From e69bedffbc5fd7fb770c9fcdb778785d35adf3ba Mon Sep 17 00:00:00 2001
From: GitHub Action <action@github.com>
Date: Fri, 3 Nov 2023 21:57:47 +0000
Subject: [PATCH] Update documentation

---
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 _modules/index.html                           |  166 +
 _modules/libpysal/cg/alpha_shapes.html        |  860 +++
 _modules/libpysal/cg/kdtree.html              |  472 ++
 _modules/libpysal/cg/locators.html            | 1023 +++
 _modules/libpysal/cg/shapes.html              | 2193 ++++++
 _modules/libpysal/cg/sphere.html              |  821 ++
 _modules/libpysal/cg/standalone.html          | 1500 ++++
 _modules/libpysal/cg/voronoi.html             |  480 ++
 _modules/libpysal/examples.html               |  238 +
 _modules/libpysal/graph/base.html             | 1604 ++++
 _modules/libpysal/io/fileio.html              |  644 ++
 _modules/libpysal/weights/contiguity.html     |  935 +++
 _modules/libpysal/weights/distance.html       | 1182 +++
 _modules/libpysal/weights/gabriel.html        |  525 ++
 _modules/libpysal/weights/raster.html         | 1019 +++
 _modules/libpysal/weights/set_operations.html |  686 ++
 _modules/libpysal/weights/spatial_lag.html    |  393 +
 _modules/libpysal/weights/spintW.html         |  437 ++
 _modules/libpysal/weights/user.html           |  308 +
 _modules/libpysal/weights/util.html           | 1901 +++++
 _modules/libpysal/weights/weights.html        | 1923 +++++
 _sources/api.rst.txt                          |  265 +
 _sources/generated/libpysal.cg.Chain.rst.txt  |   33 +
 _sources/generated/libpysal.cg.Grid.rst.txt   |   28 +
 _sources/generated/libpysal.cg.KDTree.rst.txt |    6 +
 _sources/generated/libpysal.cg.Line.rst.txt   |   24 +
 .../generated/libpysal.cg.LineSegment.rst.txt |   37 +
 _sources/generated/libpysal.cg.Point.rst.txt  |   22 +
 .../libpysal.cg.PointLocator.rst.txt          |   27 +
 .../generated/libpysal.cg.Polygon.rst.txt     |   38 +
 .../libpysal.cg.PolygonLocator.rst.txt        |   28 +
 .../libpysal.cg.RADIUS_EARTH_KM.rst.txt       |    6 +
 .../libpysal.cg.RADIUS_EARTH_MILES.rst.txt    |    6 +
 _sources/generated/libpysal.cg.Ray.rst.txt    |   22 +
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 .../generated/libpysal.cg.alpha_shape.rst.txt |    6 +
 .../libpysal.cg.alpha_shape_auto.rst.txt      |    6 +
 .../generated/libpysal.cg.arcdist.rst.txt     |    6 +
 .../libpysal.cg.arcdist2linear.rst.txt        |    6 +
 .../generated/libpysal.cg.asShape.rst.txt     |    6 +
 .../generated/libpysal.cg.bbcommon.rst.txt    |    6 +
 .../generated/libpysal.cg.brute_knn.rst.txt   |    6 +
 .../generated/libpysal.cg.convex_hull.rst.txt |    6 +
 .../libpysal.cg.distance_matrix.rst.txt       |    6 +
 .../generated/libpysal.cg.fast_knn.rst.txt    |    6 +
 .../libpysal.cg.fast_threshold.rst.txt        |    6 +
 .../generated/libpysal.cg.geogrid.rst.txt     |    6 +
 .../libpysal.cg.geointerpolate.rst.txt        |    6 +
 .../libpysal.cg.get_angle_between.rst.txt     |    6 +
 .../libpysal.cg.get_bounding_box.rst.txt      |    6 +
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 ...libpysal.cg.get_polygon_point_dist.rst.txt |    6 +
 ...sal.cg.get_polygon_point_intersect.rst.txt |    6 +
 ...pysal.cg.get_ray_segment_intersect.rst.txt |    6 +
 ...l.cg.get_rectangle_point_intersect.rst.txt |    6 +
 ...t_rectangle_rectangle_intersection.rst.txt |    6 +
 ...libpysal.cg.get_segment_point_dist.rst.txt |    6 +
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 .../libpysal.cg.get_shared_segments.rst.txt   |    6 +
 .../generated/libpysal.cg.harcdist.rst.txt    |    6 +
 .../libpysal.cg.is_clockwise.rst.txt          |    6 +
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 _sources/generated/libpysal.cg.lonlat.rst.txt |    6 +
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 .../generated/libpysal.cg.toLngLat.rst.txt    |    6 +
 _sources/generated/libpysal.cg.toXYZ.rst.txt  |    6 +
 .../libpysal.cg.voronoi_frames.rst.txt        |    6 +
 .../libpysal.examples.available.rst.txt       |    6 +
 .../libpysal.examples.explain.rst.txt         |    6 +
 .../libpysal.examples.get_path.rst.txt        |    6 +
 .../generated/libpysal.graph.Graph.rst.txt    |   72 +
 .../libpysal.io.fileio.FileIO.rst.txt         |   42 +
 _sources/generated/libpysal.io.open.rst.txt   |   42 +
 .../libpysal.weights.Delaunay.rst.txt         |   76 +
 .../libpysal.weights.DistanceBand.rst.txt     |   77 +
 .../libpysal.weights.Gabriel.rst.txt          |   76 +
 .../generated/libpysal.weights.KNN.rst.txt    |   78 +
 .../generated/libpysal.weights.Kernel.rst.txt |   77 +
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 .../generated/libpysal.weights.Queen.rst.txt  |   78 +
 ...ysal.weights.Relative_Neighborhood.rst.txt |   76 +
 .../generated/libpysal.weights.Rook.rst.txt   |   78 +
 .../libpysal.weights.Voronoi.rst.txt          |    6 +
 _sources/generated/libpysal.weights.W.rst.txt |   75 +
 .../generated/libpysal.weights.WSP.rst.txt    |   33 +
 .../generated/libpysal.weights.WSP2W.rst.txt  |    6 +
 .../libpysal.weights.attach_islands.rst.txt   |    6 +
 .../libpysal.weights.block_weights.rst.txt    |    6 +
 ...al.weights.build_lattice_shapefile.rst.txt |    6 +
 .../generated/libpysal.weights.comb.rst.txt   |    6 +
 .../generated/libpysal.weights.da2W.rst.txt   |    6 +
 .../generated/libpysal.weights.da2WSP.rst.txt |    6 +
 .../libpysal.weights.fill_diagonal.rst.txt    |    6 +
 .../generated/libpysal.weights.full.rst.txt   |    6 +
 .../generated/libpysal.weights.full2W.rst.txt |    6 +
 .../libpysal.weights.fuzzy_contiguity.rst.txt |    6 +
 .../libpysal.weights.get_ids.rst.txt          |    6 +
 ...ts.get_points_array_from_shapefile.rst.txt |    6 +
 .../libpysal.weights.hexLat2W.rst.txt         |    6 +
 .../libpysal.weights.higher_order.rst.txt     |    6 +
 .../libpysal.weights.higher_order_sp.rst.txt  |    6 +
 .../libpysal.weights.lag_categorical.rst.txt  |    6 +
 .../libpysal.weights.lag_spatial.rst.txt      |    6 +
 .../generated/libpysal.weights.lat2SW.rst.txt |    6 +
 .../generated/libpysal.weights.lat2W.rst.txt  |    6 +
 .../generated/libpysal.weights.mat2L.rst.txt  |    6 +
 ....min_threshold_dist_from_shapefile.rst.txt |    6 +
 ...sal.weights.min_threshold_distance.rst.txt |    6 +
 ...libpysal.weights.neighbor_equality.rst.txt |    6 +
 .../generated/libpysal.weights.netW.rst.txt   |    6 +
 ...bpysal.weights.nonplanar_neighbors.rst.txt |    6 +
 .../generated/libpysal.weights.order.rst.txt  |    6 +
 .../libpysal.weights.remap_ids.rst.txt        |    6 +
 .../libpysal.weights.shimbel.rst.txt          |    6 +
 .../libpysal.weights.spw_from_gal.rst.txt     |    6 +
 .../libpysal.weights.testDataArray.rst.txt    |    6 +
 .../generated/libpysal.weights.vecW.rst.txt   |    6 +
 .../generated/libpysal.weights.w2da.rst.txt   |    6 +
 .../generated/libpysal.weights.w_clip.rst.txt |    6 +
 .../libpysal.weights.w_difference.rst.txt     |    6 +
 .../libpysal.weights.w_intersection.rst.txt   |    6 +
 .../libpysal.weights.w_local_cluster.rst.txt  |    6 +
 .../libpysal.weights.w_subset.rst.txt         |    6 +
 ...sal.weights.w_symmetric_difference.rst.txt |    6 +
 .../libpysal.weights.w_union.rst.txt          |    6 +
 .../generated/libpysal.weights.wsp2da.rst.txt |    6 +
 _sources/index.rst.txt                        |  141 +-
 _sources/installation.rst.txt                 |   56 +
 .../notebooks/Raster_awareness_API.ipynb.txt  |  773 ++
 _sources/notebooks/examples.ipynb.txt         | 1093 +++
 _sources/notebooks/fetch.ipynb.txt            |  887 +++
 _sources/notebooks/io.ipynb.txt               |  310 +
 _sources/notebooks/voronoi.ipynb.txt          |  476 ++
 _sources/notebooks/weights.ipynb.txt          | 1263 +++
 _sources/references.rst.txt                   |    7 +
 _sources/tutorial.rst.txt                     |   21 +
 _static/basic.css                             |  325 +-
 .../css/bootstrap-responsive.css              | 1109 +++
 .../css/bootstrap-responsive.min.css          |    9 +
 _static/bootstrap-2.3.2/css/bootstrap.css     | 6167 +++++++++++++++
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 _static/bootstrap-2.3.2/js/bootstrap.js       | 2287 ++++++
 _static/bootstrap-2.3.2/js/bootstrap.min.js   |    6 +
 .../bootstrap-3.4.1/css/bootstrap-theme.css   |  587 ++
 .../css/bootstrap-theme.css.map               |    1 +
 .../css/bootstrap-theme.min.css               |    6 +
 .../css/bootstrap-theme.min.css.map           |    1 +
 _static/bootstrap-3.4.1/css/bootstrap.css     | 6834 +++++++++++++++++
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 _static/bootstrap-3.4.1/css/bootstrap.min.css |    6 +
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 _static/bootstrap-3.4.1/js/bootstrap.js       | 2580 +++++++
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 _static/bootstrap-3.4.1/js/npm.js             |   13 +
 _static/bootstrap-sphinx.css                  |  223 +
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 .../bootswatch-2.3.2/spruce/bootstrap.min.css |    9 +
 .../superhero/bootstrap.min.css               |    9 +
 .../bootswatch-2.3.2/united/bootstrap.min.css |    9 +
 .../cerulean/bootstrap.min.css                |   11 +
 .../bootswatch-3.4.1/cosmo/bootstrap.min.css  |   11 +
 .../bootswatch-3.4.1/cyborg/bootstrap.min.css |   11 +
 .../bootswatch-3.4.1/darkly/bootstrap.min.css |   11 +
 .../bootswatch-3.4.1/flatly/bootstrap.min.css |   11 +
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 .../bootswatch-3.4.1/lumen/bootstrap.min.css  |   11 +
 .../bootswatch-3.4.1/paper/bootstrap.min.css  |   11 +
 .../readable/bootstrap.min.css                |   11 +
 .../sandstone/bootstrap.min.css               |   11 +
 .../simplex/bootstrap.min.css                 |   11 +
 .../bootswatch-3.4.1/slate/bootstrap.min.css  |   11 +
 .../spacelab/bootstrap.min.css                |   11 +
 .../superhero/bootstrap.min.css               |   11 +
 .../bootswatch-3.4.1/united/bootstrap.min.css |   11 +
 .../bootswatch-3.4.1/yeti/bootstrap.min.css   |   11 +
 _static/doctools.js                           |  377 +-
 _static/documentation_options.js              |   12 +-
 _static/images/neighboorsetLIMA_US.png        |  Bin 0 -> 654235 bytes
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 _static/images/pysal_favicon.ico              |  Bin 0 -> 32038 bytes
 _static/images/rose_conditional.png           |  Bin 0 -> 35347 bytes
 _static/images/spatial_markov_us.png          |  Bin 0 -> 134429 bytes
 _static/js/jquery-1.12.4.min.js               |    5 +
 _static/js/jquery-fix.js                      |    2 +
 _static/language_data.js                      |  106 +-
 _static/nbsphinx-broken-thumbnail.svg         |    9 +
 _static/nbsphinx-code-cells.css               |  259 +
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 _static/nbsphinx-no-thumbnail.svg             |    9 +
 _static/plot_directive.css                    |   16 +
 _static/pygments.css                          |  144 +-
 _static/pysal-styles.css                      |   73 +
 _static/pysal_favicon.ico                     |  Bin 0 -> 32038 bytes
 _static/references.bib                        |   39 +
 _static/searchtools.js                        |  818 +-
 _static/sphinx_highlight.js                   |  154 +
 api.html                                      |  651 ++
 generated/libpysal.cg.Chain.html              |  331 +
 generated/libpysal.cg.Grid.html               |  356 +
 generated/libpysal.cg.KDTree.html             |  189 +
 generated/libpysal.cg.Line.html               |  265 +
 generated/libpysal.cg.LineSegment.html        |  504 ++
 generated/libpysal.cg.Point.html              |  197 +
 generated/libpysal.cg.PointLocator.html       |  311 +
 generated/libpysal.cg.Polygon.html            |  498 ++
 generated/libpysal.cg.PolygonLocator.html     |  419 +
 generated/libpysal.cg.RADIUS_EARTH_KM.html    |  167 +
 generated/libpysal.cg.RADIUS_EARTH_MILES.html |  167 +
 generated/libpysal.cg.Ray.html                |  214 +
 generated/libpysal.cg.Rectangle.html          |  337 +
 generated/libpysal.cg.alpha_shape.html        |  205 +
 generated/libpysal.cg.alpha_shape_auto.html   |  211 +
 generated/libpysal.cg.arcdist.html            |  197 +
 generated/libpysal.cg.arcdist2linear.html     |  200 +
 generated/libpysal.cg.asShape.html            |  190 +
 generated/libpysal.cg.bbcommon.html           |  191 +
 generated/libpysal.cg.brute_knn.html          |  194 +
 generated/libpysal.cg.convex_hull.html        |  187 +
 generated/libpysal.cg.distance_matrix.html    |  227 +
 generated/libpysal.cg.fast_knn.html           |  196 +
 generated/libpysal.cg.fast_threshold.html     |  191 +
 generated/libpysal.cg.geogrid.html            |  216 +
 generated/libpysal.cg.geointerpolate.html     |  207 +
 generated/libpysal.cg.get_angle_between.html  |  197 +
 generated/libpysal.cg.get_bounding_box.html   |  199 +
 ...bpysal.cg.get_point_at_angle_and_dist.html |  200 +
 generated/libpysal.cg.get_points_dist.html    |  192 +
 .../libpysal.cg.get_polygon_point_dist.html   |  195 +
 ...bpysal.cg.get_polygon_point_intersect.html |  195 +
 ...libpysal.cg.get_ray_segment_intersect.html |  207 +
 ...ysal.cg.get_rectangle_point_intersect.html |  195 +
 ....get_rectangle_rectangle_intersection.html |  214 +
 .../libpysal.cg.get_segment_point_dist.html   |  200 +
 ...bpysal.cg.get_segment_point_intersect.html |  195 +
 .../libpysal.cg.get_segments_intersect.html   |  201 +
 .../libpysal.cg.get_shared_segments.html      |  199 +
 generated/libpysal.cg.harcdist.html           |  206 +
 generated/libpysal.cg.is_clockwise.html       |  261 +
 generated/libpysal.cg.is_collinear.html       |  194 +
 generated/libpysal.cg.linear2arcdist.html     |  202 +
 generated/libpysal.cg.lonlat.html             |  190 +
 .../libpysal.cg.point_touches_rectangle.html  |  202 +
 generated/libpysal.cg.toLngLat.html           |  181 +
 generated/libpysal.cg.toXYZ.html              |  181 +
 generated/libpysal.cg.voronoi_frames.html     |  214 +
 generated/libpysal.examples.available.html    |  167 +
 generated/libpysal.examples.explain.html      |  167 +
 generated/libpysal.examples.get_path.html     |  167 +
 generated/libpysal.graph.Graph.html           | 1306 ++++
 generated/libpysal.io.fileio.FileIO.html      |  398 +
 generated/libpysal.io.open.html               |  167 +
 generated/libpysal.weights.Delaunay.html      |  396 +
 generated/libpysal.weights.DistanceBand.html  |  482 ++
 generated/libpysal.weights.Gabriel.html       |  359 +
 generated/libpysal.weights.KNN.html           |  605 ++
 generated/libpysal.weights.Kernel.html        |  557 ++
 generated/libpysal.weights.ODW.html           |  202 +
 generated/libpysal.weights.Queen.html         |  536 ++
 ...ibpysal.weights.Relative_Neighborhood.html |  356 +
 generated/libpysal.weights.Rook.html          |  533 ++
 generated/libpysal.weights.Voronoi.html       |  194 +
 generated/libpysal.weights.W.html             | 1337 ++++
 generated/libpysal.weights.WSP.html           |  343 +
 generated/libpysal.weights.WSP2W.html         |  205 +
 .../libpysal.weights.attach_islands.html      |  199 +
 generated/libpysal.weights.block_weights.html |  212 +
 ...pysal.weights.build_lattice_shapefile.html |  184 +
 generated/libpysal.weights.comb.html          |  196 +
 generated/libpysal.weights.da2W.html          |  248 +
 generated/libpysal.weights.da2WSP.html        |  247 +
 generated/libpysal.weights.fill_diagonal.html |  212 +
 generated/libpysal.weights.full.html          |  196 +
 generated/libpysal.weights.full2W.html        |  220 +
 .../libpysal.weights.fuzzy_contiguity.html    |  251 +
 generated/libpysal.weights.get_ids.html       |  209 +
 ...ights.get_points_array_from_shapefile.html |  204 +
 generated/libpysal.weights.hexLat2W.html      |  203 +
 generated/libpysal.weights.higher_order.html  |  204 +
 .../libpysal.weights.higher_order_sp.html     |  221 +
 .../libpysal.weights.lag_categorical.html     |  226 +
 generated/libpysal.weights.lag_spatial.html   |  237 +
 generated/libpysal.weights.lat2SW.html        |  203 +
 generated/libpysal.weights.lat2W.html         |  204 +
 generated/libpysal.weights.mat2L.html         |  185 +
 ...hts.min_threshold_dist_from_shapefile.html |  205 +
 ...bpysal.weights.min_threshold_distance.html |  198 +
 .../libpysal.weights.neighbor_equality.html   |  209 +
 generated/libpysal.weights.netW.html          |  204 +
 .../libpysal.weights.nonplanar_neighbors.html |  231 +
 generated/libpysal.weights.order.html         |  198 +
 generated/libpysal.weights.remap_ids.html     |  205 +
 generated/libpysal.weights.shimbel.html       |  192 +
 generated/libpysal.weights.spw_from_gal.html  |  190 +
 generated/libpysal.weights.testDataArray.html |  191 +
 generated/libpysal.weights.vecW.html          |  226 +
 generated/libpysal.weights.w2da.html          |  198 +
 generated/libpysal.weights.w_clip.html        |  272 +
 generated/libpysal.weights.w_difference.html  |  221 +
 .../libpysal.weights.w_intersection.html      |  212 +
 .../libpysal.weights.w_local_cluster.html     |  207 +
 generated/libpysal.weights.w_subset.html      |  205 +
 ...bpysal.weights.w_symmetric_difference.html |  219 +
 generated/libpysal.weights.w_union.html       |  208 +
 generated/libpysal.weights.wsp2da.html        |  198 +
 genindex.html                                 | 1067 ++-
 index.html                                    |  303 +-
 installation.html                             |  206 +
 notebooks/Raster_awareness_API.html           |  722 ++
 notebooks/Raster_awareness_API.ipynb          |  773 ++
 notebooks/examples.html                       | 1053 +++
 notebooks/examples.ipynb                      | 1093 +++
 notebooks/fetch.html                          |  943 +++
 notebooks/fetch.ipynb                         |  887 +++
 notebooks/io.html                             |  412 +
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+<li><a href="libpysal/weights/contiguity.html">libpysal.weights.contiguity</a></li>
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+  <h1>Source code for libpysal.cg.alpha_shapes</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Computation of alpha shape algorithm in 2-D based on original implementation</span>
+<span class="sd">by Tim Kittel (@timkittel) available at:</span>
+
+<span class="sd">    https://github.com/timkittel/alpha-shapes</span>
+
+<span class="sd">Author(s):</span>
+<span class="sd">    Dani Arribas-Bel daniel.arribas.bel@gmail.com</span>
+<span class="sd">    Levi John Wolf levi.john.wolf@gmail.com</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">scipy.spatial</span> <span class="k">as</span> <span class="nn">spat</span>
+<span class="kn">from</span> <span class="nn">packaging.version</span> <span class="kn">import</span> <span class="n">Version</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
+
+<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">HAS_JIT</span><span class="p">,</span> <span class="n">jit</span><span class="p">,</span> <span class="n">requires</span>
+
+<span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_JIT</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+
+    <span class="n">NUMBA_WARN</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="s2">&quot;Numba not imported, so alpha shape construction may be slower than expected.&quot;</span>
+    <span class="p">)</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">shapely</span>
+
+    <span class="k">assert</span> <span class="n">Version</span><span class="p">(</span><span class="n">shapely</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">Version</span><span class="p">(</span><span class="s2">&quot;2&quot;</span><span class="p">)</span>
+
+    <span class="n">HAS_SHAPELY</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="p">(</span><span class="ne">ModuleNotFoundError</span><span class="p">,</span> <span class="ne">AssertionError</span><span class="p">):</span>
+    <span class="n">HAS_SHAPELY</span> <span class="o">=</span> <span class="kc">False</span>
+
+
+<span class="n">EPS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;alpha_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;alpha_shape_auto&quot;</span><span class="p">]</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">nb_dist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;numba implementation of distance between points `x` and `y`</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x : ndarray</span>
+<span class="sd">        Coordinates of point `x`</span>
+<span class="sd">    y : ndarray</span>
+<span class="sd">        Coordinates of point `y`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dist : float</span>
+<span class="sd">        Distance between `x` and `y`</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; x = np.array([0, 0])</span>
+<span class="sd">    &gt;&gt;&gt; y = np.array([1, 1])</span>
+<span class="sd">    &gt;&gt;&gt; dist = nb_dist(x, y)</span>
+<span class="sd">    &gt;&gt;&gt; dist</span>
+<span class="sd">    1.4142135623730951</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">sum_</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span> <span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>  <span class="c1"># noqa B905</span>
+        <span class="n">sum_</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">y_i</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+    <span class="n">dist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">sum_</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dist</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">r_circumcircle_triangle_single</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computation of the circumcircle of a single triangle</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    a : ndarray</span>
+<span class="sd">        (2,) Array with coordinates of vertex `a` of the triangle</span>
+<span class="sd">    b : ndarray</span>
+<span class="sd">        (2,) Array with coordinates of vertex `b` of the triangle</span>
+<span class="sd">    c : ndarray</span>
+<span class="sd">        (2,) Array with coordinates of vertex `c` of the triangle</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    r : float</span>
+<span class="sd">        Circumcircle of the triangle</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Source for equations:</span>
+<span class="sd">    &gt; https://www.mathopenref.com/trianglecircumcircle.html</span>
+<span class="sd">    [Last accessed July 11th. 2018]</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; a = np.array([0, 0])</span>
+<span class="sd">    &gt;&gt;&gt; b = np.array([0.5, 0])</span>
+<span class="sd">    &gt;&gt;&gt; c = np.array([0.25, 0.25])</span>
+<span class="sd">    &gt;&gt;&gt; r = r_circumcircle_triangle_single(a, b, c)</span>
+<span class="sd">    &gt;&gt;&gt; r</span>
+<span class="sd">    0.2500000000000001</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ab</span> <span class="o">=</span> <span class="n">nb_dist</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+    <span class="n">bc</span> <span class="o">=</span> <span class="n">nb_dist</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
+    <span class="n">ca</span> <span class="o">=</span> <span class="n">nb_dist</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+
+    <span class="n">num</span> <span class="o">=</span> <span class="n">ab</span> <span class="o">*</span> <span class="n">bc</span> <span class="o">*</span> <span class="n">ca</span>
+    <span class="n">den</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">ab</span> <span class="o">+</span> <span class="n">bc</span> <span class="o">+</span> <span class="n">ca</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">bc</span> <span class="o">+</span> <span class="n">ca</span> <span class="o">-</span> <span class="n">ab</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">ca</span> <span class="o">+</span> <span class="n">ab</span> <span class="o">-</span> <span class="n">bc</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">ab</span> <span class="o">+</span> <span class="n">bc</span> <span class="o">-</span> <span class="n">ca</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">den</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">ab</span><span class="p">,</span> <span class="n">bc</span><span class="p">,</span> <span class="n">ca</span><span class="p">])</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">/</span> <span class="mf">2.0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">num</span> <span class="o">/</span> <span class="n">den</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">r_circumcircle_triangle</span><span class="p">(</span><span class="n">a_s</span><span class="p">,</span> <span class="n">b_s</span><span class="p">,</span> <span class="n">c_s</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computation of circumcircles for a series of triangles</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    a_s : ndarray</span>
+<span class="sd">        (N, 2) array with coordinates of vertices `a` of the triangles</span>
+<span class="sd">    b_s : ndarray</span>
+<span class="sd">        (N, 2) array with coordinates of vertices `b` of the triangles</span>
+<span class="sd">    c_s : ndarray</span>
+<span class="sd">        (N, 2) array with coordinates of vertices `c` of the triangles</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    radii : ndarray</span>
+<span class="sd">        (N,) array with circumcircles for every triangle</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; a_s = np.array([[0, 0], [2, 1], [3, 2]])</span>
+<span class="sd">    &gt;&gt;&gt; b_s = np.array([[1, 0], [5, 1], [2, 4]])</span>
+<span class="sd">    &gt;&gt;&gt; c_s = np.array([[0, 7], [1, 3], [4, 2]])</span>
+<span class="sd">    &gt;&gt;&gt; rs = r_circumcircle_triangle(a_s, b_s, c_s)</span>
+<span class="sd">    &gt;&gt;&gt; rs</span>
+<span class="sd">    array([3.53553391, 2.5       , 1.58113883])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">len_a</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">a_s</span><span class="p">)</span>
+    <span class="n">r2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">len_a</span><span class="p">,))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">len_a</span><span class="p">):</span>
+        <span class="n">r2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle_single</span><span class="p">(</span><span class="n">a_s</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">b_s</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">c_s</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">r2</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">get_faces</span><span class="p">(</span><span class="n">triangle</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract faces from a single triangle</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    triangles : ndarray</span>
+<span class="sd">        (3,) array with the vertex indices for a triangle</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    faces : ndarray</span>
+<span class="sd">        (3, 2) array with a row for each face containing the indices of the two</span>
+<span class="sd">        points that make up the face</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; triangle = np.array([3, 1, 4], dtype=np.int32)</span>
+<span class="sd">    &gt;&gt;&gt; faces = get_faces(triangle)</span>
+<span class="sd">    &gt;&gt;&gt; faces</span>
+<span class="sd">    array([[3., 1.],</span>
+<span class="sd">           [1., 4.],</span>
+<span class="sd">           [4., 3.]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">faces</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">i0</span><span class="p">,</span> <span class="n">i1</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]):</span>
+        <span class="n">faces</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">triangle</span><span class="p">[</span><span class="n">i0</span><span class="p">],</span> <span class="n">triangle</span><span class="p">[</span><span class="n">i1</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">faces</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">build_faces</span><span class="p">(</span><span class="n">faces</span><span class="p">,</span> <span class="n">triangles_is</span><span class="p">,</span> <span class="n">num_triangles</span><span class="p">,</span> <span class="n">num_faces_single</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Build facing triangles</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    faces : ndarray</span>
+<span class="sd">        (num_triangles * num_faces_single, 2) array of zeroes in int form</span>
+<span class="sd">    triangles_is : ndarray</span>
+<span class="sd">        (D, 3) array, where D is the number of Delaunay triangles, with the</span>
+<span class="sd">        vertex indices for each triangle</span>
+<span class="sd">    num_triangles : int</span>
+<span class="sd">        Number of triangles</span>
+<span class="sd">    num_faces_single : int</span>
+<span class="sd">        Number of faces a triangle has (i.e. 3)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    faces : ndarray</span>
+<span class="sd">        Two dimensional array with a row for every facing segment containing</span>
+<span class="sd">        the indices of the coordinate points</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import scipy.spatial as spat</span>
+<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
+<span class="sd">    &gt;&gt;&gt; triangulation = spat.Delaunay(pts)</span>
+<span class="sd">    &gt;&gt;&gt; triangulation.simplices</span>
+<span class="sd">    array([[3, 1, 4],</span>
+<span class="sd">           [1, 2, 4],</span>
+<span class="sd">           [2, 1, 0]], dtype=int32)</span>
+<span class="sd">    &gt;&gt;&gt; num_faces_single = 3</span>
+<span class="sd">    &gt;&gt;&gt; num_triangles = triangulation.simplices.shape[0]</span>
+<span class="sd">    &gt;&gt;&gt; num_faces = num_triangles * num_faces_single</span>
+<span class="sd">    &gt;&gt;&gt; faces = np.zeros((num_faces, 2), dtype=np.int_)</span>
+<span class="sd">    &gt;&gt;&gt; mask = np.ones((num_faces,), dtype=np.bool_)</span>
+<span class="sd">    &gt;&gt;&gt; faces = build_faces(</span>
+<span class="sd">    ...     faces, triangulation.simplices, num_triangles, num_faces_single</span>
+<span class="sd">    ... )</span>
+<span class="sd">    &gt;&gt;&gt; faces</span>
+<span class="sd">    array([[3, 1],</span>
+<span class="sd">           [1, 4],</span>
+<span class="sd">           [4, 3],</span>
+<span class="sd">           [1, 2],</span>
+<span class="sd">           [2, 4],</span>
+<span class="sd">           [4, 1],</span>
+<span class="sd">           [2, 1],</span>
+<span class="sd">           [1, 0],</span>
+<span class="sd">           [0, 2]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_triangles</span><span class="p">):</span>
+        <span class="n">from_i</span> <span class="o">=</span> <span class="n">num_faces_single</span> <span class="o">*</span> <span class="n">i</span>
+        <span class="n">to_i</span> <span class="o">=</span> <span class="n">num_faces_single</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">faces</span><span class="p">[</span><span class="n">from_i</span><span class="p">:</span><span class="n">to_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_faces</span><span class="p">(</span><span class="n">triangles_is</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">faces</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">nb_mask_faces</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">faces</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Run over each row in `faces`, if the face in the following row is the</span>
+<span class="sd">    same, then mark both as False on `mask`</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    mask : ndarray</span>
+<span class="sd">        One-dimensional boolean array set to True with as many observations as</span>
+<span class="sd">        rows in `faces`</span>
+<span class="sd">    faces : ndarray</span>
+<span class="sd">        Sorted sequence of faces for all triangles (ie. triangles split by each</span>
+<span class="sd">        segment)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    masked : ndarray</span>
+<span class="sd">         Sequence of outward-facing faces</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; faces = np.array(</span>
+<span class="sd">    ...     [</span>
+<span class="sd">    ...         [0, 1], [0, 2], [1, 2], [1, 2], [1, 3], [1, 4], [1, 4], [2, 4], [3, 4]</span>
+<span class="sd">    ...     ]</span>
+<span class="sd">    ... )</span>
+<span class="sd">    &gt;&gt;&gt; mask = np.ones((faces.shape[0], ), dtype=np.bool_)</span>
+<span class="sd">    &gt;&gt;&gt; masked = nb_mask_faces(mask, faces)</span>
+<span class="sd">    &gt;&gt;&gt; masked</span>
+<span class="sd">    array([[0, 1],</span>
+<span class="sd">           [0, 2],</span>
+<span class="sd">           [1, 3],</span>
+<span class="sd">           [2, 4],</span>
+<span class="sd">           [3, 4]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">faces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">mask</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="ow">and</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">faces</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">==</span> <span class="n">faces</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]):</span>
+            <span class="n">mask</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">mask</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">return</span> <span class="n">faces</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
+
+
+<span class="k">def</span> <span class="nf">get_single_faces</span><span class="p">(</span><span class="n">triangles_is</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract outward facing edges from collection of triangles</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    triangles_is : ndarray</span>
+<span class="sd">        (D, 3) array, where D is the number of Delaunay triangles, with the</span>
+<span class="sd">        vertex indices for each triangle</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    single_faces : ndarray</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import scipy.spatial as spat</span>
+<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
+<span class="sd">    &gt;&gt;&gt; alpha = 0.33</span>
+<span class="sd">    &gt;&gt;&gt; triangulation = spat.Delaunay(pts)</span>
+<span class="sd">    &gt;&gt;&gt; triangulation.simplices</span>
+<span class="sd">    array([[3, 1, 4],</span>
+<span class="sd">           [1, 2, 4],</span>
+<span class="sd">           [2, 1, 0]], dtype=int32)</span>
+<span class="sd">    &gt;&gt;&gt; get_single_faces(triangulation.simplices)</span>
+<span class="sd">    array([[0, 1],</span>
+<span class="sd">           [0, 2],</span>
+<span class="sd">           [1, 3],</span>
+<span class="sd">           [2, 4],</span>
+<span class="sd">           [3, 4]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">num_faces_single</span> <span class="o">=</span> <span class="mi">3</span>
+    <span class="n">num_triangles</span> <span class="o">=</span> <span class="n">triangles_is</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">num_faces</span> <span class="o">=</span> <span class="n">num_triangles</span> <span class="o">*</span> <span class="n">num_faces_single</span>
+    <span class="n">faces</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_faces</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">)</span>
+    <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">num_faces</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">bool_</span><span class="p">)</span>
+
+    <span class="n">faces</span> <span class="o">=</span> <span class="n">build_faces</span><span class="p">(</span><span class="n">faces</span><span class="p">,</span> <span class="n">triangles_is</span><span class="p">,</span> <span class="n">num_triangles</span><span class="p">,</span> <span class="n">num_faces_single</span><span class="p">)</span>
+
+    <span class="n">orderlist</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;x</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">faces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+    <span class="n">dtype_list</span> <span class="o">=</span> <span class="p">[(</span><span class="n">el</span><span class="p">,</span> <span class="n">faces</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">str</span><span class="p">)</span> <span class="k">for</span> <span class="n">el</span> <span class="ow">in</span> <span class="n">orderlist</span><span class="p">]</span>
+    <span class="c1"># Arranging each face so smallest vertex is first</span>
+    <span class="n">faces</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="c1"># Arranging faces in ascending way</span>
+    <span class="n">faces</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">dtype_list</span><span class="p">)</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="c1"># Masking</span>
+    <span class="n">single_faces</span> <span class="o">=</span> <span class="n">nb_mask_faces</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="n">faces</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">single_faces</span>
+
+
+<span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">,</span> <span class="s2">&quot;shapely&quot;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_alpha_geoms</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate alpha-shape polygon(s) from `alpha` value, vertices of</span>
+<span class="sd">    `triangles`, the `radii` for all points, and the points themselves</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    alpha : float</span>
+<span class="sd">        Alpha value to delineate the alpha-shape</span>
+<span class="sd">    triangles : ndarray</span>
+<span class="sd">         (D, 3) array, where D is the number of Delaunay triangles, with the</span>
+<span class="sd">         vertex indices for each triangle</span>
+<span class="sd">    radii : ndarray</span>
+<span class="sd">        (N,) array with circumcircles for every triangle</span>
+<span class="sd">    xys : ndarray</span>
+<span class="sd">        (N, 2) array with one point per row and coordinates structured as X and Y</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    geoms : GeoSeries</span>
+<span class="sd">        Polygon(s) resulting from the alpha shape algorithm, in a GeoSeries.</span>
+<span class="sd">        The output is a GeoSeries even if only a single polygon is returned. There is</span>
+<span class="sd">        no CRS included in the returned GeoSeries.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import scipy.spatial as spat</span>
+<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
+<span class="sd">    &gt;&gt;&gt; alpha = 0.33</span>
+<span class="sd">    &gt;&gt;&gt; triangulation = spat.Delaunay(pts)</span>
+<span class="sd">    &gt;&gt;&gt; triangles = pts[triangulation.simplices]</span>
+<span class="sd">    &gt;&gt;&gt; triangles</span>
+<span class="sd">    array([[[6, 7],</span>
+<span class="sd">            [3, 5],</span>
+<span class="sd">            [9, 3]],</span>
+<span class="sd">    &lt;BLANKLINE&gt;</span>
+<span class="sd">           [[3, 5],</span>
+<span class="sd">            [4, 1],</span>
+<span class="sd">            [9, 3]],</span>
+<span class="sd">    &lt;BLANKLINE&gt;</span>
+<span class="sd">           [[4, 1],</span>
+<span class="sd">            [3, 5],</span>
+<span class="sd">            [0, 1]]])</span>
+<span class="sd">    &gt;&gt;&gt; a_pts = triangles[:, 0, :]</span>
+<span class="sd">    &gt;&gt;&gt; b_pts = triangles[:, 1, :]</span>
+<span class="sd">    &gt;&gt;&gt; c_pts = triangles[:, 2, :]</span>
+<span class="sd">    &gt;&gt;&gt; radii = r_circumcircle_triangle(a_pts, b_pts, c_pts)</span>
+<span class="sd">    &gt;&gt;&gt; geoms = alpha_geoms(alpha, triangulation.simplices, radii, pts)</span>
+<span class="sd">    &gt;&gt;&gt; geoms</span>
+<span class="sd">    0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 4....</span>
+<span class="sd">    dtype: geometry</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">geopandas</span> <span class="kn">import</span> <span class="n">GeoSeries</span>
+    <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">LineString</span>
+    <span class="kn">from</span> <span class="nn">shapely.ops</span> <span class="kn">import</span> <span class="n">polygonize</span>
+
+    <span class="n">triangles_reduced</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[</span><span class="n">radii</span> <span class="o">&lt;</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">alpha</span><span class="p">]</span>
+    <span class="n">outer_triangulation</span> <span class="o">=</span> <span class="n">get_single_faces</span><span class="p">(</span><span class="n">triangles_reduced</span><span class="p">)</span>
+    <span class="n">face_pts</span> <span class="o">=</span> <span class="n">xys</span><span class="p">[</span><span class="n">outer_triangulation</span><span class="p">]</span>
+    <span class="n">geoms</span> <span class="o">=</span> <span class="n">GeoSeries</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">polygonize</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">LineString</span><span class="p">,</span> <span class="n">face_pts</span><span class="p">)))))</span>
+    <span class="k">return</span> <span class="n">geoms</span>
+
+
+<div class="viewcode-block" id="alpha_shape">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.alpha_shape.html#libpysal.cg.alpha_shape">[docs]</a>
+<span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">,</span> <span class="s2">&quot;shapely&quot;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">alpha_shape</span><span class="p">(</span><span class="n">xys</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Alpha-shape delineation (Edelsbrunner, Kirkpatrick &amp; Seidel, 1983)</span>
+<span class="sd">    from a collection of points</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    xys : ndarray</span>
+<span class="sd">        (N, 2) array with one point per row and coordinates structured as X and Y</span>
+<span class="sd">    alpha : float</span>
+<span class="sd">        Alpha value to delineate the alpha-shape</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    shapes : GeoSeries</span>
+<span class="sd">         Polygon(s) resulting from the alpha shape algorithm. The GeoSeries</span>
+<span class="sd">         object remains so even if only a single polygon is returned. There is</span>
+<span class="sd">         no CRS included in the object. Note that the returned shape(s) may</span>
+<span class="sd">         have holes, as per the definition of the shape in Edselbrunner et al. (1983)</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
+<span class="sd">    &gt;&gt;&gt; alpha = 0.1</span>
+<span class="sd">    &gt;&gt;&gt; poly = alpha_shape(pts, alpha)</span>
+<span class="sd">    &gt;&gt;&gt; poly</span>
+<span class="sd">    0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 6....</span>
+<span class="sd">    dtype: geometry</span>
+<span class="sd">    &gt;&gt;&gt; poly.centroid</span>
+<span class="sd">    0    POINT (4.69048 3.45238)</span>
+<span class="sd">    dtype: geometry</span>
+
+<span class="sd">    References</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</span>
+<span class="sd">        a set of points in the plane. IEEE Transactions on information theory,</span>
+<span class="sd">        29(4), 551-559.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_JIT</span><span class="p">:</span>
+        <span class="n">warn</span><span class="p">(</span><span class="n">NUMBA_WARN</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">geometry</span> <span class="k">as</span> <span class="n">geom</span>
+        <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">ops</span>
+
+        <span class="k">return</span> <span class="n">ops</span><span class="o">.</span><span class="n">unary_union</span><span class="p">([</span><span class="n">geom</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">xy</span><span class="p">)</span> <span class="k">for</span> <span class="n">xy</span> <span class="ow">in</span> <span class="n">xys</span><span class="p">])</span><span class="o">.</span><span class="n">convex_hull</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">triangulation</span> <span class="o">=</span> <span class="n">spat</span><span class="o">.</span><span class="n">Delaunay</span><span class="p">(</span><span class="n">xys</span><span class="p">)</span>
+    <span class="n">triangles</span> <span class="o">=</span> <span class="n">xys</span><span class="p">[</span><span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">]</span>
+    <span class="n">a_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
+    <span class="n">b_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
+    <span class="n">c_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">:]</span>
+    <span class="n">radii</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle</span><span class="p">(</span><span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span><span class="p">)</span>
+    <span class="k">del</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span>
+    <span class="n">geoms</span> <span class="o">=</span> <span class="n">_alpha_geoms</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
+    <span class="n">geoms</span> <span class="o">=</span> <span class="n">_filter_holes</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">geoms</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_valid_hull</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Sanity check within ``alpha_shape_auto()`` to verify the generated alpha</span>
+<span class="sd">    shape actually contains the original set of points (xys).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    geoms : GeoSeries</span>
+<span class="sd">        See alpha_geoms()</span>
+<span class="sd">    points : list</span>
+<span class="sd">        xys parameter cast as shapely.geometry.Point objects</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    flag : bool</span>
+<span class="sd">        Valid hull for alpha shape [True] or not [False]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># if there is not exactly one polygon</span>
+    <span class="k">if</span> <span class="n">geoms</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">return</span> <span class="kc">False</span>
+    <span class="c1"># if any (xys) points do not intersect the polygon</span>
+    <span class="k">if</span> <span class="n">HAS_SHAPELY</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">shapely</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">geoms</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">points</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">geoms</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">points</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="alpha_shape_auto">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.alpha_shape_auto.html#libpysal.cg.alpha_shape_auto">[docs]</a>
+<span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">,</span> <span class="s2">&quot;shapely&quot;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">alpha_shape_auto</span><span class="p">(</span>
+    <span class="n">xys</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_radius</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_circles</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computation of alpha-shape delineation with automated selection of alpha.</span>
+
+<span class="sd">    This method uses the algorithm proposed by  Edelsbrunner, Kirkpatrick &amp;</span>
+<span class="sd">    Seidel (1983) to return the tightest polygon that contains all points in</span>
+<span class="sd">    `xys`. The algorithm ranks every point based on its radius and iterates</span>
+<span class="sd">    over each point, checking whether the maximum alpha that would keep the</span>
+<span class="sd">    point and all the other ones in the set with smaller radii results in a</span>
+<span class="sd">    single polygon. If that is the case, it moves to the next point;</span>
+<span class="sd">    otherwise, it retains the previous alpha value and returns the polygon</span>
+<span class="sd">    as `shapely` geometry. Note that this geometry may have holes.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    xys : ndarray</span>
+<span class="sd">        Nx2 array with one point per row and coordinates structured as X and Y</span>
+<span class="sd">    step : int</span>
+<span class="sd">        [Optional. Default=1] Number of points in `xys` to jump ahead after</span>
+<span class="sd">        checking whether the largest possible alpha that includes the point and</span>
+<span class="sd">        all the other ones with smaller radii</span>
+<span class="sd">    verbose : Boolean</span>
+<span class="sd">        [Optional. Default=False] If True, it prints alpha values</span>
+<span class="sd">        being tried at every step.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    poly : shapely.Polygon</span>
+<span class="sd">         Tightest alpha-shape polygon containing all points in `xys`</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; pts = np.array([[0, 1], [3, 5], [4, 1], [6, 7], [9, 3]])</span>
+<span class="sd">    &gt;&gt;&gt; poly = alpha_shape_auto(pts)</span>
+<span class="sd">    &gt;&gt;&gt; poly.bounds</span>
+<span class="sd">    (0.0, 1.0, 9.0, 7.0)</span>
+<span class="sd">    &gt;&gt;&gt; poly.centroid.x, poly.centroid.y</span>
+<span class="sd">    (4.690476190476191, 3.4523809523809526)</span>
+
+<span class="sd">    References</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</span>
+<span class="sd">        a set of points in the plane. IEEE Transactions on information theory,</span>
+<span class="sd">        29(4), 551-559.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">HAS_JIT</span><span class="p">:</span>
+        <span class="n">warn</span><span class="p">(</span><span class="n">NUMBA_WARN</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">geometry</span> <span class="k">as</span> <span class="n">geom</span>
+
+    <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
+        <span class="n">return_radius</span> <span class="o">=</span> <span class="kc">True</span>
+    <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">shapely</span> <span class="kn">import</span> <span class="n">ops</span>
+
+        <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">multipoint</span> <span class="o">=</span> <span class="n">ops</span><span class="o">.</span><span class="n">cascaded_union</span><span class="p">([</span><span class="n">geom</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">xy</span><span class="p">)</span> <span class="k">for</span> <span class="n">xy</span> <span class="ow">in</span> <span class="n">xys</span><span class="p">])</span>
+            <span class="n">alpha_shape</span> <span class="o">=</span> <span class="n">multipoint</span><span class="o">.</span><span class="n">convex_hull</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha_shape</span> <span class="o">=</span> <span class="n">geom</span><span class="o">.</span><span class="n">Polygon</span><span class="p">([])</span>
+        <span class="k">if</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">return_radius</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
+                    <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">alpha_shape</span><span class="p">]</span>
+                <span class="k">return</span> <span class="n">alpha_shape</span><span class="p">,</span> <span class="mi">0</span>
+            <span class="k">return</span> <span class="n">alpha_shape</span>
+        <span class="k">elif</span> <span class="n">xys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">return_radius</span><span class="p">:</span>
+                <span class="n">r</span> <span class="o">=</span> <span class="n">spat</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">euclidean</span><span class="p">(</span><span class="n">xys</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
+                <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
+                    <span class="n">circle</span> <span class="o">=</span> <span class="n">_construct_centers</span><span class="p">(</span><span class="n">xys</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="p">)</span>
+                    <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">circle</span><span class="p">]</span>
+                <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">r</span><span class="p">]</span>
+            <span class="k">return</span> <span class="n">alpha_shape</span>
+        <span class="k">elif</span> <span class="n">return_radius</span><span class="p">:</span>  <span class="c1"># this handles xys.shape[0] == 3</span>
+            <span class="n">radius</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle_single</span><span class="p">(</span><span class="n">xys</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">xys</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
+                <span class="n">circles</span> <span class="o">=</span> <span class="n">construct_bounding_circles</span><span class="p">(</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">)</span>
+                <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="n">circles</span><span class="p">]</span>
+            <span class="k">return</span> <span class="p">[</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">alpha_shape</span>
+    <span class="n">triangulation</span> <span class="o">=</span> <span class="n">spat</span><span class="o">.</span><span class="n">Delaunay</span><span class="p">(</span><span class="n">xys</span><span class="p">)</span>
+    <span class="n">triangles</span> <span class="o">=</span> <span class="n">xys</span><span class="p">[</span><span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">]</span>
+    <span class="n">a_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
+    <span class="n">b_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">:]</span>
+    <span class="n">c_pts</span> <span class="o">=</span> <span class="n">triangles</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">:]</span>
+    <span class="n">radii</span> <span class="o">=</span> <span class="n">r_circumcircle_triangle</span><span class="p">(</span><span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span><span class="p">)</span>
+    <span class="n">radii</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">radii</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># &quot;Line&quot; triangles to be kept for sure</span>
+    <span class="k">del</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">a_pts</span><span class="p">,</span> <span class="n">b_pts</span><span class="p">,</span> <span class="n">c_pts</span>
+    <span class="n">radii_sorted_i</span> <span class="o">=</span> <span class="n">radii</span><span class="o">.</span><span class="n">argsort</span><span class="p">()</span>
+    <span class="n">triangles</span> <span class="o">=</span> <span class="n">triangulation</span><span class="o">.</span><span class="n">simplices</span><span class="p">[</span><span class="n">radii_sorted_i</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">radii</span> <span class="o">=</span> <span class="n">radii</span><span class="p">[</span><span class="n">radii_sorted_i</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">geoms_prev</span> <span class="o">=</span> <span class="n">_alpha_geoms</span><span class="p">((</span><span class="mi">1</span> <span class="o">/</span> <span class="n">radii</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="o">-</span> <span class="n">EPS</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
+    <span class="n">points</span> <span class="o">=</span> <span class="n">shapely</span><span class="o">.</span><span class="n">points</span><span class="p">(</span><span class="n">xys</span><span class="p">)</span> <span class="k">if</span> <span class="n">HAS_SHAPELY</span> <span class="k">else</span> <span class="p">[</span><span class="n">geom</span><span class="o">.</span><span class="n">Point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">xys</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Step set to </span><span class="si">%i</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">step</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">radii</span><span class="p">),</span> <span class="n">step</span><span class="p">):</span>
+        <span class="n">radi</span> <span class="o">=</span> <span class="n">radii</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">radi</span><span class="p">)</span> <span class="o">-</span> <span class="n">EPS</span>
+        <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="p">(</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">radii</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">% | Trying a = </span><span class="si">{</span><span class="n">alpha</span><span class="si">:</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">geoms</span> <span class="o">=</span> <span class="n">_alpha_geoms</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">triangles</span><span class="p">,</span> <span class="n">radii</span><span class="p">,</span> <span class="n">xys</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">_valid_hull</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
+            <span class="n">geoms_prev</span> <span class="o">=</span> <span class="n">geoms</span>
+            <span class="n">radi_prev</span> <span class="o">=</span> <span class="n">radi</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">break</span>
+    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">geoms_prev</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">return_radius</span><span class="p">:</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="p">[</span><span class="n">geoms_prev</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">radi_prev</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">return_circles</span><span class="p">:</span>
+            <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">construct_bounding_circles</span><span class="p">(</span><span class="n">out</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">radi_prev</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">out</span>
+    <span class="c1"># Return a shapely polygon</span>
+    <span class="k">return</span> <span class="n">geoms_prev</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">construct_bounding_circles</span><span class="p">(</span><span class="n">alpha_shape</span><span class="p">,</span> <span class="n">radius</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Construct the bounding circles for an alpha shape, given the radius</span>
+<span class="sd">    computed from the `alpha_shape_auto` method.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    alpha_shape : shapely.Polygon</span>
+<span class="sd">        An alpha-hull with the input radius.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of the input alpha_shape.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    center : numpy.ndarray of shape (n,2)</span>
+<span class="sd">        The centers of the circles defining the alpha_shape.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">coordinates</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">alpha_shape</span><span class="o">.</span><span class="n">boundary</span><span class="o">.</span><span class="n">coords</span><span class="p">)</span>
+    <span class="n">n_coordinates</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
+    <span class="n">centers</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_coordinates</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">a</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="n">centers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_construct_centers</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">radius</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">centers</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_construct_centers</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">radius</span><span class="p">):</span>
+    <span class="n">midpoint_x</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.5</span>
+    <span class="n">midpoint_y</span> <span class="o">=</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">0.5</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="p">((</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">**</span> <span class="mf">0.5</span>
+    <span class="k">if</span> <span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
+        <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">m</span> <span class="o">=</span> <span class="p">(</span><span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">axis_rotation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+    <span class="c1"># altitude is perpendicular bisector of AB</span>
+    <span class="n">interior_angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="mf">0.5</span> <span class="o">*</span> <span class="n">d</span> <span class="o">/</span> <span class="n">radius</span><span class="p">)</span>
+    <span class="n">chord</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">interior_angle</span><span class="p">)</span> <span class="o">*</span> <span class="n">radius</span>
+
+    <span class="n">dx</span> <span class="o">=</span> <span class="n">chord</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">axis_rotation</span><span class="p">)</span>
+    <span class="n">dy</span> <span class="o">=</span> <span class="n">chord</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">axis_rotation</span><span class="p">)</span>
+
+    <span class="n">up_x</span> <span class="o">=</span> <span class="n">midpoint_x</span> <span class="o">-</span> <span class="n">dx</span>
+    <span class="n">up_y</span> <span class="o">=</span> <span class="n">midpoint_y</span> <span class="o">+</span> <span class="n">dy</span>
+    <span class="n">down_x</span> <span class="o">=</span> <span class="n">midpoint_x</span> <span class="o">+</span> <span class="n">dx</span>
+    <span class="n">down_y</span> <span class="o">=</span> <span class="n">midpoint_y</span> <span class="o">-</span> <span class="n">dy</span>
+
+    <span class="c1"># sign gives us direction of point, since</span>
+    <span class="c1"># shapely shapes are clockwise-defined</span>
+    <span class="n">sign</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">((</span><span class="n">b</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">up_y</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">b</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">up_x</span> <span class="o">-</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+    <span class="k">if</span> <span class="n">sign</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">up_x</span><span class="p">,</span> <span class="n">up_y</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">down_x</span><span class="p">,</span> <span class="n">down_y</span>
+
+
+<span class="k">def</span> <span class="nf">_filter_holes</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>  <span class="c1"># noqa ARG001</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Filter hole polygons using a computational geometry solution</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">geopandas</span>
+
+    <span class="k">if</span> <span class="p">(</span><span class="n">geoms</span><span class="o">.</span><span class="n">interiors</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+        <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span>
+
+        <span class="c1"># Extract the &quot;shell&quot;, or outer ring of the polygon.</span>
+        <span class="n">shells</span> <span class="o">=</span> <span class="n">geoms</span><span class="o">.</span><span class="n">exterior</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">Polygon</span><span class="p">)</span>
+        <span class="c1"># Compute which original geometries are within each shell, self-inclusive</span>
+        <span class="k">if</span> <span class="n">Version</span><span class="p">(</span><span class="n">geopandas</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">Version</span><span class="p">(</span><span class="s2">&quot;0.13&quot;</span><span class="p">):</span>
+            <span class="n">inside</span><span class="p">,</span> <span class="n">outside</span> <span class="o">=</span> <span class="n">shells</span><span class="o">.</span><span class="n">sindex</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="s2">&quot;within&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">inside</span><span class="p">,</span> <span class="n">outside</span> <span class="o">=</span> <span class="n">shells</span><span class="o">.</span><span class="n">sindex</span><span class="o">.</span><span class="n">query_bulk</span><span class="p">(</span><span class="n">geoms</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="s2">&quot;within&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Now, create the sparse matrix relating the inner geom (rows)</span>
+        <span class="c1"># to the outer shell (cols) and take the sum.</span>
+        <span class="c1"># A z-order of 1 means the polygon is only inside if its own exterior.</span>
+        <span class="c1">#   This means it&#39;s not a hole.</span>
+        <span class="c1"># A z-order of 2 means the polygon is inside of exactly one other exterior.</span>
+        <span class="c1">#   Because the hull generation method is restricted to be planar, this</span>
+        <span class="c1">#   means the polygon is a hole.</span>
+        <span class="c1"># In general, an even z-order means that the polygon is always exactly</span>
+        <span class="c1">#   matched to one exterior, plus some number of intermediate</span>
+        <span class="c1">#   exterior-hole pairs. Therefore, the polygon is a hole.</span>
+        <span class="c1"># In general, an odd z-order means that there is an uneven number of exteriors.</span>
+        <span class="c1">#   This means the polygon is not a hole.</span>
+        <span class="n">zorder</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csc_matrix</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">inside</span><span class="p">),</span> <span class="p">(</span><span class="n">inside</span><span class="p">,</span> <span class="n">outside</span><span class="p">)))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
+            <span class="n">axis</span><span class="o">=</span><span class="mi">1</span>
+        <span class="p">)</span>
+        <span class="n">zorder</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">zorder</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="c1"># Keep only the odd z-orders</span>
+        <span class="n">to_include</span> <span class="o">=</span> <span class="p">(</span><span class="n">zorder</span> <span class="o">%</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span>
+        <span class="n">geoms</span> <span class="o">=</span> <span class="n">geoms</span><span class="p">[</span><span class="n">to_include</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">geoms</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+
+    <span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="s2">&quot;all&quot;</span><span class="p">)</span>
+    <span class="n">xys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">geoms</span> <span class="o">=</span> <span class="n">alpha_shape_auto</span><span class="p">(</span><span class="n">xys</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%.2f</span><span class="s2"> Seconds to run algorithm&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
+    <span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">({</span><span class="s2">&quot;geometry&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">geoms</span><span class="p">]})</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">xys</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">xys</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
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diff --git a/_modules/libpysal/cg/kdtree.html b/_modules/libpysal/cg/kdtree.html
new file mode 100644
index 000000000..1d0b6a753
--- /dev/null
+++ b/_modules/libpysal/cg/kdtree.html
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+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
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+  <h1>Source code for libpysal.cg.kdtree</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">KDTree for PySAL: Python Spatial Analysis Library.</span>
+
+<span class="sd">Adds support for Arc Distance to scipy.spatial.KDTree.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="c1"># ruff: noqa: ARG002, N801, N802, N816</span>
+
+<span class="kn">import</span> <span class="nn">math</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">scipy.spatial</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">inf</span>
+
+<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">sphere</span>
+<span class="kn">from</span> <span class="nn">.sphere</span> <span class="kn">import</span> <span class="n">RADIUS_EARTH_KM</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Charles R Schmidt &lt;schmidtc@gmail.com&gt;&quot;</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;DISTANCE_METRICS&quot;</span><span class="p">,</span> <span class="s2">&quot;FLOAT_EPS&quot;</span><span class="p">,</span> <span class="s2">&quot;KDTree&quot;</span><span class="p">]</span>
+
+<span class="n">DISTANCE_METRICS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Euclidean&quot;</span><span class="p">,</span> <span class="s2">&quot;Arc&quot;</span><span class="p">]</span>
+<span class="n">FLOAT_EPS</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span>
+
+
+<div class="viewcode-block" id="KDTree">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.KDTree.html#libpysal.cg.KDTree">[docs]</a>
+<span class="k">def</span> <span class="nf">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;Euclidean&quot;</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;kd-tree built on top of kd-tree functionality in scipy. If using scipy</span>
+<span class="sd">    0.12 or greater uses the scipy.spatial.cKDTree, otherwise uses</span>
+<span class="sd">    scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance. Note</span>
+<span class="sd">    that Arc distance is only appropriate when points in latitude and</span>
+<span class="sd">    longitude, and the radius set to meaningful value (see docs below).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : array</span>
+<span class="sd">        The data points to be indexed. This array is not copied, and so</span>
+<span class="sd">        modifying this data will result in bogus results. Typically nx2.</span>
+<span class="sd">    leafsize : int</span>
+<span class="sd">        The number of points at which the algorithm switches</span>
+<span class="sd">        over to brute-force. Has to be positive. Optional, default is 10.</span>
+<span class="sd">    distance_metric : string</span>
+<span class="sd">        Options: &quot;Euclidean&quot; (default) and &quot;Arc&quot;.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        Radius of the sphere on which to compute distances. Assumes data in</span>
+<span class="sd">        latitude and longitude. Ignored if distance_metric=&quot;Euclidean&quot;. Typical</span>
+<span class="sd">        values: pysal.cg.RADIUS_EARTH_KM (default) pysal.cg.RADIUS_EARTH_MILES</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">distance_metric</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;euclidean&quot;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">12</span>
+            <span class="ow">and</span> <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span>
+        <span class="p">):</span>
+            <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">distance_metric</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;arc&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Arc_KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="p">,</span> <span class="n">radius</span><span class="p">)</span></div>
+
+
+
+<span class="c1"># internal hack for the Arc_KDTree class inheritance</span>
+<span class="k">if</span> <span class="p">(</span>
+    <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">12</span>
+    <span class="ow">and</span> <span class="nb">int</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span>
+<span class="p">):</span>
+    <span class="n">temp_KDTree</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="n">temp_KDTree</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span>
+
+
+<span class="k">class</span> <span class="nc">Arc_KDTree</span><span class="p">(</span><span class="n">temp_KDTree</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">leafsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mf">1.0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;KDTree using Arc Distance instead of Euclidean Distance.</span>
+
+<span class="sd">        Returned distances are based on radius. For Example, pass in the radius</span>
+<span class="sd">        of earth in miles to get back miles. Assumes data are Lng/Lat, does not</span>
+<span class="sd">        account for geoids.</span>
+
+<span class="sd">        For more information see docs for scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; d,i = kd.query((90,0), k=4)</span>
+<span class="sd">        &gt;&gt;&gt; d</span>
+<span class="sd">        array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])</span>
+<span class="sd">        &gt;&gt;&gt; circumference = 2*math.pi*sphere.RADIUS_EARTH_KM</span>
+<span class="sd">        &gt;&gt;&gt; round(d[0],5) == round(circumference/4.0,5)</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">=</span> <span class="n">radius</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">radius</span>
+        <span class="n">temp_KDTree</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">sphere</span><span class="o">.</span><span class="n">toXYZ</span><span class="p">,</span> <span class="n">data</span><span class="p">)),</span> <span class="n">leafsize</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_toXYZ</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># assume point is already in XYZ</span>
+            <span class="k">return</span> <span class="n">x</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># assume point is already in XYZ</span>
+            <span class="k">return</span> <span class="n">x</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sphere</span><span class="o">.</span><span class="n">toXYZ</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">sphere</span><span class="o">.</span><span class="n">toXYZ</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">x</span>
+
+    <span class="k">def</span> <span class="nf">count_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.count_neighbors</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p: ignored, kept to maintain compatibility with scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; kd.count_neighbors(kd,0)</span>
+<span class="sd">        4</span>
+<span class="sd">        &gt;&gt;&gt; circumference = 2.0*math.pi*sphere.RADIUS_EARTH_KM</span>
+<span class="sd">        &gt;&gt;&gt; kd.count_neighbors(kd,circumference/2.0)</span>
+<span class="sd">        16</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
+                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
+                <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">count_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">query</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">distance_upper_bound</span><span class="o">=</span><span class="n">inf</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : array-like, last dimension self.m</span>
+<span class="sd">            query points are lng/lat.</span>
+<span class="sd">        p: ignored</span>
+<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; d,i = kd.query((90,0), k=4)</span>
+<span class="sd">        &gt;&gt;&gt; d</span>
+<span class="sd">        array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])</span>
+<span class="sd">        &gt;&gt;&gt; circumference = 2*math.pi*sphere.RADIUS_EARTH_KM</span>
+<span class="sd">        &gt;&gt;&gt; round(d[0],5) == round(circumference/4.0,5)</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; d,i = kd.query(kd.data, k=3)</span>
+<span class="sd">        &gt;&gt;&gt; d2,i2 = kd.query(pts, k=3)</span>
+<span class="sd">        &gt;&gt;&gt; (d == d2).all()</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; (i == i2).all()</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">eps</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">distance_upper_bound</span> <span class="o">!=</span> <span class="n">inf</span><span class="p">:</span>
+            <span class="n">distance_upper_bound</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span>
+                <span class="n">distance_upper_bound</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span>
+            <span class="p">)</span>
+        <span class="n">d</span><span class="p">,</span> <span class="n">i</span> <span class="o">=</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_toXYZ</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">k</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">,</span> <span class="n">distance_upper_bound</span><span class="o">=</span><span class="n">distance_upper_bound</span>
+        <span class="p">)</span>
+        <span class="n">dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span>
+        <span class="k">if</span> <span class="n">dims</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">r</span><span class="p">),</span> <span class="n">i</span>
+        <span class="k">if</span> <span class="n">dims</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="c1"># TODO: implement linear2arcdist on numpy arrays</span>
+            <span class="n">d</span> <span class="o">=</span> <span class="p">[</span><span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">d</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="n">dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">d</span> <span class="o">=</span> <span class="p">[[</span><span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">row</span><span class="p">]</span> <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">d</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="n">i</span>
+
+    <span class="k">def</span> <span class="nf">query_ball_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query_ball_point</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p : ignored</span>
+<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; circumference = 2*math.pi*sphere.RADIUS_EARTH_KM</span>
+<span class="sd">        &gt;&gt;&gt; kd.query_ball_point(pts, circumference/4.)</span>
+<span class="sd">        array([list([0, 1, 2]), list([0, 1, 3]), list([0, 2, 3]), list([1, 2, 3])],</span>
+<span class="sd">              dtype=object)</span>
+<span class="sd">        &gt;&gt;&gt; kd.query_ball_point(pts, circumference/2.)</span>
+<span class="sd">        array([list([0, 1, 2, 3]), list([0, 1, 2, 3]), list([0, 1, 2, 3]),</span>
+<span class="sd">               list([0, 1, 2, 3])], dtype=object)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">eps</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
+        <span class="c1"># scipy.sphere.KDTree.query_ball_point appears to ignore</span>
+        <span class="c1"># the eps argument. we have some floating point errors moving</span>
+        <span class="c1"># back and forth between cordinate systems, so we&#39;ll account</span>
+        <span class="c1"># for that be adding some to our radius, 3*float&#39;s eps value.</span>
+        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
+                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
+                <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
+        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query_ball_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_toXYZ</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">r</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">query_ball_tree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query_ball_tree</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p : ignored</span>
+<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; (</span>
+<span class="sd">        ...     kd.query_ball_tree(kd, kd.circumference/4.)</span>
+<span class="sd">        ...     == [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; (</span>
+<span class="sd">        ...     kd.query_ball_tree(kd, kd.circumference/2.)</span>
+<span class="sd">        ...     == [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">eps</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
+        <span class="c1"># scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.</span>
+        <span class="c1"># we have some floating point errors moving back and forth between</span>
+        <span class="c1"># coordinate systems, so we&#39;ll account for that be adding some</span>
+        <span class="c1"># to our radius, 3*float&#39;s eps value.</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">radius</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Both trees must have the same radius.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
+                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
+                <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
+        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query_ball_tree</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">query_pairs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.query_pairs</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p : ignored</span>
+<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; kd.query_pairs(kd.circumference/4.) == set([(0, 1), (1, 3), (2, 3), (0, 2)])</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; (</span>
+<span class="sd">        ...     kd.query_pairs(kd.circumference/2.)</span>
+<span class="sd">        ...     == set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)])</span>
+<span class="sd">        ... )</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;r, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
+                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
+                <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
+        <span class="k">return</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">query_pairs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">eps</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">sparse_distance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">max_distance</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;See scipy.spatial.KDTree.sparse_distance_matrix</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p : ignored</span>
+<span class="sd">            kept to maintain compatibility with scipy.spatial.KDTree</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; pts = [(0,90), (0,0), (180,0), (0,-90)]</span>
+<span class="sd">        &gt;&gt;&gt; kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)</span>
+<span class="sd">        &gt;&gt;&gt; kd.sparse_distance_matrix(kd, kd.circumference/4.).todense()</span>
+<span class="sd">        matrix([[    0.        , 10007.54339801, 10007.54339801,     0.        ],</span>
+<span class="sd">                [10007.54339801,     0.        ,     0.        , 10007.54339801],</span>
+<span class="sd">                [10007.54339801,     0.        ,     0.        , 10007.54339801],</span>
+<span class="sd">                [    0.        , 10007.54339801, 10007.54339801,     0.        ]])</span>
+<span class="sd">        &gt;&gt;&gt; kd.sparse_distance_matrix(kd, kd.circumference/2.).todense()</span>
+<span class="sd">        matrix([[    0.        , 10007.54339801, 10007.54339801, 20015.08679602],</span>
+<span class="sd">                [10007.54339801,     0.        , 20015.08679602, 10007.54339801],</span>
+<span class="sd">                [10007.54339801, 20015.08679602,     0.        , 10007.54339801],</span>
+<span class="sd">                [20015.08679602, 10007.54339801, 10007.54339801,     0.        ]])</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span> <span class="o">!=</span> <span class="n">other</span><span class="o">.</span><span class="n">radius</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Both trees must have the same radius.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_distance</span> <span class="o">&gt;</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;max_distance, must not exceed 1/2 circumference of the sphere (</span><span class="si">%f</span><span class="s2">).&quot;</span>
+                <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">circumference</span>
+                <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="p">)</span>
+        <span class="n">max_distance</span> <span class="o">=</span> <span class="n">sphere</span><span class="o">.</span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">max_distance</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span> <span class="o">+</span> <span class="n">FLOAT_EPS</span> <span class="o">*</span> <span class="mi">3</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">temp_KDTree</span><span class="o">.</span><span class="n">sparse_distance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">,</span> <span class="n">max_distance</span><span class="p">)</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">tocoo</span><span class="p">()</span>
+        <span class="c1"># print D.data</span>
+        <span class="n">a2l</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">sphere</span><span class="o">.</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">radius</span><span class="p">)</span>
+        <span class="c1"># print map(a2l,D.data)</span>
+        <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">((</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">a2l</span><span class="p">,</span> <span class="n">D</span><span class="o">.</span><span class="n">data</span><span class="p">)),</span> <span class="p">(</span><span class="n">D</span><span class="o">.</span><span class="n">row</span><span class="p">,</span> <span class="n">D</span><span class="o">.</span><span class="n">col</span><span class="p">)))</span><span class="o">.</span><span class="n">todok</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+      
+  <h1>Source code for libpysal.cg.locators</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Computational geometry code for PySAL: Python Spatial Analysis Library.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow&quot;</span>
+<span class="n">__credits__</span> <span class="o">=</span> <span class="s2">&quot;Copyright (c) 2005-2011 Sergio J. Rey&quot;</span>
+
+<span class="c1"># ruff: noqa: B028, F403, F405</span>
+
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">from</span> <span class="nn">.rtree</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.shapes</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">.standalone</span> <span class="kn">import</span> <span class="o">*</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Grid&quot;</span><span class="p">,</span> <span class="s2">&quot;BruteForcePointLocator&quot;</span><span class="p">,</span> <span class="s2">&quot;PointLocator&quot;</span><span class="p">,</span> <span class="s2">&quot;PolygonLocator&quot;</span><span class="p">]</span>
+
+<span class="n">dep_msg</span> <span class="o">=</span> <span class="s2">&quot;is deprecated and will be removed in a future version of libpysal&quot;</span>
+
+
+<div class="viewcode-block" id="Grid">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid">[docs]</a>
+<span class="k">class</span> <span class="nc">Grid</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Representation of a binning data structure.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Grid.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bounds</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a grid with specified properties.</span>
+
+<span class="sd">        __init__(Rectangle, number) -&gt; Grid</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        bounds      : the area for the grid to encompass</span>
+<span class="sd">        resolution  : the diameter of each bin</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        TODO: complete this doctest</span>
+<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Grid &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">resolution</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Cannot create grid with resolution 0&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">res</span> <span class="o">=</span> <span class="n">resolution</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hash</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">i_range</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span>
+                <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">)</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">j_range</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span>
+                <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
+                <span class="s2">&quot;Invalid arguments for Grid(): (&quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">x_range</span><span class="p">)</span>
+                <span class="o">+</span> <span class="s2">&quot;, &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">y_range</span><span class="p">)</span>
+                <span class="o">+</span> <span class="s2">&quot;, &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+                <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="p">)</span> <span class="kn">from</span> <span class="nn">e</span></div>
+
+
+<div class="viewcode-block" id="Grid.in_grid">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.in_grid">[docs]</a>
+    <span class="k">def</span> <span class="nf">in_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns whether a 2-tuple location _loc_ lies inside the grid bounds.</span>
+
+<span class="sd">        Test tag: &lt;tc&gt;#is#Grid.in_grid&lt;/tc&gt;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__grid_loc</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">i_range</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">((</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">),</span> <span class="mi">0</span><span class="p">))</span>
+        <span class="n">j</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">j_range</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="nb">int</span><span class="p">((</span><span class="n">loc</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span><span class="p">),</span> <span class="mi">0</span><span class="p">))</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Grid.add">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.add">[docs]</a>
+    <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Adds an item to the grid at a specified location.</span>
+
+<span class="sd">        add(x, Point) -&gt; x</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        item  : the item to insert into the grid</span>
+<span class="sd">        pt : the location to insert the item at</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((4.2, 8.7)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_grid</span><span class="p">(</span><span class="n">pt</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
+                <span class="s2">&quot;Attempt to insert item at location outside grid bounds: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="n">grid_loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">grid_loc</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span> <span class="o">=</span> <span class="p">[(</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">)]</span>
+        <span class="k">return</span> <span class="n">item</span></div>
+
+
+<div class="viewcode-block" id="Grid.remove">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.remove">[docs]</a>
+    <span class="k">def</span> <span class="nf">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Removes an item from the grid at a specified location.</span>
+
+<span class="sd">        remove(x, Point) -&gt; x</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        item : the item to remove from the grid</span>
+<span class="sd">        pt : the location the item was added at</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((4.2, 8.7)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.remove(&#39;A&#39;, Point((4.2, 8.7)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_grid</span><span class="p">(</span><span class="n">pt</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
+                <span class="s2">&quot;Attempt to remove item at location outside grid bounds: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="n">grid_loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span><span class="o">.</span><span class="n">remove</span><span class="p">((</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span> <span class="o">==</span> <span class="p">[]:</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[</span><span class="n">grid_loc</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">item</span></div>
+
+
+<div class="viewcode-block" id="Grid.bounds">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.bounds">[docs]</a>
+    <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bounds</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a list of items found in the grid within the bounds specified.</span>
+
+<span class="sd">        bounds(Rectangle) -&gt; x list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        item     : the item to remove from the grid</span>
+<span class="sd">        pt       : the location the item was added at</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((1.0, 1.0)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;B&#39;, Point((4.0, 4.0)))</span>
+<span class="sd">        &#39;B&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.bounds(Rectangle(0, 0, 3, 3))</span>
+<span class="sd">        [&#39;A&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; g.bounds(Rectangle(2, 2, 5, 5))</span>
+<span class="sd">        [&#39;B&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; sorted(g.bounds(Rectangle(0, 0, 5, 5)))</span>
+<span class="sd">        [&#39;A&#39;, &#39;B&#39;]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">x_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
+        <span class="n">y_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">bounds</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">bounds</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+        <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lower_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="n">upper_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
+                    <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                        <span class="p">[</span>
+                            <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="p">[</span>
+                                <span class="n">item</span>
+                                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>
+                                <span class="k">if</span> <span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                                <span class="ow">and</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                            <span class="p">]</span>
+                        <span class="p">]</span>
+                    <span class="p">)</span>
+        <span class="k">return</span> <span class="n">items</span></div>
+
+
+<div class="viewcode-block" id="Grid.proximity">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.proximity">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a list of items found in the grid</span>
+<span class="sd">        within a specified distance of a point.</span>
+
+<span class="sd">        proximity(Point, number) -&gt; x list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pt : the location to search around</span>
+<span class="sd">        r  : the distance to search around the point</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((1.0, 1.0)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;B&#39;, Point((4.0, 4.0)))</span>
+<span class="sd">        &#39;B&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.proximity(Point((2.0, 1.0)), 2)</span>
+<span class="sd">        [&#39;A&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; g.proximity(Point((6.0, 5.0)), 3.0)</span>
+<span class="sd">        [&#39;B&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; sorted(g.proximity(Point((4.0, 1.0)), 4.0))</span>
+<span class="sd">        [&#39;A&#39;, &#39;B&#39;]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lower_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">))</span>
+        <span class="n">upper_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
+                    <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                        <span class="p">[</span>
+                            <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="p">[</span>
+                                <span class="n">item</span>
+                                <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>
+                                <span class="k">if</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">r</span>
+                            <span class="p">]</span>
+                        <span class="p">]</span>
+                    <span class="p">)</span>
+        <span class="k">return</span> <span class="n">items</span></div>
+
+
+<div class="viewcode-block" id="Grid.nearest">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Grid.html#libpysal.cg.Grid.nearest">[docs]</a>
+    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the nearest item to a point.</span>
+
+<span class="sd">        nearest(Point) -&gt; x</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pt : the location to search near</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;A&#39;, Point((1.0, 1.0)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.add(&#39;B&#39;, Point((4.0, 4.0)))</span>
+<span class="sd">        &#39;B&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.nearest(Point((2.0, 1.0)))</span>
+<span class="sd">        &#39;A&#39;</span>
+<span class="sd">        &gt;&gt;&gt; g.nearest(Point((7.0, 5.0)))</span>
+<span class="sd">        &#39;B&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">search_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">res</span>
+        <span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">search_size</span><span class="p">)</span> <span class="o">==</span> <span class="p">[]</span> <span class="ow">and</span> <span class="p">(</span>
+            <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
+            <span class="ow">or</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
+            <span class="ow">or</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
+            <span class="ow">or</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">x_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">pt</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">search_size</span>
+        <span class="p">):</span>
+            <span class="n">search_size</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">search_size</span>
+        <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lower_left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">search_size</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">search_size</span><span class="p">))</span>
+        <span class="n">upper_right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__grid_loc</span><span class="p">((</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">search_size</span><span class="p">,</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">search_size</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">lower_left</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">upper_right</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">:</span>
+                    <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span>
+                        <span class="p">[</span>
+                            <span class="p">(</span><span class="n">get_points_dist</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                            <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hash</span><span class="p">[(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)]</span>
+                        <span class="p">]</span>
+                    <span class="p">)</span>
+        <span class="k">if</span> <span class="n">items</span> <span class="o">==</span> <span class="p">[]:</span>
+            <span class="k">return</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="nb">min</span><span class="p">(</span><span class="n">items</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">BruteForcePointLocator</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    A class which does naive linear search on a set of Point objects.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates a naive index of the points specified.</span>
+
+<span class="sd">        __init__(Point list) -&gt; BruteForcePointLocator</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        points : a list of points to index (Point list)</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator([Point((0, 0)), Point((5, 0)), Point((0, 10))])</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;BruteForcePointLocator &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_points</span> <span class="o">=</span> <span class="n">points</span>
+
+    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_point</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the nearest point indexed to a query point.</span>
+
+<span class="sd">        nearest(Point) -&gt; Point</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        query_point : a point to find the nearest indexed point to</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator(points)</span>
+<span class="sd">        &gt;&gt;&gt; n = pl.nearest(Point((1, 1)))</span>
+<span class="sd">        &gt;&gt;&gt; str(n)</span>
+<span class="sd">        &#39;(0.0, 0.0)&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_points</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">query_point</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">region</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">region_rect</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed points located inside a rectangular query region.</span>
+
+<span class="sd">        region(Rectangle) -&gt; Point list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        region_rect : the rectangular range to find indexed points in</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator(points)</span>
+<span class="sd">        &gt;&gt;&gt; pts = pl.region(Rectangle(-1, -1, 10, 10))</span>
+<span class="sd">        &gt;&gt;&gt; len(pts)</span>
+<span class="sd">        3</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">[</span>
+            <span class="n">p</span>
+            <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_points</span>
+            <span class="k">if</span> <span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">region_rect</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed points located within some distance of an origin point.</span>
+
+<span class="sd">        proximity(Point, number) -&gt; Point list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        origin  : the point to find indexed points near</span>
+<span class="sd">        r       : the maximum distance to find indexed point from the origin point</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = BruteForcePointLocator(points)</span>
+<span class="sd">        &gt;&gt;&gt; neighs = pl.proximity(Point((1, 0)), 2)</span>
+<span class="sd">        &gt;&gt;&gt; len(neighs)</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; p = neighs[0]</span>
+<span class="sd">        &gt;&gt;&gt; isinstance(p, Point)</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; str(p)</span>
+<span class="sd">        &#39;(0.0, 0.0)&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_points</span> <span class="k">if</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">origin</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">r</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="PointLocator">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator">[docs]</a>
+<span class="k">class</span> <span class="nc">PointLocator</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    An abstract representation of a point indexing data structure.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="PointLocator.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a point locator object.</span>
+
+<span class="sd">        __init__(Point list) -&gt; PointLocator</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        points : a list of points to index</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;PointLocator &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span> <span class="o">=</span> <span class="n">BruteForcePointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="PointLocator.nearest">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.nearest">[docs]</a>
+    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_point</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the nearest point indexed to a query point.</span>
+
+<span class="sd">        nearest(Point) -&gt; Point</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        query_point : a point to find the nearest indexed point to</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
+<span class="sd">        &gt;&gt;&gt; n = pl.nearest(Point((1, 1)))</span>
+<span class="sd">        &gt;&gt;&gt; str(n)</span>
+<span class="sd">        &#39;(0.0, 0.0)&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">query_point</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="PointLocator.region">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.region">[docs]</a>
+    <span class="k">def</span> <span class="nf">region</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">region_rect</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed points located inside a rectangular query region.</span>
+
+<span class="sd">        region(Rectangle) -&gt; Point list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        region_rect : the rectangular range to find indexed points in</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
+<span class="sd">        &gt;&gt;&gt; pts = pl.region(Rectangle(-1, -1, 10, 10))</span>
+<span class="sd">        &gt;&gt;&gt; len(pts)</span>
+<span class="sd">        3</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">region_rect</span><span class="p">)</span></div>
+
+
+    <span class="n">overlapping</span> <span class="o">=</span> <span class="n">region</span>
+
+<div class="viewcode-block" id="PointLocator.polygon">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.polygon">[docs]</a>
+    <span class="k">def</span> <span class="nf">polygon</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygon</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed points located inside a polygon</span>
+<span class="sd">        &quot;&quot;&quot;</span></div>
+
+
+        <span class="c1"># get points in polygon bounding box</span>
+
+        <span class="c1"># for points in bounding box, check for inclusion in polygon</span>
+
+<div class="viewcode-block" id="PointLocator.proximity">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.proximity">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed points located within some distance of an origin point.</span>
+
+<span class="sd">        proximity(Point, number) -&gt; Point list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        origin  : the point to find indexed points near</span>
+<span class="sd">        r       : the maximum distance to find indexed point from the origin point</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; points = [Point((0, 0)), Point((1, 6)), Point((5.4, 1.4))]</span>
+<span class="sd">        &gt;&gt;&gt; pl = PointLocator(points)</span>
+<span class="sd">        &gt;&gt;&gt; len(pl.proximity(Point((1, 0)), 2))</span>
+<span class="sd">        1</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="PolygonLocator">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator">[docs]</a>
+<span class="k">class</span> <span class="nc">PolygonLocator</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    An abstract representation of a polygon indexing data structure.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="PolygonLocator.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygons</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a polygon locator object.</span>
+
+<span class="sd">        __init__(Polygon list) -&gt; PolygonLocator</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        polygons : a list of polygons to index</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
+<span class="sd">        &gt;&gt;&gt; isinstance(pl, PolygonLocator)</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;PolygonLocator &quot;</span> <span class="o">+</span> <span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span> <span class="o">=</span> <span class="n">polygons</span>
+        <span class="c1"># create and rtree</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span> <span class="o">=</span> <span class="n">RTree</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">polygons</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+            <span class="n">X</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+            <span class="n">Y</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">Rect</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="PolygonLocator.inside">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.inside">[docs]</a>
+    <span class="k">def</span> <span class="nf">inside</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_rectangle</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns polygons that are inside query_rectangle</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2, p3])</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 5, 5)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(3, 7, 5, 8)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        0</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(10, 10, 12, 12)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        0</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 12, 12)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.inside( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        3</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        inside means the intersection of the query rectangle and a</span>
+<span class="sd">        polygon is not empty and is equal to the area of the polygon</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">left</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">left</span>
+        <span class="n">right</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">right</span>
+        <span class="n">upper</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">upper</span>
+        <span class="n">lower</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">lower</span>
+
+        <span class="c1"># rtree rect</span>
+        <span class="n">qr</span> <span class="o">=</span> <span class="n">Rect</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
+        <span class="c1"># bb overlaps</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_rect</span><span class="p">(</span><span class="n">qr</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
+
+        <span class="n">qp</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span>
+                <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span>
+                <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)),</span>
+                <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)),</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">ip</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">GPPI</span> <span class="o">=</span> <span class="n">get_polygon_point_intersect</span>
+        <span class="k">for</span> <span class="n">poly</span> <span class="ow">in</span> <span class="n">res</span><span class="p">:</span>
+            <span class="n">lower</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+            <span class="n">right</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+            <span class="n">upper</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+            <span class="n">left</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+            <span class="n">p1</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">))</span>
+            <span class="n">p2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">GPPI</span><span class="p">(</span><span class="n">qp</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">GPPI</span><span class="p">(</span><span class="n">qp</span><span class="p">,</span> <span class="n">p2</span><span class="p">):</span>
+                <span class="n">ip</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">poly</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ip</span></div>
+
+
+<div class="viewcode-block" id="PolygonLocator.overlapping">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.overlapping">[docs]</a>
+    <span class="k">def</span> <span class="nf">overlapping</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_rectangle</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns list of polygons that overlap query_rectangle</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p3 = Polygon([Point((7, 1)), Point((8, 7)), Point((9, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2, p3])</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 5, 5)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        2</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(3, 7, 5, 8)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(10, 10, 12, 12)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        0</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(0, 0, 12, 12)</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.overlapping( qr )</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        3</span>
+<span class="sd">        &gt;&gt;&gt; qr = Rectangle(8, 3, 9, 4)</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((2, 1)), Point((2, 3)), Point((4, 3)), Point((4,1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((7, 1)), Point((7, 5)), Point((10, 5)), Point((10, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
+<span class="sd">        &gt;&gt;&gt; res = pl.overlapping(qr)</span>
+<span class="sd">        &gt;&gt;&gt; len(res)</span>
+<span class="sd">        1</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        overlapping means the intersection of the query rectangle and a</span>
+<span class="sd">        polygon is not empty and is no larger than the area of the polygon</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">left</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">left</span>
+        <span class="n">right</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">right</span>
+        <span class="n">upper</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">upper</span>
+        <span class="n">lower</span> <span class="o">=</span> <span class="n">query_rectangle</span><span class="o">.</span><span class="n">lower</span>
+
+        <span class="c1"># rtree rect</span>
+        <span class="n">qr</span> <span class="o">=</span> <span class="n">Rect</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
+
+        <span class="c1"># bb overlaps</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_rect</span><span class="p">(</span><span class="n">qr</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
+        <span class="c1"># have to check for polygon overlap using segment intersection</span>
+
+        <span class="c1"># add polys whose bb contains at least one of the corners of the query</span>
+        <span class="c1"># rectangle</span>
+
+        <span class="n">sw</span> <span class="o">=</span> <span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)</span>
+        <span class="n">se</span> <span class="o">=</span> <span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)</span>
+        <span class="n">ne</span> <span class="o">=</span> <span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
+        <span class="n">nw</span> <span class="o">=</span> <span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
+        <span class="n">pnts</span> <span class="o">=</span> <span class="p">[</span><span class="n">sw</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">ne</span><span class="p">,</span> <span class="n">nw</span><span class="p">]</span>
+        <span class="n">cs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">pnts</span><span class="p">:</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
+            <span class="n">cs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+
+        <span class="n">cs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">cs</span><span class="p">))</span>
+
+        <span class="n">overlapping</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="c1"># first find polygons with at least one vertex inside query rectangle</span>
+        <span class="n">remaining</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">res</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="k">for</span> <span class="n">vertex</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">:</span>
+                <span class="n">xb</span> <span class="o">=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">left</span>
+                <span class="n">xb</span> <span class="o">*=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">right</span>
+                <span class="n">yb</span> <span class="o">=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">lower</span>
+                <span class="n">yb</span> <span class="o">*=</span> <span class="n">vertex</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">upper</span>
+                <span class="k">if</span> <span class="n">xb</span> <span class="o">*</span> <span class="n">yb</span><span class="p">:</span>
+                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
+                    <span class="n">remaining</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
+                    <span class="k">break</span>
+
+        <span class="c1"># for remaining polys in bb overlap check if vertex chains intersect</span>
+        <span class="c1"># segments of the query rectangle</span>
+        <span class="n">left_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
+        <span class="n">right_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
+        <span class="n">lower_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)))</span>
+        <span class="n">upper_edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
+        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">remaining</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">polygon</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">vertices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">vertices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>  <span class="c1"># put on closed cartographic form</span>
+            <span class="n">nv</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nv</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="n">head</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">tail</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+                <span class="n">edge</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">)</span>
+                <span class="n">li</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">left_edge</span><span class="p">)</span>
+                <span class="k">if</span> <span class="p">(</span>
+                    <span class="n">li</span>
+                    <span class="ow">or</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">right_edge</span><span class="p">)</span>
+                    <span class="ow">or</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">lower_edge</span><span class="p">)</span>
+                    <span class="ow">or</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">edge</span><span class="p">,</span> <span class="n">upper_edge</span><span class="p">)</span>
+                <span class="p">):</span>
+                    <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
+                    <span class="k">break</span>
+        <span class="c1"># check remaining for explicit containment of the bounding rectangle</span>
+        <span class="c1"># cs has candidates for this check</span>
+        <span class="n">sw</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">sw</span><span class="p">)</span>
+        <span class="n">se</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">se</span><span class="p">)</span>
+        <span class="n">ne</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">ne</span><span class="p">)</span>
+        <span class="n">nw</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span><span class="n">nw</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">cs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="p">(</span>
+                <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">sw</span><span class="p">)</span>
+                <span class="ow">or</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">se</span><span class="p">)</span>
+                <span class="ow">or</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">ne</span><span class="p">)</span>
+                <span class="ow">or</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">polygon</span><span class="p">,</span> <span class="n">nw</span><span class="p">)</span>
+            <span class="p">):</span>
+                <span class="n">overlapping</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
+                <span class="k">break</span>
+        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">overlapping</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="PolygonLocator.nearest">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.nearest">[docs]</a>
+    <span class="k">def</span> <span class="nf">nearest</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query_point</span><span class="p">,</span> <span class="n">rule</span><span class="o">=</span><span class="s2">&quot;vertex&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the nearest polygon indexed to a query point based on</span>
+<span class="sd">        various rules.</span>
+
+<span class="sd">        nearest(Polygon) -&gt; Polygon</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        query_point  : a point to find the nearest indexed polygon to</span>
+
+<span class="sd">        rule         : representative point for polygon in nearest query.</span>
+<span class="sd">                 vertex -- measures distance between vertices and query_point</span>
+<span class="sd">                 centroid -- measures distance between centroid and</span>
+<span class="sd">                 query_point</span>
+<span class="sd">                 edge   -- measures the distance between edges and query_point</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
+<span class="sd">        &gt;&gt;&gt; try: n = pl.nearest(Point((-1, 1)))</span>
+<span class="sd">        ... except NotImplementedError: print(&quot;future test: str(min(n.vertices())) == (0.0, 1.0)&quot;)</span>
+<span class="sd">        future test: str(min(n.vertices())) == (0.0, 1.0)</span>
+<span class="sd">        &quot;&quot;&quot;</span>  <span class="c1"># noqa E501</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="PolygonLocator.region">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.region">[docs]</a>
+    <span class="k">def</span> <span class="nf">region</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">region_rect</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed polygons located inside a rectangular query region.</span>
+
+<span class="sd">        region(Rectangle) -&gt; Polygon list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        region_rect  : the rectangular range to find indexed polygons in</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
+<span class="sd">        &gt;&gt;&gt; n = pl.region(Rectangle(0, 0, 4, 10))</span>
+<span class="sd">        &gt;&gt;&gt; len(n)</span>
+<span class="sd">        2</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_locator</span>
+        <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">n</span><span class="p">:</span>
+            <span class="n">points</span> <span class="o">=</span> <span class="n">polygon</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="n">pl</span> <span class="o">=</span> <span class="n">BruteForcePointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+            <span class="n">pts</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">region_rect</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">n</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">polygon</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">n</span></div>
+
+
+<div class="viewcode-block" id="PolygonLocator.contains_point">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.contains_point">[docs]</a>
+    <span class="k">def</span> <span class="nf">contains_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns polygons that contain point</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        point: point (x,y)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        list of polygons containing point</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0,0)), Point((6,0)), Point((4,4))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((1,2)), Point((4,0)), Point((4,4))])</span>
+<span class="sd">        &gt;&gt;&gt; p1.contains_point((2,2))</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; p2.contains_point((2,2))</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
+<span class="sd">        &gt;&gt;&gt; len(pl.contains_point((2,2)))</span>
+<span class="sd">        2</span>
+<span class="sd">        &gt;&gt;&gt; p2.contains_point((1,1))</span>
+<span class="sd">        0</span>
+<span class="sd">        &gt;&gt;&gt; p1.contains_point((1,1))</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; len(pl.contains_point((1,1)))</span>
+<span class="sd">        1</span>
+<span class="sd">        &gt;&gt;&gt; p1.centroid</span>
+<span class="sd">        (3.3333333333333335, 1.3333333333333333)</span>
+<span class="sd">        &gt;&gt;&gt; pl.contains_point((1,1))[0].centroid</span>
+<span class="sd">        (3.3333333333333335, 1.3333333333333333)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># bbounding box containment</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">leaf_obj</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_rtree</span><span class="o">.</span><span class="n">query_point</span><span class="p">(</span><span class="n">point</span><span class="p">)</span> <span class="k">if</span> <span class="n">r</span><span class="o">.</span><span class="n">is_leaf</span><span class="p">()]</span>
+        <span class="c1"># explicit containment check for candidate polygons needed</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">poly</span> <span class="k">for</span> <span class="n">poly</span> <span class="ow">in</span> <span class="n">res</span> <span class="k">if</span> <span class="n">poly</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">)]</span></div>
+
+
+<div class="viewcode-block" id="PolygonLocator.proximity">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.proximity">[docs]</a>
+    <span class="k">def</span> <span class="nf">proximity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">rule</span><span class="o">=</span><span class="s2">&quot;vertex&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the indexed polygons located within some distance of an</span>
+<span class="sd">        origin point based on various rules.</span>
+
+<span class="sd">        proximity(Polygon, number) -&gt; Polygon list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        origin  : the point to find indexed polygons near</span>
+<span class="sd">        r       : the maximum distance to find indexed polygon from the origin point</span>
+
+<span class="sd">        rule    : representative point for polygon in nearest query.</span>
+<span class="sd">                vertex -- measures distance between vertices and query_point</span>
+<span class="sd">                centroid -- measures distance between centroid and</span>
+<span class="sd">                query_point</span>
+<span class="sd">                edge   -- measures the distance between edges and query_point</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 1)), Point((4, 5)), Point((5, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p2 = Polygon([Point((3, 9)), Point((6, 7)), Point((1, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; pl = PolygonLocator([p1, p2])</span>
+<span class="sd">        &gt;&gt;&gt; try:</span>
+<span class="sd">        ...     len(pl.proximity(Point((0, 0)), 2))</span>
+<span class="sd">        ... except NotImplementedError:</span>
+<span class="sd">        ...     print(&quot;future test: len(pl.proximity(Point((0, 0)), 2)) == 2&quot;)</span>
+<span class="sd">        future test: len(pl.proximity(Point((0, 0)), 2)) == 2</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+</div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
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+      <a href="#">Back to top</a>
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+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
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+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
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+      
+  <h1>Source code for libpysal.cg.shapes</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Computational geometry code for PySAL: Python Spatial Analysis Library.</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow, Hu Shao&quot;</span>
+
+<span class="c1"># ruff: noqa: A003, B028, N802, E402</span>
+
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
+
+<span class="kn">from</span> <span class="nn">.sphere</span> <span class="kn">import</span> <span class="n">arcdist</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;Point&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;LineSegment&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Line&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Ray&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Chain&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Polygon&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Rectangle&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;asShape&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<span class="n">dep_msg</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="s2">&quot;Objects based on the `Geometry` class will deprecated &quot;</span>
+    <span class="s2">&quot;and removed in a future version of libpysal.&quot;</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="asShape">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.asShape.html#libpysal.cg.asShape">[docs]</a>
+<span class="k">def</span> <span class="nf">asShape</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a PySAL shape object from ``obj``, which</span>
+<span class="sd">    must support the ``__geo_interface__``.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}</span>
+<span class="sd">        A geometric representation of an object.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    TypeError</span>
+<span class="sd">        Raised when ``obj`` is not a supported shape.</span>
+<span class="sd">    NotImplementedError</span>
+<span class="sd">        Raised when ``geo_type`` is not a supported type.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    obj : {libpysal.cg.{Point, LineSegment, Line, Ray, Chain, Polygon}</span>
+<span class="sd">        A new geometric representation of the object.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">Point</span> <span class="o">|</span> <span class="n">LineSegment</span> <span class="o">|</span> <span class="n">Line</span> <span class="o">|</span> <span class="n">Ray</span> <span class="o">|</span> <span class="n">Chain</span> <span class="o">|</span> <span class="n">Polygon</span><span class="p">):</span>
+        <span class="k">pass</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">geo</span> <span class="o">=</span> <span class="n">obj</span><span class="o">.</span><span class="n">__geo_interface__</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="s2">&quot;__geo_interface__&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="n">obj</span>
+
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">geo</span><span class="p">,</span> <span class="s2">&quot;type&quot;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%r</span><span class="s2"> does not appear to be a shape object.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">obj</span><span class="p">))</span>
+
+        <span class="n">geo_type</span> <span class="o">=</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+
+        <span class="c1"># if geo_type.startswith(&#39;multi&#39;):</span>
+        <span class="c1">#    raise NotImplementedError, &quot;%s are not supported at this time.&quot;%geo_type</span>
+
+        <span class="k">if</span> <span class="n">geo_type</span> <span class="ow">in</span> <span class="n">_geoJSON_type_to_Pysal_type</span><span class="p">:</span>
+            <span class="n">obj</span> <span class="o">=</span> <span class="n">_geoJSON_type_to_Pysal_type</span><span class="p">[</span><span class="n">geo_type</span><span class="p">]</span><span class="o">.</span><span class="n">__from_geo_interface__</span><span class="p">(</span><span class="n">geo</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> is not supported at this time.&quot;</span> <span class="o">%</span> <span class="n">geo_type</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">obj</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">Geometry</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;A base class to help implement ``is_geometry``</span>
+<span class="sd">    and make geometric types extendable.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+
+<div class="viewcode-block" id="Point">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Point.html#libpysal.cg.Point">[docs]</a>
+<span class="k">class</span> <span class="nc">Point</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric class for point objects.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    loc : tuple</span>
+<span class="sd">        The point&#39;s location (number :math:`x`-tuple, :math:`x` &gt; 1).</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; p = Point((1, 3))</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Point.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Point.html#libpysal.cg.Point.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="n">loc</span><span class="p">))</span></div>
+
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">__from_geo_interface__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geo</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">])</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">__geo_interface__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Point&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">}</span>
+
+    <span class="k">def</span> <span class="fm">__lt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Tests if the point is less than another object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.Point</span>
+<span class="sd">            An object to test equality against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt; Point((0, 1))</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt; Point((1, 1))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__le__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Tests if the point is less than or equal to another object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.Point</span>
+<span class="sd">            An object to test equality against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt;= Point((0, 1))</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &lt;= Point((1, 1))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Tests if the point is equal to another object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.Point</span>
+<span class="sd">            An object to test equality against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) == Point((0, 1))</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) == Point((1, 1))</span>
+<span class="sd">        False</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="fm">__ne__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Tests if the point is not equal to another object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.Point</span>
+<span class="sd">            An object to test equality against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) != Point((0, 1))</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) != Point((1, 1))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">!=</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="fm">__gt__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Tests if the point is greater than another object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.Point</span>
+<span class="sd">            An object to test equality against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt; Point((0, 1))</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt; Point((1, 1))</span>
+<span class="sd">        False</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__ge__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Tests if the point is greater than or equal to another object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.Point</span>
+<span class="sd">            An object to test equality against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt;= Point((0, 1))</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; Point((0, 1)) &gt;= Point((1, 1))</span>
+<span class="sd">        False</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__hash__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the hash of the point&#39;s location.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; hash(Point((0, 1))) == hash(Point((0, 1)))</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; hash(Point((0, 1))) == hash(Point((1, 1)))</span>
+<span class="sd">        False</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the coordinate for the given dimension.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        *args : tuple</span>
+<span class="sd">            A singleton tuple of :math:`(i)` with :math:`i`</span>
+<span class="sd">            as the index of the desired dimension.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Point((5.5, 4.3))</span>
+<span class="sd">        &gt;&gt;&gt; p[0] == 5.5</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; p[1] == 4.3</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__getslice__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">slice</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the coordinates for the given dimensions.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        *args : tuple</span>
+<span class="sd">            A tuple of :math:`(i,j)` with :math:`i` as the index to the start</span>
+<span class="sd">            slice and :math:`j` as the index to end the slice (excluded).</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Point((3, 6, 2))</span>
+<span class="sd">        &gt;&gt;&gt; p[:2] == (3, 6)</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; p[1:2] == (6,)</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="o">.</span><span class="n">__getslice__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the dimensions of the point.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; len(Point((1, 2)))</span>
+<span class="sd">        2</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the string representation of the ``Point``.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; Point((0, 1))</span>
+<span class="sd">        (0.0, 1.0)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a string representation of a ``Point`` object.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Point((1, 3))</span>
+<span class="sd">        &gt;&gt;&gt; str(p)</span>
+<span class="sd">        &#39;(1.0, 3.0)&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__loc</span><span class="p">)</span></div>
+
+        <span class="c1"># return &quot;POINT ({} {})&quot;.format(*self.__loc)</span>
+
+
+<div class="viewcode-block" id="LineSegment">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment">[docs]</a>
+<span class="k">class</span> <span class="nc">LineSegment</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of line segment objects.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    start_pt : libpysal.cg.Point</span>
+<span class="sd">        The point where the segment begins.</span>
+<span class="sd">    end_pt : libpysal.cg.Point</span>
+<span class="sd">        The point where the segment ends.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    p1 : libpysal.cg.Point</span>
+<span class="sd">        The starting point of the line segment.</span>
+<span class="sd">    p2 : Point</span>
+<span class="sd">        The ending point of the line segment.</span>
+<span class="sd">    bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">        The bounding box of the segment.</span>
+<span class="sd">    len : float</span>
+<span class="sd">        The length of the segment.</span>
+<span class="sd">    line : libpysal.cg.Line</span>
+<span class="sd">        The line on which the segment lies.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="LineSegment.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">start_pt</span><span class="p">,</span> <span class="n">end_pt</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="o">=</span> <span class="n">start_pt</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="o">=</span> <span class="n">end_pt</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="s2">&quot;LineSegment(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;, &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+        <span class="c1"># return &quot;LINESTRING ({} {}, {} {})&quot;.format(</span>
+        <span class="c1">#    self._p1[0], self._p1[1], self._p2[0], self._p2[1]</span>
+        <span class="c1"># )</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns ``True`` if ``self`` and ``other`` are the same line segment.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; l1 = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; l2 = LineSegment(Point((5, 6)), Point((1, 2)))</span>
+<span class="sd">        &gt;&gt;&gt; l1 == l2</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; l2 == l1</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">eq</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">):</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">p1</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="ow">and</span> <span class="n">other</span><span class="o">.</span><span class="n">p2</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span>
+                <span class="n">other</span><span class="o">.</span><span class="n">p2</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="ow">and</span> <span class="n">other</span><span class="o">.</span><span class="n">p1</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span>
+            <span class="p">):</span>
+                <span class="n">eq</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">return</span> <span class="n">eq</span>
+
+<div class="viewcode-block" id="LineSegment.intersect">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.intersect">[docs]</a>
+    <span class="k">def</span> <span class="nf">intersect</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Test whether segment intersects with other segment (``True``) or</span>
+<span class="sd">        not (``False``). Handles endpoints of segments being on other segment.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        other : libpysal.cg.LineSegment</span>
+<span class="sd">            Another line segment to check against.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((5, 0)), Point((10, 0)))</span>
+<span class="sd">        &gt;&gt;&gt; ls1 = LineSegment(Point((5, 0)), Point((10, 1)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.intersect(ls1)</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; ls2 = LineSegment(Point((5, 1)), Point((10, 1)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.intersect(ls2)</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; ls2 = LineSegment(Point((7, -1)), Point((7, 2)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.intersect(ls2)</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">ccw1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+        <span class="n">ccw2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="n">other</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
+        <span class="n">ccw3</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
+        <span class="n">ccw4</span> <span class="o">=</span> <span class="n">other</span><span class="o">.</span><span class="n">sw_ccw</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+
+        <span class="n">intersects</span> <span class="o">=</span> <span class="n">ccw1</span> <span class="o">*</span> <span class="n">ccw2</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">ccw3</span> <span class="o">*</span> <span class="n">ccw4</span> <span class="o">&lt;=</span> <span class="mi">0</span>
+
+        <span class="k">return</span> <span class="n">intersects</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_reset_props</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
+<span class="sd">        Resets attributes which are functions of other attributes.</span>
+<span class="sd">        The getters for these attributes (implemented as properties)</span>
+<span class="sd">        then recompute their values if they have been reset since</span>
+<span class="sd">        the last call to the getter.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; ls._reset_props()</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="nf">_get_p1</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
+<span class="sd">        Returns the ``p1`` attribute of the line segment.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._p1 : libpysal.cg.Point</span>
+<span class="sd">            The ``_p1`` attribute.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; r = ls._get_p1()</span>
+<span class="sd">        &gt;&gt;&gt; r == Point((1, 2))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span>
+
+    <span class="k">def</span> <span class="nf">_set_p1</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
+<span class="sd">        Sets the ``p1`` attribute of the line segment.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p1 : libpysal.cg.Point</span>
+<span class="sd">            A point.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._p1 : libpysal.cg.Point</span>
+<span class="sd">            The reset ``p1`` attribute.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; r = ls._set_p1(Point((3, -1)))</span>
+<span class="sd">        &gt;&gt;&gt; r == Point((3.0, -1.0))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span> <span class="o">=</span> <span class="n">p1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span>
+
+    <span class="n">p1</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_get_p1</span><span class="p">,</span> <span class="n">_set_p1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_get_p2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
+<span class="sd">        Returns the ``p2`` attribute of the line segment.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._p2 : libpysal.cg.Point</span>
+<span class="sd">            The ``_p2`` attribute.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; r = ls._get_p2()</span>
+<span class="sd">        &gt;&gt;&gt; r == Point((5, 6))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span>
+
+    <span class="k">def</span> <span class="nf">_set_p2</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">p2</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.**</span>
+<span class="sd">        Sets the ``p2`` attribute of the line segment.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        p2 : libpysal.cg.Point</span>
+<span class="sd">            A point.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._p2 : libpysal.cg.Point</span>
+<span class="sd">            The reset ``p2`` attribute.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; r = ls._set_p2(Point((3, -1)))</span>
+<span class="sd">        &gt;&gt;&gt; r == Point((3.0, -1.0))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span> <span class="o">=</span> <span class="n">p2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span>
+
+    <span class="n">p2</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">_get_p2</span><span class="p">,</span> <span class="n">_set_p2</span><span class="p">)</span>
+
+<div class="viewcode-block" id="LineSegment.is_ccw">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_ccw">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_ccw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns whether a point is counterclockwise of the</span>
+<span class="sd">        segment (``True``) or not (``False``). Exclusive.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pt : libpysal.cg.Point</span>
+<span class="sd">            A point lying ccw or cw of a segment.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((0, 0)), Point((5, 0)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.is_ccw(Point((2, 2)))</span>
+<span class="sd">        True</span>
+
+<span class="sd">        &gt;&gt;&gt; ls.is_ccw(Point((2, -2)))</span>
+<span class="sd">        False</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">v1</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">v2</span> <span class="o">=</span> <span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span></div>
+
+
+<div class="viewcode-block" id="LineSegment.is_cw">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_cw">[docs]</a>
+    <span class="k">def</span> <span class="nf">is_cw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns whether a point is clockwise of the</span>
+<span class="sd">        segment (``True``) or not (``False``). Exclusive.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        pt : libpysal.cg.Point</span>
+<span class="sd">            A point lying ccw or cw of a segment.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((0, 0)), Point((5, 0)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.is_cw(Point((2, 2)))</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; ls.is_cw(Point((2, -2)))</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">v1</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">v2</span> <span class="o">=</span> <span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span></div>
+
+
+<div class="viewcode-block" id="LineSegment.sw_ccw">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.sw_ccw">[docs]</a>
+    <span class="k">def</span> <span class="nf">sw_ccw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sedgewick test for ``pt`` being ccw of segment.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        is_ccw : bool</span>
+<span class="sd">            ``1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is ccw.</span>
+<span class="sd">            ``-1`` if turn from ``self.p1`` to ``self.p2`` to ``pt`` is cw.</span>
+<span class="sd">            ``-1`` if the points are collinear and ``self.p1`` is in the middle.</span>
+<span class="sd">            ``1`` if the points are collinear and ``self.p2`` is in the middle.</span>
+<span class="sd">            ``0`` if the points are collinear and ``pt`` is in the middle.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">p0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p2</span>
+        <span class="n">p2</span> <span class="o">=</span> <span class="n">pt</span>
+
+        <span class="n">dx1</span> <span class="o">=</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">dy1</span> <span class="o">=</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">dx2</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">dy2</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">&lt;</span> <span class="n">dy2</span> <span class="o">*</span> <span class="n">dx1</span><span class="p">:</span>
+            <span class="n">is_ccw</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">elif</span> <span class="p">(</span><span class="n">dy1</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">&gt;</span> <span class="n">dy2</span> <span class="o">*</span> <span class="n">dx1</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">dx1</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dy2</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
+            <span class="n">is_ccw</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="k">elif</span> <span class="n">dx1</span> <span class="o">*</span> <span class="n">dx1</span> <span class="o">+</span> <span class="n">dy1</span> <span class="o">*</span> <span class="n">dy1</span> <span class="o">&gt;=</span> <span class="n">dx2</span> <span class="o">*</span> <span class="n">dx2</span> <span class="o">+</span> <span class="n">dy2</span> <span class="o">*</span> <span class="n">dy2</span><span class="p">:</span>
+            <span class="n">is_ccw</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">is_ccw</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="k">return</span> <span class="n">is_ccw</span></div>
+
+
+<div class="viewcode-block" id="LineSegment.get_swap">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.get_swap">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_swap</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a ``LineSegment`` object which has its endpoints swapped.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        line_seg : libpysal.cg.LineSegment</span>
+<span class="sd">            The ``LineSegment`` object which has its endpoints swapped.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; swap = ls.get_swap()</span>
+<span class="sd">        &gt;&gt;&gt; swap.p1[0]</span>
+<span class="sd">        5.0</span>
+
+<span class="sd">        &gt;&gt;&gt; swap.p1[1]</span>
+<span class="sd">        6.0</span>
+
+<span class="sd">        &gt;&gt;&gt; swap.p2[0]</span>
+<span class="sd">        1.0</span>
+
+<span class="sd">        &gt;&gt;&gt; swap.p2[1]</span>
+<span class="sd">        2.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">line_seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">line_seg</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the minimum bounding box of a ``LineSegment`` object.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">            The bounding box of the line segment.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((1, 2)), Point((5, 6)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.bounding_box.left</span>
+<span class="sd">        1.0</span>
+
+<span class="sd">        &gt;&gt;&gt; ls.bounding_box.lower</span>
+<span class="sd">        2.0</span>
+
+<span class="sd">        &gt;&gt;&gt; ls.bounding_box.right</span>
+<span class="sd">        5.0</span>
+
+<span class="sd">        &gt;&gt;&gt; ls.bounding_box.upper</span>
+<span class="sd">        6.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># If LineSegment attributes p1, p2 changed, recompute</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
+                <span class="nb">min</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span>
+                <span class="nb">min</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]]),</span>
+                <span class="nb">max</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span>
+                <span class="nb">max</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]]),</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="n">Rectangle</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">left</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">right</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the length of a ``LineSegment`` object.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((2, 2)), Point((5, 2)))</span>
+<span class="sd">        &gt;&gt;&gt; ls.len</span>
+<span class="sd">        3.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1"># If LineSegment attributes p1, p2 changed, recompute</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">line</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a ``Line`` object of the line on which the segment lies.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._line : libpysal.cg.Line</span>
+<span class="sd">            The ``Line`` object of the line on which the segment lies.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; ls = LineSegment(Point((2, 2)), Point((3, 3)))</span>
+<span class="sd">        &gt;&gt;&gt; l = ls.line</span>
+<span class="sd">        &gt;&gt;&gt; l.m</span>
+<span class="sd">        1.0</span>
+
+<span class="sd">        &gt;&gt;&gt; l.b</span>
+<span class="sd">        0.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">dx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">dy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">dx</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">dy</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">elif</span> <span class="n">dx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="n">VerticalLine</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">m</span> <span class="o">=</span> <span class="n">dy</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">dx</span><span class="p">)</span>
+                <span class="c1"># y - mx</span>
+                <span class="n">b</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">m</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_line</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_line</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">VerticalLine</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of verticle line objects.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x : {int, float}</span>
+<span class="sd">        The :math:`x`-intercept of the line. ``x`` is also an attribute.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; ls = VerticalLine(0)</span>
+<span class="sd">    &gt;&gt;&gt; ls.m</span>
+<span class="sd">    inf</span>
+
+<span class="sd">    &gt;&gt;&gt; ls.b</span>
+<span class="sd">    nan</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;nan&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>  <span class="c1"># noqa ARG002</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the :math:`x`-value of the line at a particular :math:`y`-value.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : {int, float}</span>
+<span class="sd">            The :math:`y`-value at which to compute :math:`x`.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; l = VerticalLine(0)</span>
+<span class="sd">        &gt;&gt;&gt; l.x(0.25)</span>
+<span class="sd">        0.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_x</span>
+
+    <span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>  <span class="c1"># noqa ARG002</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the :math:`y`-value of the line at a particular :math:`x`-value.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : {int, float}</span>
+<span class="sd">            The :math:`x`-value at which to compute :math:`y`.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; l = VerticalLine(1)</span>
+<span class="sd">        &gt;&gt;&gt; l.y(1)</span>
+<span class="sd">        nan</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;nan&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="Line">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line">[docs]</a>
+<span class="k">class</span> <span class="nc">Line</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of line objects.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    m : {int, float}</span>
+<span class="sd">        The slope of the line. ``m`` is also an attribute.</span>
+<span class="sd">    b : {int, float}</span>
+<span class="sd">        The :math:`y`-intercept of the line. ``b`` is also an attribute.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ArithmeticError</span>
+<span class="sd">        Raised when infinity is passed in as the slope.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; ls = Line(1, 0)</span>
+<span class="sd">    &gt;&gt;&gt; ls.m</span>
+<span class="sd">    1.0</span>
+
+<span class="sd">    &gt;&gt;&gt; ls.b</span>
+<span class="sd">    0.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Line.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">m</span> <span class="o">==</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ArithmeticError</span><span class="p">(</span><span class="s2">&quot;Slope cannot be infinite.&quot;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">b</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">b</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Line.x">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line.x">[docs]</a>
+    <span class="k">def</span> <span class="nf">x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the :math:`x`-value of the line at a particular :math:`y`-value.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : {int, float}</span>
+<span class="sd">            The :math:`y`-value at which to compute :math:`x`.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ArithmeticError</span>
+<span class="sd">            Raised when ``0.`` is passed in as the slope.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; l = Line(0.5, 0)</span>
+<span class="sd">        &gt;&gt;&gt; l.x(0.25)</span>
+<span class="sd">        0.5</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ArithmeticError</span><span class="p">(</span><span class="s2">&quot;Cannot solve for &#39;x&#39; when slope is zero.&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span></div>
+
+
+<div class="viewcode-block" id="Line.y">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Line.html#libpysal.cg.Line.y">[docs]</a>
+    <span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the :math:`y`-value of the line at a particular :math:`x`-value.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        x : {int, float}</span>
+<span class="sd">            The :math:`x`-value at which to compute :math:`y`.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; l = Line(1, 0)</span>
+<span class="sd">        &gt;&gt;&gt; l.y(1)</span>
+<span class="sd">        1.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">m</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">b</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Ray">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Ray.html#libpysal.cg.Ray">[docs]</a>
+<span class="k">class</span> <span class="nc">Ray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of ray objects.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    origin : libpysal.cg.Point</span>
+<span class="sd">        The point where the ray originates.</span>
+<span class="sd">    second_p :</span>
+<span class="sd">        The second point specifying the ray (not ``origin``.)</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    o : libpysal.cg.Point</span>
+<span class="sd">        The origin (point where ray originates). See ``origin``.</span>
+<span class="sd">    p : libpysal.cg.Point</span>
+<span class="sd">        The second point on the ray (not the point where the</span>
+<span class="sd">        ray originates). See ``second_p``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; l = Ray(Point((0, 0)), Point((1, 0)))</span>
+<span class="sd">    &gt;&gt;&gt; str(l.o)</span>
+<span class="sd">    &#39;(0.0, 0.0)&#39;</span>
+
+<span class="sd">    &gt;&gt;&gt; str(l.p)</span>
+<span class="sd">    &#39;(1.0, 0.0)&#39;</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Ray.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Ray.html#libpysal.cg.Ray.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">second_p</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">o</span> <span class="o">=</span> <span class="n">origin</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">second_p</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Chain">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Chain.html#libpysal.cg.Chain">[docs]</a>
+<span class="k">class</span> <span class="nc">Chain</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of a chain, also known as a polyline.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A point list or list of point lists.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        The list of points of the vertices of the chain in order.</span>
+<span class="sd">    len : float</span>
+<span class="sd">        The geometric length of the chain.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Chain.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Chain.html#libpysal.cg.Chain.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertices</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">vertices</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span></div>
+
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">__from_geo_interface__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geo</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;linestring&quot;</span><span class="p">:</span>
+            <span class="n">verts</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]]</span>
+        <span class="k">elif</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;multilinestring&quot;</span><span class="p">:</span>
+            <span class="n">verts</span> <span class="o">=</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="n">part</span><span class="p">))</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%r</span><span class="s2"> is not a Chain.&quot;</span> <span class="o">%</span> <span class="n">geo</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">verts</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">__geo_interface__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;LineString&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;MultiLineString&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_reset_props</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.** Resets attributes which are</span>
+<span class="sd">        functions of other attributes. The ``getter``s for these attributes</span>
+<span class="sd">        (implemented as ``properties``) then recompute their values if they</span>
+<span class="sd">        have been reset since the last call to the ``getter``.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">vertices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the vertices of the chain in clockwise order.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; verts = c.vertices</span>
+<span class="sd">        &gt;&gt;&gt; len(verts)</span>
+<span class="sd">        4</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">),</span> <span class="p">[])</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">parts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the parts (lists of ``libpysal.cg.Point`` objects) of the chain.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; c = Chain(</span>
+<span class="sd">        ...     [</span>
+<span class="sd">        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],</span>
+<span class="sd">        ...         [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]</span>
+<span class="sd">        ...     ]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; len(c.parts)</span>
+<span class="sd">        2</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the bounding box of the chain.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">            The bounding box of the chain.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; c.bounding_box.left</span>
+<span class="sd">        0.0</span>
+
+<span class="sd">        &gt;&gt;&gt; c.bounding_box.lower</span>
+<span class="sd">        0.0</span>
+
+<span class="sd">        &gt;&gt;&gt; c.bounding_box.right</span>
+<span class="sd">        2.0</span>
+
+<span class="sd">        &gt;&gt;&gt; c.bounding_box.upper</span>
+<span class="sd">        1.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
+                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the geometric length of the chain.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; c = Chain([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((2, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; c.len</span>
+<span class="sd">        3.0</span>
+
+<span class="sd">        &gt;&gt;&gt; c = Chain(</span>
+<span class="sd">        ...     [</span>
+<span class="sd">        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1))],</span>
+<span class="sd">        ...         [Point((10, 10)), Point((11, 10)), Point((11, 11))]</span>
+<span class="sd">        ...     ]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; c.len</span>
+<span class="sd">        4.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">dist</span><span class="p">(</span><span class="n">v1</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">,</span> <span class="n">v2</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+            <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">def</span> <span class="nf">part_perimeter</span><span class="p">(</span><span class="n">p</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">dist</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">arclen</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the geometric length of the chain</span>
+<span class="sd">        computed using &#39;arcdistance&#39; (meters).</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">part_perimeter</span><span class="p">(</span><span class="n">p</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">arcdist</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="mf">1000.0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_arclen</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">segments</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the segments that compose the chain.&quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">[</span>
+            <span class="p">[</span><span class="n">LineSegment</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">part</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">part</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
+            <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span>
+        <span class="p">]</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">Ring</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of a linear ring. Linear rings must be</span>
+<span class="sd">    closed, the first and last point must be the same. Open rings will</span>
+<span class="sd">    be closed. This class exists primarily as a geometric primitive to</span>
+<span class="sd">    form complex polygons with multiple rings and holes. The ordering</span>
+<span class="sd">    of the vertices is ignored and will not be altered.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of vertices.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of points with the vertices of the ring.</span>
+<span class="sd">    len : int</span>
+<span class="sd">        The number of vertices.</span>
+<span class="sd">    perimeter : float</span>
+<span class="sd">        The geometric length of the perimeter of the ring.</span>
+<span class="sd">    bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">        The bounding box of the ring.</span>
+<span class="sd">    area : float</span>
+<span class="sd">        The area enclosed by the ring.</span>
+<span class="sd">    centroid : {tuple, libpysal.cg.Point}</span>
+<span class="sd">        The centroid of the ring defined by the &#39;center of gravity&#39;</span>
+<span class="sd">        or &#39;center or mass&#39;.</span>
+<span class="sd">    _quad_tree_structure : libpysal.cg.QuadTreeStructureSingleRing</span>
+<span class="sd">        The quad tree structure for the ring. This structure helps</span>
+<span class="sd">        test if a point is inside the ring.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertices</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">vertices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[:]</span> <span class="o">+</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
+            <span class="c1"># msg = &quot;Supplied vertices do not form a closed ring, &quot;</span>
+            <span class="c1"># msg += &quot;the first and last vertices are not the same.&quot;</span>
+            <span class="c1"># raise ValueError(msg)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">dist</span><span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">perimeter</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span>
+            <span class="n">v</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">dist</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the bounding box of the ring.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">            The bounding box of the ring.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Ring(</span>
+<span class="sd">        ...     [</span>
+<span class="sd">        ...         Point((0, 0)),</span>
+<span class="sd">        ...         Point((2, 0)),</span>
+<span class="sd">        ...         Point((2, 1)),</span>
+<span class="sd">        ...         Point((0, 1)),</span>
+<span class="sd">        ...         Point((0, 0))</span>
+<span class="sd">        ...     ]</span>
+<span class="sd">        ... )</span>
+
+<span class="sd">        &gt;&gt;&gt; r.bounding_box.left</span>
+<span class="sd">        0.0</span>
+
+<span class="sd">        &gt;&gt;&gt; r.bounding_box.lower</span>
+<span class="sd">        0.0</span>
+
+<span class="sd">        &gt;&gt;&gt; r.bounding_box.right</span>
+<span class="sd">        2.0</span>
+
+<span class="sd">        &gt;&gt;&gt; r.bounding_box.upper</span>
+<span class="sd">        1.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the area of the ring.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Ring(</span>
+<span class="sd">        ...     [</span>
+<span class="sd">        ...         Point((0, 0)),</span>
+<span class="sd">        ...         Point((2, 0)),</span>
+<span class="sd">        ...         Point((2, 1)),</span>
+<span class="sd">        ...         Point((0, 1)),</span>
+<span class="sd">        ...         Point((0, 0))</span>
+<span class="sd">        ...     ]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; r.area</span>
+<span class="sd">        2.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">signed_area</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">signed_area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+            <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+            <span class="n">A</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="n">A</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
+            <span class="n">A</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="o">=</span> <span class="o">-</span><span class="n">A</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_area</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the centroid of the ring.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._centroid : libpysal.cg.Point</span>
+<span class="sd">            The ring&#39;s centroid.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The centroid returned by this method is the geometric centroid.</span>
+<span class="sd">        Also known as the &#39;center of gravity&#39; or &#39;center of mass&#39;.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Ring(</span>
+<span class="sd">        ...     [</span>
+<span class="sd">        ...         Point((0, 0)),</span>
+<span class="sd">        ...         Point((2, 0)),</span>
+<span class="sd">        ...         Point((2, 1)),</span>
+<span class="sd">        ...         Point((0, 1)),</span>
+<span class="sd">        ...         Point((0, 0))</span>
+<span class="sd">        ...     ]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; str(r.centroid)</span>
+<span class="sd">        &#39;(1.0, 0.5)&#39;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+            <span class="n">A</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">signed_area</span>
+            <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+            <span class="n">cx</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">cy</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">N</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="n">f</span> <span class="o">=</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">cx</span> <span class="o">+=</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">f</span>
+                <span class="n">cy</span> <span class="o">+=</span> <span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">f</span>
+            <span class="n">cx</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">6</span> <span class="o">*</span> <span class="n">A</span><span class="p">)</span> <span class="o">*</span> <span class="n">cx</span>
+            <span class="n">cy</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">6</span> <span class="o">*</span> <span class="n">A</span><span class="p">)</span> <span class="o">*</span> <span class="n">cy</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span>
+
+    <span class="k">def</span> <span class="nf">build_quad_tree_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Build the quad tree structure for this polygon. Once</span>
+<span class="sd">        the structure is built, speed for testing if a point is</span>
+<span class="sd">        inside the ring will be increased significantly.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span> <span class="o">=</span> <span class="n">QuadTreeStructureSingleRing</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">contains_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Point containment using winding number. The implementation is based on</span>
+<span class="sd">        `this &lt;http://www.engr.colostate.edu/~dga/dga/papers/point_in_polygon.pdf&gt;`_.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        point : libpysal.cg.Point</span>
+<span class="sd">            The point to test for containment.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        point_contained : bool</span>
+<span class="sd">            ``True`` if ``point`` is contained within the polygon, otherwise ``False``.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">point_contained</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">point</span>
+
+            <span class="c1"># bbox checks</span>
+            <span class="n">bbleft</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+            <span class="n">bbright</span> <span class="o">=</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+            <span class="n">bblower</span> <span class="o">=</span> <span class="n">y</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+            <span class="n">bbupper</span> <span class="o">=</span> <span class="n">y</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+
+            <span class="k">if</span> <span class="n">bbleft</span> <span class="ow">or</span> <span class="n">bbright</span> <span class="ow">or</span> <span class="n">bblower</span> <span class="ow">or</span> <span class="n">bbupper</span><span class="p">:</span>
+                <span class="k">pass</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">rn</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
+                <span class="n">xs</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rn</span><span class="p">)]</span>
+                <span class="n">ys</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rn</span><span class="p">)]</span>
+                <span class="n">w</span> <span class="o">=</span> <span class="mi">0</span>
+
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                    <span class="n">yi</span> <span class="o">=</span> <span class="n">ys</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">yj</span> <span class="o">=</span> <span class="n">ys</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+                    <span class="n">xi</span> <span class="o">=</span> <span class="n">xs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">xj</span> <span class="o">=</span> <span class="n">xs</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">yi</span> <span class="o">*</span> <span class="n">yj</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">r</span> <span class="o">=</span> <span class="n">xi</span> <span class="o">+</span> <span class="n">yi</span> <span class="o">*</span> <span class="p">(</span><span class="n">xj</span> <span class="o">-</span> <span class="n">xi</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">yi</span> <span class="o">-</span> <span class="n">yj</span><span class="p">)</span>
+                        <span class="k">if</span> <span class="n">r</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="n">yi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                                <span class="n">w</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="k">else</span><span class="p">:</span>
+                                <span class="n">w</span> <span class="o">-=</span> <span class="mi">1</span>
+                    <span class="k">elif</span> <span class="n">yi</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">xi</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">yj</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">w</span> <span class="o">+=</span> <span class="mf">0.5</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">w</span> <span class="o">-=</span> <span class="mf">0.5</span>
+                    <span class="k">elif</span> <span class="n">yj</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">xj</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">yi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">w</span> <span class="o">+=</span> <span class="mf">0.5</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">w</span> <span class="o">-=</span> <span class="mf">0.5</span>
+                <span class="k">if</span> <span class="n">w</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">pass</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">point_contained</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">point_contained</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quad_tree_structure</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">point_contained</span>
+
+
+<div class="viewcode-block" id="Polygon">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon">[docs]</a>
+<span class="k">class</span> <span class="nc">Polygon</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of polygon objects.</span>
+<span class="sd">    Returns a polygon created from the objects specified.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of vertices or a list of lists of vertices.</span>
+<span class="sd">    holes : list</span>
+<span class="sd">        A list of sub-polygons to be considered as holes.</span>
+<span class="sd">        Default is ``None``.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of points with the vertices of the polygon in clockwise order.</span>
+<span class="sd">    len : int</span>
+<span class="sd">        The number of vertices including holes.</span>
+<span class="sd">    perimeter : float</span>
+<span class="sd">        The geometric length of the perimeter of the polygon.</span>
+<span class="sd">    bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">        The bounding box of the polygon.</span>
+<span class="sd">    bbox : list</span>
+<span class="sd">        A list representation of the bounding box in the</span>
+<span class="sd">        form ``[left, lower, right, upper]``.</span>
+<span class="sd">    area : float</span>
+<span class="sd">        The area enclosed by the polygon.</span>
+<span class="sd">    centroid : tuple</span>
+<span class="sd">        The &#39;center of gravity&#39;, i.e. the mean point of the polygon.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; p1 = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Polygon.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">holes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">def</span> <span class="nf">clockwise</span><span class="p">(</span><span class="n">part</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">standalone</span><span class="o">.</span><span class="n">is_clockwise</span><span class="p">(</span><span class="n">part</span><span class="p">):</span>
+                <span class="k">return</span> <span class="n">part</span><span class="p">[:]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">part</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+        <span class="n">vl</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">vl</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Ring</span><span class="p">,</span> <span class="n">vertices</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span> <span class="o">=</span> <span class="p">[</span><span class="n">Ring</span><span class="p">(</span><span class="n">vertices</span><span class="p">)]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">vertices</span><span class="p">)]</span>
+        <span class="k">if</span> <span class="n">holes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">holes</span> <span class="o">!=</span> <span class="p">[]:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">holes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Ring</span><span class="p">,</span> <span class="n">holes</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">hole</span><span class="p">)</span> <span class="k">for</span> <span class="n">hole</span> <span class="ow">in</span> <span class="n">holes</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span> <span class="o">=</span> <span class="p">[</span><span class="n">Ring</span><span class="p">(</span><span class="n">holes</span><span class="p">)]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span> <span class="o">=</span> <span class="p">[</span><span class="n">clockwise</span><span class="p">(</span><span class="n">holes</span><span class="p">)]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span> <span class="o">=</span> <span class="p">[[]]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset_props</span><span class="p">()</span></div>
+
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">__from_geo_interface__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geo</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;While PySAL does not differentiate polygons and multipolygons</span>
+<span class="sd">        GEOS, Shapely, and geoJSON do. In GEOS, etc, polygons may only</span>
+<span class="sd">        have a single exterior ring, all other parts are holes.</span>
+<span class="sd">        MultiPolygons are simply a list of polygons.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">geo_type</span> <span class="o">=</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">geo_type</span> <span class="o">==</span> <span class="s2">&quot;multipolygon&quot;</span><span class="p">:</span>
+            <span class="n">parts</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">holes</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">polygon</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]:</span>
+                <span class="n">verts</span> <span class="o">=</span> <span class="p">[[</span><span class="n">Point</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">polygon</span><span class="p">]</span>
+                <span class="n">parts</span> <span class="o">+=</span> <span class="n">verts</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">holes</span> <span class="o">+=</span> <span class="n">verts</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">holes</span><span class="p">:</span>
+                <span class="n">holes</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">parts</span><span class="p">,</span> <span class="n">holes</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">verts</span> <span class="o">=</span> <span class="p">[[</span><span class="n">Point</span><span class="p">(</span><span class="n">pt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">]]</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">verts</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">],</span> <span class="n">verts</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">__geo_interface__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return ``__geo_interface__`` information lookup.&quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">geo</span> <span class="o">=</span> <span class="p">{</span>
+                <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;MultiPolygon&quot;</span><span class="p">,</span>
+                <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="p">[[</span><span class="n">part</span><span class="p">]</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">parts</span><span class="p">],</span>
+            <span class="p">}</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">geo</span><span class="p">[</span><span class="s2">&quot;coordinates&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span>
+            <span class="k">return</span> <span class="n">geo</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Polygon&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">}</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;Polygon&quot;</span><span class="p">,</span> <span class="s2">&quot;coordinates&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_reset_props</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Resets the geometric properties of the polygon.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_area</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_centroid</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">len</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the number of vertices in the polygon.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])</span>
+<span class="sd">        &gt;&gt;&gt; p1.len</span>
+<span class="sd">        4</span>
+
+<span class="sd">        &gt;&gt;&gt; len(p1)</span>
+<span class="sd">        4</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_len</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">vertices</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the vertices of the polygon in clockwise order.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p1 = Polygon([Point((0, 0)), Point((0, 1)), Point((1, 1)), Point((1, 0))])</span>
+<span class="sd">        &gt;&gt;&gt; len(p1.vertices)</span>
+<span class="sd">        4</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">),</span> <span class="p">[])</span> <span class="o">+</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">),</span> <span class="p">[])</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">holes</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the holes of the polygon in clockwise order.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
+<span class="sd">        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],</span>
+<span class="sd">        ...     [Point((1, 2)), Point((2, 2)), Point((2, 1)), Point((1, 1))]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; len(p.holes)</span>
+<span class="sd">        1</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">parts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the parts of the polygon in clockwise order.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
+<span class="sd">        ...     [</span>
+<span class="sd">        ...         [Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))],</span>
+<span class="sd">        ...         [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]</span>
+<span class="sd">        ...     ]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; len(p.parts)</span>
+<span class="sd">        2</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">perimeter</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the perimeter of the polygon.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p.perimeter</span>
+<span class="sd">        4.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">dist</span><span class="p">(</span><span class="n">v1</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">v2</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">v1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">v1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">v2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">def</span> <span class="nf">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">dist</span><span class="p">(</span><span class="n">part</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">part</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+
+        <span class="n">sum_perim</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">part_type</span><span class="p">:</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_perimeter</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">part_type</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span> <span class="o">=</span> <span class="n">sum_perim</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">)</span> <span class="o">+</span> <span class="n">sum_perim</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_perimeter</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">bbox</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the bounding box of the polygon as a list.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._bbox : list</span>
+<span class="sd">            The bounding box of the polygon as a list.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        libpysal.cg.bounding_box</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span>
+            <span class="p">]</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bbox</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the bounding box of the polygon.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self._bounding_box : libpysal.cg.Rectangle</span>
+<span class="sd">            The bounding box of the polygon.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon([Point((0, 0)), Point((2, 0)), Point((2, 1)), Point((0, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p.bounding_box.left</span>
+<span class="sd">        0.0</span>
+
+<span class="sd">        &gt;&gt;&gt; p.bounding_box.lower</span>
+<span class="sd">        0.0</span>
+
+<span class="sd">        &gt;&gt;&gt; p.bounding_box.right</span>
+<span class="sd">        2.0</span>
+
+<span class="sd">        &gt;&gt;&gt; p.bounding_box.upper</span>
+<span class="sd">        1.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vertices</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
+                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+                <span class="nb">min</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+                <span class="nb">max</span><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">]),</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bounding_box</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the area of the polygon.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
+<span class="sd">        &gt;&gt;&gt; p.area</span>
+<span class="sd">        1.0</span>
+
+<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
+<span class="sd">        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],</span>
+<span class="sd">        ...     [Point((2, 1)), Point((2, 2)), Point((1, 2)), Point((1, 1))]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; p.area</span>
+<span class="sd">        99.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">part_area</span><span class="p">(</span><span class="n">pv</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+            <span class="n">__area</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">pv</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="n">__area</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pv</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">pv</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">pv</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">pv</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">__area</span> <span class="o">=</span> <span class="n">__area</span> <span class="o">*</span> <span class="mf">0.5</span>
+            <span class="k">if</span> <span class="n">__area</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">__area</span> <span class="o">=</span> <span class="o">-</span><span class="n">area</span>  <span class="c1"># noqa F821</span>
+            <span class="k">return</span> <span class="n">__area</span>
+
+        <span class="n">sum_area</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">part_type</span><span class="p">:</span> <span class="nb">sum</span><span class="p">([</span><span class="n">part_area</span><span class="p">(</span><span class="n">part</span><span class="p">)</span> <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">part_type</span><span class="p">])</span>
+        <span class="n">_area</span> <span class="o">=</span> <span class="n">sum_area</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_vertices</span><span class="p">)</span> <span class="o">-</span> <span class="n">sum_area</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_holes</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">_area</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the centroid of the polygon.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The centroid returned by this method is the geometric</span>
+<span class="sd">        centroid and respects multipart polygons with holes.</span>
+<span class="sd">        Also known as the &#39;center of gravity&#39; or &#39;center of mass&#39;.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
+<span class="sd">        ...     [Point((0, 0)), Point((10, 0)), Point((10, 10)), Point((0, 10))],</span>
+<span class="sd">        ...     [Point((1, 1)), Point((1, 2)), Point((2, 2)), Point((2, 1))]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; p.centroid</span>
+<span class="sd">        (5.0353535353535355, 5.0353535353535355)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">CP</span> <span class="o">=</span> <span class="p">[</span><span class="n">ring</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">]</span>
+        <span class="n">AP</span> <span class="o">=</span> <span class="p">[</span><span class="n">ring</span><span class="o">.</span><span class="n">area</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">]</span>
+        <span class="n">CH</span> <span class="o">=</span> <span class="p">[</span><span class="n">ring</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">]</span>
+        <span class="n">AH</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="n">ring</span><span class="o">.</span><span class="n">area</span> <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">]</span>
+
+        <span class="n">A</span> <span class="o">=</span> <span class="n">AP</span> <span class="o">+</span> <span class="n">AH</span>
+        <span class="n">cx</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">area</span> <span class="k">for</span> <span class="n">pt</span><span class="p">,</span> <span class="n">area</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">CP</span> <span class="o">+</span> <span class="n">CH</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+        <span class="n">cy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">area</span> <span class="k">for</span> <span class="n">pt</span><span class="p">,</span> <span class="n">area</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">CP</span> <span class="o">+</span> <span class="n">CH</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span>
+
+<div class="viewcode-block" id="Polygon.build_quad_tree_structure">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.build_quad_tree_structure">[docs]</a>
+    <span class="k">def</span> <span class="nf">build_quad_tree_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Build the quad tree structure for this polygon. Once</span>
+<span class="sd">        the structure is built, speed for testing if a point is</span>
+<span class="sd">        inside the ring will be increased significantly.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">:</span>
+            <span class="n">ring</span><span class="o">.</span><span class="n">build_quad_tree_structure</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">:</span>
+            <span class="n">ring</span><span class="o">.</span><span class="n">build_quad_tree_structure</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_quad_tree_structure_built</span> <span class="o">=</span> <span class="kc">True</span></div>
+
+
+<div class="viewcode-block" id="Polygon.contains_point">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.contains_point">[docs]</a>
+    <span class="k">def</span> <span class="nf">contains_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Test if a polygon contains a point.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        point : libpysal.cg.Point</span>
+<span class="sd">            A point to test for containment.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        contains : bool</span>
+<span class="sd">            ``True`` if the polygon contains ``point`` otherwise ``False``.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
+<span class="sd">        ...     [Point((0,0)), Point((4,0)), Point((4,5)), Point((2,3)), Point((0,5))]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; p.contains_point((3,3))</span>
+<span class="sd">        1</span>
+
+<span class="sd">        &gt;&gt;&gt; p.contains_point((0,6))</span>
+<span class="sd">        0</span>
+
+<span class="sd">        &gt;&gt;&gt; p.contains_point((2,2.9))</span>
+<span class="sd">        1</span>
+
+<span class="sd">        &gt;&gt;&gt; p.contains_point((4,5))</span>
+<span class="sd">        0</span>
+
+<span class="sd">        &gt;&gt;&gt; p.contains_point((4,0))</span>
+<span class="sd">        0</span>
+
+<span class="sd">        Handles holes.</span>
+
+<span class="sd">        &gt;&gt;&gt; p = Polygon(</span>
+<span class="sd">        ...     [Point((0, 0)), Point((0, 10)), Point((10, 10)), Point((10, 0))],</span>
+<span class="sd">        ...     [Point((2, 2)), Point((4, 2)), Point((4, 4)), Point((2, 4))]</span>
+<span class="sd">        ... )</span>
+<span class="sd">        &gt;&gt;&gt; p.contains_point((3.0, 3.0))</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; p.contains_point((1.0, 1.0))</span>
+<span class="sd">        True</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        Points falling exactly on polygon edges may yield unpredictable results.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">searching</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_hole_rings</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">ring</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">):</span>
+                <span class="n">contains</span> <span class="o">=</span> <span class="kc">False</span>
+                <span class="n">searching</span> <span class="o">=</span> <span class="kc">False</span>
+                <span class="k">break</span>
+
+        <span class="k">if</span> <span class="n">searching</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_part_rings</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">ring</span><span class="o">.</span><span class="n">contains_point</span><span class="p">(</span><span class="n">point</span><span class="p">):</span>
+                    <span class="n">contains</span> <span class="o">=</span> <span class="kc">True</span>
+                    <span class="n">searching</span> <span class="o">=</span> <span class="kc">False</span>
+                    <span class="k">break</span>
+            <span class="k">if</span> <span class="n">searching</span><span class="p">:</span>
+                <span class="n">contains</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">return</span> <span class="n">contains</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Rectangle">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle">[docs]</a>
+<span class="k">class</span> <span class="nc">Rectangle</span><span class="p">(</span><span class="n">Geometry</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Geometric representation of rectangle objects.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    left : float</span>
+<span class="sd">        Minimum x-value of the rectangle.</span>
+<span class="sd">    lower : float</span>
+<span class="sd">        Minimum y-value of the rectangle.</span>
+<span class="sd">    right : float</span>
+<span class="sd">        Maximum x-value of the rectangle.</span>
+<span class="sd">    upper : float</span>
+<span class="sd">        Maximum y-value of the rectangle.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; r = Rectangle(-4, 3, 10, 17)</span>
+<span class="sd">    &gt;&gt;&gt; r.left #minx</span>
+<span class="sd">    -4.0</span>
+
+<span class="sd">    &gt;&gt;&gt; r.lower #miny</span>
+<span class="sd">    3.0</span>
+
+<span class="sd">    &gt;&gt;&gt; r.right #maxx</span>
+<span class="sd">    10.0</span>
+
+<span class="sd">    &gt;&gt;&gt; r.upper #maxy</span>
+<span class="sd">    17.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Rectangle.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">):</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">dep_msg</span><span class="p">,</span> <span class="ne">FutureWarning</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">right</span> <span class="o">&lt;</span> <span class="n">left</span> <span class="ow">or</span> <span class="n">upper</span> <span class="o">&lt;</span> <span class="n">lower</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ArithmeticError</span><span class="p">(</span><span class="s2">&quot;Rectangle must have positive area.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">left</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">right</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__bool__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Rectangles will evaluate to False if they have zero area.</span>
+<span class="sd">        ``___nonzero__`` is used &quot;to implement truth value</span>
+<span class="sd">        testing and the built-in operation ``bool()``&quot;</span>
+<span class="sd">        ``-- http://docs.python.org/reference/datamodel.html</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 0, 0)</span>
+<span class="sd">        &gt;&gt;&gt; bool(r)</span>
+<span class="sd">        False</span>
+
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 1, 1)</span>
+<span class="sd">        &gt;&gt;&gt; bool(r)</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">area</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__eq__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">other</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="p">[:]</span> <span class="o">==</span> <span class="n">other</span><span class="p">[:]</span>
+        <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[:]</span>
+        <span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="n">Y1</span> <span class="o">=</span> <span class="n">other</span><span class="p">[:]</span>
+
+        <span class="k">return</span> <span class="n">Rectangle</span><span class="p">(</span>
+            <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">left</span><span class="p">),</span>
+            <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">lower</span><span class="p">),</span>
+            <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">right</span><span class="p">),</span>
+            <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">upper</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(-4, 3, 10, 17)</span>
+<span class="sd">        &gt;&gt;&gt; r[:]</span>
+<span class="sd">        [-4.0, 3.0, 10.0, 17.0]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">l_</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">l_</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Rectangle.set_centroid">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_centroid">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_centroid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_center</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Moves the rectangle center to a new specified point.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        new_center : libpysal.cg.Point</span>
+<span class="sd">            The new location of the centroid of the polygon.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
+<span class="sd">        &gt;&gt;&gt; r.set_centroid(Point((4, 4)))</span>
+<span class="sd">        &gt;&gt;&gt; r.left</span>
+<span class="sd">        2.0</span>
+
+<span class="sd">        &gt;&gt;&gt; r.right</span>
+<span class="sd">        6.0</span>
+
+<span class="sd">        &gt;&gt;&gt; r.lower</span>
+<span class="sd">        2.0</span>
+
+<span class="sd">        &gt;&gt;&gt; r.upper</span>
+<span class="sd">        6.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">shift</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">new_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+            <span class="n">new_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">+</span> <span class="n">shift</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="Rectangle.set_scale">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_scale">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_scale</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">scale</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Rescales the rectangle around its center.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        scale : int, float</span>
+<span class="sd">            The ratio of the new scale to the old</span>
+<span class="sd">            scale (e.g. 1.0 is current size).</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
+<span class="sd">        &gt;&gt;&gt; r.set_scale(2)</span>
+<span class="sd">        &gt;&gt;&gt; r.left</span>
+<span class="sd">        -2.0</span>
+<span class="sd">        &gt;&gt;&gt; r.right</span>
+<span class="sd">        6.0</span>
+<span class="sd">        &gt;&gt;&gt; r.lower</span>
+<span class="sd">        -2.0</span>
+<span class="sd">        &gt;&gt;&gt; r.upper</span>
+<span class="sd">        6.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">center</span> <span class="o">=</span> <span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">left</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">area</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the area of the Rectangle.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
+<span class="sd">        &gt;&gt;&gt; r.area</span>
+<span class="sd">        16.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">width</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the width of the Rectangle.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
+<span class="sd">        &gt;&gt;&gt; r.width</span>
+<span class="sd">        4.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">right</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">left</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">height</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the height of the Rectangle.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; r = Rectangle(0, 0, 4, 4)</span>
+<span class="sd">        &gt;&gt;&gt; r.height</span>
+<span class="sd">        4.0</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
+
+
+
+<span class="n">_geoJSON_type_to_Pysal_type</span> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># noqa N816</span>
+    <span class="s2">&quot;point&quot;</span><span class="p">:</span> <span class="n">Point</span><span class="p">,</span>
+    <span class="s2">&quot;linestring&quot;</span><span class="p">:</span> <span class="n">Chain</span><span class="p">,</span>
+    <span class="s2">&quot;multilinestring&quot;</span><span class="p">:</span> <span class="n">Chain</span><span class="p">,</span>
+    <span class="s2">&quot;polygon&quot;</span><span class="p">:</span> <span class="n">Polygon</span><span class="p">,</span>
+    <span class="s2">&quot;multipolygon&quot;</span><span class="p">:</span> <span class="n">Polygon</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="c1"># moving this to top breaks unit tests !</span>
+<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">standalone</span>
+<span class="kn">from</span> <span class="nn">.polygonQuadTreeStructure</span> <span class="kn">import</span> <span class="n">QuadTreeStructureSingleRing</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/_modules/libpysal/cg/sphere.html b/_modules/libpysal/cg/sphere.html
new file mode 100644
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+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+      
+  <h1>Source code for libpysal.cg.sphere</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">sphere: Tools for working with spherical geometry.</span>
+
+<span class="sd">Author(s):</span>
+<span class="sd">    Charles R Schmidt schmidtc@gmail.com</span>
+<span class="sd">    Luc Anselin luc.anselin@asu.edu</span>
+<span class="sd">    Xun Li xun.li@asu.edu</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="s2">&quot;Charles R Schmidt &lt;schmidtc@gmail.com&gt;,&quot;</span>
+    <span class="s2">&quot;Luc Anselin &lt;luc.anselin@asu.edu,&quot;</span>
+    <span class="s2">&quot;Xun Li &lt;xun.li@asu.edu&quot;</span>
+<span class="p">)</span>
+
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">cos</span><span class="p">,</span> <span class="n">pi</span><span class="p">,</span> <span class="n">sin</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+<span class="kn">import</span> <span class="nn">scipy.constants</span>
+<span class="kn">import</span> <span class="nn">scipy.spatial</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="kn">import</span> <span class="n">euclidean</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;RADIUS_EARTH_KM&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;RADIUS_EARTH_MILES&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;arcdist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;arcdist2linear&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;brute_knn&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;fast_knn&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;fast_threshold&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;linear2arcdist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;toLngLat&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;toXYZ&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;lonlat&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;harcdist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;geointerpolate&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;geogrid&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<span class="n">RADIUS_EARTH_KM</span> <span class="o">=</span> <span class="mf">6371.0</span>
+<span class="n">RADIUS_EARTH_MILES</span> <span class="o">=</span> <span class="p">(</span><span class="n">RADIUS_EARTH_KM</span> <span class="o">*</span> <span class="n">scipy</span><span class="o">.</span><span class="n">constants</span><span class="o">.</span><span class="n">kilo</span><span class="p">)</span> <span class="o">/</span> <span class="n">scipy</span><span class="o">.</span><span class="n">constants</span><span class="o">.</span><span class="n">mile</span>
+
+
+<div class="viewcode-block" id="arcdist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.arcdist.html#libpysal.cg.arcdist">[docs]</a>
+<span class="k">def</span> <span class="nf">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Arc distance between two points on a sphere.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pt0 : tuple</span>
+<span class="sd">        A point assumed to be in form (longitude,latitude).</span>
+<span class="sd">    pt1 : tuple</span>
+<span class="sd">        A point assumed to be in form (longitude,latitude).</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dist : float</span>
+<span class="sd">        The arc distance between ``pt0`` and ``pt1`` using supplied ``radius``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; pt0 = (0, 0)</span>
+<span class="sd">    &gt;&gt;&gt; pt1 = (180, 0)</span>
+<span class="sd">    &gt;&gt;&gt; d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)</span>
+<span class="sd">    &gt;&gt;&gt; d == math.pi * RADIUS_EARTH_MILES</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">dist</span> <span class="o">=</span> <span class="n">linear2arcdist</span><span class="p">(</span><span class="n">euclidean</span><span class="p">(</span><span class="n">toXYZ</span><span class="p">(</span><span class="n">pt0</span><span class="p">),</span> <span class="n">toXYZ</span><span class="p">(</span><span class="n">pt1</span><span class="p">)),</span> <span class="n">radius</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">dist</span></div>
+
+
+
+<div class="viewcode-block" id="arcdist2linear">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.arcdist2linear.html#libpysal.cg.arcdist2linear">[docs]</a>
+<span class="k">def</span> <span class="nf">arcdist2linear</span><span class="p">(</span><span class="n">arc_dist</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert an arc distance (spherical earth)</span>
+<span class="sd">    to a linear distance (R3) in the unit sphere.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    arc_dist : float</span>
+<span class="sd">        The arc distance to convert.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    linear_dist : float</span>
+<span class="sd">        The linear distance conversion of ``arc_dist``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; pt0 = (0, 0)</span>
+<span class="sd">    &gt;&gt;&gt; pt1 = (180, 0)</span>
+<span class="sd">    &gt;&gt;&gt; d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)</span>
+<span class="sd">    &gt;&gt;&gt; d == math.pi * RADIUS_EARTH_MILES</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; arcdist2linear(d, RADIUS_EARTH_MILES)</span>
+<span class="sd">    2.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">circumference</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">radius</span>
+    <span class="n">linear_dist</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="mi">2</span> <span class="o">-</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">radians</span><span class="p">((</span><span class="n">arc_dist</span> <span class="o">*</span> <span class="mf">360.0</span><span class="p">)</span> <span class="o">/</span> <span class="n">circumference</span><span class="p">)))</span>
+    <span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">linear_dist</span></div>
+
+
+
+<div class="viewcode-block" id="linear2arcdist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.linear2arcdist.html#libpysal.cg.linear2arcdist">[docs]</a>
+<span class="k">def</span> <span class="nf">linear2arcdist</span><span class="p">(</span><span class="n">linear_dist</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a linear distance in the unit sphere</span>
+<span class="sd">    (R3) to an arc distance based on supplied radius.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    linear_dist : float</span>
+<span class="sd">        The linear distance to convert.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    arc_dist : float</span>
+<span class="sd">        The arc distance conversion of ``linear_dist``.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ValueError</span>
+<span class="sd">        Raised when ``linear_dist`` exceeds the diameter of the unit sphere.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; pt0 = (0, 0)</span>
+<span class="sd">    &gt;&gt;&gt; pt1 = (180, 0)</span>
+<span class="sd">    &gt;&gt;&gt; d = arcdist(pt0, pt1, RADIUS_EARTH_MILES)</span>
+<span class="sd">    &gt;&gt;&gt; d == linear2arcdist(2.0, radius=RADIUS_EARTH_MILES)</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">linear_dist</span> <span class="o">==</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">):</span>
+        <span class="n">arc_dist</span> <span class="o">=</span> <span class="n">linear_dist</span>
+    <span class="k">elif</span> <span class="n">linear_dist</span> <span class="o">&gt;</span> <span class="mf">2.0</span><span class="p">:</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;&#39;linear_dist&#39;, must not exceed the diameter of the unit sphere, 2.0.&quot;</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">circumference</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">radius</span>
+        <span class="n">a2</span> <span class="o">=</span> <span class="n">linear_dist</span><span class="o">**</span><span class="mi">2</span>
+        <span class="n">theta</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">degrees</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">acos</span><span class="p">((</span><span class="mi">2</span> <span class="o">-</span> <span class="n">a2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">2.0</span><span class="p">)))</span>
+        <span class="n">arc_dist</span> <span class="o">=</span> <span class="p">(</span><span class="n">theta</span> <span class="o">*</span> <span class="n">circumference</span><span class="p">)</span> <span class="o">/</span> <span class="mf">360.0</span>
+
+    <span class="k">return</span> <span class="n">arc_dist</span></div>
+
+
+
+<div class="viewcode-block" id="toXYZ">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.toXYZ.html#libpysal.cg.toXYZ">[docs]</a>
+<span class="k">def</span> <span class="nf">toXYZ</span><span class="p">(</span><span class="n">pt</span><span class="p">):</span>  <span class="c1"># noqa N802</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a point&#39;s latitude and longitude to x,y,z.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pt : tuple</span>
+<span class="sd">        A point assumed to be in form (lng,lat).</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    x, y, z : tuple</span>
+<span class="sd">        A point in form (x, y, z).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">phi</span><span class="p">,</span> <span class="n">theta</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">radians</span><span class="p">,</span> <span class="n">pt</span><span class="p">))</span>
+    <span class="n">phi</span><span class="p">,</span> <span class="n">theta</span> <span class="o">=</span> <span class="n">phi</span> <span class="o">+</span> <span class="n">pi</span><span class="p">,</span> <span class="n">theta</span> <span class="o">+</span> <span class="p">(</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span> <span class="o">*</span> <span class="n">sin</span><span class="p">(</span><span class="n">phi</span><span class="p">)</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span></div>
+
+
+
+<div class="viewcode-block" id="toLngLat">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.toLngLat.html#libpysal.cg.toLngLat">[docs]</a>
+<span class="k">def</span> <span class="nf">toLngLat</span><span class="p">(</span><span class="n">xyz</span><span class="p">):</span>  <span class="c1"># noqa N802</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a point&#39;s x,y,z to latitude and longitude.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    xyz : tuple</span>
+<span class="sd">        A point assumed to be in form (x,y,z).</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    phi, theta : tuple</span>
+<span class="sd">        A point in form (phi, theta) [y,x].</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="o">=</span> <span class="n">xyz</span>
+    <span class="k">if</span> <span class="n">z</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="ow">or</span> <span class="n">z</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">phi</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">phi</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">phi</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">phi</span> <span class="o">=</span> <span class="n">phi</span> <span class="o">-</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span>
+        <span class="k">elif</span> <span class="n">phi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">phi</span> <span class="o">=</span> <span class="n">phi</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span>
+    <span class="n">theta</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">phi</span><span class="p">,</span> <span class="n">theta</span></div>
+
+
+
+<div class="viewcode-block" id="brute_knn">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.brute_knn.html#libpysal.cg.brute_knn">[docs]</a>
+<span class="k">def</span> <span class="nf">brute_knn</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;arc&quot;</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes a brute-force :math:`k` nearest neighbors.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pts : list</span>
+<span class="sd">        A list of :math:`x,y` pairs.</span>
+<span class="sd">    k :  int</span>
+<span class="sd">        The number of points to query.</span>
+<span class="sd">    mode : str</span>
+<span class="sd">        The mode of distance. Valid modes are ``&#39;arc&#39;``</span>
+<span class="sd">        and ``&#39;xyz&#39;``. Default is ``&#39;arc&#39;``.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w : dict</span>
+<span class="sd">        A neighbor ID lookup.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+    <span class="n">full</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;arc&quot;</span><span class="p">:</span>
+                <span class="n">lng0</span><span class="p">,</span> <span class="n">lat0</span> <span class="o">=</span> <span class="n">pts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">lng1</span><span class="p">,</span> <span class="n">lat1</span> <span class="o">=</span> <span class="n">pts</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+                <span class="n">dist</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pts</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">pts</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;xyz&quot;</span><span class="p">:</span>
+                <span class="n">dist</span> <span class="o">=</span> <span class="n">euclidean</span><span class="p">(</span><span class="n">pts</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">pts</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+            <span class="n">full</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">dist</span>
+            <span class="n">full</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">dist</span>
+
+    <span class="n">w</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">full</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">argsort</span><span class="p">()[</span><span class="mi">1</span> <span class="p">:</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">w</span></div>
+
+
+
+<div class="viewcode-block" id="fast_knn">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.fast_knn.html#libpysal.cg.fast_knn">[docs]</a>
+<span class="k">def</span> <span class="nf">fast_knn</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">return_dist</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes :math:`k` nearest neighbors on a sphere.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pts : list</span>
+<span class="sd">        A list of :math:`x,y` pairs.</span>
+<span class="sd">    k :  int</span>
+<span class="sd">        The number of points to query.</span>
+<span class="sd">    return_dist : bool</span>
+<span class="sd">        Return distances in the ``wd`` container object (``True``).</span>
+<span class="sd">        Default is ``False``.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    wn : dict</span>
+<span class="sd">        A neighbor ID lookup.</span>
+<span class="sd">    wd : dict</span>
+<span class="sd">        A neighbor distance lookup (optional).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">pts</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+    <span class="n">kd</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+    <span class="n">d</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
+    <span class="n">wn</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)):</span>
+        <span class="n">wn</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">return_dist</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">d</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
+        <span class="n">wd</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)):</span>
+            <span class="n">wd</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">linear2arcdist</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">d</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()]</span>
+        <span class="k">return</span> <span class="n">wn</span><span class="p">,</span> <span class="n">wd</span>
+    <span class="k">return</span> <span class="n">wn</span></div>
+
+
+
+<div class="viewcode-block" id="fast_threshold">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.fast_threshold.html#libpysal.cg.fast_threshold">[docs]</a>
+<span class="k">def</span> <span class="nf">fast_threshold</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">dist</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Find all neighbors on a sphere within a threshold distance.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pointslist : list</span>
+<span class="sd">        A list of lat-lon tuples. This **must** be a list, even for one point.</span>
+<span class="sd">    dist: float</span>
+<span class="sd">        The threshold distance.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    wd : dict</span>
+<span class="sd">        A neighbor distance lookup where the key is the ID</span>
+<span class="sd">        of a point and the value is a list of IDs for other</span>
+<span class="sd">        points within ``dist`` of the key point,</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">d</span> <span class="o">=</span> <span class="n">arcdist2linear</span><span class="p">(</span><span class="n">dist</span><span class="p">,</span> <span class="n">radius</span><span class="p">)</span>
+    <span class="n">kd</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">query_ball_tree</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
+    <span class="n">wd</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)):</span>
+        <span class="n">l_</span> <span class="o">=</span> <span class="n">r</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">l_</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="n">wd</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">l_</span>
+
+    <span class="k">return</span> <span class="n">wd</span></div>
+
+
+
+<div class="viewcode-block" id="lonlat">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.lonlat.html#libpysal.cg.lonlat">[docs]</a>
+<span class="k">def</span> <span class="nf">lonlat</span><span class="p">(</span><span class="n">pointslist</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Converts point order from lat-lon tuples to lon-lat (x,y) tuples.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pointslist : list</span>
+<span class="sd">        A list of lat-lon tuples. This **must** be a list, even for one point.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    newpts : list</span>
+<span class="sd">        A list with tuples of points in lon-lat order.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; points = [</span>
+<span class="sd">    ...     (41.981417, -87.893517), (41.980396, -87.776787), (41.980906, -87.696450)</span>
+<span class="sd">    ... ]</span>
+<span class="sd">    &gt;&gt;&gt; newpoints = lonlat(points)</span>
+<span class="sd">    &gt;&gt;&gt; newpoints</span>
+<span class="sd">    [(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">newpts</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">pointslist</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">newpts</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">haversine</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes the haversine formula.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    x : float</span>
+<span class="sd">        The angle in radians.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    haversine_dist : float</span>
+<span class="sd">        The square of sine of half the radian (the haversine formula).</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; haversine(math.pi) # is 180 in radians, hence sin of 90 = 1</span>
+<span class="sd">    1.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">x</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+
+    <span class="n">haversine_dist</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">x</span>
+
+    <span class="k">return</span> <span class="n">haversine_dist</span>
+
+
+<span class="c1"># Lambda functions</span>
+
+<span class="c1"># degree to radian conversion</span>
+<span class="n">d2r</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mf">180.0</span>
+
+<span class="c1"># radian to degree conversion</span>
+<span class="n">r2d</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">*</span> <span class="mf">180.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span>
+
+
+<span class="k">def</span> <span class="nf">radangle</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Radian angle between two points on a sphere in lon-lat (x,y).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    p0 : tuple</span>
+<span class="sd">        The first point in (lon,lat) format.</span>
+<span class="sd">    p1 : tuple</span>
+<span class="sd">        The second point in (lon,lat) format.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    d : float</span>
+<span class="sd">        Radian angle in radians.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; p0 = (-87.893517, 41.981417)</span>
+<span class="sd">    &gt;&gt;&gt; p1 = (-87.519295, 41.657498)</span>
+<span class="sd">    &gt;&gt;&gt; radangle(p0, p1)</span>
+<span class="sd">    0.007460167953189258</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Uses haversine formula, function haversine and degree to radian</span>
+<span class="sd">    conversion lambda function ``d2r``.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">asin</span><span class="p">(</span>
+        <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">haversine</span><span class="p">(</span><span class="n">y1</span> <span class="o">-</span> <span class="n">y0</span><span class="p">)</span> <span class="o">+</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="n">haversine</span><span class="p">(</span><span class="n">x1</span> <span class="o">-</span> <span class="n">x0</span><span class="p">))</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">d</span>
+
+
+<div class="viewcode-block" id="harcdist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.harcdist.html#libpysal.cg.harcdist">[docs]</a>
+<span class="k">def</span> <span class="nf">harcdist</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_KM</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Alternative the arc distance function, uses the haversine formula.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    p0 : tuple</span>
+<span class="sd">        The first point decimal degrees.</span>
+<span class="sd">    p1 : tuple</span>
+<span class="sd">        The second point decimal degrees.</span>
+<span class="sd">    lonx : bool</span>
+<span class="sd">        The method to assess the order of the coordinates.</span>
+<span class="sd">        ``True`` for (lon,lat); ``False`` for (lat,lon).</span>
+<span class="sd">        Default is ``True``.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The radius of a sphere. Default is Earth&#39;s radius in</span>
+<span class="sd">        kilometers, ``RADIUS_EARTH_KM`` (``6371.0``). Earth&#39;s</span>
+<span class="sd">        radius in miles, ``RADIUS_EARTH_MILES`` (``3958.76``)</span>
+<span class="sd">        is also an option. Set to ``None`` for radians.</span>
+<span class="sd">        Source: http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    harc_dist : harc_dist</span>
+<span class="sd">        The distance in units specified, km, miles or radians.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; p0 = (-87.893517, 41.981417)</span>
+<span class="sd">    &gt;&gt;&gt; p1 = (-87.519295, 41.657498)</span>
+<span class="sd">    &gt;&gt;&gt; harcdist(p0, p1)</span>
+<span class="sd">    47.52873002976876</span>
+
+<span class="sd">    &gt;&gt;&gt; harcdist(p0, p1, radius=None)</span>
+<span class="sd">    0.007460167953189258</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Uses the ``radangle`` function to compute radian angle.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lonx</span><span class="p">):</span>
+        <span class="n">p</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">([</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">])</span>
+        <span class="n">p0</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">harc_dist</span> <span class="o">=</span> <span class="n">radangle</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">harc_dist</span> <span class="o">=</span> <span class="n">harc_dist</span> <span class="o">*</span> <span class="n">radius</span>
+
+    <span class="k">return</span> <span class="n">harc_dist</span></div>
+
+
+
+<div class="viewcode-block" id="geointerpolate">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.geointerpolate.html#libpysal.cg.geointerpolate">[docs]</a>
+<span class="k">def</span> <span class="nf">geointerpolate</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Finds a point on a sphere along the great circle distance between</span>
+<span class="sd">    two points on a sphere also known as a way point in great circle navigation.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    p0 : tuple</span>
+<span class="sd">        The first point decimal degrees.</span>
+<span class="sd">    p1 : tuple</span>
+<span class="sd">        The second point decimal degrees.</span>
+<span class="sd">    t : float</span>
+<span class="sd">        The proportion along great circle distance between ``p0``</span>
+<span class="sd">        and ``p1`` (e.g., :math:`\mathtt{t}=0.5` would find the mid-point).</span>
+<span class="sd">    lonx : bool</span>
+<span class="sd">        The method to assess the order of the coordinates.</span>
+<span class="sd">        ``True`` for (lon,lat); ``False`` for (lat,lon).</span>
+<span class="sd">        Default is ``True``.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    newpx, newpy : tuple</span>
+<span class="sd">        The new point in decimal degrees of (lon-lat) by</span>
+<span class="sd">        default or (lat-lon) if ``lonx`` is set to ``False``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; p0 = (-87.893517, 41.981417)</span>
+<span class="sd">    &gt;&gt;&gt; p1 = (-87.519295, 41.657498)</span>
+<span class="sd">    &gt;&gt;&gt; geointerpolate(p0, p1, 0.1)             # using lon-lat</span>
+<span class="sd">    (-87.85592403438788, 41.949079912574796)</span>
+
+<span class="sd">    &gt;&gt;&gt; p3 = (41.981417, -87.893517)</span>
+<span class="sd">    &gt;&gt;&gt; p4 = (41.657498, -87.519295)</span>
+<span class="sd">    &gt;&gt;&gt; geointerpolate(p3, p4, 0.1, lonx=False) # using lat-lon</span>
+<span class="sd">    (41.949079912574796, -87.85592403438788)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lonx</span><span class="p">):</span>
+        <span class="n">p</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">([</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">])</span>
+        <span class="n">p0</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">p1</span> <span class="o">=</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">d</span> <span class="o">=</span> <span class="n">radangle</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span>
+    <span class="n">k</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="n">t</span> <span class="o">*</span> <span class="n">d</span>
+    <span class="n">A</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">d</span> <span class="o">-</span> <span class="n">t</span><span class="p">)</span> <span class="o">*</span> <span class="n">k</span>
+    <span class="n">B</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="o">*</span> <span class="n">k</span>
+
+    <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p0</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">d2r</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="n">x</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x0</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x0</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x1</span><span class="p">)</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="n">A</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">y0</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">y1</span><span class="p">)</span>
+
+    <span class="n">newpx</span> <span class="o">=</span> <span class="n">r2d</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
+    <span class="n">newpy</span> <span class="o">=</span> <span class="n">r2d</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">z</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">y</span><span class="p">)))</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">lonx</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">newpy</span><span class="p">,</span> <span class="n">newpx</span>
+
+    <span class="k">return</span> <span class="n">newpx</span><span class="p">,</span> <span class="n">newpy</span></div>
+
+
+
+<div class="viewcode-block" id="geogrid">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.geogrid.html#libpysal.cg.geogrid">[docs]</a>
+<span class="k">def</span> <span class="nf">geogrid</span><span class="p">(</span><span class="n">pup</span><span class="p">,</span> <span class="n">pdown</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes a :math:`k+1` by :math:`k+1` set of grid</span>
+<span class="sd">    points for a bounding box in lat-lon. Uses ``geointerpolate``.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pup : tuple</span>
+<span class="sd">        The lat-lon or lon-lat for the upper left corner of the bounding box.</span>
+<span class="sd">    pdown : tuple</span>
+<span class="sd">        The lat-lon or lon-lat for The lower right corner of The bounding box.</span>
+<span class="sd">    k : int</span>
+<span class="sd">        The number of grid cells (grid points will be one more).</span>
+<span class="sd">    lonx : bool</span>
+<span class="sd">        The method to assess the order of the coordinates.</span>
+<span class="sd">        ``True`` for (lon,lat); ``False`` for (lat,lon).</span>
+<span class="sd">        Default is ``True``.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    grid : list</span>
+<span class="sd">        A list of tuples with (lat-lon) or (lon-lat) for grid points,</span>
+<span class="sd">        row by row, starting with the top row and moving to the bottom;</span>
+<span class="sd">        coordinate tuples are returned in same order as input.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; pup = (42.023768, -87.946389)       # Arlington Heights, IL</span>
+<span class="sd">    &gt;&gt;&gt; pdown = (41.644415, -87.524102)     # Hammond, IN</span>
+<span class="sd">    &gt;&gt;&gt; geogrid(pup,pdown, 3, lonx=False)</span>
+<span class="sd">    [(42.023768, -87.946389),</span>
+<span class="sd">     (42.02393997819538, -87.80562679358316),</span>
+<span class="sd">     (42.02393997819538, -87.66486420641684),</span>
+<span class="sd">     (42.023768, -87.524102),</span>
+<span class="sd">     (41.897317, -87.94638900000001),</span>
+<span class="sd">     (41.8974888973743, -87.80562679296166),</span>
+<span class="sd">     (41.8974888973743, -87.66486420703835),</span>
+<span class="sd">     (41.897317, -87.524102),</span>
+<span class="sd">     (41.770866000000005, -87.94638900000001),</span>
+<span class="sd">     (41.77103781320412, -87.80562679234043),</span>
+<span class="sd">     (41.77103781320412, -87.66486420765956),</span>
+<span class="sd">     (41.770866000000005, -87.524102),</span>
+<span class="sd">     (41.644415, -87.946389),</span>
+<span class="sd">     (41.64458672568646, -87.80562679171955),</span>
+<span class="sd">     (41.64458672568646, -87.66486420828045),</span>
+<span class="sd">     (41.644415, -87.524102)]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">corners</span> <span class="o">=</span> <span class="p">[</span><span class="n">pup</span><span class="p">,</span> <span class="n">pdown</span><span class="p">]</span> <span class="k">if</span> <span class="n">lonx</span> <span class="k">else</span> <span class="n">lonlat</span><span class="p">([</span><span class="n">pup</span><span class="p">,</span> <span class="n">pdown</span><span class="p">])</span>
+
+    <span class="n">tpoints</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">/</span> <span class="n">k</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]]</span>
+    <span class="n">leftcorners</span> <span class="o">=</span> <span class="p">[</span><span class="n">corners</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="n">corners</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">corners</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])]</span>
+    <span class="n">rightcorners</span> <span class="o">=</span> <span class="p">[(</span><span class="n">corners</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">corners</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]),</span> <span class="n">corners</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+    <span class="n">leftside</span> <span class="o">=</span> <span class="p">[</span><span class="n">leftcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+    <span class="n">rightside</span> <span class="o">=</span> <span class="p">[</span><span class="n">rightcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tpoints</span><span class="p">:</span>
+        <span class="n">newpl</span> <span class="o">=</span> <span class="n">geointerpolate</span><span class="p">(</span><span class="n">leftcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">leftcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
+        <span class="n">leftside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">newpl</span><span class="p">)</span>
+        <span class="n">newpr</span> <span class="o">=</span> <span class="n">geointerpolate</span><span class="p">(</span><span class="n">rightcorners</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rightcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
+        <span class="n">rightside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">newpr</span><span class="p">)</span>
+    <span class="n">leftside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">leftcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">rightside</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rightcorners</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="n">grid</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">leftside</span><span class="p">)):</span>
+        <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">leftside</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tpoints</span><span class="p">:</span>
+            <span class="n">newp</span> <span class="o">=</span> <span class="n">geointerpolate</span><span class="p">(</span><span class="n">leftside</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">rightside</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">t</span><span class="p">)</span>
+            <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">newp</span><span class="p">)</span>
+        <span class="n">grid</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rightside</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">lonx</span><span class="p">):</span>
+        <span class="n">grid</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">grid</span></div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
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+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
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+      
+  <h1>Source code for libpysal.cg.standalone</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Helper functions for computational geometry in PySAL.</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey, Xinyue Ye, Charles Schmidt, Andrew Winslow&quot;</span>
+<span class="n">__credits__</span> <span class="o">=</span> <span class="s2">&quot;Copyright (c) 2005-2009 Sergio J. Rey&quot;</span>
+
+<span class="c1"># ruff: noqa: F403, F405</span>
+
+
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">islice</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">scipy.spatial</span>
+
+<span class="kn">from</span> <span class="nn">.shapes</span> <span class="kn">import</span> <span class="o">*</span>
+
+<span class="n">EPSILON_SCALER</span> <span class="o">=</span> <span class="mi">3</span>
+
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;bbcommon&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_bounding_box&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_angle_between&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;is_collinear&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_segments_intersect&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_segment_point_intersect&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_polygon_point_intersect&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_rectangle_point_intersect&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_ray_segment_intersect&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_rectangle_rectangle_intersection&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_polygon_point_dist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_points_dist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_segment_point_dist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_point_at_angle_and_dist&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;convex_hull&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;is_clockwise&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;point_touches_rectangle&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_shared_segments&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;distance_matrix&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<div class="viewcode-block" id="bbcommon">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.bbcommon.html#libpysal.cg.bbcommon">[docs]</a>
+<span class="k">def</span> <span class="nf">bbcommon</span><span class="p">(</span><span class="n">bb</span><span class="p">,</span> <span class="n">bbother</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Old Stars method for bounding box overlap testing.</span>
+<span class="sd">    Also defined in ``pysal.weights._cont_binning``.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    bb : list</span>
+<span class="sd">        A bounding box.</span>
+<span class="sd">    bbother : list</span>
+<span class="sd">        The bounding box to test against.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    chflag : int</span>
+<span class="sd">        ``1`` if ``bb`` overlaps ``bbother``, otherwise ``0``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; b0 = [0, 0, 10, 10]</span>
+<span class="sd">    &gt;&gt;&gt; b1 = [10, 0, 20, 10]</span>
+<span class="sd">    &gt;&gt;&gt; bbcommon(b0, b1)</span>
+<span class="sd">    1</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">chflag</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="p">((</span><span class="n">bbother</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">or</span> <span class="p">(</span><span class="n">bbother</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span> <span class="ow">and</span> <span class="p">(</span>
+        <span class="ow">not</span> <span class="p">((</span><span class="n">bbother</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">or</span> <span class="p">(</span><span class="n">bbother</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">bb</span><span class="p">[</span><span class="mi">3</span><span class="p">]))</span>
+    <span class="p">):</span>
+        <span class="n">chflag</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="k">return</span> <span class="n">chflag</span></div>
+
+
+
+<div class="viewcode-block" id="get_bounding_box">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_bounding_box.html#libpysal.cg.get_bounding_box">[docs]</a>
+<span class="k">def</span> <span class="nf">get_bounding_box</span><span class="p">(</span><span class="n">items</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Find bounding box for a list of geometries.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    items : list</span>
+<span class="sd">        PySAL shapes.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    rect = libpysal.cg.Rectangle</span>
+<span class="sd">        The bounding box for a list of geometries.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; bb = get_bounding_box([Point((-1, 5)), Rectangle(0, 6, 11, 12)])</span>
+<span class="sd">    &gt;&gt;&gt; bb.left</span>
+<span class="sd">    -1.0</span>
+
+<span class="sd">    &gt;&gt;&gt; bb.lower</span>
+<span class="sd">    5.0</span>
+
+<span class="sd">    &gt;&gt;&gt; bb.right</span>
+<span class="sd">    11.0</span>
+
+<span class="sd">    &gt;&gt;&gt; bb.upper</span>
+<span class="sd">    12.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">left</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
+        <span class="c1"># Polygon, Ellipse</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+        <span class="c1"># Rectangle</span>
+        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;left&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">left</span>
+        <span class="c1"># Point</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">right</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
+        <span class="c1"># Polygon, Ellipse</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+        <span class="c1"># Rectangle</span>
+        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;right&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">right</span>
+        <span class="c1"># Point</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">lower</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
+        <span class="c1"># Polygon, Ellipse</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+        <span class="c1"># Rectangle</span>
+        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;lower&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">lower</span>
+        <span class="c1"># Point</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">upper</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
+        <span class="c1"># Polygon, Ellipse</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+        <span class="c1"># Rectangle</span>
+        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s2">&quot;upper&quot;</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">upper</span>
+        <span class="c1"># Point</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">rect</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span>
+        <span class="nb">min</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
+        <span class="nb">min</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
+        <span class="nb">max</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
+        <span class="nb">max</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">items</span><span class="p">))),</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">rect</span></div>
+
+
+
+<div class="viewcode-block" id="get_angle_between">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_angle_between.html#libpysal.cg.get_angle_between">[docs]</a>
+<span class="k">def</span> <span class="nf">get_angle_between</span><span class="p">(</span><span class="n">ray1</span><span class="p">,</span> <span class="n">ray2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the angle formed between a pair of rays which share an origin.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ray1 : libpysal.cg.Ray</span>
+<span class="sd">        A ray forming the beginning of the angle measured.</span>
+<span class="sd">    ray2 : libpysal.cg.Ray</span>
+<span class="sd">        A ray forming the end of the angle measured.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    angle : float</span>
+<span class="sd">        The angle between ``ray1`` and ``ray2``.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ValueError</span>
+<span class="sd">        Raised when rays do not have the same origin.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; get_angle_between(</span>
+<span class="sd">    ...     Ray(Point((0, 0)), Point((1, 0))),</span>
+<span class="sd">    ...     Ray(Point((0, 0)), Point((1, 0)))</span>
+<span class="sd">    ... )</span>
+<span class="sd">    0.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">ray1</span><span class="o">.</span><span class="n">o</span> <span class="o">!=</span> <span class="n">ray2</span><span class="o">.</span><span class="n">o</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Rays must have the same origin.&quot;</span><span class="p">)</span>
+
+    <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">ray1</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray1</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray1</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray1</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">ray2</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray2</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray2</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray2</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="n">rot_theta</span> <span class="o">=</span> <span class="o">-</span><span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">rot_matrix</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">),</span> <span class="o">-</span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">)],</span>
+        <span class="p">[</span><span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">),</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">rot_theta</span><span class="p">)],</span>
+    <span class="p">]</span>
+
+    <span class="n">rot_vec2</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">rot_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+    <span class="p">)</span>
+
+    <span class="n">angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">rot_vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">rot_vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">angle</span></div>
+
+
+
+<div class="viewcode-block" id="is_collinear">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.is_collinear.html#libpysal.cg.is_collinear">[docs]</a>
+<span class="k">def</span> <span class="nf">is_collinear</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns whether a triplet of points is collinear.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    p1 : libpysal.cg.Point</span>
+<span class="sd">        A point.</span>
+<span class="sd">    p2 : libpysal.cg.Point</span>
+<span class="sd">        A point.</span>
+<span class="sd">    p3 : libpysal.cg.Point</span>
+<span class="sd">        A point.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    collinear : bool</span>
+<span class="sd">        ``True`` if ``{p1, p2, p3}`` are collinear, otherwise ``False``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 5)))</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; is_collinear(Point((0, 0)), Point((1, 1)), Point((5, 0)))</span>
+<span class="sd">    False</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">eps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span><span class="o">.</span><span class="n">eps</span>
+
+    <span class="n">slope_diff</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span>
+        <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="p">)</span>
+
+    <span class="n">very_small_dist</span> <span class="o">=</span> <span class="n">EPSILON_SCALER</span> <span class="o">*</span> <span class="n">eps</span>
+
+    <span class="n">collinear</span> <span class="o">=</span> <span class="n">slope_diff</span> <span class="o">&lt;</span> <span class="n">very_small_dist</span>
+
+    <span class="k">return</span> <span class="n">collinear</span></div>
+
+
+
+<div class="viewcode-block" id="get_segments_intersect">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect">[docs]</a>
+<span class="k">def</span> <span class="nf">get_segments_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the intersection of two segments if one exists.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    seg1 : libpysal.cg.LineSegment</span>
+<span class="sd">        A segment to check for an intersection.</span>
+<span class="sd">    seg2 : libpysal.cg.LineSegment</span>
+<span class="sd">        The segment to check against ``seg1`` for an intersection.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None}</span>
+<span class="sd">        The intersecting point or line between ``seg1`` and</span>
+<span class="sd">        ``seg2`` if an intersection exists or ``None`` if</span>
+<span class="sd">        ``seg1`` and ``seg2`` do not intersect.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; seg1 = LineSegment(Point((0, 0)), Point((0, 10)))</span>
+<span class="sd">    &gt;&gt;&gt; seg2 = LineSegment(Point((-5, 5)), Point((5, 5)))</span>
+<span class="sd">    &gt;&gt;&gt; i = get_segments_intersect(seg1, seg2)</span>
+<span class="sd">    &gt;&gt;&gt; isinstance(i, Point)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; str(i)</span>
+<span class="sd">    &#39;(0.0, 5.0)&#39;</span>
+
+<span class="sd">    &gt;&gt;&gt; seg3 = LineSegment(Point((100, 100)), Point((100, 101)))</span>
+<span class="sd">    &gt;&gt;&gt; i = get_segments_intersect(seg2, seg3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">p1</span> <span class="o">=</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p1</span>
+    <span class="n">p2</span> <span class="o">=</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span>
+    <span class="n">p3</span> <span class="o">=</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p1</span>
+    <span class="n">p4</span> <span class="o">=</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p2</span>
+    <span class="n">a</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">b</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p4</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p4</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">det</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">a</span> <span class="o">*</span> <span class="n">d</span> <span class="o">-</span> <span class="n">b</span> <span class="o">*</span> <span class="n">c</span><span class="p">)</span>
+
+    <span class="n">intersection</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">if</span> <span class="n">det</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">seg1</span> <span class="o">==</span> <span class="n">seg2</span><span class="p">:</span>
+            <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">seg1</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">a</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg2</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
+            <span class="n">b</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg2</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p1</span><span class="p">)</span>
+            <span class="n">d</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">a</span> <span class="ow">and</span> <span class="n">b</span><span class="p">:</span>  <span class="c1"># seg1 in seg2</span>
+                <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">seg1</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg1</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">c</span> <span class="ow">and</span> <span class="n">d</span><span class="p">:</span>  <span class="c1"># seg2 in seg1</span>
+                <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">seg2</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg2</span><span class="o">.</span><span class="n">p2</span><span class="p">)</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">a</span> <span class="ow">or</span> <span class="n">b</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">c</span> <span class="ow">or</span> <span class="n">d</span><span class="p">):</span>
+                <span class="n">p1</span> <span class="o">=</span> <span class="n">a</span> <span class="k">if</span> <span class="n">a</span> <span class="k">else</span> <span class="n">b</span>
+                <span class="n">p2</span> <span class="o">=</span> <span class="n">c</span> <span class="k">if</span> <span class="n">c</span> <span class="k">else</span> <span class="n">d</span>
+                <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">a_inv</span> <span class="o">=</span> <span class="n">d</span> <span class="o">/</span> <span class="n">det</span>
+        <span class="n">b_inv</span> <span class="o">=</span> <span class="o">-</span><span class="n">b</span> <span class="o">/</span> <span class="n">det</span>
+        <span class="n">c_inv</span> <span class="o">=</span> <span class="o">-</span><span class="n">c</span> <span class="o">/</span> <span class="n">det</span>
+        <span class="n">d_inv</span> <span class="o">=</span> <span class="n">a</span> <span class="o">/</span> <span class="n">det</span>
+        <span class="n">m</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">a_inv</span> <span class="o">*</span> <span class="n">m</span> <span class="o">+</span> <span class="n">b_inv</span> <span class="o">*</span> <span class="n">n</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">c_inv</span> <span class="o">*</span> <span class="n">m</span> <span class="o">+</span> <span class="n">d_inv</span> <span class="o">*</span> <span class="n">n</span>
+        <span class="n">intersect_exists</span> <span class="o">=</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">intersect_exists</span><span class="p">:</span>
+            <span class="n">intersection</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span>
+                <span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+            <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">intersection</span></div>
+
+
+
+<div class="viewcode-block" id="get_segment_point_intersect">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_segment_point_intersect.html#libpysal.cg.get_segment_point_intersect">[docs]</a>
+<span class="k">def</span> <span class="nf">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the intersection of a segment and point.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    seg : libpysal.cg.LineSegment</span>
+<span class="sd">        A segment to check for an intersection.</span>
+<span class="sd">    pt : libpysal.cg.Point</span>
+<span class="sd">        A point to check ``seg`` for an intersection.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    pt : {libpysal.cg.Point, None}</span>
+<span class="sd">        The intersection of a ``seg`` and ``pt`` if one exists, otherwise ``None``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; seg = LineSegment(Point((0, 0)), Point((0, 10)))</span>
+<span class="sd">    &gt;&gt;&gt; pt = Point((0, 5))</span>
+<span class="sd">    &gt;&gt;&gt; i = get_segment_point_intersect(seg, pt)</span>
+<span class="sd">    &gt;&gt;&gt; str(i)</span>
+<span class="sd">    &#39;(0.0, 5.0)&#39;</span>
+
+<span class="sd">    &gt;&gt;&gt; pt2 = Point((5, 5))</span>
+<span class="sd">    &gt;&gt;&gt; get_segment_point_intersect(seg, pt2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">eps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span><span class="o">.</span><span class="n">eps</span>
+
+    <span class="k">if</span> <span class="n">is_collinear</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">,</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">EPSILON_SCALER</span> <span class="o">*</span> <span class="n">eps</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">pt</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;</span> <span class="n">eps</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">pt</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">pt</span></div>
+
+
+
+<div class="viewcode-block" id="get_polygon_point_intersect">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_polygon_point_intersect.html#libpysal.cg.get_polygon_point_intersect">[docs]</a>
+<span class="k">def</span> <span class="nf">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the intersection of a polygon and point.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    poly : libpysal.cg.Polygon</span>
+<span class="sd">        A polygon to check for an intersection.</span>
+<span class="sd">    pt : libpysal.cg.Point</span>
+<span class="sd">        A point to check ``poly`` for an intersection.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    ret : {libpysal.cg.Point, None}</span>
+<span class="sd">        The intersection of a ``poly`` and ``pt`` if one exists, otherwise ``None``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
+<span class="sd">    &gt;&gt;&gt; pt = Point((0.5, 0.5))</span>
+<span class="sd">    &gt;&gt;&gt; i = get_polygon_point_intersect(poly, pt)</span>
+<span class="sd">    &gt;&gt;&gt; str(i)</span>
+<span class="sd">    &#39;(0.5, 0.5)&#39;</span>
+
+<span class="sd">    &gt;&gt;&gt; pt2 = Point((2, 2))</span>
+<span class="sd">    &gt;&gt;&gt; get_polygon_point_intersect(poly, pt2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">pt_lies_on_part_boundary</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">vx</span><span class="p">):</span>
+        <span class="n">vx_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vx</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">seg</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">vx</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">vx</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">vx_range</span> <span class="k">if</span> <span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">p</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="p">[]</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># Weed out points that aren&#39;t even close</span>
+    <span class="k">if</span> <span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="o">.</span><span class="n">bounding_box</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">pass</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="p">[</span>
+            <span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">_vertices</span> <span class="k">if</span> <span class="n">pt_lies_on_part_boundary</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)</span>
+        <span class="p">]</span> <span class="o">!=</span> <span class="p">[]</span> <span class="ow">or</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">_vertices</span> <span class="k">if</span> <span class="n">_point_in_vertices</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">pt</span>
+        <span class="k">if</span> <span class="n">poly</span><span class="o">.</span><span class="n">_holes</span> <span class="o">!=</span> <span class="p">[[]]:</span>
+            <span class="k">if</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">pt_lies_on_part_boundary</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
+                <span class="c1"># pt lies on boundary of hole.</span>
+                <span class="k">pass</span>
+            <span class="k">if</span> <span class="p">[</span><span class="n">vxs</span> <span class="k">for</span> <span class="n">vxs</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">_point_in_vertices</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vxs</span><span class="p">)]</span> <span class="o">!=</span> <span class="p">[]:</span>
+                <span class="c1"># pt lines inside a hole.</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="c1"># raise NotImplementedError,</span>
+            <span class="c1"># &#39;Cannot compute containment for polygon with holes&#39;</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+
+<div class="viewcode-block" id="get_rectangle_point_intersect">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_rectangle_point_intersect.html#libpysal.cg.get_rectangle_point_intersect">[docs]</a>
+<span class="k">def</span> <span class="nf">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">rect</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the intersection of a rectangle and point.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    rect : libpysal.cg.Rectangle</span>
+<span class="sd">        A rectangle to check for an intersection.</span>
+<span class="sd">    pt : libpysal.cg.Point</span>
+<span class="sd">        A point to check ``rect`` for an intersection.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    pt : {libpysal.cg.Point, None}</span>
+<span class="sd">        The intersection of a ``rect`` and ``pt`` if one exists, otherwise ``None``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; rect = Rectangle(0, 0, 5, 5)</span>
+<span class="sd">    &gt;&gt;&gt; pt = Point((1, 1))</span>
+<span class="sd">    &gt;&gt;&gt; i = get_rectangle_point_intersect(rect, pt)</span>
+<span class="sd">    &gt;&gt;&gt; str(i)</span>
+<span class="sd">    &#39;(1.0, 1.0)&#39;</span>
+
+<span class="sd">    &gt;&gt;&gt; pt2 = Point((10, 10))</span>
+<span class="sd">    &gt;&gt;&gt; get_rectangle_point_intersect(rect, pt2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">rect</span><span class="o">.</span><span class="n">left</span> <span class="o">&lt;=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">right</span> <span class="ow">and</span> <span class="n">rect</span><span class="o">.</span><span class="n">lower</span> <span class="o">&lt;=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">upper</span><span class="p">:</span>
+        <span class="k">pass</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">pt</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">pt</span></div>
+
+
+
+<div class="viewcode-block" id="get_ray_segment_intersect">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_ray_segment_intersect.html#libpysal.cg.get_ray_segment_intersect">[docs]</a>
+<span class="k">def</span> <span class="nf">get_ray_segment_intersect</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">seg</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the intersection of a ray and line segment.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ray : libpysal.cg.Ray</span>
+<span class="sd">        A ray to check for an intersection.</span>
+<span class="sd">    seg : libpysal.cg.LineSegment</span>
+<span class="sd">        A segment to check for an intersection against ``ray``.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, None}</span>
+<span class="sd">        The intersecting point or line between ``ray`` and</span>
+<span class="sd">        ``seg`` if an intersection exists or ``None`` if</span>
+<span class="sd">        ``ray`` and ``seg`` do not intersect.</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    libpysal.cg.get_segments_intersect</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; ray = Ray(Point((0, 0)), Point((0, 1)))</span>
+<span class="sd">    &gt;&gt;&gt; seg = LineSegment(Point((-1, 10)), Point((1, 10)))</span>
+<span class="sd">    &gt;&gt;&gt; i = get_ray_segment_intersect(ray, seg)</span>
+<span class="sd">    &gt;&gt;&gt; isinstance(i, Point)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; str(i)</span>
+<span class="sd">    &#39;(0.0, 10.0)&#39;</span>
+
+<span class="sd">    &gt;&gt;&gt; seg2 = LineSegment(Point((10, 10)), Point((10, 11)))</span>
+<span class="sd">    &gt;&gt;&gt; get_ray_segment_intersect(ray, seg2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Upper bound on origin to segment dist (+1)</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="nb">max</span><span class="p">(</span>
+            <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+        <span class="p">)</span>
+        <span class="o">+</span> <span class="mi">1</span>
+    <span class="p">)</span>
+    <span class="n">ratio</span> <span class="o">=</span> <span class="n">d</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">ray_seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span>
+        <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">,</span>
+        <span class="n">Point</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">ratio</span> <span class="o">*</span> <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+                <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">ratio</span> <span class="o">*</span> <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="p">)</span>
+        <span class="p">),</span>
+    <span class="p">)</span>
+
+    <span class="n">intersection</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">ray_seg</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">intersection</span></div>
+
+
+
+<div class="viewcode-block" id="get_rectangle_rectangle_intersection">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_rectangle_rectangle_intersection.html#libpysal.cg.get_rectangle_rectangle_intersection">[docs]</a>
+<span class="k">def</span> <span class="nf">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span> <span class="n">r1</span><span class="p">,</span> <span class="n">checkOverlap</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the intersection between two rectangles.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    r0 : libpysal.cg.Rectangle</span>
+<span class="sd">        A rectangle to check for an intersection.</span>
+<span class="sd">    r1 : libpysal.cg.Rectangle</span>
+<span class="sd">        A rectangle to check for an intersection against ``r0``.</span>
+<span class="sd">    checkOverlap : bool</span>
+<span class="sd">        Call ``bbcommon(r0, r1)`` prior to complex geometry</span>
+<span class="sd">        checking. Default is ``True``. Prior to setting as</span>
+<span class="sd">        ``False`` see the Notes section.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    intersection : {libpysal.cg.Point, libpysal.cg.LineSegment, libpysal.cg.Rectangle, None}</span>
+<span class="sd">        The intersecting point, line, or rectangle between</span>
+<span class="sd">        ``r0`` and ``r1`` if an intersection exists or ``None``</span>
+<span class="sd">        if ``r0`` and ``r1`` do not intersect.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    The algorithm assumes the rectangles overlap. The keyword</span>
+<span class="sd">    ``checkOverlap=False`` should be used with extreme caution.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; r0 = Rectangle(0,4,6,9)</span>
+<span class="sd">    &gt;&gt;&gt; r1 = Rectangle(4,0,9,7)</span>
+<span class="sd">    &gt;&gt;&gt; ri = get_rectangle_rectangle_intersection(r0,r1)</span>
+<span class="sd">    &gt;&gt;&gt; ri[:]</span>
+<span class="sd">    [4.0, 4.0, 6.0, 7.0]</span>
+
+<span class="sd">    &gt;&gt;&gt; r0 = Rectangle(0,0,4,4)</span>
+<span class="sd">    &gt;&gt;&gt; r1 = Rectangle(2,1,6,3)</span>
+<span class="sd">    &gt;&gt;&gt; ri = get_rectangle_rectangle_intersection(r0,r1)</span>
+<span class="sd">    &gt;&gt;&gt; ri[:]</span>
+<span class="sd">    [2.0, 1.0, 4.0, 3.0]</span>
+
+<span class="sd">    &gt;&gt;&gt; r0 = Rectangle(0,0,4,4)</span>
+<span class="sd">    &gt;&gt;&gt; r1 = Rectangle(2,1,3,2)</span>
+<span class="sd">    &gt;&gt;&gt; ri = get_rectangle_rectangle_intersection(r0,r1)</span>
+<span class="sd">    &gt;&gt;&gt; ri[:] == r1[:]</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>  <span class="c1"># noqa E501</span>
+
+    <span class="n">intersection</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">common_bb</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">if</span> <span class="n">checkOverlap</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">bbcommon</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span> <span class="n">r1</span><span class="p">):</span>
+        <span class="c1"># raise ValueError, &quot;Rectangles do not intersect&quot;</span>
+        <span class="n">common_bb</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">if</span> <span class="n">common_bb</span><span class="p">:</span>
+        <span class="n">left</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">left</span><span class="p">)</span>
+        <span class="n">lower</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
+        <span class="n">right</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
+        <span class="n">upper</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">upper</span> <span class="o">==</span> <span class="n">lower</span> <span class="ow">and</span> <span class="n">left</span> <span class="o">==</span> <span class="n">right</span><span class="p">:</span>
+            <span class="n">intersection</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">))</span>
+        <span class="k">elif</span> <span class="n">upper</span> <span class="o">==</span> <span class="n">lower</span><span class="p">:</span>
+            <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">right</span><span class="p">,</span> <span class="n">lower</span><span class="p">)))</span>
+        <span class="k">elif</span> <span class="n">left</span> <span class="o">==</span> <span class="n">right</span><span class="p">:</span>
+            <span class="n">intersection</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="n">left</span><span class="p">,</span> <span class="n">upper</span><span class="p">)))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">intersection</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">right</span><span class="p">,</span> <span class="n">upper</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">intersection</span></div>
+
+
+
+<div class="viewcode-block" id="get_polygon_point_dist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_polygon_point_dist.html#libpysal.cg.get_polygon_point_dist">[docs]</a>
+<span class="k">def</span> <span class="nf">get_polygon_point_dist</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the distance between a polygon and point.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    poly : libpysal.cg.Polygon</span>
+<span class="sd">        A polygon to compute distance from.</span>
+
+<span class="sd">    pt : libpysal.cg.Point</span>
+<span class="sd">        a point to compute distance from</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dist : float</span>
+<span class="sd">        The distance between ``poly`` and ``point``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; poly = Polygon([Point((0, 0)), Point((1, 0)), Point((1, 1)), Point((0, 1))])</span>
+<span class="sd">    &gt;&gt;&gt; pt = Point((2, 0.5))</span>
+<span class="sd">    &gt;&gt;&gt; get_polygon_point_dist(poly, pt)</span>
+<span class="sd">    1.0</span>
+
+<span class="sd">    &gt;&gt;&gt; pt2 = Point((0.5, 0.5))</span>
+<span class="sd">    &gt;&gt;&gt; get_polygon_point_dist(poly, pt2)</span>
+<span class="sd">    0.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">part_prox</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">vertices</span> <span class="ow">in</span> <span class="n">poly</span><span class="o">.</span><span class="n">_vertices</span><span class="p">:</span>
+            <span class="n">vx_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">seg</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>  <span class="c1"># noqa B023</span>
+            <span class="n">_min_dist</span> <span class="o">=</span> <span class="nb">min</span><span class="p">([</span><span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">pt</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">vx_range</span><span class="p">])</span>
+            <span class="n">part_prox</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_min_dist</span><span class="p">)</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">part_prox</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">dist</span></div>
+
+
+
+<div class="viewcode-block" id="get_points_dist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_points_dist.html#libpysal.cg.get_points_dist">[docs]</a>
+<span class="k">def</span> <span class="nf">get_points_dist</span><span class="p">(</span><span class="n">pt1</span><span class="p">,</span> <span class="n">pt2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the distance between a pair of points.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pt1 : libpysal.cg.Point</span>
+<span class="sd">        A point.</span>
+
+<span class="sd">    pt2 : libpysal.cg.Point</span>
+<span class="sd">        The other point.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dist : float</span>
+<span class="sd">        The distance between ``pt1`` and ``pt2``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; get_points_dist(Point((4, 4)), Point((4, 8)))</span>
+<span class="sd">    4.0</span>
+
+<span class="sd">    &gt;&gt;&gt; get_points_dist(Point((0, 0)), Point((0, 0)))</span>
+<span class="sd">    0.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">dist</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">hypot</span><span class="p">(</span><span class="n">pt1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">pt2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">pt2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">dist</span></div>
+
+
+
+<div class="viewcode-block" id="get_segment_point_dist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_segment_point_dist.html#libpysal.cg.get_segment_point_dist">[docs]</a>
+<span class="k">def</span> <span class="nf">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns (1) the distance between a line segment and point</span>
+<span class="sd">    and (2) the distance along the segment to the closest location on the</span>
+<span class="sd">    segment from the point as a ratio of the length of the segment.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    seg : libpysal.cg.LineSegment</span>
+<span class="sd">        A line segment to compute distance from.</span>
+<span class="sd">    pt : libpysal.cg.Point</span>
+<span class="sd">        A point to compute distance from.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dist : float</span>
+<span class="sd">        The distance between ``seg`` and ``pt``.</span>
+<span class="sd">    ratio : float</span>
+<span class="sd">        The distance along ``seg`` to the closest location on</span>
+<span class="sd">        ``seg`` from ``pt`` as a ratio of the length of ``seg``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; seg = LineSegment(Point((0, 0)), Point((10, 0)))</span>
+<span class="sd">    &gt;&gt;&gt; pt = Point((5, 5))</span>
+<span class="sd">    &gt;&gt;&gt; get_segment_point_dist(seg, pt)</span>
+<span class="sd">    (5.0, 0.5)</span>
+
+<span class="sd">    &gt;&gt;&gt; pt2 = Point((0, 0))</span>
+<span class="sd">    &gt;&gt;&gt; get_segment_point_dist(seg, pt2)</span>
+<span class="sd">    (0.0, 0.0)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">src_p</span> <span class="o">=</span> <span class="n">seg</span><span class="o">.</span><span class="n">p1</span>
+    <span class="n">dest_p</span> <span class="o">=</span> <span class="n">seg</span><span class="o">.</span><span class="n">p2</span>
+
+    <span class="c1"># Shift line to go through origin</span>
+    <span class="n">points_0</span> <span class="o">=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">points_1</span> <span class="o">=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">points_2</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">points_3</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">points_4</span> <span class="o">=</span> <span class="n">dest_p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">points_5</span> <span class="o">=</span> <span class="n">dest_p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">src_p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">segment_length</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">src_p</span><span class="p">,</span> <span class="n">dest_p</span><span class="p">)</span>
+
+    <span class="c1"># Meh, robustness...</span>
+    <span class="c1"># maybe should incorporate this into a more general approach later</span>
+    <span class="k">if</span> <span class="n">segment_length</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">src_p</span><span class="p">),</span> <span class="mi">0</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">u_x</span> <span class="o">=</span> <span class="n">points_4</span> <span class="o">/</span> <span class="n">segment_length</span>
+        <span class="n">u_y</span> <span class="o">=</span> <span class="n">points_5</span> <span class="o">/</span> <span class="n">segment_length</span>
+
+        <span class="n">inter_x</span> <span class="o">=</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">points_0</span> <span class="o">+</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">points_1</span>
+        <span class="n">inter_y</span> <span class="o">=</span> <span class="n">u_x</span> <span class="o">*</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">points_0</span> <span class="o">+</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">u_y</span> <span class="o">*</span> <span class="n">points_1</span>
+
+        <span class="n">src_proj_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="n">inter_x</span><span class="p">,</span> <span class="n">inter_y</span><span class="p">))</span>
+        <span class="n">dest_proj_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">inter_x</span><span class="p">,</span> <span class="n">inter_y</span><span class="p">),</span> <span class="p">(</span><span class="n">points_4</span><span class="p">,</span> <span class="n">points_5</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">src_proj_dist</span> <span class="o">&gt;</span> <span class="n">segment_length</span> <span class="ow">or</span> <span class="n">dest_proj_dist</span> <span class="o">&gt;</span> <span class="n">segment_length</span><span class="p">:</span>
+            <span class="n">src_pt_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">points_2</span><span class="p">,</span> <span class="n">points_3</span><span class="p">),</span> <span class="p">(</span><span class="n">points_0</span><span class="p">,</span> <span class="n">points_1</span><span class="p">))</span>
+            <span class="n">dest_pt_dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">points_4</span><span class="p">,</span> <span class="n">points_5</span><span class="p">),</span> <span class="p">(</span><span class="n">points_0</span><span class="p">,</span> <span class="n">points_1</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">src_pt_dist</span> <span class="o">&lt;</span> <span class="n">dest_pt_dist</span><span class="p">:</span>
+                <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">src_pt_dist</span><span class="p">,</span> <span class="mi">0</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span> <span class="o">=</span> <span class="n">dest_pt_dist</span><span class="p">,</span> <span class="mi">1</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">get_points_dist</span><span class="p">((</span><span class="n">inter_x</span><span class="p">,</span> <span class="n">inter_y</span><span class="p">),</span> <span class="p">(</span><span class="n">points_0</span><span class="p">,</span> <span class="n">points_1</span><span class="p">))</span>
+            <span class="n">ratio</span> <span class="o">=</span> <span class="n">src_proj_dist</span> <span class="o">/</span> <span class="n">segment_length</span>
+
+    <span class="k">return</span> <span class="n">dist</span><span class="p">,</span> <span class="n">ratio</span></div>
+
+
+
+<div class="viewcode-block" id="get_point_at_angle_and_dist">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_point_at_angle_and_dist.html#libpysal.cg.get_point_at_angle_and_dist">[docs]</a>
+<span class="k">def</span> <span class="nf">get_point_at_angle_and_dist</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">angle</span><span class="p">,</span> <span class="n">dist</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the point at a distance and angle relative to the origin of a ray.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ray : libpysal.cg.Ray</span>
+<span class="sd">        The ray to which ``angle`` and ``dist`` are relative.</span>
+<span class="sd">    angle : float</span>
+<span class="sd">        The angle relative to ``ray`` at which ``point`` is located.</span>
+<span class="sd">    dist : float</span>
+<span class="sd">        The distance from the origin of ``ray`` at which ``point`` is located.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    point : libpysal.cg.Point</span>
+<span class="sd">        The point at ``dist`` and ``angle`` relative to the origin of ``ray``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; ray = Ray(Point((0, 0)), Point((1, 0)))</span>
+<span class="sd">    &gt;&gt;&gt; pt = get_point_at_angle_and_dist(ray, math.pi, 1.0)</span>
+<span class="sd">    &gt;&gt;&gt; isinstance(pt, Point)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; round(pt[0], 8)</span>
+<span class="sd">    -1.0</span>
+
+<span class="sd">    &gt;&gt;&gt; round(pt[1], 8)</span>
+<span class="sd">    0.0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">v</span> <span class="o">=</span> <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ray</span><span class="o">.</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">cur_angle</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">dest_angle</span> <span class="o">=</span> <span class="n">cur_angle</span> <span class="o">+</span> <span class="n">angle</span>
+
+    <span class="n">point</span> <span class="o">=</span> <span class="n">Point</span><span class="p">(</span>
+        <span class="p">(</span><span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">dist</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">dest_angle</span><span class="p">),</span> <span class="n">ray</span><span class="o">.</span><span class="n">o</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">dist</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">dest_angle</span><span class="p">))</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">point</span></div>
+
+
+
+<div class="viewcode-block" id="convex_hull">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.convex_hull.html#libpysal.cg.convex_hull">[docs]</a>
+<span class="k">def</span> <span class="nf">convex_hull</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the convex hull of a set of points.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    points : list</span>
+<span class="sd">        A list of points for computing the convex hull.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    stack : list</span>
+<span class="sd">        A list of points representing the convex hull.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; points = [Point((0, 0)), Point((4, 4)), Point((4, 0)), Point((3, 1))]</span>
+<span class="sd">    &gt;&gt;&gt; convex_hull(points)</span>
+<span class="sd">    [(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">right_turn</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns if ``p1`` -&gt; ``p2`` -&gt; ``p3`` forms a &#39;right turn&#39;.&quot;&quot;&quot;</span>
+        <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">_rt</span> <span class="o">=</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span>
+        <span class="k">return</span> <span class="n">_rt</span>
+
+    <span class="n">points</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+    <span class="n">lowest</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+    <span class="n">points</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">lowest</span><span class="p">)</span>
+    <span class="n">points</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="n">math</span><span class="o">.</span><span class="n">atan2</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lowest</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">lowest</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+
+    <span class="n">stack</span> <span class="o">=</span> <span class="p">[</span><span class="n">lowest</span><span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">points</span><span class="p">:</span>
+        <span class="n">stack</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
+        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">stack</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">right_turn</span><span class="p">(</span><span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">stack</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">stack</span></div>
+
+
+
+<div class="viewcode-block" id="is_clockwise">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.is_clockwise.html#libpysal.cg.is_clockwise">[docs]</a>
+<span class="k">def</span> <span class="nf">is_clockwise</span><span class="p">(</span><span class="n">vertices</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns whether a list of points describing</span>
+<span class="sd">    a polygon are clockwise or counterclockwise.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of points that form a single ring.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    clockwise : bool</span>
+<span class="sd">        ``True`` if ``vertices`` are clockwise, otherwise ``False``.</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    libpysal.cg.ccw</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; is_clockwise([Point((0, 0)), Point((10, 0)), Point((0, 10))])</span>
+<span class="sd">    False</span>
+
+<span class="sd">    &gt;&gt;&gt; is_clockwise([Point((0, 0)), Point((0, 10)), Point((10, 0))])</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; v = [</span>
+<span class="sd">    ...     (-106.57798, 35.174143999999998),</span>
+<span class="sd">    ...     (-106.583412, 35.174141999999996),</span>
+<span class="sd">    ...     (-106.58417999999999, 35.174143000000001),</span>
+<span class="sd">    ...     (-106.58377999999999, 35.175542999999998),</span>
+<span class="sd">    ...     (-106.58287999999999, 35.180543),</span>
+<span class="sd">    ...     (-106.58263099999999, 35.181455),</span>
+<span class="sd">    ...     (-106.58257999999999, 35.181643000000001),</span>
+<span class="sd">    ...     (-106.58198299999999, 35.184615000000001),</span>
+<span class="sd">    ...     (-106.58148, 35.187242999999995),</span>
+<span class="sd">    ...     (-106.58127999999999, 35.188243),</span>
+<span class="sd">    ...     (-106.58138, 35.188243),</span>
+<span class="sd">    ...     (-106.58108, 35.189442999999997),</span>
+<span class="sd">    ...     (-106.58104, 35.189644000000001),</span>
+<span class="sd">    ...     (-106.58028, 35.193442999999995),</span>
+<span class="sd">    ...     (-106.580029, 35.194541000000001),</span>
+<span class="sd">    ...     (-106.57974399999999, 35.195785999999998),</span>
+<span class="sd">    ...     (-106.579475, 35.196961999999999),</span>
+<span class="sd">    ...     (-106.57922699999999, 35.198042999999998),</span>
+<span class="sd">    ...     (-106.578397, 35.201665999999996),</span>
+<span class="sd">    ...     (-106.57827999999999, 35.201642999999997),</span>
+<span class="sd">    ...     (-106.57737999999999, 35.201642999999997),</span>
+<span class="sd">    ...     (-106.57697999999999, 35.201543000000001),</span>
+<span class="sd">    ...     (-106.56436599999999, 35.200311999999997),</span>
+<span class="sd">    ...     (-106.56058, 35.199942999999998),</span>
+<span class="sd">    ...     (-106.56048, 35.197342999999996),</span>
+<span class="sd">    ...     (-106.56048, 35.195842999999996),</span>
+<span class="sd">    ...     (-106.56048, 35.194342999999996),</span>
+<span class="sd">    ...     (-106.56048, 35.193142999999999),</span>
+<span class="sd">    ...     (-106.56048, 35.191873999999999),</span>
+<span class="sd">    ...     (-106.56048, 35.191742999999995),</span>
+<span class="sd">    ...     (-106.56048, 35.190242999999995),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.188642999999999),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.187242999999995),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.186842999999996),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.186552999999996),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.185842999999998),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.184443000000002),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.182943000000002),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.181342999999998),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.180433000000001),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.179943000000002),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.178542999999998),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.177790999999999),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.177143999999998),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.175643999999998),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.174444000000001),</span>
+<span class="sd">    ...     (-106.56037999999999, 35.174043999999995),</span>
+<span class="sd">    ...     (-106.560526, 35.174043999999995),</span>
+<span class="sd">    ...     (-106.56478, 35.174043999999995),</span>
+<span class="sd">    ...     (-106.56627999999999, 35.174143999999998),</span>
+<span class="sd">    ...     (-106.566541, 35.174144999999996),</span>
+<span class="sd">    ...     (-106.569023, 35.174157000000001),</span>
+<span class="sd">    ...     (-106.56917199999999, 35.174157999999998),</span>
+<span class="sd">    ...     (-106.56938, 35.174143999999998),</span>
+<span class="sd">    ...     (-106.57061499999999, 35.174143999999998),</span>
+<span class="sd">    ...     (-106.57097999999999, 35.174143999999998),</span>
+<span class="sd">    ...     (-106.57679999999999, 35.174143999999998),</span>
+<span class="sd">    ...     (-106.57798, 35.174143999999998)</span>
+<span class="sd">    ... ]</span>
+<span class="sd">    &gt;&gt;&gt; is_clockwise(v)</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">clockwise</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">area</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="n">ax</span><span class="p">,</span> <span class="n">ay</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">bx</span><span class="p">,</span> <span class="n">by</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
+            <span class="n">area</span> <span class="o">+=</span> <span class="n">ax</span> <span class="o">*</span> <span class="n">by</span> <span class="o">-</span> <span class="n">ay</span> <span class="o">*</span> <span class="n">bx</span>
+            <span class="n">ax</span><span class="p">,</span> <span class="n">ay</span> <span class="o">=</span> <span class="n">bx</span><span class="p">,</span> <span class="n">by</span>
+        <span class="n">bx</span><span class="p">,</span> <span class="n">by</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">area</span> <span class="o">+=</span> <span class="n">ax</span> <span class="o">*</span> <span class="n">by</span> <span class="o">-</span> <span class="n">ay</span> <span class="o">*</span> <span class="n">bx</span>
+
+        <span class="n">clockwise</span> <span class="o">=</span> <span class="n">area</span> <span class="o">&lt;</span> <span class="mf">0.0</span>
+
+    <span class="k">return</span> <span class="n">clockwise</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">ccw</span><span class="p">(</span><span class="n">vertices</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns whether a list of points is counterclockwise.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of points that form a single ring.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    counter_clockwise : bool</span>
+<span class="sd">        ``True`` if ``vertices`` are counter clockwise, otherwise ``False``.</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    libpysal.cg.is_clockwise</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; ccw([Point((0, 0)), Point((10, 0)), Point((0, 10))])</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; ccw([Point((0, 0)), Point((0, 10)), Point((10, 0))])</span>
+<span class="sd">    False</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">counter_clockwise</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">if</span> <span class="n">is_clockwise</span><span class="p">(</span><span class="n">vertices</span><span class="p">):</span>
+        <span class="n">counter_clockwise</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">return</span> <span class="n">counter_clockwise</span>
+
+
+<span class="k">def</span> <span class="nf">seg_intersect</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Tests if two segments (a,b) and (c,d) intersect.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    a : libpysal.cg.Point</span>
+<span class="sd">        The first vertex for the first segment.</span>
+<span class="sd">    b : libpysal.cg.Point</span>
+<span class="sd">        The second vertex for the first segment.</span>
+<span class="sd">    c : libpysal.cg.Point</span>
+<span class="sd">        The first vertex for the second segment.</span>
+<span class="sd">    d : libpysal.cg.Point</span>
+<span class="sd">        The second vertex for the second segment.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    segments_intersect : bool</span>
+<span class="sd">        ``True`` if segments ``(a,b)`` and ``(c,d)``, otherwise ``False``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; a = Point((0,1))</span>
+<span class="sd">    &gt;&gt;&gt; b = Point((0,10))</span>
+<span class="sd">    &gt;&gt;&gt; c = Point((-2,5))</span>
+<span class="sd">    &gt;&gt;&gt; d = Point((2,5))</span>
+<span class="sd">    &gt;&gt;&gt; e = Point((-3,5))</span>
+<span class="sd">    &gt;&gt;&gt; seg_intersect(a, b, c, d)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &gt;&gt;&gt; seg_intersect(a, b, c, e)</span>
+<span class="sd">    False</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">segments_intersect</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="n">acd_bcd</span> <span class="o">=</span> <span class="n">ccw</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">])</span> <span class="o">==</span> <span class="n">ccw</span><span class="p">([</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">])</span>
+
+    <span class="n">abc_abd</span> <span class="o">=</span> <span class="n">ccw</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">])</span> <span class="o">==</span> <span class="n">ccw</span><span class="p">([</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">d</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">acd_bcd</span> <span class="ow">or</span> <span class="n">abc_abd</span><span class="p">:</span>
+        <span class="n">segments_intersect</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">return</span> <span class="n">segments_intersect</span>
+
+
+<span class="k">def</span> <span class="nf">_point_in_vertices</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">vertices</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;**HELPER METHOD. DO NOT CALL.** Returns whether a point</span>
+<span class="sd">    is contained in a polygon specified by a sequence of vertices.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    pt : libpysal.cg.Point</span>
+<span class="sd">        A point.</span>
+<span class="sd">    vertices : list</span>
+<span class="sd">        A list of vertices representing as polygon.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    pt_in_poly : bool</span>
+<span class="sd">        ``True`` if ``pt`` is contained in ``vertices``, otherwise ``False``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; _point_in_vertices(</span>
+<span class="sd">    ...     Point((1, 1)),</span>
+<span class="sd">    ...     [Point((0, 0)), Point((10, 0)), Point((0, 10))]</span>
+<span class="sd">    ... )</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">neg_ray_intersect</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns whether a ray in the negative-x</span>
+<span class="sd">        direction from ``p3`` intersects the segment between.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">min</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="nb">max</span><span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">nr_inters</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">vec1</span> <span class="o">=</span> <span class="p">(</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="n">vec2</span> <span class="o">=</span> <span class="p">(</span><span class="n">p3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p3</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="n">nr_inters</span> <span class="o">=</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">vec2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">vec1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="mi">0</span>
+
+        <span class="k">return</span> <span class="n">nr_inters</span>
+
+    <span class="n">vert_y_set</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">}</span>
+    <span class="k">while</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="n">vert_y_set</span><span class="p">:</span>
+        <span class="c1"># Perturb the location very slightly</span>
+        <span class="n">pt</span> <span class="o">=</span> <span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="o">-</span><span class="mf">1e-14</span> <span class="o">+</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">*</span> <span class="mf">2e-14</span>
+
+    <span class="n">inters</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">v1</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">v2</span> <span class="o">=</span> <span class="n">vertices</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">neg_ray_intersect</span><span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">,</span> <span class="n">pt</span><span class="p">):</span>
+            <span class="n">inters</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="n">pt_in_poly</span> <span class="o">=</span> <span class="n">inters</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span>
+
+    <span class="k">return</span> <span class="n">pt_in_poly</span>
+
+
+<div class="viewcode-block" id="point_touches_rectangle">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.point_touches_rectangle.html#libpysal.cg.point_touches_rectangle">[docs]</a>
+<span class="k">def</span> <span class="nf">point_touches_rectangle</span><span class="p">(</span><span class="n">point</span><span class="p">,</span> <span class="n">rect</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns ``True`` (``1``) if the point is in the rectangle</span>
+<span class="sd">    or touches it&#39;s boundary, otherwise ``False`` (``0``).</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    point : {libpysal.cg.Point, tuple}</span>
+<span class="sd">        A point or point coordinates.</span>
+<span class="sd">    rect : libpysal.cg.Rectangle</span>
+<span class="sd">        A rectangle.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    chflag : int</span>
+<span class="sd">        ``1`` if ``point`` is in (or touches</span>
+<span class="sd">        boundary of) ``rect``, otherwise ``0``.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; rect = Rectangle(0, 0, 10, 10)</span>
+<span class="sd">    &gt;&gt;&gt; a = Point((5, 5))</span>
+<span class="sd">    &gt;&gt;&gt; b = Point((10, 5))</span>
+<span class="sd">    &gt;&gt;&gt; c = Point((11, 11))</span>
+<span class="sd">    &gt;&gt;&gt; point_touches_rectangle(a, rect)</span>
+<span class="sd">    1</span>
+
+<span class="sd">    &gt;&gt;&gt; point_touches_rectangle(b, rect)</span>
+<span class="sd">    1</span>
+
+<span class="sd">    &gt;&gt;&gt; point_touches_rectangle(c, rect)</span>
+<span class="sd">    0</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">chflag</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">if</span> <span class="p">(</span><span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">left</span> <span class="ow">and</span> <span class="n">point</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">right</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span>
+        <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">lower</span> <span class="ow">and</span> <span class="n">point</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">rect</span><span class="o">.</span><span class="n">upper</span>
+    <span class="p">):</span>
+        <span class="n">chflag</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="k">return</span> <span class="n">chflag</span></div>
+
+
+
+<div class="viewcode-block" id="get_shared_segments">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.get_shared_segments.html#libpysal.cg.get_shared_segments">[docs]</a>
+<span class="k">def</span> <span class="nf">get_shared_segments</span><span class="p">(</span><span class="n">poly1</span><span class="p">,</span> <span class="n">poly2</span><span class="p">,</span> <span class="n">bool_ret</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the line segments in common to both polygons.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    poly1 : libpysal.cg.Polygon</span>
+<span class="sd">        A Polygon.</span>
+<span class="sd">    poly2 : libpysal.cg.Polygon</span>
+<span class="sd">        A Polygon.</span>
+<span class="sd">    bool_ret : bool</span>
+<span class="sd">        Return only a ``bool``. Default is ``False``.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    common : list</span>
+<span class="sd">        The shared line segments between ``poly1`` and ``poly2``.</span>
+<span class="sd">    _ret_bool : bool</span>
+<span class="sd">        Whether ``poly1`` and ``poly2`` share a</span>
+<span class="sd">        segment (``True``) or not (``False``).</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.cg.shapes import Polygon</span>
+<span class="sd">    &gt;&gt;&gt; x = [0, 0, 1, 1]</span>
+<span class="sd">    &gt;&gt;&gt; y = [0, 1, 1, 0]</span>
+<span class="sd">    &gt;&gt;&gt; poly1 = Polygon(list(map(Point, zip(x, y))) )</span>
+<span class="sd">    &gt;&gt;&gt; x = [a+1 for a in x]</span>
+<span class="sd">    &gt;&gt;&gt; poly2 = Polygon(list(map(Point, zip(x, y))) )</span>
+<span class="sd">    &gt;&gt;&gt; get_shared_segments(poly1, poly2, bool_ret=True)</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># get_rectangle_rectangle_intersection inlined for speed.</span>
+    <span class="n">r0</span> <span class="o">=</span> <span class="n">poly1</span><span class="o">.</span><span class="n">bounding_box</span>
+    <span class="n">r1</span> <span class="o">=</span> <span class="n">poly2</span><span class="o">.</span><span class="n">bounding_box</span>
+    <span class="n">wLeft</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">left</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">left</span><span class="p">)</span>
+    <span class="n">wLower</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">lower</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">lower</span><span class="p">)</span>
+    <span class="n">wRight</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">right</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">right</span><span class="p">)</span>
+    <span class="n">wUpper</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">r0</span><span class="o">.</span><span class="n">upper</span><span class="p">,</span> <span class="n">r1</span><span class="o">.</span><span class="n">upper</span><span class="p">)</span>
+
+    <span class="n">segmentsA</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+    <span class="n">common</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">poly1</span><span class="o">.</span><span class="n">parts</span> <span class="o">+</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">poly1</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">p</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">part</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>  <span class="c1"># not closed</span>
+            <span class="n">part</span> <span class="o">=</span> <span class="n">part</span><span class="p">[:]</span> <span class="o">+</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">islice</span><span class="p">(</span><span class="n">part</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="c1"># inlining point_touches_rectangle for speed</span>
+            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">a</span>
+            <span class="c1"># check if point a is in the bounding box intersection</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
+                <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">b</span>
+                <span class="c1"># check if point b is in the bounding box intersection</span>
+                <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">a</span> <span class="o">&gt;</span> <span class="n">b</span><span class="p">:</span>
+                        <span class="n">segmentsA</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">segmentsA</span><span class="o">.</span><span class="n">add</span><span class="p">((</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">))</span>
+            <span class="n">a</span> <span class="o">=</span> <span class="n">b</span>
+
+    <span class="n">_ret_bool</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">for</span> <span class="n">part</span> <span class="ow">in</span> <span class="n">poly2</span><span class="o">.</span><span class="n">parts</span> <span class="o">+</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">poly2</span><span class="o">.</span><span class="n">holes</span> <span class="k">if</span> <span class="n">p</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">part</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>  <span class="c1"># not closed</span>
+            <span class="n">part</span> <span class="o">=</span> <span class="n">part</span><span class="p">[:]</span> <span class="o">+</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="n">part</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">islice</span><span class="p">(</span><span class="n">part</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="c1"># inlining point_touches_rectangle for speed</span>
+            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">a</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
+                <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">b</span>
+                <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="n">wLeft</span> <span class="ow">and</span> <span class="n">x</span> <span class="o">&lt;=</span> <span class="n">wRight</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&gt;=</span> <span class="n">wLower</span> <span class="ow">and</span> <span class="n">y</span> <span class="o">&lt;=</span> <span class="n">wUpper</span><span class="p">:</span>
+                    <span class="n">seg</span> <span class="o">=</span> <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span> <span class="k">if</span> <span class="n">a</span> <span class="o">&gt;</span> <span class="n">b</span> <span class="k">else</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">seg</span> <span class="ow">in</span> <span class="n">segmentsA</span><span class="p">:</span>
+                        <span class="n">common</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">LineSegment</span><span class="p">(</span><span class="o">*</span><span class="n">seg</span><span class="p">))</span>
+                        <span class="k">if</span> <span class="n">bool_ret</span><span class="p">:</span>
+                            <span class="n">_ret_bool</span> <span class="o">=</span> <span class="kc">True</span>
+                            <span class="k">return</span> <span class="n">_ret_bool</span>
+            <span class="n">a</span> <span class="o">=</span> <span class="n">b</span>
+
+    <span class="k">if</span> <span class="n">bool_ret</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">common</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">_ret_bool</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">return</span> <span class="n">_ret_bool</span>
+
+    <span class="k">return</span> <span class="n">common</span></div>
+
+
+
+<div class="viewcode-block" id="distance_matrix">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.distance_matrix.html#libpysal.cg.distance_matrix">[docs]</a>
+<span class="k">def</span> <span class="nf">distance_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">5e7</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Calculate a distance matrix.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    X : numpy.ndarray</span>
+<span class="sd">        An :math:`n \\times k` array where :math:`n` is the number</span>
+<span class="sd">        of observations and :math:`k` is the number of dimensions</span>
+<span class="sd">        (2 for :math:`x,y`).</span>
+<span class="sd">    p : float</span>
+<span class="sd">        Minkowski `p`-norm distance metric parameter where</span>
+<span class="sd">        :math:`1&lt;=\mathtt{p}&lt;=\infty`. ``2`` is Euclidean distance and</span>
+<span class="sd">        ``1`` is Manhattan distance. Default is ``2.0``.</span>
+<span class="sd">    threshold : int</span>
+<span class="sd">        If :math:`(\mathtt{n}**2)*32 &gt; \mathtt{threshold}` use</span>
+<span class="sd">        ``scipy.spatial.distance_matrix`` instead of working in RAM,</span>
+<span class="sd">        this is roughly the amount of RAM (in bytes) that will be used.</span>
+<span class="sd">        Must be positive. Default is ``5e7``.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    D : numpy.ndarray</span>
+<span class="sd">        An n by :math:`m` :math:`p`-norm distance matrix.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    TypeError</span>
+<span class="sd">        Raised when an invalid dimensional array is passed in.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Needs optimization/integration with other weights in PySAL.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; x, y = [r.flatten() for r in np.indices((3, 3))]</span>
+<span class="sd">    &gt;&gt;&gt; data = np.array([x, y]).T</span>
+<span class="sd">    &gt;&gt;&gt; d = distance_matrix(data)</span>
+<span class="sd">    &gt;&gt;&gt; np.array(d)</span>
+<span class="sd">    array([[0.        , 1.        , 2.        , 1.        , 1.41421356,</span>
+<span class="sd">            2.23606798, 2.        , 2.23606798, 2.82842712],</span>
+<span class="sd">           [1.        , 0.        , 1.        , 1.41421356, 1.        ,</span>
+<span class="sd">            1.41421356, 2.23606798, 2.        , 2.23606798],</span>
+<span class="sd">           [2.        , 1.        , 0.        , 2.23606798, 1.41421356,</span>
+<span class="sd">            1.        , 2.82842712, 2.23606798, 2.        ],</span>
+<span class="sd">           [1.        , 1.41421356, 2.23606798, 0.        , 1.        ,</span>
+<span class="sd">            2.        , 1.        , 1.41421356, 2.23606798],</span>
+<span class="sd">           [1.41421356, 1.        , 1.41421356, 1.        , 0.        ,</span>
+<span class="sd">            1.        , 1.41421356, 1.        , 1.41421356],</span>
+<span class="sd">           [2.23606798, 1.41421356, 1.        , 2.        , 1.        ,</span>
+<span class="sd">            0.        , 2.23606798, 1.41421356, 1.        ],</span>
+<span class="sd">           [2.        , 2.23606798, 2.82842712, 1.        , 1.41421356,</span>
+<span class="sd">            2.23606798, 0.        , 1.        , 2.        ],</span>
+<span class="sd">           [2.23606798, 2.        , 2.23606798, 1.41421356, 1.        ,</span>
+<span class="sd">            1.41421356, 1.        , 0.        , 1.        ],</span>
+<span class="sd">           [2.82842712, 2.23606798, 2.        , 2.23606798, 1.41421356,</span>
+<span class="sd">            1.        , 2.        , 1.        , 0.        ]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">X</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;Should be 2D point coordinates: </span><span class="si">%s</span><span class="s2"> dimensions present.&quot;</span> <span class="o">%</span> <span class="n">X</span><span class="o">.</span><span class="n">ndim</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+
+    <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
+
+    <span class="k">if</span> <span class="p">(</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">32</span> <span class="o">&gt;</span> <span class="n">threshold</span><span class="p">:</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">col</span><span class="p">]</span>
+            <span class="n">xM</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">M</span>
+            <span class="n">dx</span> <span class="o">=</span> <span class="n">xM</span> <span class="o">-</span> <span class="n">xM</span><span class="o">.</span><span class="n">T</span>
+            <span class="k">if</span> <span class="n">p</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">dx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">dx</span><span class="p">)</span>
+            <span class="n">dx2</span> <span class="o">=</span> <span class="n">dx</span><span class="o">**</span><span class="n">p</span>
+            <span class="n">D</span> <span class="o">+=</span> <span class="n">dx2</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">D</span> <span class="o">**</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">p</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">D</span></div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
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+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
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+    <p>
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+    </p>
+  </div>
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+      
+  <h1>Source code for libpysal.cg.voronoi</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Voronoi tesslation of 2-d point sets.</span>
+
+<span class="sd">Adapted from https://gist.github.com/pv/8036995</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">Voronoi</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Serge Rey &lt;sjsrey@gmail.com&gt;&quot;</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;voronoi_frames&quot;</span><span class="p">]</span>
+
+
+<span class="k">def</span> <span class="nf">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Determine finite Voronoi diagram for a 2-d point set.</span>
+<span class="sd">    See also ``voronoi_regions()``.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    points : array_like</span>
+<span class="sd">        An nx2 array of points.</span>
+<span class="sd">    radius : float (optional)</span>
+<span class="sd">        The distance to &#39;points at infinity&#39;. Default is ``None.``</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    vor : tuple</span>
+<span class="sd">        A two-element tuple consisting of a list and an array. Each element of</span>
+<span class="sd">        the list contains the sequence of the indices of Voronoi vertices</span>
+<span class="sd">        composing a Voronoi polygon (region), whereas the array contains</span>
+<span class="sd">        the Voronoi vertex coordinates.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]</span>
+<span class="sd">    &gt;&gt;&gt; regions, coordinates = voronoi(points)</span>
+<span class="sd">    &gt;&gt;&gt; regions</span>
+<span class="sd">    [[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]</span>
+
+<span class="sd">    &gt;&gt;&gt; coordinates</span>
+<span class="sd">    array([[  4.21783296,   4.08408578],</span>
+<span class="sd">           [  7.51956025,   3.51807539],</span>
+<span class="sd">           [  9.4642193 ,  19.3994576 ],</span>
+<span class="sd">           [ 14.98210684, -10.63503022],</span>
+<span class="sd">           [ -9.22691341,  -4.58994414],</span>
+<span class="sd">           [ 14.98210684, -10.63503022],</span>
+<span class="sd">           [  1.78491801,  19.89803294],</span>
+<span class="sd">           [  9.4642193 ,  19.3994576 ],</span>
+<span class="sd">           [  1.78491801,  19.89803294],</span>
+<span class="sd">           [ -9.22691341,  -4.58994414]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">vor</span> <span class="o">=</span> <span class="n">voronoi_regions</span><span class="p">(</span><span class="n">Voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">),</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">vor</span>
+
+
+<span class="k">def</span> <span class="nf">voronoi_regions</span><span class="p">(</span><span class="n">vor</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Finite voronoi regions for a 2-d point set. See also ``voronoi()``.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    vor : scipy.spatial.Voronoi</span>
+<span class="sd">        A planar Voronoi diagram.</span>
+<span class="sd">    radius : float (optional)</span>
+<span class="sd">        Distance to &#39;points at infinity&#39;. Default is ``None.``</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    regions_vertices : tuple</span>
+<span class="sd">        A two-element tuple consisting of a list of finite voronoi regions</span>
+<span class="sd">        and an array Voronoi vertex coordinates.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">new_regions</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">new_vertices</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">vertices</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+    <span class="n">center</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">radius</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="o">.</span><span class="n">ptp</span><span class="p">()</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="mi">2</span>
+
+    <span class="n">all_ridges</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">),</span> <span class="p">(</span><span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">vor</span><span class="o">.</span><span class="n">ridge_points</span><span class="p">,</span> <span class="n">vor</span><span class="o">.</span><span class="n">ridge_vertices</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+        <span class="n">all_ridges</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">p1</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">p2</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">))</span>
+        <span class="n">all_ridges</span><span class="o">.</span><span class="n">setdefault</span><span class="p">(</span><span class="n">p2</span><span class="p">,</span> <span class="p">[])</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">p1</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span><span class="p">))</span>
+
+    <span class="k">for</span> <span class="n">p1</span><span class="p">,</span> <span class="n">region</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vor</span><span class="o">.</span><span class="n">point_region</span><span class="p">):</span>
+        <span class="n">vertices</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">regions</span><span class="p">[</span><span class="n">region</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="nb">all</span><span class="p">(</span><span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span><span class="p">):</span>
+            <span class="n">new_regions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+            <span class="k">continue</span>
+
+        <span class="n">ridges</span> <span class="o">=</span> <span class="n">all_ridges</span><span class="p">[</span><span class="n">p1</span><span class="p">]</span>
+        <span class="n">new_region</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">vertices</span> <span class="k">if</span> <span class="n">v</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">p2</span><span class="p">,</span> <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span> <span class="ow">in</span> <span class="n">ridges</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">v2</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">v1</span><span class="p">,</span> <span class="n">v2</span> <span class="o">=</span> <span class="n">v2</span><span class="p">,</span> <span class="n">v1</span>
+            <span class="k">if</span> <span class="n">v1</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">continue</span>
+
+            <span class="n">t</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="p">[</span><span class="n">p2</span><span class="p">]</span> <span class="o">-</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="p">[</span><span class="n">p1</span><span class="p">]</span>
+            <span class="n">t</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+            <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="n">t</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
+
+            <span class="n">midpoint</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">points</span><span class="p">[[</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">]]</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">direction</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">midpoint</span> <span class="o">-</span> <span class="n">center</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span> <span class="o">*</span> <span class="n">n</span>
+            <span class="n">far_point</span> <span class="o">=</span> <span class="n">vor</span><span class="o">.</span><span class="n">vertices</span><span class="p">[</span><span class="n">v2</span><span class="p">]</span> <span class="o">+</span> <span class="n">direction</span> <span class="o">*</span> <span class="n">radius</span>
+
+            <span class="n">new_region</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">new_vertices</span><span class="p">))</span>
+            <span class="n">new_vertices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">far_point</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+
+        <span class="n">vs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">new_vertices</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">new_region</span><span class="p">])</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="n">vs</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">vs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">vs</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">new_region</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">new_region</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">angles</span><span class="p">)]</span>
+
+        <span class="n">new_regions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_region</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
+
+    <span class="n">regions_vertices</span> <span class="o">=</span> <span class="n">new_regions</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">new_vertices</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">regions_vertices</span>
+
+
+<span class="k">def</span> <span class="nf">as_dataframes</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to store finite Voronoi regions and</span>
+<span class="sd">    originator points as ``geopandas`` (or ``pandas``) dataframes.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    regions : list</span>
+<span class="sd">        Each element of the list contains sequence of the indexes of</span>
+<span class="sd">        voronoi vertices composing a vornoi polygon (region).</span>
+<span class="sd">    vertices : array_like</span>
+<span class="sd">        The coordinates of the vornoi vertices.</span>
+<span class="sd">    points : array_like</span>
+<span class="sd">        The originator points.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    region_df : geopandas.GeoDataFrame</span>
+<span class="sd">        Finite Voronoi polygons as geometries.</span>
+<span class="sd">    points_df : geopandas.GeoDataFrame</span>
+<span class="sd">        Originator points as geometries.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ImportError</span>
+<span class="sd">        Raised when ``geopandas`` is not available.</span>
+<span class="sd">    ImportError</span>
+<span class="sd">        Raised when ``shapely`` is not available.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="n">gpd</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Point</span><span class="p">,</span> <span class="n">Polygon</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">.shapes</span> <span class="kn">import</span> <span class="n">Point</span><span class="p">,</span> <span class="n">Polygon</span>
+
+    <span class="k">if</span> <span class="n">gpd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">region_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span>
+            <span class="n">geometry</span><span class="o">=</span><span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">(</span><span class="n">Polygon</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">region</span><span class="p">])</span> <span class="k">for</span> <span class="n">region</span> <span class="ow">in</span> <span class="n">regions</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">point_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span>
+            <span class="n">geometry</span><span class="o">=</span><span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">(</span><span class="n">Point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">points</span><span class="p">)</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+        <span class="n">region_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
+        <span class="n">region_df</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">Polygon</span><span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">region</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> <span class="k">for</span> <span class="n">region</span> <span class="ow">in</span> <span class="n">regions</span>
+        <span class="p">]</span>
+        <span class="n">point_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
+        <span class="n">point_df</span><span class="p">[</span><span class="s2">&quot;geometry&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">(</span><span class="n">pnt</span><span class="p">)</span> <span class="k">for</span> <span class="n">pnt</span> <span class="ow">in</span> <span class="n">points</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">region_df</span><span class="p">,</span> <span class="n">point_df</span>
+
+
+<div class="viewcode-block" id="voronoi_frames">
+<a class="viewcode-back" href="../../../generated/libpysal.cg.voronoi_frames.html#libpysal.cg.voronoi_frames">[docs]</a>
+<span class="k">def</span> <span class="nf">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;extent&quot;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Composite helper to return Voronoi regions and</span>
+<span class="sd">    generator points as individual dataframes.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    points : array_like</span>
+<span class="sd">        The originator points.</span>
+<span class="sd">    radius : float</span>
+<span class="sd">        The distance to &#39;points at infinity&#39; used in building voronoi cells.</span>
+<span class="sd">        Default is ``None``.</span>
+<span class="sd">    clip : {str, shapely.geometry.Polygon}</span>
+<span class="sd">        An overloaded option about how to clip the voronoi cells.</span>
+<span class="sd">        Default is ``&#39;extent&#39;``. Options are as follows.</span>
+
+<span class="sd">        * ``&#39;none&#39;``/``None`` -- No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended.</span>
+<span class="sd">        * ``&#39;bbox&#39;``/``&#39;extent&#39;``/``&#39;bounding box&#39;`` -- Clip the voronoi cells to the bounding box of the input points.</span>
+<span class="sd">        * ``&#39;chull``/``&#39;convex hull&#39;`` -- Clip the voronoi cells to the convex hull of the input points.</span>
+<span class="sd">        * ``&#39;ashape&#39;``/``&#39;ahull&#39;`` -- Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using ``libpysal.cg.alpha_shape_auto``).</span>
+<span class="sd">        * Polygon -- Clip to an arbitrary Polygon.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    reg_vtx : tuple</span>
+<span class="sd">        Two ``geopandas.GeoDataFrame`` (or ``pandas.DataFrame`` if ``geopandas``</span>
+<span class="sd">        is unavailable) objects--``(region_df, points_df)``--of finite</span>
+<span class="sd">        Voronoi polygons and the originator points as geometries.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    If ``geopandas`` is not available the return types will be</span>
+<span class="sd">    ``pandas.DataFrame`` objects, each with a geometry column populated</span>
+<span class="sd">    with PySAL shapes. If ``geopandas`` is available, return types are</span>
+<span class="sd">    ``pandas.GeoDataFrame`` objects with a geometry column populated</span>
+<span class="sd">    with shapely geometry types.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]</span>
+<span class="sd">    &gt;&gt;&gt; regions_df, points_df = voronoi_frames(points)</span>
+<span class="sd">    &gt;&gt;&gt; regions_df.shape</span>
+<span class="sd">    (4, 1)</span>
+
+<span class="sd">    &gt;&gt;&gt; regions_df.shape == points_df.shape</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>  <span class="c1"># noqa E501</span>
+
+    <span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
+    <span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">as_dataframes</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">points</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">clip</span><span class="p">:</span>
+        <span class="n">regions</span> <span class="o">=</span> <span class="n">clip_voronoi_frames_to_extent</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">)</span>
+
+    <span class="n">reg_vtx</span> <span class="o">=</span> <span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span>
+    <span class="k">return</span> <span class="n">reg_vtx</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">clip_voronoi_frames_to_extent</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">vertices</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;extent&quot;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate a geopandas.GeoDataFrame of Voronoi cells clipped to</span>
+<span class="sd">    a specified extent.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    regions : geopandas.GeoDataFrame</span>
+<span class="sd">        A (geo)dataframe containing voronoi cells to clip.</span>
+<span class="sd">    vertices : geopandas.GeoDataFrame</span>
+<span class="sd">        A (geo)dataframe containing vertices used to build voronoi cells.</span>
+<span class="sd">    clip : str, shapely.geometry.Polygon</span>
+<span class="sd">        An overloaded option about how to clip the voronoi cells.</span>
+<span class="sd">        The options are:</span>
+<span class="sd">          - &#39;none&#39;/None: No clip is applied. Voronoi cells may be arbitrarily</span>
+<span class="sd">            larger that the source map. Note that this may lead to cells that</span>
+<span class="sd">            are many orders of magnitude larger in extent than</span>
+<span class="sd">            the original map. Not recommended.</span>
+<span class="sd">          - &#39;bbox&#39;/&#39;extent&#39;/&#39;bounding box&#39;: Clip the voronoi cells to the</span>
+<span class="sd">            bounding box of the input points.</span>
+<span class="sd">          - &#39;chull&#39;/&#39;convex hull&#39;: Clip the voronoi cells to the</span>
+<span class="sd">            convex hull of the input points.</span>
+<span class="sd">          - &#39;ashape&#39;/&#39;ahull&#39;: Clip the voronoi cells to the tightest hull that</span>
+<span class="sd">            contains all points (e.g. the smallest alphashape,</span>
+<span class="sd">            using ``libpysal.cg.alpha_shape_auto``).</span>
+<span class="sd">          - Polygon: Clip to an arbitrary Polygon.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    clipped_regions : geopandas.GeoDataFrame</span>
+<span class="sd">        A ``geopandas.GeoDataFrame`` of clipped voronoi regions.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ImportError</span>
+<span class="sd">        Raised when ``shapely`` is not available.</span>
+<span class="sd">    ImportError</span>
+<span class="sd">        Raised when ``geopandas`` is not available.</span>
+<span class="sd">    ValueError</span>
+<span class="sd">        Raised when in invalid value for ``clip`` is passed in.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Shapely is required to clip voronoi regions.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">geopandas</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Geopandas is required to clip voronoi regions.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">clip</span><span class="p">,</span> <span class="n">Polygon</span><span class="p">):</span>
+        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">clip</span><span class="p">])</span>
+    <span class="k">elif</span> <span class="n">clip</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;none&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">regions</span>
+    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;bounds&quot;</span><span class="p">,</span> <span class="s2">&quot;bounding box&quot;</span><span class="p">,</span> <span class="s2">&quot;bbox&quot;</span><span class="p">,</span> <span class="s2">&quot;extent&quot;</span><span class="p">):</span>
+        <span class="n">min_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">,</span> <span class="n">max_x</span><span class="p">,</span> <span class="n">max_y</span> <span class="o">=</span> <span class="n">vertices</span><span class="o">.</span><span class="n">total_bounds</span>
+        <span class="n">bounding_poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">),</span>
+                <span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">max_y</span><span class="p">),</span>
+                <span class="p">(</span><span class="n">max_x</span><span class="p">,</span> <span class="n">max_y</span><span class="p">),</span>
+                <span class="p">(</span><span class="n">max_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">),</span>
+                <span class="p">(</span><span class="n">min_x</span><span class="p">,</span> <span class="n">min_y</span><span class="p">),</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">bounding_poly</span><span class="p">])</span>
+    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;chull&quot;</span><span class="p">,</span> <span class="s2">&quot;convex hull&quot;</span><span class="p">,</span> <span class="s2">&quot;convex_hull&quot;</span><span class="p">):</span>
+        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span>
+            <span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">vertices</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">unary_union</span><span class="o">.</span><span class="n">convex_hull</span><span class="p">]</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">clip</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">(</span>
+        <span class="s2">&quot;ahull&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;alpha hull&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;alpha_hull&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;ashape&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;alpha shape&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;alpha_shape&quot;</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="kn">from</span> <span class="nn">..weights.distance</span> <span class="kn">import</span> <span class="n">get_points_array</span>
+        <span class="kn">from</span> <span class="nn">.alpha_shapes</span> <span class="kn">import</span> <span class="n">alpha_shape_auto</span>
+
+        <span class="n">coordinates</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">vertices</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+        <span class="n">clipper</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="p">[</span><span class="n">alpha_shape_auto</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Clip type &#39;</span><span class="si">{</span><span class="n">clip</span><span class="si">}</span><span class="s2">&#39; not understood. Try one of the supported options: &quot;</span>
+            <span class="s2">&quot;[None, &#39;extent&#39;, &#39;chull&#39;, &#39;ahull&#39;].&quot;</span>
+        <span class="p">)</span>
+    <span class="n">clipped_regions</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">regions</span><span class="p">,</span> <span class="n">clipper</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;intersection&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">clipped_regions</span>
+</pre></div>
+
+    </div>
+      
+  </div>
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+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+      
+  <h1>Source code for libpysal.examples</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;The :mod:`libpysal.examples` module provides example datasets.</span>
+
+<span class="sd">The datasets consist of two sets, built-ins which are installed with</span>
+<span class="sd">this module and remotes that can be downloaded.</span>
+
+<span class="sd">This module provides functionality for working with these example datasets.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">Example</span><span class="p">,</span> <span class="n">example_manager</span>
+<span class="kn">from</span> <span class="nn">.builtin</span> <span class="kn">import</span> <span class="n">LocalExample</span>
+<span class="kn">from</span> <span class="nn">.builtin</span> <span class="kn">import</span> <span class="n">datasets</span> <span class="k">as</span> <span class="n">builtin_datasets</span>
+<span class="kn">from</span> <span class="nn">.remotes</span> <span class="kn">import</span> <span class="n">datasets</span> <span class="k">as</span> <span class="n">remote_datasets</span>
+
+<span class="n">available_datasets</span> <span class="o">=</span> <span class="n">builtin_datasets</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="n">available_datasets</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">remote_datasets</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;get_path&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;available&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;explain&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;fetch_all&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_url&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;load_example&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;summary&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+<span class="n">example_manager</span><span class="o">.</span><span class="n">add_examples</span><span class="p">(</span><span class="n">available_datasets</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">fetch_all</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Fetch and install all remote datasets.&quot;&quot;&quot;</span>
+    <span class="n">datasets</span> <span class="o">=</span> <span class="n">remote_datasets</span><span class="o">.</span><span class="n">datasets</span>
+    <span class="n">names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">datasets</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="n">names</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span><span class="p">:</span>
+        <span class="n">example</span> <span class="o">=</span> <span class="n">datasets</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">example</span><span class="o">.</span><span class="n">download</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">:</span>  <span class="c1"># noqa E722</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Example not downloaded: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">example_manager</span><span class="o">.</span><span class="n">add_examples</span><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="available">
+<a class="viewcode-back" href="../../generated/libpysal.examples.available.html#libpysal.examples.available">[docs]</a>
+<span class="k">def</span> <span class="nf">available</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Return a dataframe with available datasets.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">available</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="explain">
+<a class="viewcode-back" href="../../generated/libpysal.examples.explain.html#libpysal.examples.explain">[docs]</a>
+<span class="k">def</span> <span class="nf">explain</span><span class="p">(</span><span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Explain a dataset by name.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">explain</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">get_url</span><span class="p">(</span><span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get url for remote dataset.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">get_remote_url</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">load_example</span><span class="p">(</span><span class="n">example_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Example</span> <span class="o">|</span> <span class="n">LocalExample</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Load example dataset instance.&quot;&quot;&quot;</span>
+    <span class="n">example</span> <span class="o">=</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">example_name</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">example</span>
+
+
+<div class="viewcode-block" id="get_path">
+<a class="viewcode-back" href="../../generated/libpysal.examples.get_path.html#libpysal.examples.get_path">[docs]</a>
+<span class="k">def</span> <span class="nf">get_path</span><span class="p">(</span><span class="n">file_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the path for a file by searching installed datasets.&quot;&quot;&quot;</span>
+    <span class="n">installed</span> <span class="o">=</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">get_installed_names</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">installed</span><span class="p">:</span>
+        <span class="n">example</span> <span class="o">=</span> <span class="n">example_manager</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">name</span><span class="p">]</span>
+        <span class="n">pth</span> <span class="o">=</span> <span class="n">example</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">pth</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">pth</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s2"> is not a file in any installed dataset.&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">summary</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Summary of datasets.&quot;&quot;&quot;</span>
+    <span class="n">example_manager</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/_modules/libpysal/graph/base.html b/_modules/libpysal/graph/base.html
new file mode 100644
index 000000000..730a48a05
--- /dev/null
+++ b/_modules/libpysal/graph/base.html
@@ -0,0 +1,1604 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>libpysal.graph.base &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/pysal-styles.css?v=38b0ce36" />
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+    <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
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+    <link rel="icon" href="../../../_static/pysal_favicon.ico"/>
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+<meta name="apple-mobile-web-app-capable" content="yes">
+<script type="text/javascript" src="../../../_static/js/jquery-1.12.4.min.js"></script>
+<script type="text/javascript" src="../../../_static/js/jquery-fix.js"></script>
+<script type="text/javascript" src="../../../_static/bootstrap-3.4.1/js/bootstrap.min.js"></script>
+<script type="text/javascript" src="../../../_static/bootstrap-sphinx.js"></script>
+
+  </head><body>
+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
+    <div class="container">
+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </button>
+        <a class="navbar-brand" href="../../../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="../../../installation.html">Installation</a></li>
+                <li><a href="../../../tutorial.html">Tutorial</a></li>
+                <li><a href="../../../api.html">API</a></li>
+                <li><a href="../../../references.html">References</a></li>
+            
+            
+              <li class="dropdown globaltoc-container">
+  <a role="button"
+     id="dLabelGlobalToc"
+     data-toggle="dropdown"
+     data-target="#"
+     href="../../../index.html">Site <b class="caret"></b></a>
+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../api.html">API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#io">io</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#examples">examples</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#experimental">Experimental</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#graph">graph</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
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+     data-target="#"
+     href="#">Page <b class="caret"></b></a>
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+      role="menu"
+      aria-labelledby="dLabelLocalToc"></ul>
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+
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+<form class="navbar-form navbar-right" action="../../../search.html" method="get">
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+  <input type="hidden" name="area" value="default" />
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+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <h1>Source code for libpysal.graph.base</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">cached_property</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
+
+<span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
+
+<span class="kn">from</span> <span class="nn">._contiguity</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">_block_contiguity</span><span class="p">,</span>
+    <span class="n">_fuzzy_contiguity</span><span class="p">,</span>
+    <span class="n">_queen</span><span class="p">,</span>
+    <span class="n">_rook</span><span class="p">,</span>
+    <span class="n">_vertex_set_intersection</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">._kernel</span> <span class="kn">import</span> <span class="n">_distance_band</span><span class="p">,</span> <span class="n">_kernel</span>
+<span class="kn">from</span> <span class="nn">._parquet</span> <span class="kn">import</span> <span class="n">_read_parquet</span><span class="p">,</span> <span class="n">_to_parquet</span>
+<span class="kn">from</span> <span class="nn">._plotting</span> <span class="kn">import</span> <span class="n">_explore_graph</span><span class="p">,</span> <span class="n">_plot</span>
+<span class="kn">from</span> <span class="nn">._set_ops</span> <span class="kn">import</span> <span class="n">SetOpsMixin</span>
+<span class="kn">from</span> <span class="nn">._spatial_lag</span> <span class="kn">import</span> <span class="n">_lag_spatial</span>
+<span class="kn">from</span> <span class="nn">._triangulation</span> <span class="kn">import</span> <span class="n">_delaunay</span><span class="p">,</span> <span class="n">_gabriel</span><span class="p">,</span> <span class="n">_relative_neighborhood</span><span class="p">,</span> <span class="n">_voronoi</span>
+<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">_evaluate_index</span><span class="p">,</span> <span class="n">_neighbor_dict_to_edges</span><span class="p">,</span> <span class="n">_sparse_to_arrays</span>
+
+<span class="n">ALLOWED_TRANSFORMATIONS</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;R&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="s2">&quot;V&quot;</span><span class="p">)</span>
+
+<span class="c1"># listed alphabetically</span>
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;&quot;</span>
+<span class="s2">Martin Fleischmann (martin@martinfleischmann.net)</span>
+<span class="s2">Eli Knaap (ek@knaaptime.com)</span>
+<span class="s2">Serge Rey (sjsrey@gmail.com)</span>
+<span class="s2">Levi John Wolf (levi.john.wolf@gmail.com)</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+
+<div class="viewcode-block" id="Graph">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph">[docs]</a>
+<span class="k">class</span> <span class="nc">Graph</span><span class="p">(</span><span class="n">SetOpsMixin</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Graph class encoding spatial weights matrices</span>
+
+<span class="sd">    The :class:`Graph` is currently experimental</span>
+<span class="sd">    and its API is incomplete and unstable.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Graph.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">adjacency</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="s2">&quot;O&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Weights base class based on adjacency list</span>
+
+<span class="sd">        It is recommenced to use one of the ``from_*`` or ``build_*`` constructors</span>
+<span class="sd">        rather than invoking ``__init__`` directly.</span>
+
+<span class="sd">        Each observation needs to be present in the focal,</span>
+<span class="sd">        at least as a self-loop with a weight 0.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        adjacency : pandas.Series</span>
+<span class="sd">            A MultiIndexed pandas.Series with ``&quot;focal&quot;`` and ``&quot;neigbor&quot;`` levels</span>
+<span class="sd">            encoding adjacency, and values encoding weights. By convention,</span>
+<span class="sd">            isolates are encoded as self-loops with a weight 0.</span>
+<span class="sd">        transformation : str, default &quot;O&quot;</span>
+<span class="sd">            weights transformation used to produce the table.</span>
+
+<span class="sd">            - **O** -- Original</span>
+<span class="sd">            - **B** -- Binary</span>
+<span class="sd">            - **R** -- Row-standardization (global sum :math:`=n`)</span>
+<span class="sd">            - **D** -- Double-standardization (global sum :math:`=1`)</span>
+<span class="sd">            - **V** -- Variance stabilizing</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">adjacency</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;The adjacency table needs to be a pandas.Series. </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">adjacency</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">names</span> <span class="o">==</span> <span class="p">[</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;The index of the adjacency table needs to be a MultiIndex named &quot;</span>
+                <span class="s2">&quot;[&#39;focal&#39;, &#39;neighbor&#39;].&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;weight&quot;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;The adjacency needs to be named &#39;weight&#39;. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">adjacency</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; was given instead.&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">pd</span><span class="o">.</span><span class="n">api</span><span class="o">.</span><span class="n">types</span><span class="o">.</span><span class="n">is_numeric_dtype</span><span class="p">(</span><span class="n">adjacency</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;The &#39;weight&#39; needs to be of a numeric dtype. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">adjacency</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">&#39; dtype was given instead.&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The adjacency table cannot contain missing values.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">transformation</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ALLOWED_TRANSFORMATIONS</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;transformation&#39; needs to be one of </span><span class="si">{</span><span class="n">ALLOWED_TRANSFORMATIONS</span><span class="si">}</span><span class="s2">. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">transformation</span><span class="si">}</span><span class="s2">&#39; was given instead.&quot;</span>
+            <span class="p">)</span>
+        <span class="c1"># adjacency always ordered i--&gt;j on both levels</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">adjacency</span> <span class="o">=</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">reindex</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span> <span class="o">=</span> <span class="n">adjacency</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="n">transformation</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Easy lookup based on focal index</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        item : hashable</span>
+<span class="sd">            hashable represting an index value</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        pandas.Series</span>
+<span class="sd">            subset of the adjacency table for `item`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">item</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">isolates</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+                <span class="p">[],</span>
+                <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([],</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">),</span>
+                <span class="n">name</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">item</span><span class="p">]</span>
+
+<div class="viewcode-block" id="Graph.copy">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.copy">[docs]</a>
+    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Make a copy of this Graph&#39;s adjacency table and transformation</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        deep : bool, optional</span>
+<span class="sd">            Make a deep copy of the adjacency table, by default True</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph as a copy of the original</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">Graph</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="n">deep</span><span class="p">),</span> <span class="n">transformation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transformation</span>
+        <span class="p">)</span></div>
+
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">adjacency</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a copy of the adjacency list</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        pandas.Series</span>
+<span class="sd">            Underlying adjacency list</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+<div class="viewcode-block" id="Graph.from_W">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_W">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_W</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span>  <span class="c1"># noqa N802</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create an experimental Graph from libpysal.weights.W object</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        w : libpysal.weights.W</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph from W</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_weights_dict</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">w</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="Graph.to_W">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.to_W">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>  <span class="c1"># noqa N802</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert Graph to a libpysal.weights.W object</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        libpysal.weights.W</span>
+<span class="sd">            representation of graph as a weights.W object</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">factorize</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="s2">&quot;focal&quot;</span><span class="p">),</span> <span class="n">sort</span><span class="o">=</span><span class="kc">False</span>
+        <span class="p">)</span>
+        <span class="n">neighbors</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="k">lambda</span> <span class="n">group</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span>
+                    <span class="n">group</span><span class="p">[</span>
+                        <span class="o">~</span><span class="p">((</span><span class="n">group</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">group</span><span class="o">.</span><span class="n">neighbor</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">weight</span> <span class="o">==</span> <span class="mi">0</span><span class="p">))</span>
+                    <span class="p">]</span><span class="o">.</span><span class="n">neighbor</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">neighbors</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="n">neighbors</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="k">lambda</span> <span class="n">group</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span>
+                    <span class="n">group</span><span class="p">[</span>
+                        <span class="o">~</span><span class="p">((</span><span class="n">group</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">group</span><span class="o">.</span><span class="n">neighbor</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">weight</span> <span class="o">==</span> <span class="mi">0</span><span class="p">))</span>
+                    <span class="p">]</span><span class="o">.</span><span class="n">weight</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">weights</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="n">weights</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="n">weights</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="n">id_order</span><span class="o">=</span><span class="n">labels</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="Graph.from_adjacency">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_adjacency">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_adjacency</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">adjacency</span><span class="p">,</span> <span class="n">focal_col</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span> <span class="n">weight_col</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a Graph from a pandas DataFrame formatted as an adjacency list</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        adjacency : pandas.DataFrame</span>
+<span class="sd">            a dataframe formatted as an ajacency list. Should have columns</span>
+<span class="sd">            &quot;focal&quot;, &quot;neighbor&quot;, and &quot;weight&quot;, or columns that can be mapped</span>
+<span class="sd">            to these (e.g. origin, destination, cost)</span>
+<span class="sd">        focal : str, optional</span>
+<span class="sd">            name of column holding focal/origin index, by default &#39;focal&#39;</span>
+<span class="sd">        neighbor : str, optional</span>
+<span class="sd">            name of column holding neighbor/destination index, by default &#39;neighbor&#39;</span>
+<span class="sd">        weight : str, optional</span>
+<span class="sd">            name of column holding weight values, by default &#39;weight&#39;</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">cols</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+            <span class="nb">zip</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">focal_col</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="p">,</span> <span class="n">weight_col</span><span class="p">],</span>
+                <span class="p">[</span><span class="s2">&quot;focal_col&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor_col&quot;</span><span class="p">,</span> <span class="s2">&quot;weight_col&quot;</span><span class="p">],</span>
+                <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="p">(</span>
+                <span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">col</span><span class="si">}</span><span class="s1">&quot; was given for `</span><span class="si">{</span><span class="n">cols</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="si">}</span><span class="s1">`, but the &#39;</span>
+                <span class="sa">f</span><span class="s2">&quot;columns available in `adjacency` are:  </span><span class="si">{</span><span class="n">adjacency</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span><span class="si">}</span><span class="s2">.&quot;</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span>
+            <span class="n">adjacency</span><span class="p">[</span><span class="n">focal_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+            <span class="n">adjacency</span><span class="p">[</span><span class="n">neighbor_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+            <span class="n">adjacency</span><span class="p">[</span><span class="n">weight_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.from_sparse">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_sparse">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_sparse</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sparse</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert a ``scipy.sparse`` array to a PySAL ``Graph`` object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        sparse : scipy.sparse array</span>
+<span class="sd">            sparse representation of a graph</span>
+<span class="sd">        ids : list-like, default None</span>
+<span class="sd">            list-like of ids for geometries that is mappable to</span>
+<span class="sd">            positions from sparse. If None, the positions are used as labels.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph based on sparse</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="o">*</span><span class="n">_sparse_to_arrays</span><span class="p">(</span><span class="n">sparse</span><span class="p">,</span> <span class="n">ids</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="Graph.from_arrays">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_arrays">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_arrays</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">focal_ids</span><span class="p">,</span> <span class="n">neighbor_ids</span><span class="p">,</span> <span class="n">weight</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from arrays of indices and weights of the same length</span>
+
+<span class="sd">        The arrays needs to be sorted in a way ensuring that focal_ids.unique() is</span>
+<span class="sd">        equal to the index of original observations from which the Graph is being built</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        focal_index : array-like</span>
+<span class="sd">            focal indices</span>
+<span class="sd">        neighbor_index : array-like</span>
+<span class="sd">            neighbor indices</span>
+<span class="sd">        weight : array-like</span>
+<span class="sd">            weights</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph based on arrays</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span>
+            <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+                <span class="n">weight</span><span class="p">,</span>
+                <span class="n">name</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">,</span>
+                <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span>
+                    <span class="p">[</span><span class="n">focal_ids</span><span class="p">,</span> <span class="n">neighbor_ids</span><span class="p">],</span> <span class="n">names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor&quot;</span><span class="p">]</span>
+                <span class="p">),</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="Graph.from_weights_dict">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_weights_dict">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_weights_dict</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">weights_dict</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from a dict of dicts</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        weights_dict : dictionary of dictionaries</span>
+<span class="sd">            weights dictionary with the ``{focal: {neighbor: weight}}`` structure.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph based on weights dictionary of dictionaries</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="p">{</span><span class="n">f</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="n">weights_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="n">f</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="n">weights_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_dicts</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.from_dicts">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_dicts">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dicts</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from dictionaries of neighbors and weights</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        neighbors : dict</span>
+<span class="sd">            dictionary of neighbors with the ``{focal: [neighbor1, neighbor2]}``</span>
+<span class="sd">            structure</span>
+<span class="sd">        weights : dict, optional</span>
+<span class="sd">            dictionary of neighbors with the ``{focal: [weight1, weight2]}``</span>
+<span class="sd">            structure. If None, assumes binary weights.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph based on dictionaries</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">_neighbor_dict_to_edges</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_contiguity">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_contiguity">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_contiguity</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">geometry</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">by_perimeter</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from geometry based on contiguity</span>
+
+<span class="sd">        Contiguity builder assumes that all geometries are forming a coverage, i.e.</span>
+<span class="sd">        a non-overlapping mesh and neighbouring geometries share only points or</span>
+<span class="sd">        segments of their exterior boundaries. In practice, ``build_contiguity`` is</span>
+<span class="sd">        capable of creating a Graph of partially overlapping geometries when</span>
+<span class="sd">        ``strict=False, by_perimeter=False``, but that would not strictly follow the</span>
+<span class="sd">        definition of queen or rook contiguity.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        geometry : array-like of shapely.Geometry objects</span>
+<span class="sd">            Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case the</span>
+<span class="sd">            resulting Graph is indexed by the original index. If an array of</span>
+<span class="sd">            shapely.Geometry objects is passed, Graph will assume a RangeIndex.</span>
+<span class="sd">        rook : bool, optional</span>
+<span class="sd">            Contiguity method. If True, two geometries are considered neighbours if</span>
+<span class="sd">            they share at least one edge. If False, two geometries are considered</span>
+<span class="sd">            neighbours if they share at least one vertex. By default True</span>
+<span class="sd">        by_perimeter : bool, optional</span>
+<span class="sd">            If True, ``weight`` represents the length of the shared boundary between</span>
+<span class="sd">            adjacent units, by default False. For row-standardized version of perimeter</span>
+<span class="sd">            weights, use</span>
+<span class="sd">            ``Graph.build_contiguity(gdf, by_perimeter=True).transform(&quot;r&quot;)``.</span>
+<span class="sd">        strict : bool, optional</span>
+<span class="sd">            Use the strict topological method. If False, the contiguity is determined</span>
+<span class="sd">            based on shared coordinates or coordinate sequences representing edges.</span>
+<span class="sd">            This assumes geometry coverage that is topologically correct. This method</span>
+<span class="sd">            is faster but can miss some relations. If True, the contiguity is</span>
+<span class="sd">            determined based on geometric relations that do not require precise</span>
+<span class="sd">            topology. This method is slower but will result in correct contiguity</span>
+<span class="sd">            even if the topology of geometries is not optimal. By default False.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding contiguity weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">geometry</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="s2">&quot;geometry&quot;</span><span class="p">):</span>
+            <span class="c1"># potentially cast GeoDataFrame to GeoSeries</span>
+            <span class="n">geometry</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">geometry</span>
+
+        <span class="k">if</span> <span class="n">strict</span><span class="p">:</span>
+            <span class="c1"># use shapely-based constructors</span>
+            <span class="k">if</span> <span class="n">rook</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span>
+                    <span class="o">*</span><span class="n">_rook</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">by_perimeter</span><span class="o">=</span><span class="n">by_perimeter</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span>
+                <span class="o">*</span><span class="n">_queen</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">by_perimeter</span><span class="o">=</span><span class="n">by_perimeter</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="c1"># use vertex-based constructor</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span>
+            <span class="o">*</span><span class="n">_vertex_set_intersection</span><span class="p">(</span>
+                <span class="n">geometry</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="n">rook</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">by_perimeter</span><span class="o">=</span><span class="n">by_perimeter</span>
+            <span class="p">)</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_kernel">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_kernel">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_kernel</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;gaussian&quot;</span><span class="p">,</span>
+        <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">bandwidth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
+        <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">coincident</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from geometry data based on a kernel function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame</span>
+<span class="sd">            geometries over which to compute a kernel. If a geopandas object with Point</span>
+<span class="sd">            geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray</span>
+<span class="sd">            with shapely geoemtry is used, then the coordinates are extracted and used.</span>
+<span class="sd">            If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y</span>
+<span class="sd">            coordinates. If metric=&quot;precomputed&quot;, data is assumed to contain a</span>
+<span class="sd">            precomputed distance metric.</span>
+<span class="sd">        kernel : string or callable (default: &#39;gaussian&#39;)</span>
+<span class="sd">            kernel function to apply over the distance matrix computed by `metric`.</span>
+<span class="sd">            The following kernels are supported:</span>
+
+<span class="sd">            - ``&quot;triangular&quot;``:</span>
+<span class="sd">            - ``&quot;parabolic&quot;``:</span>
+<span class="sd">            - ``&quot;gaussian&quot;``:</span>
+<span class="sd">            - ``&quot;bisquare&quot;``:</span>
+<span class="sd">            - ``&quot;cosine&quot;``:</span>
+<span class="sd">            - ``&#39;boxcar&#39;``/discrete: all distances less than `bandwidth` are 1, and all</span>
+<span class="sd">              other distances are 0</span>
+<span class="sd">            - ``&quot;identity&quot;``/None : do nothing, weight similarity based on raw distance</span>
+<span class="sd">            - ``callable`` : a user-defined function that takes the distance vector and</span>
+<span class="sd">              the bandwidth and returns the kernel: kernel(distances, bandwidth)</span>
+
+<span class="sd">        k : int (default: None)</span>
+<span class="sd">            number of nearest neighbors used to truncate the kernel. This is assumed</span>
+<span class="sd">            to be constant across samples. If None, no truncation is conduted.</span>
+<span class="sd">        bandwidth : float (default: None)</span>
+<span class="sd">            distance to use in the kernel computation. Should be on the same scale as</span>
+<span class="sd">            the input coordinates.</span>
+<span class="sd">        metric : string or callable (default: &#39;euclidean&#39;)</span>
+<span class="sd">            distance function to apply over the input coordinates. Supported options</span>
+<span class="sd">            depend on whether or not scikit-learn is installed. If so, then any</span>
+<span class="sd">            distance function supported by scikit-learn is supported here. Otherwise,</span>
+<span class="sd">            only euclidean, minkowski, and manhattan/cityblock distances are admitted.</span>
+<span class="sd">        p : int (default: 2)</span>
+<span class="sd">            parameter for minkowski metric, ignored if metric != &quot;minkowski&quot;.</span>
+<span class="sd">        coincident: str, optional (default &quot;raise&quot;)</span>
+<span class="sd">            Method for handling coincident points when ``k`` is not None. Options are</span>
+<span class="sd">            ``&#39;raise&#39;`` (raising an exception when coincident points are present),</span>
+<span class="sd">            ``&#39;jitter&#39;`` (randomly displace coincident points to produce uniqueness), &amp;</span>
+<span class="sd">            ``&#39;clique&#39;`` (induce fully-connected sub cliques for coincident points).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding kernel weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">_kernel</span><span class="p">(</span>
+            <span class="n">data</span><span class="p">,</span>
+            <span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span>
+            <span class="n">metric</span><span class="o">=</span><span class="n">metric</span><span class="p">,</span>
+            <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
+            <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span>
+            <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span>
+            <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
+            <span class="n">coincident</span><span class="o">=</span><span class="n">coincident</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_knn">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_knn">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_knn</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coincident</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from geometry data based on k-nearest neighbors search</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame</span>
+<span class="sd">            geometries over which to compute a kernel. If a geopandas object with Point</span>
+<span class="sd">            geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray</span>
+<span class="sd">            with shapely geoemtry is used, then the coordinates are extracted and used.</span>
+<span class="sd">            If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y</span>
+<span class="sd">            coordinates.</span>
+<span class="sd">        k : int</span>
+<span class="sd">            number of nearest neighbors.</span>
+<span class="sd">        metric : string or callable (default: &#39;euclidean&#39;)</span>
+<span class="sd">            distance function to apply over the input coordinates. Supported options</span>
+<span class="sd">            depend on whether or not scikit-learn is installed. If so, then any</span>
+<span class="sd">            distance function supported by scikit-learn is supported here. Otherwise,</span>
+<span class="sd">            only euclidean, minkowski, and manhattan/cityblock distances are admitted.</span>
+<span class="sd">        p : int (default: 2)</span>
+<span class="sd">            parameter for minkowski metric, ignored if metric != &quot;minkowski&quot;.</span>
+<span class="sd">        coincident: str, optional (default &quot;raise&quot;)</span>
+<span class="sd">            Method for handling coincident points. Options include</span>
+<span class="sd">            ``&#39;raise&#39;`` (raising an exception when coincident points are present),</span>
+<span class="sd">            ``&#39;jitter&#39;`` (randomly displace coincident points to produce uniqueness), &amp;</span>
+<span class="sd">            ``&#39;clique&#39;`` (induce fully-connected sub cliques for coincident points).</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding KNN weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">_kernel</span><span class="p">(</span>
+            <span class="n">data</span><span class="p">,</span>
+            <span class="n">bandwidth</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
+            <span class="n">metric</span><span class="o">=</span><span class="n">metric</span><span class="p">,</span>
+            <span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;boxcar&quot;</span><span class="p">,</span>
+            <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span>
+            <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span>
+            <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
+            <span class="n">coincident</span><span class="o">=</span><span class="n">coincident</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_triangulation">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_triangulation">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_triangulation</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;delaunay&quot;</span><span class="p">,</span>
+        <span class="n">bandwidth</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
+        <span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;boxcar&quot;</span><span class="p">,</span>
+        <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;extent&quot;</span><span class="p">,</span>
+        <span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">coincident</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from geometry based on triangulation</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame</span>
+<span class="sd">            geometries containing locations to compute the</span>
+<span class="sd">            delaunay triangulation. If a geopandas object with Point</span>
+<span class="sd">            geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray</span>
+<span class="sd">            with shapely geoemtry is used, then the coordinates are extracted and used.</span>
+<span class="sd">            If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y</span>
+<span class="sd">            coordinates.</span>
+<span class="sd">        method : str, (default &quot;delaunay&quot;)</span>
+<span class="sd">            method of extracting the weights from triangulation. Supports:</span>
+
+<span class="sd">            - ``&quot;delaunay&quot;``</span>
+<span class="sd">            - ``&quot;gabriel&quot;``</span>
+<span class="sd">            - ``&quot;relative_neighborhood&quot;``</span>
+<span class="sd">            - ``&quot;voronoi&quot;``</span>
+
+<span class="sd">        bandwidth : float, optional</span>
+<span class="sd">            distance to use in the kernel computation. Should be on the same scale as</span>
+<span class="sd">            the input coordinates, by default numpy.inf</span>
+<span class="sd">        kernel : str, optional</span>
+<span class="sd">            kernel function to use in order to weight the output graph. See</span>
+<span class="sd">            :meth:`Graph.build_kernel` for details. By default &quot;boxcar&quot;</span>
+<span class="sd">        clip : str (default: &#39;bbox&#39;)</span>
+<span class="sd">            Clipping method when ``method=&quot;voronoi&quot;``. Ignored otherwise.</span>
+<span class="sd">            Default is ``&#39;extent&#39;``. Options are as follows.</span>
+
+<span class="sd">            - ``&#39;none&#39;``/``None``: No clip is applied. Voronoi cells may be</span>
+<span class="sd">              arbitrarily larger that the source map. Note that this may lead to</span>
+<span class="sd">              cells that are many orders of magnitude larger in extent than the</span>
+<span class="sd">              original map. Not recommended.</span>
+<span class="sd">            - ``&#39;bbox&#39;``/``&#39;extent&#39;``/``&#39;bounding box&#39;``: Clip the voronoi cells to</span>
+<span class="sd">              the bounding box of the input points.</span>
+<span class="sd">            - ``&#39;chull``/``&#39;convex hull&#39;``: Clip the voronoi cells to the convex hull</span>
+<span class="sd">              of the input points.</span>
+<span class="sd">            - ``&#39;ashape&#39;``/``&#39;ahull&#39;``: Clip the voronoi cells to the tightest hull</span>
+<span class="sd">              that contains all points (e.g. the smallest alphashape, using</span>
+<span class="sd">              :func:`libpysal.cg.alpha_shape_auto`).</span>
+<span class="sd">            - ``shapely.Polygon``: Clip to an arbitrary Polygon.</span>
+
+<span class="sd">        rook : bool, optional</span>
+<span class="sd">            Contiguity method when ``method=&quot;voronoi&quot;``. Ignored otherwise.</span>
+<span class="sd">            If True, two geometries are considered neighbours if they</span>
+<span class="sd">            share at least one edge. If False, two geometries are considered neighbours</span>
+<span class="sd">            if they share at least one vertex. By default True</span>
+<span class="sd">        coincident: str, optional (default &quot;raise&quot;)</span>
+<span class="sd">            Method for handling coincident points. Options include</span>
+<span class="sd">            ``&#39;raise&#39;`` (raising an exception when coincident points are present),</span>
+<span class="sd">            ``&#39;jitter&#39;`` (randomly displace coincident points to produce uniqueness), &amp;</span>
+<span class="sd">            ``&#39;clique&#39;`` (induce fully-connected sub cliques for coincident points).</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding triangulation weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;delaunay&quot;</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">_delaunay</span><span class="p">(</span>
+                <span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">coincident</span><span class="o">=</span><span class="n">coincident</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;gabriel&quot;</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">_gabriel</span><span class="p">(</span>
+                <span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">coincident</span><span class="o">=</span><span class="n">coincident</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;relative_neighborhood&quot;</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">_relative_neighborhood</span><span class="p">(</span>
+                <span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span> <span class="n">coincident</span><span class="o">=</span><span class="n">coincident</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;voronoi&quot;</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">_voronoi</span><span class="p">(</span>
+                <span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="n">rook</span><span class="p">,</span> <span class="n">coincident</span><span class="o">=</span><span class="n">coincident</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Method &#39;</span><span class="si">{</span><span class="n">method</span><span class="si">}</span><span class="s2">&#39; is not supported. Use one of [&#39;delaunay&#39;, &quot;</span>
+                <span class="s2">&quot;&#39;gabriel&#39;, &#39;relative_neighborhood&#39;, &#39;voronoi&#39;].&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_distance_band">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_distance_band">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_distance_band</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from geometry based on a distance band</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        data : numpy.ndarray, geopandas.GeoSeries, geopandas.GeoDataFrame</span>
+<span class="sd">            geometries containing locations to compute the</span>
+<span class="sd">            delaunay triangulation. If a geopandas object with Point</span>
+<span class="sd">            geometry is provided, the .geometry attribute is used. If a numpy.ndarray</span>
+<span class="sd">            with shapely geometry is used, then the coordinates are extracted and used.</span>
+<span class="sd">            If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y</span>
+<span class="sd">            coordinates.</span>
+<span class="sd">        threshold : float</span>
+<span class="sd">            distance band</span>
+<span class="sd">        binary : bool, optional</span>
+<span class="sd">            If True :math:`w_{ij}=1` if :math:`d_{i,j}&lt;=threshold`, otherwise</span>
+<span class="sd">            :math:`w_{i,j}=0`.</span>
+<span class="sd">            If False :math:`wij=dij^{alpha}`, by default True.</span>
+<span class="sd">        alpha : float, optional</span>
+<span class="sd">            distance decay parameter for weight (default -1.0)</span>
+<span class="sd">            if alpha is positive the weights will not decline with</span>
+<span class="sd">            distance. Ignored if ``binary=True`` or ``kernel`` is not None.</span>
+<span class="sd">        kernel : str, optional</span>
+<span class="sd">            kernel function to use in order to weight the output graph. See</span>
+<span class="sd">            :meth:`Graph.build_kernel` for details. Ignored if ``binary=True``.</span>
+<span class="sd">        bandwidth : float (default: None)</span>
+<span class="sd">            distance to use in the kernel computation. Should be on the same scale as</span>
+<span class="sd">            the input coordinates. Ignored if ``binary=True`` or ``kernel=None``.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding distance band weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">_distance_band</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">threshold</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">binary</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">_kernel</span><span class="p">(</span>
+                <span class="n">dist</span><span class="p">,</span>
+                <span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;boxcar&quot;</span><span class="p">,</span>
+                <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;precomputed&quot;</span><span class="p">,</span>
+                <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
+                <span class="n">bandwidth</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">kernel</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">_kernel</span><span class="p">(</span>
+                <span class="n">dist</span><span class="p">,</span>
+                <span class="n">kernel</span><span class="o">=</span><span class="n">kernel</span><span class="p">,</span>
+                <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;precomputed&quot;</span><span class="p">,</span>
+                <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
+                <span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">head</span><span class="p">,</span> <span class="n">tail</span><span class="p">,</span> <span class="n">weight</span> <span class="o">=</span> <span class="n">_kernel</span><span class="p">(</span>
+                <span class="n">dist</span><span class="p">,</span>
+                <span class="n">kernel</span><span class="o">=</span><span class="k">lambda</span> <span class="n">distances</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">distances</span><span class="p">,</span> <span class="n">alpha</span><span class="p">),</span>
+                <span class="n">metric</span><span class="o">=</span><span class="s2">&quot;precomputed&quot;</span><span class="p">,</span>
+                <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
+                <span class="n">bandwidth</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="n">adjacency</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+            <span class="p">{</span><span class="s2">&quot;focal&quot;</span><span class="p">:</span> <span class="n">head</span><span class="p">,</span> <span class="s2">&quot;neighbor&quot;</span><span class="p">:</span> <span class="n">tail</span><span class="p">,</span> <span class="s2">&quot;weight&quot;</span><span class="p">:</span> <span class="n">weight</span><span class="p">}</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;focal&quot;</span><span class="p">)</span>
+
+        <span class="c1"># drop diagonal</span>
+        <span class="n">counts</span> <span class="o">=</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
+        <span class="n">no_isolates</span> <span class="o">=</span> <span class="n">counts</span><span class="p">[</span><span class="n">counts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="n">adjacency</span> <span class="o">=</span> <span class="n">adjacency</span><span class="p">[</span>
+            <span class="o">~</span><span class="p">(</span>
+                <span class="n">adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">no_isolates</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+                <span class="o">&amp;</span> <span class="p">(</span><span class="n">adjacency</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">neighbor</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="p">]</span>
+        <span class="c1"># set isolates to 0 - distance band should never contain self-weight</span>
+        <span class="n">adjacency</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="o">~</span><span class="n">adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">no_isolates</span><span class="o">.</span><span class="n">index</span><span class="p">),</span> <span class="s2">&quot;weight&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span>
+            <span class="n">adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">neighbor</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">adjacency</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">values</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_block_contiguity">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_block_contiguity">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_block_contiguity</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">regimes</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from block contiguity (regime neighbors)</span>
+
+<span class="sd">        Block contiguity structures are relevant when defining neighbor relations</span>
+<span class="sd">        based on membership in a regime. For example, all counties belonging to</span>
+<span class="sd">        the same state could be defined as neighbors, in an analysis of all</span>
+<span class="sd">        counties in the US.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        regimes : list-like</span>
+<span class="sd">            list-like of regimes. If pandas.Series, its index is used to encode Graph.</span>
+<span class="sd">            Otherwise a default RangeIndex is used.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding block contiguity</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_dicts</span><span class="p">(</span><span class="n">_block_contiguity</span><span class="p">(</span><span class="n">regimes</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="Graph.build_fuzzy_contiguity">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_fuzzy_contiguity">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">build_fuzzy_contiguity</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">geometry</span><span class="p">,</span>
+        <span class="n">tolerance</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">predicate</span><span class="o">=</span><span class="s2">&quot;intersects&quot;</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate Graph from fuzzy contiguity</span>
+
+<span class="sd">        Fuzzy contiguity relaxes the notion of contiguity neighbors</span>
+<span class="sd">        for the case of geometry collections that violate the condition</span>
+<span class="sd">        of planar enforcement. It handles three types of conditions present</span>
+<span class="sd">        in such collections that would result in missing links when using</span>
+<span class="sd">        the regular contiguity methods.</span>
+
+<span class="sd">        The first are edges for nearby polygons that should be shared, but are</span>
+<span class="sd">        digitized separately for the individual polygons and the resulting edges</span>
+<span class="sd">        do not coincide, but instead the edges intersect. This case can also be</span>
+<span class="sd">        covered by ``build_contiguty`` with the ``strict=False`` parameter.</span>
+
+<span class="sd">        The second case is similar to the first, only the resultant edges do not</span>
+<span class="sd">        intersect but are &quot;close&quot;. The optional buffering of geometry then closes the</span>
+<span class="sd">        gaps between the polygons and a resulting intersection is encoded as a link.</span>
+
+<span class="sd">        The final case arises when one polygon is &quot;inside&quot; a second polygon but is not</span>
+<span class="sd">        encoded to represent a hole in the containing polygon.</span>
+
+<span class="sd">        It is also possible to create a contiguity based on a custom spatial predicate.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        geoms :  array-like of shapely.Geometry objects</span>
+<span class="sd">            Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case the</span>
+<span class="sd">            resulting Graph is indexed by the original index. If an array of</span>
+<span class="sd">            shapely.Geometry objects is passed, Graph will assume a RangeIndex.</span>
+<span class="sd">        tolerance : float, optional</span>
+<span class="sd">            The percentage of the length of the minimum side of the bounding rectangle</span>
+<span class="sd">            for the ``geoms`` to use in determining the buffering distance. Either</span>
+<span class="sd">            ``tolerance`` or ``buffer`` may be specified but not both.</span>
+<span class="sd">            By default None.</span>
+<span class="sd">        buffer : float, optional</span>
+<span class="sd">            Exact buffering distance in the units of ``geoms.crs``. Either</span>
+<span class="sd">            ``tolerance`` or ``buffer`` may be specified but not both.</span>
+<span class="sd">            By default None.</span>
+<span class="sd">        predicate : str, optional</span>
+<span class="sd">            The predicate to use for determination of neighbors. Default is</span>
+<span class="sd">            &#39;intersects&#39;. If None is passed, neighbours are determined based</span>
+<span class="sd">            on the intersection of bounding boxes. See the documentation of</span>
+<span class="sd">            ``geopandas.GeoSeries.sindex.query`` for allowed predicates.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            libpysal.graph.Graph encoding fuzzy contiguity</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">_evaluate_index</span><span class="p">(</span><span class="n">geometry</span><span class="p">)</span>
+
+        <span class="n">heads</span><span class="p">,</span> <span class="n">tails</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">_fuzzy_contiguity</span><span class="p">(</span>
+            <span class="n">geometry</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="n">tolerance</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="n">buffer</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="n">predicate</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_arrays</span><span class="p">(</span><span class="n">heads</span><span class="p">,</span> <span class="n">tails</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span></div>
+
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get neighbors dictionary</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        It is recommended to work directly with :meth:`Graph.adjacency` rather than</span>
+<span class="sd">        using the :meth:`Graph.neighbors`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        dict</span>
+<span class="sd">            dict of tuples representing neighbors</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="k">lambda</span> <span class="n">group</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span>
+                    <span class="n">group</span><span class="p">[</span>
+                        <span class="o">~</span><span class="p">((</span><span class="n">group</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">group</span><span class="o">.</span><span class="n">neighbor</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">weight</span> <span class="o">==</span> <span class="mi">0</span><span class="p">))</span>
+                    <span class="p">]</span><span class="o">.</span><span class="n">neighbor</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
+        <span class="p">)</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get weights dictionary</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        It is recommended to work directly with :meth:`Graph.adjacency` rather than</span>
+<span class="sd">        using the :meth:`Graph.weights`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        dict</span>
+<span class="sd">            dict of tuples representing weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+                <span class="k">lambda</span> <span class="n">group</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span>
+                    <span class="n">group</span><span class="p">[</span>
+                        <span class="o">~</span><span class="p">((</span><span class="n">group</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">group</span><span class="o">.</span><span class="n">neighbor</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">weight</span> <span class="o">==</span> <span class="mi">0</span><span class="p">))</span>
+                    <span class="p">]</span><span class="o">.</span><span class="n">weight</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
+        <span class="p">)</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a scipy.sparse array (COO)</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scipy.sparse.COO</span>
+<span class="sd">            sparse representation of the adjacency</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># pivot to COO sparse matrix and cast to array</span>
+        <span class="k">return</span> <span class="n">sparse</span><span class="o">.</span><span class="n">coo_array</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;Sparse[float]&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">to_coo</span><span class="p">(</span><span class="n">sort_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="p">)</span>
+
+<div class="viewcode-block" id="Graph.transform">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transformation of weights</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        transformation : str</span>
+<span class="sd">            Transformation method. The following are</span>
+<span class="sd">            valid transformations.</span>
+
+<span class="sd">            - **B** -- Binary</span>
+<span class="sd">            - **R** -- Row-standardization (global sum :math:`=n`)</span>
+<span class="sd">            - **D** -- Double-standardization (global sum :math:`=1`)</span>
+<span class="sd">            - **V** -- Variance stabilizing</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            transformed weights</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Value error for unsupported transformation</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">transformation</span> <span class="o">=</span> <span class="n">transformation</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">==</span> <span class="n">transformation</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">transformation</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span><span class="p">:</span>
+            <span class="n">standardized</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="s2">&quot;sum&quot;</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">values</span>
+            <span class="p">)</span>  <span class="c1"># isolate comes as NaN -&gt; 0</span>
+
+        <span class="k">elif</span> <span class="n">transformation</span> <span class="o">==</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span>
+            <span class="n">standardized</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span><span class="o">.</span><span class="n">values</span>
+
+        <span class="k">elif</span> <span class="n">transformation</span> <span class="o">==</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span>
+            <span class="n">standardized</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+
+        <span class="k">elif</span> <span class="n">transformation</span> <span class="o">==</span> <span class="s2">&quot;V&quot;</span><span class="p">:</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span>
+                <span class="k">lambda</span> <span class="n">group</span><span class="p">:</span> <span class="n">group</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">group</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
+            <span class="p">)</span>
+            <span class="n">n_q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">/</span> <span class="n">s</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="n">standardized</span> <span class="o">=</span> <span class="p">(</span><span class="n">s</span> <span class="o">*</span> <span class="n">n_q</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">values</span>  <span class="c1"># isolate comes as NaN -&gt; 0</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Transformation &#39;</span><span class="si">{</span><span class="n">transformation</span><span class="si">}</span><span class="s2">&#39; is not supported. &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Use one of </span><span class="si">{</span><span class="n">ALLOWED_TRANSFORMATIONS</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">standardized_adjacency</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+            <span class="n">standardized</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">index</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">Graph</span><span class="p">(</span><span class="n">standardized_adjacency</span><span class="p">,</span> <span class="n">transformation</span><span class="p">)</span></div>
+
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">_components</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;helper for n_components and component_labels&quot;&quot;&quot;</span>
+        <span class="c1"># TODO: remove casting to matrix once scipy supports arrays here</span>
+        <span class="k">return</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csgraph</span><span class="o">.</span><span class="n">connected_components</span><span class="p">(</span><span class="n">sparse</span><span class="o">.</span><span class="n">coo_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">))</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">n_components</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get a number of connected components</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        int</span>
+<span class="sd">            number of components</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_components</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">component_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get component labels per observation</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        numpy.array</span>
+<span class="sd">            Array of component labels</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_components</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">unique_ids</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;component labels&quot;</span>
+        <span class="p">)</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">cardinalities</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of neighbors for each observation</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        pandas.Series</span>
+<span class="sd">            Series with a number of neighbors per each observation</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">cardinalities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+        <span class="n">cardinalities</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;cardinalities&quot;</span>
+        <span class="k">return</span> <span class="n">cardinalities</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">isolates</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Index of observations with no neighbors</span>
+
+<span class="sd">        Isolates are encoded as a self-loop with</span>
+<span class="sd">        the weight == 0 in the adjacency table.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        pandas.Index</span>
+<span class="sd">            Index with a subset of observations that do not have any neighbor</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">nulls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="c1"># since not all zeros are necessarily isolates, do the focal == neighbor check</span>
+        <span class="k">return</span> <span class="n">nulls</span><span class="p">[</span><span class="n">nulls</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">nulls</span><span class="o">.</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">unique_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Unique IDs used in the Graph&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="s2">&quot;focal&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">n</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of observations.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unique_ids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">n_nodes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of observations.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">unique_ids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">n_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of observations.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">isolates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">pct_nonzero</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Percentage of nonzero weights.&quot;&quot;&quot;</span>
+        <span class="n">p</span> <span class="o">=</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">p</span>
+
+    <span class="nd">@cached_property</span>
+    <span class="k">def</span> <span class="nf">nonzero</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of nonzero weights.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">isolates</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+<div class="viewcode-block" id="Graph.asymmetry">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.asymmetry">[docs]</a>
+    <span class="k">def</span> <span class="nf">asymmetry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intrinsic</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Asymmetry check.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        intrinsic : bool, optional</span>
+<span class="sd">            Default is ``True``. Intrinsic symmetry is defined as:</span>
+
+<span class="sd">            .. math::</span>
+
+<span class="sd">                w_{i,j} == w_{j,i}</span>
+
+<span class="sd">            If ``intrinsic`` is ``False`` symmetry is defined as:</span>
+
+<span class="sd">            .. math::</span>
+
+<span class="sd">                i \in N_j \ \&amp; \ j \in N_i</span>
+
+<span class="sd">            where :math:`N_j` is the set of neighbors for :math:`j`,</span>
+<span class="sd">            e.g., ``True`` requires equality of the weight to consider</span>
+<span class="sd">            two links equal, ``False`` requires only a presence of a link</span>
+<span class="sd">            with a non-zero weight.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        pandas.Series</span>
+<span class="sd">            A ``Series`` of ``(i,j)`` pairs of asymmetries sorted</span>
+<span class="sd">            ascending by the focal observation (index value),</span>
+<span class="sd">            where ``i`` is the focal and ``j`` is the neighbor.</span>
+<span class="sd">            An empty ``Series`` is returned if no asymmetries are found.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">intrinsic</span><span class="p">:</span>
+            <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">transformed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">)</span>
+            <span class="n">wd</span> <span class="o">=</span> <span class="n">transformed</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="n">transformed</span><span class="o">.</span><span class="n">sparse</span>
+
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+                <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">([],</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">),</span>
+                <span class="n">name</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="s2">&quot;focal&quot;</span><span class="p">],</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">i2id</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+                <span class="nb">zip</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">unique_ids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="bp">self</span><span class="o">.</span><span class="n">unique_ids</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="p">)</span>
+            <span class="n">focal</span><span class="p">,</span> <span class="n">neighbor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
+            <span class="n">focal</span> <span class="o">=</span> <span class="n">focal</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="s2">&quot;focal&quot;</span><span class="p">])</span>
+            <span class="n">neighbor</span> <span class="o">=</span> <span class="n">neighbor</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">dtypes</span><span class="p">[</span><span class="s2">&quot;focal&quot;</span><span class="p">])</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">i2id</span><span class="p">:</span>
+                <span class="n">focal</span><span class="p">[</span><span class="n">focal</span> <span class="o">==</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i2id</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">neighbor</span><span class="p">[</span><span class="n">neighbor</span> <span class="o">==</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i2id</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">ijs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
+                <span class="n">neighbor</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">(</span><span class="n">focal</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">sort_index</span><span class="p">()</span>
+            <span class="k">return</span> <span class="n">ijs</span></div>
+
+
+<div class="viewcode-block" id="Graph.higher_order">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.higher_order">[docs]</a>
+    <span class="k">def</span> <span class="nf">higher_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shortest_path</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lower_order</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Contiguity weights object of order :math:`k`.</span>
+
+<span class="sd">        Proper higher order neighbors are returned such that :math:`i` and :math:`j`</span>
+<span class="sd">        are :math:`k`-order neighbors if the shortest path from :math:`i-j` is of</span>
+<span class="sd">        length :math:`k`.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        k : int, optional</span>
+<span class="sd">            Order of contiguity. By default 2.</span>
+<span class="sd">        shortest_path : bool, optional</span>
+<span class="sd">            If True, :math:`i,j` and :math:`k`-order neighbors if the shortest</span>
+<span class="sd">            path for :math:`i,j` is :math:`k`. If False, :math:`i,j` are</span>
+<span class="sd">            `k`-order neighbors if there is a path from :math:`i,j` of length</span>
+<span class="sd">            :math:`k`. By default True.</span>
+<span class="sd">        diagonal : bool, optional</span>
+<span class="sd">            If True, keep :math:`k`-order (:math:`i,j`) joins when :math:`i==j`.</span>
+<span class="sd">            If False, remove :math:`k`-order (:math:`i,j`) joins when</span>
+<span class="sd">            :math:`i==j`. By default False.</span>
+<span class="sd">        lower_order : bool, optional</span>
+<span class="sd">            If True, include lower order contiguities. If False return only weights of</span>
+<span class="sd">            order :math:`k`. By default False.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Graph</span>
+<span class="sd">            higher order weights</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># TODO: remove casting to matrix once scipy implements matrix_power on array.</span>
+        <span class="c1"># [https://github.com/scipy/scipy/pull/18544]</span>
+        <span class="n">binary</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">)</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span><span class="n">binary</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">lower_order</span><span class="p">:</span>
+            <span class="n">wk</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">sp</span><span class="o">**</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+            <span class="n">shortest_path</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">wk</span> <span class="o">=</span> <span class="n">sp</span><span class="o">**</span><span class="n">k</span>
+
+        <span class="n">rk</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">wk</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
+        <span class="n">sk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rk</span><span class="p">,</span> <span class="n">ck</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="n">shortest_path</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
+                <span class="n">wj</span> <span class="o">=</span> <span class="n">sp</span><span class="o">**</span><span class="n">j</span>
+                <span class="n">rj</span><span class="p">,</span> <span class="n">cj</span> <span class="o">=</span> <span class="n">wj</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
+                <span class="n">sj</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rj</span><span class="p">,</span> <span class="n">cj</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+                <span class="n">sk</span><span class="o">.</span><span class="n">difference_update</span><span class="p">(</span><span class="n">sj</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">diagonal</span><span class="p">:</span>
+            <span class="n">sk</span> <span class="o">=</span> <span class="p">{(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">sk</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">}</span>
+
+        <span class="k">return</span> <span class="n">Graph</span><span class="o">.</span><span class="n">from_sparse</span><span class="p">(</span>
+            <span class="n">sparse</span><span class="o">.</span><span class="n">coo_array</span><span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sk</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">),</span>
+                    <span class="p">([</span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sk</span><span class="p">],</span> <span class="p">[</span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sk</span><span class="p">]),</span>
+                <span class="p">),</span>
+                <span class="n">shape</span><span class="o">=</span><span class="n">sp</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">ids</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">unique_ids</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.lag">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.lag">[docs]</a>
+    <span class="k">def</span> <span class="nf">lag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Spatial lag operator</span>
+
+<span class="sd">        If weights are row standardized, returns the mean of each</span>
+<span class="sd">        observation&#39;s neighbors; if not, returns the weighted sum</span>
+<span class="sd">        of each observation&#39;s neighbors.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : array-like</span>
+<span class="sd">            array-like (N,) shape where N is equal to number of observations in self.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        numpy.ndarray</span>
+<span class="sd">            array of numeric values for the spatial lag</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">_lag_spatial</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.to_parquet">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.to_parquet">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_parquet</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Save Graph to a Apache Parquet</span>
+
+<span class="sd">        Graph is serialized to the Apache Parquet using the underlying adjacency</span>
+<span class="sd">        object stored as a Parquet table and custom metadata containing transformation.</span>
+
+<span class="sd">        Requires pyarrow package.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        path : str | pyarrow.NativeFile</span>
+<span class="sd">            path or any stream supported by pyarrow</span>
+<span class="sd">        **kwargs</span>
+<span class="sd">            additional keyword arguments passed to pyarrow.parquet.write_table</span>
+
+<span class="sd">        See also</span>
+<span class="sd">        --------</span>
+<span class="sd">        read_parquet</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">_to_parquet</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.to_networkx">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.to_networkx">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_networkx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert Graph to a ``networkx`` graph.</span>
+
+<span class="sd">        If Graph is symmetric, returns ``nx.Graph``, otherwise returns a ``nx.DiGraph``.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        networkx.Graph | networkx.DiGraph</span>
+<span class="sd">            Representation of libpysal Graph as networkx graph</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;NetworkX is required.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
+
+        <span class="n">graph_type</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span><span class="o">.</span><span class="n">empty</span> <span class="k">else</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span>
+
+        <span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">from_pandas_edgelist</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_adjacency</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(),</span>
+            <span class="n">source</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span>
+            <span class="n">target</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span>
+            <span class="n">edge_attr</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">,</span>
+            <span class="n">create_using</span><span class="o">=</span><span class="n">graph_type</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.plot">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.plot">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">gdf</span><span class="p">,</span>
+        <span class="n">focal</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">nodes</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span>
+        <span class="n">edge_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">node_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">focal_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">limit_extent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Plot edges and nodes of the Graph</span>
+
+<span class="sd">        Creates a ``maptlotlib`` plot based on the topology stored in the</span>
+<span class="sd">        Graph and spatial location defined in ``gdf``.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        gdf : geopandas.GeoDataFrame</span>
+<span class="sd">            Geometries indexed using the same index as Graph. Geometry types other than</span>
+<span class="sd">            points are converted to centroids encoding start and end point of Graph</span>
+<span class="sd">            edges.</span>
+<span class="sd">        focal : hashable | array-like[hashable] | None, optional</span>
+<span class="sd">            ID or an array-like of IDs of focal geometries whose weights shall be</span>
+<span class="sd">            plotted. If None, all weights from all focal geometries are plotted.</span>
+<span class="sd">            By default None</span>
+<span class="sd">        nodes : bool, optional</span>
+<span class="sd">            Plot nodes as points, by default True</span>
+<span class="sd">        color : str, optional</span>
+<span class="sd">            The color of all objects, by default &quot;k&quot;</span>
+<span class="sd">        edge_kws : dict, optional</span>
+<span class="sd">            Keyword arguments dictionary to send to ``LineCollection``,</span>
+<span class="sd">            which provides fine-grained control over the aesthetics</span>
+<span class="sd">            of the edges in the plot. By default None</span>
+<span class="sd">        node_kws : dict, optional</span>
+<span class="sd">            Keyword arguments dictionary to send to ``ax.scatter``,</span>
+<span class="sd">            which provides fine-grained control over the aesthetics</span>
+<span class="sd">            of the nodes in the plot. By default None</span>
+<span class="sd">        focal_kws : dict, optional</span>
+<span class="sd">            Keyword arguments dictionary to send to ``ax.scatter``,</span>
+<span class="sd">            which provides fine-grained control over the aesthetics</span>
+<span class="sd">            of the focal nodes in the plot on top of generic ``node_kws``.</span>
+<span class="sd">            Values of ``node_kws`` are updated from ``focal_kws``.</span>
+<span class="sd">            Ignored if ``focal=None``. By default None</span>
+<span class="sd">        ax : matplotlib.axes.Axes, optional</span>
+<span class="sd">            Axis on which to plot the weights. If None, a new figure and axis are</span>
+<span class="sd">            created. By default None</span>
+<span class="sd">        figsize : tuple, optional</span>
+<span class="sd">            figsize used to create a new axis. By default None</span>
+<span class="sd">        limit_extent : bool, optional</span>
+<span class="sd">            limit the extent of the axis to the extent of the plotted graph, by default</span>
+<span class="sd">            False</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        matplotlib.axes.Axes</span>
+<span class="sd">            Axis with the resulting plot</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        If you&#39;d like to overlay the actual geometries from the</span>
+<span class="sd">        ``geopandas.GeoDataFrame``, create an axis by plotting the ``GeoDataFrame``</span>
+<span class="sd">        and plot the Graph on top.</span>
+
+<span class="sd">            ax = gdf.plot()</span>
+<span class="sd">            gdf_graph.plot(gdf, ax=ax)</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">_plot</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">gdf</span><span class="p">,</span>
+            <span class="n">focal</span><span class="o">=</span><span class="n">focal</span><span class="p">,</span>
+            <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
+            <span class="n">node_kws</span><span class="o">=</span><span class="n">node_kws</span><span class="p">,</span>
+            <span class="n">edge_kws</span><span class="o">=</span><span class="n">edge_kws</span><span class="p">,</span>
+            <span class="n">focal_kws</span><span class="o">=</span><span class="n">focal_kws</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">,</span>
+            <span class="n">limit_extent</span><span class="o">=</span><span class="n">limit_extent</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Graph.explore">
+<a class="viewcode-back" href="../../../generated/libpysal.graph.Graph.html#libpysal.graph.Graph.explore">[docs]</a>
+    <span class="k">def</span> <span class="nf">explore</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">gdf</span><span class="p">,</span>
+        <span class="n">focal</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">nodes</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+        <span class="n">edge_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">node_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">focal_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">m</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Plot graph as an interactive Folium Map</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        gdf : geopandas.GeoDataFrame</span>
+<span class="sd">            geodataframe used to instantiate to Graph</span>
+<span class="sd">        focal : list, optional</span>
+<span class="sd">            subset of focal observations to plot in the map, by default None.</span>
+<span class="sd">            If none, all relationships are plotted</span>
+<span class="sd">        nodes : bool, optional</span>
+<span class="sd">            whether to display observations as nodes in the map, by default True</span>
+<span class="sd">        color : str, optional</span>
+<span class="sd">            color applied to nodes and edges, by default &quot;black&quot;</span>
+<span class="sd">        edge_kws : dict, optional</span>
+<span class="sd">            additional keyword arguments passed to geopandas explore function</span>
+<span class="sd">            when plotting edges, by default None</span>
+<span class="sd">        node_kws : dict, optional</span>
+<span class="sd">            additional keyword arguments passed to geopandas explore function</span>
+<span class="sd">            when plotting nodes, by default None</span>
+<span class="sd">        focal_kws : dict, optional</span>
+<span class="sd">            additional keyword arguments passed to geopandas explore function</span>
+<span class="sd">            when plotting focal observations, by default None. Only applicable when</span>
+<span class="sd">            passing a subset of nodes with the `focal` argument</span>
+<span class="sd">        m : Folilum.Map, optional</span>
+<span class="sd">            folium map objecto to plot on top of, by default None</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        folium.Map</span>
+<span class="sd">            folium map</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">_explore_graph</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">gdf</span><span class="p">,</span>
+            <span class="n">focal</span><span class="o">=</span><span class="n">focal</span><span class="p">,</span>
+            <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">,</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span>
+            <span class="n">edge_kws</span><span class="o">=</span><span class="n">edge_kws</span><span class="p">,</span>
+            <span class="n">node_kws</span><span class="o">=</span><span class="n">node_kws</span><span class="p">,</span>
+            <span class="n">focal_kws</span><span class="o">=</span><span class="n">focal_kws</span><span class="p">,</span>
+            <span class="n">m</span><span class="o">=</span><span class="n">m</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+
+
+<span class="k">def</span> <span class="nf">_arrange_arrays</span><span class="p">(</span><span class="n">heads</span><span class="p">,</span> <span class="n">tails</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Rearrange input arrays so that observation indices</span>
+<span class="sd">    are well-ordered with respect to the input ids. That is,</span>
+<span class="sd">    an &quot;early&quot; identifier should always preceed a &quot;later&quot; identifier</span>
+<span class="sd">    in the heads, but the tails should be sorted with respect</span>
+<span class="sd">    to heads *first*, then sorted within the tails.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">heads</span><span class="p">,</span> <span class="n">tails</span><span class="p">)))</span>
+    <span class="n">lookup</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">input_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span>
+        <span class="p">{</span><span class="s2">&quot;focal&quot;</span><span class="p">:</span> <span class="n">heads</span><span class="p">,</span> <span class="s2">&quot;neighbor&quot;</span><span class="p">:</span> <span class="n">tails</span><span class="p">,</span> <span class="s2">&quot;weight&quot;</span><span class="p">:</span> <span class="n">weights</span><span class="p">}</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">input_df</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor&quot;</span><span class="p">])</span>
+        <span class="o">.</span><span class="n">assign</span><span class="p">(</span>
+            <span class="n">focal_loc</span><span class="o">=</span><span class="n">input_df</span><span class="o">.</span><span class="n">focal</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">lookup</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+            <span class="n">neighbor_loc</span><span class="o">=</span><span class="n">input_df</span><span class="o">.</span><span class="n">neighbor</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">lookup</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s2">&quot;focal_loc&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor_loc&quot;</span><span class="p">])</span>
+        <span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
+        <span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;focal_loc&quot;</span><span class="p">,</span> <span class="s2">&quot;neighbor_loc&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">T</span>
+    <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">read_parquet</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Read Graph from a Apache Parquet</span>
+
+<span class="sd">    Read Graph serialized using `Graph.to_parquet()` back into the `Graph` object.</span>
+<span class="sd">    The Parquet file needs to contain adjacency table with a structure required</span>
+<span class="sd">    by the `Graph` constructor and optional metadata with the type of transformation.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    path : str | pyarrow.NativeFile | file-like object</span>
+<span class="sd">        path or any stream supported by pyarrow</span>
+<span class="sd">    **kwargs</span>
+<span class="sd">        additional keyword arguments passed to pyarrow.parquet.read_table</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Graph</span>
+<span class="sd">        deserialized Graph</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">adjacency</span><span class="p">,</span> <span class="n">transformation</span> <span class="o">=</span> <span class="n">_read_parquet</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">Graph</span><span class="p">(</span><span class="n">adjacency</span><span class="p">,</span> <span class="n">transformation</span><span class="p">)</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
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diff --git a/_modules/libpysal/io/fileio.html b/_modules/libpysal/io/fileio.html
new file mode 100644
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+++ b/_modules/libpysal/io/fileio.html
@@ -0,0 +1,644 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>libpysal.io.fileio &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bootstrap-sphinx.css?v=9afac83c" />
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+    <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
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+    <link rel="icon" href="../../../_static/pysal_favicon.ico"/>
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+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
+    <div class="container">
+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
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+        <a class="navbar-brand" href="../../../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
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+          <ul class="nav navbar-nav">
+            
+                <li><a href="../../../installation.html">Installation</a></li>
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+                <li><a href="../../../references.html">References</a></li>
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+              <li class="dropdown globaltoc-container">
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+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
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+</ul>
+</li>
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+  <h1>Source code for libpysal.io.fileio</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">FileIO: Module for reading and writing various file types in a Pythonic way.</span>
+<span class="sd">This module should not be used directly, instead...</span>
+
+<span class="sd">```</span>
+<span class="sd">import pysal.core.FileIO as FileIO</span>
+<span class="sd">```</span>
+
+<span class="sd">Readers and Writers will mimic python file objects.</span>
+<span class="sd">    .seek(n) seeks to the n&#39;th object</span>
+<span class="sd">    .read(n) reads n objects, default == all</span>
+<span class="sd">    .next() reads the next object</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="c1"># ruff: noqa: ARG002, N801, N802, N803, N806, SIM115</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Charles R Schmidt &lt;schmidtc@gmail.com&gt;&quot;</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;FileIO&quot;</span><span class="p">]</span>
+
+<span class="kn">import</span> <span class="nn">os.path</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+
+<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">MISSINGVALUE</span>
+
+
+<span class="k">class</span> <span class="nc">FileIO_MetaCls</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;This meta class is instantiated when the class is first defined. All</span>
+<span class="sd">    subclasses of `FileIO` also inherit this meta class, which registers</span>
+<span class="sd">    their abilities with the FileIO registry. Subclasses must contain</span>
+<span class="sd">    ``FORMATS`` and ``MODES`` (both are ``type(list)``).</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    TypeError</span>
+<span class="sd">        FileIO subclasses must have ``FORMATS`` and ``MODES`` defined.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">dict_</span><span class="p">):</span>
+        <span class="bp">cls</span> <span class="o">=</span> <span class="nb">type</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">bases</span><span class="p">,</span> <span class="n">dict_</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">name</span> <span class="o">!=</span> <span class="s2">&quot;FileIO&quot;</span> <span class="ow">and</span> <span class="n">name</span> <span class="o">!=</span> <span class="s2">&quot;DataTable&quot;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;FORMATS&quot;</span> <span class="ow">in</span> <span class="n">dict_</span> <span class="ow">and</span> <span class="s2">&quot;MODES&quot;</span> <span class="ow">in</span> <span class="n">dict_</span><span class="p">:</span>
+                <span class="c1"># msg = &quot;Registering %s with FileIO.\n\tFormats: %r\n\tModes: %r&quot;</span>
+                <span class="c1"># msg = msg % (name, dict[&quot;FORMATS&quot;], dict[&quot;MODES&quot;])</span>
+                <span class="n">FileIO</span><span class="o">.</span><span class="n">_register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dict_</span><span class="p">[</span><span class="s2">&quot;FORMATS&quot;</span><span class="p">],</span> <span class="n">dict_</span><span class="p">[</span><span class="s2">&quot;MODES&quot;</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;FileIO subclasses must have &#39;FORMATS&#39; and &#39;MODES&#39; defined.&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span>
+
+
+<div class="viewcode-block" id="FileIO">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO">[docs]</a>
+<span class="k">class</span> <span class="nc">FileIO</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">FileIO_MetaCls</span><span class="p">):</span>  <span class="c1"># should be a type?</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Metaclass for supporting spatial data file read and write.</span>
+
+<span class="sd">    How this works:</span>
+
+<span class="sd">    ``FileIO.open(\\*args) == FileIO(\\*args)``</span>
+
+<span class="sd">    When creating a new instance of `FileIO` the ``.__new__`` method intercepts.</span>
+<span class="sd">    ``.__new__`` parses the filename to determine the ``fileType``. Next,</span>
+<span class="sd">    ``.__registry`` and checked for that type. Each type supports one or more modes</span>
+<span class="sd">    (``[&#39;r&#39;, &#39;w&#39;, &#39;a&#39;, etc.]``). If we support the type and mode, an instance of the</span>
+<span class="sd">    appropriate handler is created and returned. All handlers must inherit from this</span>
+<span class="sd">    class, and by doing so are automatically added to the ``.__registry`` and are</span>
+<span class="sd">    forced to conform to the prescribed API. The metaclass takes care of the</span>
+<span class="sd">    registration by parsing the class definition. It doesn&#39;t make much sense to</span>
+<span class="sd">    treat weights in the same way as shapefiles and dbfs, so...</span>
+
+<span class="sd">    * ... for now we&#39;ll just return an instance of `W` on ``mode=&#39;r&#39;``.</span>
+<span class="sd">    * ... on ``mode=&#39;w&#39;``, ``.write`` will expect an instance of `W`.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">__registry</span> <span class="o">=</span> <span class="p">{}</span>  <span class="c1"># {&#39;shp&#39;:{&#39;r&#39;:[OGRshpReader,pysalShpReader]}}</span>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dataPath</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Intercepts the instantiation of ``FileIO`` and dispatches</span>
+<span class="sd">        to the correct handler. If no suitable handler is found a</span>
+<span class="sd">        python file object is returned.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="bp">cls</span> <span class="ow">is</span> <span class="n">FileIO</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">newCls</span> <span class="o">=</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span>
+                    <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="n">getType</span><span class="p">(</span><span class="n">dataPath</span><span class="p">,</span> <span class="n">mode</span><span class="p">,</span> <span class="n">dataFormat</span><span class="p">)][</span><span class="n">mode</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="p">)</span>
+            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+                <span class="k">return</span> <span class="nb">open</span><span class="p">(</span><span class="n">dataPath</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">newCls</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">object</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FileIO.getType">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.getType">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">getType</span><span class="p">(</span><span class="n">dataPath</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">mode</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Parse the ``dataPath`` and return the data type.&quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">dataFormat</span><span class="p">:</span>
+            <span class="n">ext</span> <span class="o">=</span> <span class="n">dataFormat</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ext</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">dataPath</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">ext</span> <span class="o">=</span> <span class="n">ext</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+            <span class="n">ext</span> <span class="o">=</span> <span class="n">ext</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s2">&quot;txt&quot;</span><span class="p">:</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">dataPath</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="n">l1</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+                <span class="n">l2</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">l1</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+                    <span class="n">n</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+                    <span class="n">fields</span> <span class="o">=</span> <span class="n">l2</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span>
+                    <span class="k">return</span> <span class="s2">&quot;geoda_txt&quot;</span>
+                <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
+                    <span class="k">return</span> <span class="n">ext</span>
+
+        <span class="k">return</span> <span class="n">ext</span></div>
+
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_register</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">parser</span><span class="p">,</span> <span class="n">formats</span><span class="p">,</span> <span class="n">modes</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;This method is called automatically via the Metaclass of `FileIO` subclasses</span>
+<span class="sd">        This should be private, but that hides it from the Metaclass.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">assert</span> <span class="bp">cls</span> <span class="ow">is</span> <span class="n">FileIO</span>
+
+        <span class="k">for</span> <span class="n">format_</span> <span class="ow">in</span> <span class="n">formats</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">format_</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">:</span>
+                <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="n">format_</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">mode</span> <span class="ow">in</span> <span class="n">modes</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="n">format_</span><span class="p">]:</span>
+                    <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="n">format_</span><span class="p">][</span><span class="n">mode</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="p">[</span><span class="n">format_</span><span class="p">][</span><span class="n">mode</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">parser</span><span class="p">)</span>
+        <span class="c1"># cls.check()</span>
+
+<div class="viewcode-block" id="FileIO.check">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.check">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">cls</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Prints the contents of the registry.&quot;&quot;&quot;</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;PySAL File I/O understands the following file extensions:&quot;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">__registry</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Ext: &#39;.</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&#39;, Modes: </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="FileIO.open">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.open">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">open</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>  <span class="c1"># noqa A001</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Alias for ``FileIO()``.&quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">class</span> <span class="nc">_By_Row</span><span class="p">:</span>
+        <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">parent</span>
+
+        <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">:</span>
+                <span class="k">return</span> <span class="s2">&quot;keys: range(0,n)&quot;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="s2">&quot;keys: &quot;</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="fm">__repr__</span><span class="p">()</span>
+
+        <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span> <span class="o">|</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">r</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">key</span><span class="p">:</span>
+                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">key</span><span class="p">:</span>
+                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+                <span class="k">return</span> <span class="n">r</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">ids</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+        <span class="fm">__call__</span> <span class="o">=</span> <span class="fm">__getitem__</span>
+
+<div class="viewcode-block" id="FileIO.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataPath</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dataPath</span> <span class="o">=</span> <span class="n">dataPath</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dataObj</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span>
+        <span class="c1"># pos Should ALWAYS be in the range 0,...,n</span>
+        <span class="c1"># for custom IDs set the ids property.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="kc">None</span>  <span class="c1"># {&#39;id&#39;:n}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">closed</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">header</span> <span class="o">=</span> <span class="p">[]</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">by_row</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">by_row</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_By_Row</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__getIds</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span>
+
+    <span class="k">def</span> <span class="nf">__setIds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="p">:</span> <span class="nb">list</span> <span class="o">|</span> <span class="p">(</span><span class="nb">dict</span> <span class="o">|</span> <span class="kc">None</span><span class="p">)):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Property method for ``.ids``. Takes a list of ids and maps then</span>
+<span class="sd">        to a 0-based index. Need to provide a method to set ID&#39;s based on</span>
+<span class="sd">        a ``fieldName`` preferably without reading the whole file.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        AssertionError</span>
+<span class="sd">            Raised when IDs are not unique.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">ids</span><span class="p">))</span>
+            <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;IDs must be unique.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
+            <span class="c1"># keys: ID values: i</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="c1"># keys: i values: ID</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">id_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ids</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="n">ids</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">id_</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="n">ids</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__ids</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="n">ids</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">fget</span><span class="o">=</span><span class="n">__getIds</span><span class="p">,</span> <span class="n">fset</span><span class="o">=</span><span class="n">__setIds</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">rIds</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">__rIds</span>
+
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_complain_ifclosed</span><span class="p">(</span><span class="n">closed</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;From `StringIO`.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised when a file is already closed.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">closed</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;I/O operation on closed file.&quot;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FileIO.cast">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.cast">[docs]</a>
+    <span class="k">def</span> <span class="nf">cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">typ</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Cast ``key`` as ``typ``.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        TypeError</span>
+<span class="sd">            Raised when a cast object in not callable.</span>
+<span class="sd">        KeyError</span>
+<span class="sd">            Raised when a key is not present.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="o">=</span> <span class="p">[</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">typ</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)]</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="k">assert</span> <span class="nb">callable</span><span class="p">(</span><span class="n">typ</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">header</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">key</span><span class="p">)]</span> <span class="o">=</span> <span class="n">typ</span>
+                <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Cast objects must be callable.&quot;</span><span class="p">)</span> <span class="kn">from</span> <span class="kc">None</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">key</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_cast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised when a value could not be cast a particular type.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spec</span> <span class="ow">and</span> <span class="n">row</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="k">return</span> <span class="p">[</span><span class="n">f</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
+            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                <span class="n">r</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_spec</span><span class="p">,</span> <span class="n">row</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+                    <span class="k">try</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">and</span> <span class="n">f</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">:</span>
+                            <span class="k">raise</span> <span class="ne">ValueError</span>
+                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">v</span><span class="p">))</span>
+                    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                        <span class="n">msg</span> <span class="o">=</span> <span class="s2">&quot;Value &#39;</span><span class="si">%r</span><span class="s2">&#39; could not be cast to </span><span class="si">%s</span><span class="s2">, &quot;</span>
+                        <span class="n">msg</span> <span class="o">+=</span> <span class="s2">&quot;value set to MISSINGVALUE.&quot;</span>
+                        <span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span> <span class="o">%</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
+                        <span class="n">warn</span><span class="p">(</span><span class="n">msg</span><span class="p">,</span> <span class="ne">RuntimeWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+                        <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">MISSINGVALUE</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">r</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">row</span>
+
+    <span class="k">def</span> <span class="fm">__next__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;A `FileIO` object is its own iterator, see `StringIO`.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        StopIteration</span>
+<span class="sd">            Raised at the EOF.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">r</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+
+        <span class="k">return</span> <span class="n">r</span>
+
+<div class="viewcode-block" id="FileIO.close">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.close">[docs]</a>
+    <span class="k">def</span> <span class="nf">close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Subclasses should clean themselves up and then call this method.&quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">closed</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataObj</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span></div>
+
+
+<div class="viewcode-block" id="FileIO.get">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.get">[docs]</a>
+    <span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Seeks the file to ``n`` and returns ``n``. If ``.ids`` is set</span>
+<span class="sd">        ``n`` should be an id, else, ``n`` should be an offset.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">prev_pos</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tell</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+        <span class="n">obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="n">prev_pos</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">obj</span></div>
+
+
+<div class="viewcode-block" id="FileIO.seek">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.seek">[docs]</a>
+    <span class="k">def</span> <span class="nf">seek</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Seek the `FileObj` to the beginning of the ``n``&#39;th record.</span>
+<span class="sd">        If IDs are set, seeks to the beginning of the record at ID, ``n``.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pos</span> <span class="o">=</span> <span class="n">n</span></div>
+
+
+<div class="viewcode-block" id="FileIO.tell">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.tell">[docs]</a>
+    <span class="k">def</span> <span class="nf">tell</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return ID (or offset) of next object.&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos</span></div>
+
+
+<div class="viewcode-block" id="FileIO.read">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.read">[docs]</a>
+    <span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Read at most ``n`` objects, less if read hits EOF.</span>
+<span class="sd">        If size is negative or omitted read all objects until EOF.</span>
+<span class="sd">        Returns ``None`` if EOF is reached before any objects.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        StopIteration</span>
+<span class="sd">            Raised at the EOF.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">n</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># return list(self)</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">while</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">())</span>
+                <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span>
+                    <span class="k">break</span>
+            <span class="k">return</span> <span class="n">result</span>
+        <span class="k">elif</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">None</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">__read</span><span class="p">())</span>
+                <span class="k">except</span> <span class="ne">StopIteration</span><span class="p">:</span>
+                    <span class="k">break</span>
+            <span class="k">return</span> <span class="n">result</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__read</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Gets one row from the file handler, and if necessary casts it&#39;s objects.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        StopIteration</span>
+<span class="sd">            Raised at the EOF.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">row</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">row</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">StopIteration</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cast</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">row</span>
+
+    <span class="k">def</span> <span class="nf">_read</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Must be implemented by subclasses that support &#39;r&#39; subclasses.</span>
+<span class="sd">        Should increment ``.pos`` and redefine this doc string.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        NotImplementedError</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+
+<div class="viewcode-block" id="FileIO.truncate">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.truncate">[docs]</a>
+    <span class="k">def</span> <span class="nf">truncate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Should be implemented by subclasses and redefine this doc string.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        NotImplementedError</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="FileIO.write">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.write">[docs]</a>
+    <span class="k">def</span> <span class="nf">write</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">obj</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Must be implemented by subclasses that support &#39;w&#39; subclasses</span>
+<span class="sd">        Should increment ``.pos``. Subclasses should also check if ``obj``</span>
+<span class="sd">        is an instance of type(list) and redefine this doc string.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        NotImplementedError</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+        <span class="s2">&quot;Write obj to dataObj&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+
+
+<div class="viewcode-block" id="FileIO.flush">
+<a class="viewcode-back" href="../../../generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.flush">[docs]</a>
+    <span class="k">def</span> <span class="nf">flush</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        NotImplementedError</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_complain_ifclosed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">closed</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
+</div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/_modules/libpysal/weights/contiguity.html b/_modules/libpysal/weights/contiguity.html
new file mode 100644
index 000000000..7da4c9a8c
--- /dev/null
+++ b/_modules/libpysal/weights/contiguity.html
@@ -0,0 +1,935 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>libpysal.weights.contiguity &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/pysal-styles.css?v=38b0ce36" />
+    <script src="../../../_static/documentation_options.js?v=7a99edf7"></script>
+    <script src="../../../_static/doctools.js?v=888ff710"></script>
+    <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="icon" href="../../../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../../../genindex.html" />
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+<script type="text/javascript" src="../../../_static/js/jquery-1.12.4.min.js"></script>
+<script type="text/javascript" src="../../../_static/js/jquery-fix.js"></script>
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+<script type="text/javascript" src="../../../_static/bootstrap-sphinx.js"></script>
+
+  </head><body>
+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
+    <div class="container">
+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </button>
+        <a class="navbar-brand" href="../../../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="../../../installation.html">Installation</a></li>
+                <li><a href="../../../tutorial.html">Tutorial</a></li>
+                <li><a href="../../../api.html">API</a></li>
+                <li><a href="../../../references.html">References</a></li>
+            
+            
+              <li class="dropdown globaltoc-container">
+  <a role="button"
+     id="dLabelGlobalToc"
+     data-toggle="dropdown"
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+     href="../../../index.html">Site <b class="caret"></b></a>
+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../api.html">API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#io">io</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#examples">examples</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../api.html#graph">graph</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
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+     data-toggle="dropdown"
+     data-target="#"
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+      role="menu"
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+  <input type="hidden" name="area" value="default" />
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+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <h1>Source code for libpysal.weights.contiguity</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">itertools</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="kn">from</span> <span class="nn">..cg</span> <span class="kn">import</span> <span class="n">voronoi_frames</span>
+<span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span>
+<span class="kn">from</span> <span class="nn">._contW_lists</span> <span class="kn">import</span> <span class="n">ContiguityWeightsLists</span>
+<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">get_ids</span><span class="p">,</span> <span class="n">get_points_array</span>
+<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">W</span>
+<span class="kn">from</span> <span class="nn">.raster</span> <span class="kn">import</span> <span class="n">da2W</span><span class="p">,</span> <span class="n">da2WSP</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Point</span> <span class="k">as</span> <span class="n">shapely_point</span>
+    <span class="kn">from</span> <span class="nn">..cg.shapes</span> <span class="kn">import</span> <span class="n">Point</span> <span class="k">as</span> <span class="n">pysal_point</span>
+
+    <span class="n">point_type</span> <span class="o">=</span> <span class="p">(</span><span class="n">shapely_point</span><span class="p">,</span> <span class="n">pysal_point</span><span class="p">)</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">..cg.shapes</span> <span class="kn">import</span> <span class="n">Point</span> <span class="k">as</span> <span class="n">point_type</span>
+
+<span class="n">WT_TYPE</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;rook&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>  <span class="c1"># for _contW_Binning</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt; , Levi John Wolf &lt;levi.john.wolf@gmail.com&gt;&quot;</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Rook&quot;</span><span class="p">,</span> <span class="s2">&quot;Queen&quot;</span><span class="p">,</span> <span class="s2">&quot;Voronoi&quot;</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="Rook">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook">[docs]</a>
+<span class="k">class</span> <span class="nc">Rook</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Construct a weights object from a collection of pysal polygons that share at least one edge.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    polygons    : list</span>
+<span class="sd">                a collection of PySAL shapes to build weights from</span>
+<span class="sd">    ids         : list</span>
+<span class="sd">                a list of names to use to build the weights</span>
+<span class="sd">    **kw        : keyword arguments</span>
+<span class="sd">                optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :class:`libpysal.weights.weights.W`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Rook.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygons</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
+        <span class="n">criterion</span> <span class="o">=</span> <span class="s2">&quot;rook&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">polygons</span><span class="p">,</span> <span class="n">backup</span> <span class="o">=</span> <span class="n">itertools</span><span class="o">.</span><span class="n">tee</span><span class="p">(</span><span class="n">polygons</span><span class="p">)</span>
+        <span class="n">first_shape</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">backup</span><span class="p">))</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first_shape</span><span class="p">,</span> <span class="n">point_type</span><span class="p">):</span>
+            <span class="n">polygons</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">get_points_array</span><span class="p">(</span><span class="n">polygons</span><span class="p">))</span>
+            <span class="n">polygons</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">polygons</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+        <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">_build</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="n">criterion</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Rook.from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_shapefile">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Rook contiguity weights from a polygon shapefile.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        shapefile : string</span>
+<span class="sd">                    name of polygon shapefile including suffix.</span>
+<span class="sd">        sparse    : boolean</span>
+<span class="sd">                    If True return WSP instance</span>
+<span class="sd">                    If False return W instance</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        w          : W</span>
+<span class="sd">                     instance of spatial weights</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import Rook</span>
+<span class="sd">        &gt;&gt;&gt; import libpysal</span>
+<span class="sd">        &gt;&gt;&gt; wr=Rook.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;), &quot;POLYID&quot;)</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%wr.pct_nonzero</span>
+<span class="sd">        &#39;8.330&#39;</span>
+<span class="sd">        &gt;&gt;&gt; wr=Rook.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;), sparse=True)</span>
+<span class="sd">        &gt;&gt;&gt; pct_sp = wr.sparse.nnz *1. / wr.n**2</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%pct_sp</span>
+<span class="sd">        &#39;0.083&#39;</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        Rook contiguity defines as neighbors any pair of polygons that share a</span>
+<span class="sd">        common edge in their polygon definitions.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.contiguity.Rook`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">sparse</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;sparse&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">FileIO</span><span class="p">(</span><span class="n">filepath</span><span class="p">),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">set_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="n">full</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">to_WSP</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="Rook.from_iterable">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_iterable">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_iterable</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">iterable</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a weights object from a collection of arbitrary polygons. This</span>
+<span class="sd">        will cast the polygons to PySAL polygons, then build the W.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        iterable    : iterable</span>
+<span class="sd">                      a collection of of shapes to be cast to PySAL shapes. Must</span>
+<span class="sd">                      support iteration. Can be either Shapely or PySAL shapes.</span>
+<span class="sd">        **kw        : keyword arguments</span>
+<span class="sd">                      optional arguments for  :class:`pysal.weights.W`</span>
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.contiguity.Rook`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">new_iterable</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">new_iterable</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">from_W</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="Rook.from_dataframe">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_dataframe">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">df</span><span class="p">,</span>
+        <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">use_index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a weights object from a (geo)pandas dataframe with a geometry</span>
+<span class="sd">        column. This will cast the polygons to PySAL polygons, then build the W</span>
+<span class="sd">        using ids from the dataframe.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df          : DataFrame</span>
+<span class="sd">                      a :class: `pandas.DataFrame` containing geometries to use</span>
+<span class="sd">                      for spatial weights</span>
+<span class="sd">        geom_col    : string</span>
+<span class="sd">                      the name of the column in `df` that contains the</span>
+<span class="sd">                      geometries. Defaults to active geometry column.</span>
+<span class="sd">        idVariable  : string</span>
+<span class="sd">                      DEPRECATED - use `ids` instead.</span>
+<span class="sd">                      the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                      provided, the dataframe index is used</span>
+<span class="sd">        ids         : list-like, string</span>
+<span class="sd">                      a list-like of ids to use to index the spatial weights object or</span>
+<span class="sd">                      the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                      provided, the dataframe index is used if `use_index=True` or</span>
+<span class="sd">                      a positional index is used if `use_index=False`.</span>
+<span class="sd">                      Order of the resulting W is not respected from this list.</span>
+<span class="sd">        id_order    : list</span>
+<span class="sd">                      DEPRECATED - argument is deprecated and will be removed.</span>
+<span class="sd">                      An ordered list of ids to use to index the spatial weights</span>
+<span class="sd">                      object. If used, the resulting weights object will iterate</span>
+<span class="sd">                      over results in the order of the names provided in this</span>
+<span class="sd">                      argument.</span>
+<span class="sd">        use_index   : bool</span>
+<span class="sd">                      use index of `df` as `ids` to index the spatial weights object.</span>
+<span class="sd">                      Defaults to False but in future will default to True.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.contiguity.Rook`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+
+        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;`id_order` is deprecated and will be removed in future.&quot;</span><span class="p">,</span>
+                <span class="ne">FutureWarning</span><span class="p">,</span>
+                <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="p">((</span><span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
+                <span class="c1"># if idVariable is None, we want ids. Otherwise, we want the</span>
+                <span class="c1"># idVariable column</span>
+                <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">ids</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">id_order</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">id_order</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;`idVariable` is deprecated and will be removed in future. &quot;</span>
+                    <span class="s2">&quot;Use `ids` instead.&quot;</span><span class="p">,</span>
+                    <span class="ne">FutureWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">idVariable</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;Both `idVariable` and `ids` passed, using `ids`.&quot;</span><span class="p">,</span>
+                    <span class="ne">UserWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;`use_index` defaults to False but will default to True in future. &quot;</span>
+                    <span class="s2">&quot;Set True/False directly to control this behavior and silence this &quot;</span>
+                    <span class="s2">&quot;warning&quot;</span><span class="p">,</span>
+                    <span class="ne">FutureWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">use_index</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">if</span> <span class="n">use_index</span><span class="p">:</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The length of `ids` does not match the length of df.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">id_order</span> <span class="o">=</span> <span class="n">ids</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span>
+            <span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Rook.from_xarray">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_xarray">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_xarray</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">da</span><span class="p">,</span>
+        <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
+        <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a weights object from a xarray.DataArray with an additional</span>
+<span class="sd">        attribute index containing coordinate values of the raster</span>
+<span class="sd">        in the form of Pandas.Index/MultiIndex.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        da : xarray.DataArray</span>
+<span class="sd">            Input 2D or 3D DataArray with shape=(z, y, x)</span>
+<span class="sd">        z_value : int/string/float</span>
+<span class="sd">            Select the z_value of 3D DataArray with multiple layers.</span>
+<span class="sd">        coords_labels : dictionary</span>
+<span class="sd">            Pass dimension labels for coordinates and layers if they do not</span>
+<span class="sd">            belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
+<span class="sd">            e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
+<span class="sd">            Default is {} empty dictionary.</span>
+<span class="sd">        sparse : boolean</span>
+<span class="sd">            type of weight object. Default is True. For libpysal.weights.W, sparse = False</span>
+<span class="sd">        k : int</span>
+<span class="sd">            Order of contiguity, this will select all neighbors upto kth order.</span>
+<span class="sd">            Default is 1.</span>
+<span class="sd">        include_nodata : boolean</span>
+<span class="sd">            If True, missing values will be assumed as non-missing when</span>
+<span class="sd">            selecting higher_order neighbors, Default is False</span>
+<span class="sd">        n_jobs : int</span>
+<span class="sd">            Number of cores to be used in the sparse weight construction. If -1,</span>
+<span class="sd">            all available cores are used. Default is 1.</span>
+<span class="sd">        **kwargs : keyword arguments</span>
+<span class="sd">            optional arguments passed when sparse = False</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w : libpysal.weights.W/libpysal.weights.WSP</span>
+<span class="sd">            instance of spatial weights class W or WSP with an index attribute</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        1. Lower order contiguities are also selected.</span>
+<span class="sd">        2. Returned object contains `index` attribute that includes a</span>
+<span class="sd">           `Pandas.MultiIndex` object from the DataArray.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.weights.WSP`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Queen">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen">[docs]</a>
+<span class="k">class</span> <span class="nc">Queen</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Construct a weights object from a collection of pysal polygons that share at least one vertex.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    polygons    : list</span>
+<span class="sd">                  a collection of PySAL shapes to build weights from</span>
+<span class="sd">    ids         : list</span>
+<span class="sd">                  a list of names to use to build the weights</span>
+<span class="sd">    **kw        : keyword arguments</span>
+<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :class:`libpysal.weights.weights.W`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Queen.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">polygons</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
+        <span class="n">criterion</span> <span class="o">=</span> <span class="s2">&quot;queen&quot;</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">polygons</span><span class="p">,</span> <span class="n">backup</span> <span class="o">=</span> <span class="n">itertools</span><span class="o">.</span><span class="n">tee</span><span class="p">(</span><span class="n">polygons</span><span class="p">)</span>
+        <span class="n">first_shape</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">backup</span><span class="p">))</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">first_shape</span><span class="p">,</span> <span class="n">point_type</span><span class="p">):</span>
+            <span class="n">polygons</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">get_points_array</span><span class="p">(</span><span class="n">polygons</span><span class="p">))</span>
+            <span class="n">polygons</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">polygons</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span>
+        <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">_build</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="n">criterion</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Queen.from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_shapefile">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Queen contiguity weights from a polygon shapefile.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        shapefile   : string</span>
+<span class="sd">                      name of polygon shapefile including suffix.</span>
+<span class="sd">        idVariable  : string</span>
+<span class="sd">                      name of a column in the shapefile&#39;s DBF to use for ids.</span>
+<span class="sd">        sparse      : boolean</span>
+<span class="sd">                      If True return WSP instance</span>
+<span class="sd">                      If False return W instance</span>
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        w            : W</span>
+<span class="sd">                       instance of spatial weights</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import Queen</span>
+<span class="sd">        &gt;&gt;&gt; import libpysal</span>
+<span class="sd">        &gt;&gt;&gt; wq=Queen.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%wq.pct_nonzero</span>
+<span class="sd">        &#39;9.829&#39;</span>
+<span class="sd">        &gt;&gt;&gt; wq=Queen.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;),&quot;POLYID&quot;)</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%wq.pct_nonzero</span>
+<span class="sd">        &#39;9.829&#39;</span>
+<span class="sd">        &gt;&gt;&gt; wq=Queen.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;), sparse=True)</span>
+<span class="sd">        &gt;&gt;&gt; pct_sp = wq.sparse.nnz *1. / wq.n**2</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%pct_sp</span>
+<span class="sd">        &#39;0.098&#39;</span>
+
+<span class="sd">        Notes</span>
+
+<span class="sd">        Queen contiguity defines as neighbors any pair of polygons that share at</span>
+<span class="sd">        least one vertex in their polygon definitions.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.contiguity.Queen`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">sparse</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;sparse&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">FileIO</span><span class="p">(</span><span class="n">filepath</span><span class="p">),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">set_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="n">full</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">to_WSP</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="Queen.from_iterable">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_iterable">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_iterable</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">iterable</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a weights object from a collection of arbitrary polygons. This</span>
+<span class="sd">        will cast the polygons to PySAL polygons, then build the W.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        iterable    : iterable</span>
+<span class="sd">                      a collection of of shapes to be cast to PySAL shapes. Must</span>
+<span class="sd">                      support iteration. Contents may either be a shapely or PySAL shape.</span>
+<span class="sd">        **kw        : keyword arguments</span>
+<span class="sd">                      optional arguments for  :class:`pysal.weights.W`</span>
+<span class="sd">        See Also</span>
+<span class="sd">        ---------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.contiguiyt.Queen`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">new_iterable</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">new_iterable</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">from_W</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="Queen.from_dataframe">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_dataframe">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">df</span><span class="p">,</span>
+        <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">use_index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a weights object from a (geo)pandas dataframe with a geometry</span>
+<span class="sd">        column. This will cast the polygons to PySAL polygons, then build the W</span>
+<span class="sd">        using ids from the dataframe.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df          : DataFrame</span>
+<span class="sd">                      a :class: `pandas.DataFrame` containing geometries to use</span>
+<span class="sd">                      for spatial weights</span>
+<span class="sd">        geom_col    : string</span>
+<span class="sd">                      the name of the column in `df` that contains the</span>
+<span class="sd">                      geometries. Defaults to active geometry column.</span>
+<span class="sd">        idVariable  : string</span>
+<span class="sd">                      DEPRECATED - use `ids` instead.</span>
+<span class="sd">                      the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                      provided, the dataframe index is used</span>
+<span class="sd">        ids         : list-like, string</span>
+<span class="sd">                      a list-like of ids to use to index the spatial weights object or</span>
+<span class="sd">                      the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                      provided, the dataframe index is used if `use_index=True` or</span>
+<span class="sd">                      a positional index is used if `use_index=False`.</span>
+<span class="sd">                      Order of the resulting W is not respected from this list.</span>
+<span class="sd">        id_order    : list</span>
+<span class="sd">                      DEPRECATED - argument is deprecated and will be removed.</span>
+<span class="sd">                      An ordered list of ids to use to index the spatial weights</span>
+<span class="sd">                      object. If used, the resulting weights object will iterate</span>
+<span class="sd">                      over results in the order of the names provided in this</span>
+<span class="sd">                      argument.</span>
+<span class="sd">        use_index   : bool</span>
+<span class="sd">                      use index of `df` as `ids` to index the spatial weights object.</span>
+<span class="sd">                      Defaults to False but in future will default to True.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.contiguity.Queen`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+
+        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;`id_order` is deprecated and will be removed in future.&quot;</span><span class="p">,</span>
+                <span class="ne">FutureWarning</span><span class="p">,</span>
+                <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">True</span> <span class="ow">and</span> <span class="p">((</span><span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
+                <span class="c1"># if idVariable is None, we want ids. Otherwise, we want the</span>
+                <span class="c1"># idVariable column</span>
+                <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">idVariable</span><span class="p">,</span> <span class="n">ids</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">id_order</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">id_order</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;`idVariable` is deprecated and will be removed in future. &quot;</span>
+                    <span class="s2">&quot;Use `ids` instead.&quot;</span><span class="p">,</span>
+                    <span class="ne">FutureWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">idVariable</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;Both `idVariable` and `ids` passed, using `ids`.&quot;</span><span class="p">,</span>
+                    <span class="ne">UserWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;`use_index` defaults to False but will default to True in future. &quot;</span>
+                    <span class="s2">&quot;Set True/False directly to control this behavior and silence this &quot;</span>
+                    <span class="s2">&quot;warning&quot;</span><span class="p">,</span>
+                    <span class="ne">FutureWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">use_index</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">if</span> <span class="n">use_index</span><span class="p">:</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The length of `ids` does not match the length of df.&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">id_order</span> <span class="o">=</span> <span class="n">ids</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span>
+            <span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Queen.from_xarray">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_xarray">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_xarray</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">da</span><span class="p">,</span>
+        <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
+        <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a weights object from a xarray.DataArray with an additional</span>
+<span class="sd">        attribute index containing coordinate values of the raster</span>
+<span class="sd">        in the form of Pandas.Index/MultiIndex.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        da : xarray.DataArray</span>
+<span class="sd">            Input 2D or 3D DataArray with shape=(z, y, x)</span>
+<span class="sd">        z_value : int/string/float</span>
+<span class="sd">            Select the z_value of 3D DataArray with multiple layers.</span>
+<span class="sd">        coords_labels : dictionary</span>
+<span class="sd">            Pass dimension labels for coordinates and layers if they do not</span>
+<span class="sd">            belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
+<span class="sd">            e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
+<span class="sd">            Default is {} empty dictionary.</span>
+<span class="sd">        sparse : boolean</span>
+<span class="sd">            type of weight object. Default is True. For libpysal.weights.W, sparse = False</span>
+<span class="sd">        k : int</span>
+<span class="sd">            Order of contiguity, this will select all neighbors upto kth order.</span>
+<span class="sd">            Default is 1.</span>
+<span class="sd">        include_nodata : boolean</span>
+<span class="sd">            If True, missing values will be assumed as non-missing when</span>
+<span class="sd">            selecting higher_order neighbors, Default is False</span>
+<span class="sd">        n_jobs : int</span>
+<span class="sd">            Number of cores to be used in the sparse weight construction. If -1,</span>
+<span class="sd">            all available cores are used. Default is 1.</span>
+<span class="sd">        **kwargs : keyword arguments</span>
+<span class="sd">            optional arguments passed when sparse = False</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w : libpysal.weights.W/libpysal.weights.WSP</span>
+<span class="sd">            instance of spatial weights class W or WSP with an index attribute</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        1. Lower order contiguities are also selected.</span>
+<span class="sd">        2. Returned object contains `index` attribute that includes a</span>
+<span class="sd">           `Pandas.MultiIndex` object from the DataArray.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        :class:`libpysal.weights.weights.WSP`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Voronoi">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Voronoi.html#libpysal.weights.Voronoi">[docs]</a>
+<span class="k">def</span> <span class="nf">Voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="s2">&quot;ahull&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Voronoi weights for a 2-d point set</span>
+
+
+<span class="sd">    Points are Voronoi neighbors if their polygons share an edge or vertex.</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    points      : array</span>
+<span class="sd">                  (n,2)</span>
+<span class="sd">                  coordinates for point locations</span>
+<span class="sd">    kwargs      : arguments to pass to Rook, the underlying contiguity class.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w           : W</span>
+<span class="sd">                  instance of spatial weights</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import Voronoi</span>
+<span class="sd">    &gt;&gt;&gt; np.random.seed(12345)</span>
+<span class="sd">    &gt;&gt;&gt; points= np.random.random((5,2))*10 + 10</span>
+<span class="sd">    &gt;&gt;&gt; w = Voronoi(points)</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors</span>
+<span class="sd">    {0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">..cg.voronoi</span> <span class="kn">import</span> <span class="n">voronoi_frames</span>
+
+    <span class="n">region_df</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">clip</span><span class="o">=</span><span class="n">clip</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span>
+        <span class="bp">cls</span> <span class="o">=</span> <span class="n">Queen</span>
+    <span class="k">elif</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span><span class="p">:</span>
+        <span class="bp">cls</span> <span class="o">=</span> <span class="n">Rook</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;Contiguity criterion </span><span class="si">{}</span><span class="s2"> not supported. &quot;</span>
+            <span class="s1">&#39;Only &quot;rook&quot; and &quot;queen&quot; are supported.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">criterion</span><span class="p">)</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">region_df</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_from_dataframe</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Construct a voronoi contiguity weight directly from a dataframe.</span>
+<span class="sd">    Note that if criterion=&#39;rook&#39;, this is identical to the delaunay</span>
+<span class="sd">    graph for the points if no clipping of the voronoi cells is applied.</span>
+
+<span class="sd">    If the input dataframe is of any other geometry type than &quot;Point&quot;,</span>
+<span class="sd">    a value error is raised.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    df          :   pandas.DataFrame</span>
+<span class="sd">                    dataframe containing point geometries for a</span>
+<span class="sd">                    voronoi diagram.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w           :   W</span>
+<span class="sd">                    instance of spatial weights.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span>
+    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
+            <span class="s2">&quot;Voronoi weights are only&quot;</span>
+            <span class="s2">&quot; implemented for point geometries. &quot;</span>
+            <span class="s2">&quot;You may consider using df.centroid.&quot;</span>
+        <span class="p">)</span>
+    <span class="n">coords</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">Voronoi</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+
+<span class="n">Voronoi</span><span class="o">.</span><span class="n">from_dataframe</span> <span class="o">=</span> <span class="n">_from_dataframe</span>
+
+
+<span class="k">def</span> <span class="nf">_build</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This is a developer-facing function to construct a spatial weights object.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    polygons    : list</span>
+<span class="sd">                  list of pysal polygons to use to build contiguity</span>
+<span class="sd">    criterion   : string</span>
+<span class="sd">                  option of which kind of contiguity to build. Is either &quot;rook&quot; or &quot;queen&quot;</span>
+<span class="sd">    ids         : list</span>
+<span class="sd">                  list of ids to use to index the neighbor dictionary</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    tuple containing (neighbors, ids), where neighbors is a dictionary</span>
+<span class="sd">    describing contiguity relations and ids is the list of ids used to index</span>
+<span class="sd">    that dictionary.</span>
+
+<span class="sd">    NOTE: this is different from the prior behavior of buildContiguity, which</span>
+<span class="sd">          returned an actual weights object. Since this just dispatches for the</span>
+<span class="sd">          classes above, this returns the raw ingredients for a spatial weights</span>
+<span class="sd">          object, not the object itself.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">ids</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">ids</span><span class="p">)):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;The argument to the ids parameter contains duplicate entries.&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">wttype</span> <span class="o">=</span> <span class="n">WT_TYPE</span><span class="p">[</span><span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+    <span class="n">geo</span> <span class="o">=</span> <span class="n">polygons</span>
+    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">geo</span><span class="p">),</span> <span class="n">FileIO</span><span class="p">):</span>
+        <span class="n">geo</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># Make sure we read from the beginning of the file.</span>
+
+    <span class="n">neighbor_data</span> <span class="o">=</span> <span class="n">ContiguityWeightsLists</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">wttype</span><span class="o">=</span><span class="n">wttype</span><span class="p">)</span><span class="o">.</span><span class="n">w</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1"># weights={}</span>
+    <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbor_data</span><span class="p">:</span>
+            <span class="n">ida</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">ida</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+                <span class="n">neighbors</span><span class="p">[</span><span class="n">ida</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">ida</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">([</span><span class="n">ids</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">neighbor_data</span><span class="p">[</span><span class="n">key</span><span class="p">]])</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbor_data</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbor_data</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="nb">dict</span><span class="p">(</span>
+            <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()),</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">values</span><span class="p">())))))</span>
+        <span class="p">),</span>
+        <span class="n">ids</span><span class="p">,</span>
+    <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">buildContiguity</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This is a deprecated function.</span>
+
+<span class="sd">    It builds a contiguity W from the polygons provided. As such, it is now</span>
+<span class="sd">    identical to calling the class constructors for Rook or Queen.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Warn(&#39;This function is deprecated. Please use the Rook or Queen classes&#39;,</span>
+    <span class="c1">#        UserWarning)</span>
+    <span class="k">if</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Rook</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">criterion</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">Queen</span><span class="p">(</span><span class="n">polygons</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Weights criterion &quot;</span><span class="si">{}</span><span class="s1">&quot; was not found.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">criterion</span><span class="p">))</span>
+</pre></div>
+
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+      
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diff --git a/_modules/libpysal/weights/distance.html b/_modules/libpysal/weights/distance.html
new file mode 100644
index 000000000..ecb556ed2
--- /dev/null
+++ b/_modules/libpysal/weights/distance.html
@@ -0,0 +1,1182 @@
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+      
+  <h1>Source code for libpysal.weights.distance</h1><div class="highlight"><pre>
+<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;KNN&quot;</span><span class="p">,</span> <span class="s2">&quot;Kernel&quot;</span><span class="p">,</span> <span class="s2">&quot;DistanceBand&quot;</span><span class="p">]</span>
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;, Levi John Wolf &lt;levi.john.wolf@gmail.com&gt;&quot;</span>
+
+
+<span class="kn">from</span> <span class="nn">..cg.kdtree</span> <span class="kn">import</span> <span class="n">KDTree</span>
+<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
+<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">isKDTree</span><span class="p">,</span>
+    <span class="n">get_ids</span><span class="p">,</span>
+    <span class="n">get_points_array_from_shapefile</span><span class="p">,</span>
+    <span class="n">get_points_array</span><span class="p">,</span>
+    <span class="n">WSP2W</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span> <span class="k">as</span> <span class="n">Warn</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">distance_matrix</span>
+<span class="kn">import</span> <span class="nn">scipy.sparse</span> <span class="k">as</span> <span class="nn">sp</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<span class="k">def</span> <span class="nf">knnW</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This is deprecated. Use the pysal.weights.KNN class instead.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Warn(&#39;This function is deprecated. Please use pysal.weights.KNN&#39;, UserWarning)</span>
+    <span class="k">return</span> <span class="n">KNN</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="KNN">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN">[docs]</a>
+<span class="k">class</span> <span class="nc">KNN</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates nearest neighbor weights matrix based on k nearest</span>
+<span class="sd">    neighbors.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    kdtree      : object</span>
+<span class="sd">                  PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)</span>
+<span class="sd">                  n observations on k characteristics used to measure</span>
+<span class="sd">                  distances between the n objects</span>
+<span class="sd">    k           : int</span>
+<span class="sd">                  number of nearest neighbors</span>
+<span class="sd">    p           : float</span>
+<span class="sd">                  Minkowski p-norm distance metric parameter:</span>
+<span class="sd">                  1&lt;=p&lt;=infinity</span>
+<span class="sd">                  2: Euclidean distance</span>
+<span class="sd">                  1: Manhattan distance</span>
+<span class="sd">                  Ignored if the KDTree is an ArcKDTree</span>
+<span class="sd">    ids         : list</span>
+<span class="sd">                  identifiers to attach to each observation</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w         : W</span>
+<span class="sd">                instance</span>
+<span class="sd">                Weights object with binary weights</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
+<span class="sd">    &gt;&gt;&gt; kd = libpysal.cg.KDTree(np.array(points))</span>
+<span class="sd">    &gt;&gt;&gt; wnn2 = libpysal.weights.KNN(kd, 2)</span>
+<span class="sd">    &gt;&gt;&gt; [1,3] == wnn2.neighbors[0]</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; wnn2 = KNN(kd,2)</span>
+<span class="sd">    &gt;&gt;&gt; wnn2[0]</span>
+<span class="sd">    {1: 1.0, 3: 1.0}</span>
+<span class="sd">    &gt;&gt;&gt; wnn2[1]</span>
+<span class="sd">    {0: 1.0, 3: 1.0}</span>
+
+<span class="sd">    now with 1 rather than 0 offset</span>
+
+<span class="sd">    &gt;&gt;&gt; wnn2 = libpysal.weights.KNN(kd, 2, ids=range(1,7))</span>
+<span class="sd">    &gt;&gt;&gt; wnn2[1]</span>
+<span class="sd">    {2: 1.0, 4: 1.0}</span>
+<span class="sd">    &gt;&gt;&gt; wnn2[2]</span>
+<span class="sd">    {1: 1.0, 4: 1.0}</span>
+<span class="sd">    &gt;&gt;&gt; 0 in wnn2.neighbors</span>
+<span class="sd">    False</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    Ties between neighbors of equal distance are arbitrarily broken.</span>
+
+<span class="sd">    Further, if many points occupy the same spatial location (i.e. observations are</span>
+<span class="sd">    coincident), then you may need to increase k for those observations to</span>
+<span class="sd">    acquire neighbors at different spatial locations. For example, if five</span>
+<span class="sd">    points are coincident, then their four nearest neighbors will all</span>
+<span class="sd">    occupy the same spatial location; only the fifth nearest neighbor will</span>
+<span class="sd">    result in those coincident points becoming connected to the graph as a</span>
+<span class="sd">    whole.</span>
+
+<span class="sd">    Solutions to this problem include jittering the points (by adding</span>
+<span class="sd">    a small random value to each observation&#39;s location) or by adding</span>
+<span class="sd">    higher-k neighbors only to the coincident points, using the</span>
+<span class="sd">    weights.w_sets.w_union() function.</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :class:`libpysal.weights.weights.W`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="KNN.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">distance_metric</span> <span class="o">=</span> <span class="s2">&quot;arc&quot;</span>
+        <span class="k">if</span> <span class="n">isKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">data</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">k</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span>
+
+        <span class="c1"># these are both n x k+1</span>
+        <span class="n">distances</span><span class="p">,</span> <span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
+        <span class="n">full_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
+
+        <span class="c1"># if an element in the indices matrix is equal to the corresponding</span>
+        <span class="c1"># index for that row, we want to mask that site from its neighbors</span>
+        <span class="n">not_self_mask</span> <span class="o">=</span> <span class="n">indices</span> <span class="o">!=</span> <span class="n">full_indices</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="c1"># if there are *too many duplicates per site*, then we may get some</span>
+        <span class="c1"># rows where the site index is not in the set of k+1 neighbors</span>
+        <span class="c1"># So, we need to know where these sites are</span>
+        <span class="n">has_one_too_many</span> <span class="o">=</span> <span class="n">not_self_mask</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="c1"># if a site has k+1 neighbors, drop its k+1th neighbor</span>
+        <span class="n">not_self_mask</span><span class="p">[</span><span class="n">has_one_too_many</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&amp;=</span> <span class="kc">False</span>
+        <span class="n">not_self_indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">not_self_mask</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">n</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">to_weight</span> <span class="o">=</span> <span class="n">not_self_indices</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">full_indices</span><span class="p">)</span>
+            <span class="n">named_indices</span> <span class="o">=</span> <span class="n">not_self_indices</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">named_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ids</span><span class="p">)[</span><span class="n">not_self_indices</span><span class="p">]</span>
+        <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="n">idx</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">indices</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">named_indices</span><span class="p">)}</span>
+
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="KNN.from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_shapefile">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Nearest neighbor weights from a shapefile.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        data       : string</span>
+<span class="sd">                     shapefile containing attribute data.</span>
+<span class="sd">        k          : int</span>
+<span class="sd">                     number of nearest neighbors</span>
+<span class="sd">        p          : float</span>
+<span class="sd">                     Minkowski p-norm distance metric parameter:</span>
+<span class="sd">                     1&lt;=p&lt;=infinity</span>
+<span class="sd">                     2: Euclidean distance</span>
+<span class="sd">                     1: Manhattan distance</span>
+<span class="sd">        ids        : list</span>
+<span class="sd">                     identifiers to attach to each observation</span>
+<span class="sd">        radius     : float</span>
+<span class="sd">                     If supplied arc_distances will be calculated</span>
+<span class="sd">                     based on the given radius. p will be ignored.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        w         : KNN</span>
+<span class="sd">                    instance; Weights object with binary weights.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        Polygon shapefile</span>
+<span class="sd">        &gt;&gt;&gt; import libpysal</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import KNN</span>
+<span class="sd">        &gt;&gt;&gt; wc=KNN.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.4f&quot;%wc.pct_nonzero</span>
+<span class="sd">        &#39;4.0816&#39;</span>
+<span class="sd">        &gt;&gt;&gt; set([2,1]) == set(wc.neighbors[0])</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; wc3=KNN.from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;),k=3)</span>
+<span class="sd">        &gt;&gt;&gt; set(wc3.neighbors[0]) == set([2,1,3])</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; set(wc3.neighbors[2]) == set([4,3,0])</span>
+<span class="sd">        True</span>
+
+
+<span class="sd">        Point shapefile</span>
+
+<span class="sd">        &gt;&gt;&gt; w=KNN.from_shapefile(libpysal.examples.get_path(&quot;juvenile.shp&quot;))</span>
+<span class="sd">        &gt;&gt;&gt; w.pct_nonzero</span>
+<span class="sd">        1.1904761904761905</span>
+<span class="sd">        &gt;&gt;&gt; w1=KNN.from_shapefile(libpysal.examples.get_path(&quot;juvenile.shp&quot;),k=1)</span>
+<span class="sd">        &gt;&gt;&gt; &quot;%.3f&quot;%w1.pct_nonzero</span>
+<span class="sd">        &#39;0.595&#39;</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        Ties between neighbors of equal distance are arbitrarily broken.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="KNN.from_array">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_array">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates nearest neighbor weights matrix based on k nearest</span>
+<span class="sd">        neighbors.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        array       : np.ndarray</span>
+<span class="sd">                      (n, k) array representing n observations on</span>
+<span class="sd">                      k characteristics used to measure distances</span>
+<span class="sd">                      between the n objects</span>
+<span class="sd">        **kwargs    : keyword arguments, see Rook</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w         : W</span>
+<span class="sd">                    instance</span>
+<span class="sd">                    Weights object with binary weights</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import KNN</span>
+<span class="sd">        &gt;&gt;&gt; points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
+<span class="sd">        &gt;&gt;&gt; wnn2 = KNN.from_array(points, 2)</span>
+<span class="sd">        &gt;&gt;&gt; [1,3] == wnn2.neighbors[0]</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; wnn2 = KNN.from_array(points,2)</span>
+<span class="sd">        &gt;&gt;&gt; wnn2[0]</span>
+<span class="sd">        {1: 1.0, 3: 1.0}</span>
+<span class="sd">        &gt;&gt;&gt; wnn2[1]</span>
+<span class="sd">        {0: 1.0, 3: 1.0}</span>
+
+<span class="sd">        now with 1 rather than 0 offset</span>
+
+<span class="sd">        &gt;&gt;&gt; wnn2 = KNN.from_array(points, 2, ids=range(1,7))</span>
+<span class="sd">        &gt;&gt;&gt; wnn2[1]</span>
+<span class="sd">        {2: 1.0, 4: 1.0}</span>
+<span class="sd">        &gt;&gt;&gt; wnn2[2]</span>
+<span class="sd">        {1: 1.0, 4: 1.0}</span>
+<span class="sd">        &gt;&gt;&gt; 0 in wnn2.neighbors</span>
+<span class="sd">        False</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        Ties between neighbors of equal distance are arbitrarily broken.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="KNN.from_dataframe">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_dataframe">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Make KNN weights from a dataframe.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df      :   pandas.dataframe</span>
+<span class="sd">                    a dataframe with a geometry column that can be used to</span>
+<span class="sd">                    construct a W object</span>
+<span class="sd">        geom_col :  string</span>
+<span class="sd">                    the name of the column in `df` that contains the</span>
+<span class="sd">                    geometries. Defaults to active geometry column.</span>
+<span class="sd">        ids     :   list-like, string</span>
+<span class="sd">                    a list-like of ids to use to index the spatial weights object or</span>
+<span class="sd">                    the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                    provided, the dataframe index is used if `use_index=True` or</span>
+<span class="sd">                    a positional index is used if `use_index=False`.</span>
+<span class="sd">                    Order of the resulting W is not respected from this list.</span>
+<span class="sd">        use_index   : bool</span>
+<span class="sd">                    use index of `df` as `ids` to index the spatial weights object.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">pts</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">use_index</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="KNN.reweight">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.KNN.html#libpysal.weights.KNN.reweight">[docs]</a>
+    <span class="k">def</span> <span class="nf">reweight</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">new_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">new_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Redo K-Nearest Neighbor weights construction using given parameters</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        new_data    : np.ndarray</span>
+<span class="sd">                      an array containing additional data to use in the KNN</span>
+<span class="sd">                      weight</span>
+<span class="sd">        new_ids     : list</span>
+<span class="sd">                      a list aligned with new_data that provides the ids for</span>
+<span class="sd">                      each new observation</span>
+<span class="sd">        inplace     : bool</span>
+<span class="sd">                      a flag denoting whether to modify the KNN object</span>
+<span class="sd">                      in place or to return a new KNN object</span>
+<span class="sd">        k           : int</span>
+<span class="sd">                      number of nearest neighbors</span>
+<span class="sd">        p           : float</span>
+<span class="sd">                      Minkowski p-norm distance metric parameter:</span>
+<span class="sd">                      1&lt;=p&lt;=infinity</span>
+<span class="sd">                      2: Euclidean distance</span>
+<span class="sd">                      1: Manhattan distance</span>
+<span class="sd">                      Ignored if the KDTree is an ArcKDTree</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        A copy of the object using the new parameterization, or None if the</span>
+<span class="sd">        object is reweighted in place.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">new_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">new_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">new_data</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">new_data</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">new_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
+                <span class="n">ids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">new_ids</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="k">elif</span> <span class="p">(</span><span class="n">new_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">new_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="c1"># If not, we can use the same kdtree we have</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span>
+        <span class="k">elif</span> <span class="p">(</span><span class="n">new_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">new_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
+            <span class="n">Warn</span><span class="p">(</span><span class="s2">&quot;Remapping ids must be done using w.remap_ids&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">k</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span>
+        <span class="k">if</span> <span class="n">p</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span>
+        <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">KNN</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Kernel">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel">[docs]</a>
+<span class="k">class</span> <span class="nc">Kernel</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Spatial weights based on kernel functions.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    data        : array</span>
+<span class="sd">                  (n,k) or KDTree where KDtree.data is array (n,k)</span>
+<span class="sd">                  n observations on k characteristics used to measure</span>
+<span class="sd">                  distances between the n objects</span>
+<span class="sd">    bandwidth   : float</span>
+<span class="sd">                  or array-like (optional)</span>
+<span class="sd">                  the bandwidth :math:`h_i` for the kernel.</span>
+<span class="sd">    fixed       : binary</span>
+<span class="sd">                  If true then :math:`h_i=h \\forall i`. If false then</span>
+<span class="sd">                  bandwidth is adaptive across observations.</span>
+<span class="sd">    k           : int</span>
+<span class="sd">                  the number of nearest neighbors to use for determining</span>
+<span class="sd">                  bandwidth. For fixed bandwidth, :math:`h_i=max(dknn) \\forall i`</span>
+<span class="sd">                  where :math:`dknn` is a vector of k-nearest neighbor</span>
+<span class="sd">                  distances (the distance to the kth nearest neighbor for each</span>
+<span class="sd">                  observation).  For adaptive bandwidths, :math:`h_i=dknn_i`</span>
+<span class="sd">    diagonal    : boolean</span>
+<span class="sd">                  If true, set diagonal weights = 1.0, if false (default),</span>
+<span class="sd">                  diagonals weights are set to value according to kernel</span>
+<span class="sd">                  function.</span>
+<span class="sd">    function    : {&#39;triangular&#39;,&#39;uniform&#39;,&#39;quadratic&#39;,&#39;quartic&#39;,&#39;gaussian&#39;}</span>
+<span class="sd">                  kernel function defined as follows with</span>
+
+<span class="sd">                  .. math::</span>
+
+<span class="sd">                      z_{i,j} = d_{i,j}/h_i</span>
+
+<span class="sd">                  triangular</span>
+
+<span class="sd">                  .. math::</span>
+
+<span class="sd">                      K(z) = (1 - |z|) \\ if |z| \\le 1</span>
+
+<span class="sd">                  uniform</span>
+
+<span class="sd">                  .. math::</span>
+
+<span class="sd">                      K(z) = 1/2 \\ if |z| \\le 1</span>
+
+<span class="sd">                  quadratic</span>
+
+<span class="sd">                  .. math::</span>
+
+<span class="sd">                      K(z) = (3/4)(1-z^2) \\ if |z| \\le 1</span>
+
+<span class="sd">                  quartic</span>
+
+<span class="sd">                  .. math::</span>
+
+<span class="sd">                      K(z) = (15/16)(1-z^2)^2 \\ if |z| \\le 1</span>
+
+<span class="sd">                  gaussian</span>
+
+<span class="sd">                  .. math::</span>
+
+<span class="sd">                      K(z) = (2\\pi)^{(-1/2)} exp(-z^2 / 2)</span>
+
+<span class="sd">    eps         : float</span>
+<span class="sd">                  adjustment to ensure knn distance range is closed on the</span>
+<span class="sd">                  knnth observations</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    weights : dict</span>
+<span class="sd">              Dictionary keyed by id with a list of weights for each neighbor</span>
+
+<span class="sd">    neighbors : dict</span>
+<span class="sd">                of lists of neighbors keyed by observation id</span>
+
+<span class="sd">    bandwidth : array</span>
+<span class="sd">                array of bandwidths</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import Kernel</span>
+<span class="sd">    &gt;&gt;&gt; points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
+<span class="sd">    &gt;&gt;&gt; kw=Kernel(points)</span>
+<span class="sd">    &gt;&gt;&gt; kw.weights[0]</span>
+<span class="sd">    [1.0, 0.500000049999995, 0.4409830615267465]</span>
+<span class="sd">    &gt;&gt;&gt; kw.neighbors[0]</span>
+<span class="sd">    [0, 1, 3]</span>
+<span class="sd">    &gt;&gt;&gt; kw.bandwidth</span>
+<span class="sd">    array([[20.000002],</span>
+<span class="sd">           [20.000002],</span>
+<span class="sd">           [20.000002],</span>
+<span class="sd">           [20.000002],</span>
+<span class="sd">           [20.000002],</span>
+<span class="sd">           [20.000002]])</span>
+<span class="sd">    &gt;&gt;&gt; kw15=Kernel(points,bandwidth=15.0)</span>
+<span class="sd">    &gt;&gt;&gt; kw15[0]</span>
+<span class="sd">    {0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}</span>
+<span class="sd">    &gt;&gt;&gt; kw15.neighbors[0]</span>
+<span class="sd">    [0, 1, 3]</span>
+<span class="sd">    &gt;&gt;&gt; kw15.bandwidth</span>
+<span class="sd">    array([[15.],</span>
+<span class="sd">           [15.],</span>
+<span class="sd">           [15.],</span>
+<span class="sd">           [15.],</span>
+<span class="sd">           [15.],</span>
+<span class="sd">           [15.]])</span>
+
+<span class="sd">    Adaptive bandwidths user specified</span>
+
+<span class="sd">    &gt;&gt;&gt; bw=[25.0,15.0,25.0,16.0,14.5,25.0]</span>
+<span class="sd">    &gt;&gt;&gt; kwa=Kernel(points,bandwidth=bw)</span>
+<span class="sd">    &gt;&gt;&gt; kwa.weights[0]</span>
+<span class="sd">    [1.0, 0.6, 0.552786404500042, 0.10557280900008403]</span>
+<span class="sd">    &gt;&gt;&gt; kwa.neighbors[0]</span>
+<span class="sd">    [0, 1, 3, 4]</span>
+<span class="sd">    &gt;&gt;&gt; kwa.bandwidth</span>
+<span class="sd">    array([[25. ],</span>
+<span class="sd">           [15. ],</span>
+<span class="sd">           [25. ],</span>
+<span class="sd">           [16. ],</span>
+<span class="sd">           [14.5],</span>
+<span class="sd">           [25. ]])</span>
+
+<span class="sd">    Endogenous adaptive bandwidths</span>
+
+<span class="sd">    &gt;&gt;&gt; kwea=Kernel(points,fixed=False)</span>
+<span class="sd">    &gt;&gt;&gt; kwea.weights[0]</span>
+<span class="sd">    [1.0, 0.10557289844279438, 9.99999900663795e-08]</span>
+<span class="sd">    &gt;&gt;&gt; kwea.neighbors[0]</span>
+<span class="sd">    [0, 1, 3]</span>
+<span class="sd">    &gt;&gt;&gt; kwea.bandwidth</span>
+<span class="sd">    array([[11.18034101],</span>
+<span class="sd">           [11.18034101],</span>
+<span class="sd">           [20.000002  ],</span>
+<span class="sd">           [11.18034101],</span>
+<span class="sd">           [14.14213704],</span>
+<span class="sd">           [18.02775818]])</span>
+
+<span class="sd">    Endogenous adaptive bandwidths with Gaussian kernel</span>
+
+<span class="sd">    &gt;&gt;&gt; kweag=Kernel(points,fixed=False,function=&#39;gaussian&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; kweag.weights[0]</span>
+<span class="sd">    [0.3989422804014327, 0.2674190291577696, 0.2419707487162134]</span>
+<span class="sd">    &gt;&gt;&gt; kweag.bandwidth</span>
+<span class="sd">    array([[11.18034101],</span>
+<span class="sd">           [11.18034101],</span>
+<span class="sd">           [20.000002  ],</span>
+<span class="sd">           [11.18034101],</span>
+<span class="sd">           [14.14213704],</span>
+<span class="sd">           [18.02775818]])</span>
+
+<span class="sd">    Diagonals to 1.0</span>
+
+<span class="sd">    &gt;&gt;&gt; kq = Kernel(points,function=&#39;gaussian&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; kq.weights</span>
+<span class="sd">    {0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}</span>
+<span class="sd">    &gt;&gt;&gt; kqd = Kernel(points, function=&#39;gaussian&#39;, diagonal=True)</span>
+<span class="sd">    &gt;&gt;&gt; kqd.weights</span>
+<span class="sd">    {0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Kernel.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">bandwidth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">fixed</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">function</span><span class="o">=</span><span class="s2">&quot;triangular&quot;</span><span class="p">,</span>
+        <span class="n">eps</span><span class="o">=</span><span class="mf">1.0000001</span><span class="p">,</span>
+        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">diagonal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
+        <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span>
+    <span class="p">):</span>
+        <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">distance_metric</span> <span class="o">=</span> <span class="s2">&quot;arc&quot;</span>
+        <span class="k">if</span> <span class="n">isKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">data</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fixed</span> <span class="o">=</span> <span class="n">fixed</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
+        <span class="k">if</span> <span class="n">bandwidth</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
+                <span class="n">bandwidth</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="s2">&quot;float&quot;</span><span class="p">)</span> <span class="o">*</span> <span class="n">bandwidth</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_set_bw</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_eval_kernel</span><span class="p">()</span>
+        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_k_to_W</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">diagonal</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+                <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="mf">1.0</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Kernel.from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_shapefile">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Kernel based weights from shapefile</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        shapefile   : string</span>
+<span class="sd">                      shapefile name with shp suffix</span>
+<span class="sd">        idVariable  : string</span>
+<span class="sd">                      name of column in shapefile&#39;s DBF to use for ids</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Kernel Weights Object</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">points</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Kernel.from_array">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_array">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a Kernel weights from an array. Supports all the same options</span>
+<span class="sd">        as :class:`libpysal.weights.Kernel`</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Kernel.from_dataframe">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_dataframe">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Make Kernel weights from a dataframe.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df      :   pandas.dataframe</span>
+<span class="sd">                    a dataframe with a geometry column that can be used to</span>
+<span class="sd">                    construct a W object</span>
+<span class="sd">        geom_col :  string</span>
+<span class="sd">                    the name of the column in `df` that contains the</span>
+<span class="sd">                    geometries. Defaults to active geometry column.</span>
+<span class="sd">        ids     :   list-like, string</span>
+<span class="sd">                    a list-like of ids to use to index the spatial weights object or</span>
+<span class="sd">                    the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                    provided, the dataframe index is used if `use_index=True` or</span>
+<span class="sd">                    a positional index is used if `use_index=False`.</span>
+<span class="sd">                    Order of the resulting W is not respected from this list.</span>
+<span class="sd">        use_index   : bool</span>
+<span class="sd">                    use index of `df` as `ids` to index the spatial weights object.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        :class:`libpysal.weights.weights.W`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">pts</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">use_index</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_k_to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">allneighbors</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neigh</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">allneighbors</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="n">weights</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">allneighbors</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">neigh</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
+                <span class="n">weights</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">allneighbors</span><span class="p">,</span> <span class="n">weights</span>
+
+    <span class="k">def</span> <span class="nf">_set_bw</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">dmat</span><span class="p">,</span> <span class="n">neigh</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fixed</span><span class="p">:</span>
+            <span class="c1"># use max knn distance as bandwidth</span>
+            <span class="n">bandwidth</span> <span class="o">=</span> <span class="n">dmat</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span>
+            <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dmat</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="s2">&quot;float&quot;</span><span class="p">)</span> <span class="o">*</span> <span class="n">bandwidth</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># use local max knn distance</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">dmat</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="c1"># identify knn neighbors for each point</span>
+            <span class="n">nnq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">neigh</span> <span class="o">=</span> <span class="n">nnq</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_eval_kernel</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="c1"># get points within bandwidth distance of each point</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;neigh&quot;</span><span class="p">):</span>
+            <span class="n">kdtq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query_ball_point</span>
+            <span class="n">neighbors</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">kdtq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">r</span><span class="o">=</span><span class="n">bwi</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">bwi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
+            <span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">neigh</span> <span class="o">=</span> <span class="n">neighbors</span>
+        <span class="c1"># get distances for neighbors</span>
+        <span class="n">bw</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span>
+
+        <span class="n">kdtq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span>
+        <span class="n">z</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">nids</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neigh</span><span class="p">):</span>
+            <span class="n">di</span><span class="p">,</span> <span class="n">ni</span> <span class="o">=</span> <span class="n">kdtq</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">k</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">nids</span><span class="p">))</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">di</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="n">di</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">di</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">nids</span><span class="p">))</span>
+                <span class="n">ni</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">ni</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">nids</span><span class="p">))</span>
+            <span class="n">zi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ni</span><span class="p">,</span> <span class="n">di</span><span class="p">)))[</span><span class="n">nid</span><span class="p">]</span> <span class="k">for</span> <span class="n">nid</span> <span class="ow">in</span> <span class="n">nids</span><span class="p">])</span> <span class="o">/</span> <span class="n">bw</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">z</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">zi</span><span class="p">)</span>
+        <span class="n">zs</span> <span class="o">=</span> <span class="n">z</span>
+        <span class="c1"># functions follow Anselin and Rey (2010) table 5.4</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;triangular&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zi</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;uniform&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">zi</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.5</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;quadratic&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">3.0</span> <span class="o">/</span> <span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;quartic&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">15.0</span> <span class="o">/</span> <span class="mi">16</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">zi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span> <span class="o">==</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">:</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="mi">2</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="n">c</span> <span class="o">**</span> <span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">zi</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span> <span class="k">for</span> <span class="n">zi</span> <span class="ow">in</span> <span class="n">zs</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">((</span><span class="s2">&quot;Unsupported kernel function&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">function</span><span class="p">))</span></div>
+
+
+
+<div class="viewcode-block" id="DistanceBand">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand">[docs]</a>
+<span class="k">class</span> <span class="nc">DistanceBand</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Spatial weights based on distance band.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    data        : array</span>
+<span class="sd">                  (n,k) or KDTree where KDtree.data is array (n,k)</span>
+<span class="sd">                  n observations on k characteristics used to measure</span>
+<span class="sd">                  distances between the n objects</span>
+<span class="sd">    threshold  : float</span>
+<span class="sd">                 distance band</span>
+<span class="sd">    p          : float</span>
+<span class="sd">                 DEPRECATED: use `distance_metric`</span>
+<span class="sd">                 Minkowski p-norm distance metric parameter:</span>
+<span class="sd">                 1&lt;=p&lt;=infinity</span>
+<span class="sd">                 2: Euclidean distance</span>
+<span class="sd">                 1: Manhattan distance</span>
+<span class="sd">    binary     : boolean</span>
+<span class="sd">                 If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0</span>
+<span class="sd">                 If false wij=dij^{alpha}</span>
+<span class="sd">    alpha      : float</span>
+<span class="sd">                 distance decay parameter for weight (default -1.0)</span>
+<span class="sd">                 if alpha is positive the weights will not decline with</span>
+<span class="sd">                 distance. If binary is True, alpha is ignored</span>
+
+<span class="sd">    ids         : list</span>
+<span class="sd">                  values to use for keys of the neighbors and weights dicts</span>
+
+<span class="sd">    build_sp    : boolean</span>
+<span class="sd">                  DEPRECATED</span>
+<span class="sd">                  True to build sparse distance matrix and false to build dense</span>
+<span class="sd">                  distance matrix; significant speed gains may be obtained</span>
+<span class="sd">                  dending on the sparsity of the of distance_matrix and</span>
+<span class="sd">                  threshold that is applied</span>
+<span class="sd">    silent      : boolean</span>
+<span class="sd">                  By default libpysal will print a warning if the</span>
+<span class="sd">                  dataset contains any disconnected observations or</span>
+<span class="sd">                  islands. To silence this warning set this</span>
+<span class="sd">                  parameter to True.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    weights : dict</span>
+<span class="sd">              of neighbor weights keyed by observation id</span>
+
+<span class="sd">    neighbors : dict</span>
+<span class="sd">                of neighbors keyed by observation id</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; points=[(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]</span>
+<span class="sd">    &gt;&gt;&gt; wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3], 2: [], 3: [0, 1], 4: [5], 5: [4]})</span>
+
+<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
+<span class="sd">    Island id:  [2]</span>
+<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2)</span>
+
+<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
+<span class="sd">    Island id:  [2]</span>
+<span class="sd">    &gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=14.2)</span>
+<span class="sd">    &gt;&gt;&gt; wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]})</span>
+<span class="sd">    &gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    inverse distance weights</span>
+
+<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False)</span>
+
+<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
+<span class="sd">    Island id:  [2]</span>
+<span class="sd">    &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">    [0.1, 0.08944271909999159]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[0].tolist()</span>
+<span class="sd">    [1, 3]</span>
+
+<span class="sd">    gravity weights</span>
+
+<span class="sd">    &gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2,binary=False,alpha=-2.)</span>
+
+<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
+<span class="sd">    Island id:  [2]</span>
+<span class="sd">    &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">    [0.01, 0.007999999999999998]</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="DistanceBand.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">threshold</span><span class="p">,</span>
+        <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">build_sp</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">distance_metric</span><span class="o">=</span><span class="s2">&quot;euclidean&quot;</span><span class="p">,</span>
+        <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Casting to floats is a work around for a bug in scipy.spatial.</span>
+<span class="sd">        See detail in pysal issue #126.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">distance_metric</span> <span class="o">=</span> <span class="s2">&quot;arc&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="n">threshold</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">binary</span> <span class="o">=</span> <span class="n">binary</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">build_sp</span> <span class="o">=</span> <span class="n">build_sp</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span> <span class="o">=</span> <span class="n">silence_warnings</span>
+
+        <span class="k">if</span> <span class="n">isKDTree</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">data</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sp</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">kind</span> <span class="o">!=</span> <span class="s2">&quot;f&quot;</span><span class="p">:</span>
+                        <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span>
+                        <span class="n">data</span><span class="p">,</span> <span class="n">distance_metric</span><span class="o">=</span><span class="n">distance_metric</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span>
+                    <span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">data</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Could not make array from data&quot;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_band</span><span class="p">()</span>
+        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_distance_to_W</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DistanceBand.from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_shapefile">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filepath</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Distance-band based weights from shapefile</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        shapefile   : string</span>
+<span class="sd">                      shapefile name with shp suffix</span>
+<span class="sd">        idVariable  : string</span>
+<span class="sd">                      name of column in shapefile&#39;s DBF to use for ids</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Kernel Weights Object</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">points</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">filepath</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">idVariable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">filepath</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DistanceBand.from_array">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_array">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">array</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a DistanceBand weights from an array. Supports all the same options</span>
+<span class="sd">        as :class:`libpysal.weights.DistanceBand`</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="DistanceBand.from_dataframe">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_dataframe">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_index</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+    <span class="p">):</span>
+
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Make DistanceBand weights from a dataframe.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df      :   pandas.dataframe</span>
+<span class="sd">                    a dataframe with a geometry column that can be used to</span>
+<span class="sd">                    construct a W object</span>
+<span class="sd">        geom_col :  string</span>
+<span class="sd">                    the name of the column in `df` that contains the</span>
+<span class="sd">                    geometries. Defaults to active geometry column.</span>
+<span class="sd">        ids     :   list-like, string</span>
+<span class="sd">                    a list-like of ids to use to index the spatial weights object or</span>
+<span class="sd">                    the name of the column to use as IDs. If nothing is</span>
+<span class="sd">                    provided, the dataframe index is used if `use_index=True` or</span>
+<span class="sd">                    a positional index is used if `use_index=False`.</span>
+<span class="sd">                    Order of the resulting W is not respected from this list.</span>
+<span class="sd">        use_index   : bool</span>
+<span class="sd">                    use index of `df` as `ids` to index the spatial weights object.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">pts</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">use_index</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_band</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Find all pairs within threshold.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sp</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">sparse_distance_matrix</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="p">,</span> <span class="n">max_distance</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span>
+            <span class="p">)</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">10</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Arc_KDTree&quot;</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Unable to calculate dense arc distance matrix;&quot;</span>
+                    <span class="s1">&#39; parameter &quot;build_sp&quot; must be set to True for arc&#39;</span>
+                    <span class="s2">&quot; distance type weight&quot;</span>
+                <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_spdistance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_distance_to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">dmat</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+            <span class="n">tempW</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span>
+                <span class="n">WSP</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">),</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span>
+            <span class="p">)</span>
+            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tempW</span><span class="o">.</span><span class="n">neighbors</span>
+            <span class="n">weight_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+            <span class="n">weight_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weight_keys</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">weight_vals</span><span class="p">)))))</span>
+            <span class="k">return</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">weighted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dmat</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+            <span class="n">weighted</span><span class="p">[</span><span class="n">weighted</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">weighted</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+            <span class="n">tempW</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span>
+                <span class="n">WSP</span><span class="p">(</span><span class="n">weighted</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">),</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span>
+            <span class="p">)</span>
+            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">tempW</span><span class="o">.</span><span class="n">neighbors</span>
+            <span class="n">weight_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+            <span class="n">weight_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tempW</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">weight_keys</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">weight_vals</span><span class="p">)))))</span>
+            <span class="k">return</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span>
+
+    <span class="k">def</span> <span class="nf">_spdistance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">distance_matrix</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">zeros</span> <span class="o">=</span> <span class="n">dist</span> <span class="o">&gt;</span> <span class="n">threshold</span>
+            <span class="n">dist</span><span class="p">[</span><span class="n">zeros</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">return</span> <span class="n">sp</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_test</span><span class="p">():</span>
+    <span class="kn">import</span> <span class="nn">doctest</span>
+
+    <span class="c1"># the following line could be used to define an alternative to the &#39;&lt;BLANKLINE&gt;&#39; flag</span>
+    <span class="c1"># doctest.BLANKLINE_MARKER = &#39;something better than &lt;BLANKLINE&gt;&#39;</span>
+    <span class="n">start_suppress</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_printoptions</span><span class="p">()[</span><span class="s2">&quot;suppress&quot;</span><span class="p">]</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">()</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="n">start_suppress</span><span class="p">)</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">_test</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
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+    <p>
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diff --git a/_modules/libpysal/weights/gabriel.html b/_modules/libpysal/weights/gabriel.html
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+<!DOCTYPE html>
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+  <head>
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+      
+  <h1>Source code for libpysal.weights.gabriel</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">Delaunay</span> <span class="k">as</span> <span class="n">_Delaunay</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
+<span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
+<span class="kn">import</span> <span class="nn">pandas</span><span class="o">,</span> <span class="nn">numpy</span><span class="o">,</span> <span class="nn">warnings</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">njit</span>
+<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">libpysal.common</span> <span class="kn">import</span> <span class="n">jit</span> <span class="k">as</span> <span class="n">njit</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;&quot;</span>
+<span class="s2">Levi John Wolf (levi.john.wolf@gmail.com)</span>
+<span class="s2">Martin Fleischmann (martin@martinfleischmann.net)</span>
+<span class="s2">&quot;&quot;&quot;</span>
+
+<span class="c1">#### Classes</span>
+
+
+<div class="viewcode-block" id="Delaunay">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay">[docs]</a>
+<span class="k">class</span> <span class="nc">Delaunay</span><span class="p">(</span><span class="n">W</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructor of the Delaunay graph of a set of input points.</span>
+<span class="sd">    Relies on scipy.spatial.Delaunay and numba to quickly construct</span>
+<span class="sd">    a graph from the input set of points. Will be slower without numba,</span>
+<span class="sd">    and will warn if this is missing.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    coordinates :   array of points, (N,2)</span>
+<span class="sd">        numpy array of coordinates containing locations to compute the</span>
+<span class="sd">        delaunay triangulation</span>
+<span class="sd">    **kwargs    :   keyword argument list</span>
+<span class="sd">        keyword arguments passed directly to weights.W</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    The Delaunay triangulation can result in quite a few non-local links among</span>
+<span class="sd">    spatial coordinates. For a more useful graph, consider the weights.Voronoi</span>
+<span class="sd">    constructor or the Gabriel graph.</span>
+
+<span class="sd">    The weights.Voronoi class builds a voronoi diagram among the points, clips the</span>
+<span class="sd">    Voronoi cells, and then constructs an adjacency graph among the clipped cells.</span>
+<span class="sd">    This graph among the clipped Voronoi cells generally represents the structure</span>
+<span class="sd">    of local adjacencies better than the &quot;raw&quot; Delaunay graph.</span>
+
+<span class="sd">    The weights.gabriel.Gabriel graph constructs a Delaunay graph, but only</span>
+<span class="sd">    includes the &quot;short&quot; links in the Delaunay graph.</span>
+
+<span class="sd">    However, if the unresricted Delaunay triangulation is needed, this class</span>
+<span class="sd">    will compute it much more quickly than Voronoi(coordinates, clip=None).</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Delaunay.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">njit</span>
+        <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;The numba package is used extensively in this module&quot;</span>
+                <span class="s2">&quot; to accelerate the computation of graphs. Without numba,&quot;</span>
+                <span class="s2">&quot; these computations may become unduly slow on large data.&quot;</span>
+            <span class="p">)</span>
+        <span class="n">edges</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_voronoi_edges</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+            <span class="n">edges</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">ids</span><span class="p">[</span><span class="n">edges</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]],</span> <span class="n">ids</span><span class="p">[</span><span class="n">edges</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]]))</span>
+            <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="n">voronoi_neighbors</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">voronoi_neighbors</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_voronoi_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">):</span>
+        <span class="n">dt</span> <span class="o">=</span> <span class="n">_Delaunay</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
+        <span class="n">edges</span> <span class="o">=</span> <span class="n">_edges_from_simplices</span><span class="p">(</span><span class="n">dt</span><span class="o">.</span><span class="n">simplices</span><span class="p">)</span>
+        <span class="n">edges</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">edges</span><span class="p">)))</span>
+            <span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+            <span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">values</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">edges</span><span class="p">,</span> <span class="n">dt</span>
+
+<div class="viewcode-block" id="Delaunay.from_dataframe">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay.from_dataframe">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_dataframe</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">geom_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">use_index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Construct a Delaunay triangulation from a geopandas GeoDataFrame.</span>
+<span class="sd">        Not that the input geometries in the dataframe must be Points.</span>
+<span class="sd">        Polygons or lines must be converted to points (e.g. using</span>
+<span class="sd">        df.geometry.centroid).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        df  :   geopandas.GeoDataFrame</span>
+<span class="sd">            GeoDataFrame containing points to construct the Delaunay</span>
+<span class="sd">            Triangulation.</span>
+<span class="sd">        geom_col :  string</span>
+<span class="sd">            the name of the column in `df` that contains the</span>
+<span class="sd">            geometries. Defaults to active geometry column.</span>
+<span class="sd">        ids   :   list-like, string</span>
+<span class="sd">            a list-like of ids to use to index the spatial weights object or</span>
+<span class="sd">            the name of the column to use as IDs. If nothing is</span>
+<span class="sd">            provided, the dataframe index is used if `use_index=True` or</span>
+<span class="sd">            a positional index is used if `use_index=False`.</span>
+<span class="sd">            Order of the resulting W is not respected from this list.</span>
+<span class="sd">        use_index  : bool</span>
+<span class="sd">            use index of `df` as `ids` to index the spatial weights object.</span>
+<span class="sd">        **kwargs :  keyword arguments</span>
+<span class="sd">            Keyword arguments that are passed downwards to the weights.W</span>
+<span class="sd">            constructor.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">pandas</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
+            <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">to_frame</span><span class="p">(</span><span class="s2">&quot;geometry&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">geom_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">geom_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">geomtypes</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">geom_type</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                    <span class="s2">&quot;`use_index` defaults to False but will default to True in future. &quot;</span>
+                    <span class="s2">&quot;Set True/False directly to control this behavior and silence this &quot;</span>
+                    <span class="s2">&quot;warning&quot;</span><span class="p">,</span>
+                    <span class="ne">FutureWarning</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">use_index</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="k">if</span> <span class="n">use_index</span><span class="p">:</span>
+                <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">geomtypes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+            <span class="k">assert</span> <span class="n">geomtypes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Point&quot;</span>
+            <span class="n">point_array</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span>
+                <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">geom_col</span><span class="p">]</span><span class="o">.</span><span class="n">y</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+            <span class="p">)</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">point_array</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;The input dataframe has geometry types </span><span class="si">{</span><span class="n">geomtypes</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="sa">f</span><span class="s2">&quot; but this delaunay triangulation is only well-defined for points.&quot;</span>
+                <span class="sa">f</span><span class="s2">&quot; Choose a method to convert your dataframe into points (like using&quot;</span>
+                <span class="sa">f</span><span class="s2">&quot; the df.centroid) and use that to estimate this graph.&quot;</span>
+            <span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Gabriel">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Gabriel.html#libpysal.weights.Gabriel">[docs]</a>
+<span class="k">class</span> <span class="nc">Gabriel</span><span class="p">(</span><span class="n">Delaunay</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the Gabriel graph of a set of points. This graph is a subset of</span>
+<span class="sd">    the Delaunay triangulation where only &quot;short&quot; links are retained. This</span>
+<span class="sd">    function is also accelerated using numba, and implemented on top of the</span>
+<span class="sd">    scipy.spatial.Delaunay class.</span>
+
+<span class="sd">    For a link (i,j) connecting node i to j in the Delaunay triangulation</span>
+<span class="sd">    to be retained in the Gabriel graph, it must pass a point set exclusion test:</span>
+
+<span class="sd">    1. Construct the circle C_ij containing link (i,j) as its diameter</span>
+<span class="sd">    2. If any other node k is contained within C_ij, then remove link (i,j)</span>
+<span class="sd">       from the graph.</span>
+<span class="sd">    3. Once all links are evaluated, the remaining graph is the Gabriel graph.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    coordinates :   array of points, (N,2)</span>
+<span class="sd">        numpy array of coordinates containing locations to compute the</span>
+<span class="sd">        delaunay triangulation</span>
+<span class="sd">    **kwargs    :   keyword argument list</span>
+<span class="sd">        keyword arguments passed directly to weights.W</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Gabriel.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Gabriel.html#libpysal.weights.Gabriel.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">njit</span>
+        <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;The numba package is used extensively in this module&quot;</span>
+                <span class="s2">&quot; to accelerate the computation of graphs. Without numba,&quot;</span>
+                <span class="s2">&quot; these computations may become unduly slow on large data.&quot;</span>
+            <span class="p">)</span>
+        <span class="n">edges</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_voronoi_edges</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
+        <span class="n">droplist</span> <span class="o">=</span> <span class="n">_filter_gabriel</span><span class="p">(</span>
+            <span class="n">edges</span><span class="p">,</span>
+            <span class="n">dt</span><span class="o">.</span><span class="n">points</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">row_stack</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="n">edges</span><span class="p">))</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">droplist</span><span class="p">))))</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+            <span class="n">output</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">column_stack</span><span class="p">((</span><span class="n">ids</span><span class="p">[</span><span class="n">output</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]],</span> <span class="n">ids</span><span class="p">[</span><span class="n">output</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]]))</span>
+            <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="n">gabriel_neighbors</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">output</span><span class="p">)</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">gabriel_neighbors</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="Relative_Neighborhood">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Relative_Neighborhood.html#libpysal.weights.Relative_Neighborhood">[docs]</a>
+<span class="k">class</span> <span class="nc">Relative_Neighborhood</span><span class="p">(</span><span class="n">Delaunay</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs the Relative Neighborhood graph from a set of points.</span>
+<span class="sd">    This graph is a subset of the Delaunay triangulation, where only</span>
+<span class="sd">    &quot;relative neighbors&quot; are retained. Further, it is a superset of</span>
+<span class="sd">    the Minimum Spanning Tree, with additional &quot;relative neighbors&quot;</span>
+<span class="sd">    introduced.</span>
+
+<span class="sd">    A relative neighbor pair of points i,j must be closer than the </span>
+<span class="sd">    maximum distance between i (or j) and each other point k. </span>
+<span class="sd">    This means that the points are at least as close to one another </span>
+<span class="sd">    as they are to any other point. </span>
+<span class="sd">    </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    coordinates :   array of points, (N,2)</span>
+<span class="sd">        numpy array of coordinates containing locations to compute the</span>
+<span class="sd">        delaunay triangulation</span>
+<span class="sd">    **kwargs    :   keyword argument list</span>
+<span class="sd">        keyword arguments passed directly to weights.W</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="Relative_Neighborhood.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.Relative_Neighborhood.html#libpysal.weights.Relative_Neighborhood.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span> <span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">njit</span>
+        <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;The numba package is used extensively in this module&quot;</span>
+                <span class="s2">&quot; to accelerate the computation of graphs. Without numba,&quot;</span>
+                <span class="s2">&quot; these computations may become unduly slow on large data.&quot;</span>
+            <span class="p">)</span>
+        <span class="n">edges</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_voronoi_edges</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
+        <span class="n">output</span><span class="p">,</span> <span class="n">dkmax</span> <span class="o">=</span> <span class="n">_filter_relativehood</span><span class="p">(</span><span class="n">edges</span><span class="p">,</span> <span class="n">dt</span><span class="o">.</span><span class="n">points</span><span class="p">,</span> <span class="n">return_dkmax</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">output</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">binary</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">sp</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csc_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)))</span>  <span class="c1"># TODO: faster way than this?</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;ids&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ids</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">sp</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;ids&quot;</span><span class="p">]</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sp</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">to_W</span><span class="p">()</span>
+        <span class="n">W</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tmp</span><span class="o">.</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">tmp</span><span class="o">.</span><span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+</div>
+
+
+
+<span class="c1">#### utilities</span>
+
+
+<span class="nd">@njit</span>
+<span class="k">def</span> <span class="nf">_edges_from_simplices</span><span class="p">(</span><span class="n">simplices</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Construct the sets of links that correspond to the edges of each</span>
+<span class="sd">    simplex. Each simplex has three &quot;sides,&quot; and thus six undirected</span>
+<span class="sd">    edges. Thus, the input should be a list of three-length tuples,</span>
+<span class="sd">    that are then converted into the six non-directed edges for</span>
+<span class="sd">    each simplex.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">edges</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">simplex</span> <span class="ow">in</span> <span class="n">simplices</span><span class="p">:</span>
+        <span class="n">edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">simplex</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">simplex</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="n">edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">simplex</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">simplex</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="n">edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">simplex</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">simplex</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+        <span class="n">edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">simplex</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">simplex</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="n">edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">simplex</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">simplex</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="n">edges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">simplex</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">simplex</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+    <span class="k">return</span> <span class="n">numpy</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span>
+
+
+<span class="nd">@njit</span>
+<span class="k">def</span> <span class="nf">_filter_gabriel</span><span class="p">(</span><span class="n">edges</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    For an input set of edges and coordinates, filter the input edges</span>
+<span class="sd">    depending on the Gabriel rule:</span>
+
+<span class="sd">    For each simplex, let i,j be the diameter of the circle defined by</span>
+<span class="sd">    edge (i,j), and let k be the third point defining the simplex. The</span>
+<span class="sd">    limiting case for the Gabriel rule is when k is also on the circle</span>
+<span class="sd">    with diameter (i,j). In this limiting case, then simplex ijk must</span>
+<span class="sd">    be a right triangle, and dij**2 = djk**2 + dki**2 (by thales theorem).</span>
+
+<span class="sd">    This means that when dij**2 &gt; djk**2 + dki**2, then k is inside the circle.</span>
+<span class="sd">    In contrast, when dij**2 &lt; djk**2 + dji*2, k is outside of the circle.</span>
+
+<span class="sd">    Therefore, it&#39;s sufficient to take each observation i, iterate over its</span>
+<span class="sd">    Delaunay neighbors j,k, and remove links whre dij**2 &gt; djk**2 + dki**2</span>
+<span class="sd">    in order to construct the Gabriel graph.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">edge_pointer</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">edges</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+    <span class="n">n_edges</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span>
+    <span class="n">to_drop</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">while</span> <span class="n">edge_pointer</span> <span class="o">&lt;</span> <span class="n">n_edges</span><span class="p">:</span>
+        <span class="n">edge</span> <span class="o">=</span> <span class="n">edges</span><span class="p">[</span><span class="n">edge_pointer</span><span class="p">]</span>
+        <span class="n">cardinality</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1"># look ahead to find all neighbors of edge[0]</span>
+        <span class="k">for</span> <span class="n">joff</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">edge_pointer</span><span class="p">,</span> <span class="n">n_edges</span><span class="p">):</span>
+            <span class="n">next_edge</span> <span class="o">=</span> <span class="n">edges</span><span class="p">[</span><span class="n">joff</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">next_edge</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">edge</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="k">break</span>
+            <span class="n">cardinality</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">edge_pointer</span><span class="p">,</span> <span class="n">edge_pointer</span> <span class="o">+</span> <span class="n">cardinality</span><span class="p">):</span>
+            <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">edges</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>  <span class="c1"># lookahead ensures that i is always edge[0]</span>
+            <span class="n">dij2</span> <span class="o">=</span> <span class="p">((</span><span class="n">coordinates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">ix2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">edge_pointer</span><span class="p">,</span> <span class="n">edge_pointer</span> <span class="o">+</span> <span class="n">cardinality</span><span class="p">):</span>
+                <span class="n">_</span><span class="p">,</span> <span class="n">k</span> <span class="o">=</span> <span class="n">edges</span><span class="p">[</span><span class="n">ix2</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="n">k</span><span class="p">:</span>
+                    <span class="k">continue</span>
+                <span class="n">dik2</span> <span class="o">=</span> <span class="p">((</span><span class="n">coordinates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+                <span class="n">djk2</span> <span class="o">=</span> <span class="p">((</span><span class="n">coordinates</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">-</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+
+                <span class="k">if</span> <span class="n">dij2</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">dik2</span> <span class="o">+</span> <span class="n">djk2</span><span class="p">):</span>
+                    <span class="n">to_drop</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">))</span>
+                    <span class="n">to_drop</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
+        <span class="n">edge_pointer</span> <span class="o">+=</span> <span class="n">cardinality</span>
+    <span class="k">return</span> <span class="nb">set</span><span class="p">(</span><span class="n">to_drop</span><span class="p">)</span>
+
+
+<span class="nd">@njit</span>
+<span class="k">def</span> <span class="nf">_filter_relativehood</span><span class="p">(</span><span class="n">edges</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span> <span class="n">return_dkmax</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This is a direct implementation of the algorithm from Toussaint (1980), RNG-2</span>
+
+<span class="sd">    1. Compute the delaunay</span>
+<span class="sd">    2. for each edge of the delaunay (i,j), compute</span>
+<span class="sd">       dkmax = max(d(k,i), d(k,j)) for k in 1..n, k != i, j</span>
+<span class="sd">    3. for each edge of the delaunay (i,j), prune</span>
+<span class="sd">       if any dkmax is greater than d(i,j)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">edge_pointer</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">edges</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+    <span class="n">n_edges</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span>
+    <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
+        <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">=</span> <span class="n">edge</span>
+        <span class="n">pi</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">pj</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+        <span class="n">dkmax</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">dij</span> <span class="o">=</span> <span class="p">((</span><span class="n">pi</span> <span class="o">-</span> <span class="n">pj</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">**</span> <span class="mf">0.5</span>
+        <span class="n">prune</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+            <span class="n">pk</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">dik</span> <span class="o">=</span> <span class="p">((</span><span class="n">pi</span> <span class="o">-</span> <span class="n">pk</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">**</span> <span class="mf">0.5</span>
+            <span class="n">djk</span> <span class="o">=</span> <span class="p">((</span><span class="n">pj</span> <span class="o">-</span> <span class="n">pk</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">**</span> <span class="mf">0.5</span>
+            <span class="n">distances</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">dik</span><span class="p">,</span> <span class="n">djk</span><span class="p">,</span> <span class="n">dkmax</span><span class="p">])</span>
+            <span class="n">dkmax</span> <span class="o">=</span> <span class="n">distances</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+            <span class="n">prune</span> <span class="o">=</span> <span class="n">dkmax</span> <span class="o">&lt;</span> <span class="n">dij</span>
+            <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">return_dkmax</span><span class="p">)</span> <span class="o">&amp;</span> <span class="n">prune</span><span class="p">:</span>
+                <span class="k">break</span>
+        <span class="k">if</span> <span class="n">prune</span><span class="p">:</span>
+            <span class="k">continue</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">dij</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">return_dkmax</span><span class="p">:</span>
+            <span class="n">r</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dkmax</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="n">r</span>
+</pre></div>
+
+    </div>
+      
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+    <p>
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diff --git a/_modules/libpysal/weights/raster.html b/_modules/libpysal/weights/raster.html
new file mode 100644
index 000000000..4330339ee
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+<!DOCTYPE html>
+
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+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+      
+  <h1>Source code for libpysal.weights.raster</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">lat2SW</span>
+<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">W</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
+
+<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;pytest&quot;</span><span class="p">,</span> <span class="s2">&quot;py.test&quot;</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="nf">jit</span><span class="p">(</span><span class="o">*</span><span class="n">dec_args</span><span class="p">,</span> <span class="o">**</span><span class="n">dec_kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        decorator mimicking numba.jit</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">intercepted_function</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="o">*</span><span class="n">f_args</span><span class="p">,</span> <span class="o">**</span><span class="n">f_kwargs</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">f</span>
+
+        <span class="k">return</span> <span class="n">intercepted_function</span>
+
+
+<span class="k">else</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">jit</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;da2W&quot;</span><span class="p">,</span> <span class="s2">&quot;da2WSP&quot;</span><span class="p">,</span> <span class="s2">&quot;w2da&quot;</span><span class="p">,</span> <span class="s2">&quot;wsp2da&quot;</span><span class="p">,</span> <span class="s2">&quot;testDataArray&quot;</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="da2W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.da2W.html#libpysal.weights.da2W">[docs]</a>
+<span class="k">def</span> <span class="nf">da2W</span><span class="p">(</span>
+    <span class="n">da</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;queen&quot;</span><span class="p">,</span>
+    <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
+    <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a W object from xarray.DataArray with an additional</span>
+<span class="sd">    attribute index containing coordinate values of the raster</span>
+<span class="sd">    in the form of Pandas.Index/MultiIndex.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        Input 2D or 3D DataArray with shape=(z, y, x)</span>
+<span class="sd">    criterion : {&quot;rook&quot;, &quot;queen&quot;}</span>
+<span class="sd">        Type of contiguity. Default is queen.</span>
+<span class="sd">    z_value : int/string/float</span>
+<span class="sd">        Select the z_value of 3D DataArray with multiple layers.</span>
+<span class="sd">    coords_labels : dictionary</span>
+<span class="sd">        Pass dimension labels for coordinates and layers if they do not</span>
+<span class="sd">        belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
+<span class="sd">        e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
+<span class="sd">        Default is {} empty dictionary.</span>
+<span class="sd">    k : int</span>
+<span class="sd">        Order of contiguity, this will select all neighbors upto kth order.</span>
+<span class="sd">        Default is 1.</span>
+<span class="sd">    include_nodata : boolean</span>
+<span class="sd">        If True, missing values will be assumed as non-missing when</span>
+<span class="sd">        selecting higher_order neighbors, Default is False</span>
+<span class="sd">    n_jobs : int</span>
+<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
+<span class="sd">        all available cores are used. Default is 1.</span>
+<span class="sd">    **kwargs : keyword arguments</span>
+<span class="sd">        Optional arguments for :class:`libpysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w : libpysal.weights.W</span>
+<span class="sd">       instance of spatial weights class W with an index attribute</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    1. Lower order contiguities are also selected.</span>
+<span class="sd">    2. Returned object contains `index` attribute that includes a</span>
+<span class="sd">       `Pandas.MultiIndex` object from the DataArray.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.raster import da2W, testDataArray</span>
+<span class="sd">    &gt;&gt;&gt; da = testDataArray().rename(</span>
+<span class="sd">            {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
+<span class="sd">    &gt;&gt;&gt; da.dims</span>
+<span class="sd">    (&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; da.shape</span>
+<span class="sd">    (3, 4, 4)</span>
+<span class="sd">    &gt;&gt;&gt; da.coords</span>
+<span class="sd">    Coordinates:</span>
+<span class="sd">        * layer      (layer) int64 1 2 3</span>
+<span class="sd">        * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
+<span class="sd">        * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
+<span class="sd">    &gt;&gt;&gt; da.attrs</span>
+<span class="sd">    {&#39;nodatavals&#39;: (-32768.0,)}</span>
+<span class="sd">    &gt;&gt;&gt; coords_labels = {</span>
+<span class="sd">        &quot;z_label&quot;: &quot;layer&quot;,</span>
+<span class="sd">        &quot;y_label&quot;: &quot;latitude&quot;,</span>
+<span class="sd">        &quot;x_label&quot;: &quot;longitude&quot;</span>
+<span class="sd">    }</span>
+<span class="sd">    &gt;&gt;&gt; w = da2W(da, z_value=2, coords_labels=coords_labels)</span>
+<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w.pct_nonzero</span>
+<span class="sd">    &#39;30.000&#39;</span>
+<span class="sd">    &gt;&gt;&gt; w[(2, 90.0, 180.0)] == {(2, 90.0, 60.0): 1, (2, 30.0, 180.0): 1}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; len(w.index)</span>
+<span class="sd">    10</span>
+<span class="sd">    &gt;&gt;&gt; w.index[:2]</span>
+<span class="sd">    MultiIndex([(2, 90.0,  60.0),</span>
+<span class="sd">                (2, 90.0, 180.0)],</span>
+<span class="sd">               names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :class:`libpysal.weights.weights.W`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">warn</span><span class="p">(</span>
+        <span class="s2">&quot;You are trying to build a full W object from &quot;</span>
+        <span class="s2">&quot;xarray.DataArray (raster) object. This computation &quot;</span>
+        <span class="s2">&quot;can be very slow and not scale well. It is recommended, &quot;</span>
+        <span class="s2">&quot;if possible, to instead build WSP object, which is more &quot;</span>
+        <span class="s2">&quot;efficient and faster. You can do this by using da2WSP method.&quot;</span>
+    <span class="p">)</span>
+    <span class="n">wsp</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_nodata</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span>
+    <span class="n">w</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">to_W</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="c1"># temp addition of index attribute</span>
+    <span class="n">w</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">index</span>
+    <span class="k">return</span> <span class="n">w</span></div>
+
+
+
+<div class="viewcode-block" id="da2WSP">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.da2WSP.html#libpysal.weights.da2WSP">[docs]</a>
+<span class="k">def</span> <span class="nf">da2WSP</span><span class="p">(</span>
+    <span class="n">da</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;queen&quot;</span><span class="p">,</span>
+    <span class="n">z_value</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">coords_labels</span><span class="o">=</span><span class="p">{},</span>
+    <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">include_nodata</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a WSP object from xarray.DataArray with an additional</span>
+<span class="sd">    attribute index containing coordinate values of the raster</span>
+<span class="sd">    in the form of Pandas.Index/MultiIndex.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        Input 2D or 3D DataArray with shape=(z, y, x)</span>
+<span class="sd">    criterion : {&quot;rook&quot;, &quot;queen&quot;}</span>
+<span class="sd">        Type of contiguity. Default is queen.</span>
+<span class="sd">    z_value : int/string/float</span>
+<span class="sd">        Select the z_value of 3D DataArray with multiple layers.</span>
+<span class="sd">    coords_labels : dictionary</span>
+<span class="sd">        Pass dimension labels for coordinates and layers if they do not</span>
+<span class="sd">        belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
+<span class="sd">        e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
+<span class="sd">        Default is {} empty dictionary.</span>
+<span class="sd">    k : int</span>
+<span class="sd">        Order of contiguity, this will select all neighbors upto kth order.</span>
+<span class="sd">        Default is 1.</span>
+<span class="sd">    include_nodata : boolean</span>
+<span class="sd">        If True, missing values will be assumed as non-missing when</span>
+<span class="sd">        selecting higher_order neighbors, Default is False</span>
+<span class="sd">    n_jobs : int</span>
+<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
+<span class="sd">        all available cores are used. Default is 1.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    wsp : libpysal.weights.WSP</span>
+<span class="sd">       instance of spatial weights class WSP with an index attribute</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    1. Lower order contiguities are also selected.</span>
+<span class="sd">    2. Returned object contains `index` attribute that includes a</span>
+<span class="sd">       `Pandas.MultiIndex` object from the DataArray.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.raster import da2WSP, testDataArray</span>
+<span class="sd">    &gt;&gt;&gt; da = testDataArray().rename(</span>
+<span class="sd">            {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
+<span class="sd">    &gt;&gt;&gt; da.dims</span>
+<span class="sd">    (&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; da.shape</span>
+<span class="sd">    (3, 4, 4)</span>
+<span class="sd">    &gt;&gt;&gt; da.coords</span>
+<span class="sd">    Coordinates:</span>
+<span class="sd">        * layer      (layer) int64 1 2 3</span>
+<span class="sd">        * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
+<span class="sd">        * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
+<span class="sd">    &gt;&gt;&gt; da.attrs</span>
+<span class="sd">    {&#39;nodatavals&#39;: (-32768.0,)}</span>
+<span class="sd">    &gt;&gt;&gt; coords_labels = {</span>
+<span class="sd">        &quot;z_label&quot;: &quot;layer&quot;,</span>
+<span class="sd">        &quot;y_label&quot;: &quot;latitude&quot;,</span>
+<span class="sd">        &quot;x_label&quot;: &quot;longitude&quot;</span>
+<span class="sd">    }</span>
+<span class="sd">    &gt;&gt;&gt; wsp = da2WSP(da, z_value=2, coords_labels=coords_labels)</span>
+<span class="sd">    &gt;&gt;&gt; wsp.n</span>
+<span class="sd">    10</span>
+<span class="sd">    &gt;&gt;&gt; pct_sp = wsp.sparse.nnz *1. / wsp.n**2</span>
+<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%pct_sp</span>
+<span class="sd">    &#39;0.300&#39;</span>
+<span class="sd">    &gt;&gt;&gt; print(wsp.sparse[4].todense())</span>
+<span class="sd">    [[0 0 1 0 0 1 1 1 0 0]]</span>
+<span class="sd">    &gt;&gt;&gt; wsp.index[:2]</span>
+<span class="sd">    MultiIndex([(2, 90.0,  60.0),</span>
+<span class="sd">                (2, 90.0, 180.0)],</span>
+<span class="sd">               names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
+
+<span class="sd">    See Also</span>
+<span class="sd">    --------</span>
+<span class="sd">    :class:`libpysal.weights.weights.WSP`</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">z_id</span><span class="p">,</span> <span class="n">coords_labels</span> <span class="o">=</span> <span class="n">_da_checker</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">)</span>
+    <span class="n">shape</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">if</span> <span class="n">z_id</span><span class="p">:</span>
+        <span class="n">slice_dict</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">slice_dict</span><span class="p">[</span><span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="n">slice_dict</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">slice_dict</span><span class="p">[</span><span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">z_id</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">z_id</span><span class="p">)</span>
+        <span class="n">da</span> <span class="o">=</span> <span class="n">da</span><span class="p">[</span><span class="n">slice_dict</span><span class="p">]</span>
+
+    <span class="n">ser</span> <span class="o">=</span> <span class="n">da</span><span class="o">.</span><span class="n">to_series</span><span class="p">()</span>
+    <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span> <span class="k">if</span> <span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">&lt;</span> <span class="mi">46340</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">int64</span>
+    <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span> <span class="ow">and</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]:</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">ser</span> <span class="o">!=</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">id_map</span> <span class="o">=</span> <span class="n">_idmap</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
+        <span class="n">ser</span> <span class="o">=</span> <span class="n">ser</span><span class="p">[</span><span class="n">ser</span> <span class="o">!=</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ser</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">id_map</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">numba</span>
+    <span class="k">except</span> <span class="p">(</span><span class="ne">ModuleNotFoundError</span><span class="p">,</span> <span class="ne">ImportError</span><span class="p">):</span>
+        <span class="n">warn</span><span class="p">(</span>
+            <span class="s2">&quot;numba cannot be imported, parallel processing &quot;</span>
+            <span class="s2">&quot;and include_nodata functionality will be disabled. &quot;</span>
+            <span class="s2">&quot;falling back to slower method&quot;</span>
+        <span class="p">)</span>
+        <span class="n">include_nodata</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="c1"># Fallback method to build sparse matrix</span>
+        <span class="n">sw</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">criterion</span><span class="p">)</span>
+        <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span> <span class="ow">and</span> <span class="n">da</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]:</span>
+            <span class="n">sw</span> <span class="o">=</span> <span class="n">sw</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
+            <span class="n">sw</span> <span class="o">=</span> <span class="n">sw</span><span class="p">[:,</span> <span class="n">mask</span><span class="p">]</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">k_nas</span> <span class="o">=</span> <span class="n">k</span> <span class="k">if</span> <span class="n">include_nodata</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="kn">import</span> <span class="nn">joblib</span>
+            <span class="k">except</span> <span class="p">(</span><span class="ne">ModuleNotFoundError</span><span class="p">,</span> <span class="ne">ImportError</span><span class="p">):</span>
+                <span class="n">warn</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Parallel processing is requested (n_jobs=</span><span class="si">{</span><span class="n">n_jobs</span><span class="si">}</span><span class="s2">),&quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot; but joblib cannot be imported. n_jobs will be set&quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot; to 1.&quot;</span><span class="p">,</span>
+                    <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="n">n_jobs</span> <span class="o">=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">sw_tup</span> <span class="o">=</span> <span class="n">_SWbuilder</span><span class="p">(</span>
+                <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k_nas</span><span class="p">,</span> <span class="n">dtype</span>
+            <span class="p">)</span>  <span class="c1"># -&gt; (data, (row, col))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="n">n_jobs</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
+            <span class="c1"># Parallel implementation</span>
+            <span class="n">sw_tup</span> <span class="o">=</span> <span class="n">_parSWbuilder</span><span class="p">(</span>
+                <span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k_nas</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">n_jobs</span>
+            <span class="p">)</span>  <span class="c1"># -&gt; (data, (row, col))</span>
+
+        <span class="n">sw</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">(</span>
+            <span class="n">sw_tup</span><span class="p">,</span>
+            <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span>
+            <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Higher_order functionality, this uses idea from</span>
+    <span class="c1"># libpysal#313 for adding higher order neighbors.</span>
+    <span class="c1"># Since diagonal elements are also added in the result,</span>
+    <span class="c1"># this method set the diagonal elements to zero and</span>
+    <span class="c1"># then eliminate zeros from the data. This changes the</span>
+    <span class="c1"># sparcity of the csr_matrix !!</span>
+    <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">include_nodata</span><span class="p">:</span>
+        <span class="n">sw</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">sw</span> <span class="o">**</span> <span class="n">x</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)))</span>
+        <span class="n">sw</span><span class="o">.</span><span class="n">setdiag</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">sw</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+        <span class="n">sw</span><span class="o">.</span><span class="n">data</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">sw</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
+
+    <span class="n">index</span> <span class="o">=</span> <span class="n">ser</span><span class="o">.</span><span class="n">index</span>
+    <span class="n">wsp</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sw</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="n">index</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">wsp</span></div>
+
+
+
+<div class="viewcode-block" id="w2da">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w2da.html#libpysal.weights.w2da">[docs]</a>
+<span class="k">def</span> <span class="nf">w2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="p">{},</span> <span class="n">coords</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates xarray.DataArray object from passed data aligned with W object.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : array/list/pd.Series</span>
+<span class="sd">        1d array-like data with dimensionality conforming to w</span>
+<span class="sd">    w : libpysal.weights.W</span>
+<span class="sd">        Spatial weights object aligned with passed data</span>
+<span class="sd">    attrs : Dictionary</span>
+<span class="sd">        Attributes stored in dict related to DataArray, e.g. da.attrs</span>
+<span class="sd">        Default is {} empty dictionary.</span>
+<span class="sd">    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates</span>
+<span class="sd">        Coordinates corresponding to DataArray, e.g. da.coords</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        instance of xarray.DataArray</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.raster import da2W, testDataArray, w2da</span>
+<span class="sd">    &gt;&gt;&gt; da = testDataArray()</span>
+<span class="sd">    &gt;&gt;&gt; da.shape</span>
+<span class="sd">    (3, 4, 4)</span>
+<span class="sd">    &gt;&gt;&gt; w = da2W(da, z_value=2)</span>
+<span class="sd">    &gt;&gt;&gt; data = np.random.randint(0, 255, len(w.index))</span>
+<span class="sd">    &gt;&gt;&gt; da1 = w2da(data, w)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;w must be an instance of weights.W&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="s2">&quot;index&quot;</span><span class="p">):</span>
+        <span class="n">da</span> <span class="o">=</span> <span class="n">_index2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="s2">&quot;This method requires `w` object to include `index` attribute that is built as a `pandas.MultiIndex` object.&quot;</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="n">da</span></div>
+
+
+
+<div class="viewcode-block" id="wsp2da">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.wsp2da.html#libpysal.weights.wsp2da">[docs]</a>
+<span class="k">def</span> <span class="nf">wsp2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">wsp</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="p">{},</span> <span class="n">coords</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates xarray.DataArray object from passed data aligned with WSP object.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : array/list/pd.Series</span>
+<span class="sd">        1d array-like data with dimensionality conforming to wsp</span>
+<span class="sd">    wsp : libpysal.weights.WSP</span>
+<span class="sd">        Sparse weights object aligned with passed data</span>
+<span class="sd">    attrs : Dictionary</span>
+<span class="sd">        Attributes stored in dict related to DataArray, e.g. da.attrs</span>
+<span class="sd">        Default is {} empty dictionary.</span>
+<span class="sd">    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates</span>
+<span class="sd">        coordinates corresponding to DataArray, e.g. da.coords</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        instance of xarray.DataArray</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.raster import da2WSP, testDataArray, wsp2da</span>
+<span class="sd">    &gt;&gt;&gt; da = testDataArray()</span>
+<span class="sd">    &gt;&gt;&gt; da.shape</span>
+<span class="sd">    (3, 4, 4)</span>
+<span class="sd">    &gt;&gt;&gt; wsp = da2WSP(da, z_value=2)</span>
+<span class="sd">    &gt;&gt;&gt; data = np.random.randint(0, 255, len(wsp.index))</span>
+<span class="sd">    &gt;&gt;&gt; da1 = w2da(data, wsp)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="n">WSP</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;wsp must be an instance of weights.WSP&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="s2">&quot;index&quot;</span><span class="p">):</span>
+        <span class="n">da</span> <span class="o">=</span> <span class="n">_index2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">wsp</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="s2">&quot;This method requires `wsp` object to include `index` attribute that is built as a `pandas.MultiIndex` object.&quot;</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="n">da</span></div>
+
+
+
+<div class="viewcode-block" id="testDataArray">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.testDataArray.html#libpysal.weights.testDataArray">[docs]</a>
+<span class="k">def</span> <span class="nf">testDataArray</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">time</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">rand</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">missing_vals</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates 2 or 3 dimensional test xarray.DataArray object</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    shape : tuple</span>
+<span class="sd">        Tuple containing shape of the DataArray aligned with</span>
+<span class="sd">        following dimension = (lat, lon) or (layer, lat, lon)</span>
+<span class="sd">        Default shape = (3, 4, 4)</span>
+<span class="sd">    time : boolean</span>
+<span class="sd">        Type of layer, if True then layer=time else layer=band</span>
+<span class="sd">        Default is False.</span>
+<span class="sd">    rand : boolean</span>
+<span class="sd">        If True, creates a DataArray filled with unique and random data.</span>
+<span class="sd">        Default is false (generates seeded random data)</span>
+<span class="sd">    missing_vals : boolean</span>
+<span class="sd">        Create a DataArray filled with missing values. Default is True.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        instance of xarray.DataArray</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;xarray must be installed to use this functionality&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">rand</span><span class="p">:</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
+    <span class="n">coords</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">n</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">layer</span> <span class="o">=</span> <span class="s2">&quot;time&quot;</span> <span class="k">if</span> <span class="n">time</span> <span class="k">else</span> <span class="s2">&quot;band&quot;</span>
+        <span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">time</span><span class="p">:</span>
+            <span class="n">layers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">datetime64</span><span class="p">(</span><span class="s2">&quot;2020-07-30&quot;</span><span class="p">),</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s2">&quot;datetime64[D]&quot;</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">layers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">coords</span><span class="p">[</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]]</span> <span class="o">=</span> <span class="n">layers</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">dims</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;y&quot;</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span>
+    <span class="n">coords</span><span class="p">[</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">90</span><span class="p">,</span> <span class="o">-</span><span class="mi">90</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
+    <span class="n">coords</span><span class="p">[</span><span class="n">dims</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span> <span class="mi">180</span><span class="p">,</span> <span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="n">shape</span><span class="p">)</span>
+    <span class="n">attrs</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">if</span> <span class="n">missing_vals</span><span class="p">:</span>
+        <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">32768.0</span><span class="p">,)</span>
+        <span class="n">miss_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">data</span><span class="p">[</span><span class="n">miss_ids</span><span class="p">]</span> <span class="o">=</span> <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">da</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">coords</span><span class="p">,</span> <span class="n">dims</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">da</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_da_checker</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="p">,</span> <span class="n">coords_labels</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    xarray.dataarray checker for raster interface</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        Input 2D or 3D DataArray with shape=(z, y, x)</span>
+<span class="sd">    z_value : int/string/float</span>
+<span class="sd">        Select the z_value of 3D DataArray with multiple layers.</span>
+<span class="sd">    coords_labels : dictionary</span>
+<span class="sd">        Pass dimension labels for coordinates and layers if they do not</span>
+<span class="sd">        belong to default dimensions, which are (band/time, y/lat, x/lon)</span>
+<span class="sd">        e.g. coords_labels = {&quot;y_label&quot;: &quot;latitude&quot;, &quot;x_label&quot;: &quot;longitude&quot;, &quot;z_label&quot;: &quot;year&quot;}</span>
+<span class="sd">        Default is {} empty dictionary.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    z_id : int</span>
+<span class="sd">        Returns the index of layer</span>
+<span class="sd">    dims : dictionary</span>
+<span class="sd">        Mapped dimensions of the DataArray</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;xarray must be installed to use this functionality&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">DataArray</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;da must be an instance of xarray.DataArray&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">ndim</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;da must be 2D or 3D&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">)</span>
+        <span class="ow">or</span> <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="n">da</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">floating</span><span class="p">)</span>
+    <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;da must be an array of integers or float&quot;</span><span class="p">)</span>
+
+    <span class="c1"># default dimensions</span>
+    <span class="n">def_labels</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;x_label&quot;</span><span class="p">:</span> <span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;x_label&quot;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="s2">&quot;x_label&quot;</span> <span class="ow">in</span> <span class="n">coords_labels</span>
+        <span class="k">else</span> <span class="p">(</span><span class="s2">&quot;x&quot;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;x&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="s2">&quot;lon&quot;</span><span class="p">),</span>
+        <span class="s2">&quot;y_label&quot;</span><span class="p">:</span> <span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;y_label&quot;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="s2">&quot;y_label&quot;</span> <span class="ow">in</span> <span class="n">coords_labels</span>
+        <span class="k">else</span> <span class="p">(</span><span class="s2">&quot;y&quot;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;y&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="s2">&quot;lat&quot;</span><span class="p">),</span>
+    <span class="p">}</span>
+
+    <span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">def_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">coords_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]</span>
+            <span class="k">if</span> <span class="s2">&quot;z_label&quot;</span> <span class="ow">in</span> <span class="n">coords_labels</span>
+            <span class="k">else</span> <span class="p">(</span><span class="s2">&quot;band&quot;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="s2">&quot;band&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="s2">&quot;time&quot;</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="n">z_id</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="n">z_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">da</span><span class="o">.</span><span class="n">sizes</span><span class="p">[</span><span class="n">def_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Multiple layers detected. Using first layer as default.&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">z_id</span> <span class="o">+=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">da</span><span class="p">[</span><span class="n">def_labels</span><span class="p">[</span><span class="s2">&quot;z_label&quot;</span><span class="p">]])</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">z_value</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">z_id</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">return</span> <span class="n">z_id</span><span class="p">,</span> <span class="n">def_labels</span>
+
+
+<span class="k">def</span> <span class="nf">_index2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">attrs</span><span class="p">,</span> <span class="n">coords</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates xarray.DataArray object from passed data</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    data : array/list/pd.Series</span>
+<span class="sd">        1d array-like data with dimensionality conforming to index</span>
+<span class="sd">    index : pd.MultiIndex</span>
+<span class="sd">        indices of the DataArray when converted to pd.Series</span>
+<span class="sd">    attrs : Dictionary</span>
+<span class="sd">        Attributes stored in dict related to DataArray, e.g. da.attrs</span>
+<span class="sd">    coords : Dictionary/xarray.core.coordinates.DataArrayCoordinates</span>
+<span class="sd">        coordinates corresponding to DataArray, e.g. da[n-1:n].coords</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    da : xarray.DataArray</span>
+<span class="sd">        instance of xarray.DataArray</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">xarray</span> <span class="kn">import</span> <span class="n">DataArray</span>
+    <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ModuleNotFoundError</span><span class="p">(</span><span class="s2">&quot;xarray must be installed to use this functionality&quot;</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="n">index</span>
+    <span class="n">dims</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">names</span>
+    <span class="n">indexer</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">idx</span><span class="o">.</span><span class="n">codes</span><span class="p">)</span>
+    <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">lev</span><span class="o">.</span><span class="n">size</span> <span class="k">for</span> <span class="n">lev</span> <span class="ow">in</span> <span class="n">idx</span><span class="o">.</span><span class="n">levels</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">coords</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">missing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">idx</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">missing</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">attrs</span><span class="p">:</span>
+                <span class="n">fill_value</span> <span class="o">=</span> <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">min_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+                <span class="n">fill_value</span> <span class="o">=</span> <span class="n">min_data</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">min_data</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="mi">1</span>
+                <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">fill_value</span><span class="p">])</span>
+            <span class="n">data_complete</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill_value</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">data_complete</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">data_complete</span><span class="p">[</span><span class="n">indexer</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="n">coords</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">dim</span><span class="p">,</span> <span class="n">lev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">dims</span><span class="p">,</span> <span class="n">idx</span><span class="o">.</span><span class="n">levels</span><span class="p">):</span>
+            <span class="n">coords</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">lev</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fill</span> <span class="o">=</span> <span class="n">attrs</span><span class="p">[</span><span class="s2">&quot;nodatavals&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="s2">&quot;nodatavals&quot;</span> <span class="ow">in</span> <span class="n">attrs</span> <span class="k">else</span> <span class="mi">0</span>
+        <span class="n">data_complete</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">fill</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+        <span class="n">data_complete</span><span class="p">[</span><span class="n">indexer</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
+
+    <span class="n">da</span> <span class="o">=</span> <span class="n">DataArray</span><span class="p">(</span><span class="n">data_complete</span><span class="p">,</span> <span class="n">coords</span><span class="o">=</span><span class="n">coords</span><span class="p">,</span> <span class="n">dims</span><span class="o">=</span><span class="n">dims</span><span class="p">,</span> <span class="n">attrs</span><span class="o">=</span><span class="n">attrs</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">da</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_idmap</span><span class="p">(</span><span class="n">ids</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">dtype</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Utility function computes id_map of non-missing raster data</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    ids : ndarray</span>
+<span class="sd">        1D array containing ids of non-missing raster data</span>
+<span class="sd">    mask : ndarray</span>
+<span class="sd">        1D array mask array</span>
+<span class="sd">    dtype : type</span>
+<span class="sd">        Data type of the id_map array</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    id_map : ndarray</span>
+<span class="sd">        1D array containing id_maps of non-missing raster data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">id_map</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">*</span> <span class="mi">1</span>
+    <span class="n">id_map</span><span class="p">[</span><span class="n">ids</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">id_map</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_SWbuilder</span><span class="p">(</span>
+    <span class="n">nrows</span><span class="p">,</span>
+    <span class="n">ncols</span><span class="p">,</span>
+    <span class="n">ids</span><span class="p">,</span>
+    <span class="n">id_map</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="p">,</span>
+    <span class="n">k</span><span class="p">,</span>
+    <span class="n">dtype</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Computes data and orders rows, cols, data for a single chunk</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    nrows : int</span>
+<span class="sd">        Number of rows in the raster data</span>
+<span class="sd">    ncols : int</span>
+<span class="sd">        Number of columns in the raster data</span>
+<span class="sd">    ids : ndarray</span>
+<span class="sd">        1D array containing ids of non-missing raster data</span>
+<span class="sd">    id_map : ndarray</span>
+<span class="sd">        1D array containing id_maps of non-missing raster data</span>
+<span class="sd">    criterion : str</span>
+<span class="sd">        Type of contiguity.</span>
+<span class="sd">    k : int</span>
+<span class="sd">        Order of contiguity, Default is 1</span>
+<span class="sd">    dtype : type</span>
+<span class="sd">        Data type of the id_map array</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    data : ndarray</span>
+<span class="sd">        1D ones array containing weight of each neighbor</span>
+<span class="sd">    rows : ndarray</span>
+<span class="sd">        1D ones array containing row value of each id</span>
+<span class="sd">        in the sparse weight object</span>
+<span class="sd">    cols : ndarray</span>
+<span class="sd">        1D ones array containing columns value of each id</span>
+<span class="sd">        in the sparse weight object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">_compute_chunk</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">nogil</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_compute_chunk</span><span class="p">(</span>
+    <span class="n">nrows</span><span class="p">,</span>
+    <span class="n">ncols</span><span class="p">,</span>
+    <span class="n">ids</span><span class="p">,</span>
+    <span class="n">id_map</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="p">,</span>
+    <span class="n">k</span><span class="p">,</span>
+    <span class="n">dtype</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Computes rows cols for a single chunk</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    nrows : int</span>
+<span class="sd">        Number of rows in the raster data</span>
+<span class="sd">    ncols : int</span>
+<span class="sd">        Number of columns in the raster data</span>
+<span class="sd">    ids : ndarray</span>
+<span class="sd">        1D array containing ids of non-missing raster data</span>
+<span class="sd">    id_map : ndarray</span>
+<span class="sd">        1D array containing id_maps of non-missing raster data</span>
+<span class="sd">    criterion : str</span>
+<span class="sd">        Type of contiguity.</span>
+<span class="sd">    k : int</span>
+<span class="sd">        Order of contiguity, Default is 1</span>
+<span class="sd">    dtype : type</span>
+<span class="sd">        Data type of the rows and cols array</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    rows : ndarray</span>
+<span class="sd">        1D ones array containing row value of each id</span>
+<span class="sd">        in the sparse weight object</span>
+<span class="sd">    cols : ndarray</span>
+<span class="sd">        1D ones array containing columns value of each id</span>
+<span class="sd">        in the sparse weight object</span>
+<span class="sd">    ni : int</span>
+<span class="sd">        Number of rows and cols</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+    <span class="c1"># Setting d which is used for row, col preallocation</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="mi">4</span> <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span> <span class="k">else</span> <span class="mi">8</span>
+    <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">k</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="mi">8</span> <span class="o">+</span> <span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mi">8</span><span class="p">))</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">d</span> <span class="o">*</span> <span class="n">n</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span>
+    <span class="n">ni</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># -&gt; Pointer to store rows and cols in array</span>
+    <span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">condition</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span>
+            <span class="k">else</span> <span class="p">((</span><span class="n">k</span> <span class="o">-</span> <span class="n">order</span><span class="p">)</span> <span class="k">if</span> <span class="p">((</span><span class="n">k</span> <span class="o">-</span> <span class="n">order</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">order</span><span class="p">)</span> <span class="k">else</span> <span class="p">(</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+            <span class="n">id_i</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">og_id</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="p">((</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">order</span><span class="p">:</span>
+                <span class="c1"># east neighbor</span>
+                <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="c1"># north-east to south-east neighbors</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">condition</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">//</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">nrows</span> <span class="o">-</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[(</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))]</span>
+                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">//</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
+                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[(</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))]</span>
+                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+
+            <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">//</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&lt;</span> <span class="p">(</span><span class="n">nrows</span> <span class="o">-</span> <span class="n">order</span><span class="p">):</span>
+                <span class="c1"># south neighbor</span>
+                <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)]</span>
+                <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                    <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="c1"># south-west to south-east neighbors</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">condition</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
+                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">-</span> <span class="n">j</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="p">((</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:</span>
+                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+
+                <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span> <span class="ow">or</span> <span class="p">((</span><span class="n">k</span> <span class="o">/</span> <span class="n">order</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mf">2.0</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="p">(</span><span class="n">id_i</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">order</span><span class="p">:</span>
+                        <span class="c1"># south-west neighbor</span>
+                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">-</span> <span class="n">order</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="p">((</span><span class="n">id_i</span> <span class="o">+</span> <span class="n">order</span><span class="p">)</span> <span class="o">%</span> <span class="n">ncols</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">order</span><span class="p">:</span>
+                        <span class="c1"># south-east neighbor</span>
+                        <span class="n">id_neighbor</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">[</span><span class="n">id_i</span> <span class="o">+</span> <span class="p">(</span><span class="n">ncols</span> <span class="o">*</span> <span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="n">order</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">id_neighbor</span><span class="p">:</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">og_id</span><span class="p">,</span> <span class="n">id_neighbor</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">rows</span><span class="p">[</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[</span><span class="n">ni</span><span class="p">]</span> <span class="o">=</span> <span class="n">id_neighbor</span><span class="p">,</span> <span class="n">og_id</span>
+                            <span class="n">ni</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="k">return</span> <span class="n">rows</span><span class="p">[:</span><span class="n">ni</span><span class="p">],</span> <span class="n">cols</span><span class="p">[:</span><span class="n">ni</span><span class="p">]</span>
+
+
+<span class="nd">@jit</span><span class="p">(</span><span class="n">nopython</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">_chunk_generator</span><span class="p">(</span>
+    <span class="n">n_jobs</span><span class="p">,</span>
+    <span class="n">starts</span><span class="p">,</span>
+    <span class="n">ids</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Construct chunks to iterate over within numba in parallel</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_jobs : int</span>
+<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
+<span class="sd">        all available cores are used.</span>
+<span class="sd">    starts : ndarray</span>
+<span class="sd">        (n_chunks+1,) array of positional starts for ids chunk</span>
+<span class="sd">    ids : ndarray</span>
+<span class="sd">        1D array containing ids of non-missing raster data</span>
+
+<span class="sd">    Yields</span>
+<span class="sd">    ------</span>
+<span class="sd">    ids_chunk : numpy.ndarray</span>
+<span class="sd">        (n_chunk,) array containing the chunk of non-missing raster data</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">chunk_size</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">starts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">):</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">ids_chunk</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">start</span> <span class="p">:</span> <span class="p">(</span><span class="n">start</span> <span class="o">+</span> <span class="n">chunk_size</span><span class="p">)]</span>
+        <span class="k">yield</span> <span class="p">(</span><span class="n">ids_chunk</span><span class="p">,)</span>
+
+
+<span class="k">def</span> <span class="nf">_parSWbuilder</span><span class="p">(</span>
+    <span class="n">nrows</span><span class="p">,</span>
+    <span class="n">ncols</span><span class="p">,</span>
+    <span class="n">ids</span><span class="p">,</span>
+    <span class="n">id_map</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="p">,</span>
+    <span class="n">k</span><span class="p">,</span>
+    <span class="n">dtype</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Computes data and orders rows, cols, data in parallel using numba</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    nrows : int</span>
+<span class="sd">        Number of rows in the raster data</span>
+<span class="sd">    ncols : int</span>
+<span class="sd">        Number of columns in the raster data</span>
+<span class="sd">    ids : ndarray</span>
+<span class="sd">        1D array containing ids of non-missing raster data</span>
+<span class="sd">    id_map : ndarray</span>
+<span class="sd">        1D array containing id_maps of non-missing raster data</span>
+<span class="sd">    criterion : str</span>
+<span class="sd">        Type of contiguity.</span>
+<span class="sd">    k : int</span>
+<span class="sd">        Order of contiguity, Default is 1</span>
+<span class="sd">    dtype : type</span>
+<span class="sd">        Data type of the rows and cols array</span>
+<span class="sd">    n_jobs : int</span>
+<span class="sd">        Number of cores to be used in the sparse weight construction. If -1,</span>
+<span class="sd">        all available cores are used.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    data : ndarray</span>
+<span class="sd">        1D ones array containing weight of each neighbor</span>
+<span class="sd">    rows : ndarray</span>
+<span class="sd">        1D ones array containing row value of each id</span>
+<span class="sd">        in the sparse weight object</span>
+<span class="sd">    cols : ndarray</span>
+<span class="sd">        1D ones array containing columns value of each id</span>
+<span class="sd">        in the sparse weight object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span><span class="p">,</span> <span class="n">parallel_backend</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+    <span class="n">chunk_size</span> <span class="o">=</span> <span class="n">n</span> <span class="o">//</span> <span class="n">n_jobs</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_jobs</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">chunk_size</span>
+    <span class="n">chunk</span> <span class="o">=</span> <span class="n">_chunk_generator</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">starts</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
+    <span class="k">with</span> <span class="n">parallel_backend</span><span class="p">(</span><span class="s2">&quot;threading&quot;</span><span class="p">):</span>
+        <span class="n">worker_out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
+            <span class="n">delayed</span><span class="p">(</span><span class="n">_compute_chunk</span><span class="p">)(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="o">*</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_map</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dtype</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">ids</span> <span class="ow">in</span> <span class="n">chunk</span>
+        <span class="p">)</span>
+    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">worker_out</span><span class="p">)</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span>
+    <span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">))</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
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+  </body>
+</html>
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diff --git a/_modules/libpysal/weights/set_operations.html b/_modules/libpysal/weights/set_operations.html
new file mode 100644
index 000000000..23cc33249
--- /dev/null
+++ b/_modules/libpysal/weights/set_operations.html
@@ -0,0 +1,686 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>libpysal.weights.set_operations &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../../api.html">API</a><ul>
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+      
+  <h1>Source code for libpysal.weights.set_operations</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Set-like manipulation of weights matrices.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;, Charles Schmidt &lt;schmidtc@gmail.com&gt;, David Folch &lt;david.folch@asu.edu&gt;, Dani Arribas-Bel &lt;darribas@asu.edu&gt;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
+<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">isspmatrix_csr</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">ones</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;w_union&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;w_intersection&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;w_difference&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;w_symmetric_difference&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;w_subset&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;w_clip&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<div class="viewcode-block" id="w_union">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_union.html#libpysal.weights.w_union">[docs]</a>
+<span class="k">def</span> <span class="nf">w_union</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a binary weights object, w, that includes all neighbor pairs that</span>
+<span class="sd">    exist in either w1 or w2.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w1                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w2                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w       : W</span>
+<span class="sd">              object</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
+<span class="sd">    equivalent to the float ID 2.0. Returns a matrix with all the unique IDs</span>
+<span class="sd">    from w1 and w2.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Construct rook weights matrices for two regions, one is 4x4 (16 areas)</span>
+<span class="sd">    and the other is 6x4 (24 areas). A union of these two weights matrices</span>
+<span class="sd">    results in the new weights matrix matching the larger one.</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_union</span>
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4)</span>
+<span class="sd">    &gt;&gt;&gt; w2 = lat2W(6,4)</span>
+<span class="sd">    &gt;&gt;&gt; w = w_union(w1, w2)</span>
+<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
+<span class="sd">    [11, 14]</span>
+<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
+<span class="sd">    [11, 14, 19]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
+<span class="sd">    [19, 11, 14]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+            <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="w_intersection">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_intersection.html#libpysal.weights.w_intersection">[docs]</a>
+<span class="k">def</span> <span class="nf">w_intersection</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w_shape</span><span class="o">=</span><span class="s2">&quot;w1&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a binary weights object, w, that includes only</span>
+<span class="sd">    those neighbor pairs that exist in both w1 and w2.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w1                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w2                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w_shape                 : string</span>
+<span class="sd">                              Defines the shape of the returned weights matrix. &#39;w1&#39; returns a</span>
+<span class="sd">                              matrix with the same IDs as w1; &#39;all&#39; returns a matrix with all</span>
+<span class="sd">                              the unique IDs from w1 and w2; and &#39;min&#39; returns a matrix with</span>
+<span class="sd">                              only the IDs occurring in both w1 and w2.</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w       : W</span>
+<span class="sd">              object</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
+<span class="sd">    equivalent to the float ID 2.0.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Construct rook weights matrices for two regions, one is 4x4 (16 areas)</span>
+<span class="sd">    and the other is 6x4 (24 areas). An intersection of these two weights</span>
+<span class="sd">    matrices results in the new weights matrix matching the smaller one.</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_intersection</span>
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4)</span>
+<span class="sd">    &gt;&gt;&gt; w2 = lat2W(6,4)</span>
+<span class="sd">    &gt;&gt;&gt; w = w_intersection(w1, w2)</span>
+<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
+<span class="sd">    [11, 14]</span>
+<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
+<span class="sd">    [11, 14, 19]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
+<span class="sd">    [11, 14]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;w1&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;invalid string passed to w_shape&quot;</span><span class="p">)</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neigh_keys</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span> <span class="ow">and</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+            <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="w_difference">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_difference.html#libpysal.weights.w_difference">[docs]</a>
+<span class="k">def</span> <span class="nf">w_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w_shape</span><span class="o">=</span><span class="s2">&quot;w1&quot;</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a binary weights object, w, that includes only neighbor pairs</span>
+<span class="sd">    in w1 that are not in w2. The w_shape and constrained parameters</span>
+<span class="sd">    determine which pairs in w1 that are not in w2 are returned.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w1                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w2                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w_shape                 : string</span>
+<span class="sd">                              Defines the shape of the returned weights matrix. &#39;w1&#39; returns a</span>
+<span class="sd">                              matrix with the same IDs as w1; &#39;all&#39; returns a matrix with all</span>
+<span class="sd">                              the unique IDs from w1 and w2; and &#39;min&#39; returns a matrix with</span>
+<span class="sd">                              the IDs occurring in w1 and not in w2.</span>
+<span class="sd">    constrained             : boolean</span>
+<span class="sd">                              If False then the full set of neighbor pairs in w1 that are</span>
+<span class="sd">                              not in w2 are returned. If True then those pairs that would</span>
+<span class="sd">                              not be possible if w_shape=&#39;min&#39; are dropped. Ignored if</span>
+<span class="sd">                              w_shape is set to &#39;min&#39;.</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w       : W</span>
+<span class="sd">              object</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
+<span class="sd">    equivalent to the float ID 2.0.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions</span>
+<span class="sd">    (16 areas). A queen matrix has all the joins a rook matrix does plus joins</span>
+<span class="sd">    between areas that share a corner. The new matrix formed by the difference</span>
+<span class="sd">    of rook from queen contains only join at corners (typically called a</span>
+<span class="sd">    bishop matrix). Note that the difference of queen from rook would result</span>
+<span class="sd">    in a weights matrix with no joins.</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_difference</span>
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4,rook=False)</span>
+<span class="sd">    &gt;&gt;&gt; w2 = lat2W(4,4,rook=True)</span>
+<span class="sd">    &gt;&gt;&gt; w = w_difference(w1, w2, constrained=False)</span>
+<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
+<span class="sd">    False</span>
+<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
+<span class="sd">    [10, 11, 14]</span>
+<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
+<span class="sd">    [11, 14]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
+<span class="sd">    [10]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;w1&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">neigh_keys</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;returned an empty weights matrix&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;invalid string passed to w_shape&quot;</span><span class="p">)</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neigh_keys</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+                <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">if</span> <span class="n">constrained</span> <span class="ow">or</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
+        <span class="n">constrained_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+        <span class="n">island_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">island_keys</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">constrained_keys</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="w_symmetric_difference">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_symmetric_difference.html#libpysal.weights.w_symmetric_difference">[docs]</a>
+<span class="k">def</span> <span class="nf">w_symmetric_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">w_shape</span><span class="o">=</span><span class="s2">&quot;all&quot;</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a binary weights object, w, that includes only neighbor pairs</span>
+<span class="sd">    that are not shared by w1 and w2. The w_shape and constrained parameters</span>
+<span class="sd">    determine which pairs that are not shared by w1 and w2 are returned.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w1                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w2                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    w_shape                 : string</span>
+<span class="sd">                              Defines the shape of the returned weights matrix. &#39;all&#39; returns a</span>
+<span class="sd">                              matrix with all the unique IDs from w1 and w2; and &#39;min&#39; returns</span>
+<span class="sd">                              a matrix with the IDs not shared by w1 and w2.</span>
+<span class="sd">    constrained             : boolean</span>
+<span class="sd">                              If False then the full set of neighbor pairs that are not</span>
+<span class="sd">                              shared by w1 and w2 are returned. If True then those pairs</span>
+<span class="sd">                              that would not be possible if w_shape=&#39;min&#39; are dropped.</span>
+<span class="sd">                              Ignored if w_shape is set to &#39;min&#39;.</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w       : W</span>
+<span class="sd">              object</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    ID comparisons are performed using ==, therefore the integer ID 2 is</span>
+<span class="sd">    equivalent to the float ID 2.0.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook</span>
+<span class="sd">    matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these</span>
+<span class="sd">    two matrices (with w_shape set to &#39;all&#39; and constrained set to False)</span>
+<span class="sd">    contains the corner joins in the overlap area, all the joins in the</span>
+<span class="sd">    non-overlap area.</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_symmetric_difference</span>
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(4,4,rook=False)</span>
+<span class="sd">    &gt;&gt;&gt; w2 = lat2W(6,4,rook=True)</span>
+<span class="sd">    &gt;&gt;&gt; w = w_symmetric_difference(w1, w2, constrained=False)</span>
+<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
+<span class="sd">    False</span>
+<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
+<span class="sd">    [10, 11, 14]</span>
+<span class="sd">    &gt;&gt;&gt; w2.neighbors[15]</span>
+<span class="sd">    [11, 14, 19]</span>
+<span class="sd">    &gt;&gt;&gt; set(w.neighbors[15]) == set([10, 19])</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;all&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">union</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+    <span class="k">elif</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
+        <span class="n">neigh_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">symmetric_difference</span><span class="p">(</span>
+            <span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;invalid string passed to w_shape&quot;</span><span class="p">)</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">neigh_keys</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+                <span class="n">add_neigh</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">symmetric_difference</span><span class="p">(</span>
+                    <span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="p">)</span>
+                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">add_neigh</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">if</span> <span class="n">constrained</span> <span class="ow">or</span> <span class="n">w_shape</span> <span class="o">==</span> <span class="s2">&quot;min&quot;</span><span class="p">:</span>
+        <span class="n">constrained_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
+        <span class="n">island_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">island_keys</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">constrained_keys</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">constrained_keys</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="w_subset">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_subset.html#libpysal.weights.w_subset">[docs]</a>
+<span class="k">def</span> <span class="nf">w_subset</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a binary weights object, w, that includes only those</span>
+<span class="sd">    observations in ids.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w1                      : W</span>
+<span class="sd">                              object</span>
+<span class="sd">    ids                     : list</span>
+<span class="sd">                              A list containing the IDs to be include in the returned weights</span>
+<span class="sd">                              object.</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w       : W</span>
+<span class="sd">              object</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Construct a rook weights matrix for a 6x4 region (24 areas). By default</span>
+<span class="sd">    PySAL assigns integer IDs to the areas in a region. By passing in a list</span>
+<span class="sd">    of integers from 0 to 15, the first 16 areas are extracted from the</span>
+<span class="sd">    previous weights matrix, and only those joins relevant to the new region</span>
+<span class="sd">    are retained.</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_subset</span>
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(6,4)</span>
+<span class="sd">    &gt;&gt;&gt; ids = range(16)</span>
+<span class="sd">    &gt;&gt;&gt; w = w_subset(w1, ids)</span>
+<span class="sd">    &gt;&gt;&gt; w1[0] == w[0]</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w1.neighbors[15]</span>
+<span class="sd">    [11, 14, 19]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[15]</span>
+<span class="sd">    [11, 14]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">ids_set</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">:</span>
+            <span class="n">neigh_add</span> <span class="o">=</span> <span class="n">ids_set</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">neigh_add</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">ids</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="w_clip">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_clip.html#libpysal.weights.w_clip">[docs]</a>
+<span class="k">def</span> <span class="nf">w_clip</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">outSP</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Clip a continuous W object (w1) with a different W object (w2) so only cells where</span>
+<span class="sd">    w2 has a non-zero value remain with non-zero values in w1.</span>
+
+<span class="sd">    Checks on w1 and w2 are performed to make sure they conform to the</span>
+<span class="sd">    appropriate format and, if not, they are converted.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    w1                      : W</span>
+<span class="sd">                              W, scipy.sparse.csr.csr_matrix</span>
+<span class="sd">                              Potentially continuous weights matrix to be clipped. The clipped</span>
+<span class="sd">                              matrix wc will have at most the same elements as w1.</span>
+<span class="sd">    w2                      : W</span>
+<span class="sd">                              W, scipy.sparse.csr.csr_matrix</span>
+<span class="sd">                              Weights matrix to use as shell to clip w1. Automatically</span>
+<span class="sd">                              converted to binary format. Only non-zero elements in w2 will be</span>
+<span class="sd">                              kept non-zero in wc. NOTE: assumed to be of the same shape as w1</span>
+<span class="sd">    outSP                   : boolean</span>
+<span class="sd">                              If True (default) return sparse version of the clipped W, if</span>
+<span class="sd">                              False, return W object of the clipped matrix</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    wc      : W</span>
+<span class="sd">              W, scipy.sparse.csr.csr_matrix</span>
+<span class="sd">              Clipped W object (sparse if outSP=Ture). It inherits ``id_order`` from w1.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+
+<span class="sd">    First create a W object from a lattice using queen contiguity and</span>
+<span class="sd">    row-standardize it (note that these weights will stay when we clip the</span>
+<span class="sd">    object, but they will not neccesarily represent a row-standardization</span>
+<span class="sd">    anymore):</span>
+
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(3, 2, rook=False)</span>
+<span class="sd">    &gt;&gt;&gt; w1.transform = &#39;R&#39;</span>
+
+<span class="sd">    We will clip that geography assuming observations 0, 2, 3 and 4 belong to</span>
+<span class="sd">    one group and 1, 5 belong to another group and we don&#39;t want both groups</span>
+<span class="sd">    to interact with each other in our weights (i.e. w_ij = 0 if i and j in</span>
+<span class="sd">    different groups). For that, we use the following method:</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; w2 = libpysal.weights.block_weights([&#39;r1&#39;, &#39;r2&#39;, &#39;r1&#39;, &#39;r1&#39;, &#39;r1&#39;, &#39;r2&#39;])</span>
+
+<span class="sd">    To illustrate that w2 will only be considered as binary even when the</span>
+<span class="sd">    object passed is not, we can row-standardize it</span>
+
+<span class="sd">    &gt;&gt;&gt; w2.transform = &#39;R&#39;</span>
+
+<span class="sd">    The clipped object ``wc`` will contain only the spatial queen</span>
+<span class="sd">    relationships that occur within one group (&#39;r1&#39; or &#39;r2&#39;) but will have</span>
+<span class="sd">    gotten rid of those that happen across groups</span>
+
+<span class="sd">    &gt;&gt;&gt; wcs = libpysal.weights.w_clip(w1, w2, outSP=True)</span>
+
+<span class="sd">    This will create a sparse object (recommended when n is large).</span>
+
+<span class="sd">    &gt;&gt;&gt; wcs.sparse.toarray()</span>
+<span class="sd">    array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="sd">            0.        ]])</span>
+
+
+<span class="sd">    If we wanted an original W object, we can control that with the argument</span>
+<span class="sd">    ``outSP``:</span>
+
+<span class="sd">    &gt;&gt;&gt; wc = libpysal.weights.w_clip(w1, w2, outSP=False)</span>
+<span class="sd">    &gt;&gt;&gt; wc.full()[0]</span>
+<span class="sd">    array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="sd">            0.        ],</span>
+<span class="sd">           [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="sd">            0.        ]])</span>
+
+<span class="sd">    You can check they are actually the same:</span>
+
+<span class="sd">    &gt;&gt;&gt; wcs.sparse.toarray() == wc.full()[0]</span>
+<span class="sd">    array([[ True,  True,  True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True,  True,  True]])</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">WSP2W</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">w1</span><span class="o">.</span><span class="n">id_order</span><span class="p">:</span>
+        <span class="n">w1</span><span class="o">.</span><span class="n">id_order</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">id_order</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">id_order</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">w1</span><span class="p">):</span>
+        <span class="n">w1</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">sparse</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">w2</span><span class="p">):</span>
+        <span class="n">w2</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">sparse</span>
+    <span class="n">w2</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">ones</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+    <span class="n">wc</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">w2</span><span class="p">)</span>
+    <span class="n">wc</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">wc</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">id_order</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">outSP</span><span class="p">:</span>
+        <span class="n">wc</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">wc</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">wc</span></div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
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+  <h1>Source code for libpysal.weights.spatial_lag</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Spatial lag operations.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;, David C. Folch &lt;david.folch@asu.edu&gt;, Levi John Wolf &lt;ljw2@asu.edu&quot;</span>
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;lag_spatial&quot;</span><span class="p">,</span> <span class="s2">&quot;lag_categorical&quot;</span><span class="p">]</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="lag_spatial">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.lag_spatial.html#libpysal.weights.lag_spatial">[docs]</a>
+<span class="k">def</span> <span class="nf">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Spatial lag operator.</span>
+
+<span class="sd">    If w is row standardized, returns the average of each observation&#39;s neighbors;</span>
+<span class="sd">    if not, returns the weighted sum of each observation&#39;s neighbors.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w                   : W</span>
+<span class="sd">                          libpysal spatial weightsobject</span>
+<span class="sd">    y                   : array</span>
+<span class="sd">                          numpy array with dimensionality conforming to w (see examples)</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    wy                  : array</span>
+<span class="sd">                          array of numeric values for the spatial lag</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Setup a 9x9 binary spatial weights matrix and vector of data; compute the</span>
+<span class="sd">    spatial lag of the vector.</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; w = libpysal.weights.lat2W(3, 3)</span>
+<span class="sd">    &gt;&gt;&gt; y = np.arange(9)</span>
+<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, y)</span>
+<span class="sd">    &gt;&gt;&gt; yl</span>
+<span class="sd">    array([ 4.,  6.,  6., 10., 16., 14., 10., 18., 12.])</span>
+
+<span class="sd">    Row standardize the weights matrix and recompute the spatial lag</span>
+
+<span class="sd">    &gt;&gt;&gt; w.transform = &#39;r&#39;</span>
+<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, y)</span>
+<span class="sd">    &gt;&gt;&gt; yl</span>
+<span class="sd">    array([2.        , 2.        , 3.        , 3.33333333, 4.        ,</span>
+<span class="sd">           4.66666667, 5.        , 6.        , 6.        ])</span>
+
+
+<span class="sd">    Explicitly define data vector as 9x1 and recompute the spatial lag</span>
+
+<span class="sd">    &gt;&gt;&gt; y.shape = (9, 1)</span>
+<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, y)</span>
+<span class="sd">    &gt;&gt;&gt; yl</span>
+<span class="sd">    array([[2.        ],</span>
+<span class="sd">           [2.        ],</span>
+<span class="sd">           [3.        ],</span>
+<span class="sd">           [3.33333333],</span>
+<span class="sd">           [4.        ],</span>
+<span class="sd">           [4.66666667],</span>
+<span class="sd">           [5.        ],</span>
+<span class="sd">           [6.        ],</span>
+<span class="sd">           [6.        ]])</span>
+
+
+<span class="sd">    Take the spatial lag of a 9x2 data matrix</span>
+
+<span class="sd">    &gt;&gt;&gt; yr = np.arange(8, -1, -1)</span>
+<span class="sd">    &gt;&gt;&gt; yr.shape = (9, 1)</span>
+<span class="sd">    &gt;&gt;&gt; x = np.hstack((y, yr))</span>
+<span class="sd">    &gt;&gt;&gt; yl = libpysal.weights.lag_spatial(w, x)</span>
+<span class="sd">    &gt;&gt;&gt; yl</span>
+<span class="sd">    array([[2.        , 6.        ],</span>
+<span class="sd">           [2.        , 6.        ],</span>
+<span class="sd">           [3.        , 5.        ],</span>
+<span class="sd">           [3.33333333, 4.66666667],</span>
+<span class="sd">           [4.        , 4.        ],</span>
+<span class="sd">           [4.66666667, 3.33333333],</span>
+<span class="sd">           [5.        , 3.        ],</span>
+<span class="sd">           [6.        , 2.        ],</span>
+<span class="sd">           [6.        , 2.        ]])</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">w</span><span class="o">.</span><span class="n">sparse</span> <span class="o">*</span> <span class="n">y</span></div>
+
+
+
+<div class="viewcode-block" id="lag_categorical">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.lag_categorical.html#libpysal.weights.lag_categorical">[docs]</a>
+<span class="k">def</span> <span class="nf">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">ties</span><span class="o">=</span><span class="s2">&quot;tryself&quot;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Spatial lag operator for categorical variables.</span>
+
+<span class="sd">    Constructs the most common categories of neighboring observations, weighted</span>
+<span class="sd">    by their weight strength.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w                   : W</span>
+<span class="sd">                          PySAL spatial weightsobject</span>
+<span class="sd">    y                   : iterable</span>
+<span class="sd">                          iterable collection of categories (either int or</span>
+<span class="sd">                          string) with dimensionality conforming to w (see examples)</span>
+<span class="sd">    ties                : str</span>
+<span class="sd">                          string describing the method to use when resolving</span>
+<span class="sd">                          ties. By default, the option is &quot;tryself&quot;,</span>
+<span class="sd">                          and the category of the focal observation</span>
+<span class="sd">                          is included with its neighbors to try</span>
+<span class="sd">                          and break a tie. If this does not resolve the tie,</span>
+<span class="sd">                          a winner is chosen randomly. To just use random choice to</span>
+<span class="sd">                          break ties, pass &quot;random&quot; instead.</span>
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    an (n x k) column vector containing the most common neighboring observation</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    This works on any array where the number of unique elements along the column</span>
+<span class="sd">    axis is less than the number of elements in the array, for any dtype.</span>
+<span class="sd">    That means the routine should work on any dtype that np.unique() can</span>
+<span class="sd">    compare.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of</span>
+<span class="sd">    categorical variables with no ties.</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; np.random.seed(12345)</span>
+<span class="sd">    &gt;&gt;&gt; w = libpysal.weights.lat2W(3, 3)</span>
+<span class="sd">    &gt;&gt;&gt; y = [&#39;a&#39;,&#39;b&#39;,&#39;a&#39;,&#39;b&#39;,&#39;c&#39;,&#39;b&#39;,&#39;c&#39;,&#39;b&#39;,&#39;c&#39;]</span>
+<span class="sd">    &gt;&gt;&gt; y_l = libpysal.weights.lag_categorical(w, y)</span>
+<span class="sd">    &gt;&gt;&gt; np.array_equal(y_l, np.array([&#39;b&#39;, &#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;]))</span>
+<span class="sd">    True</span>
+
+<span class="sd">    Explicitly reshape y into a (9x1) array and calculate lag again</span>
+
+<span class="sd">    &gt;&gt;&gt; yvect = np.array(y).reshape(9,1)</span>
+<span class="sd">    &gt;&gt;&gt; yvect_l = libpysal.weights.lag_categorical(w,yvect)</span>
+<span class="sd">    &gt;&gt;&gt; check = np.array( [ [i] for i in  [&#39;b&#39;, &#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;, &#39;c&#39;, &#39;b&#39;]] )</span>
+<span class="sd">    &gt;&gt;&gt; np.array_equal(yvect_l, check)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    compute the lag of a 9x2 matrix of categories</span>
+
+<span class="sd">    &gt;&gt;&gt; y2 = [&#39;a&#39;, &#39;c&#39;, &#39;c&#39;, &#39;d&#39;, &#39;b&#39;, &#39;a&#39;, &#39;d&#39;, &#39;d&#39;, &#39;c&#39;]</span>
+<span class="sd">    &gt;&gt;&gt; ym = np.vstack((y,y2)).T</span>
+<span class="sd">    &gt;&gt;&gt; ym_lag = libpysal.weights.lag_categorical(w,ym)</span>
+<span class="sd">    &gt;&gt;&gt; check = np.array([[&#39;b&#39;, &#39;d&#39;], [&#39;a&#39;, &#39;c&#39;], [&#39;b&#39;, &#39;c&#39;], [&#39;c&#39;, &#39;d&#39;], [&#39;b&#39;, &#39;d&#39;], [&#39;c&#39;, &#39;c&#39;], [&#39;b&#39;, &#39;d&#39;], [&#39;c&#39;, &#39;d&#39;], [&#39;b&#39;, &#39;c&#39;]])</span>
+<span class="sd">    &gt;&gt;&gt; np.array_equal(check, ym_lag)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="n">orig_shape</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">orig_shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">orig_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">col</span><span class="p">)</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="n">normalized_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
+        <span class="n">normalized_labels</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+    <span class="k">for</span> <span class="n">focal_name</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
+        <span class="n">focal_idx</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id2i</span><span class="p">[</span><span class="n">focal_name</span><span class="p">]</span>
+        <span class="n">neighborhood_tally</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">labels</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">neighb_name</span><span class="p">,</span> <span class="n">weight</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="n">neighb_idx</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id2i</span><span class="p">[</span><span class="n">neighb_name</span><span class="p">]</span>
+            <span class="n">neighb_label</span> <span class="o">=</span> <span class="n">normalized_labels</span><span class="p">[</span><span class="n">neighb_idx</span><span class="p">]</span>
+            <span class="n">neighborhood_tally</span><span class="p">[</span><span class="n">neighb_label</span><span class="p">]</span> <span class="o">+=</span> <span class="n">weight</span>
+        <span class="n">out_label_idx</span> <span class="o">=</span> <span class="n">_resolve_ties</span><span class="p">(</span>
+            <span class="n">focal_idx</span><span class="p">,</span> <span class="n">normalized_labels</span><span class="p">,</span> <span class="n">neighborhood_tally</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">ties</span><span class="p">,</span> <span class="n">w</span>
+        <span class="p">)</span>
+        <span class="n">output</span><span class="p">[</span><span class="n">focal_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="n">out_label_idx</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">orig_shape</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_resolve_ties</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">normalized_labels</span><span class="p">,</span> <span class="n">tally</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">w</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Helper function to resolve ties if lag is multimodal</span>
+
+<span class="sd">    first, if this function gets called when there&#39;s actually no tie, then the</span>
+<span class="sd">    correct value will be picked.</span>
+
+<span class="sd">    if &#39;random&#39; is selected as the method, a random tiebeaker is picked</span>
+
+<span class="sd">    if &#39;tryself&#39; is selected, then the observation&#39;s own value will be used in</span>
+<span class="sd">    an attempt to break the tie, but if it fails, a random tiebreaker will be</span>
+<span class="sd">    selected.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    idx                 : int</span>
+<span class="sd">                          index (aligned with `normalized_labels`) of the</span>
+<span class="sd">                          current observation being resolved.</span>
+<span class="sd">    normalized_labels   : (n,) array of ints</span>
+<span class="sd">                          normalized array of labels for each observation</span>
+<span class="sd">    tally               : (p,) array of floats</span>
+<span class="sd">                          current tally of neighbors&#39; labels around `idx` to resolve.</span>
+<span class="sd">    neighbors           : dict of (neighbor_name : weight)</span>
+<span class="sd">                          the elements of the weights object, identical to w[idx]</span>
+<span class="sd">    method              : string</span>
+<span class="sd">                          configuration option to use a specific tiebreaking method.</span>
+<span class="sd">                          supported options are:</span>
+<span class="sd">                          1. tryself: Use the focal observation&#39;s label to tiebreak.</span>
+<span class="sd">                                      If this doesn&#39;t successfully break the tie,</span>
+<span class="sd">                                      (which only occurs if it induces a new tie),</span>
+<span class="sd">                                      decide randomly.</span>
+<span class="sd">                          2. random: Resolve the tie randomly amongst winners.</span>
+<span class="sd">                          3. lowest: Pick the lowest-value label amongst winners.</span>
+<span class="sd">                          4. highest: Pick the highest-value label amongst winners.</span>
+<span class="sd">    w                   : pysal.W object</span>
+<span class="sd">                          a PySAL weights object aligned with normalized_labels.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    integer denoting which label to use to label the observation.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="p">(</span><span class="n">ties</span><span class="p">,)</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">tally</span> <span class="o">==</span> <span class="n">tally</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>  <span class="c1"># returns a tuple for flat arrays</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tally</span><span class="p">[</span><span class="n">tally</span> <span class="o">==</span> <span class="n">tally</span><span class="o">.</span><span class="n">max</span><span class="p">()])</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># no tie, pick the highest</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tally</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;random&quot;</span><span class="p">:</span>  <span class="c1"># choose randomly from tally</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">ties</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;lowest&quot;</span><span class="p">:</span>  <span class="c1"># pick lowest tied value</span>
+        <span class="k">return</span> <span class="n">ties</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;highest&quot;</span><span class="p">:</span>  <span class="c1"># pick highest tied value</span>
+        <span class="k">return</span> <span class="n">ties</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="p">(</span>
+        <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;tryself&quot;</span>
+    <span class="p">):</span>  <span class="c1"># add self-label as observation, try again, random if fail</span>
+        <span class="n">mean_neighbor_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+        <span class="n">tally</span><span class="p">[</span><span class="n">normalized_labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span> <span class="o">+=</span> <span class="n">mean_neighbor_value</span>
+        <span class="k">return</span> <span class="n">_resolve_ties</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">normalized_labels</span><span class="p">,</span> <span class="n">tally</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="s2">&quot;random&quot;</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;Tie-breaking method for categorical lag not recognized&quot;</span><span class="p">)</span>
+</pre></div>
+
+    </div>
+      
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+      
+  <h1>Source code for libpysal.weights.spintW</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Spatial weights for spatial interaction including contiguity OD weights (ODW),</span>
+<span class="sd">network based weights (netW), and distance-decay based vector weights (vecW).</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Taylor Oshan  &lt;tayoshan@gmail.com&gt; &quot;</span>
+
+<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">kron</span>
+<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
+<span class="kn">from</span> <span class="nn">.distance</span> <span class="kn">import</span> <span class="n">DistanceBand</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
+
+
+<div class="viewcode-block" id="ODW">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.ODW.html#libpysal.weights.ODW">[docs]</a>
+<span class="k">def</span> <span class="nf">ODW</span><span class="p">(</span><span class="n">Wo</span><span class="p">,</span> <span class="n">Wd</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs an o*d by o*d origin-destination style spatial weight for o*d</span>
+<span class="sd">    flows using standard spatial weights on o origins and d destinations. Input</span>
+<span class="sd">    spatial weights must be binary or able to be sutiably transformed to binary.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    Wo          : W object for origin locations</span>
+<span class="sd">                  o x o spatial weight object amongst o origins</span>
+
+<span class="sd">    Wd          : W object for destination locations</span>
+<span class="sd">                  d x d spatial weight object amongst d destinations</span>
+
+<span class="sd">    transform   : Transformation for standardization of final OD spatial weight; default</span>
+<span class="sd">                  is &#39;r&#39; for row standardized</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    W           : spatial contiguity W object for assocations between flows</span>
+<span class="sd">                 o*d x o*d spatial weight object amongst o*d flows between o</span>
+<span class="sd">                 origins and d destinations</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; O = libpysal.weights.lat2W(2,2)</span>
+<span class="sd">    &gt;&gt;&gt; D = libpysal.weights.lat2W(2,2)</span>
+<span class="sd">    &gt;&gt;&gt; OD = libpysal.weights.ODW(O,D)</span>
+<span class="sd">    &gt;&gt;&gt; OD.weights[0]</span>
+<span class="sd">    [0.25, 0.25, 0.25, 0.25]</span>
+<span class="sd">    &gt;&gt;&gt; OD.neighbors[0]</span>
+<span class="sd">    [5, 6, 9, 10]</span>
+<span class="sd">    &gt;&gt;&gt; OD.full()[0][0]</span>
+<span class="sd">    array([0.  , 0.  , 0.  , 0.  , 0.  , 0.25, 0.25, 0.  , 0.  , 0.25, 0.25,</span>
+<span class="sd">           0.  , 0.  , 0.  , 0.  , 0.  ])</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">Wo</span><span class="o">.</span><span class="n">transform</span> <span class="o">!=</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">Wo</span><span class="o">.</span><span class="n">tranform</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s2">&quot;Wo is not binary and cannot be transformed to &quot;</span>
+                <span class="s2">&quot;binary. Wo must be binary or suitably transformed to binary.&quot;</span>
+            <span class="p">)</span>
+    <span class="k">if</span> <span class="n">Wd</span><span class="o">.</span><span class="n">transform</span> <span class="o">!=</span> <span class="s2">&quot;b&quot;</span><span class="p">:</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">Wd</span><span class="o">.</span><span class="n">tranform</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s2">&quot;Wd is not binary and cannot be transformed to &quot;</span>
+                <span class="s2">&quot;binary. Wd must be binary or suitably transformed to binary.&quot;</span>
+            <span class="p">)</span>
+    <span class="n">Wo</span> <span class="o">=</span> <span class="n">Wo</span><span class="o">.</span><span class="n">sparse</span>
+    <span class="n">Wo</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+    <span class="n">Wd</span> <span class="o">=</span> <span class="n">Wd</span><span class="o">.</span><span class="n">sparse</span>
+    <span class="n">Wd</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+    <span class="n">Ww</span> <span class="o">=</span> <span class="n">kron</span><span class="p">(</span><span class="n">Wo</span><span class="p">,</span> <span class="n">Wd</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;csr&quot;</span><span class="p">)</span>
+    <span class="n">Ww</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+    <span class="n">Ww</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">Ww</span><span class="p">)</span><span class="o">.</span><span class="n">to_W</span><span class="p">(</span><span class="n">silence_warnings</span><span class="o">=</span><span class="n">silence_warnings</span><span class="p">)</span>
+    <span class="n">Ww</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
+    <span class="k">return</span> <span class="n">Ww</span></div>
+
+
+
+<div class="viewcode-block" id="netW">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.netW.html#libpysal.weights.netW">[docs]</a>
+<span class="k">def</span> <span class="nf">netW</span><span class="p">(</span><span class="n">link_list</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a network-contiguity based weight object based on different nodal</span>
+<span class="sd">    relationships encoded in a network.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    link_list   : list</span>
+<span class="sd">                  of tuples where each tuple is of the form (o,d) where o is an</span>
+<span class="sd">                  origin id and d is a destination id</span>
+
+<span class="sd">    share       : string</span>
+<span class="sd">                  denoting how to define the nodal relationship used to determine neighboring edges; defualt is &#39;A&#39; for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two.</span>
+
+<span class="sd">    transform   : Transformation for standardization of final OD spatial weight; default</span>
+<span class="sd">                  is &#39;r&#39; for row standardized</span>
+<span class="sd">    **kwargs    : keyword arguments</span>
+<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">     W          : nodal contiguity W object for networkd edges or flows</span>
+<span class="sd">                  W Object representing the binary adjacency of the network edges</span>
+<span class="sd">                  given a definition of nodal relationshilibpysal.weights.spintW.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; links = [(&#39;a&#39;,&#39;b&#39;), (&#39;a&#39;,&#39;c&#39;), (&#39;a&#39;,&#39;d&#39;), (&#39;c&#39;,&#39;d&#39;), (&#39;c&#39;, &#39;b&#39;), (&#39;c&#39;,&#39;a&#39;)]</span>
+<span class="sd">    &gt;&gt;&gt; O = libpysal.weights.netW(links, share=&#39;O&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; O.neighbors[(&#39;a&#39;, &#39;b&#39;)]</span>
+<span class="sd">    [(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;)]</span>
+<span class="sd">    &gt;&gt;&gt; OD = libpysal.weights.netW(links, share=&#39;OD&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; OD.neighbors[(&#39;a&#39;, &#39;b&#39;)]</span>
+<span class="sd">    [(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;)]</span>
+<span class="sd">    &gt;&gt;&gt; any_common = libpysal.weights.netW(links, share=&#39;A&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; any_common.neighbors[(&#39;a&#39;, &#39;b&#39;)]</span>
+<span class="sd">    [(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;), (&#39;c&#39;, &#39;a&#39;)]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
+    <span class="n">edges</span> <span class="o">=</span> <span class="n">link_list</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
+        <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">neigh</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">:</span>
+                <span class="k">continue</span>
+            <span class="k">if</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;OD&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;O&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;C&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">share</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;A&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="p">(</span>
+                    <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="ow">or</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neigh</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="p">):</span>
+                    <span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neigh</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Parameter &#39;share&#39; must be &#39;O&#39;, &#39;D&#39;,&quot;</span> <span class="s2">&quot; &#39;OD&#39;, or &#39;C&#39;&quot;</span>
+                <span class="p">)</span>
+    <span class="n">netW</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">netW</span><span class="o">.</span><span class="n">tranform</span> <span class="o">=</span> <span class="n">transform</span>
+    <span class="k">return</span> <span class="n">netW</span></div>
+
+
+
+<div class="viewcode-block" id="vecW">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.vecW.html#libpysal.weights.vecW">[docs]</a>
+<span class="k">def</span> <span class="nf">vecW</span><span class="p">(</span>
+    <span class="n">origin_x</span><span class="p">,</span>
+    <span class="n">origin_y</span><span class="p">,</span>
+    <span class="n">dest_x</span><span class="p">,</span>
+    <span class="n">dest_y</span><span class="p">,</span>
+    <span class="n">threshold</span><span class="p">,</span>
+    <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">alpha</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span>
+    <span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">build_sp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Distance-based spatial weight for vectors that is computed using a</span>
+<span class="sd">    4-dimensional distance between the origin x,y-coordinates and the</span>
+<span class="sd">    destination x,y-coordinates</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    origin_x   : list or array</span>
+<span class="sd">                 of vector origin x-coordinates</span>
+<span class="sd">    origin_y   : list or array</span>
+<span class="sd">                 of vector origin y-coordinates</span>
+<span class="sd">    dest_x     : list or array</span>
+<span class="sd">                 of vector destination x-coordinates</span>
+<span class="sd">    dest_y     : list or array</span>
+<span class="sd">                 of vector destination y-coordinates</span>
+<span class="sd">    threshold  : float</span>
+<span class="sd">                 distance band</span>
+<span class="sd">    p          : float</span>
+<span class="sd">                 Minkowski p-norm distance metric parameter:</span>
+<span class="sd">                 1&lt;=p&lt;=infinity</span>
+<span class="sd">                 2: Euclidean distance</span>
+<span class="sd">                 1: Manhattan distance</span>
+<span class="sd">    binary     : boolean</span>
+<span class="sd">                 If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0</span>
+<span class="sd">                 If false wij=dij^{alpha}</span>
+<span class="sd">    alpha      : float</span>
+<span class="sd">                 distance decay parameter for weight (default -1.0)</span>
+<span class="sd">                 if alpha is positive the weights will not decline with</span>
+<span class="sd">                 distance. If binary is True, alpha is ignored</span>
+
+<span class="sd">    ids         : list</span>
+<span class="sd">                  values to use for keys of the neighbors and weights dicts</span>
+<span class="sd">    build_sp    : boolean</span>
+<span class="sd">                  True to build sparse distance matrix and false to build dense</span>
+<span class="sd">                  distance matrix; significant speed gains may be obtained</span>
+<span class="sd">                  dending on the sparsity of the of distance_matrix and</span>
+<span class="sd">                  threshold that is applied</span>
+<span class="sd">    **kwargs    : keyword arguments</span>
+<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    W           : DistanceBand W object that uses 4-dimenional distances between</span>
+<span class="sd">                  vectors origin and destination coordinates.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; x1 = [5,6,3]</span>
+<span class="sd">    &gt;&gt;&gt; y1 = [1,8,5]</span>
+<span class="sd">    &gt;&gt;&gt; x2 = [2,4,9]</span>
+<span class="sd">    &gt;&gt;&gt; y2 = [3,6,1]</span>
+<span class="sd">    &gt;&gt;&gt; W1 = libpysal.weights.vecW(x1, y1, x2, y2, threshold=999)</span>
+<span class="sd">    &gt;&gt;&gt; list(W1.neighbors[0])</span>
+<span class="sd">    [1, 2]</span>
+<span class="sd">    &gt;&gt;&gt; W2 = libpysal.weights.vecW(x1, y2, x1, y2, threshold=8.5)</span>
+<span class="sd">    &gt;&gt;&gt; list(W2.neighbors[0])</span>
+<span class="sd">    [1, 2]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">origin_x</span><span class="p">,</span> <span class="n">origin_y</span><span class="p">,</span> <span class="n">dest_x</span><span class="p">,</span> <span class="n">dest_y</span><span class="p">))</span>
+    <span class="n">W</span> <span class="o">=</span> <span class="n">DistanceBand</span><span class="p">(</span>
+        <span class="n">data</span><span class="p">,</span>
+        <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
+        <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">,</span>
+        <span class="n">binary</span><span class="o">=</span><span class="n">binary</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span>
+        <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span>
+        <span class="n">build_sp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">W</span></div>
+
+
+
+<div class="viewcode-block" id="mat2L">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.mat2L.html#libpysal.weights.mat2L">[docs]</a>
+<span class="k">def</span> <span class="nf">mat2L</span><span class="p">(</span><span class="n">edge_matrix</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert a matrix denoting network connectivity (edges or flows) to a list</span>
+<span class="sd">    denoting edges</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    edge_matrix   : array</span>
+<span class="sd">                    where rows denote network edge origins, columns denote</span>
+<span class="sd">                    network edge destinations, and non-zero entries denote the</span>
+<span class="sd">                    existence of an edge between a given origin and destination</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">     edge_list    : list</span>
+<span class="sd">                    of tuples where each tuple is of the form (o,d) where o is an</span>
+<span class="sd">                    origin id and d is a destination id</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">edge_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+            <span class="s2">&quot;Matrix of network edges should be two dimensions&quot;</span>
+            <span class="s2">&quot;with edge origins on one axis and edge destinations on the&quot;</span>
+            <span class="s2">&quot;second axis with non-zero matrix entires denoting an edge&quot;</span>
+            <span class="s2">&quot;between and origin and destination&quot;</span>
+        <span class="p">)</span>
+    <span class="n">edge_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">edge_matrix</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cols</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">edge_matrix</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">edge_list</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">edge_list</span></div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/_modules/libpysal/weights/user.html b/_modules/libpysal/weights/user.html
new file mode 100644
index 000000000..fea548050
--- /dev/null
+++ b/_modules/libpysal/weights/user.html
@@ -0,0 +1,308 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../../../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>libpysal.weights.user &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bootstrap-sphinx.css?v=9afac83c" />
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+    <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
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+    <link rel="icon" href="../../../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+<meta charset='utf-8'>
+<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
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+
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+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+            
+                <li><a href="../../../installation.html">Installation</a></li>
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+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+</ul>
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+      
+  <h1>Source code for libpysal.weights.user</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Convenience functions for the construction of spatial weights based on</span>
+<span class="sd">contiguity and distance criteria.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt; &quot;</span>
+
+<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">get_points_array_from_shapefile</span><span class="p">,</span> <span class="n">min_threshold_distance</span>
+<span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span> <span class="k">as</span> <span class="n">ps_open</span>
+<span class="kn">from</span> <span class="nn">..</span> <span class="kn">import</span> <span class="n">cg</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;min_threshold_dist_from_shapefile&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;build_lattice_shapefile&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;spw_from_gal&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<div class="viewcode-block" id="spw_from_gal">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.spw_from_gal.html#libpysal.weights.spw_from_gal">[docs]</a>
+<span class="k">def</span> <span class="nf">spw_from_gal</span><span class="p">(</span><span class="n">galfile</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Sparse scipy matrix for w from a gal file.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    galfile  : string</span>
+<span class="sd">               name of gal file including suffix</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    spw      : sparse_matrix</span>
+<span class="sd">               scipy sparse matrix in CSR format</span>
+
+<span class="sd">    ids      : array</span>
+<span class="sd">               identifiers for rows/cols of spw</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; spw = libpysal.weights.spw_from_gal(libpysal.examples.get_path(&quot;sids2.gal&quot;))</span>
+<span class="sd">    &gt;&gt;&gt; spw.sparse.nnz</span>
+<span class="sd">    462</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">return</span> <span class="n">ps_open</span><span class="p">(</span><span class="n">galfile</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="min_threshold_dist_from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.min_threshold_dist_from_shapefile.html#libpysal.weights.min_threshold_dist_from_shapefile">[docs]</a>
+<span class="k">def</span> <span class="nf">min_threshold_dist_from_shapefile</span><span class="p">(</span><span class="n">shapefile</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get the maximum nearest neighbor distance between observations in the</span>
+<span class="sd">    shapefile.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    shapefile  : string</span>
+<span class="sd">                 shapefile name with shp suffix.</span>
+<span class="sd">    radius     : float</span>
+<span class="sd">                 If supplied arc_distances will be calculated</span>
+<span class="sd">                 based on the given radius. p will be ignored.</span>
+<span class="sd">    p          : float</span>
+<span class="sd">                 Minkowski p-norm distance metric parameter:</span>
+<span class="sd">                 1&lt;=p&lt;=infinity</span>
+<span class="sd">                 2: Euclidean distance</span>
+<span class="sd">                 1: Manhattan distance</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    d          : float</span>
+<span class="sd">                 Maximum nearest neighbor distance between the n</span>
+<span class="sd">                 observations.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; md = libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
+<span class="sd">    &gt;&gt;&gt; md</span>
+<span class="sd">    0.6188641580768541</span>
+<span class="sd">    &gt;&gt;&gt; libpysal.weights.min_threshold_dist_from_shapefile(libpysal.examples.get_path(&quot;stl_hom.shp&quot;), libpysal.cg.sphere.RADIUS_EARTH_MILES)</span>
+<span class="sd">    31.846942936393717</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Supports polygon or point shapefiles. For polygon shapefiles, distance is</span>
+<span class="sd">    based on polygon centroids. Distances are defined using coordinates in</span>
+<span class="sd">    shapefile which are assumed to be projected and not geographical</span>
+<span class="sd">    coordinates.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">points</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">shapefile</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">kdt</span> <span class="o">=</span> <span class="n">cg</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">Arc_KDTree</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">radius</span><span class="p">)</span>
+        <span class="n">nn</span> <span class="o">=</span> <span class="n">kdt</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">kdt</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">nnd</span> <span class="o">=</span> <span class="n">nn</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">nnd</span>
+    <span class="k">return</span> <span class="n">min_threshold_distance</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">p</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="build_lattice_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.build_lattice_shapefile.html#libpysal.weights.build_lattice_shapefile">[docs]</a>
+<span class="k">def</span> <span class="nf">build_lattice_shapefile</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">outFileName</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Build a lattice shapefile with nrows rows and ncols cols.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    nrows       : int</span>
+<span class="sd">                  Number of rows</span>
+<span class="sd">    ncols       : int</span>
+<span class="sd">                  Number of cols</span>
+<span class="sd">    outFileName : str</span>
+<span class="sd">                  shapefile name with shp suffix</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    None</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">outFileName</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;.shp&quot;</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;outFileName must end with .shp&quot;</span><span class="p">)</span>
+    <span class="n">o</span> <span class="o">=</span> <span class="n">ps_open</span><span class="p">(</span><span class="n">outFileName</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
+    <span class="n">dbf_name</span> <span class="o">=</span> <span class="n">outFileName</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;.dbf&quot;</span>
+    <span class="n">d</span> <span class="o">=</span> <span class="n">ps_open</span><span class="p">(</span><span class="n">dbf_name</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">header</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;ID&quot;</span><span class="p">]</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">field_spec</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">&quot;N&quot;</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span>
+    <span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nrows</span><span class="p">):</span>
+            <span class="n">ll</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span>
+            <span class="n">ul</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="n">ur</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="n">lr</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">j</span>
+            <span class="n">o</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">cg</span><span class="o">.</span><span class="n">Polygon</span><span class="p">([</span><span class="n">ll</span><span class="p">,</span> <span class="n">ul</span><span class="p">,</span> <span class="n">ur</span><span class="p">,</span> <span class="n">lr</span><span class="p">,</span> <span class="n">ll</span><span class="p">]))</span>
+            <span class="n">d</span><span class="o">.</span><span class="n">write</span><span class="p">([</span><span class="n">c</span><span class="p">])</span>
+            <span class="n">c</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+    <span class="n">o</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">_test</span><span class="p">():</span>
+    <span class="kn">import</span> <span class="nn">doctest</span>
+
+    <span class="c1"># the following line could be used to define an alternative to the &#39;&lt;BLANKLINE&gt;&#39; flag</span>
+    <span class="c1"># doctest.BLANKLINE_MARKER = &#39;something better than &lt;BLANKLINE&gt;&#39;</span>
+    <span class="n">start_suppress</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">get_printoptions</span><span class="p">()[</span><span class="s2">&quot;suppress&quot;</span><span class="p">]</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">doctest</span><span class="o">.</span><span class="n">testmod</span><span class="p">()</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="n">start_suppress</span><span class="p">)</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="n">_test</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
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+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
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\ No newline at end of file
diff --git a/_modules/libpysal/weights/util.html b/_modules/libpysal/weights/util.html
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+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../tutorial.html#example-datasets">Example Datasets</a></li>
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+      
+  <h1>Source code for libpysal.weights.util</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span> <span class="k">as</span> <span class="n">psopen</span>
+<span class="kn">from</span> <span class="nn">.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
+<span class="kn">from</span> <span class="nn">.set_operations</span> <span class="kn">import</span> <span class="n">w_subset</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">sparse</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">KDTree</span>
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">import</span> <span class="nn">scipy.spatial</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">scipy</span>
+<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
+<span class="kn">import</span> <span class="nn">numbers</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">tee</span>
+<span class="kn">from</span> <span class="nn">..common</span> <span class="kn">import</span> <span class="n">requires</span>
+<span class="kn">from</span> <span class="nn">packaging.version</span> <span class="kn">import</span> <span class="n">Version</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
+
+    <span class="n">GPD_013</span> <span class="o">=</span> <span class="n">Version</span><span class="p">(</span><span class="n">gpd</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">Version</span><span class="p">(</span><span class="s2">&quot;0.13.0&quot;</span><span class="p">)</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;geopandas not available. Some functionality will be disabled.&quot;</span><span class="p">)</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">shapely.geometry.base</span> <span class="kn">import</span> <span class="n">BaseGeometry</span>
+
+    <span class="n">HAS_SHAPELY</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="n">HAS_SHAPELY</span> <span class="o">=</span> <span class="kc">False</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;lat2W&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;block_weights&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;comb&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;order&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;higher_order&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;shimbel&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;remap_ids&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;full2W&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;full&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;WSP2W&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;insert_diagonal&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;fill_diagonal&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_ids&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;get_points_array_from_shapefile&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;min_threshold_distance&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;lat2SW&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;w_local_cluster&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;higher_order_sp&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;hexLat2W&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;neighbor_equality&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;attach_islands&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;nonplanar_neighbors&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;fuzzy_contiguity&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<span class="n">KDTREE_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">,</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">cKDTree</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="hexLat2W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.hexLat2W.html#libpysal.weights.hexLat2W">[docs]</a>
+<span class="k">def</span> <span class="nf">hexLat2W</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a W object for a hexagonal lattice.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    nrows      : int</span>
+<span class="sd">                 number of rows</span>
+<span class="sd">    ncols      : int</span>
+<span class="sd">                 number of columns</span>
+<span class="sd">    **kwargs   : keyword arguments</span>
+<span class="sd">                 optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w : W</span>
+<span class="sd">        instance of spatial weights class W</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</span>
+
+<span class="sd">    Construction is based on shifting every other column of a regular lattice</span>
+<span class="sd">    down 1/2 of a cell.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, hexLat2W</span>
+<span class="sd">    &gt;&gt;&gt; w = lat2W()</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[1]</span>
+<span class="sd">    [0, 6, 2]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[21]</span>
+<span class="sd">    [16, 20, 22]</span>
+<span class="sd">    &gt;&gt;&gt; wh = hexLat2W()</span>
+<span class="sd">    &gt;&gt;&gt; wh.neighbors[1]</span>
+<span class="sd">    [0, 6, 2, 5, 7]</span>
+<span class="sd">    &gt;&gt;&gt; wh.neighbors[21]</span>
+<span class="sd">    [16, 20, 22]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">nrows</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">ncols</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Hexagon lattice requires at least 2 rows and columns&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Returning a linear contiguity structure&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">lat2W</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">)</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="n">ncols</span>
+    <span class="n">rid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">//</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
+    <span class="n">cid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">%</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
+    <span class="n">r1</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">c1</span> <span class="o">=</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span>
+
+    <span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">)</span><span class="o">.</span><span class="n">neighbors</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="n">odd</span> <span class="o">=</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">%</span> <span class="mi">2</span>
+        <span class="k">if</span> <span class="n">odd</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>  <span class="c1"># odd col index above last row</span>
+                <span class="c1"># new sw neighbor</span>
+                <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">j</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span>
+                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">j</span><span class="p">]</span>
+                <span class="c1"># new se neighbor</span>
+                <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">c1</span><span class="p">:</span>
+                    <span class="n">j</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span>
+                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">j</span><span class="p">]</span>
+
+        <span class="k">else</span><span class="p">:</span>  <span class="c1"># even col</span>
+            <span class="c1"># nw</span>
+            <span class="n">jnw</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="c1"># ne</span>
+            <span class="n">jne</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jne</span>
+                <span class="k">elif</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">c1</span><span class="p">:</span>
+                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jnw</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jne</span>
+                    <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="n">jnw</span>
+
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="lat2W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.lat2W.html#libpysal.weights.lat2W">[docs]</a>
+<span class="k">def</span> <span class="nf">lat2W</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">id_type</span><span class="o">=</span><span class="s2">&quot;int&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a W object for a regular lattice.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    nrows      : int</span>
+<span class="sd">                 number of rows</span>
+<span class="sd">    ncols      : int</span>
+<span class="sd">                 number of columns</span>
+<span class="sd">    rook       : boolean</span>
+<span class="sd">                 type of contiguity. Default is rook. For queen, rook =False</span>
+<span class="sd">    id_type    : string</span>
+<span class="sd">                 string defining the type of IDs to use in the final W object;</span>
+<span class="sd">                 options are &#39;int&#39; (0, 1, 2 ...; default), &#39;float&#39; (0.0,</span>
+<span class="sd">                 1.0, 2.0, ...) and &#39;string&#39; (&#39;id0&#39;, &#39;id1&#39;, &#39;id2&#39;, ...)</span>
+<span class="sd">    **kwargs   : keyword arguments</span>
+<span class="sd">                 optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w : W</span>
+<span class="sd">        instance of spatial weights class W</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">    &gt;&gt;&gt; w9 = lat2W(3,3)</span>
+<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w9.pct_nonzero</span>
+<span class="sd">    &#39;29.630&#39;</span>
+<span class="sd">    &gt;&gt;&gt; w9[0] == {1: 1.0, 3: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w9[3] == {0: 1.0, 4: 1.0, 6: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="n">ncols</span>
+    <span class="n">r1</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">c1</span> <span class="o">=</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">rid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">//</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>  <span class="c1"># must be floor!</span>
+    <span class="n">cid</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">%</span> <span class="n">ncols</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
+    <span class="n">w</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">below</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>
+            <span class="n">below</span> <span class="o">=</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="n">r</span> <span class="o">=</span> <span class="n">below</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">+</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">r</span><span class="p">]</span>
+            <span class="n">w</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">c1</span><span class="p">:</span>
+            <span class="n">right</span> <span class="o">=</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">+</span> <span class="n">right</span>
+            <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">c</span><span class="p">]</span>
+            <span class="n">w</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">rook</span><span class="p">:</span>
+            <span class="c1"># southeast bishop</span>
+            <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">c1</span> <span class="ow">and</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>
+                <span class="n">r</span> <span class="o">=</span> <span class="p">(</span><span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">r</span><span class="p">]</span>
+                <span class="n">w</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="c1"># southwest bishop</span>
+            <span class="k">if</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">r1</span><span class="p">:</span>
+                <span class="n">r</span> <span class="o">=</span> <span class="p">(</span><span class="n">rid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">cid</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">r</span><span class="p">]</span>
+                <span class="n">w</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="p">[])</span> <span class="o">+</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
+        <span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+    <span class="n">ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;string&quot;</span><span class="p">:</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;id&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">]</span>
+    <span class="k">elif</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;float&quot;</span><span class="p">:</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="mf">1.0</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;string&quot;</span> <span class="ow">or</span> <span class="n">id_type</span> <span class="o">==</span> <span class="s2">&quot;float&quot;</span><span class="p">:</span>
+        <span class="n">id_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> <span class="n">ids</span><span class="p">)))</span>
+        <span class="n">alt_w</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">alt_weights</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w</span><span class="p">:</span>
+            <span class="n">values</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_dict</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+            <span class="n">key</span> <span class="o">=</span> <span class="n">id_dict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">alt_w</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
+            <span class="n">alt_weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">alt_w</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">alt_weights</span>
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">[:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="block_weights">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.block_weights.html#libpysal.weights.block_weights">[docs]</a>
+<span class="k">def</span> <span class="nf">block_weights</span><span class="p">(</span><span class="n">regimes</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Construct spatial weights for regime neighbors.</span>
+
+<span class="sd">    Block contiguity structures are relevant when defining neighbor relations</span>
+<span class="sd">    based on membership in a regime. For example, all counties belonging to</span>
+<span class="sd">    the same state could be defined as neighbors, in an analysis of all</span>
+<span class="sd">    counties in the US.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    regimes     : list, array</span>
+<span class="sd">                  ids of which regime an observation belongs to</span>
+<span class="sd">    ids         : list, array</span>
+<span class="sd">                  Ordered sequence of IDs for the observations</span>
+<span class="sd">    sparse      : boolean</span>
+<span class="sd">                  If True return WSP instance</span>
+<span class="sd">                  If False return W instance</span>
+<span class="sd">    **kwargs    : keyword arguments</span>
+<span class="sd">                  optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    W : spatial weights instance</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import block_weights</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; regimes = np.ones(25)</span>
+<span class="sd">    &gt;&gt;&gt; regimes[range(10,20)] = 2</span>
+<span class="sd">    &gt;&gt;&gt; regimes[range(21,25)] = 3</span>
+<span class="sd">    &gt;&gt;&gt; regimes</span>
+<span class="sd">    array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2.,</span>
+<span class="sd">           2., 2., 2., 1., 3., 3., 3., 3.])</span>
+<span class="sd">    &gt;&gt;&gt; w = block_weights(regimes)</span>
+<span class="sd">    &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">    [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[0]</span>
+<span class="sd">    [1, 2, 3, 4, 5, 6, 7, 8, 9, 20]</span>
+<span class="sd">    &gt;&gt;&gt; regimes = [&#39;n&#39;,&#39;n&#39;,&#39;s&#39;,&#39;s&#39;,&#39;e&#39;,&#39;e&#39;,&#39;w&#39;,&#39;w&#39;,&#39;e&#39;]</span>
+<span class="sd">    &gt;&gt;&gt; n = len(regimes)</span>
+<span class="sd">    &gt;&gt;&gt; w = block_weights(regimes)</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors == {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5]}</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">rids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">NPNZ</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span>
+    <span class="n">regimes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">rid</span> <span class="ow">in</span> <span class="n">rids</span><span class="p">:</span>
+        <span class="n">members</span> <span class="o">=</span> <span class="n">NPNZ</span><span class="p">(</span><span class="n">regimes</span> <span class="o">==</span> <span class="n">rid</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">member</span> <span class="ow">in</span> <span class="n">members</span><span class="p">:</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">member</span><span class="p">]</span> <span class="o">=</span> <span class="n">members</span><span class="p">[</span><span class="n">NPNZ</span><span class="p">(</span><span class="n">members</span> <span class="o">!=</span> <span class="n">member</span><span class="p">)[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+    <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">remap_ids</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">sparse</span><span class="p">:</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">w</span></div>
+
+
+
+<div class="viewcode-block" id="comb">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.comb.html#libpysal.weights.comb">[docs]</a>
+<span class="k">def</span> <span class="nf">comb</span><span class="p">(</span><span class="n">items</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Combinations of size n taken from items</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    items : list</span>
+<span class="sd">            items to be drawn from</span>
+<span class="sd">    n     : integer</span>
+<span class="sd">            size of combinations to take from items</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    implicit : generator</span>
+<span class="sd">               combinations of size n taken from items</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; x = range(4)</span>
+<span class="sd">    &gt;&gt;&gt; for c in comb(x, 2):</span>
+<span class="sd">    ...     print(c)</span>
+<span class="sd">    ...</span>
+<span class="sd">    [0, 1]</span>
+<span class="sd">    [0, 2]</span>
+<span class="sd">    [0, 3]</span>
+<span class="sd">    [1, 2]</span>
+<span class="sd">    [1, 3]</span>
+<span class="sd">    [2, 3]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">items</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">n</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">items</span><span class="p">))):</span>
+        <span class="n">v</span> <span class="o">=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">yield</span> <span class="n">v</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">rest</span> <span class="o">=</span> <span class="n">items</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:]</span>
+            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">(</span><span class="n">rest</span><span class="p">,</span> <span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+                <span class="k">yield</span> <span class="n">v</span> <span class="o">+</span> <span class="n">c</span></div>
+
+
+
+<div class="viewcode-block" id="order">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.order.html#libpysal.weights.order">[docs]</a>
+<span class="k">def</span> <span class="nf">order</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">kmax</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Determine the non-redundant order of contiguity up to a specific</span>
+<span class="sd">    order.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w       : W</span>
+<span class="sd">              spatial weights object</span>
+
+<span class="sd">    kmax    : int</span>
+<span class="sd">              maximum order of contiguity</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    info : dictionary</span>
+<span class="sd">           observation id is the key, value is a list of contiguity</span>
+<span class="sd">           orders with a negative 1 in the ith position</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Implements the algorithm in :cite:`Anselin1996b`.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import order, Rook</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; w = Rook.from_shapefile(libpysal.examples.get_path(&#39;10740.shp&#39;))</span>
+
+<span class="sd">    WARNING: there is one disconnected observation (no neighbors)</span>
+<span class="sd">    Island id:  [163]</span>
+<span class="sd">    &gt;&gt;&gt; w3 = order(w, kmax = 3)</span>
+<span class="sd">    &gt;&gt;&gt; w3[1][0:5]</span>
+<span class="sd">    [1, -1, 1, 2, 1]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">ids</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
+    <span class="n">info</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">id_</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">:</span>
+        <span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span>
+        <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">id_</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">id_</span><span class="p">]:</span>
+            <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">while</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">kmax</span><span class="p">:</span>
+            <span class="n">knext</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
+                <span class="c1"># get neighbors of order k</span>
+                <span class="n">js</span> <span class="o">=</span> <span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="k">if</span> <span class="n">val</span> <span class="o">==</span> <span class="n">k</span><span class="p">]</span>
+                <span class="c1"># get first order neighbors for order k neighbors</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">js</span><span class="p">:</span>
+                    <span class="n">next_neighbors</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+                    <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">next_neighbors</span><span class="p">:</span>
+                        <span class="n">nid</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
+                        <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                            <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">=</span> <span class="n">knext</span>
+            <span class="n">k</span> <span class="o">=</span> <span class="n">knext</span>
+        <span class="n">info</span><span class="p">[</span><span class="n">id_</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span>
+    <span class="k">return</span> <span class="n">info</span></div>
+
+
+
+<div class="viewcode-block" id="higher_order">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.higher_order.html#libpysal.weights.higher_order">[docs]</a>
+<span class="k">def</span> <span class="nf">higher_order</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Contiguity weights object of order k.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w        : W</span>
+<span class="sd">               spatial weights object</span>
+<span class="sd">    k        : int</span>
+<span class="sd">               order of contiguity</span>
+<span class="sd">    **kwargs : keyword arguments</span>
+<span class="sd">               optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    implicit : W</span>
+<span class="sd">               spatial weights object</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Proper higher order neighbors are returned such that i and j are k-order</span>
+<span class="sd">    neighbors iff the shortest path from i-j is of length k.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, higher_order</span>
+<span class="sd">    &gt;&gt;&gt; w10 = lat2W(10, 10)</span>
+<span class="sd">    &gt;&gt;&gt; w10_2 = higher_order(w10, 2)</span>
+<span class="sd">    &gt;&gt;&gt; w10_2[0] ==  {2: 1.0, 11: 1.0, 20: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w5 = lat2W()</span>
+<span class="sd">    &gt;&gt;&gt; w5[0] ==  {1: 1.0, 5: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w5[1] == {0: 1.0, 2: 1.0, 6: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w5_2 = higher_order(w5,2)</span>
+<span class="sd">    &gt;&gt;&gt; w5_2[0] == {10: 1.0, 2: 1.0, 6: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="higher_order_sp">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.higher_order_sp.html#libpysal.weights.higher_order_sp">[docs]</a>
+<span class="k">def</span> <span class="nf">higher_order_sp</span><span class="p">(</span>
+    <span class="n">w</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shortest_path</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lower_order</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Contiguity weights for either a sparse W or W for order k.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    w             : W</span>
+<span class="sd">                            sparse_matrix, spatial weights object or</span>
+<span class="sd">                            scipy.sparse.csr.csr_instance</span>
+<span class="sd">    k             : int</span>
+<span class="sd">                    Order of contiguity</span>
+<span class="sd">    shortest_path : boolean</span>
+<span class="sd">                    True: i,j and k-order neighbors if the</span>
+<span class="sd">                    shortest path for i,j is k.</span>
+<span class="sd">                    False: i,j are k-order neighbors if there</span>
+<span class="sd">                    is a path from i,j of length k.</span>
+<span class="sd">    diagonal      : boolean</span>
+<span class="sd">                    True:  keep k-order (i,j) joins when i==j</span>
+<span class="sd">                    False: remove k-order (i,j) joins when i==j</span>
+<span class="sd">    lower_order   : boolean</span>
+<span class="sd">                    True: include lower order contiguities</span>
+<span class="sd">                    False: return only weights of order k</span>
+<span class="sd">    **kwargs      : keyword arguments</span>
+<span class="sd">                    optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    wk : W</span>
+<span class="sd">             WSP, type matches type of w argument</span>
+
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, higher_order_sp</span>
+<span class="sd">    &gt;&gt;&gt; w25 = lat2W(5,5)</span>
+<span class="sd">    &gt;&gt;&gt; w25.n</span>
+<span class="sd">    25</span>
+<span class="sd">    &gt;&gt;&gt; w25[0] == {1: 1.0, 5: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w25_2 = higher_order_sp(w25, 2)</span>
+<span class="sd">    &gt;&gt;&gt; w25_2[0] == {10: 1.0, 2: 1.0, 6: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w25_2 = higher_order_sp(w25, 2, diagonal=True)</span>
+<span class="sd">    &gt;&gt;&gt; w25_2[0] ==  {0: 1.0, 10: 1.0, 2: 1.0, 6: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w25_3 = higher_order_sp(w25, 3)</span>
+<span class="sd">    &gt;&gt;&gt; w25_3[0] == {15: 1.0, 3: 1.0, 11: 1.0, 7: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w25_3 = higher_order_sp(w25, 3, shortest_path=False)</span>
+<span class="sd">    &gt;&gt;&gt; w25_3[0] == {1: 1.0, 3: 1.0, 5: 1.0, 7: 1.0, 11: 1.0, 15: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w25_3 = higher_order_sp(w25, 3, lower_order=True)</span>
+<span class="sd">    &gt;&gt;&gt; w25_3[0] == {5: 1.0, 7: 1.0, 11: 1.0, 2: 1.0, 15: 1.0, 6: 1.0, 10: 1.0, 1: 1.0, 3: 1.0}</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">id_order</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">w</span><span class="p">),</span> <span class="n">W</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]):</span>
+            <span class="n">id_order</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">sparse</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights are not binary (0,1)&quot;</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">isspmatrix_csr</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">]):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Sparse weights matrix is not binary (0,1) weights matrix.&quot;</span>
+            <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Weights provided are neither a binary W object nor &quot;</span>
+            <span class="s2">&quot;a scipy.sparse.csr_matrix&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">lower_order</span><span class="p">:</span>
+        <span class="n">wk</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">w</span><span class="o">**</span><span class="n">x</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)))</span>
+        <span class="n">shortest_path</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">wk</span> <span class="o">=</span> <span class="n">w</span><span class="o">**</span><span class="n">k</span>
+
+    <span class="n">rk</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">wk</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
+    <span class="n">sk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rk</span><span class="p">,</span> <span class="n">ck</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">shortest_path</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
+            <span class="n">wj</span> <span class="o">=</span> <span class="n">w</span><span class="o">**</span><span class="n">j</span>
+            <span class="n">rj</span><span class="p">,</span> <span class="n">cj</span> <span class="o">=</span> <span class="n">wj</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
+            <span class="n">sj</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rj</span><span class="p">,</span> <span class="n">cj</span><span class="p">))</span>
+            <span class="n">sk</span><span class="o">.</span><span class="n">difference_update</span><span class="p">(</span><span class="n">sj</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">diagonal</span><span class="p">:</span>
+        <span class="n">sk</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">sk</span> <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">i</span><span class="p">,</span> <span class="p">[])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">id_order</span><span class="p">])</span>
+        <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">sk</span><span class="p">:</span>
+            <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">pair</span>
+            <span class="n">k</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">v</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">v</span><span class="p">]</span>
+            <span class="n">d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">d</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">pair</span> <span class="ow">in</span> <span class="n">sk</span><span class="p">:</span>
+            <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="n">pair</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="p">:</span>
+                <span class="n">d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">d</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">WSP</span><span class="p">(</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">d</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="w_local_cluster">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.w_local_cluster.html#libpysal.weights.w_local_cluster">[docs]</a>
+<span class="k">def</span> <span class="nf">w_local_cluster</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Local clustering coefficients for each unit as a node in a graph.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w   : W</span>
+<span class="sd">          spatial weights object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    c     : array</span>
+<span class="sd">            (w.n,1)</span>
+<span class="sd">            local clustering coefficients</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    The local clustering coefficient :math:`c_i` quantifies how close the</span>
+<span class="sd">    neighbors of observation :math:`i` are to being a clique:</span>
+
+<span class="sd">    .. math::</span>
+
+<span class="sd">       c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i</span>
+
+<span class="sd">    where :math:`N_i` is the set of neighbors to :math:`i`, :math:`k_i =</span>
+<span class="sd">    |N_i|` and :math:`\{w_{j,k}\}` is the set of non-zero elements of the</span>
+<span class="sd">    weights between pairs in :math:`N_i` :cite:`Watts1998`.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, w_local_cluster</span>
+<span class="sd">    &gt;&gt;&gt; w = lat2W(3,3, rook=False)</span>
+<span class="sd">    &gt;&gt;&gt; w_local_cluster(w)</span>
+<span class="sd">    array([[1.        ],</span>
+<span class="sd">           [0.6       ],</span>
+<span class="sd">           [1.        ],</span>
+<span class="sd">           [0.6       ],</span>
+<span class="sd">           [0.42857143],</span>
+<span class="sd">           [0.6       ],</span>
+<span class="sd">           [1.        ],</span>
+<span class="sd">           [0.6       ],</span>
+<span class="sd">           [1.        ]])</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">w</span><span class="o">.</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="nb">float</span><span class="p">)</span>
+    <span class="n">w</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="nb">id</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">):</span>
+        <span class="n">ki</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">cardinalities</span><span class="p">[</span><span class="nb">id</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># deal with islands</span>
+        <span class="n">Ni</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span>
+        <span class="n">wi</span> <span class="o">=</span> <span class="n">w_subset</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">Ni</span><span class="p">)</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">wi</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="p">(</span><span class="n">ki</span> <span class="o">*</span> <span class="p">(</span><span class="n">ki</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">c</span></div>
+
+
+
+<div class="viewcode-block" id="shimbel">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.shimbel.html#libpysal.weights.shimbel">[docs]</a>
+<span class="k">def</span> <span class="nf">shimbel</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Find the Shimbel matrix for first order contiguity matrix.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    w     : W</span>
+<span class="sd">            spatial weights object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    info  : list</span>
+<span class="sd">            list of lists; one list for each observation which stores</span>
+<span class="sd">            the shortest order between it and each of the the other observations.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, shimbel</span>
+<span class="sd">    &gt;&gt;&gt; w5 = lat2W()</span>
+<span class="sd">    &gt;&gt;&gt; w5_shimbel = shimbel(w5)</span>
+<span class="sd">    &gt;&gt;&gt; w5_shimbel[0][24]</span>
+<span class="sd">    8</span>
+<span class="sd">    &gt;&gt;&gt; w5_shimbel[0][0:4]</span>
+<span class="sd">    [-1, 1, 2, 3]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">info</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">ids</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids</span><span class="p">:</span>
+        <span class="n">s</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span>
+        <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span>
+            <span class="n">s</span><span class="p">[</span><span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">flag</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">while</span> <span class="n">flag</span><span class="p">:</span>
+            <span class="n">p</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="n">knext</span> <span class="o">=</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="n">k</span><span class="p">)):</span>
+                <span class="n">neighbor</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+                <span class="n">p</span> <span class="o">=</span> <span class="n">neighbor</span>
+                <span class="n">next_neighbors</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">p</span><span class="p">]]</span>
+                <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">next_neighbors</span><span class="p">:</span>
+                    <span class="n">nid</span> <span class="o">=</span> <span class="n">ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">s</span><span class="p">[</span><span class="n">nid</span><span class="p">]</span> <span class="o">=</span> <span class="n">knext</span>
+            <span class="n">k</span> <span class="o">=</span> <span class="n">knext</span>
+            <span class="n">flag</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">info</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">s</span>
+    <span class="k">return</span> <span class="n">info</span></div>
+
+
+
+<div class="viewcode-block" id="full">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.full.html#libpysal.weights.full">[docs]</a>
+<span class="k">def</span> <span class="nf">full</span><span class="p">(</span><span class="n">w</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate a full numpy array.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    w        : W</span>
+<span class="sd">               spatial weights object</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    (fullw, keys) : tuple</span>
+<span class="sd">                    first element being the full numpy array and second element</span>
+<span class="sd">                    keys being the ids associated with each row in the array.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import W, full</span>
+<span class="sd">    &gt;&gt;&gt; neighbors = {&#39;first&#39;:[&#39;second&#39;],&#39;second&#39;:[&#39;first&#39;,&#39;third&#39;],&#39;third&#39;:[&#39;second&#39;]}</span>
+<span class="sd">    &gt;&gt;&gt; weights = {&#39;first&#39;:[1],&#39;second&#39;:[1,1],&#39;third&#39;:[1]}</span>
+<span class="sd">    &gt;&gt;&gt; w = W(neighbors, weights)</span>
+<span class="sd">    &gt;&gt;&gt; wf, ids = full(w)</span>
+<span class="sd">    &gt;&gt;&gt; wf</span>
+<span class="sd">    array([[0., 1., 0.],</span>
+<span class="sd">           [1., 0., 1.],</span>
+<span class="sd">           [0., 1., 0.]])</span>
+<span class="sd">    &gt;&gt;&gt; ids</span>
+<span class="sd">    [&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="full2W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.full2W.html#libpysal.weights.full2W">[docs]</a>
+<span class="k">def</span> <span class="nf">full2W</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a PySAL W object from a full array.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    m          : array</span>
+<span class="sd">                 nxn array with the full weights matrix</span>
+<span class="sd">    ids        : list</span>
+<span class="sd">                 User ids assumed to be aligned with m</span>
+<span class="sd">    **kwargs   : keyword arguments</span>
+<span class="sd">                 optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w       : W</span>
+<span class="sd">              PySAL weights object</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import full2W</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+
+<span class="sd">    Create an array of zeros</span>
+
+<span class="sd">    &gt;&gt;&gt; a = np.zeros((4, 4))</span>
+
+<span class="sd">    For loop to fill it with random numbers</span>
+
+<span class="sd">    &gt;&gt;&gt; for i in range(len(a)):</span>
+<span class="sd">    ...     for j in range(len(a[i])):</span>
+<span class="sd">    ...         if i!=j:</span>
+<span class="sd">    ...             a[i, j] = np.random.random(1)</span>
+
+<span class="sd">    Create W object</span>
+
+<span class="sd">    &gt;&gt;&gt; w = full2W(a)</span>
+<span class="sd">    &gt;&gt;&gt; w.full()[0] == a</span>
+<span class="sd">    array([[ True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True]])</span>
+
+<span class="sd">    Create list of user ids</span>
+
+<span class="sd">    &gt;&gt;&gt; ids = [&#39;myID0&#39;, &#39;myID1&#39;, &#39;myID2&#39;, &#39;myID3&#39;]</span>
+<span class="sd">    &gt;&gt;&gt; w = full2W(a, ids=ids)</span>
+<span class="sd">    &gt;&gt;&gt; w.full()[0] == a</span>
+<span class="sd">    array([[ True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True],</span>
+<span class="sd">           [ True,  True,  True,  True]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Your array is not square&quot;</span><span class="p">)</span>
+    <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="c1"># for i, row in enumerate(m):</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">m</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="n">ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">ngh</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="n">ngh</span><span class="p">])</span>
+        <span class="n">ngh</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ngh</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">ids</span><span class="p">:</span>
+            <span class="n">ngh</span> <span class="o">=</span> <span class="p">[</span><span class="n">ids</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">ngh</span><span class="p">]</span>
+        <span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">ngh</span>
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="WSP2W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.WSP2W.html#libpysal.weights.WSP2W">[docs]</a>
+<span class="k">def</span> <span class="nf">WSP2W</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert a pysal WSP object (thin weights matrix) to a pysal W object.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    wsp                     : WSP</span>
+<span class="sd">                              PySAL sparse weights object</span>
+<span class="sd">    **kwargs                : keyword arguments</span>
+<span class="sd">                              optional arguments for :class:`pysal.weights.W`</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w       : W</span>
+<span class="sd">              PySAL weights object</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, WSP, WSP2W</span>
+
+<span class="sd">    Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells</span>
+<span class="sd">    (rook contiguity), then construct a PySAL sparse weights object (wsp).</span>
+
+<span class="sd">    &gt;&gt;&gt; sp = lat2SW(2, 5)</span>
+<span class="sd">    &gt;&gt;&gt; wsp = WSP(sp)</span>
+<span class="sd">    &gt;&gt;&gt; wsp.n</span>
+<span class="sd">    10</span>
+<span class="sd">    &gt;&gt;&gt; wsp.sparse[0].todense()</span>
+<span class="sd">    matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)</span>
+
+<span class="sd">    Convert this sparse weights object to a standard PySAL weights object.</span>
+
+<span class="sd">    &gt;&gt;&gt; w = WSP2W(wsp)</span>
+<span class="sd">    &gt;&gt;&gt; w.n</span>
+<span class="sd">    10</span>
+<span class="sd">    &gt;&gt;&gt; print(w.full()[0][0])</span>
+<span class="sd">    [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">data</span>
+    <span class="n">indptr</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indptr</span>
+    <span class="n">id_order</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">id_order</span>
+    <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
+        <span class="c1"># replace indices with user IDs</span>
+        <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indices</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
+    <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+    <span class="n">start</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span><span class="p">):</span>
+        <span class="n">oid</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">end</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="n">neighbors</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+        <span class="n">weights</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">end</span>
+    <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
+    <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
+    <span class="n">w</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span>
+    <span class="k">return</span> <span class="n">w</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">insert_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">wsp</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;This function is deprecated. Use fill_diagonal instead.&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">fill_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="n">val</span><span class="p">,</span> <span class="n">wsp</span><span class="o">=</span><span class="n">wsp</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="fill_diagonal">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.fill_diagonal.html#libpysal.weights.fill_diagonal">[docs]</a>
+<span class="k">def</span> <span class="nf">fill_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">val</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">wsp</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a new weights object with values inserted along the main diagonal.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    w        : W</span>
+<span class="sd">               Spatial weights object</span>
+
+<span class="sd">    diagonal : float, int or array</span>
+<span class="sd">               Defines the value(s) to which the weights matrix diagonal should</span>
+<span class="sd">               be set. If a constant is passed then each element along the</span>
+<span class="sd">               diagonal will get this value (default is 1.0). An array of length</span>
+<span class="sd">               w.n can be passed to set explicit values to each element along</span>
+<span class="sd">               the diagonal (assumed to be in the same order as w.id_order).</span>
+
+<span class="sd">    wsp      : boolean</span>
+<span class="sd">               If True return a thin weights object of the type WSP, if False</span>
+<span class="sd">               return the standard W object.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    w        : W</span>
+<span class="sd">               Spatial weights object</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+
+<span class="sd">    Build a basic rook weights matrix, which has zeros on the diagonal, then</span>
+<span class="sd">    insert ones along the diagonal.</span>
+
+<span class="sd">    &gt;&gt;&gt; w = lat2W(5, 5, id_type=&#39;string&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; w_const = insert_diagonal(w)</span>
+<span class="sd">    &gt;&gt;&gt; w[&#39;id0&#39;] ==  {&#39;id5&#39;: 1.0, &#39;id1&#39;: 1.0}</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w_const[&#39;id0&#39;] == {&#39;id5&#39;: 1.0, &#39;id0&#39;: 1.0, &#39;id1&#39;: 1.0}</span>
+<span class="sd">    True</span>
+
+<span class="sd">    Insert different values along the main diagonal.</span>
+
+<span class="sd">    &gt;&gt;&gt; diag = np.arange(100, 125)</span>
+<span class="sd">    &gt;&gt;&gt; w_var = insert_diagonal(w, diag)</span>
+<span class="sd">    &gt;&gt;&gt; w_var[&#39;id0&#39;] == {&#39;id5&#39;: 1.0, &#39;id0&#39;: 100.0, &#39;id1&#39;: 1.0}</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">w_new</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
+    <span class="n">w_new</span> <span class="o">=</span> <span class="n">w_new</span><span class="o">.</span><span class="n">tolil</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span> <span class="o">!=</span> <span class="n">val</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;shape of w and diagonal do not match&quot;</span><span class="p">)</span>
+        <span class="n">w_new</span><span class="o">.</span><span class="n">setdiag</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">numbers</span><span class="o">.</span><span class="n">Number</span><span class="p">):</span>
+        <span class="n">w_new</span><span class="o">.</span><span class="n">setdiag</span><span class="p">([</span><span class="n">val</span><span class="p">]</span> <span class="o">*</span> <span class="n">w</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Invalid value passed to diagonal&quot;</span><span class="p">)</span>
+    <span class="n">w_out</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">w_new</span><span class="p">,</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">wsp</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">w_out</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">w_out</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="remap_ids">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.remap_ids.html#libpysal.weights.remap_ids">[docs]</a>
+<span class="k">def</span> <span class="nf">remap_ids</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">old2new</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="p">[],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Remaps the IDs in a spatial weights object.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    w        : W</span>
+<span class="sd">               Spatial weights object</span>
+
+<span class="sd">    old2new  : dictionary</span>
+<span class="sd">               Dictionary where the keys are the IDs in w (i.e. &quot;old IDs&quot;) and</span>
+<span class="sd">               the values are the IDs to replace them (i.e. &quot;new IDs&quot;)</span>
+
+<span class="sd">    id_order : list</span>
+<span class="sd">               An ordered list of new IDs, which defines the order of observations when</span>
+<span class="sd">               iterating over W. If not set then the id_order in w will be</span>
+<span class="sd">               used.</span>
+<span class="sd">    **kwargs : keyword arguments</span>
+<span class="sd">               optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    implicit : W</span>
+<span class="sd">               Spatial weights object with new IDs</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">    &gt;&gt;&gt; w = lat2W(3,2)</span>
+<span class="sd">    &gt;&gt;&gt; w.id_order</span>
+<span class="sd">    [0, 1, 2, 3, 4, 5]</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[0]</span>
+<span class="sd">    [2, 1]</span>
+<span class="sd">    &gt;&gt;&gt; old_to_new = {0:&#39;a&#39;, 1:&#39;b&#39;, 2:&#39;c&#39;, 3:&#39;d&#39;, 4:&#39;e&#39;, 5:&#39;f&#39;}</span>
+<span class="sd">    &gt;&gt;&gt; w_new = remap_ids(w, old_to_new)</span>
+<span class="sd">    &gt;&gt;&gt; w_new.id_order</span>
+<span class="sd">    [&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;]</span>
+<span class="sd">    &gt;&gt;&gt; w_new.neighbors[&#39;a&#39;]</span>
+<span class="sd">    [&#39;c&#39;, &#39;b&#39;]</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;w must be a spatial weights object&quot;</span><span class="p">)</span>
+    <span class="n">new_neigh</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">new_weights</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+        <span class="n">new_values</span> <span class="o">=</span> <span class="p">[</span><span class="n">old2new</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">value</span><span class="p">]</span>
+        <span class="n">new_key</span> <span class="o">=</span> <span class="n">old2new</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="n">new_neigh</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_values</span>
+        <span class="n">new_weights</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">new_neigh</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">:</span>
+            <span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="n">old2new</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">]</span>
+            <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">new_neigh</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">new_neigh</span><span class="p">,</span> <span class="n">new_weights</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="get_ids">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.get_ids.html#libpysal.weights.get_ids">[docs]</a>
+<span class="k">def</span> <span class="nf">get_ids</span><span class="p">(</span><span class="n">in_shps</span><span class="p">,</span> <span class="n">idVariable</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Gets the IDs from the DBF file that moves with a given shape file or</span>
+<span class="sd">    a geopandas.GeoDataFrame.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    in_shps      : str or geopandas.GeoDataFrame</span>
+<span class="sd">                   The input geographic data. Either</span>
+<span class="sd">                   (1) a path to a shapefile including suffix (str); or</span>
+<span class="sd">                   (2) a geopandas.GeoDataFrame.</span>
+<span class="sd">    idVariable   : str</span>
+<span class="sd">                   name of a column in the shapefile&#39;s DBF or the</span>
+<span class="sd">                   geopandas.GeoDataFrame to use for ids.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    ids          : list</span>
+<span class="sd">                   a list of IDs</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import get_ids</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; polyids = get_ids(libpysal.examples.get_path(&quot;columbus.shp&quot;), &quot;POLYID&quot;)</span>
+<span class="sd">    &gt;&gt;&gt; polyids[:5]</span>
+<span class="sd">    [1, 2, 3, 4, 5]</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import get_ids</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; import geopandas as gpd</span>
+<span class="sd">    &gt;&gt;&gt; gdf = gpd.read_file(libpysal.examples.get_path(&quot;columbus.shp&quot;))</span>
+<span class="sd">    &gt;&gt;&gt; polyids = gdf[&quot;POLYID&quot;]</span>
+<span class="sd">    &gt;&gt;&gt; polyids[:5]</span>
+<span class="sd">    0    1</span>
+<span class="sd">    1    2</span>
+<span class="sd">    2    3</span>
+<span class="sd">    3    4</span>
+<span class="sd">    4    5</span>
+<span class="sd">    Name: POLYID, dtype: int64</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">in_shps</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="n">dbname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">in_shps</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;.dbf&quot;</span>
+            <span class="n">db</span> <span class="o">=</span> <span class="n">psopen</span><span class="p">(</span><span class="n">dbname</span><span class="p">)</span>
+            <span class="n">cols</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">header</span>
+            <span class="n">var</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">by_col</span><span class="p">[</span><span class="n">idVariable</span><span class="p">]</span>
+            <span class="n">db</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">cols</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">in_shps</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
+            <span class="n">var</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">in_shps</span><span class="p">[</span><span class="n">idVariable</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">var</span>
+
+    <span class="k">except</span> <span class="ne">IOError</span><span class="p">:</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="s1">&#39;The shapefile &quot;</span><span class="si">%s</span><span class="s1">&quot; appears to be missing its DBF file. &#39;</span>
+            <span class="o">+</span> <span class="s1">&#39; The DBF file &quot;</span><span class="si">%s</span><span class="s1">&quot; could not be found.&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">in_shps</span><span class="p">,</span> <span class="n">dbname</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span>
+    <span class="k">except</span> <span class="p">(</span><span class="ne">AttributeError</span><span class="p">,</span> <span class="ne">KeyError</span><span class="p">):</span>
+        <span class="n">msg</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="s1">&#39;The variable &quot;</span><span class="si">%s</span><span class="s1">&quot; not found in the DBF/GDF. The the following &#39;</span>
+            <span class="o">+</span> <span class="s2">&quot;variables are present: </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">idVariable</span><span class="p">,</span> <span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">cols</span><span class="p">))</span>
+        <span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="n">msg</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">get_points_array</span><span class="p">(</span><span class="n">iterable</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Gets a data array of x and y coordinates from a given iterable</span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    iterable      : iterable</span>
+<span class="sd">                    arbitrary collection of shapes that supports iteration</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    points        : array</span>
+<span class="sd">                    (n, 2)</span>
+<span class="sd">                    a data array of x and y coordinates</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    If the given shape file includes polygons,</span>
+<span class="sd">    this function returns x and y coordinates of the polygons&#39; centroids</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">first_choice</span><span class="p">,</span> <span class="n">backup</span> <span class="o">=</span> <span class="n">tee</span><span class="p">(</span><span class="n">iterable</span><span class="p">)</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">HAS_SHAPELY</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">coords</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">BaseGeometry</span><span class="p">)</span>
+                    <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">first_choice</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span> <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">first_choice</span><span class="p">])</span>
+    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">shape</span> <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">backup</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">data</span>
+
+
+<div class="viewcode-block" id="get_points_array_from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.get_points_array_from_shapefile.html#libpysal.weights.get_points_array_from_shapefile">[docs]</a>
+<span class="k">def</span> <span class="nf">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">shapefile</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Gets a data array of x and y coordinates from a given shapefile.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    shapefile     : string</span>
+<span class="sd">                    name of a shape file including suffix</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    points        : array</span>
+<span class="sd">                    (n, 2)</span>
+<span class="sd">                    a data array of x and y coordinates</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    If the given shape file includes polygons,</span>
+<span class="sd">    this function returns x and y coordinates of the polygons&#39; centroids</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    Point shapefile</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import get_points_array_from_shapefile</span>
+<span class="sd">    &gt;&gt;&gt; xy = get_points_array_from_shapefile(libpysal.examples.get_path(&#39;juvenile.shp&#39;))</span>
+<span class="sd">    &gt;&gt;&gt; xy[:3]</span>
+<span class="sd">    array([[94., 93.],</span>
+<span class="sd">           [80., 95.],</span>
+<span class="sd">           [79., 90.]])</span>
+
+
+<span class="sd">    Polygon shapefile</span>
+
+<span class="sd">    &gt;&gt;&gt; xy = get_points_array_from_shapefile(libpysal.examples.get_path(&#39;columbus.shp&#39;))</span>
+<span class="sd">    &gt;&gt;&gt; xy[:3]</span>
+<span class="sd">    array([[ 8.82721847, 14.36907602],</span>
+<span class="sd">           [ 8.33265837, 14.03162401],</span>
+<span class="sd">           [ 9.01226541, 13.81971908]])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">f</span> <span class="o">=</span> <span class="n">psopen</span><span class="p">(</span><span class="n">shapefile</span><span class="p">)</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">get_points_array</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">data</span></div>
+
+
+
+<div class="viewcode-block" id="min_threshold_distance">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.min_threshold_distance.html#libpysal.weights.min_threshold_distance">[docs]</a>
+<span class="k">def</span> <span class="nf">min_threshold_distance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get the maximum nearest neighbor distance.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    data    : array</span>
+<span class="sd">              (n,k) or KDTree where KDtree.data is array (n,k)</span>
+<span class="sd">              n observations on k attributes</span>
+<span class="sd">    p       : float</span>
+<span class="sd">              Minkowski p-norm distance metric parameter:</span>
+<span class="sd">              1&lt;=p&lt;=infinity</span>
+<span class="sd">              2: Euclidean distance</span>
+<span class="sd">              1: Manhattan distance</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    nnd    : float</span>
+<span class="sd">             maximum nearest neighbor distance between the n observations</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import min_threshold_distance</span>
+<span class="sd">    &gt;&gt;&gt; import numpy as np</span>
+<span class="sd">    &gt;&gt;&gt; x, y = np.indices((5, 5))</span>
+<span class="sd">    &gt;&gt;&gt; x.shape = (25, 1)</span>
+<span class="sd">    &gt;&gt;&gt; y.shape = (25, 1)</span>
+<span class="sd">    &gt;&gt;&gt; data = np.hstack([x, y])</span>
+<span class="sd">    &gt;&gt;&gt; min_threshold_distance(data)</span>
+<span class="sd">    1.0</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">KDTree</span><span class="p">):</span>
+        <span class="n">kd</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">data</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">kd</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">nn</span> <span class="o">=</span> <span class="n">kd</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="n">p</span><span class="p">)</span>
+    <span class="n">nnd</span> <span class="o">=</span> <span class="n">nn</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">nnd</span></div>
+
+
+
+<div class="viewcode-block" id="lat2SW">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.lat2SW.html#libpysal.weights.lat2SW">[docs]</a>
+<span class="k">def</span> <span class="nf">lat2SW</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">criterion</span><span class="o">=</span><span class="s2">&quot;rook&quot;</span><span class="p">,</span> <span class="n">row_st</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a sparse W matrix for a regular lattice.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    nrows   : int</span>
+<span class="sd">              number of rows</span>
+<span class="sd">    ncols   : int</span>
+<span class="sd">              number of columns</span>
+<span class="sd">    rook    : {&quot;rook&quot;, &quot;queen&quot;, &quot;bishop&quot;}</span>
+<span class="sd">              type of contiguity. Default is rook.</span>
+<span class="sd">    row_st  : boolean</span>
+<span class="sd">              If True, the created sparse W object is row-standardized so</span>
+<span class="sd">              every row sums up to one. Defaults to False.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w : scipy.sparse.dia_matrix</span>
+<span class="sd">        instance of a scipy sparse matrix</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</span>
+<span class="sd">    This method directly creates the W matrix using the strucuture of the contiguity type.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2SW</span>
+<span class="sd">    &gt;&gt;&gt; w9 = lat2SW(3,3)</span>
+<span class="sd">    &gt;&gt;&gt; w9[0,1] == 1</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w9[3,6] == 1</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w9r = lat2SW(3,3, row_st=True)</span>
+<span class="sd">    &gt;&gt;&gt; w9r[3,6] == 1./3</span>
+<span class="sd">    True</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="n">nrows</span> <span class="o">*</span> <span class="n">ncols</span>
+    <span class="n">diagonals</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">offsets</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;rook&quot;</span> <span class="ow">or</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">ncols</span><span class="p">):</span>
+            <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="n">ncols</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;queen&quot;</span> <span class="ow">or</span> <span class="n">criterion</span> <span class="o">==</span> <span class="s2">&quot;bishop&quot;</span><span class="p">:</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">ncols</span><span class="p">):</span>
+            <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
+
+        <span class="n">d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">ncols</span><span class="p">):</span>
+            <span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">diagonals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+        <span class="n">offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">ncols</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">diagonals</span><span class="p">)</span>
+    <span class="n">offsets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">offsets</span><span class="p">)</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">dia_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="n">offsets</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">m</span> <span class="o">+</span> <span class="n">m</span><span class="o">.</span><span class="n">T</span>
+    <span class="k">if</span> <span class="n">row_st</span><span class="p">:</span>
+        <span class="n">m</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">spdiags</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">m</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">*</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">*</span> <span class="n">m</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">tocsc</span><span class="p">()</span>
+    <span class="n">m</span><span class="o">.</span><span class="n">eliminate_zeros</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">m</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">write_gal</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+    <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">k</span> <span class="o">*</span> <span class="n">k</span>
+    <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;0 </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">i</span> <span class="o">/</span> <span class="n">k</span>
+        <span class="n">col</span> <span class="o">=</span> <span class="n">i</span> <span class="o">%</span> <span class="n">k</span>
+        <span class="n">neighs</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span> <span class="o">-</span> <span class="n">k</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span>
+        <span class="n">neighs</span> <span class="o">=</span> <span class="p">[</span><span class="n">j</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">neighs</span> <span class="k">if</span> <span class="n">j</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">&lt;</span> <span class="n">n</span><span class="p">]</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">%d</span><span class="s2"> </span><span class="si">%d</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighs</span><span class="p">)))</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="n">neighs</span><span class="p">)))</span>
+    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+
+<div class="viewcode-block" id="neighbor_equality">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.neighbor_equality.html#libpysal.weights.neighbor_equality">[docs]</a>
+<span class="k">def</span> <span class="nf">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Test if the neighbor sets are equal between two weights objects</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w1 : W</span>
+<span class="sd">        instance of spatial weights class W</span>
+
+<span class="sd">    w2 : W</span>
+<span class="sd">        instance of spatial weights class W</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    Boolean</span>
+
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    Only set membership is evaluated, no check of the weight values is carried out.</span>
+
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights.util import neighbor_equality</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, W</span>
+<span class="sd">    &gt;&gt;&gt; w1 = lat2W(3,3)</span>
+<span class="sd">    &gt;&gt;&gt; w2 = lat2W(3,3)</span>
+<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w2)</span>
+<span class="sd">    True</span>
+<span class="sd">    &gt;&gt;&gt; w3 = lat2W(5,5)</span>
+<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w3)</span>
+<span class="sd">    False</span>
+<span class="sd">    &gt;&gt;&gt; n4 = w1.neighbors.copy()</span>
+<span class="sd">    &gt;&gt;&gt; n4[0] = [1]</span>
+<span class="sd">    &gt;&gt;&gt; n4[1] = [4, 2]</span>
+<span class="sd">    &gt;&gt;&gt; w4 = W(n4)</span>
+<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w4)</span>
+<span class="sd">    False</span>
+<span class="sd">    &gt;&gt;&gt; n5 = w1.neighbors.copy()</span>
+<span class="sd">    &gt;&gt;&gt; n5[0]</span>
+<span class="sd">    [3, 1]</span>
+<span class="sd">    &gt;&gt;&gt; n5[0] = [1, 3]</span>
+<span class="sd">    &gt;&gt;&gt; w5 = W(n5)</span>
+<span class="sd">    &gt;&gt;&gt; neighbor_equality(w1, w5)</span>
+<span class="sd">    True</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n1</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span>
+    <span class="n">n2</span> <span class="o">=</span> <span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span>
+    <span class="n">ids_1</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">n1</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="n">ids_2</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">n2</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+    <span class="k">if</span> <span class="n">ids_1</span> <span class="o">!=</span> <span class="n">ids_2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="kc">False</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids_1</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">]):</span>
+            <span class="k">return</span> <span class="kc">False</span>
+    <span class="k">return</span> <span class="kc">True</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">isKDTree</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This is a utility function to determine whether or not an object is a</span>
+<span class="sd">    KDTree, since KDTree and cKDTree have no common parent type</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="nb">any</span><span class="p">([</span><span class="nb">issubclass</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">),</span> <span class="n">KDTYPE</span><span class="p">)</span> <span class="k">for</span> <span class="n">KDTYPE</span> <span class="ow">in</span> <span class="n">KDTREE_TYPES</span><span class="p">])</span>
+
+
+<div class="viewcode-block" id="attach_islands">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.attach_islands.html#libpysal.weights.attach_islands">[docs]</a>
+<span class="k">def</span> <span class="nf">attach_islands</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">w_knn1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Attach nearest neighbor to islands in spatial weight w.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w            : libpysal.weights.W</span>
+<span class="sd">                   pysal spatial weight object (unstandardized).</span>
+<span class="sd">    w_knn1       : libpysal.weights.W</span>
+<span class="sd">                   Nearest neighbor pysal spatial weight object (k=1).</span>
+<span class="sd">    **kwargs     : keyword arguments</span>
+<span class="sd">                   optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">                 : libpysal.weights.W</span>
+<span class="sd">                   pysal spatial weight object w without islands.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W, Rook, KNN, attach_islands</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; w = Rook.from_shapefile(libpysal.examples.get_path(&#39;10740.shp&#39;))</span>
+<span class="sd">    &gt;&gt;&gt; w.islands</span>
+<span class="sd">    [163]</span>
+<span class="sd">    &gt;&gt;&gt; w_knn1 = KNN.from_shapefile(libpysal.examples.get_path(&#39;10740.shp&#39;),k=1)</span>
+<span class="sd">    &gt;&gt;&gt; w_attach = attach_islands(w, w_knn1)</span>
+<span class="sd">    &gt;&gt;&gt; w_attach.islands</span>
+<span class="sd">    []</span>
+<span class="sd">    &gt;&gt;&gt; w_attach[w.islands[0]]</span>
+<span class="sd">    {166: 1.0}</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">),</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">islands</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;There are no disconnected observations (no islands)!&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">w</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">islands</span><span class="p">:</span>
+            <span class="n">nb</span> <span class="o">=</span> <span class="n">w_knn1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">island</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">island</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">float</span><span class="p">:</span>
+                <span class="n">nb</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">nb</span><span class="p">)</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">island</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">nb</span><span class="p">]</span>
+            <span class="n">weights</span><span class="p">[</span><span class="n">island</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">island</span><span class="p">]</span>
+            <span class="n">weights</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span><span class="p">[</span><span class="n">nb</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="nonplanar_neighbors">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.nonplanar_neighbors.html#libpysal.weights.nonplanar_neighbors">[docs]</a>
+<span class="k">def</span> <span class="nf">nonplanar_neighbors</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">geodataframe</span><span class="p">,</span> <span class="n">tolerance</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Detect neighbors for non-planar polygon collections</span>
+
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    w:   pysal W</span>
+<span class="sd">         A spatial weights object with reported islands</span>
+
+
+<span class="sd">    geodataframe: GeoDataframe</span>
+<span class="sd">                  The polygon dataframe from which w was constructed.</span>
+
+<span class="sd">    tolerance: float</span>
+<span class="sd">               The percentage of the minimum horizontal or vertical extent (minextent) of</span>
+<span class="sd">               the dataframe to use in defining  a buffering distance to allow for fuzzy</span>
+<span class="sd">               contiguity detection. The buffering distance is equal to tolerance*minextent.</span>
+<span class="sd">    **kwargs:  keyword arguments</span>
+<span class="sd">               optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    non_planar_joins : dictionary</span>
+<span class="sd">               Stores the new joins detected. Key is the id of the focal unit, value is a list of neighbor ids.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w: pysal W</span>
+<span class="sd">       Spatial weights object that encodes fuzzy neighbors.</span>
+<span class="sd">       This will have an attribute `non_planar_joins` to indicate what new joins were detected.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    This relaxes the notion of contiguity neighbors for the case of shapefiles</span>
+<span class="sd">    that violate the condition of planar enforcement. It handles three types</span>
+<span class="sd">    of conditions present in such files that would result in islands when using</span>
+<span class="sd">    the regular PySAL contiguity methods. The first are edges for nearby</span>
+<span class="sd">    polygons that should be shared, but are digitized separately for the</span>
+<span class="sd">    individual polygons and the resulting edges do not coincide, but instead</span>
+<span class="sd">    the edges intersect. The second case is similar to the first, only the</span>
+<span class="sd">    resultant edges do not intersect but are &quot;close&quot;. The final case arises</span>
+<span class="sd">    when one polygon is &quot;inside&quot; a second polygon but is not encoded to</span>
+<span class="sd">    represent a hole in the containing polygon.</span>
+
+<span class="sd">    The buffering check assumes the geometry coordinates are projected.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; import geopandas as gpd</span>
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; df = gpd.read_file(libpysal.examples.get_path(&#39;map_RS_BR.shp&#39;))</span>
+<span class="sd">    &gt;&gt;&gt; w = libpysal.weights.Queen.from_dataframe(df)</span>
+<span class="sd">    &gt;&gt;&gt; w.islands</span>
+<span class="sd">    [0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374]</span>
+<span class="sd">    &gt;&gt;&gt; wnp = libpysal.weights.nonplanar_neighbors(w, df)</span>
+<span class="sd">    &gt;&gt;&gt; wnp.islands</span>
+<span class="sd">    []</span>
+<span class="sd">    &gt;&gt;&gt; w.neighbors[0]</span>
+<span class="sd">    []</span>
+<span class="sd">    &gt;&gt;&gt; wnp.neighbors[0]</span>
+<span class="sd">    [23, 59, 152, 239]</span>
+<span class="sd">    &gt;&gt;&gt; wnp.neighbors[23]</span>
+<span class="sd">    [0, 45, 59, 107, 152, 185, 246]</span>
+
+<span class="sd">    Also see `nonplanarweights.ipynb`</span>
+
+<span class="sd">    References</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">gdf</span> <span class="o">=</span> <span class="n">geodataframe</span>
+    <span class="k">assert</span> <span class="p">(</span>
+        <span class="n">gdf</span><span class="o">.</span><span class="n">sindex</span>
+    <span class="p">),</span> <span class="s2">&quot;GeoDataFrame must have a spatial index. Please make sure you have `libspatialindex` installed&quot;</span>
+    <span class="n">islands</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">islands</span>
+    <span class="n">joins</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">)</span>
+    <span class="n">candidates</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span>
+    <span class="n">fixes</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+
+    <span class="c1"># first check for intersecting polygons</span>
+    <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="n">islands</span><span class="p">:</span>
+        <span class="n">focal</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span>
+        <span class="n">neighbors</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">j</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">focal</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">island</span>
+        <span class="p">]</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]:</span>
+                    <span class="n">fixes</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
+                    <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">island</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]:</span>
+                    <span class="n">fixes</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
+                    <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
+
+    <span class="c1"># if any islands remain, dilate them and check for intersection</span>
+    <span class="k">if</span> <span class="n">islands</span><span class="p">:</span>
+        <span class="n">x0</span><span class="p">,</span> <span class="n">y0</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y1</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">total_bounds</span>
+        <span class="n">distance</span> <span class="o">=</span> <span class="n">tolerance</span> <span class="o">*</span> <span class="nb">min</span><span class="p">(</span><span class="n">x1</span> <span class="o">-</span> <span class="n">x0</span><span class="p">,</span> <span class="n">y1</span> <span class="o">-</span> <span class="n">y0</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="n">islands</span><span class="p">:</span>
+            <span class="n">dilated</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
+            <span class="n">neighbors</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">j</span>
+                <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">dilated</span><span class="o">.</span><span class="n">intersects</span><span class="p">(</span><span class="n">candidate</span><span class="p">)</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">!=</span> <span class="n">island</span>
+            <span class="p">]</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">neighbor</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]:</span>
+                        <span class="n">fixes</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
+                        <span class="n">joins</span><span class="p">[</span><span class="n">island</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neighbor</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">island</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]:</span>
+                        <span class="n">fixes</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
+                        <span class="n">joins</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">island</span><span class="p">)</span>
+
+    <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">joins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+    <span class="n">w</span><span class="o">.</span><span class="n">non_planar_joins</span> <span class="o">=</span> <span class="n">fixes</span>
+    <span class="k">return</span> <span class="n">w</span></div>
+
+
+
+<div class="viewcode-block" id="fuzzy_contiguity">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.fuzzy_contiguity.html#libpysal.weights.fuzzy_contiguity">[docs]</a>
+<span class="nd">@requires</span><span class="p">(</span><span class="s2">&quot;geopandas&quot;</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">fuzzy_contiguity</span><span class="p">(</span>
+    <span class="n">gdf</span><span class="p">,</span>
+    <span class="n">tolerance</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span>
+    <span class="n">buffering</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">predicate</span><span class="o">=</span><span class="s2">&quot;intersects&quot;</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Fuzzy contiguity spatial weights</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    gdf:   GeoDataFrame</span>
+
+<span class="sd">    tolerance: float</span>
+<span class="sd">               The percentage of the length of the minimum side of the bounding rectangle for the GeoDataFrame to use in determining the buffering distance.</span>
+
+<span class="sd">    buffering: boolean</span>
+<span class="sd">               If False (default) joins will only be detected for features that intersect (touch, contain, within).</span>
+<span class="sd">               If True then features will be buffered and intersections will be based on buffered features.</span>
+
+<span class="sd">    drop: boolean</span>
+<span class="sd">          If True (default), the buffered features are removed from the GeoDataFrame. If False, buffered features are added to the GeoDataFrame.</span>
+
+<span class="sd">    buffer : float</span>
+<span class="sd">             Specify exact buffering distance. Ignores `tolerance`.</span>
+
+<span class="sd">    predicate : {&#39;intersects&#39;, &#39;within&#39;, &#39;contains&#39;, &#39;overlaps&#39;, &#39;crosses&#39;, &#39;touches&#39;}</span>
+<span class="sd">                The predicate to use for determination of neighbors. Default is &#39;intersects&#39;. If None is passed, neighbours are determined based on</span>
+<span class="sd">                the intersection of bounding boxes.</span>
+
+<span class="sd">    **kwargs: keyword arguments</span>
+<span class="sd">              optional arguments for :class:`pysal.weights.W`</span>
+
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+
+<span class="sd">    w:  PySAL W</span>
+<span class="sd">        Spatial weights based on fuzzy contiguity. Weights are binary.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import fuzzy_contiguity</span>
+<span class="sd">    &gt;&gt;&gt; import geopandas as gpd</span>
+<span class="sd">    &gt;&gt;&gt; rs = libpysal.examples.get_path(&#39;map_RS_BR.shp&#39;)</span>
+<span class="sd">    &gt;&gt;&gt; rs_df = gpd.read_file(rs)</span>
+<span class="sd">    &gt;&gt;&gt; wq = libpysal.weights.Queen.from_dataframe(rs_df)</span>
+<span class="sd">    &gt;&gt;&gt; len(wq.islands)</span>
+<span class="sd">    29</span>
+<span class="sd">    &gt;&gt;&gt; wq[0]</span>
+<span class="sd">    {}</span>
+<span class="sd">    &gt;&gt;&gt; wf = fuzzy_contiguity(rs_df)</span>
+<span class="sd">    &gt;&gt;&gt; wf.islands</span>
+<span class="sd">    []</span>
+<span class="sd">    &gt;&gt;&gt; wf[0] == dict({239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0})</span>
+<span class="sd">    True</span>
+
+<span class="sd">    Example needing to use buffering</span>
+
+<span class="sd">    &gt;&gt;&gt; from shapely.geometry import Polygon</span>
+<span class="sd">    &gt;&gt;&gt; p0 = Polygon([(0,0), (10,0), (10,10)])</span>
+<span class="sd">    &gt;&gt;&gt; p1 = Polygon([(10,1), (10,2), (15,2)])</span>
+<span class="sd">    &gt;&gt;&gt; p2 = Polygon([(12,2.001), (14, 2.001), (13,10)])</span>
+<span class="sd">    &gt;&gt;&gt; gs = gpd.GeoSeries([p0,p1,p2])</span>
+<span class="sd">    &gt;&gt;&gt; gdf = gpd.GeoDataFrame(geometry=gs)</span>
+<span class="sd">    &gt;&gt;&gt; wf = fuzzy_contiguity(gdf)</span>
+<span class="sd">    &gt;&gt;&gt; wf.islands</span>
+<span class="sd">    [2]</span>
+<span class="sd">    &gt;&gt;&gt; wfb = fuzzy_contiguity(gdf, buffering=True)</span>
+<span class="sd">    &gt;&gt;&gt; wfb.islands</span>
+<span class="sd">    []</span>
+<span class="sd">    &gt;&gt;&gt; wfb[2]</span>
+<span class="sd">    {1: 1.0}</span>
+
+<span class="sd">    Example with a custom index</span>
+
+<span class="sd">    &gt;&gt;&gt; rs_df_ix = rs_df.set_index(&quot;NM_MUNICIP&quot;)</span>
+<span class="sd">    &gt;&gt;&gt; wf_ix = fuzzy_contiguity(rs_df)</span>
+<span class="sd">    &gt;&gt;&gt; wf_ix.neighbors[&quot;TAVARES&quot;]</span>
+<span class="sd">    [&#39;SÃO JOSÉ DO NORTE&#39;, &#39;MOSTARDAS&#39;]</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+
+<span class="sd">    This relaxes the notion of contiguity neighbors for the case of feature</span>
+<span class="sd">    collections that violate the condition of planar enforcement. It handles</span>
+<span class="sd">    three types of conditions present in such collections that would result in</span>
+<span class="sd">    islands when using the regular PySAL contiguity methods. The first are</span>
+<span class="sd">    edges for nearby polygons that should be shared, but are digitized</span>
+<span class="sd">    separately for the individual polygons and the resulting edges do not</span>
+<span class="sd">    coincide, but instead the edges intersect. The second case is similar to</span>
+<span class="sd">    the first, only the resultant edges do not intersect but are &quot;close&quot;. The</span>
+<span class="sd">    final case arises when one polygon is &quot;inside&quot; a second polygon but is not</span>
+<span class="sd">    encoded to represent a hole in the containing polygon.</span>
+
+<span class="sd">    Detection of the second case will require setting buffering=True and exploring different values for tolerance.</span>
+
+<span class="sd">    The buffering check assumes the geometry coordinates are projected.</span>
+
+
+<span class="sd">    References</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    Planar Enforcement: http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</span>
+
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">buffering</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">buffer</span><span class="p">:</span>
+            <span class="c1"># buffer each shape</span>
+            <span class="n">minx</span><span class="p">,</span> <span class="n">miny</span><span class="p">,</span> <span class="n">maxx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">total_bounds</span>
+            <span class="n">buffer</span> <span class="o">=</span> <span class="n">tolerance</span> <span class="o">*</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="nb">abs</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">maxx</span> <span class="o">-</span> <span class="n">minx</span><span class="p">,</span> <span class="n">maxy</span> <span class="o">-</span> <span class="n">miny</span><span class="p">))</span>
+        <span class="c1"># create new geometry column</span>
+        <span class="n">new_geometry</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="n">buffer</span><span class="p">)</span>
+        <span class="n">gdf</span><span class="p">[</span><span class="s2">&quot;_buffer&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_geometry</span>
+        <span class="n">old_geometry_name</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">name</span>
+        <span class="n">gdf</span><span class="o">.</span><span class="n">set_geometry</span><span class="p">(</span><span class="s2">&quot;_buffer&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">if</span> <span class="n">GPD_013</span><span class="p">:</span>
+        <span class="c1"># query tree based on set predicate</span>
+        <span class="n">inp</span><span class="p">,</span> <span class="n">res</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">sindex</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="n">predicate</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">inp</span><span class="p">,</span> <span class="n">res</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">sindex</span><span class="o">.</span><span class="n">query_bulk</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="n">predicate</span><span class="o">=</span><span class="n">predicate</span><span class="p">)</span>
+    <span class="c1"># remove self hits</span>
+    <span class="n">itself</span> <span class="o">=</span> <span class="n">inp</span> <span class="o">==</span> <span class="n">res</span>
+    <span class="n">inp</span> <span class="o">=</span> <span class="n">inp</span><span class="p">[</span><span class="o">~</span><span class="n">itself</span><span class="p">]</span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">res</span><span class="p">[</span><span class="o">~</span><span class="n">itself</span><span class="p">]</span>
+
+    <span class="c1"># extract index values of neighbors</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">index</span><span class="p">):</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">res</span><span class="p">[</span><span class="n">inp</span> <span class="o">==</span> <span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="n">neighbors</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">ids</span>
+
+    <span class="k">if</span> <span class="n">buffering</span><span class="p">:</span>
+        <span class="n">gdf</span><span class="o">.</span><span class="n">set_geometry</span><span class="p">(</span><span class="n">old_geometry_name</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">drop</span><span class="p">:</span>
+            <span class="n">gdf</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;_buffer&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+
+    <span class="k">assert</span> <span class="p">(</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+    <span class="k">assert</span> <span class="p">(</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+    <span class="k">assert</span> <span class="p">(</span>
+        <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
+    <span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+    <span class="k">assert</span> <span class="p">(</span>
+        <span class="n">lat2W</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span> <span class="o">==</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="s2">&quot;queen&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
+    <span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
+</pre></div>
+
+    </div>
+      
+  </div>
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diff --git a/_modules/libpysal/weights/weights.html b/_modules/libpysal/weights/weights.html
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index 000000000..86148554e
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+<!DOCTYPE html>
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+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+      
+  <h1>Source code for libpysal.weights.weights</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Weights.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Sergio J. Rey &lt;srey@asu.edu&gt;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">basename</span> <span class="k">as</span> <span class="n">BASENAME</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">scipy.sparse</span>
+<span class="kn">from</span> <span class="nn">scipy.sparse.csgraph</span> <span class="kn">import</span> <span class="n">connected_components</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+
+
+<span class="c1"># from .util import full, WSP2W resolve import cycle by</span>
+<span class="c1"># forcing these into methods</span>
+<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">adjtools</span>
+<span class="kn">from</span> <span class="nn">..io.fileio</span> <span class="kn">import</span> <span class="n">FileIO</span> <span class="k">as</span> <span class="n">popen</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;W&quot;</span><span class="p">,</span> <span class="s2">&quot;WSP&quot;</span><span class="p">]</span>
+
+
+<span class="k">class</span> <span class="nc">_LabelEncoder</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Encode labels with values between 0 and n_classes-1.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+<span class="sd">    classes_: array of shape [n_classes]</span>
+<span class="sd">        Class labels for each index.</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+<span class="sd">    &gt;&gt;&gt; le = _LabelEncoder()</span>
+<span class="sd">    &gt;&gt;&gt; le.fit([&quot;NY&quot;, &quot;CA&quot;, &quot;NY&quot;, &quot;CA&quot;, &quot;TX&quot;, &quot;TX&quot;])</span>
+<span class="sd">    &gt;&gt;&gt; le.classes_</span>
+<span class="sd">    array([&#39;CA&#39;, &#39;NY&#39;, &#39;TX&#39;])</span>
+<span class="sd">    &gt;&gt;&gt; le.transform([&quot;NY&quot;, &quot;CA&quot;, &quot;NY&quot;, &quot;CA&quot;, &quot;TX&quot;, &quot;TX&quot;])</span>
+<span class="sd">    array([1, 0, 1, 0, 2, 2])</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit label encoder.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : list</span>
+<span class="sd">            list of labels</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        self : instance of self.</span>
+<span class="sd">          Fitted label encoder.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span>
+
+    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform labels to normalized encoding.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        y : list</span>
+<span class="sd">            list of labels</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        y : array</span>
+<span class="sd">            array of normalized labels.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W">[docs]</a>
+<span class="k">class</span> <span class="nc">W</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Spatial weights class. Class attributes are described by their</span>
+<span class="sd">    docstrings. to view, use the ``help`` function.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    neighbors : dict</span>
+<span class="sd">        Key is region ID, value is a list of neighbor IDS.</span>
+<span class="sd">        For example, ``{&#39;a&#39;:[&#39;b&#39;],&#39;b&#39;:[&#39;a&#39;,&#39;c&#39;],&#39;c&#39;:[&#39;b&#39;]}``.</span>
+<span class="sd">    weights : dict</span>
+<span class="sd">       Key is region ID, value is a list of edge weights.</span>
+<span class="sd">       If not supplied all edge weights are assumed to have a weight of 1.</span>
+<span class="sd">       For example, ``{&#39;a&#39;:[0.5],&#39;b&#39;:[0.5,1.5],&#39;c&#39;:[1.5]}``.</span>
+<span class="sd">    id_order : list</span>
+<span class="sd">       An ordered list of ids, defines the order of observations when</span>
+<span class="sd">       iterating over ``W`` if not set, lexicographical ordering is used</span>
+<span class="sd">       to iterate and the ``id_order_set`` property will return ``False``.</span>
+<span class="sd">       This can be set after creation by setting the ``id_order`` property.</span>
+<span class="sd">    silence_warnings : bool</span>
+<span class="sd">       By default ``libpysal`` will print a warning if the dataset contains</span>
+<span class="sd">       any disconnected components or islands. To silence this warning set this</span>
+<span class="sd">       parameter to ``True``.</span>
+<span class="sd">    ids : list</span>
+<span class="sd">        Values to use for keys of the neighbors and weights ``dict`` objects.</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    asymmetries</span>
+<span class="sd">    cardinalities</span>
+<span class="sd">    component_labels</span>
+<span class="sd">    diagW2</span>
+<span class="sd">    diagWtW</span>
+<span class="sd">    diagWtW_WW</span>
+<span class="sd">    histogram</span>
+<span class="sd">    id2i</span>
+<span class="sd">    id_order</span>
+<span class="sd">    id_order_set</span>
+<span class="sd">    islands</span>
+<span class="sd">    max_neighbors</span>
+<span class="sd">    mean_neighbors</span>
+<span class="sd">    min_neighbors</span>
+<span class="sd">    n</span>
+<span class="sd">    n_components</span>
+<span class="sd">    neighbor_offsets</span>
+<span class="sd">    nonzero</span>
+<span class="sd">    pct_nonzero</span>
+<span class="sd">    s0</span>
+<span class="sd">    s1</span>
+<span class="sd">    s2</span>
+<span class="sd">    s2array</span>
+<span class="sd">    sd</span>
+<span class="sd">    sparse</span>
+<span class="sd">    trcW2</span>
+<span class="sd">    trcWtW</span>
+<span class="sd">    trcWtW_WW</span>
+<span class="sd">    transform</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import W</span>
+<span class="sd">    &gt;&gt;&gt; neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}</span>
+<span class="sd">    &gt;&gt;&gt; weights = {0: [1, 1], 1: [1, 1, 1], 2: [1, 1], 3: [1, 1, 1], 4: [1, 1, 1, 1], 5: [1, 1, 1], 6: [1, 1], 7: [1, 1, 1], 8: [1, 1]}</span>
+<span class="sd">    &gt;&gt;&gt; w = W(neighbors, weights)</span>
+<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w.pct_nonzero</span>
+<span class="sd">    &#39;29.630&#39;</span>
+
+<span class="sd">    Read from external `.gal file &lt;https://geodacenter.github.io/workbook/4a_contig_weights/lab4a.html#gal-weights-file&gt;`_.</span>
+
+<span class="sd">    &gt;&gt;&gt; import libpysal</span>
+<span class="sd">    &gt;&gt;&gt; w = libpysal.io.open(libpysal.examples.get_path(&quot;stl.gal&quot;)).read()</span>
+<span class="sd">    &gt;&gt;&gt; w.n</span>
+<span class="sd">    78</span>
+<span class="sd">    &gt;&gt;&gt; &quot;%.3f&quot;%w.pct_nonzero</span>
+<span class="sd">    &#39;6.542&#39;</span>
+
+<span class="sd">    Set weights implicitly.</span>
+
+<span class="sd">    &gt;&gt;&gt; neighbors = {0: [3, 1], 1: [0, 4, 2], 2: [1, 5], 3: [0, 6, 4], 4: [1, 3, 7, 5], 5: [2, 4, 8], 6: [3, 7], 7: [4, 6, 8], 8: [5, 7]}</span>
+<span class="sd">    &gt;&gt;&gt; w = W(neighbors)</span>
+<span class="sd">    &gt;&gt;&gt; round(w.pct_nonzero,3)</span>
+<span class="sd">    29.63</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">    &gt;&gt;&gt; w = lat2W(100, 100)</span>
+<span class="sd">    &gt;&gt;&gt; w.trcW2</span>
+<span class="sd">    39600.0</span>
+<span class="sd">    &gt;&gt;&gt; w.trcWtW</span>
+<span class="sd">    39600.0</span>
+<span class="sd">    &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
+<span class="sd">    &gt;&gt;&gt; round(w.trcW2, 3)</span>
+<span class="sd">    2530.722</span>
+<span class="sd">    &gt;&gt;&gt; round(w.trcWtW, 3)</span>
+<span class="sd">    2533.667</span>
+
+<span class="sd">    Cardinality Histogram:</span>
+
+<span class="sd">    &gt;&gt;&gt; w.histogram</span>
+<span class="sd">    [(2, 4), (3, 392), (4, 9604)]</span>
+
+<span class="sd">    Disconnected observations (islands):</span>
+
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import W</span>
+<span class="sd">    &gt;&gt;&gt; w = W({1:[0],0:[1],2:[], 3:[]})</span>
+
+<span class="sd">    UserWarning: The weights matrix is not fully connected:</span>
+<span class="sd">    There are 3 disconnected components.</span>
+<span class="sd">    There are 2 islands with ids: 2, 3.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="W.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span> <span class="o">=</span> <span class="n">silence_warnings</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">=</span> <span class="n">neighbors</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">weights</span><span class="p">:</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">neighbors</span><span class="p">:</span>
+                <span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>  <span class="c1"># original weights</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s2">&quot;O&quot;</span>
+        <span class="k">if</span> <span class="n">id_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="n">id_order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span>
+        <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="n">message</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="s2">&quot;The weights matrix is not fully connected: &quot;</span>
+                <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> There are </span><span class="si">%d</span><span class="s2"> disconnected components.&quot;</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span>
+            <span class="p">)</span>
+            <span class="n">ni</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ni</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">message</span> <span class="o">=</span> <span class="n">message</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> There is 1 island with id: </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span>
+                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="n">ni</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">message</span> <span class="o">=</span> <span class="n">message</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2"> There are </span><span class="si">%d</span><span class="s2"> islands with ids: </span><span class="si">%s</span><span class="s2">.&quot;</span> <span class="o">%</span> <span class="p">(</span>
+                    <span class="n">ni</span><span class="p">,</span>
+                    <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">island</span><span class="p">)</span> <span class="k">for</span> <span class="n">island</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">message</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Reset properties.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="W.to_file">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_file">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_file</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Write a weights to a file. The format is guessed automatically</span>
+<span class="sd">        from the path, but can be overridden with the format argument.</span>
+
+<span class="sd">        See libpysal.io.FileIO for more information.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        path    :   string</span>
+<span class="sd">                    location to save the file</span>
+<span class="sd">        format  :   string</span>
+<span class="sd">                    string denoting the format to write the weights to.</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        None</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">f</span> <span class="o">=</span> <span class="n">popen</span><span class="p">(</span><span class="n">dataPath</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;w&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="nb">format</span><span class="p">)</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="W.from_file">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_file">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_file</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Read a weights file into a W object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        path    :   string</span>
+<span class="sd">                    location to save the file</span>
+<span class="sd">        format  :   string</span>
+<span class="sd">                    string denoting the format to write the weights to.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        W object</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">f</span> <span class="o">=</span> <span class="n">popen</span><span class="p">(</span><span class="n">dataPath</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">dataFormat</span><span class="o">=</span><span class="nb">format</span><span class="p">)</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="W.from_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_shapefile">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_shapefile</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="c1"># we could also just &quot;do the right thing,&quot; but I think it&#39;d make sense to</span>
+        <span class="c1"># try and get people to use `Rook.from_shapefile(shapefile)` rather than</span>
+        <span class="c1"># W.from_shapefile(shapefile, type=`rook`), otherwise we&#39;d need to build</span>
+        <span class="c1"># a type dispatch table. Generic W should be for stuff we don&#39;t know</span>
+        <span class="c1"># anything about.</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
+            <span class="s2">&quot;Use type-specific constructors, like Rook, Queen, DistanceBand, or Kernel&quot;</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="W.from_WSP">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_WSP">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_WSP</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a pysal W from a pysal WSP object (thin weights matrix).</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        wsp                     : WSP</span>
+<span class="sd">                                PySAL sparse weights object</span>
+
+<span class="sd">        silence_warnings        : bool</span>
+<span class="sd">           By default ``libpysal`` will print a warning if the dataset contains</span>
+<span class="sd">           any disconnected components or islands. To silence this warning set this</span>
+<span class="sd">           parameter to ``True``.</span>
+
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w       : W</span>
+<span class="sd">                PySAL weights object</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W, WSP, W</span>
+
+<span class="sd">        Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells</span>
+<span class="sd">        (rook contiguity), then construct a PySAL sparse weights object (wsp).</span>
+
+<span class="sd">        &gt;&gt;&gt; sp = lat2SW(2, 5)</span>
+<span class="sd">        &gt;&gt;&gt; wsp = WSP(sp)</span>
+<span class="sd">        &gt;&gt;&gt; wsp.n</span>
+<span class="sd">        10</span>
+<span class="sd">        &gt;&gt;&gt; wsp.sparse[0].todense()</span>
+<span class="sd">        matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)</span>
+
+<span class="sd">        Create a standard PySAL W from this sparse weights object.</span>
+
+<span class="sd">        &gt;&gt;&gt; w = W.from_WSP(wsp)</span>
+<span class="sd">        &gt;&gt;&gt; w.n</span>
+<span class="sd">        10</span>
+<span class="sd">        &gt;&gt;&gt; print(w.full()[0][0])</span>
+<span class="sd">        [0 1 0 0 0 1 0 0 0 0]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">data</span>
+        <span class="n">indptr</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indptr</span>
+        <span class="n">id_order</span> <span class="o">=</span> <span class="n">WSP</span><span class="o">.</span><span class="n">id_order</span>
+        <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
+            <span class="c1"># replace indices with user IDs</span>
+            <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">WSP</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indices</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">WSP</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
+        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">WSP</span><span class="o">.</span><span class="n">n</span><span class="p">):</span>
+            <span class="n">oid</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">end</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+            <span class="n">weights</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+            <span class="n">start</span> <span class="o">=</span> <span class="n">end</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">WSP</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="n">silence_warnings</span><span class="p">)</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">WSP</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+<div class="viewcode-block" id="W.from_adjlist">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_adjlist">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_adjlist</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">adjlist</span><span class="p">,</span> <span class="n">focal_col</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span> <span class="n">weight_col</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Return an adjacency list representation of a weights object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        adjlist : pandas.DataFrame</span>
+<span class="sd">            Adjacency list with a minimum of two columns.</span>
+<span class="sd">        focal_col : str</span>
+<span class="sd">            Name of the column with the &quot;source&quot; node ids.</span>
+<span class="sd">        neighbor_col : str</span>
+<span class="sd">            Name of the column with the &quot;destination&quot; node ids.</span>
+<span class="sd">        weight_col : str</span>
+<span class="sd">            Name of the column with the weight information. If not provided and</span>
+<span class="sd">            the dataframe has no column named &quot;weight&quot; then all weights</span>
+<span class="sd">            are assumed to be 1.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">weight_col</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">weight_col</span> <span class="o">=</span> <span class="s2">&quot;weight&quot;</span>
+        <span class="n">try_weightcol</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">adjlist</span><span class="p">,</span> <span class="n">weight_col</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">try_weightcol</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">adjlist</span> <span class="o">=</span> <span class="n">adjlist</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">adjlist</span><span class="p">[</span><span class="s2">&quot;weight&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">grouper</span> <span class="o">=</span> <span class="n">adjlist</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">focal_col</span><span class="p">)</span>
+        <span class="n">neighbors</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">ix</span><span class="p">,</span> <span class="n">chunk</span> <span class="ow">in</span> <span class="n">grouper</span><span class="p">:</span>
+            <span class="n">neighbors_to_ix</span> <span class="o">=</span> <span class="n">chunk</span><span class="p">[</span><span class="n">neighbor_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">weights_to_ix</span> <span class="o">=</span> <span class="n">chunk</span><span class="p">[</span><span class="n">weight_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">neighbors_to_ix</span> <span class="o">!=</span> <span class="n">ix</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">neighbors_to_ix</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="n">weights</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights_to_ix</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="W.to_adjlist">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_adjlist">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_adjlist</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">remove_symmetric</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">drop_islands</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">focal_col</span><span class="o">=</span><span class="s2">&quot;focal&quot;</span><span class="p">,</span>
+        <span class="n">neighbor_col</span><span class="o">=</span><span class="s2">&quot;neighbor&quot;</span><span class="p">,</span>
+        <span class="n">weight_col</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">,</span>
+        <span class="n">sort_joins</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Compute an adjacency list representation of a weights object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        remove_symmetric : bool</span>
+<span class="sd">            Whether or not to remove symmetric entries. If the ``W``</span>
+<span class="sd">            is symmetric, a standard directed adjacency list will contain</span>
+<span class="sd">            both the forward and backward links by default because adjacency</span>
+<span class="sd">            lists are a directed graph representation. If this is ``True``,</span>
+<span class="sd">            a ``W`` created from this adjacency list **MAY NOT BE THE SAME**</span>
+<span class="sd">            as the original ``W``. If you would like to consider (1,2) and</span>
+<span class="sd">            (2,1) as distinct links, leave this as ``False``.</span>
+<span class="sd">        drop_islands : bool</span>
+<span class="sd">            Whether or not to preserve islands as entries in the adjacency</span>
+<span class="sd">            list. By default, observations with no neighbors do not appear</span>
+<span class="sd">            in the adjacency list. If islands are kept, they are coded as</span>
+<span class="sd">            self-neighbors with zero weight.</span>
+<span class="sd">        focal_col : str</span>
+<span class="sd">            Name of the column in which to store &quot;source&quot; node ids.</span>
+<span class="sd">        neighbor_col : str</span>
+<span class="sd">            Name of the column in which to store &quot;destination&quot; node ids.</span>
+<span class="sd">        weight_col : str</span>
+<span class="sd">            Name of the column in which to store weight information.</span>
+<span class="sd">        sort_joins : bool</span>
+<span class="sd">            Whether or not to lexicographically sort the adjacency</span>
+<span class="sd">            list by (focal_col, neighbor_col). Default is False.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">pandas</span>
+        <span class="k">except</span> <span class="p">(</span><span class="ne">ImportError</span><span class="p">,</span> <span class="ne">ModuleNotFoundError</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
+                <span class="s2">&quot;pandas must be installed &amp; importable to use this method&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">drop_islands</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span><span class="p">):</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;In the next version of libpysal, observations with no neighbors will be included in adjacency lists as loops (row with the same focal and neighbor) with zero weight. In the current version, observations with no neighbors are dropped. If you would like to keep the current behavior, use drop_islands=True in this function&quot;</span><span class="p">,</span>
+                <span class="ne">DeprecationWarning</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">drop_islands</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="n">links</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">focal_ix</span><span class="p">,</span> <span class="n">neighbor_ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span>
+        <span class="n">idxs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
+        <span class="n">focal_ix</span> <span class="o">=</span> <span class="n">idxs</span><span class="p">[</span><span class="n">focal_ix</span><span class="p">]</span>
+        <span class="n">neighbor_ix</span> <span class="o">=</span> <span class="n">idxs</span><span class="p">[</span><span class="n">neighbor_ix</span><span class="p">]</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">data</span>
+        <span class="n">adjlist</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+            <span class="p">{</span><span class="n">focal_col</span><span class="p">:</span> <span class="n">focal_ix</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="p">:</span> <span class="n">neighbor_ix</span><span class="p">,</span> <span class="n">weight_col</span><span class="p">:</span> <span class="n">weights</span><span class="p">}</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">remove_symmetric</span><span class="p">:</span>
+            <span class="n">adjlist</span> <span class="o">=</span> <span class="n">adjtools</span><span class="o">.</span><span class="n">filter_adjlist</span><span class="p">(</span><span class="n">adjlist</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">drop_islands</span><span class="p">:</span>
+            <span class="n">island_adjlist</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+                <span class="p">{</span><span class="n">focal_col</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">,</span> <span class="n">weight_col</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
+            <span class="p">)</span>
+            <span class="n">adjlist</span> <span class="o">=</span> <span class="n">pandas</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">adjlist</span><span class="p">,</span> <span class="n">island_adjlist</span><span class="p">))</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">sort_joins</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">adjlist</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="n">focal_col</span><span class="p">,</span> <span class="n">neighbor_col</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">adjlist</span></div>
+
+
+<div class="viewcode-block" id="W.to_networkx">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_networkx">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_networkx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert a weights object to a ``networkx`` graph.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        A ``networkx`` graph representation of the ``W`` object.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;NetworkX 2.7+ is required to use this function.&quot;</span><span class="p">)</span>
+        <span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">DiGraph</span><span class="p">()</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">asymmetries</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">nx</span><span class="o">.</span><span class="n">from_scipy_sparse_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="n">G</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="W.from_networkx">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_networkx">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_networkx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">weight_col</span><span class="o">=</span><span class="s2">&quot;weight&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert a ``networkx`` graph to a PySAL ``W`` object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        graph : networkx.Graph</span>
+<span class="sd">            The graph to convert to a ``W``.</span>
+<span class="sd">        weight_col : string</span>
+<span class="sd">            If the graph is labeled, this should be the name of the field</span>
+<span class="sd">            to use as the weight for the ``W``.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w : libpysal.weights.W</span>
+<span class="sd">            A ``W`` object containing the same graph as the ``networkx`` graph.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;NetworkX 2.7+ is required to use this function.&quot;</span><span class="p">)</span>
+        <span class="n">sparse_array</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">to_scipy_sparse_array</span><span class="p">(</span><span class="n">graph</span><span class="p">)</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sparse_array</span><span class="p">)</span><span class="o">.</span><span class="n">to_W</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sparse matrix object. For any matrix manipulations required for w,</span>
+<span class="sd">        ``w.sparse`` should be used. This is based on ``scipy.sparse``.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;sparse&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_build_sparse</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sparse</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sparse</span>
+
+<div class="viewcode-block" id="W.from_sparse">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.from_sparse">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_sparse</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">sparse</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert a ``scipy.sparse`` array to a PySAL ``W`` object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        sparse : scipy.sparse array</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w : libpysal.weights.W</span>
+<span class="sd">            A ``W`` object containing the same graph as the ``scipy.sparse`` graph.</span>
+
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        When the sparse array has a zero in its data attribute, and</span>
+<span class="sd">        the corresponding row and column values are equal, the value</span>
+<span class="sd">        for the pysal weight will be 0 for the &quot;loop&quot;.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">coo</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">tocoo</span><span class="p">()</span>
+        <span class="n">neighbors</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">coo</span><span class="o">.</span><span class="n">row</span><span class="p">,</span> <span class="n">coo</span><span class="o">.</span><span class="n">col</span><span class="p">,</span> <span class="n">coo</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+            <span class="n">weights</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="o">=</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="n">weights</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="W.to_sparse">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_sparse">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;coo&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a ``scipy.sparse`` array object from a pysal W.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        fmt : {&#39;bsr&#39;, &#39;coo&#39;, &#39;csc&#39;, &#39;csr&#39;}</span>
+<span class="sd">          scipy.sparse format</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        scipy.sparse array</span>
+<span class="sd">          A scipy.sparse array with a format of fmt.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        The keys of the w.neighbors are encoded</span>
+<span class="sd">        to determine row,col in the sparse array.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">disp</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">disp</span><span class="p">[</span><span class="s2">&quot;bsr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">bsr_array</span>
+        <span class="n">disp</span><span class="p">[</span><span class="s2">&quot;coo&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">coo_array</span>
+        <span class="n">disp</span><span class="p">[</span><span class="s2">&quot;csc&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csc_array</span>
+        <span class="n">disp</span><span class="p">[</span><span class="s2">&quot;csr&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_array</span>
+        <span class="n">fmt_l</span> <span class="o">=</span> <span class="n">fmt</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">fmt_l</span> <span class="ow">in</span> <span class="n">disp</span><span class="p">:</span>
+            <span class="n">adj_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_adjlist</span><span class="p">(</span><span class="n">drop_islands</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">adj_list</span><span class="o">.</span><span class="n">weight</span>
+            <span class="n">row</span> <span class="o">=</span> <span class="n">adj_list</span><span class="o">.</span><span class="n">focal</span>
+            <span class="n">col</span> <span class="o">=</span> <span class="n">adj_list</span><span class="o">.</span><span class="n">neighbor</span>
+            <span class="n">le</span> <span class="o">=</span> <span class="n">_LabelEncoder</span><span class="p">()</span>
+            <span class="n">le</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">row</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">col</span> <span class="o">=</span> <span class="n">le</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
+            <span class="n">n</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span>
+            <span class="k">return</span> <span class="n">disp</span><span class="p">[</span><span class="n">fmt_l</span><span class="p">]((</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;unsupported sparse format: </span><span class="si">{</span><span class="n">fmt</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">n_components</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Store whether the adjacency matrix is fully connected.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;n_components&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span> <span class="o">=</span> <span class="n">connected_components</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;n_components&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;component_labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">component_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Store the graph component in which each observation falls.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;component_labels&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span> <span class="o">=</span> <span class="n">connected_components</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;n_components&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_components</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;component_labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_component_labels</span>
+
+    <span class="k">def</span> <span class="nf">_build_sparse</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct the sparse attribute.&quot;&quot;&quot;</span>
+
+        <span class="n">row</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">col</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">id2i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id2i</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">neigh_list</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbor_offsets</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="n">card</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">row</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">id2i</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">*</span> <span class="n">card</span><span class="p">)</span>
+            <span class="n">col</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">neigh_list</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+        <span class="n">col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">s</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">s</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">id2i</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Dictionary where the key is an ID and the value is that ID&#39;s</span>
+<span class="sd">        index in ``W.id_order``.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;id2i&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">id_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span><span class="p">[</span><span class="n">id_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;id2i&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id2i</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">n</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of units.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;n&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;n&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">s0</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s0`` is defined as</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">               s0=\sum_i \sum_j w_{i,j}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;s0&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">s1</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s1`` is defined as</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">               s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;s1&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="n">t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+            <span class="n">t</span> <span class="o">=</span> <span class="n">t</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="n">t2</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>  <span class="c1"># element-wise square</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_s1</span> <span class="o">=</span> <span class="n">t2</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="mf">2.0</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s1&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s1</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s1</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">s2array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Individual elements comprising ``s2``.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        s2</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;s2array&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_s2array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">s</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">())</span> <span class="o">**</span> <span class="mi">2</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s2array&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2array</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2array</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">s2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s2`` is defined as</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">                s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;s2&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_s2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s2array</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s2&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s2</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">trcW2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Trace of :math:`WW`.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        diagW2</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;trcW2&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_trcW2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagW2</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcw2&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcW2</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcW2</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">diagW2</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Diagonal of :math:`WW`.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        trcW2</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;diagw2&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_diagW2</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagW2&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagW2</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagW2</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">diagWtW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Diagonal of :math:`W^{&#39;}W`.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        trcWtW</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;diagWtW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagWtW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">trcWtW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Trace of :math:`W^{&#39;}W`.</span>
+
+<span class="sd">        See Also</span>
+<span class="sd">        --------</span>
+<span class="sd">        diagWtW</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;trcWtW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagWtW</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcWtW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">diagWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Diagonal of :math:`W^{&#39;}W + WW`.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;diagWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="n">wt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span> <span class="o">=</span> <span class="p">(</span><span class="n">wt</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">w</span> <span class="o">*</span> <span class="n">w</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">trcWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Trace of :math:`W^{&#39;}W + WW`.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;trcWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagWtW_WW</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">pct_nonzero</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Percentage of nonzero weights.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;pct_nonzero&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_pct_nonzero</span> <span class="o">=</span> <span class="mf">100.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;pct_nonzero&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pct_nonzero</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pct_nonzero</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">cardinalities</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of neighbors for each observation.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;cardinalities&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="n">c</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">:</span>
+                <span class="n">c</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cardinalities</span> <span class="o">=</span> <span class="n">c</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;cardinalities&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cardinalities</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cardinalities</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">max_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Largest number of neighbors.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;max_neighbors&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_max_neighbors</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;max_neighbors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_neighbors</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_max_neighbors</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">mean_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Average number of neighbors.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;mean_neighbors&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_mean_neighbors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;mean_neighbors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_neighbors</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_mean_neighbors</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">min_neighbors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Minimum number of neighbors.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;min_neighbors&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_min_neighbors</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;min_neighbors&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_neighbors</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_min_neighbors</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">nonzero</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of nonzero weights.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;nonzero&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_nonzero</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;nonzero&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonzero</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nonzero</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">sd</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Standard deviation of number of neighbors.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;sd&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_sd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sd&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sd</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sd</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">asymmetries</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;List of id pairs with asymmetric weights</span>
+<span class="sd">        sorted in ascending *index location* order.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;asymmetries&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_asymmetries</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;asymmetries&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asymmetries</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asymmetries</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">islands</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;List of ids without any neighbors.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;islands&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_islands</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">items</span><span class="p">())</span> <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;islands&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_islands</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_islands</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">histogram</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Cardinality histogram as a dictionary where key is the id and</span>
+<span class="sd">        value is the number of neighbors for that unit.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;histogram&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="n">ct</span><span class="p">,</span> <span class="nb">bin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span>
+                <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span><span class="o">.</span><span class="n">values</span><span class="p">()),</span>
+                <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_neighbors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_neighbors</span> <span class="o">+</span> <span class="mi">2</span><span class="p">)),</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_histogram</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">ct</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;histogram&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_histogram</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_histogram</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Allow a dictionary like interaction with the weights class.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w = lat2W()</span>
+
+<span class="sd">        &gt;&gt;&gt; w[0] == dict({1: 1.0, 5: 1.0})</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">key</span><span class="p">])))</span>
+
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Support iteration over weights.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w=lat2W(3,3)</span>
+<span class="sd">        &gt;&gt;&gt; for i,wi in enumerate(w):</span>
+<span class="sd">        ...     print(i,wi[0])</span>
+<span class="sd">        ...</span>
+<span class="sd">        0 0</span>
+<span class="sd">        1 1</span>
+<span class="sd">        2 2</span>
+<span class="sd">        3 3</span>
+<span class="sd">        4 4</span>
+<span class="sd">        5 5</span>
+<span class="sd">        6 6</span>
+<span class="sd">        7 7</span>
+<span class="sd">        8 8</span>
+<span class="sd">        &gt;&gt;&gt;</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">:</span>
+            <span class="k">yield</span> <span class="n">i</span><span class="p">,</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">])))</span>
+
+<div class="viewcode-block" id="W.remap_ids">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.remap_ids">[docs]</a>
+    <span class="k">def</span> <span class="nf">remap_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_ids</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        In place modification throughout ``W`` of id values from</span>
+<span class="sd">        ``w.id_order`` to ``new_ids`` in all.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        new_ids : list, numpy.ndarray</span>
+<span class="sd">            Aligned list of new ids to be inserted. Note that first</span>
+<span class="sd">            element of ``new_ids`` will replace first element of</span>
+<span class="sd">            ``w.id_order``, second element of ``new_ids`` replaces second</span>
+<span class="sd">            element of ``w.id_order`` and so on.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w = lat2W(3, 3)</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order</span>
+<span class="sd">        [0, 1, 2, 3, 4, 5, 6, 7, 8]</span>
+<span class="sd">        &gt;&gt;&gt; w.neighbors[0]</span>
+<span class="sd">        [3, 1]</span>
+<span class="sd">        &gt;&gt;&gt; new_ids = [&#39;id%i&#39;%id for id in w.id_order]</span>
+<span class="sd">        &gt;&gt;&gt; _ = w.remap_ids(new_ids)</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order</span>
+<span class="sd">        [&#39;id0&#39;, &#39;id1&#39;, &#39;id2&#39;, &#39;id3&#39;, &#39;id4&#39;, &#39;id5&#39;, &#39;id6&#39;, &#39;id7&#39;, &#39;id8&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; w.neighbors[&#39;id0&#39;]</span>
+<span class="sd">        [&#39;id3&#39;, &#39;id1&#39;]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">old_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">old_ids</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_ids</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
+                <span class="s2">&quot;W.remap_ids: length of `old_ids` does not match             that of&quot;</span>
+                <span class="s2">&quot; new_ids&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">new_ids</span><span class="p">))</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_ids</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;W.remap_ids: list `new_ids` contains duplicates&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">new_neighbors</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">new_weights</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">old_transformations</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+            <span class="n">new_transformations</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">o</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">old_ids</span><span class="p">,</span> <span class="n">new_ids</span><span class="p">):</span>
+                <span class="n">o_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
+                <span class="n">o_weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
+                <span class="n">n_neighbors</span> <span class="o">=</span> <span class="p">[</span><span class="n">new_ids</span><span class="p">[</span><span class="n">old_ids</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">o_neighbors</span><span class="p">]</span>
+                <span class="n">new_neighbors</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">n_neighbors</span>
+                <span class="n">new_weights</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">o_weights</span><span class="p">[:]</span>
+                <span class="n">new_transformations</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">old_transformations</span><span class="p">[</span><span class="n">o</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">=</span> <span class="n">new_neighbors</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">new_weights</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_transformations</span>
+
+            <span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">o</span><span class="p">)</span> <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">old_ids</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">id_</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">id_order</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">[</span><span class="n">id_</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_ids</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span></div>
+
+
+    <span class="k">def</span> <span class="nf">__set_id_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ordered_ids</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the iteration order in w. ``W`` can be iterated over. On</span>
+<span class="sd">        construction the iteration order is set to the lexicographic order of</span>
+<span class="sd">        the keys in the ``w.weights`` dictionary. If a specific order</span>
+<span class="sd">        is required it can be set with this method.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        ordered_ids : sequence</span>
+<span class="sd">            Identifiers for observations in specified order.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        The ``ordered_ids`` parameter is checked against the ids implied</span>
+<span class="sd">        by the keys in ``w.weights``. If they are not equivalent sets an</span>
+<span class="sd">        exception is raised and the iteration order is not changed.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w=lat2W(3,3)</span>
+<span class="sd">        &gt;&gt;&gt; for i,wi in enumerate(w):</span>
+<span class="sd">        ...     print(i, wi[0])</span>
+<span class="sd">        ...</span>
+<span class="sd">        0 0</span>
+<span class="sd">        1 1</span>
+<span class="sd">        2 2</span>
+<span class="sd">        3 3</span>
+<span class="sd">        4 4</span>
+<span class="sd">        5 5</span>
+<span class="sd">        6 6</span>
+<span class="sd">        7 7</span>
+<span class="sd">        8 8</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order</span>
+<span class="sd">        [0, 1, 2, 3, 4, 5, 6, 7, 8]</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order=range(8,-1,-1)</span>
+<span class="sd">        &gt;&gt;&gt; list(w.id_order)</span>
+<span class="sd">        [8, 7, 6, 5, 4, 3, 2, 1, 0]</span>
+<span class="sd">        &gt;&gt;&gt; for i,w_i in enumerate(w):</span>
+<span class="sd">        ...     print(i,w_i[0])</span>
+<span class="sd">        ...</span>
+<span class="sd">        0 8</span>
+<span class="sd">        1 7</span>
+<span class="sd">        2 6</span>
+<span class="sd">        3 5</span>
+<span class="sd">        4 4</span>
+<span class="sd">        5 3</span>
+<span class="sd">        6 2</span>
+<span class="sd">        7 1</span>
+<span class="sd">        8 0</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">ordered_ids</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="n">ordered_ids</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;ordered_ids do not align with W ids&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__get_id_order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the ids for the observations in the order in which they</span>
+<span class="sd">        would be encountered if iterating over the weights.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
+
+    <span class="n">id_order</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">__get_id_order</span><span class="p">,</span> <span class="n">__set_id_order</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">id_order_set</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns ``True`` if user has set ``id_order``, ``False`` if not.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w=lat2W()</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order_set</span>
+<span class="sd">        True</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order_set</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">neighbor_offsets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Given the current ``id_order``, ``neighbor_offsets[id]`` is the</span>
+<span class="sd">        offsets of the id&#39;s neighbors in ``id_order``.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        neighbor_list : list</span>
+<span class="sd">            Offsets of the id&#39;s neighbors in ``id_order``.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import W</span>
+<span class="sd">        &gt;&gt;&gt; neighbors={&#39;c&#39;: [&#39;b&#39;], &#39;b&#39;: [&#39;c&#39;, &#39;a&#39;], &#39;a&#39;: [&#39;b&#39;]}</span>
+<span class="sd">        &gt;&gt;&gt; weights ={&#39;c&#39;: [1.0], &#39;b&#39;: [1.0, 1.0], &#39;a&#39;: [1.0]}</span>
+<span class="sd">        &gt;&gt;&gt; w=W(neighbors,weights)</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order = [&#39;a&#39;,&#39;b&#39;,&#39;c&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; w.neighbor_offsets[&#39;b&#39;]</span>
+<span class="sd">        [2, 0]</span>
+<span class="sd">        &gt;&gt;&gt; w.id_order = [&#39;b&#39;,&#39;a&#39;,&#39;c&#39;]</span>
+<span class="sd">        &gt;&gt;&gt; w.neighbor_offsets[&#39;b&#39;]</span>
+<span class="sd">        [2, 1]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="s2">&quot;neighbors_0&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">id2i</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id2i</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">neigh_list</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">id2i</span><span class="p">[</span><span class="n">neigh</span><span class="p">]</span> <span class="k">for</span> <span class="n">neigh</span> <span class="ow">in</span> <span class="n">neigh_list</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;neighbors_0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span>
+
+        <span class="n">neighbor_list</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__neighbors_0</span>
+
+        <span class="k">return</span> <span class="n">neighbor_list</span>
+
+<div class="viewcode-block" id="W.get_transform">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.get_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Getter for transform property.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        transformation : str, None</span>
+<span class="sd">            Valid transformation value. See the ``transform``</span>
+<span class="sd">            parameters in ``set_transform()`` for a detailed description.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w=lat2W()</span>
+<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">        [1.0, 1.0]</span>
+<span class="sd">        &gt;&gt;&gt; w.transform</span>
+<span class="sd">        &#39;O&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">        [0.5, 0.5]</span>
+<span class="sd">        &gt;&gt;&gt; w.transform=&#39;b&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">        [1.0, 1.0]</span>
+
+<span class="sd">        See also</span>
+<span class="sd">        --------</span>
+<span class="sd">        set_transform</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span></div>
+
+
+<div class="viewcode-block" id="W.set_transform">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.set_transform">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="s2">&quot;B&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transformations of weights.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        transform : str</span>
+<span class="sd">            This parameter is not case sensitive. The following are</span>
+<span class="sd">            valid transformations.</span>
+
+<span class="sd">            * **B** -- Binary</span>
+<span class="sd">            * **R** -- Row-standardization (global sum :math:`=n`)</span>
+<span class="sd">            * **D** -- Double-standardization (global sum :math:`=1`)</span>
+<span class="sd">            * **V** -- Variance stabilizing</span>
+<span class="sd">            * **O** -- Restore original transformation (from instantiation)</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+
+<span class="sd">        Transformations are applied only to the value of the weights at</span>
+<span class="sd">        instantiation. Chaining of transformations cannot be done on a ``W``</span>
+<span class="sd">        instance.</span>
+
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w=lat2W()</span>
+<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">        [1.0, 1.0]</span>
+<span class="sd">        &gt;&gt;&gt; w.transform</span>
+<span class="sd">        &#39;O&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">        [0.5, 0.5]</span>
+<span class="sd">        &gt;&gt;&gt; w.transform=&#39;b&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.weights[0]</span>
+<span class="sd">        [1.0, 1.0]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">value</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;R&quot;</span><span class="p">:</span>
+                <span class="c1"># row standardized weights</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
+                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">row_sum</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">wijs</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.0</span>
+                    <span class="k">if</span> <span class="n">row_sum</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">silence_warnings</span><span class="p">:</span>
+                            <span class="nb">print</span><span class="p">((</span><span class="s2">&quot;WARNING: &quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot; is an island (no neighbors)&quot;</span><span class="p">))</span>
+                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wij</span> <span class="o">/</span> <span class="n">row_sum</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;D&quot;</span><span class="p">:</span>
+                <span class="c1"># doubly-standardized weights</span>
+                <span class="c1"># update current chars before doing global sum</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+                <span class="n">s0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">s0</span>
+                <span class="n">ws</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">s0</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
+                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wij</span> <span class="o">*</span> <span class="n">ws</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;B&quot;</span><span class="p">:</span>
+                <span class="c1"># binary transformation</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
+                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1.0</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;V&quot;</span><span class="p">:</span>
+                <span class="c1"># variance stabilizing</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">q</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cardinalities</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">Q</span> <span class="o">=</span> <span class="mf">0.0</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="s2">&quot;O&quot;</span><span class="p">]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
+                    <span class="n">wijs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="n">wij</span> <span class="o">*</span> <span class="n">wij</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]))</span>
+                    <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wij</span> <span class="o">/</span> <span class="n">q</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">wij</span> <span class="ow">in</span> <span class="n">wijs</span><span class="p">]</span>
+                    <span class="n">Q</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">([</span><span class="n">si</span> <span class="k">for</span> <span class="n">si</span> <span class="ow">in</span> <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]])</span>
+                <span class="n">nQ</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">/</span> <span class="n">Q</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">:</span>
+                    <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span> <span class="o">*</span> <span class="n">nQ</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+            <span class="k">elif</span> <span class="n">value</span> <span class="o">==</span> <span class="s2">&quot;O&quot;</span><span class="p">:</span>
+                <span class="c1"># put weights back to original transformation</span>
+                <span class="n">weights</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="n">original</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">[</span><span class="n">value</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">original</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_reset</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;unsupported weights transformation&quot;</span><span class="p">)</span></div>
+
+
+    <span class="n">transform</span> <span class="o">=</span> <span class="nb">property</span><span class="p">(</span><span class="n">get_transform</span><span class="p">,</span> <span class="n">set_transform</span><span class="p">)</span>
+
+<div class="viewcode-block" id="W.asymmetry">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.asymmetry">[docs]</a>
+    <span class="k">def</span> <span class="nf">asymmetry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">intrinsic</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Asymmetry check.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+
+<span class="sd">        intrinsic : bool</span>
+<span class="sd">            Default is ``True``. Intrinsic symmetry is defined as:</span>
+
+<span class="sd">            .. math::</span>
+
+<span class="sd">                w_{i,j} == w_{j,i}</span>
+
+<span class="sd">            If ``intrinsic`` is ``False`` symmetry is defined as:</span>
+
+<span class="sd">            .. math::</span>
+
+<span class="sd">                i \in N_j \ \&amp; \ j \in N_i</span>
+
+<span class="sd">            where :math:`N_j` is the set of neighbors for :math:`j`.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        asymmetries : list</span>
+<span class="sd">            Empty if no asymmetries are found. If there are asymmetries,</span>
+<span class="sd">            then a ``list`` of ``(i,j)`` tuples is returned sorted in</span>
+<span class="sd">            ascending *index location* order.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2W</span>
+<span class="sd">        &gt;&gt;&gt; w=lat2W(3,3)</span>
+<span class="sd">        &gt;&gt;&gt; w.asymmetry()</span>
+<span class="sd">        []</span>
+<span class="sd">        &gt;&gt;&gt; w.transform=&#39;r&#39;</span>
+<span class="sd">        &gt;&gt;&gt; w.asymmetry()</span>
+<span class="sd">        [(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)]</span>
+<span class="sd">        &gt;&gt;&gt; result = w.asymmetry(intrinsic=False)</span>
+<span class="sd">        &gt;&gt;&gt; result</span>
+<span class="sd">        []</span>
+<span class="sd">        &gt;&gt;&gt; neighbors={0:[1,2,3], 1:[1,2,3], 2:[0,1], 3:[0,1]}</span>
+<span class="sd">        &gt;&gt;&gt; weights={0:[1,1,1], 1:[1,1,1], 2:[1,1], 3:[1,1]}</span>
+<span class="sd">        &gt;&gt;&gt; w=W(neighbors,weights)</span>
+<span class="sd">        &gt;&gt;&gt; w.asymmetry()</span>
+<span class="sd">        [(0, 1), (1, 0)]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">intrinsic</span><span class="p">:</span>
+            <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">transform</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s2">&quot;b&quot;</span>
+            <span class="n">wd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
+
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">[]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ijs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ids</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ids</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+            <span class="n">ijs</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+
+            <span class="n">i2id</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">id2i</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+            <span class="n">ijs</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i2id</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">i2id</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">ijs</span><span class="p">]</span>
+
+            <span class="k">return</span> <span class="n">ijs</span></div>
+
+
+<div class="viewcode-block" id="W.symmetrize">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.symmetrize">[docs]</a>
+    <span class="k">def</span> <span class="nf">symmetrize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Construct a symmetric KNN weight. This ensures that the neighbors</span>
+<span class="sd">        of each focal observation consider the focal observation itself as</span>
+<span class="sd">        a neighbor. This returns a generic ``W`` object, since the object is no</span>
+<span class="sd">        longer guaranteed to have ``k`` neighbors for each observation.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">inplace</span><span class="p">:</span>
+            <span class="n">neighbors</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">)</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">)</span>
+            <span class="n">out_W</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
+            <span class="n">out_W</span><span class="o">.</span><span class="n">symmetrize</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">out_W</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">focal</span><span class="p">,</span> <span class="n">fneighbs</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+                <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">fneighbs</span><span class="p">):</span>
+                    <span class="n">neighb_neighbors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">focal</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">neighb_neighbors</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">focal</span><span class="p">)</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">neighbor</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">focal</span><span class="p">][</span><span class="n">j</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+            <span class="k">return</span></div>
+
+
+<div class="viewcode-block" id="W.full">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.full">[docs]</a>
+    <span class="k">def</span> <span class="nf">full</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a full ``numpy.ndarray``.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        self : libpysal.weights.W</span>
+<span class="sd">            spatial weights object</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        (fullw, keys) : tuple</span>
+<span class="sd">            The first element being the full ``numpy.ndarray`` and second</span>
+<span class="sd">            element keys being the ids associated with each row in the array.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import W, full</span>
+<span class="sd">        &gt;&gt;&gt; neighbors = {&#39;first&#39;:[&#39;second&#39;],&#39;second&#39;:[&#39;first&#39;,&#39;third&#39;],&#39;third&#39;:[&#39;second&#39;]}</span>
+<span class="sd">        &gt;&gt;&gt; weights = {&#39;first&#39;:[1],&#39;second&#39;:[1,1],&#39;third&#39;:[1]}</span>
+<span class="sd">        &gt;&gt;&gt; w = W(neighbors, weights)</span>
+<span class="sd">        &gt;&gt;&gt; wf, ids = full(w)</span>
+<span class="sd">        &gt;&gt;&gt; wf</span>
+<span class="sd">        array([[0., 1., 0.],</span>
+<span class="sd">               [1., 0., 1.],</span>
+<span class="sd">               [0., 1., 0.]])</span>
+<span class="sd">        &gt;&gt;&gt; ids</span>
+<span class="sd">        [&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">wfull</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
+        <span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">:</span>
+            <span class="n">keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">wfull</span><span class="p">,</span> <span class="n">keys</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="W.to_WSP">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.to_WSP">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_WSP</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a ``WSP`` object.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+
+<span class="sd">        implicit : libpysal.weights.WSP</span>
+<span class="sd">            Thin ``W`` class</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import W, WSP</span>
+<span class="sd">        &gt;&gt;&gt; neighbors={&#39;first&#39;:[&#39;second&#39;],&#39;second&#39;:[&#39;first&#39;,&#39;third&#39;],&#39;third&#39;:[&#39;second&#39;]}</span>
+<span class="sd">        &gt;&gt;&gt; weights={&#39;first&#39;:[1],&#39;second&#39;:[1,1],&#39;third&#39;:[1]}</span>
+<span class="sd">        &gt;&gt;&gt; w=W(neighbors,weights)</span>
+<span class="sd">        &gt;&gt;&gt; wsp=w.to_WSP()</span>
+<span class="sd">        &gt;&gt;&gt; isinstance(wsp, WSP)</span>
+<span class="sd">        True</span>
+<span class="sd">        &gt;&gt;&gt; wsp.n</span>
+<span class="sd">        3</span>
+<span class="sd">        &gt;&gt;&gt; wsp.s0</span>
+<span class="sd">        4</span>
+
+<span class="sd">        See also</span>
+<span class="sd">        --------</span>
+<span class="sd">        WSP</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">WSP</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="W.set_shapefile">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.set_shapefile">[docs]</a>
+    <span class="k">def</span> <span class="nf">set_shapefile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shapefile</span><span class="p">,</span> <span class="n">idVariable</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Adding metadata for writing headers of ``.gal`` and ``.gwt`` files.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        shapefile : str</span>
+<span class="sd">            The shapefile name used to construct weights.</span>
+<span class="sd">        idVariable : str</span>
+<span class="sd">            The name of the attribute in the shapefile to associate</span>
+<span class="sd">            with ids in the weights.</span>
+<span class="sd">        full : bool</span>
+<span class="sd">            Write out the entire path for a shapefile (``True``) or</span>
+<span class="sd">            only the base of the shapefile without extension (``False``).</span>
+<span class="sd">            Default is ``True``.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">full</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_shpName</span> <span class="o">=</span> <span class="n">shapefile</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_shpName</span> <span class="o">=</span> <span class="n">BASENAME</span><span class="p">(</span><span class="n">shapefile</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_varName</span> <span class="o">=</span> <span class="n">idVariable</span></div>
+
+
+<div class="viewcode-block" id="W.plot">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.W.html#libpysal.weights.W.plot">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">gdf</span><span class="p">,</span> <span class="n">indexed_on</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="n">node_kws</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edge_kws</span><span class="o">=</span><span class="kc">None</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Plot spatial weights objects. **Requires** ``matplotlib``, and</span>
+<span class="sd">        implicitly requires a ``geopandas.GeoDataFrame`` as input.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        gdf : geopandas.GeoDataFrame</span>
+<span class="sd">            The original shapes whose topological relations are modelled in ``W``.</span>
+<span class="sd">        indexed_on : str</span>
+<span class="sd">            Column of ``geopandas.GeoDataFrame`` that the weights object uses</span>
+<span class="sd">            as an index. Default is ``None``, so the index of the</span>
+<span class="sd">            ``geopandas.GeoDataFrame`` is used.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            Axis on which to plot the weights. Default is ``None``, so</span>
+<span class="sd">            plots on the current figure.</span>
+<span class="sd">        color : str</span>
+<span class="sd">            ``matplotlib`` color string, will color both nodes and edges</span>
+<span class="sd">            the same by default.</span>
+<span class="sd">        node_kws : dict</span>
+<span class="sd">            Keyword arguments dictionary to send to ``pyplot.scatter``,</span>
+<span class="sd">            which provides fine-grained control over the aesthetics</span>
+<span class="sd">            of the nodes in the plot.</span>
+<span class="sd">        edge_kws : dict</span>
+<span class="sd">            Keyword arguments dictionary to send to ``pyplot.plot``,</span>
+<span class="sd">            which provides fine-grained control over the aesthetics</span>
+<span class="sd">            of the edges in the plot.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        f : matplotlib.figure.Figure</span>
+<span class="sd">            Figure on which the plot is made.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            Axis on which the plot is made.</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        If you&#39;d like to overlay the actual shapes from the</span>
+<span class="sd">        ``geopandas.GeoDataFrame``, call ``gdf.plot(ax=ax)`` after this.</span>
+<span class="sd">        To plot underneath, adjust the z-order of the plot as follows:</span>
+<span class="sd">        ``gdf.plot(ax=ax,zorder=0)``.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import Queen</span>
+<span class="sd">        &gt;&gt;&gt; import libpysal as lp</span>
+<span class="sd">        &gt;&gt;&gt; import geopandas</span>
+<span class="sd">        &gt;&gt;&gt; gdf = geopandas.read_file(lp.examples.get_path(&quot;columbus.shp&quot;))</span>
+<span class="sd">        &gt;&gt;&gt; weights = Queen.from_dataframe(gdf)</span>
+<span class="sd">        &gt;&gt;&gt; tmp = weights.plot(gdf, color=&#39;firebrickred&#39;, node_kws=dict(marker=&#39;*&#39;, color=&#39;k&#39;))</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
+                <span class="s2">&quot;W.plot depends on matplotlib.pyplot, and this was&quot;</span>
+                <span class="s2">&quot;not able to be imported. </span><span class="se">\n</span><span class="s2">Install matplotlib to&quot;</span>
+                <span class="s2">&quot;plot spatial weights.&quot;</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">f</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">node_kws</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;color&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node_kws</span><span class="p">:</span>
+                <span class="n">node_kws</span><span class="p">[</span><span class="s2">&quot;color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">color</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">node_kws</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">edge_kws</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;color&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">edge_kws</span><span class="p">:</span>
+                <span class="n">edge_kws</span><span class="p">[</span><span class="s2">&quot;color&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">color</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">edge_kws</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">neighbors</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">islands</span><span class="p">:</span>
+                <span class="k">continue</span>
+            <span class="k">if</span> <span class="n">indexed_on</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">neighbors</span> <span class="o">=</span> <span class="n">gdf</span><span class="p">[</span><span class="n">gdf</span><span class="p">[</span><span class="n">indexed_on</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+                <span class="n">idx</span> <span class="o">=</span> <span class="n">gdf</span><span class="p">[</span><span class="n">gdf</span><span class="p">[</span><span class="n">indexed_on</span><span class="p">]</span> <span class="o">==</span> <span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">neighbors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span>
+            <span class="n">centroids</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">neighbors</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+            <span class="n">centroids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">centroids</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">centroids</span><span class="o">.</span><span class="n">y</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">focal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">gdf</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">xy</span><span class="p">)</span>
+            <span class="n">seen</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+            <span class="k">for</span> <span class="n">nidx</span><span class="p">,</span> <span class="n">neighbor</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">centroids</span><span class="p">):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">nidx</span><span class="p">)</span> <span class="ow">in</span> <span class="n">seen</span><span class="p">:</span>
+                    <span class="k">continue</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">*</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">focal</span><span class="p">,</span> <span class="n">neighbor</span><span class="p">)),</span> <span class="n">marker</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">edge_kws</span><span class="p">)</span>
+                <span class="n">seen</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">idx</span><span class="p">,</span> <span class="n">nidx</span><span class="p">))</span>
+                <span class="n">seen</span><span class="o">.</span><span class="n">update</span><span class="p">((</span><span class="n">nidx</span><span class="p">,</span> <span class="n">idx</span><span class="p">))</span>
+        <span class="n">centroids</span> <span class="o">=</span> <span class="n">gdf</span><span class="o">.</span><span class="n">centroid</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">centroids</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">centroids</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="o">**</span><span class="n">node_kws</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">f</span><span class="p">,</span> <span class="n">ax</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="WSP">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP">[docs]</a>
+<span class="k">class</span> <span class="nc">WSP</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Thin ``W`` class for ``spreg``.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    sparse : scipy.sparse.{matrix-type}</span>
+<span class="sd">        NxN object from ``scipy.sparse``</span>
+
+<span class="sd">    Attributes</span>
+<span class="sd">    ----------</span>
+
+<span class="sd">    n           : int</span>
+<span class="sd">                  description</span>
+<span class="sd">    s0          : float</span>
+<span class="sd">                  description</span>
+<span class="sd">    trcWtW_WW   : float</span>
+<span class="sd">                  description</span>
+
+<span class="sd">    Examples</span>
+<span class="sd">    --------</span>
+
+<span class="sd">    From GAL information</span>
+
+<span class="sd">    &gt;&gt;&gt; import scipy.sparse</span>
+<span class="sd">    &gt;&gt;&gt; from libpysal.weights import WSP</span>
+<span class="sd">    &gt;&gt;&gt; rows = [0, 1, 1, 2, 2, 3]</span>
+<span class="sd">    &gt;&gt;&gt; cols = [1, 0, 2, 1, 3, 3]</span>
+<span class="sd">    &gt;&gt;&gt; weights =  [1, 0.75, 0.25, 0.9, 0.1, 1]</span>
+<span class="sd">    &gt;&gt;&gt; sparse = scipy.sparse.csr_matrix((weights, (rows, cols)), shape=(4,4))</span>
+<span class="sd">    &gt;&gt;&gt; w = WSP(sparse)</span>
+<span class="sd">    &gt;&gt;&gt; w.s0</span>
+<span class="sd">    4.0</span>
+<span class="sd">    &gt;&gt;&gt; w.trcWtW_WW</span>
+<span class="sd">    6.395</span>
+<span class="sd">    &gt;&gt;&gt; w.n</span>
+<span class="sd">    4</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="WSP.__init__">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sparse</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">sparse</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;must pass a scipy sparse object&quot;</span><span class="p">)</span>
+        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">if</span> <span class="n">rows</span> <span class="o">!=</span> <span class="n">cols</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Weights object must be square&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n</span> <span class="o">=</span> <span class="n">sparse</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">id_order</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Number of values in id_order must match shape of sparse&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="n">id_order</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;id_order&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
+        <span class="c1"># temp addition of index attribute</span>
+        <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>  <span class="c1"># will be removed after refactoring is done</span>
+
+        <span class="k">if</span> <span class="n">index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Index</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">MultiIndex</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">RangeIndex</span><span class="p">)):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;index must be an instance of pandas.Index dtype&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Number of values in index must match shape of sparse&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">RangeIndex</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">index</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">id_order</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;An ordered list of ids, assumed to match the ordering in ``sparse``.&quot;&quot;&quot;</span>
+        <span class="c1"># Temporary solution until the refactoring is finished</span>
+        <span class="k">if</span> <span class="s2">&quot;id_order&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;index&quot;</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;id_order&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_id_order</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">s0</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;``s0`` is defined as:</span>
+
+<span class="sd">        .. math::</span>
+
+<span class="sd">               s0=\sum_i \sum_j w_{i,j}</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;s0&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;s0&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_s0</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">trcWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Trace of :math:`W^{&#39;}W + WW`.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;trcWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">diagWtW_WW</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;trcWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trcWtW_WW</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">diagWtW_WW</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Diagonal of :math:`W^{&#39;}W + WW`.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="s2">&quot;diagWtW_WW&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">:</span>
+            <span class="n">wt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sparse</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span> <span class="o">=</span> <span class="p">(</span><span class="n">wt</span> <span class="o">*</span> <span class="n">w</span> <span class="o">+</span> <span class="n">w</span> <span class="o">*</span> <span class="n">w</span><span class="p">)</span><span class="o">.</span><span class="n">diagonal</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;diagWtW_WW&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_diagWtW_WW</span>
+
+<div class="viewcode-block" id="WSP.from_W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP.from_W">[docs]</a>
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_W</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Constructs a ``WSP`` object from the ``W``&#39;s sparse matrix.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        W : libpysal.weights.W</span>
+<span class="sd">            A PySAL weights object with a sparse form and ids.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        A ``WSP`` instance.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">W</span><span class="o">.</span><span class="n">sparse</span><span class="p">,</span> <span class="n">id_order</span><span class="o">=</span><span class="n">W</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="WSP.to_W">
+<a class="viewcode-back" href="../../../generated/libpysal.weights.WSP.html#libpysal.weights.WSP.to_W">[docs]</a>
+    <span class="k">def</span> <span class="nf">to_W</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Convert a pysal WSP object (thin weights matrix) to a pysal W object.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        self : WSP</span>
+<span class="sd">            PySAL sparse weights object.</span>
+<span class="sd">        silence_warnings : bool</span>
+<span class="sd">            Switch to ``True`` to turn off print statements for every</span>
+<span class="sd">            observation with islands. Default is ``False``, which does</span>
+<span class="sd">            not silence warnings.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        w : W</span>
+<span class="sd">            PySAL weights object.</span>
+
+<span class="sd">        Examples</span>
+<span class="sd">        --------</span>
+<span class="sd">        &gt;&gt;&gt; from libpysal.weights import lat2SW, WSP, WSP2W</span>
+
+<span class="sd">        Build a 10x10 ``scipy.sparse`` matrix for a rectangular 2x5</span>
+<span class="sd">        region of cells (rook contiguity), then construct a ``libpysal``</span>
+<span class="sd">        sparse weights object (``self``).</span>
+
+<span class="sd">        &gt;&gt;&gt; sp = lat2SW(2, 5)</span>
+<span class="sd">        &gt;&gt;&gt; self = WSP(sp)</span>
+<span class="sd">        &gt;&gt;&gt; self.n</span>
+<span class="sd">        10</span>
+<span class="sd">        &gt;&gt;&gt; print(self.sparse[0].todense())</span>
+<span class="sd">        [[0 1 0 0 0 1 0 0 0 0]]</span>
+
+<span class="sd">        Convert this sparse weights object to a standard PySAL weights object.</span>
+
+<span class="sd">        &gt;&gt;&gt; w = WSP2W(self)</span>
+<span class="sd">        &gt;&gt;&gt; w.n</span>
+<span class="sd">        10</span>
+<span class="sd">        &gt;&gt;&gt; print(w.full()[0][0])</span>
+<span class="sd">        [0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indices</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">indptr</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">indptr</span><span class="p">)</span>
+        <span class="n">id_order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id_order</span>
+        <span class="k">if</span> <span class="n">id_order</span><span class="p">:</span>
+            <span class="c1"># replace indices with user IDs</span>
+            <span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">id_order</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">))</span>
+        <span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n</span><span class="p">):</span>
+            <span class="n">oid</span> <span class="o">=</span> <span class="n">id_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">end</span> <span class="o">=</span> <span class="n">indptr</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">neighbors</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+            <span class="n">weights</span><span class="p">[</span><span class="n">oid</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
+            <span class="n">start</span> <span class="o">=</span> <span class="n">end</span>
+        <span class="n">ids</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id_order</span><span class="p">)</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">ids</span><span class="p">,</span> <span class="n">silence_warnings</span><span class="o">=</span><span class="n">silence_warnings</span><span class="p">)</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">)</span>
+        <span class="n">w</span><span class="o">.</span><span class="n">_cache</span><span class="p">[</span><span class="s2">&quot;sparse&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">_sparse</span>
+        <span class="k">return</span> <span class="n">w</span></div>
+</div>
+
+</pre></div>
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/_sources/api.rst.txt b/_sources/api.rst.txt
new file mode 100644
index 000000000..316db37cb
--- /dev/null
+++ b/_sources/api.rst.txt
@@ -0,0 +1,265 @@
+.. _api_ref:
+
+.. currentmodule:: libpysal
+
+
+libpysal API reference
+======================
+
+Spatial Weights
+---------------
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.W
+
+Distance Weights
+++++++++++++++++
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.DistanceBand
+   libpysal.weights.Kernel
+   libpysal.weights.KNN
+   libpysal.weights.Gabriel
+   libpysal.weights.Delaunay
+   libpysal.weights.Relative_Neighborhood
+
+Contiguity Weights
+++++++++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.Queen
+   libpysal.weights.Rook
+   libpysal.weights.Voronoi
+   libpysal.weights.W
+
+spint Weights
++++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.WSP
+   libpysal.weights.netW
+   libpysal.weights.mat2L
+   libpysal.weights.ODW
+   libpysal.weights.vecW
+
+Weights tools to interface with rasters
++++++++++++++++++++++++++++++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.da2W
+   libpysal.weights.da2WSP
+   libpysal.weights.w2da
+   libpysal.weights.wsp2da
+   libpysal.weights.testDataArray
+
+Weights Util Classes and Functions
+++++++++++++++++++++++++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.block_weights
+   libpysal.weights.lat2W
+   libpysal.weights.comb
+   libpysal.weights.order
+   libpysal.weights.higher_order
+   libpysal.weights.shimbel
+   libpysal.weights.remap_ids
+   libpysal.weights.full2W
+   libpysal.weights.full
+   libpysal.weights.WSP2W
+   libpysal.weights.get_ids
+   libpysal.weights.get_points_array_from_shapefile
+   libpysal.weights.fill_diagonal
+
+Weights user Classes and Functions
+++++++++++++++++++++++++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.min_threshold_distance
+   libpysal.weights.lat2SW
+   libpysal.weights.w_local_cluster
+   libpysal.weights.higher_order_sp
+   libpysal.weights.hexLat2W
+   libpysal.weights.attach_islands
+   libpysal.weights.nonplanar_neighbors
+   libpysal.weights.fuzzy_contiguity
+   libpysal.weights.min_threshold_dist_from_shapefile
+   libpysal.weights.build_lattice_shapefile
+   libpysal.weights.spw_from_gal
+   libpysal.weights.neighbor_equality
+
+
+Set Theoretic Weights
++++++++++++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.w_union
+   libpysal.weights.w_intersection
+   libpysal.weights.w_difference
+   libpysal.weights.w_symmetric_difference
+   libpysal.weights.w_subset
+   libpysal.weights.w_clip
+
+
+Spatial Lag
++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.weights.lag_spatial
+   libpysal.weights.lag_categorical
+
+
+cg: Computational Geometry
+--------------------------
+
+alpha_shapes
+++++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.alpha_shape
+   libpysal.cg.alpha_shape_auto
+
+voronoi
++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.voronoi_frames
+
+
+sphere
+++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.RADIUS_EARTH_KM
+   libpysal.cg.RADIUS_EARTH_MILES
+   libpysal.cg.arcdist
+   libpysal.cg.arcdist2linear
+   libpysal.cg.brute_knn
+   libpysal.cg.fast_knn
+   libpysal.cg.fast_threshold
+   libpysal.cg.linear2arcdist
+   libpysal.cg.toLngLat
+   libpysal.cg.toXYZ
+   libpysal.cg.lonlat
+   libpysal.cg.harcdist
+   libpysal.cg.geointerpolate
+   libpysal.cg.geogrid
+
+shapes
+++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.Point
+   libpysal.cg.LineSegment
+   libpysal.cg.Line
+   libpysal.cg.Ray
+   libpysal.cg.Chain
+   libpysal.cg.Polygon
+   libpysal.cg.Rectangle
+   libpysal.cg.asShape
+
+standalone
+++++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.bbcommon
+   libpysal.cg.get_bounding_box
+   libpysal.cg.get_angle_between
+   libpysal.cg.is_collinear
+   libpysal.cg.get_segments_intersect
+   libpysal.cg.get_segment_point_intersect
+   libpysal.cg.get_polygon_point_intersect
+   libpysal.cg.get_rectangle_point_intersect
+   libpysal.cg.get_ray_segment_intersect
+   libpysal.cg.get_rectangle_rectangle_intersection
+   libpysal.cg.get_polygon_point_dist
+   libpysal.cg.get_points_dist
+   libpysal.cg.get_segment_point_dist
+   libpysal.cg.get_point_at_angle_and_dist
+   libpysal.cg.convex_hull
+   libpysal.cg.is_clockwise
+   libpysal.cg.point_touches_rectangle
+   libpysal.cg.get_shared_segments
+   libpysal.cg.distance_matrix
+
+
+locators
+++++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.Grid
+   libpysal.cg.PointLocator
+   libpysal.cg.PolygonLocator
+
+
+kdtree
+++++++
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.cg.KDTree
+
+
+io
+--
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.io.open
+   libpysal.io.fileio.FileIO
+
+
+examples
+--------
+
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.examples.available
+   libpysal.examples.explain
+   libpysal.examples.get_path
+
+
+Experimental
+------------
+
+Experimental modules with unstable API.
+
+graph
+-----
+
+.. autosummary::
+   :toctree: generated/
+
+   libpysal.graph.Graph
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Chain.rst.txt b/_sources/generated/libpysal.cg.Chain.rst.txt
new file mode 100644
index 000000000..875e0b90b
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Chain.rst.txt
@@ -0,0 +1,33 @@
+libpysal.cg.Chain
+=================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Chain
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Chain.__init__
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Chain.arclen
+      ~Chain.bounding_box
+      ~Chain.len
+      ~Chain.parts
+      ~Chain.segments
+      ~Chain.vertices
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Grid.rst.txt b/_sources/generated/libpysal.cg.Grid.rst.txt
new file mode 100644
index 000000000..9d4d6f0ae
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Grid.rst.txt
@@ -0,0 +1,28 @@
+libpysal.cg.Grid
+================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Grid
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Grid.__init__
+      ~Grid.add
+      ~Grid.bounds
+      ~Grid.in_grid
+      ~Grid.nearest
+      ~Grid.proximity
+      ~Grid.remove
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.KDTree.rst.txt b/_sources/generated/libpysal.cg.KDTree.rst.txt
new file mode 100644
index 000000000..7a10fba5d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.KDTree.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.KDTree
+==================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: KDTree
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Line.rst.txt b/_sources/generated/libpysal.cg.Line.rst.txt
new file mode 100644
index 000000000..bd4249046
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Line.rst.txt
@@ -0,0 +1,24 @@
+libpysal.cg.Line
+================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Line
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Line.__init__
+      ~Line.x
+      ~Line.y
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.LineSegment.rst.txt b/_sources/generated/libpysal.cg.LineSegment.rst.txt
new file mode 100644
index 000000000..685ab2c40
--- /dev/null
+++ b/_sources/generated/libpysal.cg.LineSegment.rst.txt
@@ -0,0 +1,37 @@
+libpysal.cg.LineSegment
+=======================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: LineSegment
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~LineSegment.__init__
+      ~LineSegment.get_swap
+      ~LineSegment.intersect
+      ~LineSegment.is_ccw
+      ~LineSegment.is_cw
+      ~LineSegment.sw_ccw
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~LineSegment.bounding_box
+      ~LineSegment.len
+      ~LineSegment.line
+      ~LineSegment.p1
+      ~LineSegment.p2
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Point.rst.txt b/_sources/generated/libpysal.cg.Point.rst.txt
new file mode 100644
index 000000000..f71d89653
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Point.rst.txt
@@ -0,0 +1,22 @@
+libpysal.cg.Point
+=================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Point
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Point.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.PointLocator.rst.txt b/_sources/generated/libpysal.cg.PointLocator.rst.txt
new file mode 100644
index 000000000..97e144d5a
--- /dev/null
+++ b/_sources/generated/libpysal.cg.PointLocator.rst.txt
@@ -0,0 +1,27 @@
+libpysal.cg.PointLocator
+========================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: PointLocator
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~PointLocator.__init__
+      ~PointLocator.nearest
+      ~PointLocator.overlapping
+      ~PointLocator.polygon
+      ~PointLocator.proximity
+      ~PointLocator.region
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Polygon.rst.txt b/_sources/generated/libpysal.cg.Polygon.rst.txt
new file mode 100644
index 000000000..abd2418eb
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Polygon.rst.txt
@@ -0,0 +1,38 @@
+libpysal.cg.Polygon
+===================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Polygon
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Polygon.__init__
+      ~Polygon.build_quad_tree_structure
+      ~Polygon.contains_point
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Polygon.area
+      ~Polygon.bbox
+      ~Polygon.bounding_box
+      ~Polygon.centroid
+      ~Polygon.holes
+      ~Polygon.len
+      ~Polygon.parts
+      ~Polygon.perimeter
+      ~Polygon.vertices
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.PolygonLocator.rst.txt b/_sources/generated/libpysal.cg.PolygonLocator.rst.txt
new file mode 100644
index 000000000..e8daf826f
--- /dev/null
+++ b/_sources/generated/libpysal.cg.PolygonLocator.rst.txt
@@ -0,0 +1,28 @@
+libpysal.cg.PolygonLocator
+==========================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: PolygonLocator
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~PolygonLocator.__init__
+      ~PolygonLocator.contains_point
+      ~PolygonLocator.inside
+      ~PolygonLocator.nearest
+      ~PolygonLocator.overlapping
+      ~PolygonLocator.proximity
+      ~PolygonLocator.region
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt b/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt
new file mode 100644
index 000000000..34f748fd2
--- /dev/null
+++ b/_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.RADIUS\_EARTH\_KM
+=============================
+
+.. currentmodule:: libpysal.cg
+
+.. autodata:: RADIUS_EARTH_KM
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt b/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt
new file mode 100644
index 000000000..23840c860
--- /dev/null
+++ b/_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.RADIUS\_EARTH\_MILES
+================================
+
+.. currentmodule:: libpysal.cg
+
+.. autodata:: RADIUS_EARTH_MILES
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Ray.rst.txt b/_sources/generated/libpysal.cg.Ray.rst.txt
new file mode 100644
index 000000000..71c9009d6
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Ray.rst.txt
@@ -0,0 +1,22 @@
+libpysal.cg.Ray
+===============
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Ray
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Ray.__init__
+   
+   
+
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.Rectangle.rst.txt b/_sources/generated/libpysal.cg.Rectangle.rst.txt
new file mode 100644
index 000000000..4cddb1780
--- /dev/null
+++ b/_sources/generated/libpysal.cg.Rectangle.rst.txt
@@ -0,0 +1,32 @@
+libpysal.cg.Rectangle
+=====================
+
+.. currentmodule:: libpysal.cg
+
+.. autoclass:: Rectangle
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Rectangle.__init__
+      ~Rectangle.set_centroid
+      ~Rectangle.set_scale
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Rectangle.area
+      ~Rectangle.height
+      ~Rectangle.width
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.alpha_shape.rst.txt b/_sources/generated/libpysal.cg.alpha_shape.rst.txt
new file mode 100644
index 000000000..941eb011c
--- /dev/null
+++ b/_sources/generated/libpysal.cg.alpha_shape.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.alpha\_shape
+========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: alpha_shape
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt b/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt
new file mode 100644
index 000000000..ec4d9f795
--- /dev/null
+++ b/_sources/generated/libpysal.cg.alpha_shape_auto.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.alpha\_shape\_auto
+==============================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: alpha_shape_auto
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.arcdist.rst.txt b/_sources/generated/libpysal.cg.arcdist.rst.txt
new file mode 100644
index 000000000..c69cc2115
--- /dev/null
+++ b/_sources/generated/libpysal.cg.arcdist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.arcdist
+===================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: arcdist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.arcdist2linear.rst.txt b/_sources/generated/libpysal.cg.arcdist2linear.rst.txt
new file mode 100644
index 000000000..00482e052
--- /dev/null
+++ b/_sources/generated/libpysal.cg.arcdist2linear.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.arcdist2linear
+==========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: arcdist2linear
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.asShape.rst.txt b/_sources/generated/libpysal.cg.asShape.rst.txt
new file mode 100644
index 000000000..2be27a48d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.asShape.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.asShape
+===================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: asShape
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.bbcommon.rst.txt b/_sources/generated/libpysal.cg.bbcommon.rst.txt
new file mode 100644
index 000000000..9cae457f3
--- /dev/null
+++ b/_sources/generated/libpysal.cg.bbcommon.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.bbcommon
+====================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: bbcommon
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.brute_knn.rst.txt b/_sources/generated/libpysal.cg.brute_knn.rst.txt
new file mode 100644
index 000000000..71ed4e0fa
--- /dev/null
+++ b/_sources/generated/libpysal.cg.brute_knn.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.brute\_knn
+======================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: brute_knn
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.convex_hull.rst.txt b/_sources/generated/libpysal.cg.convex_hull.rst.txt
new file mode 100644
index 000000000..258008efd
--- /dev/null
+++ b/_sources/generated/libpysal.cg.convex_hull.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.convex\_hull
+========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: convex_hull
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.distance_matrix.rst.txt b/_sources/generated/libpysal.cg.distance_matrix.rst.txt
new file mode 100644
index 000000000..24b8f32d3
--- /dev/null
+++ b/_sources/generated/libpysal.cg.distance_matrix.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.distance\_matrix
+============================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: distance_matrix
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.fast_knn.rst.txt b/_sources/generated/libpysal.cg.fast_knn.rst.txt
new file mode 100644
index 000000000..411fe657d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.fast_knn.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.fast\_knn
+=====================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: fast_knn
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.fast_threshold.rst.txt b/_sources/generated/libpysal.cg.fast_threshold.rst.txt
new file mode 100644
index 000000000..cd13b5ab6
--- /dev/null
+++ b/_sources/generated/libpysal.cg.fast_threshold.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.fast\_threshold
+===========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: fast_threshold
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.geogrid.rst.txt b/_sources/generated/libpysal.cg.geogrid.rst.txt
new file mode 100644
index 000000000..37d01241f
--- /dev/null
+++ b/_sources/generated/libpysal.cg.geogrid.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.geogrid
+===================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: geogrid
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.geointerpolate.rst.txt b/_sources/generated/libpysal.cg.geointerpolate.rst.txt
new file mode 100644
index 000000000..634dbd210
--- /dev/null
+++ b/_sources/generated/libpysal.cg.geointerpolate.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.geointerpolate
+==========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: geointerpolate
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_angle_between.rst.txt b/_sources/generated/libpysal.cg.get_angle_between.rst.txt
new file mode 100644
index 000000000..e654e51aa
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_angle_between.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_angle\_between
+===============================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_angle_between
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_bounding_box.rst.txt b/_sources/generated/libpysal.cg.get_bounding_box.rst.txt
new file mode 100644
index 000000000..76e80a490
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_bounding_box.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_bounding\_box
+==============================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_bounding_box
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt b/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt
new file mode 100644
index 000000000..50e4546f9
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_point_at_angle_and_dist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_point\_at\_angle\_and\_dist
+============================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_point_at_angle_and_dist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_points_dist.rst.txt b/_sources/generated/libpysal.cg.get_points_dist.rst.txt
new file mode 100644
index 000000000..541128122
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_points_dist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_points\_dist
+=============================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_points_dist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt b/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt
new file mode 100644
index 000000000..931fa418d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_polygon_point_dist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_polygon\_point\_dist
+=====================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_polygon_point_dist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt b/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt
new file mode 100644
index 000000000..86aaf6f5a
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_polygon_point_intersect.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_polygon\_point\_intersect
+==========================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_polygon_point_intersect
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt b/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt
new file mode 100644
index 000000000..1f32ac522
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_ray_segment_intersect.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_ray\_segment\_intersect
+========================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_ray_segment_intersect
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt b/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt
new file mode 100644
index 000000000..9c5cc65fd
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_rectangle_point_intersect.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_rectangle\_point\_intersect
+============================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_rectangle_point_intersect
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt b/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt
new file mode 100644
index 000000000..66253a70d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_rectangle_rectangle_intersection.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_rectangle\_rectangle\_intersection
+===================================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_rectangle_rectangle_intersection
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt b/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt
new file mode 100644
index 000000000..c6c1a3909
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_segment_point_dist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_segment\_point\_dist
+=====================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_segment_point_dist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt b/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt
new file mode 100644
index 000000000..2e3b06a59
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_segment_point_intersect.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_segment\_point\_intersect
+==========================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_segment_point_intersect
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt b/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt
new file mode 100644
index 000000000..7f5740593
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_segments_intersect.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_segments\_intersect
+====================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_segments_intersect
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.get_shared_segments.rst.txt b/_sources/generated/libpysal.cg.get_shared_segments.rst.txt
new file mode 100644
index 000000000..79d76e6a8
--- /dev/null
+++ b/_sources/generated/libpysal.cg.get_shared_segments.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.get\_shared\_segments
+=================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: get_shared_segments
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.harcdist.rst.txt b/_sources/generated/libpysal.cg.harcdist.rst.txt
new file mode 100644
index 000000000..14db6e7c1
--- /dev/null
+++ b/_sources/generated/libpysal.cg.harcdist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.harcdist
+====================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: harcdist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.is_clockwise.rst.txt b/_sources/generated/libpysal.cg.is_clockwise.rst.txt
new file mode 100644
index 000000000..2ba136bf5
--- /dev/null
+++ b/_sources/generated/libpysal.cg.is_clockwise.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.is\_clockwise
+=========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: is_clockwise
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.is_collinear.rst.txt b/_sources/generated/libpysal.cg.is_collinear.rst.txt
new file mode 100644
index 000000000..964725ad8
--- /dev/null
+++ b/_sources/generated/libpysal.cg.is_collinear.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.is\_collinear
+=========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: is_collinear
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.linear2arcdist.rst.txt b/_sources/generated/libpysal.cg.linear2arcdist.rst.txt
new file mode 100644
index 000000000..c8250fe7d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.linear2arcdist.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.linear2arcdist
+==========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: linear2arcdist
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.lonlat.rst.txt b/_sources/generated/libpysal.cg.lonlat.rst.txt
new file mode 100644
index 000000000..ab7d04d96
--- /dev/null
+++ b/_sources/generated/libpysal.cg.lonlat.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.lonlat
+==================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: lonlat
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt b/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt
new file mode 100644
index 000000000..ec4e9d761
--- /dev/null
+++ b/_sources/generated/libpysal.cg.point_touches_rectangle.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.point\_touches\_rectangle
+=====================================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: point_touches_rectangle
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.toLngLat.rst.txt b/_sources/generated/libpysal.cg.toLngLat.rst.txt
new file mode 100644
index 000000000..9f3bdce3d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.toLngLat.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.toLngLat
+====================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: toLngLat
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.toXYZ.rst.txt b/_sources/generated/libpysal.cg.toXYZ.rst.txt
new file mode 100644
index 000000000..56cd7a24d
--- /dev/null
+++ b/_sources/generated/libpysal.cg.toXYZ.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.toXYZ
+=================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: toXYZ
\ No newline at end of file
diff --git a/_sources/generated/libpysal.cg.voronoi_frames.rst.txt b/_sources/generated/libpysal.cg.voronoi_frames.rst.txt
new file mode 100644
index 000000000..7acdb7b56
--- /dev/null
+++ b/_sources/generated/libpysal.cg.voronoi_frames.rst.txt
@@ -0,0 +1,6 @@
+libpysal.cg.voronoi\_frames
+===========================
+
+.. currentmodule:: libpysal.cg
+
+.. autofunction:: voronoi_frames
\ No newline at end of file
diff --git a/_sources/generated/libpysal.examples.available.rst.txt b/_sources/generated/libpysal.examples.available.rst.txt
new file mode 100644
index 000000000..fea94ba05
--- /dev/null
+++ b/_sources/generated/libpysal.examples.available.rst.txt
@@ -0,0 +1,6 @@
+libpysal.examples.available
+===========================
+
+.. currentmodule:: libpysal.examples
+
+.. autofunction:: available
\ No newline at end of file
diff --git a/_sources/generated/libpysal.examples.explain.rst.txt b/_sources/generated/libpysal.examples.explain.rst.txt
new file mode 100644
index 000000000..65795a425
--- /dev/null
+++ b/_sources/generated/libpysal.examples.explain.rst.txt
@@ -0,0 +1,6 @@
+libpysal.examples.explain
+=========================
+
+.. currentmodule:: libpysal.examples
+
+.. autofunction:: explain
\ No newline at end of file
diff --git a/_sources/generated/libpysal.examples.get_path.rst.txt b/_sources/generated/libpysal.examples.get_path.rst.txt
new file mode 100644
index 000000000..36ad29ded
--- /dev/null
+++ b/_sources/generated/libpysal.examples.get_path.rst.txt
@@ -0,0 +1,6 @@
+libpysal.examples.get\_path
+===========================
+
+.. currentmodule:: libpysal.examples
+
+.. autofunction:: get_path
\ No newline at end of file
diff --git a/_sources/generated/libpysal.graph.Graph.rst.txt b/_sources/generated/libpysal.graph.Graph.rst.txt
new file mode 100644
index 000000000..0a0a406c9
--- /dev/null
+++ b/_sources/generated/libpysal.graph.Graph.rst.txt
@@ -0,0 +1,72 @@
+libpysal.graph.Graph
+====================
+
+.. currentmodule:: libpysal.graph
+
+.. autoclass:: Graph
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Graph.__init__
+      ~Graph.asymmetry
+      ~Graph.build_block_contiguity
+      ~Graph.build_contiguity
+      ~Graph.build_distance_band
+      ~Graph.build_fuzzy_contiguity
+      ~Graph.build_kernel
+      ~Graph.build_knn
+      ~Graph.build_triangulation
+      ~Graph.copy
+      ~Graph.difference
+      ~Graph.equals
+      ~Graph.explore
+      ~Graph.from_W
+      ~Graph.from_adjacency
+      ~Graph.from_arrays
+      ~Graph.from_dicts
+      ~Graph.from_sparse
+      ~Graph.from_weights_dict
+      ~Graph.higher_order
+      ~Graph.intersection
+      ~Graph.intersects
+      ~Graph.isomorphic
+      ~Graph.issubgraph
+      ~Graph.lag
+      ~Graph.plot
+      ~Graph.symmetric_difference
+      ~Graph.to_W
+      ~Graph.to_networkx
+      ~Graph.to_parquet
+      ~Graph.transform
+      ~Graph.union
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Graph.adjacency
+      ~Graph.cardinalities
+      ~Graph.component_labels
+      ~Graph.isolates
+      ~Graph.n
+      ~Graph.n_components
+      ~Graph.n_edges
+      ~Graph.n_nodes
+      ~Graph.neighbors
+      ~Graph.nonzero
+      ~Graph.pct_nonzero
+      ~Graph.sparse
+      ~Graph.unique_ids
+      ~Graph.weights
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.io.fileio.FileIO.rst.txt b/_sources/generated/libpysal.io.fileio.FileIO.rst.txt
new file mode 100644
index 000000000..ff0499969
--- /dev/null
+++ b/_sources/generated/libpysal.io.fileio.FileIO.rst.txt
@@ -0,0 +1,42 @@
+libpysal.io.fileio.FileIO
+=========================
+
+.. currentmodule:: libpysal.io.fileio
+
+.. autoclass:: FileIO
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~FileIO.__init__
+      ~FileIO.cast
+      ~FileIO.check
+      ~FileIO.close
+      ~FileIO.flush
+      ~FileIO.get
+      ~FileIO.getType
+      ~FileIO.open
+      ~FileIO.read
+      ~FileIO.seek
+      ~FileIO.tell
+      ~FileIO.truncate
+      ~FileIO.write
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~FileIO.by_row
+      ~FileIO.ids
+      ~FileIO.rIds
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.io.open.rst.txt b/_sources/generated/libpysal.io.open.rst.txt
new file mode 100644
index 000000000..155236bbd
--- /dev/null
+++ b/_sources/generated/libpysal.io.open.rst.txt
@@ -0,0 +1,42 @@
+libpysal.io.open
+================
+
+.. currentmodule:: libpysal.io
+
+.. autoclass:: open
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~open.__init__
+      ~open.cast
+      ~open.check
+      ~open.close
+      ~open.flush
+      ~open.get
+      ~open.getType
+      ~open.open
+      ~open.read
+      ~open.seek
+      ~open.tell
+      ~open.truncate
+      ~open.write
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~open.by_row
+      ~open.ids
+      ~open.rIds
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Delaunay.rst.txt b/_sources/generated/libpysal.weights.Delaunay.rst.txt
new file mode 100644
index 000000000..3e877dfc3
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Delaunay.rst.txt
@@ -0,0 +1,76 @@
+libpysal.weights.Delaunay
+=========================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: Delaunay
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Delaunay.__init__
+      ~Delaunay.asymmetry
+      ~Delaunay.from_WSP
+      ~Delaunay.from_adjlist
+      ~Delaunay.from_dataframe
+      ~Delaunay.from_file
+      ~Delaunay.from_networkx
+      ~Delaunay.from_shapefile
+      ~Delaunay.from_sparse
+      ~Delaunay.full
+      ~Delaunay.get_transform
+      ~Delaunay.plot
+      ~Delaunay.remap_ids
+      ~Delaunay.set_shapefile
+      ~Delaunay.set_transform
+      ~Delaunay.symmetrize
+      ~Delaunay.to_WSP
+      ~Delaunay.to_adjlist
+      ~Delaunay.to_file
+      ~Delaunay.to_networkx
+      ~Delaunay.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Delaunay.asymmetries
+      ~Delaunay.cardinalities
+      ~Delaunay.component_labels
+      ~Delaunay.diagW2
+      ~Delaunay.diagWtW
+      ~Delaunay.diagWtW_WW
+      ~Delaunay.histogram
+      ~Delaunay.id2i
+      ~Delaunay.id_order
+      ~Delaunay.id_order_set
+      ~Delaunay.islands
+      ~Delaunay.max_neighbors
+      ~Delaunay.mean_neighbors
+      ~Delaunay.min_neighbors
+      ~Delaunay.n
+      ~Delaunay.n_components
+      ~Delaunay.neighbor_offsets
+      ~Delaunay.nonzero
+      ~Delaunay.pct_nonzero
+      ~Delaunay.s0
+      ~Delaunay.s1
+      ~Delaunay.s2
+      ~Delaunay.s2array
+      ~Delaunay.sd
+      ~Delaunay.sparse
+      ~Delaunay.transform
+      ~Delaunay.trcW2
+      ~Delaunay.trcWtW
+      ~Delaunay.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.DistanceBand.rst.txt b/_sources/generated/libpysal.weights.DistanceBand.rst.txt
new file mode 100644
index 000000000..9cb96b956
--- /dev/null
+++ b/_sources/generated/libpysal.weights.DistanceBand.rst.txt
@@ -0,0 +1,77 @@
+libpysal.weights.DistanceBand
+=============================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: DistanceBand
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~DistanceBand.__init__
+      ~DistanceBand.asymmetry
+      ~DistanceBand.from_WSP
+      ~DistanceBand.from_adjlist
+      ~DistanceBand.from_array
+      ~DistanceBand.from_dataframe
+      ~DistanceBand.from_file
+      ~DistanceBand.from_networkx
+      ~DistanceBand.from_shapefile
+      ~DistanceBand.from_sparse
+      ~DistanceBand.full
+      ~DistanceBand.get_transform
+      ~DistanceBand.plot
+      ~DistanceBand.remap_ids
+      ~DistanceBand.set_shapefile
+      ~DistanceBand.set_transform
+      ~DistanceBand.symmetrize
+      ~DistanceBand.to_WSP
+      ~DistanceBand.to_adjlist
+      ~DistanceBand.to_file
+      ~DistanceBand.to_networkx
+      ~DistanceBand.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~DistanceBand.asymmetries
+      ~DistanceBand.cardinalities
+      ~DistanceBand.component_labels
+      ~DistanceBand.diagW2
+      ~DistanceBand.diagWtW
+      ~DistanceBand.diagWtW_WW
+      ~DistanceBand.histogram
+      ~DistanceBand.id2i
+      ~DistanceBand.id_order
+      ~DistanceBand.id_order_set
+      ~DistanceBand.islands
+      ~DistanceBand.max_neighbors
+      ~DistanceBand.mean_neighbors
+      ~DistanceBand.min_neighbors
+      ~DistanceBand.n
+      ~DistanceBand.n_components
+      ~DistanceBand.neighbor_offsets
+      ~DistanceBand.nonzero
+      ~DistanceBand.pct_nonzero
+      ~DistanceBand.s0
+      ~DistanceBand.s1
+      ~DistanceBand.s2
+      ~DistanceBand.s2array
+      ~DistanceBand.sd
+      ~DistanceBand.sparse
+      ~DistanceBand.transform
+      ~DistanceBand.trcW2
+      ~DistanceBand.trcWtW
+      ~DistanceBand.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Gabriel.rst.txt b/_sources/generated/libpysal.weights.Gabriel.rst.txt
new file mode 100644
index 000000000..4e37c1be9
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Gabriel.rst.txt
@@ -0,0 +1,76 @@
+libpysal.weights.Gabriel
+========================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: Gabriel
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Gabriel.__init__
+      ~Gabriel.asymmetry
+      ~Gabriel.from_WSP
+      ~Gabriel.from_adjlist
+      ~Gabriel.from_dataframe
+      ~Gabriel.from_file
+      ~Gabriel.from_networkx
+      ~Gabriel.from_shapefile
+      ~Gabriel.from_sparse
+      ~Gabriel.full
+      ~Gabriel.get_transform
+      ~Gabriel.plot
+      ~Gabriel.remap_ids
+      ~Gabriel.set_shapefile
+      ~Gabriel.set_transform
+      ~Gabriel.symmetrize
+      ~Gabriel.to_WSP
+      ~Gabriel.to_adjlist
+      ~Gabriel.to_file
+      ~Gabriel.to_networkx
+      ~Gabriel.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Gabriel.asymmetries
+      ~Gabriel.cardinalities
+      ~Gabriel.component_labels
+      ~Gabriel.diagW2
+      ~Gabriel.diagWtW
+      ~Gabriel.diagWtW_WW
+      ~Gabriel.histogram
+      ~Gabriel.id2i
+      ~Gabriel.id_order
+      ~Gabriel.id_order_set
+      ~Gabriel.islands
+      ~Gabriel.max_neighbors
+      ~Gabriel.mean_neighbors
+      ~Gabriel.min_neighbors
+      ~Gabriel.n
+      ~Gabriel.n_components
+      ~Gabriel.neighbor_offsets
+      ~Gabriel.nonzero
+      ~Gabriel.pct_nonzero
+      ~Gabriel.s0
+      ~Gabriel.s1
+      ~Gabriel.s2
+      ~Gabriel.s2array
+      ~Gabriel.sd
+      ~Gabriel.sparse
+      ~Gabriel.transform
+      ~Gabriel.trcW2
+      ~Gabriel.trcWtW
+      ~Gabriel.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.KNN.rst.txt b/_sources/generated/libpysal.weights.KNN.rst.txt
new file mode 100644
index 000000000..67d6f8c4d
--- /dev/null
+++ b/_sources/generated/libpysal.weights.KNN.rst.txt
@@ -0,0 +1,78 @@
+libpysal.weights.KNN
+====================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: KNN
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~KNN.__init__
+      ~KNN.asymmetry
+      ~KNN.from_WSP
+      ~KNN.from_adjlist
+      ~KNN.from_array
+      ~KNN.from_dataframe
+      ~KNN.from_file
+      ~KNN.from_networkx
+      ~KNN.from_shapefile
+      ~KNN.from_sparse
+      ~KNN.full
+      ~KNN.get_transform
+      ~KNN.plot
+      ~KNN.remap_ids
+      ~KNN.reweight
+      ~KNN.set_shapefile
+      ~KNN.set_transform
+      ~KNN.symmetrize
+      ~KNN.to_WSP
+      ~KNN.to_adjlist
+      ~KNN.to_file
+      ~KNN.to_networkx
+      ~KNN.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~KNN.asymmetries
+      ~KNN.cardinalities
+      ~KNN.component_labels
+      ~KNN.diagW2
+      ~KNN.diagWtW
+      ~KNN.diagWtW_WW
+      ~KNN.histogram
+      ~KNN.id2i
+      ~KNN.id_order
+      ~KNN.id_order_set
+      ~KNN.islands
+      ~KNN.max_neighbors
+      ~KNN.mean_neighbors
+      ~KNN.min_neighbors
+      ~KNN.n
+      ~KNN.n_components
+      ~KNN.neighbor_offsets
+      ~KNN.nonzero
+      ~KNN.pct_nonzero
+      ~KNN.s0
+      ~KNN.s1
+      ~KNN.s2
+      ~KNN.s2array
+      ~KNN.sd
+      ~KNN.sparse
+      ~KNN.transform
+      ~KNN.trcW2
+      ~KNN.trcWtW
+      ~KNN.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Kernel.rst.txt b/_sources/generated/libpysal.weights.Kernel.rst.txt
new file mode 100644
index 000000000..64e3eb79c
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Kernel.rst.txt
@@ -0,0 +1,77 @@
+libpysal.weights.Kernel
+=======================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: Kernel
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Kernel.__init__
+      ~Kernel.asymmetry
+      ~Kernel.from_WSP
+      ~Kernel.from_adjlist
+      ~Kernel.from_array
+      ~Kernel.from_dataframe
+      ~Kernel.from_file
+      ~Kernel.from_networkx
+      ~Kernel.from_shapefile
+      ~Kernel.from_sparse
+      ~Kernel.full
+      ~Kernel.get_transform
+      ~Kernel.plot
+      ~Kernel.remap_ids
+      ~Kernel.set_shapefile
+      ~Kernel.set_transform
+      ~Kernel.symmetrize
+      ~Kernel.to_WSP
+      ~Kernel.to_adjlist
+      ~Kernel.to_file
+      ~Kernel.to_networkx
+      ~Kernel.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Kernel.asymmetries
+      ~Kernel.cardinalities
+      ~Kernel.component_labels
+      ~Kernel.diagW2
+      ~Kernel.diagWtW
+      ~Kernel.diagWtW_WW
+      ~Kernel.histogram
+      ~Kernel.id2i
+      ~Kernel.id_order
+      ~Kernel.id_order_set
+      ~Kernel.islands
+      ~Kernel.max_neighbors
+      ~Kernel.mean_neighbors
+      ~Kernel.min_neighbors
+      ~Kernel.n
+      ~Kernel.n_components
+      ~Kernel.neighbor_offsets
+      ~Kernel.nonzero
+      ~Kernel.pct_nonzero
+      ~Kernel.s0
+      ~Kernel.s1
+      ~Kernel.s2
+      ~Kernel.s2array
+      ~Kernel.sd
+      ~Kernel.sparse
+      ~Kernel.transform
+      ~Kernel.trcW2
+      ~Kernel.trcWtW
+      ~Kernel.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.ODW.rst.txt b/_sources/generated/libpysal.weights.ODW.rst.txt
new file mode 100644
index 000000000..44be31700
--- /dev/null
+++ b/_sources/generated/libpysal.weights.ODW.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.ODW
+====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: ODW
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Queen.rst.txt b/_sources/generated/libpysal.weights.Queen.rst.txt
new file mode 100644
index 000000000..c52f98ec9
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Queen.rst.txt
@@ -0,0 +1,78 @@
+libpysal.weights.Queen
+======================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: Queen
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Queen.__init__
+      ~Queen.asymmetry
+      ~Queen.from_WSP
+      ~Queen.from_adjlist
+      ~Queen.from_dataframe
+      ~Queen.from_file
+      ~Queen.from_iterable
+      ~Queen.from_networkx
+      ~Queen.from_shapefile
+      ~Queen.from_sparse
+      ~Queen.from_xarray
+      ~Queen.full
+      ~Queen.get_transform
+      ~Queen.plot
+      ~Queen.remap_ids
+      ~Queen.set_shapefile
+      ~Queen.set_transform
+      ~Queen.symmetrize
+      ~Queen.to_WSP
+      ~Queen.to_adjlist
+      ~Queen.to_file
+      ~Queen.to_networkx
+      ~Queen.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Queen.asymmetries
+      ~Queen.cardinalities
+      ~Queen.component_labels
+      ~Queen.diagW2
+      ~Queen.diagWtW
+      ~Queen.diagWtW_WW
+      ~Queen.histogram
+      ~Queen.id2i
+      ~Queen.id_order
+      ~Queen.id_order_set
+      ~Queen.islands
+      ~Queen.max_neighbors
+      ~Queen.mean_neighbors
+      ~Queen.min_neighbors
+      ~Queen.n
+      ~Queen.n_components
+      ~Queen.neighbor_offsets
+      ~Queen.nonzero
+      ~Queen.pct_nonzero
+      ~Queen.s0
+      ~Queen.s1
+      ~Queen.s2
+      ~Queen.s2array
+      ~Queen.sd
+      ~Queen.sparse
+      ~Queen.transform
+      ~Queen.trcW2
+      ~Queen.trcWtW
+      ~Queen.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Relative_Neighborhood.rst.txt b/_sources/generated/libpysal.weights.Relative_Neighborhood.rst.txt
new file mode 100644
index 000000000..62192b0ee
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Relative_Neighborhood.rst.txt
@@ -0,0 +1,76 @@
+libpysal.weights.Relative\_Neighborhood
+=======================================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: Relative_Neighborhood
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Relative_Neighborhood.__init__
+      ~Relative_Neighborhood.asymmetry
+      ~Relative_Neighborhood.from_WSP
+      ~Relative_Neighborhood.from_adjlist
+      ~Relative_Neighborhood.from_dataframe
+      ~Relative_Neighborhood.from_file
+      ~Relative_Neighborhood.from_networkx
+      ~Relative_Neighborhood.from_shapefile
+      ~Relative_Neighborhood.from_sparse
+      ~Relative_Neighborhood.full
+      ~Relative_Neighborhood.get_transform
+      ~Relative_Neighborhood.plot
+      ~Relative_Neighborhood.remap_ids
+      ~Relative_Neighborhood.set_shapefile
+      ~Relative_Neighborhood.set_transform
+      ~Relative_Neighborhood.symmetrize
+      ~Relative_Neighborhood.to_WSP
+      ~Relative_Neighborhood.to_adjlist
+      ~Relative_Neighborhood.to_file
+      ~Relative_Neighborhood.to_networkx
+      ~Relative_Neighborhood.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Relative_Neighborhood.asymmetries
+      ~Relative_Neighborhood.cardinalities
+      ~Relative_Neighborhood.component_labels
+      ~Relative_Neighborhood.diagW2
+      ~Relative_Neighborhood.diagWtW
+      ~Relative_Neighborhood.diagWtW_WW
+      ~Relative_Neighborhood.histogram
+      ~Relative_Neighborhood.id2i
+      ~Relative_Neighborhood.id_order
+      ~Relative_Neighborhood.id_order_set
+      ~Relative_Neighborhood.islands
+      ~Relative_Neighborhood.max_neighbors
+      ~Relative_Neighborhood.mean_neighbors
+      ~Relative_Neighborhood.min_neighbors
+      ~Relative_Neighborhood.n
+      ~Relative_Neighborhood.n_components
+      ~Relative_Neighborhood.neighbor_offsets
+      ~Relative_Neighborhood.nonzero
+      ~Relative_Neighborhood.pct_nonzero
+      ~Relative_Neighborhood.s0
+      ~Relative_Neighborhood.s1
+      ~Relative_Neighborhood.s2
+      ~Relative_Neighborhood.s2array
+      ~Relative_Neighborhood.sd
+      ~Relative_Neighborhood.sparse
+      ~Relative_Neighborhood.transform
+      ~Relative_Neighborhood.trcW2
+      ~Relative_Neighborhood.trcWtW
+      ~Relative_Neighborhood.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Rook.rst.txt b/_sources/generated/libpysal.weights.Rook.rst.txt
new file mode 100644
index 000000000..30f293989
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Rook.rst.txt
@@ -0,0 +1,78 @@
+libpysal.weights.Rook
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: Rook
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~Rook.__init__
+      ~Rook.asymmetry
+      ~Rook.from_WSP
+      ~Rook.from_adjlist
+      ~Rook.from_dataframe
+      ~Rook.from_file
+      ~Rook.from_iterable
+      ~Rook.from_networkx
+      ~Rook.from_shapefile
+      ~Rook.from_sparse
+      ~Rook.from_xarray
+      ~Rook.full
+      ~Rook.get_transform
+      ~Rook.plot
+      ~Rook.remap_ids
+      ~Rook.set_shapefile
+      ~Rook.set_transform
+      ~Rook.symmetrize
+      ~Rook.to_WSP
+      ~Rook.to_adjlist
+      ~Rook.to_file
+      ~Rook.to_networkx
+      ~Rook.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~Rook.asymmetries
+      ~Rook.cardinalities
+      ~Rook.component_labels
+      ~Rook.diagW2
+      ~Rook.diagWtW
+      ~Rook.diagWtW_WW
+      ~Rook.histogram
+      ~Rook.id2i
+      ~Rook.id_order
+      ~Rook.id_order_set
+      ~Rook.islands
+      ~Rook.max_neighbors
+      ~Rook.mean_neighbors
+      ~Rook.min_neighbors
+      ~Rook.n
+      ~Rook.n_components
+      ~Rook.neighbor_offsets
+      ~Rook.nonzero
+      ~Rook.pct_nonzero
+      ~Rook.s0
+      ~Rook.s1
+      ~Rook.s2
+      ~Rook.s2array
+      ~Rook.sd
+      ~Rook.sparse
+      ~Rook.transform
+      ~Rook.trcW2
+      ~Rook.trcWtW
+      ~Rook.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.Voronoi.rst.txt b/_sources/generated/libpysal.weights.Voronoi.rst.txt
new file mode 100644
index 000000000..f1ab244fb
--- /dev/null
+++ b/_sources/generated/libpysal.weights.Voronoi.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.Voronoi
+========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: Voronoi
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.W.rst.txt b/_sources/generated/libpysal.weights.W.rst.txt
new file mode 100644
index 000000000..bafed153c
--- /dev/null
+++ b/_sources/generated/libpysal.weights.W.rst.txt
@@ -0,0 +1,75 @@
+libpysal.weights.W
+==================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: W
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~W.__init__
+      ~W.asymmetry
+      ~W.from_WSP
+      ~W.from_adjlist
+      ~W.from_file
+      ~W.from_networkx
+      ~W.from_shapefile
+      ~W.from_sparse
+      ~W.full
+      ~W.get_transform
+      ~W.plot
+      ~W.remap_ids
+      ~W.set_shapefile
+      ~W.set_transform
+      ~W.symmetrize
+      ~W.to_WSP
+      ~W.to_adjlist
+      ~W.to_file
+      ~W.to_networkx
+      ~W.to_sparse
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~W.asymmetries
+      ~W.cardinalities
+      ~W.component_labels
+      ~W.diagW2
+      ~W.diagWtW
+      ~W.diagWtW_WW
+      ~W.histogram
+      ~W.id2i
+      ~W.id_order
+      ~W.id_order_set
+      ~W.islands
+      ~W.max_neighbors
+      ~W.mean_neighbors
+      ~W.min_neighbors
+      ~W.n
+      ~W.n_components
+      ~W.neighbor_offsets
+      ~W.nonzero
+      ~W.pct_nonzero
+      ~W.s0
+      ~W.s1
+      ~W.s2
+      ~W.s2array
+      ~W.sd
+      ~W.sparse
+      ~W.transform
+      ~W.trcW2
+      ~W.trcWtW
+      ~W.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.WSP.rst.txt b/_sources/generated/libpysal.weights.WSP.rst.txt
new file mode 100644
index 000000000..779f192f9
--- /dev/null
+++ b/_sources/generated/libpysal.weights.WSP.rst.txt
@@ -0,0 +1,33 @@
+libpysal.weights.WSP
+====================
+
+.. currentmodule:: libpysal.weights
+
+.. autoclass:: WSP
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~WSP.__init__
+      ~WSP.from_W
+      ~WSP.to_W
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~WSP.diagWtW_WW
+      ~WSP.id_order
+      ~WSP.s0
+      ~WSP.trcWtW_WW
+   
+   
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.WSP2W.rst.txt b/_sources/generated/libpysal.weights.WSP2W.rst.txt
new file mode 100644
index 000000000..bd3885ebc
--- /dev/null
+++ b/_sources/generated/libpysal.weights.WSP2W.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.WSP2W
+======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: WSP2W
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.attach_islands.rst.txt b/_sources/generated/libpysal.weights.attach_islands.rst.txt
new file mode 100644
index 000000000..a3d74f0f5
--- /dev/null
+++ b/_sources/generated/libpysal.weights.attach_islands.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.attach\_islands
+================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: attach_islands
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.block_weights.rst.txt b/_sources/generated/libpysal.weights.block_weights.rst.txt
new file mode 100644
index 000000000..0a410bf56
--- /dev/null
+++ b/_sources/generated/libpysal.weights.block_weights.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.block\_weights
+===============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: block_weights
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt b/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt
new file mode 100644
index 000000000..dc94f0a2b
--- /dev/null
+++ b/_sources/generated/libpysal.weights.build_lattice_shapefile.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.build\_lattice\_shapefile
+==========================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: build_lattice_shapefile
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.comb.rst.txt b/_sources/generated/libpysal.weights.comb.rst.txt
new file mode 100644
index 000000000..eb683b615
--- /dev/null
+++ b/_sources/generated/libpysal.weights.comb.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.comb
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: comb
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.da2W.rst.txt b/_sources/generated/libpysal.weights.da2W.rst.txt
new file mode 100644
index 000000000..c62aeb317
--- /dev/null
+++ b/_sources/generated/libpysal.weights.da2W.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.da2W
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: da2W
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.da2WSP.rst.txt b/_sources/generated/libpysal.weights.da2WSP.rst.txt
new file mode 100644
index 000000000..ae826288f
--- /dev/null
+++ b/_sources/generated/libpysal.weights.da2WSP.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.da2WSP
+=======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: da2WSP
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.fill_diagonal.rst.txt b/_sources/generated/libpysal.weights.fill_diagonal.rst.txt
new file mode 100644
index 000000000..c5a6c908e
--- /dev/null
+++ b/_sources/generated/libpysal.weights.fill_diagonal.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.fill\_diagonal
+===============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: fill_diagonal
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.full.rst.txt b/_sources/generated/libpysal.weights.full.rst.txt
new file mode 100644
index 000000000..01b88dfb1
--- /dev/null
+++ b/_sources/generated/libpysal.weights.full.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.full
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: full
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.full2W.rst.txt b/_sources/generated/libpysal.weights.full2W.rst.txt
new file mode 100644
index 000000000..ccb16b700
--- /dev/null
+++ b/_sources/generated/libpysal.weights.full2W.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.full2W
+=======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: full2W
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt b/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt
new file mode 100644
index 000000000..afb0e2089
--- /dev/null
+++ b/_sources/generated/libpysal.weights.fuzzy_contiguity.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.fuzzy\_contiguity
+==================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: fuzzy_contiguity
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.get_ids.rst.txt b/_sources/generated/libpysal.weights.get_ids.rst.txt
new file mode 100644
index 000000000..9d7bfcca5
--- /dev/null
+++ b/_sources/generated/libpysal.weights.get_ids.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.get\_ids
+=========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: get_ids
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt b/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt
new file mode 100644
index 000000000..bb9c1cf83
--- /dev/null
+++ b/_sources/generated/libpysal.weights.get_points_array_from_shapefile.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.get\_points\_array\_from\_shapefile
+====================================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: get_points_array_from_shapefile
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.hexLat2W.rst.txt b/_sources/generated/libpysal.weights.hexLat2W.rst.txt
new file mode 100644
index 000000000..1db489b3d
--- /dev/null
+++ b/_sources/generated/libpysal.weights.hexLat2W.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.hexLat2W
+=========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: hexLat2W
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.higher_order.rst.txt b/_sources/generated/libpysal.weights.higher_order.rst.txt
new file mode 100644
index 000000000..f389456a1
--- /dev/null
+++ b/_sources/generated/libpysal.weights.higher_order.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.higher\_order
+==============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: higher_order
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.higher_order_sp.rst.txt b/_sources/generated/libpysal.weights.higher_order_sp.rst.txt
new file mode 100644
index 000000000..f20cbeaf4
--- /dev/null
+++ b/_sources/generated/libpysal.weights.higher_order_sp.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.higher\_order\_sp
+==================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: higher_order_sp
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.lag_categorical.rst.txt b/_sources/generated/libpysal.weights.lag_categorical.rst.txt
new file mode 100644
index 000000000..0878afbd8
--- /dev/null
+++ b/_sources/generated/libpysal.weights.lag_categorical.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.lag\_categorical
+=================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: lag_categorical
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.lag_spatial.rst.txt b/_sources/generated/libpysal.weights.lag_spatial.rst.txt
new file mode 100644
index 000000000..ec8169071
--- /dev/null
+++ b/_sources/generated/libpysal.weights.lag_spatial.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.lag\_spatial
+=============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: lag_spatial
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.lat2SW.rst.txt b/_sources/generated/libpysal.weights.lat2SW.rst.txt
new file mode 100644
index 000000000..037437703
--- /dev/null
+++ b/_sources/generated/libpysal.weights.lat2SW.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.lat2SW
+=======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: lat2SW
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.lat2W.rst.txt b/_sources/generated/libpysal.weights.lat2W.rst.txt
new file mode 100644
index 000000000..a01a0d8de
--- /dev/null
+++ b/_sources/generated/libpysal.weights.lat2W.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.lat2W
+======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: lat2W
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.mat2L.rst.txt b/_sources/generated/libpysal.weights.mat2L.rst.txt
new file mode 100644
index 000000000..b2b90516a
--- /dev/null
+++ b/_sources/generated/libpysal.weights.mat2L.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.mat2L
+======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: mat2L
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt b/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt
new file mode 100644
index 000000000..2a66c6f9c
--- /dev/null
+++ b/_sources/generated/libpysal.weights.min_threshold_dist_from_shapefile.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.min\_threshold\_dist\_from\_shapefile
+======================================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: min_threshold_dist_from_shapefile
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt b/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt
new file mode 100644
index 000000000..e50a50764
--- /dev/null
+++ b/_sources/generated/libpysal.weights.min_threshold_distance.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.min\_threshold\_distance
+=========================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: min_threshold_distance
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.neighbor_equality.rst.txt b/_sources/generated/libpysal.weights.neighbor_equality.rst.txt
new file mode 100644
index 000000000..7c9c4d948
--- /dev/null
+++ b/_sources/generated/libpysal.weights.neighbor_equality.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.neighbor\_equality
+===================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: neighbor_equality
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.netW.rst.txt b/_sources/generated/libpysal.weights.netW.rst.txt
new file mode 100644
index 000000000..79d5731ab
--- /dev/null
+++ b/_sources/generated/libpysal.weights.netW.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.netW
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: netW
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt b/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt
new file mode 100644
index 000000000..5482c075d
--- /dev/null
+++ b/_sources/generated/libpysal.weights.nonplanar_neighbors.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.nonplanar\_neighbors
+=====================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: nonplanar_neighbors
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.order.rst.txt b/_sources/generated/libpysal.weights.order.rst.txt
new file mode 100644
index 000000000..bec83f111
--- /dev/null
+++ b/_sources/generated/libpysal.weights.order.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.order
+======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: order
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.remap_ids.rst.txt b/_sources/generated/libpysal.weights.remap_ids.rst.txt
new file mode 100644
index 000000000..514267494
--- /dev/null
+++ b/_sources/generated/libpysal.weights.remap_ids.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.remap\_ids
+===========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: remap_ids
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.shimbel.rst.txt b/_sources/generated/libpysal.weights.shimbel.rst.txt
new file mode 100644
index 000000000..6e63754f0
--- /dev/null
+++ b/_sources/generated/libpysal.weights.shimbel.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.shimbel
+========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: shimbel
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.spw_from_gal.rst.txt b/_sources/generated/libpysal.weights.spw_from_gal.rst.txt
new file mode 100644
index 000000000..61d7c72d0
--- /dev/null
+++ b/_sources/generated/libpysal.weights.spw_from_gal.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.spw\_from\_gal
+===============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: spw_from_gal
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.testDataArray.rst.txt b/_sources/generated/libpysal.weights.testDataArray.rst.txt
new file mode 100644
index 000000000..42cc4ca1d
--- /dev/null
+++ b/_sources/generated/libpysal.weights.testDataArray.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.testDataArray
+==============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: testDataArray
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.vecW.rst.txt b/_sources/generated/libpysal.weights.vecW.rst.txt
new file mode 100644
index 000000000..92c05a895
--- /dev/null
+++ b/_sources/generated/libpysal.weights.vecW.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.vecW
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: vecW
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w2da.rst.txt b/_sources/generated/libpysal.weights.w2da.rst.txt
new file mode 100644
index 000000000..eae3cb5f7
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w2da.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w2da
+=====================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w2da
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_clip.rst.txt b/_sources/generated/libpysal.weights.w_clip.rst.txt
new file mode 100644
index 000000000..f66ced35c
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_clip.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_clip
+========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_clip
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_difference.rst.txt b/_sources/generated/libpysal.weights.w_difference.rst.txt
new file mode 100644
index 000000000..761d84534
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_difference.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_difference
+==============================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_difference
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_intersection.rst.txt b/_sources/generated/libpysal.weights.w_intersection.rst.txt
new file mode 100644
index 000000000..03b296e12
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_intersection.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_intersection
+================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_intersection
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_local_cluster.rst.txt b/_sources/generated/libpysal.weights.w_local_cluster.rst.txt
new file mode 100644
index 000000000..3ebf14465
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_local_cluster.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_local\_cluster
+==================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_local_cluster
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_subset.rst.txt b/_sources/generated/libpysal.weights.w_subset.rst.txt
new file mode 100644
index 000000000..493b8ea3c
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_subset.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_subset
+==========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_subset
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt b/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt
new file mode 100644
index 000000000..dcee1a0dc
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_symmetric_difference.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_symmetric\_difference
+=========================================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_symmetric_difference
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.w_union.rst.txt b/_sources/generated/libpysal.weights.w_union.rst.txt
new file mode 100644
index 000000000..603e14116
--- /dev/null
+++ b/_sources/generated/libpysal.weights.w_union.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.w\_union
+=========================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: w_union
\ No newline at end of file
diff --git a/_sources/generated/libpysal.weights.wsp2da.rst.txt b/_sources/generated/libpysal.weights.wsp2da.rst.txt
new file mode 100644
index 000000000..477a9f5f7
--- /dev/null
+++ b/_sources/generated/libpysal.weights.wsp2da.rst.txt
@@ -0,0 +1,6 @@
+libpysal.weights.wsp2da
+=======================
+
+.. currentmodule:: libpysal.weights
+
+.. autofunction:: wsp2da
\ No newline at end of file
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
index 68ae86737..bf71e97fb 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -1,20 +1,133 @@
-.. No Errors Test Project documentation master file, created by
-   sphinx-quickstart on Fri Aug 30 17:07:56 2019.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
+.. libpysal documentation master file
 
-Welcome to No Errors Test Project's documentation!
-==================================================
+libpysal: Python Spatial Analysis Library Core
+==============================================
 
-.. toctree::
-   :maxdepth: 2
-   :caption: Hello World!
+.. image:: https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg
+   :target: https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml
+
+.. image:: https://badges.gitter.im/pysal/pysal.svg
+   :target: https://gitter.im/pysal/pysal
+
+.. image:: https://badge.fury.io/py/libpysal.svg
+    :target: https://badge.fury.io/py/libpysal
+
+.. raw:: html
+
+    <div class="container-fluid">
+      <div class="row equal-height">
+        <div class="col-sm-1 col-xs-hidden">
+        </div>
+        <div class="col-sm-10 col-xs-12">
+            <a href="http://nbviewer.jupyter.org/github/pysal/libpysal/blob/main/notebooks/weights.ipynb" class="thumbnail">
+                <img src="_static/images/npweights.png" class="img-responsive center-block">
+                <div class="caption text-center">
+                <h6>Weights for nonplanar enforced geometries</h6>
+                </div>
+            </a>
+        </div>
+        <div class="col-sm-1 col-xs-hidden">
+        </div>
+      </div>
+    </div>
+
+
+************
+Introduction
+************
+
+**libpysal** offers four modules that form the building blocks in many upstream packages in the `PySAL family <https://pysal.org>`_:
+
+- Spatial Weights: libpysal.weights
+- Input-and output: libpysal.io
+- Computational geometry: libpysal.cg
+- Built-in example datasets libpysal.examples
+
+
+Examples demonstrating some of **libpysal** functionality are available in the `tutorial <tutorial.html>`_.
+
+Details are available in the `libpysal api <api.html>`_.
+
+For background information see :cite:`pysal2007`.
+
+***********
+Development
+***********
+
+libpysal development is hosted on github_.
+
+.. _github : https://github.com/pysal/libpysal
+
+Discussions of development occurs on the
+`developer list <http://groups.google.com/group/pysal-dev>`_
+as well as gitter_.
+
+.. _gitter : https://gitter.im/pysal/pysal?
+
+****************
+Getting Involved
+****************
 
+If you are interested in contributing to PySAL please see our
+`development guidelines  <https://github.com/pysal/pysal/wiki>`_.
 
 
-Indices and tables
-==================
+***********
+Bug reports
+***********
+
+To search for or report bugs, please see libpysal's issues_.
+
+.. _issues :  http://github.com/pysal/libpysal/issues
+
+
+***************
+Citing libpysal
+***************
+
+If you use PySAL in a scientific publication, we would appreciate citations to the following paper:
+
+  `PySAL: A Python Library of Spatial Analytical Methods <http://journal.srsa.org/ojs/index.php/RRS/article/view/134/85>`_, *Rey, S.J. and L. Anselin*, Review of Regional Studies 37, 5-27 2007.
+
+  Bibtex entry::
+
+      @Article{pysal2007,
+        author={Rey, Sergio J. and Anselin, Luc},
+        title={{PySAL: A Python Library of Spatial Analytical Methods}},
+        journal={The Review of Regional Studies},
+        year=2007,
+        volume={37},
+        number={1},
+        pages={5-27},
+        keywords={Open Source; Software; Spatial}
+      }
+
+
+
+*******************
+License information
+*******************
+
+See the file "LICENSE.txt" for information on the history of this
+software, terms & conditions for usage, and a DISCLAIMER OF ALL
+WARRANTIES.
+
+
+libpysal
+========
+
+Core components of the Python Spatial Analysis Library (`PySAL`_)
+
+
+
+.. toctree::
+   :hidden:
+   :maxdepth: 4
+   :caption: Contents:
 
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
+   Installation <installation>
+   Tutorial <tutorial>
+   API <api>
+   References <references>
+   
+.. _PySAL: https://github.com/pysal/pysal
diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt
new file mode 100644
index 000000000..cfa3f4fb0
--- /dev/null
+++ b/_sources/installation.rst.txt
@@ -0,0 +1,56 @@
+.. Installation
+
+Installation
+============
+
+libpysal supports python >= `3.8`_ only. Please make sure that you are
+operating in a python 3 environment.
+
+Installing released version
+---------------------------
+
+conda
++++++
+
+libpysal is available through conda::
+
+
+  conda install -c conda-forge libpysal
+
+
+pypi
+++++
+
+
+libpysal is available on the `Python Package Index`_. Therefore, you can either
+install directly with `pip` from the command line::
+
+  pip install -U libpysal
+
+
+or download the source distribution (.tar.gz) and decompress it to your selected
+destination. Open a command shell and navigate to the decompressed folder.
+Type::
+
+  pip install .
+
+Installing development version
+------------------------------
+
+Potentially, you might want to use the newest features in the development
+version of libpysal on github - `pysal/libpysal`_ while have not been incorporated
+in the Pypi released version. You can achieve that by installing `pysal/libpysal`_
+by running the following from a command shell::
+
+  pip install git+https://github.com/pysal/libpysal.git
+
+You can  also `fork`_ the `pysal/libpysal`_ repo and create a local clone of
+your fork. By making changes
+to your local clone and submitting a pull request to `pysal/libpysal`_, you can
+contribute to libpysal development.
+
+
+.. _3.8: https://docs.python.org/3.8/
+.. _Python Package Index: https://pypi.org/project/libpysal/
+.. _pysal/libpysal: https://github.com/pysal/libpysal
+.. _fork: https://help.github.com/articles/fork-a-repo/
diff --git a/_sources/notebooks/Raster_awareness_API.ipynb.txt b/_sources/notebooks/Raster_awareness_API.ipynb.txt
new file mode 100644
index 000000000..7a3814bdd
--- /dev/null
+++ b/_sources/notebooks/Raster_awareness_API.ipynb.txt
@@ -0,0 +1,773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Raster awareness API\n",
+    "\n",
+    "This notebook will give an overview of newly developed raster interface. We'll cover \n",
+    "basic usage of the functionality offered by the interface which mainly involves:\n",
+    "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n",
+    "2. going back to the `xarray.DataArray` from weights object.\n",
+    "\n",
+    "using different datasets:\n",
+    "- with missing values.\n",
+    "- with multiple layers.\n",
+    "- with non conventional dimension names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from libpysal.weights import Rook, Queen, raster\n",
+    "import matplotlib.pyplot as plt\n",
+    "from splot import libpysal as splot\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "from esda import Moran_Local"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data\n",
+    "\n",
+    "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n",
+    "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n",
+    "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n",
+    "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n",
+    "\n",
+    "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n",
+    "\n",
+    "### Using xarray example dataset\n",
+    "First lets load up a `netCDF` dataset offered by xarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+      "[3869000 values with dtype=float32]\n",
+      "Coordinates:\n",
+      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+      "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+      "Attributes:\n",
+      "    long_name:     4xDaily Air temperature at sigma level 995\n",
+      "    units:         degK\n",
+      "    precision:     2\n",
+      "    GRIB_id:       11\n",
+      "    GRIB_name:     TMP\n",
+      "    var_desc:      Air temperature\n",
+      "    dataset:       NMC Reanalysis\n",
+      "    level_desc:    Surface\n",
+      "    statistic:     Individual Obs\n",
+      "    parent_stat:   Other\n",
+      "    actual_range:  [185.16 322.1 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = xr.tutorial.open_dataset(\"air_temperature.nc\")  # -> returns a xarray.Dataset object\n",
+    "da = ds[\"air\"]  # we'll use the \"air\" data variable for further analysis\n",
+    "print(da)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n",
+    "\n",
+    "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n",
+    "\n",
+    "We'll now group `da` by month and take average over the `time` dimension\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates:\n",
+      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+      "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "da = da.groupby('time.month').mean()\n",
+    "print(da.coords)  # as a result time dim is replaced by month "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x7f68f71b8100>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x648 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's plot over month, each facet will represent the mean air temperature in a given month.\n",
+    "da.plot(col=\"month\", col_wrap=4,) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n",
+    "\n",
+    "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n",
+    "\n",
+    "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n",
+    "\n",
+    "e.g. `dims` dictionary:\n",
+    "```python\n",
+    ">>> da.dims                  # none of the dimension belong to the default dimension list\n",
+    "('year', 'height', 'width')\n",
+    ">>> coords_labels = {                 # dimension values should be properly aligned with the following keys\n",
+    "        \"z_label\": \"year\",\n",
+    "        \"y_label\": \"height\",\n",
+    "        \"x_label\": \"width\"\n",
+    "    }\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/GSoC/libpysal/libpysal/weights/raster.py:119: UserWarning: You are trying to build a full W object from xarray.DataArray (raster) object. This computation can be very slow and not scale well. It is recommended, if possible, to instead build WSP object, which is more efficient and faster. You can do this by using da2WSP method.\n",
+      "  warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "coords_labels = {}\n",
+    "coords_labels[\"z_label\"] = \"month\"  # since month does not belong to the default list we need to pass it using a dictionary\n",
+    "w_queen = Queen.from_xarray(\n",
+    "    da, z_value=12, coords_labels=coords_labels, sparse=False)  # We'll use data from 12th layer (in our case layer=month)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex([(12, 75.0, 200.0),\n",
+       "            (12, 75.0, 202.5),\n",
+       "            (12, 75.0, 205.0),\n",
+       "            (12, 75.0, 207.5),\n",
+       "            (12, 75.0, 210.0)],\n",
+       "           names=['month', 'lat', 'lon'])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.index[:5]  # indices are aligned to the ids of the weight object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = da.to_series()[w_queen.index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quickly computing and loading a LISA\n",
+    "np.random.seed(12345)\n",
+    "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n",
+    "\n",
+    "*Your use case might differ but the steps for using the interface will be similar to this example.* "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (month: 1, lat: 25, lon: 53)>\n",
+      "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
+      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
+      "        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
+      "        ...,\n",
+      "        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
+      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
+      "        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n",
+      "Coordinates:\n",
+      "  * month    (month) int64 12\n",
+      "  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+      "  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Converting obtained data back to DataArray\n",
+    "moran_da = raster.w2da(lisa.p_sim, w_queen)  # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n",
+    "print(moran_da)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f68f4044940>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "moran_da.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using local `NetCDF` dataset\n",
+    "\n",
+    "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n",
+    "\n",
+    "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n",
+    "\n",
+    "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:    (latitude: 73, longitude: 144, time: 62)\n",
+      "Coordinates:\n",
+      "  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n",
+      "  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
+      "  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n",
+      "Data variables:\n",
+      "    tcw        (time, latitude, longitude) float32 ...\n",
+      "    tcwv       (time, latitude, longitude) float32 ...\n",
+      "    lsp        (time, latitude, longitude) float32 ...\n",
+      "    cp         (time, latitude, longitude) float32 ...\n",
+      "    msl        (time, latitude, longitude) float32 ...\n",
+      "    blh        (time, latitude, longitude) float32 ...\n",
+      "    tcc        (time, latitude, longitude) float32 ...\n",
+      "    p10u       (time, latitude, longitude) float32 ...\n",
+      "    p10v       (time, latitude, longitude) float32 ...\n",
+      "    p2t        (time, latitude, longitude) float32 ...\n",
+      "    p2d        (time, latitude, longitude) float32 ...\n",
+      "    e          (time, latitude, longitude) float32 ...\n",
+      "    lcc        (time, latitude, longitude) float32 ...\n",
+      "    mcc        (time, latitude, longitude) float32 ...\n",
+      "    hcc        (time, latitude, longitude) float32 ...\n",
+      "    tco3       (time, latitude, longitude) float32 ...\n",
+      "    tp         (time, latitude, longitude) float32 ...\n",
+      "Attributes:\n",
+      "    Conventions:  CF-1.0\n",
+      "    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Lets load a netCDF Surface dataset\n",
+    "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc')    # After loading netCDF dataset we obtained a xarray.Dataset object\n",
+    "print(ds)                                         # This Dataset object containes several data variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('day', 'latitude', 'longitude')\n"
+     ]
+    }
+   ],
+   "source": [
+    "da = ds[\"p2t\"]  # this will give us the required DataArray with p2t (2 metre temperature) data variable\n",
+    "da = da.groupby('time.day').mean()\n",
+    "print(da.dims)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n",
+    "\n",
+    "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords_labels = {}\n",
+    "coords_labels[\"y_label\"] = \"latitude\"\n",
+    "coords_labels[\"x_label\"] = \"longitude\"\n",
+    "coords_labels[\"z_label\"] = \"day\"\n",
+    "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = da.to_series()[w_rook.index]  # we derived the data from DataArray similar to our last example "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n",
+    "\n",
+    "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n",
+    "\n",
+    "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n",
+    "xr.DataArray.equals(da[12:13], da1)  # method to compare 2 DataArray, if true then w2da was successfull"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using local `GeoTIFF` dataset\n",
+    "\n",
+    "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n",
+    "\n",
+    "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n",
+    "\n",
+    "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (band: 1, y: 880, x: 940)>\n",
+      "[827200 values with dtype=float32]\n",
+      "Coordinates:\n",
+      "  * band     (band) int64 1\n",
+      "  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n",
+      "  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n",
+      "Attributes:\n",
+      "    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n",
+      "    crs:            +init=epsg:4326\n",
+      "    res:            (0.0008333333297872345, 0.0008333333295454553)\n",
+      "    is_tiled:       0\n",
+      "    nodatavals:     (-99999.0,)\n",
+      "    scales:         (1.0,)\n",
+      "    offsets:        (0.0,)\n",
+      "    AREA_OR_POINT:  Area\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading raster data with missing values\n",
+    "da = xr.open_rasterio('/data/Downloads/lux_ppp_2019.tif')\n",
+    "print(da)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f68ef4c5ca0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n",
+    "\n",
+    "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Slicing the dataarray\n",
+    "da_s = da[:, 330:340, 129:139]\n",
+    "w_queen = raster.da2WSP(da_s)  # default contiguity is queen\n",
+    "w_rook = raster.da2WSP(da_s, \"rook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n",
+    "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n",
+    "ax[0].set_title(\"Sliced raster\")\n",
+    "splot.plot_spatial_weights(w_rook, data=da_s, ax=ax[1])\n",
+    "ax[1].set_title(\"Rook contiguity\")\n",
+    "splot.plot_spatial_weights(w_queen, data=da_s, ax=ax[2])\n",
+    "ax[2].set_title(\"Queen contiguity\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `higher_order` neighbors\n",
+    "\n",
+    "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n",
+    "\n",
+    "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Building a test DataArray \n",
+    "da_s = raster.testDataArray((1,5,10), rand=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
+      "  self._set_arrayXarray(i, j, x)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n",
+    "splot.plot_spatial_weights(w_rook2, data=da_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n",
+    "\n",
+    "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nodata=True)\n",
+    "splot.plot_spatial_weights(w_rook2, data=da_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional resources\n",
+    "\n",
+    "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n",
+    "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)\n",
+    "3. Dataset links:\n",
+    "    - [ECMWF_ERA-40_subset.nc](https://www.unidata.ucar.edu/software/netcdf/examples/files.html)\n",
+    "    - [lux_ppp_2019.tif](https://data.humdata.org/dataset/worldpop-population-counts-for-luxembourg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/notebooks/examples.ipynb.txt b/_sources/notebooks/examples.ipynb.txt
new file mode 100644
index 000000000..5905f568d
--- /dev/null
+++ b/_sources/notebooks/examples.ipynb.txt
@@ -0,0 +1,1093 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Datasets for use with libpysal\n",
+    "As of version 4.2, libpysal has refactored the `examples` package to:\n",
+    "\n",
+    "- reduce the size of the source installation\n",
+    "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n",
+    "\n",
+    "This notebook highlights the new functionality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Backwards compatibility is maintained"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import get_path \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/serge/Documents/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_path(\"mexicojoin.dbf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import libpysal\n",
+    "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['POLY_ID',\n",
+       " 'AREA',\n",
+       " 'CODE',\n",
+       " 'NAME',\n",
+       " 'PERIMETER',\n",
+       " 'ACRES',\n",
+       " 'HECTARES',\n",
+       " 'PCGDP1940',\n",
+       " 'PCGDP1950',\n",
+       " 'PCGDP1960',\n",
+       " 'PCGDP1970',\n",
+       " 'PCGDP1980',\n",
+       " 'PCGDP1990',\n",
+       " 'PCGDP2000',\n",
+       " 'HANSON03',\n",
+       " 'HANSON98',\n",
+       " 'ESQUIVEL99',\n",
+       " 'INEGI',\n",
+       " 'INEGI2',\n",
+       " 'MAXP',\n",
+       " 'GR4000',\n",
+       " 'GR5000',\n",
+       " 'GR6000',\n",
+       " 'GR7000',\n",
+       " 'GR8000',\n",
+       " 'GR9000',\n",
+       " 'LPCGDP40',\n",
+       " 'LPCGDP50',\n",
+       " 'LPCGDP60',\n",
+       " 'LPCGDP70',\n",
+       " 'LPCGDP80',\n",
+       " 'LPCGDP90',\n",
+       " 'LPCGDP00',\n",
+       " 'TEST']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dbf.header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will report on what datasets are available, either as builtin or remotes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98, 3)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "98 datasets available, 27 installed, 71 remote.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., `examples` has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downloading Remote Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Installed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10740</td>\n",
+       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirBnB</td>\n",
+       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>Baltimore house sales prices and hedonics</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bostonhsg</td>\n",
+       "      <td>Boston housing and neighborhood data</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Name                                        Description  Installed\n",
+       "0      10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1     AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
+       "2    Atlanta       Atlanta, GA region homicide counts and rates      False\n",
+       "3  Baltimore          Baltimore house sales prices and hedonics      False\n",
+       "4  Bostonhsg               Boston housing and neighborhood data      False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The remote `AirBnB` can be installed by calling `load_example`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading AirBnB to /home/serge/.local/share/pysal/AirBnB\n"
+     ]
+    }
+   ],
+   "source": [
+    "airbnb = libpysal.examples.load_example(\"AirBnB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "98 datasets available, 28 installed, 70 remote.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we see that the number of remotes as declined by one and the number of installed has increased by 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trying to load an example that doesn't exist will return None and alert the user:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Example not available: dataset42\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.load_example('dataset42')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting remote urls\n",
+    "\n",
+    "If the url, rather than the dataset, is needed this can be obtained on a remote with `get_url`. \n",
+    "As the `Baltimore` dataset has not yet been downloaded in this example, we can grab it's url:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "balt_url = libpysal.examples.get_url('Baltimore')\n",
+    "balt_url"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explaining a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "taz\n",
+      "===\n",
+      "\n",
+      "Dataset used for regionalization\n",
+      "--------------------------------\n",
+      "\n",
+      "* taz.dbf: attribute data. (k=14)\n",
+      "* taz.shp: Polygon shapefile. (n=4109)\n",
+      "* taz.shx: spatial index.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading taz to /home/serge/.local/share/pysal/taz\n"
+     ]
+    }
+   ],
+   "source": [
+    "taz = libpysal.examples.load_example('taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/home/serge/.local/share/pysal/taz/taz-master/taz.dbf',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shp',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/README.md',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shx']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "taz.get_file_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"700\"\n",
+       "            height=\"350\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//baltim/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7febc3b56c50>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('Baltimore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Baltimore to /home/serge/.local/share/pysal/Baltimore\n"
+     ]
+    }
+   ],
+   "source": [
+    "balt = libpysal.examples.load_example('Baltimore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Installed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10740</td>\n",
+       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirBnB</td>\n",
+       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>Baltimore house sales prices and hedonics</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bostonhsg</td>\n",
+       "      <td>Boston housing and neighborhood data</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>taz</td>\n",
+       "      <td>Traffic Analysis Zones in So. California</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>tokyo</td>\n",
+       "      <td>Tokyo Mortality data</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>us_income</td>\n",
+       "      <td>Per-capita income for the lower 48 US states 1...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>virginia</td>\n",
+       "      <td>Virginia counties shapefile</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>wmat</td>\n",
+       "      <td>Datasets used for spatial weights testing</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Name                                        Description  Installed\n",
+       "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
+       "2     Atlanta       Atlanta, GA region homicide counts and rates      False\n",
+       "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
+       "4   Bostonhsg               Boston housing and neighborhood data      False\n",
+       "..        ...                                                ...        ...\n",
+       "93        taz           Traffic Analysis Zones in So. California       True\n",
+       "94      tokyo                               Tokyo Mortality data       True\n",
+       "95  us_income  Per-capita income for the lower 48 US states 1...       True\n",
+       "96   virginia                        Virginia counties shapefile       True\n",
+       "97       wmat          Datasets used for spatial weights testing       True\n",
+       "\n",
+       "[98 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with an example dataset\n",
+    "\n",
+    "`explain` will render maps for an example if available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"700\"\n",
+       "            height=\"350\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//tampa1/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7febc3b56890>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from libpysal.examples import explain\n",
+    "explain('Tampa1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Tampa1 to /home/serge/.local/share/pysal/Tampa1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from libpysal.examples import load_example\n",
+    "tampa1 = load_example('Tampa1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tampa1.installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tampa1.get_file_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tampa_counties_shp = tampa1.load('tampa_counties.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7febc3b1bd00>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tampa_counties_shp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "tampa_df.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other Remotes\n",
+    "\n",
+    "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rio_Grande_do_Sul\n",
+      "======================\n",
+      "\n",
+      "Cities of the Brazilian State of Rio Grande do Sul\n",
+      "-------------------------------------------------------\n",
+      "\n",
+      "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n",
+      "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n",
+      "* 43MUE250GC_SIR.shx: spatial index\n",
+      "* 43MUE250GC_SIR.cpg: encoding file \n",
+      "* 43MUE250GC_SIR.prj: projection information \n",
+      "* map_RS_BR.dbf: attribute data (k=3)\n",
+      "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n",
+      "* map_RS_BR.prj: projection information\n",
+      "* map_RS_BR.shx: spatial index\n",
+      "\n",
+      "\n",
+      "\n",
+      "Source: Renan Xavier Cortes \n",
+      "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('Rio Grande do Sul')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Rio Grande do Sul to /home/serge/.local/share/pysal/Rio_Grande_do_Sul\n"
+     ]
+    }
+   ],
+   "source": [
+    "rio = libpysal.examples.load_example('Rio Grande do Sul')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AirBnB': <libpysal.examples.base.Example at 0x7febc78c00d0>,\n",
+       " 'Atlanta': <libpysal.examples.base.Example at 0x7febc4a5efb0>,\n",
+       " 'Baltimore': <libpysal.examples.base.Example at 0x7febc4a5ed40>,\n",
+       " 'Bostonhsg': <libpysal.examples.base.Example at 0x7febc4a5ef50>,\n",
+       " 'Buenosaires': <libpysal.examples.base.Example at 0x7febc4a5efe0>,\n",
+       " 'Charleston1': <libpysal.examples.base.Example at 0x7febc4a5ef80>,\n",
+       " 'Charleston2': <libpysal.examples.base.Example at 0x7febc4a5ee90>,\n",
+       " 'Chicago Health': <libpysal.examples.base.Example at 0x7febc4a5ef20>,\n",
+       " 'Chicago commpop': <libpysal.examples.base.Example at 0x7febc4a5f070>,\n",
+       " 'Chicago parcels': <libpysal.examples.base.Example at 0x7febc4a5f0a0>,\n",
+       " 'Chile Labor': <libpysal.examples.base.Example at 0x7febc4a5f100>,\n",
+       " 'Chile Migration': <libpysal.examples.base.Example at 0x7febc4a5f130>,\n",
+       " 'Cincinnati': <libpysal.examples.base.Example at 0x7febc4a5f1f0>,\n",
+       " 'Cleveland': <libpysal.examples.base.Example at 0x7febc4a5f190>,\n",
+       " 'Columbus': <libpysal.examples.base.Example at 0x7febc4a5f250>,\n",
+       " 'Elections': <libpysal.examples.base.Example at 0x7febc4a5f0d0>,\n",
+       " 'Grid100': <libpysal.examples.base.Example at 0x7febc4a5f220>,\n",
+       " 'Groceries': <libpysal.examples.base.Example at 0x7febc4a5f160>,\n",
+       " 'Guerry': <libpysal.examples.base.Example at 0x7febc4a5f280>,\n",
+       " 'Health+': <libpysal.examples.base.Example at 0x7febc4a5f040>,\n",
+       " 'Health Indicators': <libpysal.examples.base.Example at 0x7febc4a5f2b0>,\n",
+       " 'Hickory1': <libpysal.examples.base.Example at 0x7febc4a5f2e0>,\n",
+       " 'Hickory2': <libpysal.examples.base.Example at 0x7febc4a5f310>,\n",
+       " 'Home Sales': <libpysal.examples.base.Example at 0x7febc4a5f340>,\n",
+       " 'Houston': <libpysal.examples.base.Example at 0x7febc4a5f370>,\n",
+       " 'Juvenile': <libpysal.examples.base.Example at 0x7febc4a5f3a0>,\n",
+       " 'Lansing1': <libpysal.examples.base.Example at 0x7febc4a5f3d0>,\n",
+       " 'Lansing2': <libpysal.examples.base.Example at 0x7febc4a5f400>,\n",
+       " 'Laozone': <libpysal.examples.base.Example at 0x7febc4a5f430>,\n",
+       " 'LasRosas': <libpysal.examples.base.Example at 0x7febc4a5f460>,\n",
+       " 'Liquor Stores': <libpysal.examples.base.Example at 0x7febc4a5f490>,\n",
+       " 'Malaria': <libpysal.examples.base.Example at 0x7febc4a5f4c0>,\n",
+       " 'Milwaukee1': <libpysal.examples.base.Example at 0x7febc4a5f4f0>,\n",
+       " 'Milwaukee2': <libpysal.examples.base.Example at 0x7febc4a5f520>,\n",
+       " 'NCOVR': <libpysal.examples.base.Example at 0x7febc4a5f550>,\n",
+       " 'Natregimes': <libpysal.examples.base.Example at 0x7febc4a5f580>,\n",
+       " 'NDVI': <libpysal.examples.base.Example at 0x7febc4a5f5b0>,\n",
+       " 'Nepal': <libpysal.examples.base.Example at 0x7febc4a5f5e0>,\n",
+       " 'NYC': <libpysal.examples.base.Example at 0x7febc4a5f610>,\n",
+       " 'NYC Earnings': <libpysal.examples.base.Example at 0x7febc4a5f640>,\n",
+       " 'NYC Education': <libpysal.examples.base.Example at 0x7febc4a5f670>,\n",
+       " 'NYC Neighborhoods': <libpysal.examples.base.Example at 0x7febc4a5f6a0>,\n",
+       " 'NYC Socio-Demographics': <libpysal.examples.base.Example at 0x7febc4a5f6d0>,\n",
+       " 'Ohiolung': <libpysal.examples.base.Example at 0x7febc4a5f700>,\n",
+       " 'Orlando1': <libpysal.examples.base.Example at 0x7febc4a5f730>,\n",
+       " 'Orlando2': <libpysal.examples.base.Example at 0x7febc4a5f760>,\n",
+       " 'Oz9799': <libpysal.examples.base.Example at 0x7febc4a5f790>,\n",
+       " 'Phoenix ACS': <libpysal.examples.base.Example at 0x7febc4a5f7c0>,\n",
+       " 'Pittsburgh': <libpysal.examples.base.Example at 0x7febc4a5f7f0>,\n",
+       " 'Police': <libpysal.examples.base.Example at 0x7febc4a5f820>,\n",
+       " 'Sacramento1': <libpysal.examples.base.Example at 0x7febc4a5f850>,\n",
+       " 'Sacramento2': <libpysal.examples.base.Example at 0x7febc4a5f880>,\n",
+       " 'SanFran Crime': <libpysal.examples.base.Example at 0x7febc4a5f8b0>,\n",
+       " 'Savannah1': <libpysal.examples.base.Example at 0x7febc4a5f8e0>,\n",
+       " 'Savannah2': <libpysal.examples.base.Example at 0x7febc4a5f910>,\n",
+       " 'Scotlip': <libpysal.examples.base.Example at 0x7febc4a5f940>,\n",
+       " 'Seattle1': <libpysal.examples.base.Example at 0x7febc4a5f970>,\n",
+       " 'Seattle2': <libpysal.examples.base.Example at 0x7febc4a5f9a0>,\n",
+       " 'SIDS': <libpysal.examples.base.Example at 0x7febc4a5f9d0>,\n",
+       " 'SIDS2': <libpysal.examples.base.Example at 0x7febc4a5fa00>,\n",
+       " 'Snow': <libpysal.examples.base.Example at 0x7febc4a5fa30>,\n",
+       " 'South': <libpysal.examples.base.Example at 0x7febc4a5fa60>,\n",
+       " 'Spirals': <libpysal.examples.base.Example at 0x7febc4a5fa90>,\n",
+       " 'StLouis': <libpysal.examples.base.Example at 0x7febc4a5fac0>,\n",
+       " 'Tampa1': <libpysal.examples.base.Example at 0x7febc4a5faf0>,\n",
+       " 'US SDOH': <libpysal.examples.base.Example at 0x7febc4a5fb20>,\n",
+       " 'Rio Grande do Sul': <libpysal.examples.base.Example at 0x7febc78c0130>,\n",
+       " 'nyc_bikes': <libpysal.examples.base.Example at 0x7febc4a5fb80>,\n",
+       " 'taz': <libpysal.examples.base.Example at 0x7febc4a5fc70>,\n",
+       " 'clearwater': <libpysal.examples.base.Example at 0x7febc4a5fbe0>,\n",
+       " 'newHaven': <libpysal.examples.base.Example at 0x7febc4a5eec0>}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.remote_datasets.datasets # a listing of all remotes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/notebooks/fetch.ipynb.txt b/_sources/notebooks/fetch.ipynb.txt
new file mode 100644
index 000000000..c8f002b9d
--- /dev/null
+++ b/_sources/notebooks/fetch.ipynb.txt
@@ -0,0 +1,887 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/guerry.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/ncovr.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://github.com/sjsrey/nyc_bikes/archive/master.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/SacramentoMSA2.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/south.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://github.com/sjsrey/taz/archive/master.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.fetch_all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import nat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "nat.fetch_nat()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import environ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "environ.get(\"PYSALDATA\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import south"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "sd = south.fetch_south()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import guerry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "guerry.fetch_guerry()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "guerry\n",
+      "======\n",
+      "\n",
+      "Andre-Michel Guerry data on \"moral statistics\" 1930 crime, suicide, literacy and other “moral statistics” in 1830s France.\n",
+      "\n",
+      "- Observations = 85\n",
+      "- Variables = 23\n",
+      "- Years = 1915-1934\n",
+      "- Support = polygon\n",
+      "\n",
+      "Files\n",
+      "-----\n",
+      "Guerry.dbf  Guerry_documentation.html  Guerry.geojson  Guerry.prj  Guerry.shp  Guerry.shx  README.md\n",
+      "\n",
+      "\n",
+      "Variables\n",
+      "---------\n",
+      "\n",
+      "dept, code_de \tDepartment ID: Standard numbers for the departments \t \n",
+      "region \tRegion of France (‘N’=’North’, ‘S’=’South’, ‘E’=’East’, ‘W’=’West’, ‘C’=’Central’). Corsica is coded as NA. \t \n",
+      "dprtmnt \tDepartment name: Departments are named according to usage in 1830, but without accents. A factor with levels Ain Aisne Allier … Vosges Yonne \t \n",
+      "crm_prs \tPopulation per Crime against persons. \tA2. Compte général, 1825-1830\n",
+      "crm_prp \tPopulation per Crime against property. \tCompte général, 1825-1830\n",
+      "litercy \tPercent of military conscripts who can read and write. \tA2 \n",
+      "donatns \tDonations to the poor. \tA2. Bulletin des lois\n",
+      "infants \tPopulation per illegitimate birth. \tA2. Bureau des Longitudes, 1817-1821\n",
+      "suicids \tPopulation per suicide. \tA2. Compte général, 1827-1830\n",
+      "maincty \tSize of principal city (‘1:Sm’, ‘2:Med’, ‘3:Lg’), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium. \tA1. An ordered factor with levels: 1:Sm < 2:Med < 3:Lg\n",
+      "wealth \tPer capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant. \tA1\n",
+      "commerc \tCommerce and Industry, measured by the rank of the number of patents / population. \tA1\n",
+      "clergy \tDistribution of clergy, measured by the rank of the number of Catholic priests in active service population. \tA1. Almanach officiel du clergy, 1829\n",
+      "crim_prn \tCrimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830. \tA1. Compte général\n",
+      "infntcd \tInfanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830. \tA1. Compte général\n",
+      "dntn_cl \tDonations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824. \tA1. Bull. des lois, ordunn. d’autorisation\n",
+      "lottery \tPer capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826. \tA1. Compte rendu par le ministre des finances\n",
+      "desertn \tMilitary desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827. \tA1. Compte du ministère du guerre, 1829 état V\n",
+      "instrct \tInstruction. Ranks recorded from Guerry’s map of Instruction. Note: this is inversely related to Literacy \t \n",
+      "Prsttts \tNumber of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth \tParent-Duchatelet (1836), De la prostitution en Paris\n",
+      "distanc \tDistance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris) \tCalculated from department centroids\n",
+      "area \tArea (1000 km^2). \tAngeville (1836)\n",
+      "pop1831 \tPopulation in 1831, in 1000s \tTaken from Angeville (1836), Essai sur la Statistique de la Population français\n",
+      "Details\n",
+      "\n",
+      "Note that most of the variables (e.g., Crime_pers) are scaled so that more is “better”. \n",
+      "\n",
+      "Values for the quantitative variables displayed on Guerry’s maps were taken from Table A2 in the English translation of Guerry (1833) by Whitt and Reinking. Values for the ranked variables were taken from Table A1, with some corrections applied. The maximum is indicated by rank 1, and the minimum by rank 86. \n",
+      "Sources\n",
+      "\n",
+      "Angeville, A. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour. \n",
+      "\n",
+      "Guerry, A.-M. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: Hugh P. Whitt and Victor W. Reinking, Lewiston, N.Y. : Edwin Mellen Press, 2002. \n",
+      "\n",
+      "Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36 \n",
+      "References\n",
+      "\n",
+      "Dray, S. and Jombart, T. (2011). A Revisit Of Guerry’s Data: Introducing Spatial Constraints In Multivariate Analysis. The Annals of Applied Statistics, Vol. 5, No. 4, 2278-2299., DOI: 10.1214/10-AOAS356. \n",
+      "\n",
+      "Brunsdon, C. and Dykes, J. (2007). Geographically weighted visualization: interactive graphics for scale-varying exploratory analysis. Geographical Information Science Research Conference (GISRUK 07), NUI Maynooth, Ireland, April, 2007. \n",
+      "\n",
+      "Friendly, M. (2007). A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis. Statistical Science, 22, 368-399. \n",
+      "\n",
+      "Friendly, M. (2007). Data from A.-M. Guerry, Essay on the Moral Statistics of France (1833). \n",
+      "See Also\n",
+      "\n",
+      "The Guerry package for maps of France: gfrance and related data. \n",
+      "\n",
+      "Prepared by Center for Spatial Data Science. Last updated July 3, 2017. Data provided “as is,” no warranties.\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('guerry')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/guerry/Guerry.geojson'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('Guerry.geojson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "south\n",
+      "=====\n",
+      "\n",
+      "Homicides and selected socio-economic characteristics for Southern U.S. counties.\n",
+      "---------------------------------------------------------------------------------\n",
+      "\n",
+      "- Observations = 1,412\n",
+      "- Variables = 69\n",
+      "- Years = 1960-90s\n",
+      "- Support = polygon\n",
+      "\n",
+      "Files\n",
+      "-----\n",
+      "south.gdb     README.md  south.dbf      south.gpkg  south.kml  south.mif  south.shp  south.sqlite\n",
+      "codebook.pdf  south.csv  south.geojson  south.html  south.mid  south.prj  south.shx  south.xlsx\n",
+      "\n",
+      "Variables\n",
+      "---------\n",
+      "NAME \tcounty name\n",
+      "STATE_NAME \tstate name\n",
+      "STATE_FIPS \tstate fips code (character)\n",
+      "CNTY_FIPS \tcounty fips code (character)\n",
+      "FIPS \tcombined state and county fips code (character)\n",
+      "STFIPS \tstate fips code (numeric)\n",
+      "COFIPS \tcounty fips code (numeric)\n",
+      "FIPSNO \tfips code as numeric variable\n",
+      "SOUTH \tdummy variable for Southern counties (South = 1)\n",
+      "HR** \thomicide rate per 100,000 (1960, 1970, 1980, 1990)\n",
+      "HC** \thomicide count, three year average centered on 1960, 1970, 1980, 1990\n",
+      "PO** \tcounty population, 1960, 1970, 1980, 1990\n",
+      "RD** \tresource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)\n",
+      "PS** \tpopulation structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)\n",
+      "UE** \tunemployment rate 1960, 1970, 1980, 1990\n",
+      "DV** \tdivorce rate 1960, 1970, 1980, 1990 (% males over 14 divorced)\n",
+      "MA** \tmedian age 1960, 1970, 1980, 1990\n",
+      "POL** \tlog of population 1960, 1970, 1980, 1990\n",
+      "DNL** \tlog of population density 1960, 1970, 1980, 1990\n",
+      "MFIL** \tlog of median family income 1960, 1970, 1980, 1990\n",
+      "FP** \t% families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)\n",
+      "BLK** \t% black 1960, 1970, 1980, 1990\n",
+      "GI** \tGini index of family income inequality 1960, 1970, 1980, 1990\n",
+      "FH** \t% female headed households 1960, 1970, 1980, 1990\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('south')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/south/south.shp'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('south.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "missing.shp not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('missing.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = libpysal.examples.get_path('south.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/south/south.shp'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = gpd.read_file(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>STATE_NAME</th>\n",
+       "      <th>STATE_FIPS</th>\n",
+       "      <th>CNTY_FIPS</th>\n",
+       "      <th>FIPS</th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>COFIPS</th>\n",
+       "      <th>FIPSNO</th>\n",
+       "      <th>SOUTH</th>\n",
+       "      <th>HR60</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BLK90</th>\n",
+       "      <th>GI59</th>\n",
+       "      <th>GI69</th>\n",
+       "      <th>GI79</th>\n",
+       "      <th>GI89</th>\n",
+       "      <th>FH60</th>\n",
+       "      <th>FH70</th>\n",
+       "      <th>FH80</th>\n",
+       "      <th>FH90</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hancock</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>029</td>\n",
+       "      <td>54029</td>\n",
+       "      <td>54</td>\n",
+       "      <td>29</td>\n",
+       "      <td>54029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.682864</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.557262</td>\n",
+       "      <td>0.223645</td>\n",
+       "      <td>0.295377</td>\n",
+       "      <td>0.332251</td>\n",
+       "      <td>0.363934</td>\n",
+       "      <td>9.981297</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>9.785797</td>\n",
+       "      <td>12.604552</td>\n",
+       "      <td>POLYGON ((-80.6280517578125 40.39815902709961,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooke</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>009</td>\n",
+       "      <td>54009</td>\n",
+       "      <td>54</td>\n",
+       "      <td>9</td>\n",
+       "      <td>54009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.607233</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.748370</td>\n",
+       "      <td>0.220407</td>\n",
+       "      <td>0.318453</td>\n",
+       "      <td>0.314165</td>\n",
+       "      <td>0.350569</td>\n",
+       "      <td>10.929337</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.214990</td>\n",
+       "      <td>11.242293</td>\n",
+       "      <td>POLYGON ((-80.52625274658203 40.16244888305664...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>069</td>\n",
+       "      <td>54069</td>\n",
+       "      <td>54</td>\n",
+       "      <td>69</td>\n",
+       "      <td>54069</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.974132</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.310334</td>\n",
+       "      <td>0.272398</td>\n",
+       "      <td>0.358454</td>\n",
+       "      <td>0.376963</td>\n",
+       "      <td>0.390534</td>\n",
+       "      <td>15.621643</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>14.716681</td>\n",
+       "      <td>17.574021</td>\n",
+       "      <td>POLYGON ((-80.52516937255859 40.02275085449219...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Marshall</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>051</td>\n",
+       "      <td>54051</td>\n",
+       "      <td>54</td>\n",
+       "      <td>51</td>\n",
+       "      <td>54051</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.876248</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.546097</td>\n",
+       "      <td>0.227647</td>\n",
+       "      <td>0.319580</td>\n",
+       "      <td>0.320953</td>\n",
+       "      <td>0.377346</td>\n",
+       "      <td>11.962834</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>8.803253</td>\n",
+       "      <td>13.564159</td>\n",
+       "      <td>POLYGON ((-80.52446746826172 39.72112655639648...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New Castle</td>\n",
+       "      <td>Delaware</td>\n",
+       "      <td>10</td>\n",
+       "      <td>003</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.228385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.480294</td>\n",
+       "      <td>0.256106</td>\n",
+       "      <td>0.329678</td>\n",
+       "      <td>0.365830</td>\n",
+       "      <td>0.332703</td>\n",
+       "      <td>12.035714</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>15.169480</td>\n",
+       "      <td>16.380903</td>\n",
+       "      <td>POLYGON ((-75.77269744873047 39.38300704956055...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         NAME     STATE_NAME STATE_FIPS CNTY_FIPS   FIPS  STFIPS  COFIPS  \\\n",
+       "0     Hancock  West Virginia         54       029  54029      54      29   \n",
+       "1      Brooke  West Virginia         54       009  54009      54       9   \n",
+       "2        Ohio  West Virginia         54       069  54069      54      69   \n",
+       "3    Marshall  West Virginia         54       051  54051      54      51   \n",
+       "4  New Castle       Delaware         10       003  10003      10       3   \n",
+       "\n",
+       "   FIPSNO  SOUTH      HR60  ...      BLK90      GI59      GI69      GI79  \\\n",
+       "0   54029      1  1.682864  ...   2.557262  0.223645  0.295377  0.332251   \n",
+       "1   54009      1  4.607233  ...   0.748370  0.220407  0.318453  0.314165   \n",
+       "2   54069      1  0.974132  ...   3.310334  0.272398  0.358454  0.376963   \n",
+       "3   54051      1  0.876248  ...   0.546097  0.227647  0.319580  0.320953   \n",
+       "4   10003      1  4.228385  ...  16.480294  0.256106  0.329678  0.365830   \n",
+       "\n",
+       "       GI89       FH60  FH70       FH80       FH90  \\\n",
+       "0  0.363934   9.981297   7.8   9.785797  12.604552   \n",
+       "1  0.350569  10.929337   8.0  10.214990  11.242293   \n",
+       "2  0.390534  15.621643  12.9  14.716681  17.574021   \n",
+       "3  0.377346  11.962834   8.8   8.803253  13.564159   \n",
+       "4  0.332703  12.035714  10.7  15.169480  16.380903   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((-80.6280517578125 40.39815902709961,...  \n",
+       "1  POLYGON ((-80.52625274658203 40.16244888305664...  \n",
+       "2  POLYGON ((-80.52516937255859 40.02275085449219...  \n",
+       "3  POLYGON ((-80.52446746826172 39.72112655639648...  \n",
+       "4  POLYGON ((-75.77269744873047 39.38300704956055...  \n",
+       "\n",
+       "[5 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples.sacramento2 import fetch_sacramento2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "fetch_sacramento2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "sacramento2\n",
+      "===========\n",
+      "\n",
+      "2000 Census Tract Data for Sacramento MSA\n",
+      "-----------------------------------------\n",
+      "\n",
+      "- Observations = 83\n",
+      "- Variables = 66\n",
+      "- Years = 1998, 2001\n",
+      "- Support = polygon\n",
+      "\n",
+      "Files\n",
+      "-----\n",
+      " SacramentoMSA2.gdb       SacramentoMSA2.kml   SacramentoMSA2.shp\n",
+      " README.md                SacramentoMSA2.mid   SacramentoMSA2.shx\n",
+      " SacramentoMSA2.csv       SacramentoMSA2.mif   SacramentoMSA2.sqlite\n",
+      " SacramentoMSA2.dbf       SacramentoMSA2.prj   SacramentoMSA2.xlsx\n",
+      " SacramentoMSA2.geojson   SacramentoMSA2.sbn  'Variable Info for Zip Code File.pdf'\n",
+      " SacramentoMSA2.gpkg      SacramentoMSA2.sbx\n",
+      "\n",
+      "Variables\n",
+      "---------\n",
+      "ZIP ZIP code\n",
+      "PO_NAME \tName of ZIP code area\n",
+      "STATE \tSTATE\n",
+      "MSA \tMSA\n",
+      "CBSA_CODE \tCBSA code\n",
+      "MAN98 \t1998 total manufacturing establishments (MSA)\n",
+      "MAN98_12 \t1998 total manufacturing establishments, 1-9 employees (MSA)\n",
+      "MAN98_39 \t1998 total manufacturing establishments 10+ employees (MSA)\n",
+      "MAN01 \t2001 total manufacturing establishments (MSA)\n",
+      "MAN01_12 \t2001 total manufacturing establishments, 1-9 employees (MSA)\n",
+      "MAN01_39 \t2001 total manufacturing establishments, 10+ employees (MSA)\n",
+      "MAN98US \t1998 total manufacturing establishments (US)\n",
+      "MAN98US12 \t1998 total manufacturing establishments, 1-9 employees (US)\n",
+      "MAN98US39 \t1998 total manufacturing establishments 10+ employees (US)\n",
+      "MAN01US \t2001 total manufacturing establishments (US)\n",
+      "MAN01US_12 \t2001 total manufacturing establishments, 1-9 employees (US)\n",
+      "MAN01US_39 \t2001 total manufacturing establishments, 10+ employees (US)\n",
+      "OFF98 \t1998 total office establishments (MSA)\n",
+      "OFF98_12 \t1998 total office establishments, 1-9 employees (MSA)\n",
+      "OFF98_39 \t1998 total office establishments, 10+ employees (MSA)\n",
+      "OFF01 \t2001 total office establishments (MSA)\n",
+      "OFF01_12 \t2001 total office establishments, 1-9 employees (MSA)\n",
+      "OFF01_39 \t2001 total office establishments, 10+ employees (MSA)\n",
+      "OFF98US \t1998 total office establishments (US)\n",
+      "OFF98US12 \t1998 total office establishments, 1-9 employees (US)\n",
+      "OFF98US39 \t1998 total office establishments, 10+ employees (US)\n",
+      "OFF01US \t2001 total office establishments (US)\n",
+      "OFFUS01_12 \t2001 total office establishments, 1-9 employees (US)\n",
+      "OFFUS01_39 \t2001 total office establishments, 10+ employees (US)\n",
+      "INFO98 \t1998 total information establishments (MSA)\n",
+      "INFO98_12 \t1998 total information establishments, 1-9 employees (MSA)\n",
+      "INFO98_39 \t1998 total information establishments, 10+ employees (MSA)\n",
+      "INFO01 \t2001 total information establishments (MSA)\n",
+      "INFO01_12 \t2001 total information establishments, 1-9 employees (MSA)\n",
+      "INFO01_39 \t2001 total information establishments, 10+ employees (MSA)\n",
+      "INFO98US \t1998 total information establishments (US)\n",
+      "INFO98US12 \t1998 total information establishments, 1-9 employees (US)\n",
+      "INFO98US39 \t1998 total information establishments, 10+ employees (US)\n",
+      "INFO01US \t2001 total information establishments (US)\n",
+      "INFO01US_1 \t2001 total information establishments, 1-9 employees (US)\n",
+      "INFO01US_3 \t2001 total information establishments, 10+ employees (US)\n",
+      "INDEX \tIndex\n",
+      "NUMSEC \tNumber of sectors represented in ZIP code\n",
+      "EST98 \tTotal establishments in ZIP code, 1998\n",
+      "EST01 \tTotal establishments in ZIP code, 2001\n",
+      "PCTNGE \tNational growth effect, percent (N)\n",
+      "PCTIME \tIndustry mix effect, percent (M)\n",
+      "PCTCSE \tCompetitive shift effect, percent (S)\n",
+      "PCTGRO \tPercent growth establishments, 1998-2001 (R)\n",
+      "ID \tUnique ZIP code ID for ID variables in weights matrix creation window\n",
+      "\n",
+      "Source: US Census Bureau, 2000 Census (Summary File 3). Extracted from http://factfinder.census.gov in April 2004.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('sacramento2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/libpysal/examples/10740/10740.shx'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path(\"10740.shx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples.nyc_bikes import fetch_bikes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "fetch_bikes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/nyc_bikes/nyct2010.shp'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('nyct2010.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "from libpysal.examples.rio_grande_do_sul import fetch_rio\n",
+    "fetch_rio()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/rio_grande_do_sul/map_RS_BR.shp'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('map_RS_BR.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples.taz import fetch_taz\n",
+    "fetch_taz()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "libpysal.examples.get_path('taz.dbf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/notebooks/io.ipynb.txt b/_sources/notebooks/io.ipynb.txt
new file mode 100644
index 000000000..76f11e123
--- /dev/null
+++ b/_sources/notebooks/io.ipynb.txt
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "sys.path.append(os.path.abspath('..'))\n",
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = libpysal.weights.lat2W(5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.8"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 1]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.neighbors[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 10, 6]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.neighbors[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['georgia',\n",
+       " '__pycache__',\n",
+       " 'tests',\n",
+       " 'newHaven',\n",
+       " 'Polygon_Holes',\n",
+       " 'nat',\n",
+       " 'Polygon',\n",
+       " '10740',\n",
+       " 'berlin',\n",
+       " 'rio_grande_do_sul',\n",
+       " 'sids2',\n",
+       " 'sacramento2',\n",
+       " 'burkitt',\n",
+       " 'arcgis',\n",
+       " 'calemp',\n",
+       " 'stl',\n",
+       " 'virginia',\n",
+       " 'geodanet',\n",
+       " 'desmith',\n",
+       " 'book',\n",
+       " 'nyc_bikes',\n",
+       " 'Line',\n",
+       " 'south',\n",
+       " 'snow_maps',\n",
+       " 'Point',\n",
+       " 'street_net_pts',\n",
+       " 'guerry',\n",
+       " '__pycache__',\n",
+       " 'baltim',\n",
+       " 'networks',\n",
+       " 'us_income',\n",
+       " 'taz',\n",
+       " 'columbus',\n",
+       " 'tokyo',\n",
+       " 'mexico',\n",
+       " '__pycache__',\n",
+       " 'chicago',\n",
+       " 'wmat',\n",
+       " 'juvenile',\n",
+       " 'clearwater']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'baltim',\n",
+       " 'description': 'Baltimore house sales prices and hedonics 1978',\n",
+       " 'explanation': ['* baltim.dbf: attribute data. (k=17)',\n",
+       "  '* baltim.shp: Point shapefile. (n=211)',\n",
+       "  '* baltim.shx: spatial index.',\n",
+       "  '* baltim.tri.k12.kwt: kernel weights using a triangular kernel with 12 nearest neighbors in KWT format.',\n",
+       "  '* baltim_k4.gwt: nearest neighbor weights (4nn) in GWT format.',\n",
+       "  '* baltim_q.gal: queen contiguity weights in GAL format.',\n",
+       "  '* baltimore.geojson: spatial weights in geojson format.']}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('baltim')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = libpysal.examples.get_path('baltim.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/baltim/baltim.shp'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shp_file = libpysal.io.open(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes = [shp for shp in shp_file]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(907.0, 534.0)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shapes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = libpysal.io.open(libpysal.examples.get_path('baltim_q.gal')).read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "211"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/notebooks/voronoi.ipynb.txt b/_sources/notebooks/voronoi.ipynb.txt
new file mode 100644
index 000000000..27276c168
--- /dev/null
+++ b/_sources/notebooks/voronoi.ipynb.txt
@@ -0,0 +1,476 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voronoi Polygons for 2-D Point Sets\n",
+    "\n",
+    "Author: Serge Rey (http://github.com/sjsrey)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.abspath('..'))\n",
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.cg.voronoi  import voronoi, voronoi_frames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regions, vertices = voronoi(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  4.21783296,   4.08408578],\n",
+       "       [  7.51956025,   3.51807539],\n",
+       "       [  9.4642193 ,  19.3994576 ],\n",
+       "       [ 14.98210684, -10.63503022],\n",
+       "       [ -9.22691341,  -4.58994414],\n",
+       "       [ 14.98210684, -10.63503022],\n",
+       "       [  1.78491801,  19.89803294],\n",
+       "       [  9.4642193 ,  19.3994576 ],\n",
+       "       [  1.78491801,  19.89803294],\n",
+       "       [ -9.22691341,  -4.58994414]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vertices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "region_df, point_df = voronoi_frames(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85e38b94a8>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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QT07m38/FbBbQ3EyzdZaDAp8sKpVK4fTpKxgacovWDbGQOJ3VtNctHm5Y7nDk7obtTCaTBaEQqIPmMlDgkwVxznHx4he4c8cAp3OT1OXkBY+HVt0CwNDQAPR6XdY2LF8JlcpDHTSXgQKfLOjLL1tx/XoCgvAkGGNSl5MXHA4votH7iEZHpS5FUpkNT3I3/34uVquAlpY+6qC5RBT4ZF537tzDuXNBCMJu2cy1XwqVSgW7fR16euS96jYU6pG8nYZOZ8TYmIE6aC5RVn6LGWO/YIwFGWPNMx4rY4ydYozdnnpvzca5SG4MDAzg5MlO2c21XyqXqwp9fbelLkNS4XAvnM4NUpcBlUqgDppLlK2XbW8D+Nojj/0pgHrOeTWA+qmPSQEYHh7GsWNfwmLZDa3WIHU5ecnj2YDBwQHZdm0cHh6CQpHO+oblK1Fe7kZ7O3XQXIqsBD7n/DMA4UcefhHAL6f+/UsAL2XjXERc0WgUdXVXodE8CZPJInU5ectgMMJsdqCvT56LsAYGelBenvv593NRq7WIx60IBAJSl5L3xByYdXDO+wFg6v2ck7cZY28yxpoYY02hUEjEcshiMnPtLyEer4bVKu7uRcXA5aqS7ebmmQ1P8qcdtsHgQUcHDessRvI7cZzztzjnNZzzGru9ODfPKATpdBpnzlxFKORERcVaqcspCF5vFQIBeS7AikRy2zBtMRaLE52d1EFzMWIGfoAx5gKAqfdBEc9FVoFzjkuXvsDt2zq4XFukLqdgZPZw1WBwUF6vLMfHR5FIjMNqdUpdygNKpRLJZAX6+qiD5kLEDPyPAbwx9e83APxexHORVWhubse1a3F4PE/RXPtlcjiqZLfqdmCgB1arI++m6paUeNDaSouwFpKtaZnvAbgIYBNjzMcY+zGAvwDwAmPsNoAXpj4meebevS40NvbD7aa59iuRWXV7V+oycioU8sNuF387w+UqLbWjpydKHTQXkJU9yTjnr8zzqaPZOD4RRyAQwCef3ILT+RzU6ty2ty0WLtcaXLo0jmh0FAaDWepycmJoqAfr1j0vdRmPYYyBMTd6e/3YvHmj1OXkJXpJJ1OZufbXaa79KqlUKthsa2QzrJPZsHwoJxuWr0RpqQc3b8rrnspyUODLUDQaxfHjV6FW74TJRAugV8vtls+q22DQB6vVlrMNy5fLZLJgcDDzgoY8jgJfZiYnJ3Hy5GXEYlV5NcuikHm9VRga6pPFSs/MhuWC1GUsSKn04N49epU/Fwp8GUmn02hsvIpAoEKyTSuKkcFggtFYgf7+4n+VPzjYk/frNKY7aHLOpS4l71DgywTnHJcvX0dHhwYu11apyyk6mb1ui3vVbTKZxMjIAByO/H6xoNMZMTqqB63cfxwFvky0tHSgqSkKQaC59mLweqswMNAtdRmiCoWmNyzXSV3KotRqD+7coTn5j6LAl4Gurm40NvZBEPZAqVRKXU5RstmcAJQYGirelZ4DA72SbFi+EuXlbrS1BZBKpaQuJa9Q4Be5YDCIEyc6UFGxl+bai6zYV92Gw76CufejVmsxMWHFwMCA1KXkFQr8IjYyMoJjx76AxbIbOp1R6nKKniBUYWDgjtRliCKdTiMS8cPhWCt1KUum11MHzUdR4BepWCyG48evQKncQXPtc8TtXofR0RFEo+NSl5J14XAQWq2moFYTWywOdHZGkEgkpC4lb1DgF6HpufbR6IaCGXMtBpm9btfC5yu+YZ1Mw7TCWrehVKqQSlXA76ebt9Mo8ItMZq59E/r7baioyJ9+5XLhclWhv/+W1GVk3dCQL+/n38/FZKIOmjNR4BeZq1e/REeHCm73E1KXIkuVldUIhfxFt+p2aKgHTmdh3LCdiTpozkaBX0RaWztw9ep9CMLTNNdeIplVtzYEAsVz83Z4eAiMpWA226QuZdlmdtAkFPhFo7u7B6dP++B276a59hJzOKrh8xXPqttAoBdlZW6py1ix0lIBzc00WwegwC8KoVAIn3zSjoqKZ6BWa6UuR/YqK6swMNAldRlZEwr15m075KUwmawIhaiDJkCBX/BGR0dx7Ng1lJTU0Fz7PGGzuZBOM0QixbHoJxzuLaj593NRKAR0ddGwDgV+AYvH46iruwzGtqOkpEzqcsgMmVW3rVKXsWrR6DgSifGCHtIBgLIyD5qb/bLvoEmBX6AmJydx6tRl3L+/HuXlhf3LWIwEYQP6+wt/r9t83bB8uXQ6I8bG9BgcHJS6FEkV9k9RptLpNM6e/Rw+Xxkcjg1Sl0Pm4HKtw8hIGPF4dMXHcJ95B0d/vBb/5EUFjv54Ldxn3slihUsTCPSivNyT8/OKQa0WcOeOvG/eUuAXoKamG2hrU0AQtkldCpmHRqNBefka+HwrG9Zxn3kHO//mTRhC3WCcwxDqxs6/eTPnoR+JFP74/bSyMgGtrfLuoEmBX2Da2m7hypUxCMIummuf59zuavj9K1t1u+XXP4NqYvZfB6qJKLb8+mfZKG1JEokJjI8PwW5fk7Nzikmt1iIet8i6gyYFfgHp6elFQ0Mv3G7qay+2bAyneL1VCAZ9SKfTy/5a/WDPsh4Xw8BALyyW8rzdsHwl9HoPbt2S72wd0QOfMdbFGLvJGLvOGGsS+3zFKjPXvg12+16aay+ybA2nmExmGI1l6O9f/qrbmK1yWY+LIRjshdWa+wkBYt67sFqduH07LNsOmrl6hX+Yc/4k57wmR+crKtNz7Y3GXdDrTVKXU/SyOZzicFTD719+98y2136OpNYw67Gk1oC2136+7GOtVDjsy3n/HLHvXUx30OzrK96dyRZCQzp5Lh6P48SJK2BsG8zmcqnLkYVsDqesdK/bvkOv4sufvIWofQ04Y4ja1+DLn7yFvkOvLvtYK5FMJhGJ9OV8w/Jc3LswGgW0tspztk4uBuc4gJOMMQ7gbznnb+XgnEUhmUzi1KnLGB1dC6dTkLoc2YjZKmEIPR7SKxlOsdlcSKXSGB4OwmKpWNbX9h16NWcB/6hQyA+TqSTnG5bn4t5Faakd3d3XEY1GYTAYFv+CIpKLV/i1nPOnAfwRgJ8wxg7M/CRj7E3GWBNjrCkUCuWgnMKQmWvfBJ/PCqezSupyZCWbwykKhQIVFRsKbtVtMOiDzZb7Fxm5uHeRWUQmzw6aogc+57xv6n0QwEcA9jzy+bc45zWc8xq73S52OQXj2rWbaG1lEITtUpciO9keTnG7q1Z041ZKg4O9kkzHzNW9C4vFI8sOmqIO6TDGjAAUnPOxqX9/BcB/EvOcxaC9/TYuXRqBx/MszbWXSDaHUwRhPa5e/RjxeBQ6Xf4PIUxvWL5v39dzfu7p/+Zbfv0z6Ad7ELNVou21n2d9aMtkssLnS2NkZASlpaVZPXY+E3sM3wHgo6nQUgF4l3P+icjnLGi9vT7U13fD5XoOSmXxzH+Ws8yq20r4fO2oqnpa6nIWFQ4HodGoJNuwPFf3LhQKD7q6fNi5Uz6BL+qQDuf8Lud859TbE5zz3M0pK0CDg4M4caIVNtvenN8sI+JyOjcUzF63mf45xd+Qz2oVZNdBk15C5omxsTHU1V2D0fg0DIYSqcshWZJOp9Hf341QqBfNzecxPh6FVmuc8WaAXm+cejNApzNBp9NL2p1ycLAXTmfuFnhJRa83YWgo00FTLvcPKfDzQGau/WVwvrUg9w0ls6XTafT1daGnpx39/R3Q6bQQhE343vf+PdLpNGKxMUxM3EcsNo779/sQDkcxMRHDxEQM8XgcyeQkNBo9dDoTtFoj1GoDdLpHLw5G6HSZ99lufRAO92Lnzueyesx8pVYLuHvXT4FPciOZTKK+/gpGRtbA6SyONrRylE6n4fffexDyBoMebvdGvPDCa8u+iCeTScTj44jFxqbejyMeH0M8PoSxsV5MTEQRj8cxORlHIjEBpVIDjcYAjcYArdY4dXEwTf3FYIROp4deb4LBYIJGs3BbjtHRCIDUstcMFKqyMgEtLbdQU5OSRX8qCnwJcc5x7tzn6OkphcdTLXU5ZJkeDXmjUQ+XayO+8pXXV/WXmkqlgslkgclkWfS5nHNMTMQQi40iGh2bcZEYQzg8gERi+i+HGBKJODhn0Gim/2J4fGhpcHAApaXy+StTrdZiYsKCQCAAt7v471tQ4Evo2rWbaG7m8Hpprn2hSCaT6O/vRk9PG/r7b8Fo1EMQNmH79tWF/EoxxqDTGaDTGWC1Ohd9fiKRQDz+8OLw6NBSODyIaDSMaHQcBoM8+jbpdB50dPgo8Il4bt3qxMWLEXg8tQW/fVyxy7eQXw2NRgONxrZg3U1Np1Ff//c4evQNyaZm5lKmg+ZN1NYmoNFopC5HVBT4EvD7/Th1qovm2uexZDKJvr576O3tmAp5IwShGtu3/whmc3FvGF9TcxjXrqnw6adv48iR15c0tFTIlEoV0mkH+vr6sHbtWqnLERWlTY4NDQ2hrq4ZNts+mmufZ6ZDvru7HYHALRiNJng8m2QR8o96+un9YAyor38bhw+/XvTfv8EgoK2tkwKfZM/4+Djq6j6H0bhLFn8qF4L5Qn7nzn9a9CG3mKee2g+lUoWGhl/hyJEfFtzw1XKUltrR1VX8HTQp8HNkYmICJ05cRiq1BVZr8f7iFIKHId+GQOA2TCYTBIFCfi47duyDQqFEQ8OvcOjQD4t2uqZCoQBjmQ6amzYV74w5CvwcSKVSqK+/guFhL5xOr9TlyFIymYTffwc9PR1TIV8CQdgoeci7z7wjeqOw1dq2LbOHckPD/8WRI8Ub+mazgObmLynwycpxznH+/Ofo7i6Bx7NR6nJk5WHItyMQ6HwQ8k899eO8uBE5vZ3f9A5P09v5Aci70N+yZRcYYzh9+tc4dOjVJU0BLTQlJWVF30GTAl9kX3zRjJs30/B4dkhdiizMFfIez+a8CfmZFtrOL98CHwA2b34aCoUKZ868g/37vw+brfhWhisUQlF30KTAF9Ht23dw4UIYgvAszbUXUTKZRG9vJ3y+jrwP+ZlysZ1ftm3cuAMKhQKNjR/g4MHiC32r1YPm5ovYsWNrUe5FQYEvkr6+Ppw8eRcu136oVGqpyyk60yHf09OOUOgOSkpK82q4ZimyuXduLlVVbXsQ+s899x04HLnfGUssmQ6a2qLtoEmBL4JwOIy6ups01z7LHg15s9kMQdiCXbsOFUzIz9T22s9njeED4mznJ4b167eCMQXOnfsQtbXfhtO5TuqSskat9hRtB00K/Cy7f/8+6uqaoNc/TXPtsyCRSMDvv/tYyNfUHIbRWNjjrLnazk8s69ZthlKpxPnzv8Ozz74El2uD1CVlhdXqRmtrcXbQpMDPosxc+0uYnNxclK8OcmV2yHfCbLZAEDZj9+4jRXcRzdV2fmKprKwGYy/hwoV/xDPPfAOCUPhTGjUaHeLx4uygSYGfJalUCqdPX0U47IHLld9jsPkokUjA5+tEb++tqZC3QhA2FWXIFxuvdwMUim/j4sXfYu/er8Pr3SJ1Saum1Qq4fdtPgU8exznHhQvXcPeuEV7vJqnLKRgPQ74dodBdmM1WeL3F+Uq+2AnCWtTWfgcXLvwDOE+jsvIJqUtalbIyF27dasG+fcXVQZMCPwu+/LIVN24k4fHskrqUvDcz5AOBTlgs5VMh/zyFfIFzudagtvZ7OH/+N0ilUli3rnDXniiVKiSTdvT392PNmuKZhUSBv0qdnXdx7lwIgkB97eeTSCTQ03MLfv8tBAKdsFpt8Hg2Yffur8hmkw25cDq9eO657+PcuQ/BeRrr1z8pdUkrZjR60NraSYFPMvr7+3Hy5B04nc/RXPtHTIf89HBNWZkNHs8WCnkZcDg8OHDg+/jssw+QTqdQVVWYf/mWltrR3V1cHTRFD3zG2NcA/DUAJYD/zTn/C7HPmQuRSAR1dTdQVvYMtFq91OXkhflCfu/er1HIy4zd7sbBg6/g7NkPkE6nsXHjbqlLWjaFQgHOXUXVQVPUwGeMKQH8DYAXAPgAXGWMfcw5bxXzvGLLzLW/Cq32qYKfC75aicQEenpuU8iTx9hsThw8+DIaGz9AKpXEli37pC5p2UpLPWhpuUGBv0R7AHRyzu8CAGPsfQAvAijYwE8kEvjkk8tIJDbBbi/ONrGLeTTkrVYbvF4MxTAQAAAWTElEQVQKefK4sjIHDh9+FadPvwMgjS1baqUuaVkyHTRTGB0dhdlc+JMKxA58AUDvjI99APaKfE7RpFIpNDRcwdCQGy5X8dzIWYqHId+GUOgeysoq4PFswr59X4dOVxzjm0QcFkv5VOi/i1QqhW3bDkhd0rJMd9DcsWOr1KWsmtiBP1e7OT7rCYy9CeBNAKiszN8FS5xzXLz4Be7eNcDjkcdc+7lC3uvdgn37/phCniyLxVKOo0d/iIaGd8F5Gtu3H5K6pCWzWAQ0N1/C9u1bCr6DptiB7wMwc4snD4C+mU/gnL8F4C0AqKmpmXUxyCdfftmK69cT8HqfKfgf+kIo5IlYzGYrjhx5FadPv4dUKoUnnzwqdUlLYjCUwOfTYmhoCDZbYW9PKnbgXwVQzRhbB8AP4GUAPxD5nFl35849nDsXhCA8V5Rz7R8N+fJyBzyezRTyJOvMZguOHn0VDQ3vgfMknnrqq1KXtCSZDpo+CvyFcM6TjLGfAvgDMtMyf8E5bxHznNk2MDCAkyc74XTWFtVc+3g8hp6e2/D7Ox6EvNe7lUKeiM5kMuP5519Fff27SKWOo6bm61KXtCir1Y2WlluoqUkX9Is+0efhc86PAzgu9nnEMDw8jGPHvoTFshdabeGH4HTI9/a2YWiom0KeSMZgMD14pX/lysfYs+cbUpe0II1Gh1isFIFAAC6XS+pyVoxW2s4jGo2iru4qNJonC3JzjWkzQz4c7kZZWSbka2v/hEKeSMpgMOLIkVdw+vT7uHz599i790WpS1qQTufBrVs+Cvxik5lrfwnxeDUqKhxSl7Ns8XgM3d0d8Pk6EA53o7zcCa93C4U8yTsGgxFHj/4ADQ3vT7VX/mbeDplkOmg249lnJ6FWF+bwLgX+I9LpNM6cuYpQyAW3e63U5SzZdMj39rYjEulBebkTa9Zsxf79L9I2iySv6XR6PP/8D9DQ8AEuXvwt9u37dl6GfqaDZgX6+voKtqEaBf4MnHNcuvQFbt/WwevdLHU5i3r4Sr4d4XAm5Neu3YoDB16ikCcFRaPR4siR7+PMmQ9x/vyHqK39bl6GvtEooLX1DgV+MWhubse1a3F4vfvydq79zJAfGuqBzTb9Sp5CnhQ2jUaLQ4e+hzNnPsTZs+9j//6X8y70S0sr0N39JWKxGPT6wmuaSIE/5d69LjQ29sPtzr+59vF4DF1d7fD7OzA01AO73Y01azZTyJOio9FocOjQd9HY+Ft89tl7eO6570Olyp+Ymu6g6fP5UV1dJXU5y8Y4z5/FrTU1NbypqSnn5w0EAvjooxuw2WrzZvplNHofPT234PO1IRz2wW53w+vdgsrKJyjkSdFLJpNobPwdGIvhwIFX8yr0x8bC0Olu4FvfOiR1KQ8wxj7nnNcs9rz8+a8okcxc++soLZV+rv10yPf2tiESyYR8Zkz+WxTyRFZUKhUOHvwWPvvsH9HY+A4OHsyf0DeZrPD7kwXZQTM//gtKJBqN4vjxq1Crd0o2136uV/Lr1m3FwYPfKarNkwlZLpVKhQMHXsK5cx/j9Olf4uDB1/Lid4IxBoXCU5AdNGU7pDM5OYm6unMYGlqLiop1OTnntGj0Prq7M2Py4bAPDocAQdiCysptefE/NCH5JJ1O4+zZjzExMYhDh17Pi9+RaHQMqdQlvPLK83kxwYOGdBaQTqfR2HgVgUAF3O7chP10yPt87YhE/HA4BKxf/wQOHKBX8oQsRKFQYP/+b+D8+To0NPw9jhx5Q/IhToOhBL29moLroCm7wOec4/Ll6+jo0MDrFffPsWh0fGoxVBtGR/tht7uxYcMT8Hi+SyFPyDIoFArU1v4xLl48gU8/fRtHjrwu+apxtdqDe/f8FPj5rKWlA01NUdHm2s8O+T5UVHhQVbUNHs/3KOQJWQWFQoF9+/4Ily//AQ0Nb+PIkR9JGvplZQKam89g167teTeVez6yCvyurm40NvZBEJ6DUqnM2nGj0XHcu9cGv7/jQchXV++AIFDIE5JND0P/FOrr38bhw69Lto+yRqNDPF5YHTRlE/jBYBAnTnSgoqIWavXqQ3g65H2+doyN9cNup5AnJFf27n0BV68qpl7pvw6DQZrpkVqtgNu3C6eDpiwCf2RkBMeOfQGLZQ90OuOKjzNXyG/cuBOC8H0KeUJybPfuo2hqUqC+/pc4evQNSULfanWho6MF+/YVRgfNog/8WCyG48evQKncAZPJuuyvHx8ffdDWYGbIe72v5M1CEELkqqbmMK5dUz24kZvr9TQqlRrJpB39/f2orKzM6blXoqgTa3JyEidPXkY0ugEVFUv/k+vRkHc4KinkCclTTz+9H0plZnjn0KHXYTaX5fT8RqMHra13KfCllJlr34SBATvc7vWLPn865H2+NoyPB+BwVGLTpqfg8VDIE5Lvdu6shUKhREPDr3DkyA9hNuduqmQhddAs2iS7evVLdHSoFpxrP1fIb968Cx7PZgp5QgrM9u3PQKlUor7+Vzh8+IewWCpycl6FQoF0ujA6aBZlqrW2duDq1fvweB6faz8+Pjo1hbJ9KuTXUMgTUiS2bt0NxhRoaPg1Dh9+FVarMyfnNZsFNDffpMDPte7uHpw544fbXftgrv2jIe90rsGWLbsgCBTyhBSbLVt2QalU4syZd3DgwCsoL3eLfs6SkrKC6KBZVGkXCoVw4kQ77PZaxGIxtLdfh9/fhvHxIJzONdi6tQZu9yYKeUKK3MaNTwJQ4MyZd3Hw4Muw2Tyino8xBsYE9PT4sW0bBb7oRkdH8cEHn6G/34ibNz+cEfK7KeQJkaGNG3dAoVCgsfEDHDjwXdjt4s6isVo9uHHjEp54YnNedNCci2gpyBj7cwD/HEBo6qE/45wfF+t8/+W//DV8PsBgsGDNmm1Yu/ZPoNeXZGVVLSGkMFVVbYNCocDZsx+itvY7cDjE23zcYCiBz6dBOBxGeXm5aOdZDbFf9v4V5/y/inwOAMC//JevY3JyEvfvTyAcjmJ4uBkjIzHE4xyMGQAYwLkBSqUBWu30mx5KJb3yJ6SYrV+/FUqlEufOfYja2m/D6RSvJbpK5cHduz7ZBn7OrFkz95V7cnIS0Wj0wdvIyDjC4SAikShCoSiSSRUYM4CxzAVBrX54QdBo9AXTBY8QMr81azaBMQXOn/8dnn32JbhcG0Q5T1mZgJaW/O2gKXbg/5Qx9jqAJgD/jnMeefQJjLE3AbwJQJSVamq1GqWlpSgtLZ3z8/F4HNFoFLFYDNFoFJHIMCKRPkQiUQQCcaTTGgCGB28azcwLgi5vx+oIIbNVVlaDsZdw/vw/Yt++fwJB2JT1c2g0OsRi+dtBc1VbHDLGPgUw10TXnwG4BGAQAAfwnwG4OOf/bKHj5XKLw6XgnD+4EESjUdy/H50aLopheDiKsbEEAD0YMyCd1kOhmDlcZIBarZX6WyCEPMLv78KlS7/Fnj1fh9e7JevHD4V6UF0dxP79i+44mDU52eKQc/78Eov5OwDHVnMuKTDGYDAYYDDMvclCOp2eNVw0NhZFJNKPSCSK4eEoYrH0vPcPNBo9VKr8765HSLERhLV49tnv4MKFf0AqlcLatduyenyr1YX29hY880z+ddAUc5aOi3PeP/XhNwE0i3UuqSgUCphMJphMc2/AkEwmH7l/EMXw8CAikcxfCpOTilkXBJVq9gUhm5u0EEIecrnWoLb2ezh//jfgPI1163Zk7dj53EFTzDH8v2SMPYnMkE4XgH8h4rnykkqlgtlsnnflXSKRmHVBGB4eRTg8gOHhKILBGFIpNRSKzMXg8fsHerp/QMgqOJ1e7N//Ms6ezYT++vVPZu3YRqMHbW3510FTtMDnnL8m1rGLhUajgUajgcXyeA9vzjkmJiZm3T+IRMKIRHyIRKIYGpoA57oHs4sA/WP3D+iCQMjCKioEHDz4Chob30M6nURVVXbG3UtLK3Dv3vW866BZNNMyiw1jDDqdDjqdDmVlj/f3TqfTs24oj49HMTwcnLqpHMX9+5OzhosevaFM9w8IybDZnDh8+AdobHwf6TTHxo27V31MhUIBzvOvgyYFfoFSKBQwGo0wGufesjGVSs0aLhodjSIcDiMSiSISiSKRwAIL0gx0/4DISlmZA4cO/QBnzryHVCqJLVv2rfqYZrMHLS3NFPhEfEqlEiUlJSgpKZnz80tdkDa9/oAWpJFiZ7Xacfjwqzh9+l1wnsLWrc+t6niZDpqTedVBkwJfphZakDbz/gEtSCNyYrGU4+jRH6K+/h2k0yls23ZwxcfKxw6aFPjkMTPvH8zl0QVp4+NRRCKhBwvSBgdpQRopXGazFUeP/hANDZnQ37HjyIqPZbUKaG6+kjcdNCnwybIttiAtlUrNuiDQgjRSaMxmC55//jU0NLyDVCqFp556YUXHMRjM8PnUedNBkwKfZJ1SqVz2grRIZBDDw4svSNNqDXT/gOSEyWTGkSOvoqHhPaTTJ7Br1x+t6DhKpYB79/KjgyYFPsm51SxICwRiU/cP9Jj7/gEtSCPZYzKZ8fzzr6K+/l1wfhw1NV9f9jHKygQ0Nzfi6ael76BJgU/yzmIL0mZ2OF1sQRpjDy8EtCCNrITBYMLRo5lX+leufIw9e76xrK/XavUIhcwIBoNwOnOzqfp8KPBJQWGMQa/Xz7t6ca4FaZHIwIP1B9FokhakkWUzGIwPXulfuvQRnnnmm8v6eq3Wg9u3fRT4hGTTYgvSksnkrAsCLUgjS6XT6XH06A9w+vQHuHDhH/DMM99a8hBNvnTQpMAnsqJSqWhBGlmxTOi/goaGD3Dx4m+xb9+3l/QzV6nUmJy0Sd5Bc1UboGRbvm2AQshMcy9Ie/g2OkoL0uQikZjAmTMfQqdT4bnnvrek0A+H++FydeErX1l924ZH5WQDFELkhBakkWkajRaHDn0PjY2/xdmz72P//pcXDX2LxYF7975EPB6f9/8hsVHgE5IlYi5Iy9w/oF/XfKLRaHDo0HfQ2PhbNDa+g/37X4FKNf/PSKFQIJ3OdNCsqhJnE/XF0JAOIXlicnJy1gUhsyAt+uCCkEhkFqRNTzmlBWn5IZlMorHxd2AshgMHXl0w9EdHh2AyNePFF1feo2cuNKRDSIFRq9VQq9XzLkibmJiYdUGYa0Fa5mKgB+ezLwa0IE08KpUKBw9+C2fP/iPOnPk1Dh16bd7Qz3TQTGBsbGzeiQNiolf4hBSBmQvSHu6QFsXwcAyRSBRjYwsvSNNopBlTLibJZBLnzn2MyckIDh58DRqNZs7n9fW1oraWYdu2LVk791Jf4VPgEyIDcy9Ii9KCtCxLp9M4f/7/IRoN4vDh16HRPH4jPhodBedX8P3vH83aX100pEMIeYAWpOWGQqFAbe2f4Pz5OjQ0vI0jR9547K+nTAdNFSKRyJzbl4qJAp8QQgvSsigT+n+MS5c+QX392zh8+HXodLNnbimVHty758t54NOQDiFkVZa2IE2L+TucFu+CtIsX/4BI5A6OHPnRrNCfmIjh/v3P8NprL2TlYkhDOoSQnMjmgrSZN5S1Wn3BL0jbt++ruHz5FD799Bc4cuRHMBgye0RkOmiW5LyD5qoCnzH2XQB/DmALgD2c86YZn/sPAH4MIAXgX3HO/7CacxFCCtNKFqSFw8MP1h/E44W9IG3v3hdw9apiakz/dRgMmWm3Wq0HnZ257aC52v9SzQC+BeBvZz7IGNsK4GUATwBwA/iUMbaRc55a5fkIIUVmsR3S5l6QNji1/8HDBWnTw0X5uCBt9+6j+PxzFT799O9x5MgbMJkssFpdaGtrwd69ueuguarA55y3AZhr/O1FAO9zzicA3GOMdQLYA+Dias5HCJGfpSxIm31DOT8XpO3adRAKhQL19W/j6NEfwWSyIJm0YWBgAF6vNyc1iPW3kADg0oyPfVOPEUJIVmm1Wmi1Wlit1sc+N/eCtCFEIr1TfyEkcrog7amn9kOhUDx4pW8weNDW1pU/gc8Y+xTAXINMP+Oc/36+L5vjsTmnAzHG3gTwJgBJ+0QTQorPzB3S5tpEfLEd0kKhFBjLzC7K1oK0nTtroVAo0dDwKxw69APcuzeSsw6aiwY+5/z5FRzXB2DmJcsDoG+e478F4C0gMy1zBecihJAVkWpB2vbtz0CpVOL06f+LjRt35qyDplhDOh8DeJcx9t+RuWlbDeCKSOcihBBRiLkgbfPmXVAolLhx4yTWrk3kf+Azxr4J4H8AsAOoY4xd55x/lXPewhj7DYBWAEkAP6EZOoSQYqNWq1FaWorS0tLHPjdzQdr0orRIZBiRSB8ikSgCgThKSrRYs8aLCxe+wAsv7Jlz2CmbVjtL5yMAH83zuZ8D+Plqjk8IIYVq5oK0uVoopNPpBzeU+/v75x1Wyqb8XrFACCFFSqFQPFiQZrPZcnPOnJyFEEKI5CjwCSFEJijwCSFEJijwCSFEJijwCSFEJijwCSFEJijwCSFEJijwCSFEJvJqT1vGWAhA9xyfsgEYzHE5+UKu37tcv2+Avnf63pdvDefcvtiT8irw58MYa1rKBr3FSK7fu1y/b4C+d/rexUNDOoQQIhMU+IQQIhOFEvhvSV2AhOT6vcv1+wboe5cr0b/3ghjDJ4QQsnqF8gqfEELIKuVt4DPGvssYa2GMpRljNY987j8wxjoZYx2Msa9KVWMuMMb+nDHmZ4xdn3r7utQ1iY0x9rWpn20nY+xPpa4nlxhjXYyxm1M/6yap6xETY+wXjLEgY6x5xmNljLFTjLHbU++tUtYolnm+d9F/1/M28AE0A/gWgM9mPsgY2wrgZQBPAPgagP/JGJt7p+Di8Vec8yen3o5LXYyYpn6WfwPgjwBsBfDK1M9cTg5P/ayLfXri28j8Ds/0pwDqOefVAOqnPi5Gb+Px7x0Q+Xc9bwOfc97GOe+Y41MvAnifcz7BOb8HoBPAntxWR0S0B0An5/wu5zwB4H1kfuakyHDOPwMQfuThFwH8curfvwTwUk6LypF5vnfR5W3gL0AA0DvjY9/UY8Xsp4yxG1N/Bhbln7gzyPHnOxMHcJIx9jlj7E2pi5GAg3PeDwBT7yskrifXRP1dlzTwGWOfMsaa53hb6BUdm+Oxgp5qtMh/h/8FYAOAJwH0A/hvkhYrvqL7+S5TLef8aWSGtH7CGDsgdUEkZ0T/XZd0E3PO+fMr+DIfAO+Mjz0A+rJTkTSW+t+BMfZ3AI6JXI7Uiu7nuxyc876p90HG2EfIDHF9tvBXFZUAY8zFOe9njLkABKUuKFc454Hpf4v1u16IQzofA3iZMaZljK0DUA3gisQ1iWbqf/pp30TmZnYxuwqgmjG2jjGmQeYG/ccS15QTjDEjY6xk+t8AvoLi/3k/6mMAb0z9+w0Av5ewlpzKxe+6pK/wF8IY+yaA/wHADqCOMXadc/5VznkLY+w3AFoBJAH8hHOekrJWkf0lY+xJZIY1ugD8C2nLERfnPMkY+ymAPwBQAvgF57xF4rJyxQHgI8YYkPndfJdz/om0JYmHMfYegEMAbIwxH4D/COAvAPyGMfZjAD0AvitdheKZ53s/JPbvOq20JYQQmSjEIR1CCCErQIFPCCEyQYFPCCEyQYFPCCEyQYFPCCEyQYFPCCEyQYFPCCEyQYFPCCEy8f8B6hdEGySK3VIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
+    "point_df.plot(ax=ax, color='red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Larger Problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_points = 200\n",
+    "np.random.seed(12345)\n",
+    "points = np.random.random((n_points,2))*10 + 10\n",
+    "results = voronoi(points)\n",
+    "mins = points.min(axis=0)\n",
+    "maxs = points.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regions, vertices = voronoi(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regions_df, points_df = voronoi_frames(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85e39dae10>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "points_df.plot(ax=ax, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85e1fbd240>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
+    "points_df.plot(ax=ax, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trimming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = np.array(points)\n",
+    "maxs = points.max(axis=0)\n",
+    "mins = points.min(axis=0)\n",
+    "xr = maxs[0] - mins[0]\n",
+    "yr = maxs[1] - mins[1]\n",
+    "buff = 0.05\n",
+    "r = max(yr, xr) * buff\n",
+    "minx = mins[0] - r\n",
+    "miny = mins[1] - r\n",
+    "maxx = maxs[0] + r\n",
+    "maxy = maxs[1] + r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n",
+    "points_df.plot(ax=ax, color='red')\n",
+    "plt.xlim(minx, maxx)\n",
+    "plt.ylim(miny, maxy)\n",
+    "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voronoi Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.weights.contiguity import Voronoi as Vornoi_weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = Vornoi_weights(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.915"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(3, 3),\n",
+       " (4, 28),\n",
+       " (5, 52),\n",
+       " (6, 65),\n",
+       " (7, 34),\n",
+       " (8, 10),\n",
+       " (9, 5),\n",
+       " (10, 2),\n",
+       " (11, 0),\n",
+       " (12, 1)]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = [i for i in range(w.n) if w.cardinalities[i]==12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16.50851787, 13.12932895]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/notebooks/weights.ipynb.txt b/_sources/notebooks/weights.ipynb.txt
new file mode 100644
index 000000000..bb2e9fc6d
--- /dev/null
+++ b/_sources/notebooks/weights.ipynb.txt
@@ -0,0 +1,1263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.append(os.path.abspath('..'))\n",
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['georgia',\n",
+       " '__pycache__',\n",
+       " 'tests',\n",
+       " 'newHaven',\n",
+       " 'Polygon_Holes',\n",
+       " 'nat',\n",
+       " 'Polygon',\n",
+       " '10740',\n",
+       " 'berlin',\n",
+       " 'rio_grande_do_sul',\n",
+       " 'sids2',\n",
+       " 'sacramento2',\n",
+       " 'burkitt',\n",
+       " 'arcgis',\n",
+       " 'calemp',\n",
+       " 'stl',\n",
+       " 'virginia',\n",
+       " 'geodanet',\n",
+       " 'desmith',\n",
+       " 'book',\n",
+       " 'nyc_bikes',\n",
+       " 'Line',\n",
+       " 'south',\n",
+       " 'snow_maps',\n",
+       " 'Point',\n",
+       " 'street_net_pts',\n",
+       " 'guerry',\n",
+       " '__pycache__',\n",
+       " 'baltim',\n",
+       " 'networks',\n",
+       " 'us_income',\n",
+       " 'taz',\n",
+       " 'columbus',\n",
+       " 'tokyo',\n",
+       " 'mexico',\n",
+       " '__pycache__',\n",
+       " 'chicago',\n",
+       " 'wmat',\n",
+       " 'juvenile',\n",
+       " 'clearwater']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'mexico',\n",
+       " 'description': 'Decennial per capita incomes of Mexican states 1940-2000',\n",
+       " 'explanation': ['* mexico.csv: attribute data. (n=32, k=13)',\n",
+       "  '* mexico.gal: spatial weights in GAL format.',\n",
+       "  '* mexicojoin.shp: Polygon shapefile. (n=32)',\n",
+       "  'Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.']}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('mexico')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weights from GeoDataFrames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
+    "gdf = geopandas.read_file(pth)\n",
+    "\n",
+    "from libpysal.weights import Queen, Rook, KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contiguity Weights\n",
+    "\n",
+    "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_rook = Rook.from_dataframe(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.6953125"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7hvPnz4sTJkyos8Yrdc3ff/9tPnToUKbJZOrP8/y5mo6zcOHCv7p27drn2WefZRnGMi+oyWTC0qVLRYPBwAJowvN8FT5DBYXHj7LjL0Kr1WaGh4f/lJube/3ChQvBycnJaNGihYrjyulH7+0N3LwJXL0K0qED2rZty6WlpblnZWX9X1hYWIRWq72r0+mCABw9fvx4v0OHDh3WarUZFcwrhoWFbc7Kyhqi1+udWrRoUc6EVWBlBfTpA7i7A889B4wcSX9u1oy6hk6eBC5fphXIhw8DLi5AWhq9MVhV7t26cOECAEj+/v5PbDzIy8uLcXZ2to2Li3sjKiqq16FDh2K1Wm0FxRnlIwhCDxsbmxmjRo2yYlnL73Esy8LPz4/5+++/oVKpmoeFhRXqdLpYrVZb3ctQUHhkKIa/GFqttjAkJORsWFjY8qysrHYXLlzwzsrKImazmW3YsOGDLlcMQ10q+/YB7dqBcXSEr68v5+joaB8fHz9ep9MVyrIsN2rUaFDHjh19kpOTe/Xq1WtVJfOawsLC8lQq1cAOHTrUqC8wALrTX7CApp4OHkx3+kFBQL9+QI8eQP/+1NCnpgJHjwJ79wKCQIPGDANkZACOjkDRzS4uLg6HDx9GkyZN5NatWz+xhh8A3Nzc0K1bN87GxqZlQkLCiLCwsN+1Wm22pecfPnz4LT8/v2BfX99q33itra0REBCABg0a+CYnJw81Go3XtVrtxeqOo6DwqFBcPRUgCAIHQMswTHeVSvVaixYtvEJDQ61LPAGsW0eN6Mcf3/enZ2Zm4rfffivIzs7+EcDotm3bul68eFFUqVQ/m0ym7XPmzNlbwXzvAPjBxcUlb8SIEXbu7u41W3hiIjB1Kl2bWl35sZJEnwQuXKDG/tgxICoK6NQJ8PfH0bw8JGRlyf0mTyZuNV3PY+DIkSPmI0eOGFmWDdfr9X8CuMjzfERFxwuC0IzjuOgJEyZYu7i41GruxMRE/Prrr3pZllebTKYZNe3YpqDwMFEMvwUIgmCjVqu3urq6al977TWNSqWibxiNwCef0DTMPn3uH5+fn48lS5YAQH+VSrWIYZiOfn5+5PLly39PmzatXCkHQRDe4jhurizLzn379rUKCgqq+YKTk6lv/8ABGgyuDqJI4wNXryI+IQHypk1yq6QkgtmzAZWKxgp8fGhF8xPcUCYnJwfXr19HfHy8/uLFi9YAPgawDcDN4tk/giDYEEJ+l2V56OzZs2Gpb78y8vPzsW/fvsK4uLgbRqOxfV1kGyko1CWKq8cCtFqtOSws7Hej0aiNj4939/f3V7EsS7NtGjUCNm8GunQBipq4q9VqmEwmY3JycrDJZOrBsmy/nJwcd5PJJIWFhS3XarUlkuIFQWipVqt3h4aGOl2+fJmkp6frn3nmGVWNF2xnR337eXm0MKw6MAzQuDHg5wdTq1bYYDSS3hs2ADY2QH4+kJUFvPMOTSUlBPj7bxovcHOrMl7wKLGysoK7uzvatWvHNW7cGAUFBT3y8/M/ZFm2WVhY2B6dTgedTkfUavUvTZs2HTxixAjOwaFuZH/UajV8fX1V58+ftzWbzU3DwsL2KD5/hSeJJ9pv+yTB87xkMBiGpKamnj927NgDw92lC91Vb91a4vh+/fqpCSEeANyMRuPzBoPhhNFo9AbwTDnDd/P29jb5+vrCy8urMDMzU2MyVSsztCxffw0UFFCXTw1xdXWF2WyGQaWi1zlqFDBuHHDlCrBhA40paDQ0XjBwIE0v/eUXYPly4NAhoLbXUEe0atUKY8eObTBlyhRrZ2fnUQDMHMdFEEL+q9Foho0aNcq60iyuGkAIwZtvvqlhGGYcgJZ1OriCQi1RDH814HlebzAYXg0PD2dWrVqVn5FRlKgzaxYtrrp8ucTx1tbWEoBYtVp9wmAwhABw4Xn+aOlx1Wr1ABsbGw4AnJ2dVSqV6u7KlSvza9IY/j4MA3TtCnz1FVUWrdEQDNRqtZyZmVn2TXd3+jQxdiwwbx41/nPm0NdYlt543n2X1h98/z0tOrtw4bFKUVhbW2PChAm2jRo1ksxmc3CHDh3eHjt2rEZdVSykFvO5ubmZWZYd/1AmUFCoIYqrp5potdosnU63Kj8/3yopKamjv7+/imFZmjkzYgSt7C1KB2zSpIna1tZWvHv3rg3LsqNYls3u3bv3meLjCYLAsCw7LTs7uxHDMFxkZCQnSVIHs9lccPTo0ZDk5GRz+/btmRrl9zduTOMPX38N9O5NbwbV5NSpU2Ljxo0ZV1fXyg8khGY6ubvTG86rr9LMojZtqDvs2DFgyxbgxg1g0yZg504gPp7eKFQ192pVl7i4OJw+fZpMmTIFAQEBnEajeajzeXt7W50+fTqwqMDP4iwjBYWHibLjrwE8z9+RZXlmcnJy+IoVK4wpKSk0dXLqVODTT+8f17hxYzz77LPswIEDNT179mxHCJlRzljS9OnTg/Lz8/vqdLoDKpXqEoCkGTNm8ABw5coVrlYBeA8PIC4O+H//r9qnZmdno7CwkLWqqe+eYYAWLahxX7iQxgWmTQMmTaKuosWL6VPAkiXAzz/XbI5qIEkSdu3aJWq1WtnO7qHI+5TB2dkZAQEBhOO4dx7JhAoKFqAY/hrC87zeaDS+mJeXt3fVqlWmwsJCuuMnBPjzzxLH+vv7o0ePHjCbzZ6CIDhXMN7xzz77bMCMGTP8eJ6/55tppFar4/bv329Bc+AKYBhg2TKax3/2bLVOzc3NhSzLpFwdoZqiVtPYQ8eOwLBhVGrCYKBusvPngaFD6ZPAQyAsLAwsyzLPPPPMI01HCgwMtCKEfPzll1+GPsp5FRQqQjH8tYDnecP06dNDWZZdtX79+jxoNNTV88svQHbJp3qWZeHp6akH0K8a46cYjcbAc+fOZV69erXmC/XwoPn9n35KM30sxMvLCxzHSVUql1aXW7doFXHXrkBEBP1+8WKqgOrnR2sKhgwB3niDppeKtZfAKSgowMmTJ+WhQ4eS6lTm1gVubm7o06ePjSiK2wRB8HykkysolINi+CtAEITGlsrxGo3GvwghNNPH35/m0K8qW6gbFBRkp1ar1y5YsCB9wYIFlWr43KOoQfubGzduLFy9enXuiRMnINbEEL7yCnWxREdX6zRXV1dy5cqVutVkPnuWxgMkicpN38PFBZg/n0pNzJ1L4xPHj1NRuqgo2gLTbK7RlFu2bJGaNm0q+fj41M01VJPu3e+Xb1Tdx0FB4SGjGP5yEAShCYBEtVq9xIJjObVa/Z/AwED7+y8OG0bz3Y8cKXGsr68vM3XqVOvx48c7y7Lsa+l65syZs0cUReekpKSx+/btw61bNVQAHjiQGv9Sqaf3kCQJW7ZswapVq+SNGzciMTERHTt2JJcuXao714jZDCQkUFfP6NHARx/Rz6o0nTrRXX/PnsD//kefBP7v/6hrKC+PCuVZGPu4efMmEhMTmRdeeOGxCc0RQhAcHGxWq9XLFH1/hceNYvjLRw0AkiRNKNJnr4wmZrPZp4Qf3N6eVvIeOVLGtaJSqeDi4gKWZdXz5s0bbemCeJ7X8zy/i+O42fv27cs312Tna2VFu3txXLnGdvv27fKVK1fg4+MDa2trcd26dfjzzz9lZ2fnuqs8PXSIFoLZ2tJCszVrgNWrKz8nIIB+pvv3AwcPAtevAx98QLWStm+n/1bCtm3bxKCgIMnR0bHOLqMmBAUFcSqVqgPLsusFQai+VKqCQh2hGP5yIIREOjo6SpIkWQOwruxYnuevcxy3NzY2tuQbzz1Hjf7u3WXOsbKywrBhw6ytrKw+ru7azGbzV1lZWeG7du3S1yjbp3t3qi80YQJ1tRQjMTFR0mq18nPPPUdCQ0PZTz/9FK+88goZO3Zs3f2dHDsGvPzyg5/feQcorEYPG2dn6k6LjgYGDABOnKB1AklJ9Jpu3ixxeGRkJMxmM9O7d+/H/rdeVEdg1apVq+Ecx8VWFOhXUHjYKFo95TB37tz/yrI8iWGY/NmzZ9tXcex4WZZ/sbW1hbNzyf/HqsJCaFeuxPExY5Dr5lbivfz8fBQVgLXgef5addYnCIKtWq0+26FDB+++ffuqASA1NRWenp6Wac0YDNRIzp9PA6pFXLlyBdu3b8fkyZNhWyQ/UecsXkwrgJs0efDawoXUpdO7d83HTUoCPv+cFoz9/jug18O4dCmWLVsmDxs2jPj6WuxZeyTs27fPcPbs2WMmk+klnufTHvd6FOoXiuGvAEEQrACYAdhUpLAoCMLHNjY2XxYWFtpotVqUV2TlcOYM3PfsQcynn0IulQ8fEREBAK/yPL+xButzUqvVB8xmcydJku77rgcOHAh7e3v4+Pig0opUUaT+8h9+oNk1RaxevVp0dnbGiBEj6t4ffvfuA+XQ4jeo776jQdx7jedry969QEwM9plMcvf//pc4bt4M0rXrE6UlZDKZsGnTJvPNmze/mzFjxpTHvR6F+oVi+KtAEASZ47gslmULAEQYDIZtAG5YWVnNMBgMLwLARx99BEII7Ozsyt9xf/oprWB9660SLy9evFhfUFDwfzUx/EVrYwFYMwyzRJKkiQB4AAJAbwCBgYGVD7BmDc2fX7r0/ks5OTn47rvvMGbMGHh7e9dkWRVz+DB1fS1eXPL13FwqKte2Le0HUEcsW7xYfNnKivV68UX6hJOaSl1Nt27RzKHHQF5eHqKioqDVapGVlYXVq1dnmc3mF3iej3osC1KolyiSDVVw7NixAZIktezatWuDNm3a+JtMpgEMw7zWtm3bto0aNTLfvXuXO378OI4fP467d+/q/f39yzby6N2bavYHB5eQSXZycuJiY2OfCw8P36PValOquzatVitrtVpTSEjIHp1O9x+e5w/pdDpbAD2HDx9e+Y4foJkzDEODq889B4DGH8xmM/bs2QNXV1fZ1dW17jJQ5s6lRW7NS2U0WllRnZ/kZOCZ8jTsasaV2FhibNFCbtGpE8GIEdSdlJ1Nfw/Nm9PYQmYm7Vn8CIiLi8PatWvlrKwsREZGyh4eHiQ2NtYawPiIiIjdWq327iNZiEK957EHvJ50jEbjIIZhFh87dgyyLItvvPGG/eTJk+1feOEF6yFDhliPGTMGzZo1k0NCQhAQEFC+L8HGhroz3n23REDV19cXWq3WgeO4yIULF66tTXEPz/OFAEAIGQrAMh89IVR1c98+oFiBWJ8+fRAUFIRDhw6hRtlD5WE20w5fHTqU//4771TdOKaa+Pr6kpiYGPqDtTXNDmrbluoFDRxIXUsjRtAsoy+/pJ3IHiJ//fWX+Mwzz+DDDz8kQUFB2L59Oxo3biwzDKMCcHbRokWnihq+Kyg8VBTDXwU8z2fOmDFjGoDWYWFh+bt37zakFxkIQghatmyJ1157jWi1WrRv377i3fG9vPVibg5CCHr27Im33367QefOnUezLBsrCEKt/CuyLH/s6uqaa/EJzs5UWfPnn4GUBw8dvXr1giiK0vLly+U6Mf43bgDPPkvnK4/u3WmmTlTdeTw6d+6MrKwsprB01pBaTW/Gy5ZRV1dmJk29PXaMFt4JgsU1ApZSUFCAzMxMtkuXLoQQgt69ezNDhw5FYmIiee655+QZM2agRYsW/gCm1unECgrloBh+C+F5Ps5sNvv/888/i3/44YfCahdREUIrejMygHPnSrzl5uaG/v37q9u0aUMADKjlUnWpqakNUlNTLT+jQQPq8vn4QXapWq3GBx98wGZnZ9eNq+fEiar1+b28gLVr62Q6gF6DRqMRr1+/XvFBhNB59++naqK2tjRDKDmZuoROnKjVGnJycrB582asWbMGHh4eYvFmLzY2NhBFEeHh4SQnJwfNmzdXq9XqQbWaUEHBAhQffzXQarXZwcHB4eHh4TLHcb1btWpVvcyXBg1oNs3+/dTvX0ozRhRF1fXr15seOnToJ61WWyOBGq1Wazpy5MgbJ06ccAwMDITKUsnjnj1pO8V//gFatwYAyLKMY8eOya1btyYNGjSoyXIesHgxbfZemWRChw70iaBhwzrLwElISGCys7PFtm3bWrbJCQigFcMMA+Tk0KD8rFk0VbR5c9p60kIiIyOxdetWODo6Ss2bNye9evViirvgXFxc0Lt3bxT1ZGYyMzPNt24zfrQKAAAgAElEQVTdOhAREbFN6dil8DBRdvw1QJblsFOnTnHx8fHIz8+HJFVDyub556lR2batzFt+fn4wGAwBADYVNXuvEZIkDQaA9evXF1jcyUutptWxixYBRcVoDMPc/6oVWVk0qNq3b+XH2dnRHf/GGiU5lUtAQADi4+OrfwEaDU09bd+eVgnHxNB/LSQsLAxHjx7FSy+9hFGjRjHPPvss3ErVcgD0M/b09GRv3rwpdurUibOxsRkK4Nlqr1dBoRoohr8G8Dx/nGGY17ds2XJnyZIlWLx4ceGVK1csO5kQquB59GiJgCpAjcDYsWPRsGHD5ziOy5g/f/6aopTN6q7vIgA2IyNj73/+8x/D2rVrc8LDwxEZGSlXmr7r60szb1JSaCN5ACaTidx7ajAajSgoKKjucoDTp2nNgCXNZCZMoGJtdUTbtm1hMBhIuV3EqiI3l+7+9+yhbqrYWKokOm5clacaDAZ4eXmJjRo1qlJUz93dHenp6cTNzQ1F0h91nEeroFASxdVTQ4KDg8+HhYUtAzDPbDYfjI2NHU0IUefk5MDFxaVMMZcsy8jKyoK1tTWIoyMtZvr7b6qTX2xH7ezsjMDAQHVaWhp7586djgCWaLXaajev1Wq1cq9evbaEh4evzs7OPnPr1q2rV69eDXZwcICHh0fFJzZrRusOrlwBtFrEx8dLFy5ckKOjo7F3715y7NgxqNVquLi4gOMsfCi5eJG6kVpa0Hq2SROA5+nxddAHlxCCf/75R7Szs2M8PauRNBUbS7N9CgupxMSYMdTgu7jQfgLt2lG3XQWZSAUFBYiKimJOnjyJY8eOoU2bNhVmWp0/fx4ARH9/f+bUqVOGnJycmJCQkPDqX62CgmUohr8WaLXae7n0iREREWdu374tx8TE2BiNRnsfH58SO/WkpCSsWLECTZs2hZOTE82h//Zb2qqwnGKivLw8+caNGzclScrX6XR3tFqt5Zk6JdeYr9Vqo0NCQsJ0Ot0XMTExqNJ/HBxMNW+aNoV/UBCJj4/HjRs3yIQJE+Dm5iYeOXKE6HQ6olKp0KS49EJ5yDLdJQ8bBpTj6iiX6Gh6syiqLagtycnJTFJSkhgQEGDZE25SEm2l2a4dfQJJSwN0Oipt7e5Ob9aCQIPh779f7pOMm5sbnJyc4O3tjdTUVNHJyYlxd3cvd7pjx44hPz+fadSoEVq2bGl15syZnhEREfu1Wu3tWl24gkIFKK6eOmL27Nl/fv755+ONRmOvEydO5ERFRUm5ubkQBAG//PILfvzxRwCAvX0x6Z8ff6S5/Tk5Zcbr2rUr6+Pj4wngewA3v/zyyym18fsX4adSqQz30hvNZnP5bohGjWgh1bBh4PLyYGNjQ+zs7GRXV1d06dKF/eSTT4hWq5VPnz5ddXDj9m2aMtm+fYWHZGVlYdWqVeKKFSvEnJwc4LPPqGsoP79GF2k0GkvEXbp27YqEhAS2yliMLAMLFtD17tv3QEzOxwf444+Sx371FZW3Pn+eykaXgmEYdOzYEc888wxEUaw0DhQcHAyNRoOff/4Zzs7OGDVqFMtx3CFFxE3hYaHs+OsYrVZbEBYWtiUxMfH5qKgoJ1mWb+Tn59+QJMnDzc0NrVu3xv0MGSsralS+/PJB28YiCCFITU1lExISSMeOHQnLsr30ev1LYWFhP9Q040Or1aYePXrU58aNG21NJpPqp59+wuHDh1Fu9o+nJ0xXr2Lb4cNigtlMRo8eTYr3qXV1dSUnTpzAnTt34OvrW7Hz/uBBICSkUsO/ZMkS5OTkMM2bN5f//PNPwqjVxHX1arAmE0inTtW6xh07dsh//PEHOXz4MKKionDs2DHp+vXrkl6vZ3x8fEreeItjMtFag3nzaDFZixYP3uvTh+74hw9/8Boh9AnmwAGa9VNq53/z5k3s3bsX586dk3Jzc9kBAwZUWEnt6OgIg8GApKQk+Pv7w8vLC8nJyeb09PTckJCQyGp9AAoKFqDs+B8CPM9fNxqNAZIkefI832LGjBmdAfRKSUnBqlWrkJZWTIyxf3+aIlhOs/Fu3boRADh37hwCAgKsjEajP4BaiacZjcZ3b9++PS88PHw7ISQFAP744w+D0ViyrW98fDyWOTrKgUeOMJPs7UlpN4VGo8Frr71GYmJiSKVtIaOjS8hUlEaSJMiyjI8//hgjRoxgR44cSY4fPy5vcnbG3gsXpOoUj4WHh+PKlSvkvffew/Tp0/Hee+9hzJgxjJ+fH2NnZyf//vvvck45T1dISaG/h3PnaN5+6ZTT9eupa6c8xo+n17hpE1UdlSRERkZi/fr1yM7OlhMSEpi33nqrykrqtLQ0mM1m+fvvv0dERITYo0cPDcuyU2sS3FdQqAplx/+Q0Gq1klarLSj28y2dTvcVy7I5p0+f7iVJEvHy8mIYjqN58998AwQF0ZaERajVavj5+eHs2bNS8+bN5czMTIMkSaGHDh3apNVqDTVclxgcHHy0V69em0JCQpbodLr/5Ofnt7p8+XKLrl27qggh2LdvHw4ePIi+ffui3fPPE27VKlp1XMqXrdFocOPGDdFoNDItiu+Q72E2A+HhtIG6RlPues6cOYOkpCRZq9USAGjYsCF69OhBWvbpg4YffUT+uHZNvi1JJCYmBjt37pRiYmJktVpNTp8+DRcXF9jY2AAAzp49i/DwcIwbNw7u7u5gWRbW1tawt7eHt7c36datG0lLS5P//PNPtGrV6sHTy4UL9Lpycqg/v7zU1ZUraSpqRcFsQgArK8hnz2JTRoYUffYseWXsWPTt25d06tQJxYu2KqJVq1bo1asXsbe3x5EjR8iAAQPIqVOniMlkSgkJCTld5QAKCtVAUed8DAiC0ITjuB22trb+kyZNUnEcR6WE//qLyghUkPaYn5+PpUuXSrIsN+R5vpx+hTVeD1Gr1UkDBw50P3bsmFhQUMC8+uqr5H72T1wcMG0asGVLGeO3f/9+KSUlhRlXXorjP/8AkydTN0kpbty4gfT0dISHh0uBgYEkODi4zEXLn32GDJbFtubNJZZl5Y4dO7JxcXHi7du3GVtbWzklJYWxs7MTn332WXbPnj0YMWIE2rRpU+F1yrIMnU4nHTt2jIwaNYo0z8ykmTorV1bcC6CwkGYa3b1bseEHVd1cs2aN6P/330xIVBRhY2JqVIT2ww8/yDY2NhgzZgyJj4/H1q1bcyRJ+laSpDAAF3mer7aYn4JCaRTD/5gQBEGjVqt3qdXq7hqNhgns3NnGb88eWAUEACNHlnuOJEnYsGGD/tatW8tnzJhRZ5ougiAQQohelmW1nZ0dJk2aBKviRkuSaOPzN96g2TnFOH36NI4fPy6+//77ZV0SX3xBlS/ff7/MW0uXLpVEUWQCAgKkPn36MFblGUmTCfjpJ+D118sYUVmWkZubC51OJ547d44dNGiQ3KVLF4vkJY7odDL35Zdy0Dff0O19HTRpOXLkCHQ6HT6aPBl2Z8/SMc+doy6kahAXF4fNmzfD1tZWfu6554gkSbh9+7Z0+fLlQoPBYDKZTH0AXOF5Xl/rRSvUWxQf/2OC5/kCo9HYLy8vr09GRsbav8LDo7ebTIW4dAlITCz3HIZh0KlTJ2uWZetOu5jyhizL9/oMm8sYYYah1bS3b9Pag2I4OzsjNzeXiYqKQlbpPr5qNdUnKofmzZszjRs3FgcMGFC+0QcAlQqIiCibUQMa/La3t8eQIUPYWbNmwVKjD1FE+7Q04hkfz8DZuWqjP28ebfJeBb1790bDhg3Frdu2SVLv3sDZs/RpopqZSa1atcLMmTPRtGlTeefOnXB3d8fAgQOZTz75xHb48OGODMOcYhgme8GCBbsFQVAqfBVqhLLjf0IQBEEFwPghy8LRyQl4771yXT5GoxFff/11QWFhoRZANAA7nudrrOMuCIItgDwAmDx5MgwGAyrKN8emTVRz58SJ+75ws9mMgwcPijdu3CBpaWkMx3Gyvb299M7IkSw3fTqwYkW5RU4xMTHYunUrZs6cWfkCjx+nmkbdutX0Eh+QkAC89BLOT5qEo9nZ4nsffFB14PTAAdrAZcyYKg81Go349ttvJY1Gg1deeYVxtrWlMYSlS2mAmK1enHblypVio0aNWD8/P7Rq1eq+RHZBQQFOnDiByMj7CT/NARgBmABIADJ4nlf+YytUSG3zwhXqCJ7nTfPnz/96pShO/OjQIWvrbt1oLnsp1Go1GjRoYC4sLOwG4AQAGbV4clOpVJ+3bNmy8OWXX7ap8uBXXqFpmb/8Qt0+ADiOw4ABA1iAuqLS0tLI5s2bmdP/+x+e8fQs1+iHh4cjKioKvXr1ElFVltIzz9Bd+fbttXPJXLxIew0PH444jUZsZGtrmRVu3Zqmc1qAWq3Ghx9+yPz888/Q6XRiaGgoCw8PIC+PVvuq1dXy+wcFBbFhYWFidHQ06+TkJKekpBCGYeDo6Ci/8847pEuXLvj111/NeXl5V0wmk5VKpTLIsgxZlrFo0aJrer3+HZ7njwqCQAC4A8i617dBoX6juHqeIGbMmDHFBPBh3bsXYuHCCo9LT0+3AdAaAAgh5yo80AJYlm3ZokWLqo3+Pby9qdunHJE5hmHg5uaGHj16EPXq1bKxAiXO2NhYUavVIiQkpGrjSwiVS66NPPL//ge89BIVoZsxA5xKBYufdNu3p08dFsJxHLp3747o6Gj2xo0bQOPGtN3k6dP0JpJmeV/1Dh06YMqUKezrr78Oe3t7fPbZZ5gyZQoKCwvlu3fvwsnJCZMnT+ZmzJhhNW3aNMyYMcNq5syZVp988olVv379fAEcEQThBbVavQdAEoACQRCcLF6AwlOLYvifMCRJWn7WZBJTV66s8BgvLy8zy7LvAMCcOXM612Y+WZZJfjl+6Bs3biAvr5we8w0aUNeFpyeVNiiHjh06wCMnh6y7dUssr2LVZDLdT8O0iEWLaNzDUqXRexiNtKWjnx+wc+d9SWW1Wk0sVi1NSaGS1dXA398fHh4eKNEHIDgY+M9/qALp0aPVGs/T0xNjxowh1tbWsLOzAyEEbCm3UfHP08bGBp07d8bo0aPRoEGDTUajceDUqVOh0WjyANRdU2OFfy2K4X/C4Hk+X5blA79VIk3cp08fG4ZhZAC1dnybzWbX8PBw7Nu3z3zPh5yamopffvkFd+7cKf8kf3+aejp1aolWkvdgEhPh8n//h3x7e0ZXLJVTkiRs3rxZzs3NZavU+CkOy9LU0N27LT9HlqnE86lTgJPT/R4DAJCdnW3ZGJGRVK20BrLUDg4OSEhIkO4/WTAMdZVduUIrgG/XXIZHluUyhr88WrdujY8++sh2+vTpsLW1vSccaGGDBoWnGcXwP4GIoqgtkyFTDG9vb/Tp08fGysrqq9pWdoqiGAEAJ06c4L766its3bpV+v777wEALStT05w+nbZLvHmz7Htnz4ITRQT16EH+/vtv+fvvvzd/++235mXLlkl3797FxIkT4VrdBufTplneBOXCBSqCN2QIvVkUK6A6e/asfP36dTKogmyjEqSkANXttFbEoEGDkJKSgo0bN0oZGRkP3ujYEbh2jbqw3ngD0Fc/K1OWZWKpMirDMFCr1UhPT4fBYJAB1OyCFJ4qFMP/ZFLBVvsBXbp0IU5OTj05jksXBKFHLeb6CkAHAJ8CwKVLl+7/TSxfvrziQCDHUbmCgQNp6mJxLl6kypagbpVu3bpxwcHB3ODBg5mJEycSJ6cauJk7dqSGvLI2igDtcRAbS3seeHiUyKTR6/XYu3cvefHFF4kl1bQIDQV++636awVgbW2NcePGMXFxccy3335bUgzP3p42f9+wAZgypdpjS5KEM2fOoLTMRmVERUUZACxXgrsKgGL4n1TGEEIqlZJUq9WYMGGCrb+/vw2A6jmhi8HzvMTz/Hme55cAcOE4LhqAkeO4P9LT023++uuvig2MoyOVXL5+/YH/3WCgFb79++Pu3bto3Lix2K1bN3To0AFt27atUKisSjiOBmgvXqz4mBUrgAEDqJxzJd2yyjRfr4i2bWnz9Rri4OBwv3vZ7dKuHWdnqs/0+uvV3vkHBweTEydOIC4uzqLj8/LycP78edlsNv/X4kkUnmoUw/9kclelUlmkxRMQEKBWqVTTBUGoZVNcgOf59JkzZ7bjed7KbDa/DABRUVFYsGABEhMTUa5g2tixdIc9fTr9+dQpanzVamRkZMDFxaXuRMa+/BI4fJg2QClOVhYNOPfuDYSFlXDtFIfjODRq1Eg+d+6cZSk9K1cCL7xQ4+VqNBrMnj0bWq0W69evx+rVq8WNGzeKCQkJtKagSRPqkgJohbIFmUa3b99GZGSk1KRJE6lcfaRy2Lt3byEh5Pva1HsoPF0ohv/J5K6mAlGz0jRr1gzu7u4sgCoa2lYPnucl0L+P3hzHfb127drcr776Cn/99VfZG8Abb1CDn5tLjXCRbLQoirLFzd4twcqK6gb9+WfJ1+fOBaKiaIevSoLGGRkZSEpKIoGBgVVX+ebkAKmpkCrrVlYFN27cwE8//SSfOXNGZFlWNpvNbGxsLLt27Vpgxw76VKJWA6tX00bzQ4bQz7ACDh8+jF9++QXdu3fHa6+9xlhbW1e5hmvXriE+Pj7XZDLNrvGFKDx1KJW7TxiCIHir1eob/fv3R5cuXSw65+TJk/KhQ4dOGAyGYACeABLqunJTEIQJAFYCgEajMfbq1Yvr3r07c7/FZGEhlWfw8QEmTgS6dcP27dtRUFAgjhkzpu52/f/8Q90+7dtTrf8vvqA9ce3tLerpe/z4cTk8PBxarZYEBQWVeX/16tViTk4OPGJjmSG//UaWTZ0KGxsbqVGjRqRr167E19fXoubzWVlZWL58Obp16yZ7eXkRvV6PiIgI2cHBAV5eXuT5558veYIo0hvBCy/QG2eRjPPWrVslKysrJjk5WUpPT2deeeWVe315q0SWZXz99dcFubm5r8yZM6dstxiFeotSufvk8abJZEJ0dLTFomNdunQhsbGx/gkJCbdNJpOLtbX1fgAD6nhdqxiGuStJ0o6CggL1gQMHkJeXJ/fr14+u0cYG6NoV+O476rLw8MCgffsQcecOm96kCRqePEkNWlYWzckfNoy6bZo0ob11b96kNw2zmX65udFU0dJPDK1a0bz8zZuBM2doENeSQG0R3bt3J87OztiyZQs8PDxKGNGTJ08iJSWF9ff3xzNjx4JZtgwzrayQmJjIXL58Wdq3bx927dpFgoKCqmxfqdPpYG9vL/ft25fcS73s3Lnzg9/nyy/T4PjgwfRnlgW+/54Wxo0cCaxfj29/+03KyMhgXFxcZGdnZ3nUqFEo3gynKpKSkmAwGDIUo69QGkWP/wlDp9P9CYDNzMwknTp1giWP84QQ+Pv7q7Kysri0tDTZaDS21ul0P2m12nK6jtSMokrbGJ1OdxfAYAC4desWKWEAtVraV8DfH3j+eXCXLiFDkuT47GypVUoKAy8vaqwjIqg/ftYsmi4pirS4yd6etjT84gvg+eeBzp2ppPOdOzT7pWFDYMYM4NIlYM0aavyfqb5eXcOGDREXFyfq9XqmVVGKaGpqKrZs2QI/Pz9cuXIFz/7xB6xv3wYTFARHR0e0atWKBAUFkcaNG2Pfvn3Iy8uDm5tbhb8fT09P6HQ6kpmZKZXboezkSSrJ0bhxydfbtgXi42G2t8eJs2fJ2IkT0bdvX+Lv789UNzB+8uRJ8datW2uCg4MPVOtEhacexdXzBCEIQm8Ah4u/NmfOHBALXBgAcOfOHfzyyy/JZrN5qyiKi3meT3gY61y4cOFOg8Ew1NbWVpw6dWpJN44kUZdLVBTQsycKCgrw9ddfIzQ0FG3atLHITVJiLEmiTwDp6bQQ684d4PPPaUwhLo6+Z8HNsTSxsbHYunUr1Gq15OzsTPR6PfH09JSHDx9OfvjhB7HniRPEb+hQprQMNUAF5iIiIqSMjAzy+uuvk127dslFaavw8/MDQH3rmzZtwltvvVW2ZuHedVWQix8XF4e7CxbAU6dDs4gIsNUpdivGf//739ysrKzneZ4/VqMBFJ5aFFfPk8VNlmU3iKJ4Xwry+PHjUlBQkEXWMjMzE4SQ1FmzZk16eEsEDAbDWAA5+fn5ZX33DEON/gsvAKmp0Gg06NevH3bs2CG7u7vL48aNYywtPgLD0C+Oe7AzbtEC+PBDajiNRsDFhergVFPArXXr1pg6dSoSExOZ6Oho6dSpU6Rp06YSANbHx4e9IElmv2HDyv3c27Rpg4YNGzLff/89fvrpJ7i7u8PW1lbatWsXk5GRgeDgYKSkpMDJyUlydXUtO8bBg7RGoJRURnZ2NtauXSvq9Xq21eDBotFkYr2uXgUrioCFfv175OXlITc3lwUV8lNQKIGy43/CEATBBUAqIeQ3WZZnqlSqk6GhoS5qtRpVpe9t3LgxLzY29gOe59c+gnWOBfArAEyaNAkNS/fVNZmoYcvNBZo0gdlsxvLly0VnZ2fy6quvMpZIDlTI9u10p//SS9Tt0749sGABzS6qSFK6ChISEiBJEpo3b44DmzfjudGjwej1ZWMMlXDz5k1s2LABnp6ecnJyMmnSpIk4evToshcqSUByMi0wu39J2+Xz58+TTp06iYMGDWLv3xxXraLSE9u2VevaoqOjsXv37qPTpk2roLWYQn1GSed8wuB5Po1hmGWyLL9qZWV1SBRFh82bN2P9+vVli4BKIdO7uOXlnLVjA4AVAPDdd9/h4MGDEAQBt+5JHKhUwNtv329iwnEc3n33XTY5ORmnTp2q3W7jxAn6VAFQoy/LwNatVAfHQuLi4nDt2jXk5ORAkiR4e3vD29sber0eBisr/O+bb8TqGH0AaNq0Kd566y3Y2NhIBoMB2dnZrFi65gCgFbuXLt3/8c8//8T58+fJSy+9hKFDh7IlnojeeYfGP3Q64Px5i9dy8+ZNk8Fg+KtaF6BQb1CCu08gISEhB3Q63RmO49qazeaWAMAwzP+Li4vr4OXlpapIbiA3N9fq1q1bGb179y5XzUwQBLVOp2O0Wm2lVcGWoNVqodVq9xQFo9+6Z/CTkpIKu3btSi3m8OG0gUlSEuDgAJZloVaryZEjR9C9e3dCCEFSUhKsrKwsEh27T9euQEgIzSQCaExhwgTqDunSBcjMBHr1KvfUGzdu4OeffxYvXLjAxMTEyJGRkUSn0yEqKgpHjhzB0aNH0SA8HC0lCe79+1sWXCmGlZUVDh06BE9PTzk7O1u6e/eu7Ovry5SI03z9NdXnDw4GQOUdLl26JCckJOCZZ54hZWI6TZtSd9aUKcBrr5Xb46A0Bw8ezM/Ly1us1Wqr0LhQqI8orp4nmKJeuGNlWV6nUql+NJlM4QA2MAyDWbNmlQn6pqamYvXq1Xkmk6kbz/NXBEHorlKpdjMMs/nzzz9/XxCEdQDG8TxfbYNWxTqtGIbZJEnSMACYNWvWA0Muyw/kCYYPhyRJWLJkidSlSxcmOztbvHz5MqvRaORRo0YRT09PyyacNo0azu++K/teWBj199+6RYPBRVk7er0eGzdulO7cucP07NlT6tmz5/1Yg8lkQkFBAYxGI1xcXKiUc0YGyPLl1f4sfv/9dzk7O1t+8803Gb1ej+XLl0uBgYHo06dPpU/XkiRh/vz5ePfdd8u6ze5x+zatUH7pJaCcGoR7iKKI+fPnmyRJcuZ5vhxtbYX6jmL4/wUIgqDheb6g6PsVACY2atQo7/nnn7dr3rx5iWPPnTsn7dmzx8AwzH6j0diGENLWzs5Or9frfzGZTEdAXTR+PM9fKjtTrdfZWqVS/ebh4eE7fvx4zX3jn5ZGg7CZmYCTE/755x9ERkaKHMfJo0aN4iIjI3H27FkMGDBA6ty5c9Xux7VrqeF/772Kjxk9mt50fv8dALBv3z7ExcXJr7/+OrG3t6+Dqy1LfHw8Nm/ejIkTJ8LZ2RkAlVj4+eefMXPmTHqjNpupb//mzQdPLADOnTuHsLAwecqUKWV3/MVxcKBB7fz8CuWic3Jy8N1332XPmDFD0d5XKBfFx/8v4J7RL/r+XQBccnLyxN9///3umjVr8otLOHfs2JGZNGmSTfv27YcD8JVlmUycONHGx8fndSsrq1cBgGGYxQ9pnbEmkykoOTk58ocffsi/L0fs4kK7WDVpAphM6NChA9577z32nXfe4ezt7TFw4EAMGzYMBw4cYCxqkBIYWHVzlI0bgY0bkTF7Nq537CifOXMGHTt2FC0y+l5eVNq5GhQUFGDbtm1S37595XtGHwAaN24MhmGQk1NUUiGK9ImlVCOazMxMODk5SRUa/UOHaO/fmBjaC6GStNjCwkKwLFtnNRwKTx+K4f8XwvO8yPP8BqPR2PTOnTvzV69eXZhWrKWfg4MDhg4dinHjxmHWrFnQaDQYNmyYtdlsfp5l2VyO4x5aXjfP8yaDwfB8RkbGrJUrVxZeuGdAu3enwUmWpQqepWjfvj00Go24f//+cqKhpdi8GVi3rurFEIJd2dlSTq9e8gfvvYdgV1fL8kg/+4zq/lhITk4Oli9fLjVv3hyldYDuBY/vV9wmJwOvvlpmjCZNmiApKYmtMPD96afAjz/SzB4fH1r1WwGFhYUghGRUeIBCvUcx/P9ieJ43zZo1a75er3/vxx9/LCzdMcvHx+e+r93GxgYTJ07kWrVqZSeKooXO9BqvS541a9bXJpOp5+7du29v375dbzQaqcEaO5Zq+JdDu3bt2PT09KonGDIEGDHCorXcbtSIabloEWP/v//RgHBV3LlDm6tb2BoyOTkZK1askNu0aSOPHDmSKS/uwjAMCgqKHtrmzqWSDKVo2bIlQkNDsWfPHpJbXKjtP/+hef+nTz9QQLW3p8Vr5dxAAdyby4IPUqG+ohj+p4DZs2evNRqNr65du7YwIaHiYl0XFxf07NmTsCw7fu7cuXWt5VMGnufPmkwm3+jo6IO7d++mIvjLlgG//ifOspEAACAASURBVFpWWhmAvb098vPzqw4837hBpaCrQJIkmM1m2o923DjqV09NBfr3p37y8vjpJ6qjYwERERFYs2YNAgMDpSFDhrDluWlatGiBxo0bS+vWrZONRiPdtd9LRS2G0WhEeHi46OTkVLIR/JUrtLlM8bHt7IBjx4CMspt6k8mEixcvQpblZIsuQqFeohj+p4Q5c+bsMJlMQzZs2JAfGRkpVeQr9/LywgsvvKCxtrb+/FGsi+f5XJPJNPbKlStiamoqdVV4etKvHTtKHOvu7o7s7GzGUMFO9j7nzlGtmypgGAYcxyHpXlN4a2uqIurgQOsMypNAnjmz8mYvAMxmM1atWiWePHkSY8aMQZ8+fco1+vcYN24cYzQa5cjISBmvvAJER99/78cffzTPmzcPCxcuBCGETJw4Efb29rT+gefpjWjChLKD5uTQNM+7JSX2MzMzER0dDb1eP7rSi1Co1yiG/ylizpw5h0wmU7cjR478tWzZssKTJ0/K5RUQeXh4wGQyBS1YsOAnQRDaPex18TyfLcvy3G3bthUUFhbS3evmzVSZstjutmnTpmjQoIEYFhZWuZ9/6lRg3rwq542MjITZbMbNmzeLT0I7hOXkAK6utCFKccaPB44cqXDMgoICrFu3DmazmZk0aRK8vb2rXAfDMBBFEZ6engR6/X03UnR0NFJTU7kpU6Zg9uzZeO+99xg1x9GnoT596FdF2NvTHX+pal43N7d731YdK1GotyiG/ymD5/nozz//fIBer+998ODBMwcOHCjj03B1dcUHH3yg7t69+ziO404IglAzFbBqIIri0oyMjJ9XrVpVkJWVRf3tp09Tw1XM7fLyyy+zZ86cYYsHq8uwZAlV6ayEixcvQqfTYeTIkejRo0fZ7biDA3D5MuDtTX3n954KOA6ooAnOpUuX8PXXX4NlWXHUqFHExsI4AEC18TUaDbBzJ1CUgvvXX3+JvXv3lu3s7B7UZISGAq+8Ql1TVUg/w9YWCAigMtfFCAgI0AOYbPHiFOodiuF/SuF5/rTRaAw9e/aslJdXtobH3t4effr04bp06WLFcVz1O35Xfz3S9OnTJ+Xl5c3+4YcfClNSUoBu3YBvvy1Rierm5obGjRvLJ06cqLi6OCCAVuhWQEFBAXbs2IHhw4ejffv2FS/Kx4c+cRw8SLNtRJF2w+rW7f4hZrMZhw8fxoEDB7Bz504MHz4cr732Gls8ZdMSbG1tZXHZsvtG/8KFC8jLy2PT0tLodeblUbfNokU0DmIJDEPjFaXcTLm5uUYAd8o/SUFBMfxPNTzP3wLw/3777bf8io4hhMBsNj+yQODMmTOXGQyG6fv27csDw9BA6ocfAi++eP+YFi1akPuaP+XRpg1txlIBaWlpUKvVaNfOAi8WITRe0KkTLayysaE3gSJOnz6N8PBwxMfHSy+//LJlY5aDJEnICA29H9fYvXu37OjoCLPZTK32G29QeYu2bak7ylKWLKExj2IN5HNzc2Uohl+hEhRZ5qcck8k0KzU1dVJ2djbK0/hJTEwsBBD/KNcky/IPiYmJ86Kjo+Hr60v96tnZ998PCAjA0aNHyYEDB6R+/fqVSZHE7t1ASkqFsgXp6emwtra+1zO4akQROHqU+vwzM4HffgPOnYPUvj3OpKXJQ156CZ27davVJslkMiF382assrZG1v79YBiGDBw4EM3NZgb79wPr11vUbL1cJk4Eli8Hhg4tuhyRALCgEk6hvqLs+J9yeJ6XOI6LuHbtWonXc3JysHXr1sLk5OR0AHsf8Zr0DMNM2bZtW254eLhZ7tyZBjIDAoCDB+Hg4IC3336bnDlzhixatAhRUVEl3T7Dh9N6gHLQ6/XYt2+f3KFDB8usqCjSXsFLl9IK4zVraIHVtGlAy5YYtHMncf/kE7pT37+fSkXUgEGDBrFt/vwTQxs0wIsvvgiDwYC7d+9KWLWKdi2zsqpRQxkAVJeoyOgDgLW1tQzA8n6UCvUORZ2zHnDo0CEbQshzfn5+agDIyMjAihUr9Onp6d+ZTKZRxSUhHhW9e/c+Gx4e/tOdO3cGZWdnO7Rq3ZojhYXUZ21rC41Ggy5dupDDhw8jISGB/PPPP9KpU6dkSZLQKCaGsHFxVLqhFFFRUcjLy5NHjhxZudyn2UwN7urVNFA8bBiNNRw9Snf9gweD+Ppip729VNiundzC25vBgQO0LWRGBu0d7OxcYSC4NC4uLrCbPBl2Wi0cHR3hcPQoTKtXk8zPPpPdP/7/7J13WFTX1sbffc40OggISFFEpQiKqIAFwULssZPYE5NYU9R8iUZjxpOqubkxRm+KehNbjCUqVsSGFBVFQaUoioJIEZUidZiZc/b3xwYEAUvaNcn8nmcemTl1n3HW3nvttd41/7eL5vn6soSuwECkpaVpi4uLI0NCQi4//kAD/0QMrp5/BgevX7/+dUZGBgoKCmhMTIyGUvr2okWLvv1f3pRarS4QBKFXcnJyUosWLVr3fPttHpcusRF4fDwkSQLHcRg/fjzKysq4iooKxMfHi9p9+3hvCwva4vXXGxnMoqIi2NjYPNrNc+kSM9zx8ayAi6srG+lXVjLZ43r+ckopym1tWY3goCBWYGbfPpaElZnJ/vXzY7UHLCwaLbTWcf0661xSUsBxHDq5uSEtMpLeysmROnfu/Buq0tTwySd1rq8bN26YA/ABsOs3n9fA3xKDq+cfgFqtvi1J0tQdO3bkxMbG/qLVagP/10a/FrVaXarVakOOHDnCL1u2jFI3N6BnT4AQHDlyRK9QKKi5uTl8fX3Rq1cvzJ07l9e98Qb29e7dZNRPWVkZLCwseIDJEzdAr2eF3t98k43at26ti7IBxwH9+zPjuWEDq5IFYOTIkdzly5f5uiQwuZwtRH/6KUusevttduzMmUwtdNcuVjRFeuj2VCrmolqzBggOxvrKSml3aCgJCgr67UYfYKUuf/kFuqNHaz/5zTUXDPx9McgyG3gmEAShC8/zkZ6enmYjRoxQcStXIj48HPcXLcLgeto+er0eCSNGSC3Ky4njgQPExMSkQV2C77//XuzUqRNva2uLLVu2wMLCQgoICKCBKhWPBQuY797dnSlw1nLzJptlpKSwEbufH5NW8PMDABw+fJieP3+eTJw4ES7NRdxQyvICiorYsTExwL/+BVhaMpeUKDJXUkEBEBWFtB49cOjQIVGj0fDe3t5i9+7d+bKyMlpaWgp3d3diZmb29A9x4kTES5LumLd3yuLFi/2e/gQG/ikYDL+BZwZBEIwVCkW4t7d30PCqKlVRcjK+USqxcOFC1BZNOX36NHK+/x6OMhk91ro1sba2ll555RVOqVQCAL7++mt93759ZTKZDAcOHJDsLCw46/Bw9BkxAqZGRmxR+GF3zPLlzIXz/vvs/c2bbGRfrzBMbGwsYmNjMWnSJLi4uIBSikuXLiEnJ0dycnLiOnXq1LAwTnExEBUFnDvHEq0++YQlqun1DS5969YtHD9+XMrLy+O09RLZpkyZgodrLTwOSik+XLoUcp0uedGnn3Z6qoMN/KMwuHoMPDOo1epKrVY78cKFC1y0nR3iAwPFkVu3oujkybp95HI5SoyMUGFjQ+fNm4eqqip6tZ5gm06nIyYmJqiuroZUXs51+/JL2Ofl4ZusLCR5e1MK4NKlS6hvZEEp89HXEhMDvPFGzSY2MAoKCkKXLl2wd+9eWlBQgIiICPHQoUO0pKSEO3TokHT06NGGfiUrqwcuofnzmRw10KDWLsDkmKdOncotWLAAAwYMwNixY+Hr64vdu3dT/UOdRFPcuXMH9RVNJ23ejJd/+MHksQca+EdjiOox8EwREhJSeeLEiXY5OTm+PM8jID+fWPfvDzg6AmBCbl1SU9Huzh2iGDUKFRUVXFRUFHJzc8XDhw8TrVbLBXbtirabNsFj0yZsGzUKqT4+sLS3F21tbTlJkrBt2zZcuHCBuri4EPO8POaXrz8TcHCAdOoUNhQVSYcOHSI9evQAx3Fwc3NDUlKSGB8fT+7du4fXXnuN8/f3x82bN1FSUkJ9fX2bHkilp7MkqxdeYJIN4eFMmrpeMXdCCFxcXNCyZUt4eHjgzJkzUlJSEnQ6HYyNjYmRkREyMzNx7NgxMTw8nIuPj5cSExPpyZMnyblz51BdXQ1XV1dcdXaW9rZuvbV3SMifGqJr4K+FIarHwLPIfAAvhIWFqYxfeolJGbzyCvDf/4LjOKZnXyNDERoaWmuQeb1eD9uMDFx6/30qy84m58eMQZWJCVq0aCFWVFQQZ2dnqFQq8DwPZ2dnsm7dOkz18EDr4OA6N01lZSVSsrJQUVFBucuXoTU2xpdffomXXnoJLVu2xMyZM2t/M3V+nby8PDpmzJjmF2nPnGGLwCtWME2gFi2YQmfHjix+vwlmzpzJnz59GhcvXhSjo6N5SilkMhlt1aoVr9frodPpOG9vbzg5OVErKyuyc+dOeubMGeLg4AC/s2dfBCGloPS93+XbMPC3w+DqMfDMoVari+RyeWpdJI1Wy3Tpa+WaExKAiIi6/du2bYsxgwfDtqBAGn7gAGydnUnnX37BvM8/BwC4urryWq2Ws7e3R1xcnGhmZib2798fVpaWVD5/PjR+D9ZBt2/fLp48eVJy02rJRGNjTq1Ww8bGRv/999/j/PnzTd6vTCajzZaMpJQVTWnXjq0h7NgBDBrE1EHbtmUhpU2gUqnQt29fzJ49m1+4cCFeeuklvPPOO6S2qlqHDh2kpKQk2NraEicnJ7z11lvkueeeQ25uLleqUrWoViialekwYMAw4jfwrKKU17pCXFyAkyeBw4dZhayCgoaFWM6fh3bRItiYmHBmKSmws7Gp29SyZUvY2NhALpcjJiYGly9f5mbPnk0sLCzQQacjMlNTGPn6AgDKy8uRk5PDz507F6ajRtUtxE6bNk32xRdfSElJSaRVq1bEwcGh4Y0qlShrStsfYCP80lKmwePoyBQ3p01jiWCrVjF10MhIIDS02Tq6HMfBscbVVfu+W7duXGZm5oOSjgC6d+8OOzs7rF+/HndtbbfOJMQXlF54wudt4B+EYcRv4JmEUmqiqKfaCQA4e5YtvE6fDixbBlRUsFq05eW4M2wYLnXtipKHKmvNmjULfn5+sLCw0F+5ckUcPXo0MTIyQmJiIvirV6WLEyeKIASiKGLnzp1Sy5YtRVNTU8DamoVh1swywsLCOFNTU7J+/XrExsbS2sXh0tJSlJaW1kUVNWL3bqYBRAjT0JfLgd69gdWrgXv3WMTPG2+w3IEnJDs7G+Hh4VJwcDCtb/gJIXX1ATqmpFwD8ASFiQ38EzGM+A08q1y/cuWKs4ODw4P/o7XhlsOGsSgZd3c2KvfxgVNwMAKOHsWGDRswYcKEBgVSFApFnW/+2rVr+OKLL2BibEwHpqQQ52nTOIBp49+7d4+88cYbzFd/7Ro7f41Bd3Z2Rrt27ejVq1fJ+fPnpdOnT/P9+/dHVFSU5OXlBW9v78Ypu5SyovC76iXQHjzI8gU6d2YJZE5OQGIi23f8eOCDDwBPz2YfiiRJ+PHHHwGACwwMbHIfW1tbHO/fH8dDQ19VP8GDNvDPw2D4DTyTaDSaKadPn77s6upq0aZNmwcbKGUhkXfuMJmFem6dAQMGwMjICD/99BPCwsLQrl27BudMTk7GgQMHaL9+/RDYoQNBTg4QEACAjaJ9fHw4uVzOYu47d2bupRq2bNki3rp1ixs/fjzat2/Pnzt3DgcPHoQkSVxqairy8vKkF154gbO0tHwQz3/pEiusUs9Ng8JCttB75gxT1Lx3j8lHODoyyYdz54A2bZot9s5xHJRKJa2uriZ6vR4833hNeeTIkVi7di08U1LOUELOE0q7Pd3TN/B3xxDOaeBPRRCEPjExMZNOnDiRGhISUtXcfiEhIeVRUVFpN2/eHB4QEPDA50MIk0e4fx/Yv5/p39TDxcUFt2/fRmZmpti5c+cGrsydO3dKRkZG3IgRIwj57DNW/atG38bExIQ7evQo4o8dg8f336Ng+HBQW1vwPI/IyEgxPT2dmzdvHrGzswMAWFpa4uTJkxg3bhz8/Pxw+/ZteuTIERITE4Pz589TW1tb0iIhgcXzd6qXS2Vtzf7t2JEliG3ezOrqjhvHFDa9vdlMQyZrUoSutLQUiYmJ0Ol0xMfHByYmjUP2VSoVYmNjUWlkhDJLS6OfMjK+DgkJeXxSgIF/DIYRv4E/FZVKtVySpABCiC+AcY/al1K6v7y8XHPjxg3Ttm3b1j8J8/HHxDBRte+/byBpnJ+fLwYGBjYaCrdq1YrT6/UiAXjs2cP87zV4eHjAOz2ddtBqyalvv8XV9HRRm5zM6XQ6Ym5ujhkzZhBVvWsoFArI5XKYmJigdevWaNOmDQewcNDExESyfetWTEpPp87vvUdAKfbu3SumpaXxpqamdIxSSRwWLwZZsQJYuJDVH758mRl9ngfduRNn4uPFqsWLSd+FCznUk2/Yt28fraysJLXhpU0hl8thZWVVXgxsvejt/Wr3+PgyQRDs1Wr1I+pZGvgtCIKgAqAAUAngUwBBALYASARwXq1Wa+rtawnADEAfmUzWS6/X/6hWqxP+zPs1jPgN/KnExsZKWq12JCFEERUVxUdHR6eGhIQ0qgsMACEhITQ6OvpaamrqoMzMTL2FhYXC0tKSbeR5Fu2TmspCJIcOBQhBUlISLl68yAUHB8Pc3BwAkJCQgHPnzuHy5cu0e/fucCgqInBza1TTtuVXX5ELKhXsBw3CuHHjuKCgIBIUFIQePXpwxg/JL+/evRscx0l9+/Yl9aUa5HI5XFxc0F6SgJUryY92djQ7OxsZGRlk0qRJhOd5khATQ9ts3UouBQVRm5YtSWppqcSNHEmohwd4Nzd8vWMHvVZWRnqvX8+ZpKSAr1edrLS0lOTk5KB169bUzs6uWTlnMzMzRUZGhoVrevr24JgY/6RevdoG9emz4+m+LQOPQxCEjmfOnImUJGkFx3HvUkqXAugFwKlTp059CwoKpgN4Pzo6eml0dPSS6OhoEcBhAPMAjJYkqbtcLp94/PjxwyEhIXl/1n0btHoM/KkIgkAALALwcc1Hw9Rq9YHHHGMKYJGNjc0bc+bMMW2wUZLYyH/FCmDjRuhNTLBjxw4pMzOTCwkJoT169CDfffcd7ty5g+eee07q0aMHh6++YjOEmTPZOVJSAEGAuGkTPl+xAo8UY6th7969KC8vFydMmNB04tZ770Hy8sIpV1dkZmZi0KBBsLW1rbllCZe2bUNSWpqYp1LxlFJ45+RQnV5Pcl1cQM3MxHnz5vGrVq4Ue3buzHfdtYsplr74IgDW6ej1enHcuHHNJo1RSrF///6qxMREIwDgCfnX+x988O4jG2XgkQiCYAbArfZlZGTUX6/XBw0cOFDl4+NDFAoFatddagcDGo0Gx48fR0JC0wP6ESNGiIQQ/sCBA4Ucx32xcOHCZX9GWwyuHgN/Kmq1mgqCsAxAFIB7arX66hMcU/7ZZ5+18vLyalz1hOOAPn2YLv6XX0L2zjsYP348d/36dWzdupW0adMGlpaW1M7OTurRowcPSWJ6+0OHAgBycnKgX7sW1WVldM/XXxMjIyPRxcXlsVLJHTp0QHh4eNP71VyDCw1Fb3t79O7d+6Fb5uCbng7f4mI+8+23sWnTJgz/z39I9ty5GJiUBNNffuEBwN3Tk0+4ckVqbWnJWSQmQj58OIq1Wly+fBmUUr62XkFTEEIwbNgwo8TERFgWFuLNVavegFq9GJQaSjI+BkEQOgLoQghpr1QqfQgh7jqdzpnneZWZmVlVixYt0LJlS2MHBweZq6sr6iup1ooJ1qJSqTBkyBAMGTIEO3fuBMdx4pAhQ/jS0lIYGRnB1NSUp5RCq9VaR0VFTQRgMPwG/p6o1WoRwKmnOUav1+dID2vc18JxwHvvAQcOQDt+PBJefhlphYVUoVAQa2trZGVlQZIk/vTp02hXWAibEydA3nsPVKOBZtAgRI0YIVm/9ho3xN0d3t7ejzT6kiQhOjoaZ86coV26dJEANN4/JYVV5rK3b749kyYhZd06VOTlgeM4JCUloduyZcCcOUza2dYWPXv2xPnz57nvlUq4tG9PRw4aRCoAGI0eLZaWlvK3b9/GyZMnpeeff77JPIIa/R+azZbEgwxGv3kEQbACMEypVC4wMjJydXFxEW1tbU2sra05KysrtGjRAqampiCEyB97skdACIFSqayb/dV+5uLiAkmSLARBIGq1+g93wxgMv4G/BDzPmyuVyuYTDgkBhg7FhQ0bULl9O203fDjtMm4cUSqV8PX1xf3793H27Fk9v2OH7Ky9Pap37RK9y8t5aLV4+YMPOFlzCVgPsW7dOn1FRQU/YsQI4uHh0XQnUSvR8Agi09Nhd/IkCquqpPY9e1JPT08eJibAunW43749dg4ZQgvbtKFarZbr06cPMjIy6Jb+/SXH0lIyuEULPlWnE9euXcsD4G7fvk1fffVVYtRECOj48ePJihUrNBumTNFPJWQXgDEw+HfrEATBUqFQfMbz/MsuLi46f39/0w4dOjQ7k/otPOqxW1paQi6X2+r1ejWApb/7xR/CYPgNPPN8+OGHfWUy2UvOzs51n127dg0nTpyQfHx8HiQyEYKMUaPgdfEifA8c4NCrF2BpicGDBzOHq14vg06HzP79oV+wgE9xdpaSJ03iXisqwsMyDE2RmJiIwsJC2ezZs2Fh0Uwtc0qZHMPB5sUxNRoNLly4gBkeHuimVHJ44YUHG2UyRHfqhC5ZWUQ5Zw5xd3cHz/Po27fvA0v07rvwiIzkB0RH4/qNG9i3bx9JSUlB9+7dG11LpVKhZ8+e8tOUfgDACoANgLuPbew/AEEQestksgNeXl6K0NBQpbGx8ZP1/r8SvV4PubzpCYMoiuA4joqi+KdIbBgMv4FnGkEQXGQy2Z4XXnjBTKVSYfXq1aJOp4NGo+F9fX1JVFQUzp8/L/bv35/38PBAWVmZWNCvH0FmJsHZsyyzt3b0ffYscPUqXF95BaiqQvuPPuJG2Ns/0eiuoKAAhw4dwpgxYxobfUqBsjJWfGXDBhaDL5czVdHz5yENHozN//qXvkSn4xQ2NpRSypuamko2q1dzOHOGrQnU3ENRUREudeuGAd7eMP72W6bn8zCffw6o1bD4739hduoULHv0oD4+Ps1G+AQGBvKnTp0aJCxd6qdeurTpzLB/JrF2dnYY8VAuyB+BRqNBZmYmHTt2bJOzxEuXLqGqqioOwN4//GZgMPwGnmEEQfDmef5wUFCQsaOjI77++mvJy8uLtGnThnNzc4ORkRHx9/fHpUuX+N27d2Pw4MG4c+cOP3HiRGDgQGDTJiaBoFazpKiSEqa988UXQGwsgCcTq5LKyrB71SppgL093BMTOSQlsfMkJTGl0I4dAVNT5puPjGSdTVER8/UvWoSjcjme++YbmcrHB+mLF8O/d29c3bePYNUqtjYxZQrw+ecoVyqxdu1a6h8QIBkHBvL45BPmNmrfvtE9Zd29i5MpKbTT5cuk44svUhXHEQBIS0uDVquFm5tb3aKjUqlE7969FSdjYz8DMAiE9AKlib/bF/UXRBCEAQCr7/BnsHPnTsnR0ZG2b9++keEXRRGJiYnlOp3uP2q1+k+plWww/AaeWTiOe0EURYeoqCgaExMDV1dXDB06lKsfN29tbY2+ffvCysqKHjx4kMjl8geKlZMnM4McH88E3d56C/DxYaGRtVRXs5F6cTFLoiotZSP40lLmrtFoUO3tDe+LF7kuLi5Ahw4sE7dlS1ao/c03mdyCUsnkGGpDRVUqwMsLmuefx/kVK2j7EyeIvasrAigFbt6Eu0JBsHo14ODA6vEOHYr7LVuinZcXDe3alYe5OYtUWryYFXWvt2aQnJyMXbt2oUPfvrBeuhQdvvuOgyCgOiYGO3fuBM/ztE+fPqR+NFHXrl356OjowTmOjtZOOTn/eMlmmUw2Ua/XN1A9fWK6dQNyc5lS7BOQn5+PrKwsbtasWY221YTdVpeWliYA2P/0N/PrMBh+A88sS5YsWSIIwlIAZhzHZQcGBpqRh+vl1uDr60vatm2L7OzshhtGjQJ++YVV2MrJYWqeN28yg7ppE/sRd+rEau7m5QF9+zJjbG7O/m7bFkYmJri8bp2Yb2mJR8XOY+RIJr3w5pt1H92/fx+SJJGcnBzR1dWVByHMBbR0KfB//8c6DCMjXCQEaatWUYv8fI5Mn84M/eTJrIOZPx/Y+8ADULse0bVrV9KqVSskDhtG0zMzSaFaDU+dTrzv60uSk5Nha2tL3N3dAQDGxsawtrbWb5g1a/5iQoYBmANKz/2a7+XvgFwu76LX69FIAfZxZGcDa9YwragnICEhAceOHaOBgYG0RYsWDSaYlFLExMSIaWlpuVqtdgQAK0EQTNRq9c2nu6mnx2D4DTyT1CR69QbgwfO8DwB5/XjppjA3N4e3t3fjDWPHAt9+y0b0lpaAszMQHMzkkFu2bFYHvz5GRkakfm3bRlDKEqxefrnBx3Z2dpg8eTI2btzI9+7ZE+TsWXbdjRsBLy/kHjqErKVL6cnQUNLrlVfQtWtXlmeQmckMvlzOavd+9BGwZAkAYPfu3aKnpyc6dOjAnzp1Sjpx4gTHe3vD5cIFPH/0KK9fsgRH4+Oxfft2vPPOO6iVmpg4caLJ2rVrF6Z7eJxwv3Kl6LGN/huj1+tdTUxM8MsvvyA8PBxz5szB3bt30b4Jt1od4eEs3Pb6deY6DAlhs0Ljxukld+/exbZt28TKykp+2LBhpGPHjg1GLFVVVdi2bVtlfn5+plar/TchpIhSKiOEbAQw9XdubiMMht/AM4cgCLxSqYxVKpU+rVu35lq0aGHk6+tL6uQafg2+vuxHO2oUM7yff97kD7YpkpOTcf36dS6gRsmzSV54Afj44ybP6eLiAiO9HpWffAKTixeZKHvcQwAAIABJREFU6mdN+OiV8+fhf+kSCTx0CDzPM+OgUjFxt8OHmSTF/v1sneLIEeDYMVRVVsLS0pL77rvvpOLiYtK3b18cPnwYt7p0ofjuO3JCEPRuR47Irrz4YoOFa3Nzc0ybNs14nSSFdD90aE4IIV+A0ifzV/zN4DiuYsKECeaUUuzYsYOuXLmSAMBrr72GVq1aNT6gpITVUdi794EulI0NcxHW+871ej327NlDr1y5Qrp06YL+/fvj4RwLSil27dpVmZeXt12n082Ry+U3/Pz8ZGfPntVSSuf8ca1+gMHwG3gWkWm1Wv8pU6bwTf4Inxa9HujXj2XrlpYCcXHs32YMf15eHnbu3ClqNBpCKYVOp+NsbW1x5swZ2NraomvXrsjOzsb+/fvF6upqjH/uOd7+6lWmrd8UGRnwu3RJytRoiPf+/aT+DOO2szPOfP21NICQpqcd7u5s1J+QAERFAatXY/ayZfynM2agbfv28Pb2JtHR0bRLly70+eef5wDgpqUlCWjZEi+NHQtFRQWoXF4nIWBlZYWXX37ZmH7xxdyUjh3NvYHXfsuj/avCcdzd6upqB1dXV8ydO5dUVlbi7Nmz0pYtWzB37lyuQQZuVRVTcf3kEzb7quWXX4Djx9n/r9atcfHiRURGRkoWFhZ49dVXiZ2dXZNuwaSkJJqdnZ2n0+lmAnDU6XR2ycnJlQDGqtXq8j+25QyDSJuBZ46QkBAxOjr6xrVr1wb5+/vLf3MyzenTzJ8+cyYbac+cCSgUTOStXz/AwQEFBQU4ceIEjh07Jp4+fZrz9fVF3759OV9fXxIcHIwePXrg5s2bNDU1ldy8eRPx8fFSmzZtOIVCQap376Ztdu4k9RVC60hKAj77DGZ+fmSDvT0puHNH8vLyIoQQrF+/Xn8zO5sbs2gRKfL1hVlTbgZBAH78Edizh1XyOnEC3H/+g+Bx49Bp0CBySaORbpmYcGFhYXUJXCeTkqh8wgTiXlxMKvv0wbd37iC/slJ0cHDgVCoVjI2NUT5lCtkmip5RUVGXgoODHyub8XcjJibmw549e5rUiu/ViOuRtLQ0euXKFdq5c2fWU1LKaj5bWwNhYSxRsD7jx6OyuBjrrl4VU1JSuIEDB5KhQ4eS+pXR6lNSUoKff/5Zo9Pp+qvV6vzo6Oj7AD4QRfHNDz74YOsf2eb6GAy/gWeS4ODgSzExMa+0adPGqtlkqSdFqWSFVepLO/M8YGGBiw4OiFyxQjyRlsYplUrR09OTHzx4MDp27EjMzc1hZmYGhUIBQggcHR1JSUmJaGZmRs3NzTFs2DDOJjGROH31FTH6v/9Dxo0buH//PszMzMARAvz0E3PTzJwJ47AwVFZW4tKlSyQ3N1fy8vIihw8fJlOmTCHV+fl0X04OOZ6cLFVWVhI3Nzd2j1otcOMGi0aytGSuhXfeYdFEvXsDgYHQuLuTdhs2UNW338Js1iwCAFZWVtzhw4cRk51N7rZsSUfMnUsqfvwR29PTiaOjY538gP+778rNr18fuy4nJzIkJCT3tz3kvw6CIHCSJH06YMAArn4hG0II2rdvT44dO0ZMTEzYIvqiRcDXXwP/+lcjoy9JEva3bIm9xcXo4OREx0+Zwjk7O6O5AAQAiIuL0+fm5n6/ZMmSjQBToA0JCRGCg4PP/0HNbRKDq8fAM4skSccyMzNdnZ2dm/8lPQnffNNIghkA7o4YgfDVq7H42295umUL5MOHP1Knx9bWtmFUD6XgevfG1qlTUbVypajT6XiO40DLytC3tFTqdvcud2PGDLT180NleTmSk5Npv379SExMDPftt99Ktra2cHR0JNycOWR8UREy7ey4iIgIGhoaSqSqKpT7+eHAsGFiiShi/PjxvKWzM6s6lpTEFhiDgtAJwC+JibQyPx+t8vMJQkPhHh6OhQsXkj179sB5yBDSkuNgFRnJpY8e3UBQTLlsGcx5Xi6PiTkgCILnP0ivn+c4TqfRaPiHM2nNzc0xatQo7N69m7q5uhKLrl1ZcMBDXL58Gfv375eMjY3xekEBZ/7zzzzCwh574bt371bpdLo/VXu/KQzF1g08s2i12pVxcXFVj4ymeRIOHqwrml4fuVwOTibD8jffxK7KSune1KmQUlKe/LybNqFVWBjGvPMOBg8ezL/99tt4d8wYTL9zBxUpKdznnp7YGR+P5cuX0//85z+0ffv2Uu/evdG6dWuprKyMmzBhAsdxHHD4MCx27ECtq+bu3buI3bgRN83M4DZ2LK/RaLjExJp8q9GjWXtGjQLApCuuiiLX+cMPOVhbs8iili2B2bMxIjUVfn5+KLe3x1czZ9IeoaHUbtw44G6NYkNwMNxv3sSQ8nJLpVK5WxCEx6qS/k3geJ6/V1BQ0ORGDw8P+NnaSmjfHlJwMNC1a9220tJSrF27VgwPD0dISAiZNWsWZ/7hh8DOnU904atXr5oB+Oj3aMRvweDqMfDMEhIScjc6Orrsxo0bffz8/BS/ytev1bLyjL6+jabqKpUKJiYm0AFUkiRis3kzKXJ1hXWXLo19uU1RVAT06AHTbt1ga2sLPioKOH0aKmdnkHnzENirF0JDQ+Hm5kasra1J3759OY7jkJqaSouKioi/vz+L+HB1BXx90cLDA/fv36fXV6+mDhERxOTnn+HTqROioqJIdnY2Tp8+Tc+dOye5LVzImZw6BbRpgy1xcWKXLl04T09P5r7q04e5tu7dY+4hhQKVU6cizc0Nw196iclYdO7MfNcmJsD69WipUnGX7eysy8vL80JCQv4UrZj/FYIgOCmVyqi2bds6BQUFyZtzy7RWKLiLqan0gr093N3diSRJiIyMxJ49e+Dk5EQnTZrEtWnThhXhUamYZMdzzwHTpjUbHpydnY0LFy4AgGVISIjwx7Xy8RgMv4FnmqioqARRFPtrNBpHNze3px+RRkQAr776oOjKQ7Rq1QqdO3cmqampSOjQgfSeOBFGgYEsoetR4Zvbt7PMzYkTmRH9+Wcm1xAaCjJ6NCytrGBsbAxCCMzMzODo6FgXWhkREUEnTpxI6konajTAK6+AvPwyWrduTYyXLYOiXz+0CwsjhBBYWlpSNzc39OnTh+j1ehyMikLQ2LEEgwYhs1Mn7nJhIVq1agVJklBXKczXlxn4igrg6FHE2NuToP37mdvC2ZklrXXpAsyaBdK3L4x0OsWN3NwWvXv3/vGpn/FfiNOnT1/q2bOn25AhQ5TN+uInTgRvYQHF/PkkMjKSSJKEXbt2ScXFxQgLCyM9e/bkGiV+mZkBZ84AgwaxKKx6SJKE7OxsbNq0SUsp5QGMDAkJSf9jWvhkGFw9Bp5p1Go1ra6ufvHcuXOFe/bs0VQ34bJ5JM7OwNtvP3Y3W1tbolQqqaWlJavnO2ECkJwMVFY2fcCOHSwktLKSLQDeucN0d4KCHnmdyspKaDQaroFUgKUlcOUKoNNBtXIl2v30E2m/fHldSUcfHx/i5+dHbG1tUVFRQSRJIv++eFG66u4O5/h4mJuaYtOmTVi9ejUuXrzY4Hr3TE3xXb9+tE27diKKiljWcE4Oyzvo0oWJwE2YAI/XX4dOp/OvqTL1t0QQBDNRFFsFBQXJmjX6ej2b7fn7w87ODgEBATQ2NhaBgYHk9ddfJ61bt276OJmMRV9t3MhmgvVITU3F+vXrIUnSXQDLCSEv/r4te3oMht/AM49arb6r0+k80tLSDuzYseOxOjMlJSXYsmULMjIymCJnQABKS0vRbCEXAP369UN5eTnS0tJQHBjIImhefpkZ84dJSWGZwGPGsEQwU1Omp1NPNro5VCoVeJ5HZf0ORalkujznzzPDUVOk4/jx49i4caNYu9uVK1eQkpJCOnbsSIcNG8alqtWif0YGZp09iwULFiAkJATh4eFISkqqO3VCQgLu379PevbqxePHH5nBz8kBLl5kxurzz4FOncCdPAlHR8dqAIMe24i/Ll6WlpZVzboMDx8GXnkF2Ly5ThvJ3NyctGjRQurVqxepHwHUgNqQz88/B959lz3betTmolBKHQEsoJQaDL8BA0+CWq0u1Wq1r2ZlZclKS0sfue+mTZvEqqoqumPHDpQsWoRf1qwRV65ciVWrVol6vb7JYziOg6Wlpbh//366adMmVjHjzBlWy/fLL5muD8B+5OPH10kjo2tXNuJvoghKU+j1eshkMpqb+1D05GuvAVu2AMeOQZLLcX7zZpyKikLlqVN87LJlQFUVvGJj4VBcTO8eOULEBQukYcHBvDwwENi+Haq8PAQHB8PU1BQHDx7E8ePHIUkSBg8eDFtbW/HGjRsPCoGMGAGcOMHWBGxsAE9PwM0NY1asMFcqlROfqCF/Tbzt7e2bdxdevgy0adPgI0pp0+GZGg2bESYmMrfaDz8wAb8tW5gcSD2sra0xul7iF8dxS35TK34HDIbfwF8GtVpdQgj5ZMOGDZXXr19HSUlJ3bbS0lIkJiZizZo1UmlpKR8cHEzmzZ2L1K+/lqyee4689dZbMDMzI//973+btvwAZs2aJevevTupM5A8zxbqCgvZi1L2GjQIsLNjawfDhzdcCNZqWRo/AOzaxdYBbtwA5s5FaUkJzg4aREPj4mBnYQG4uQHR0cxoHDvG3C5+frgVFES9pk/HNBMTjMvMhNMPP1BtaSmweTPsCgtpf19f4MYNknDypITu3Zl7afBgQKfD1KlT0bt3b8TGxmLdunUSAHTv3p0/c+YM/emnn6TTp0/j/v37TBH0xg2WjZqWBpSUwCw5GWFr1gz7bNGiFiDE/A/4Cv+nyGSy3g4ODiaNNkgS+y7HjGEJc/VoYPirqths4No1YMAANiNs3559fzNmMJG+YcOaXNy9efOm5sHlpD8tUas5DHH8Bv5S6PX6j0tKSm5v3rx5DQBMmDABhw8fFouLi3mFQgFfX1+8+OKLMDc3BzZsQK+tWzlERAAAgoODufDwcGbVJYm9SkqQdeMGYk6flqpv3CCVRkZkVI8eBIcPs8XRmBigVy/mfvH3B86dYyPkiAim+Pnaa2ykN3QoGzG+/DKT7L1wAZg3j80YXFyACxeQeOYMtFZW6D10KIG9PVMI9fZmCqE//8xcPl26QHvpEinRammrL78k1MYGd8zNybXYWHRcsAB3zp+XNL16ceaDB5M9mzaB79ePBrz1FqH29ij4979x3sdHunjxImdiYiIFBARwANC9e3e4u7qSjV99RQq3bsW9khJqMW8ewTffsAXsiROB558HWbMGeT4++m67dmVJgIojRAmgDYCsv3K5RkEQZAqF4isjI6Owjh07Nt4hI4PlRtjYNNpEKUXHEyeYKme/fsyP37YtcwvVLqTXC/dsjvT09PrP7zfV7f09IH/h79PAPxhBEOwAjOF5/lNRFC18fX3h4eEBZ2fnB5EtO3YwhcvWrYE33sD9f/8bGZaWtCvPE5qVBbJyJej06cgVRRSFhdH2qamkNCAALfPyQAoLH2RtOjmxTNmICJaNa23NRn3ffMPCJ11dmZianx+bJTThGkhLS0N4eDjGjx8PV1fXphtFKRAQgIIOHfBDx45oXVoqdtbp+Pzycol3d+f6JifjSloatXBzIw6mpsgbPBgnd+yA/aBBSC8slF744ANuZ1gY/OfPh5enJ5tNxMUB3t7IevddWuLgQMpcXanvoEHEbMgQJj1dS24uW7f4+GPcDwlBbmGhZsfYsSbqpUsvAwgHsARAZ1D6P08+ehoEQXBQKpXb7ezs/F588UXjRnWJf/mFhbUOGvTge9NomJ/+gw+QNmQINBER1G/QIIK5c3/1feTm5mLv3r3SvXv3PlqyZMnSX9+i3weD4Tfwl0YQBAWAF1Uq1Wi9Xu/bqVMnl8GDBxOZTMYMaUICM3BOTkg4dgznrl0TvTp35mNPn0a7Dh1w48YNuLi4iBMnTuQflWpfhyiyKf+uXUB5OTB79mMPuXXrFjZt2oTnn3++adnokhI2irx3DygsRNTGjbh//77k3rYtZ7x8OX6eOhWUUgQEBCApKUkc6e/Pu5WUAO3aoXzePFSlpiJr2DB03boVpKwMZPJkwMODaRR17QpMm4Zv1q8XB4wbx3fo0KHpmzx5ks1Wrl4FpRQ//vBDRbvNm2Xd4uOPGGs0owH0B/ATWM3egQDOgdJnNtNXEAQVIWQqz/NfBAYGKvv27dtY84lSNtt66y0W4dS6NRAayjrvDRuAU6dw2tERyTduiNOnT//NyW2rV68uLSwsHKJWq0/+1nP9VgyuHgN/adRqtRbARgAbBUHwTEtL25CcnNzJy8uLBgQEqBz8/QEAWq0WpaamKNZq+fjERPQPDcXp06dpQEAA7d+//5P/qHmeRfGUlLBRvygyn+4jOo19+/ZJnTt3Jt7e3k3vxPPM7cNxKJHLkaLTSVPXrePM/+//gLIyLFiwABkZGYiIiKA6nY5XOjqyNQdLS5j26wfTjAzYzp3LJIMrK4H795n09IIFdZfQm5lRrVbbfLt69GDuKTDNmpenTTM5U1amSwT43idOAEAWAJsap/enADaBkE0AxoHSb5/4+f3BCIJgw3HcNgD9KKWYNm1aXeGaBhQXs/oGixez8MsJE5iv/vvvmevO2Bho3x5iXNwjtXceRf31AY1Gg5KSEhWAZ2LGZDD8Bv42qNXqywD8BUFwSk5OnpiWlvZ+aGiocbdu3biTJ08iLi4OEyZMqCu2ERAQQMiv/VXXVtlauJDJJZ8502iXb775Rrp//z6hlHLV1dVSUFAQMTdvYs20qAjSiBHgAGzdulW0a9uWGMXEMOOTmQly5gzad+0Kk9hYcjczE1bDhjGjvnYt8PzzLGR15EgWppmXx1wX9WSF8/PzUV5eLnuk2N2JE6xNNZIVhBBctrCQZ4eEDC5Xqe4MOHJEJ9PrWcYZpX41Ow0BMBPAtyDk/wAcAqVPoXnx+yIIQjsA12rDdt999100cu3o9SxkduVKVonNwYEptzaTcdtsVM8jkCQJx44d02s0Gv3w4cNVAHDz5k0oFIoL77777iN63z8Pg+E38LdDrVbnAFguCMLOo0ePHjlx4kTr6upqMmTIkAYVln6z3DPADP+AAWwEnprKfP41FBUVcRYWFhg5ciQiIyMRHR2N4cOHNzpFxRtvID89nR6YPVsqLy/nx4wZA7mtLfMzf/8980PHxeGsQoEKd3f4eHkB6elsjUEmAz79lC1MEsLq/yYns3u6fh0wNUVkZCRt164ddXZ2btRgjUYDpVIJ4uDA3Bz1GD9+PJYvX44zgYGWCd26wWT+fDp58+bM72bM+EmSybRYuvQGgC5qtvsgAOkghAKYAUrffPhavxVBEFQA2gNwJ4R4yuXyNlqtdimAzgD2AQAhJAuAjUKhkJeXlyvrDP/Nm8C+few7MjZmrpzOnR8rzfG0hv/OnTv49ttvAUBmaWlZCUBFKcXZs2erqqurdz1tm/8oDIbfwN8WtVqdIQhCJwCxLi4u7t27d29CMP/pyMjIwJUrV6DRaNCxY0d4enoyI/vjjyyu/+bNOmNiamqq79+/v8zZ2Rk+Pj7cuXPnRAC8VqvFjh07aEFBgaTRaHjq749+8+bBQ6sl586dQ0VFBWxtbFhM+eefo3ZRMTsgQCouLubEwkJwSiVbvB0yhLkm6uPjw3IPaoxeUFAQ2bp1K/nuu++kkJAQzsPDo27XdevWUZlMRod07865jBzZ4DQqlQrjx49HSUkJIiIiwIki9D4+rkH9+r1Pysrolfz8yuLi4kWCIAxTUzoAAEBIKACHmr/XA1gHSuN+zbMWBIHwPP+NQqHwFUWxEwBjc3PzMltbW2pqampy8eJFHsC0eodMpZTuBaCtrq6u+Oabb/BBz54g27YBgYFMqO+991iU1RPytIY/Jyen9s/T1dXVnQH2fyY3NzdfkqSvnvhEfzAGw2/gb41arS4TBGFcZmbm1YSEBKl79+6/epgvSRK2bdsGV1dXUS6Xk/DwcFJeXk4yMjLo3bIy0eyDD/hWy5aRruHh0GzbBmNjYz4zM5N6eXmRNm3a4Pjx45wkSTh69CgKCwsxdOhQ3sbGBiaTJ0M1fjyBmxuxsrKiP//8MybHxJBcSZJM/v1vzhtAcXExHB0dOWNRlOS+vhw+/hjYtq35m504EZgyBejQAW7vv4/nn38et27d4sLDwzF48GCkpqaKAGBpaUlu377Nnf3qK2p0/jxs09IaWLnaxWD/mrUSAHC4cwdwcSF9MjNNYq5e9YiLi0v+/PPPL+v1+n26pUs/UqvVR2p25QGUgpDBAKaD0lFP+qwFQWinUCg2abXawM6dO6OyslLKyMigLVq0MJMkCRqNpv7u/6p5lfA8H26k13dzSU+nA0SRkKIilrA2YMATJ9nVR5KkJzL8VVVVKC8vR1ZWlk6pVMYAKPf19VUBQGFhISilx9Vq9VPqjfxxGAy/gb89arX6miAInkeOHDlhbm5u5+7u/qvPJYoiwsLCeJlMhpMnT0qnTp2Ck5OT1KdPH1lJSQnVSpJ4S6vl9/74I+RaLcnPz4eNjY3k7+/PWVlZ0RUrVpDq6mpMmDCBtKnNEtXpmF8egL+/P6moqJDSjx4l3MCB3IkDB2h2dja5HB+PwTt34siAAZw+MhKypqKDHmbw4Lravj4+PvDx8cGdO3fEY8eOwdXVlb9y5QoopQgLC0OrMWPIro8+wqiKCpiYNM5xakDLlkB6OoiDA4KXL+d8Z882uctx3Q4cOOB1//79DABbAACUTgYAEGIEILnm73gAi4WlS7UANimVygscxykIIaaVlZVBAGBkZHRFLpe3DgoKUhw7dgyOjo5wdXXlLC0tIUkSLl++TO3t7cmkSZOwefNmAHgHwDtWRUXwTE2Fd58+sDc3B5kwoVEW7dPyJFGP1dXVWLlyZTWAexzHZVVXV78ok8meP3fuXAgAhSiKcq1We+033cjvjCGc08A/BkEQeiuVysOjR482cnV1xcNFOJ6Ejz/+GPPmzXukcayoqIDs4kXIBw7E8tdfh5OXlzR58mSuoKAAx44dE/v168fb29uznWtFwWp1YFJTgTfegBQRAaJQYNWqVdQ8L4+GhIZyjqtXI3PxYnTo1u2R95ibm4tdu3aJvr6+fFC7dsDrr7OchnprGpIk4d///rfUuXNn7rnnngMSE3H2gw+klNGjMXnyZO6Jno1eD/TsyRZL27dH7u3bWL9+fZler++gVqtvP7y7IAgmo3/5JfLQoEG9OqamomNqKm5u2oQWLVpALpdj61aW0Dpt2jRYW1vD2NgYt2/fZpLX9XRytm/frjcyMpINHz4c1zMykPDdd9ILGg2noxSVrVvDYvx4kCfQTXoSjh07hlu3bokvvfRSs5FfcXFxUlxc3L6FCxc28JUJgtAKQK02h69arb7Y+Oj/DYYRv4F/DGq1Ou7jjz+euXv37nk6nc7L2dm52svLy8zT0xPN1UgFmKJmamoqEhMTRblc3qhq08OYmJgwg3jqFBa4uwNffcVBkmBnZ4cJEyY0NCCrV7NXRgZ7X1gIuLszHz6AvjduoM2PP3KFkyZBvns3monCb8C2bdvE6upq/uLFi2JQt2487t2DNj8fpUolbGxskJ+fj40bN1JTU1MSUluZ7PJldLt3j4spKpLi4uIQHBz8+MVvmYxFFAGAqyscFyxAQECA6ty5c5sEQRgCQALgBuA5mUz2Is/z3XaNHasEANfZs2F75w5aBwWxyJrNm7Fo4UJU63QNvou6DrIeoihCrtUC27bBTaOBW0ICh+nToXjhBShkv69Jq/HxNzs6zsvLQ1xcXHV1dfX7D29Tq9V5Qo0ExLNk9AGD4TfwD+P999+vjfm3ysrKGpifnx92+PDhwZ6enrRnz55GTRmaNWvWiIQQzs3NDZMnT0YjLfbm8PEBd/s2i74ZP75p9c5Jkx5EAr31FjB1KsugXb8eiIxEu6++ItE9eiBx717a4+5d9O3b95EO55KSEpSXl/O9evVCUlIS0fI8Iv/v/2jFwoVEJ5cj38ND0ul0XGBgIO3Xrx9X579+8UVwL7yAPomJXEREBGJiYmBtbS05OTnRfv368U2GodZn82agSxeEHDkiLxPFHmkyWbEkSQojI6NqU1NTWWFhoWrevHmQyWSQyWQNRvBYswbo3RvyRYsgP3qURTNpNGiyeP3t2+iycydfXlpKxe7dCT9iBBAdDb1ej5iYGFRXV6NVq1bw8fH5XaK2HrW4SynF5s2bNVqtdpZarW4ujPVdQkhJM9v+ZxhcPQb+8QiCYM3z/EyO4+ZzHGdkY2OjdXR0NPby8pK7uLhg+fLldNy4ccTCwgLW1ta/LqGnrIxlie7Zw7Jqa9m6lUk9uLgwsbXvvmOZxkePsu2zZkGn0yExMRHHjh1DUFAQHB0dcefOHXh7e8PU1BTbt2+XCgoKJEmS0LNnT9nx48dpWFgYiY2NlW7fvg1Jkrg3r12DqroaecuWQaVSNW7H2rVMjuLECdy9exd6vR65ubk4d+4cVSgU0rRp054syW3kSKBtW9xXq2FkZASFQgG9Xo9ly5bhlVdeaTqZCkBWVhasjIxgcfs2KxJjbMyE67y82DrFxYtML2fJEminTcN2U1Mpv7ISo0aN4tq1a4eLFy/iwIEDaN26tXjnzh1Oo9EgKCiIeHp6wsTEBKqmOpEn4PDhwygoKNBPnjxZBgAFBQXQ6XRwcnJCSkoK9u7dW6DT6RzUavVfypAaDL8BAzUIgkAAOAHw4nm+p0wmmwLArrq62ojneS3HcWUtW7ZUjRw50sSmCUGvR0Ip8PHHLDTzzh2mzAkw4bcBA5jhnzGDdQpDh7JwzIc4e/asdP78eZSXl3NKpZKWl5fD1NQUer0e/fr1I+Xl5YiJiQHAYvCdnZ2xc+dOsVOnTrynhwcTpTt+vFG8fs3JWU2Ah/RokpKSEBUVRefPn/90vd28eSyhbMcOAMC6deskV1dXrn///o12TU9Px7Zt28BxHExMTERbW1syKTj2H6JNAAAQNElEQVSYg5MTy0DesgUYOJB1KmPHAjyP6upq/Oc//0FZWRkAQCaTwdXVVax1paWnp+PgwYO0qqqK6PV6zJkzB9bW1k/VBACIjIzE3bt3xUmTJvEpKSnYs2dPKSFEb25ublFcXKyXJCngWXPjPAkGw2/AwCMQBMETwOsAvgSQxfP86zzPL5s+fbrq1xgS6HRM0vnHH1mYIQBMn87UHr/4gilAtmjx2NNQSrFixQpaXV1Npk+fXmfU8vLyYGRkBCsrq8YH3bvHZITPnXvQ8dRy4wYz1PUS0LRaLb766iupd+/epGfPnk9n+JOTgaws1p60NGxIS5Nu375NJk6cSJycnBrsum3bNiiVSjE0NJS/evUqDh48iMWLFz/YwdaWuZIGDgTAZgdbt26l1dXVBGCJeFOnToWdnR2rYfwQy5cvR9u2bXWmpqa1xq5RwvZD7wkAWnMtvqqqCl5eXlJubi6Xl5e3Sq/Xq2Uy2VYAhxcvXrziqZ7LM4LBx2/AwCOokYGYU++jlR9//LG0devWz2bMmGEie9rFRLmcLeRaWrIR/s8/swxbpZIZ3Scw+gAzVJIk0dGjR5P6HVBttacmsbEBbt1if5eUsHuo5cABJttQz/Cnp6dDFEUuMDCwwWkopbh8+TJKS0ulwMDAph3pPj7s9fXXwMqVmJKRwUVGRko//PADcXd3x/Dhw+tUVG/fvq0PCQmRmZiYwN7eHnK5XEL9WiF37gCEICIigiYnJ1NJkjgTExNMnz4dVlZW+PDDDxEZGYnXXnutuZZXp6WlLQdwv/bxPbT9Ue9NAdicPn36Zs37SLVafR/A4OYu9lfAYPgNGHhKRFFcXVZWNuiHH34I9vPzM+rWrdvTrSK2aMFcP1evMt//8uXARx8B77/PInyekMrKSq5ZiefmMDUFJk9mHQATX2OMGsWkp+vRsWNHRERESOnp6ZynpycopUhISKAJCQn03r17nEKhaNQpNOLNN4E5c0CuXMHADz/kHL/5BvEJCdL69evp7NmzeY1Gg4qKClltLVulUglRFLmHz5GiUiHJyoqMHTuWtGvXDhzH1RnnMWPGICIiAsnJyfDx8WnqLkQAa2ukPAzAUIHLgIGnpqYA/PP5+fmzDx8+fPvUqVNUFMXHH1gfQpi4W24uU4jMyWEuGIBpw0+Zwv4WhAcVvZrgV7lqv/uOlZKsr9aZmMjcTfXgOA7dunXjIiMj6alTp7Bx40bp+PHj8PHx4VxcXKi9vb30RNevUTQl3brBx9cX44ODueLiYj4+Ph4rV66krq6uYq2AXG2S1g8//KCXJAmSJCHp1i16PT8fL730Ejp06NAoWsfb2xstWrQQU1NTG9YybojBp10Pg4/fgIHfgCAI7kqlcpNcLvcaMGCAye8SRpiVxcIZO3RgRV4uXGDGet8+tgD7009Ar15YuWeP6OHhQQYOHPj0FywrY/rzp06xBeUvv2TF3n/6qcFukiRhx44dtLi4mDo4OHD9+/eHqakpNBoNVq5cKYWEhJCAgIAn9//n5gJubti8bJmUWVbG+fv70+eee66Bz/3evXvYvHkzdXV1JZmZmZKxXI4JY8dypnZ2zZ72/PnzYnx8PCktLSXTpk0jdvX2Xb58eaVGo2mvVqvznuIJ/a0xGH4DBn4HBEHoq1Qqv7S1tW0/bdo0k1+r9twsN28y11BoKCuuMn8+rioUsJsxAxZFRcyAW1mxspBPyq5dbHZhbMyKy0hSnXTEk3Djxg38/PPP6NKli37IkCFP7jYuKsKFmzfBvfwy2m/fDqMmisPcunULW7duRbt27cTnIyN5/sqVhq6pJpAkCeHh4WJ6ejr33nvv1X0BBsPfGIPhN2Dgd0IQBE6hUJw3Nzd369atm5m3t/fjdW9+AwWpqTi3aBF8Vq2Cy9y5bO1g7VqgXTsgNpZJKlRUPLozWLaMReC0a8cWnR8a8T+O7OxsbNy4ET169ICpqSmMjY0bvIyMjCCXy+uiZkRRxMmTJ6W4qChu+s6dsImIYFE7j0qKy81lM5T6+Q/NUCuLbGlpWUophV6v5yoqKowBtFSr1YVP1bi/MQbDb8DA74ggCDIA/ZVK5Qy9Xj/YxcVF7+fnZ+ru7v6rtIEex5EjR5CQkAAvLy9x+PDhLB920SLgs8+ApUtZwtjFi0y++Y03WHhlXh5zIQGsNGVsLODry8I9w8Ke+h7i4uKQkZEBjUYjarVaqtfriV6vJ6Iocnq9HpIkged51EZAyeVyady4cZxLrTxyq1bAhx8Cr77a9AVyctj6w7RpTW+vR3V1NXbs2KHJysqSiaK4AMAeAKVqtfruUzfsb4zB8Bsw8AchCIIpgFEqlWqWXq/38/T0lLp06WLUunXr36cITA3379/HunXrJG9vbzpw4MCmM2yXLAFefJEZ/VGjWL3ghQtZ4Zbp01mH8N57wLBhv9t91aLX61FZWYmioiJs2LAB06dPb5jBm5wMuLszmWlvb1b/tj6HDrHooKtXn/iaNXWOy/R6/fgPPvjgwO/UlL8NBsNvwMCfgCAIrTiOmyCXy2dSSh1tbW213bp1M/fx8WmoW/MrWbdundS2bVvSr1+/xy8uSBJT6vzyS8DeHhg3jrlaTE1ZIteMGaykY0AAcOkSyyxWqZie/W/osNLS0rBjxw4sWLCgaQmFsWNZVawlS371Nepz7do1bN++vUqv178OYGdN/L0BGAy/AQN/OoIgOAPoKpfLP9LpdN4zZsxoUoXyafjXv/4lhYWFcbXx8E+NRsOiiVxcWNFxV1fWQaxZw1xHs2cD+flAXBxzGU2cyPZNSWEzidJSptNfk5T1MNnZ2di2bZvk6OhIGymUPszLL7Pz7dzJ3ut0LMLp+vWn7nji4+N1kZGRcp7nL4ii+AlYB/CPN3oGw2/AwP8IQRBMOI47CsC/f//+xMnJidja2jYuEP4IsrKyEB8fj+vXr+Odd955cuXQp4VS5h6iFDh4kEk/ZGUB27ez8pChoWxG8PXXrATlnDms40hPB+bPx4FPPgFt3x5DXnrp8W6ujAzmkurYka1P9OvHVEvXrKkrLPM0aDQaXLhwAWfOnKmoqqrasHDhwjmPP+rvjcHwGzDwP0YQBH+lUvkWIaSrVqt1VSgUeltbW32rVq2M7Ozs5G3btkVtgtPDfP/997h9+zZee+21R8s1/BnUdg5nzzItoHPngIgI4PPPUdipE3SenrCfPJnJVC9dynz72dksc/nQIRam6uLyYFS/YgXw3/+yWUVuLpOc+BWGvxaNRoNVq1ZVVVZWjlar1Yd+n0b/NTEYfgMGniEEQeAAuADwJoR4K5XKQL1eH9qqVStJr9dj9OjRprXaPEeOHEFSUhImTZr0vzf6jyAnJwcbN27ErFmzYGVkxBZpnZyY7HJSEqsQNnXqA8mIbduYu2nNGra2MHw4e/38/+3db2gTdxzH8c8ll5x2taE4xdXNOQsLo5SNsmmVIhZWmCiICpMp2yMfzOEjn2wo5OcVcdDB/oiiMPbIzVVFV9YHE3QtaWJBN5U+8E9x1lWZTYmITROTu8vltwftxM0/a9PYJP19XtBHyYVvn7wJl7vv/Tj2e8QUHDp0yB0eHrYALBBCjE79vytPDD9RiRu/Omitx+N51TAMsXXr1tkPHjxAR0cHgsGgbGlpKfDdYpPjui7OnTsng8Gg9qSNpX19fThz5ozcsWPHY1sxHyPl2FK2QGBscdzo6NgVPx9/PHaT2hTvi7h48SI6OzsBAJqmfSalbFPxnD/DT1RG9uzZ84nX6/06m836ampq3FWrVnlr/7tieRqNjIygvb09dffu3b+y2ezr1dXV6ZqaGm39+vWz4vE45s+fDwBoa2uTmzdv1h5eu19kvb29OH36NADME0LcLfY80827e/fuYs9ARBO0cuXK37q7u/1+v39hMpmsjMViqfPnz+v37t3z1NbW5vd0sDwlEgkcPHgQIyMj/lwuNxcAMpmMLx6P6z6fD8ePH8etW7csy7L0wcFBVFdXa68U6CHoU9XX15eLxWJ/CiE+L/YsxcBv/ERlyjTNBQDeANAFAI2NjW5TU5P3ea6JAMZ24kSj0Ww4HNY8Hs8fHo8nbdv2W/+87vP57jiOswCPbP/VdR11dXVy+fLl/1qgVijpdHpS7z98+HAuFotdCoVCbxd8mDLA8BPNAKZpBv1+/07btj/atm3bw1MshZbL5dDe3p4eHBy8bNv2hwD6n3aO3DTNeQBOAmgCxtY853I5bN++Pa/HID5NT09PNhwOS03TnIke47puhaZpn4ZCobaCDVJGGH6iGWTv3r07XNfdq+t6rqKiwpkzZ46sqqrSA4HArLlz53rr6+snvTPItm0MDAzgypUr6f7+fk3TtKuWZTVP9E7Y8WcZtwD4HsC8hoYGd/Xq1d5JP73sCe7fv48DBw6ks9nsG0KIwf8/ggCGn2jGGV8U9yKABY/+GYbR4vF4Gpubm2c3NDRoz1oVIaXE5cuXceHChdHbt28bfr+/L5PJ/CCl/FkIcXMKsy0yDONbwzCa1q1bV7FkyZJ8PwoAcOTIkQc3b978YteuXbun9EGKYfiJFGKa5juGYXyl6/pbS5curVi0aJG2cOFCSCkRj8chpUQikUBXV1cymUzesixrD4BfhBD3CzlHa2vrWl3Xv6urq6tas2bNrHy+/d+4cQNHjx6NOY7zmhAiU8j5ZjqGn0hBpmk2+Xy+971eb4tt27W6ruc0TRvyeDwZKWXSsqzPpZQ/Pc9r3E3TnGMYxtFAILByy5YtL1RVVU34WNd1sW/fvtTo6OgHoVCo83nNOFMx/ESKM01zNoAlAK5M981Mpmlquq7v1HV916ZNm2YvXrx4QsedPXvWjUQi0fHfGhixSWL4iajoWltbW3RdP7lx48bKYDD4zPcmEgns378/7TjOm0KI69M04oxSuKdBEBHlKRQKnXYcp/nEiRPJ69ef3fJTp06lAexn9PPH8BNRSRBC/O44zrvHjh1LdnZ25oaGhh57TyqVwtWrV2c5jvNlEUacMXiqh4hKimmatQD+AIANGzagvr7+X693dHRY165d67Isaw3P7+eH3/iJqKQIIW4AqAaA7u7ux8K+YsUKw7bt9wC8Pt2zzRQMPxGVnPH7Bl5KpVK3w+Fw9tHXLl26lAXwjRCivzjTlT+Gn4hKkhAiZtt2Y29v71BPT8/D+A8MDGSklOeLOVu5Y/iJqGQJIYZs214WjUZj/8TfcZwcxn8DoPww/ERU0oQQQ47jLItGo8ORSCRbWVmpAXi52HOVM4afiEqeEOKO4zjLIpHIcCwWSwP4tdgzlTNezklEZcM0zQCArBAiVexZyhnDT0SkGJ7qISJSDMNPRKQYhp+ISDEMPxGRYhh+IiLFMPxERIph+ImIFMPwExEphuEnIlIMw09EpBiGn4hIMQw/EZFiGH4iIsUw/EREimH4iYgUw/ATESmG4SciUgzDT0SkGIafiEgxDD8RkWIYfiIixTD8RESKYfiJiBTD8BMRKYbhJyJSDMNPRKQYhp+ISDEMPxGRYhh+IiLFMPxERIph+ImIFPM3SEgQfL3nRRkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_rook.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>POLY_ID</th>\n",
+       "      <th>AREA</th>\n",
+       "      <th>CODE</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>PERIMETER</th>\n",
+       "      <th>ACRES</th>\n",
+       "      <th>HECTARES</th>\n",
+       "      <th>PCGDP1940</th>\n",
+       "      <th>PCGDP1950</th>\n",
+       "      <th>PCGDP1960</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GR9000</th>\n",
+       "      <th>LPCGDP40</th>\n",
+       "      <th>LPCGDP50</th>\n",
+       "      <th>LPCGDP60</th>\n",
+       "      <th>LPCGDP70</th>\n",
+       "      <th>LPCGDP80</th>\n",
+       "      <th>LPCGDP90</th>\n",
+       "      <th>LPCGDP00</th>\n",
+       "      <th>TEST</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.252751e+10</td>\n",
+       "      <td>MX02</td>\n",
+       "      <td>Baja California Norte</td>\n",
+       "      <td>2040312.385</td>\n",
+       "      <td>1.792187e+07</td>\n",
+       "      <td>7252751.376</td>\n",
+       "      <td>22361.0</td>\n",
+       "      <td>20977.0</td>\n",
+       "      <td>17865.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.35</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.25</td>\n",
+       "      <td>4.40</td>\n",
+       "      <td>4.47</td>\n",
+       "      <td>4.43</td>\n",
+       "      <td>4.48</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>(POLYGON ((-113.1397171020508 29.0177764892578...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.225988e+10</td>\n",
+       "      <td>MX03</td>\n",
+       "      <td>Baja California Sur</td>\n",
+       "      <td>2912880.772</td>\n",
+       "      <td>1.785573e+07</td>\n",
+       "      <td>7225987.769</td>\n",
+       "      <td>9573.0</td>\n",
+       "      <td>16013.0</td>\n",
+       "      <td>16707.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.98</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>4.22</td>\n",
+       "      <td>4.39</td>\n",
+       "      <td>4.46</td>\n",
+       "      <td>4.41</td>\n",
+       "      <td>4.42</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>(POLYGON ((-111.2061233520508 25.8027763366699...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.731957e+10</td>\n",
+       "      <td>MX18</td>\n",
+       "      <td>Nayarit</td>\n",
+       "      <td>1034770.341</td>\n",
+       "      <td>6.750785e+06</td>\n",
+       "      <td>2731956.859</td>\n",
+       "      <td>4836.0</td>\n",
+       "      <td>7515.0</td>\n",
+       "      <td>7621.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.05</td>\n",
+       "      <td>3.68</td>\n",
+       "      <td>3.88</td>\n",
+       "      <td>3.88</td>\n",
+       "      <td>4.04</td>\n",
+       "      <td>4.13</td>\n",
+       "      <td>4.11</td>\n",
+       "      <td>4.06</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>(POLYGON ((-106.6210784912109 21.5653114318847...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7.961008e+10</td>\n",
+       "      <td>MX14</td>\n",
+       "      <td>Jalisco</td>\n",
+       "      <td>2324727.436</td>\n",
+       "      <td>1.967200e+07</td>\n",
+       "      <td>7961008.285</td>\n",
+       "      <td>5309.0</td>\n",
+       "      <td>8232.0</td>\n",
+       "      <td>9953.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>4.21</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.30</td>\n",
+       "      <td>4.33</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>POLYGON ((-101.52490234375 21.85663986206055, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.467030e+09</td>\n",
+       "      <td>MX01</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>313895.530</td>\n",
+       "      <td>1.350927e+06</td>\n",
+       "      <td>546702.985</td>\n",
+       "      <td>10384.0</td>\n",
+       "      <td>6234.0</td>\n",
+       "      <td>8714.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>4.02</td>\n",
+       "      <td>3.79</td>\n",
+       "      <td>3.94</td>\n",
+       "      <td>4.21</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.44</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>POLYGON ((-101.8461990356445 22.01176071166992...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   POLY_ID          AREA  CODE                   NAME    PERIMETER  \\\n",
+       "0        1  7.252751e+10  MX02  Baja California Norte  2040312.385   \n",
+       "1        2  7.225988e+10  MX03    Baja California Sur  2912880.772   \n",
+       "2        3  2.731957e+10  MX18                Nayarit  1034770.341   \n",
+       "3        4  7.961008e+10  MX14                Jalisco  2324727.436   \n",
+       "4        5  5.467030e+09  MX01         Aguascalientes   313895.530   \n",
+       "\n",
+       "          ACRES     HECTARES  PCGDP1940  PCGDP1950  PCGDP1960  \\\n",
+       "0  1.792187e+07  7252751.376    22361.0    20977.0    17865.0   \n",
+       "1  1.785573e+07  7225987.769     9573.0    16013.0    16707.0   \n",
+       "2  6.750785e+06  2731956.859     4836.0     7515.0     7621.0   \n",
+       "3  1.967200e+07  7961008.285     5309.0     8232.0     9953.0   \n",
+       "4  1.350927e+06   546702.985    10384.0     6234.0     8714.0   \n",
+       "\n",
+       "                         ...                          GR9000  LPCGDP40  \\\n",
+       "0                        ...                            0.05      4.35   \n",
+       "1                        ...                            0.00      3.98   \n",
+       "2                        ...                           -0.05      3.68   \n",
+       "3                        ...                            0.03      3.73   \n",
+       "4                        ...                            0.13      4.02   \n",
+       "\n",
+       "   LPCGDP50  LPCGDP60  LPCGDP70  LPCGDP80  LPCGDP90  LPCGDP00  TEST  \\\n",
+       "0      4.32      4.25      4.40      4.47      4.43      4.48   1.0   \n",
+       "1      4.20      4.22      4.39      4.46      4.41      4.42   2.0   \n",
+       "2      3.88      3.88      4.04      4.13      4.11      4.06   3.0   \n",
+       "3      3.92      4.00      4.21      4.32      4.30      4.33   4.0   \n",
+       "4      3.79      3.94      4.21      4.32      4.32      4.44   5.0   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  (POLYGON ((-113.1397171020508 29.0177764892578...  \n",
+       "1  (POLYGON ((-111.2061233520508 25.8027763366699...  \n",
+       "2  (POLYGON ((-106.6210784912109 21.5653114318847...  \n",
+       "3  POLYGON ((-101.52490234375 21.85663986206055, ...  \n",
+       "4  POLYGON ((-101.8461990356445 22.01176071166992...  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 22]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Baja California Norte\n",
+       "1       Baja California Sur\n",
+       "22                   Sonora\n",
+       "Name: NAME, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf['NAME'][[0, 1,22]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_queen = Queen.from_dataframe(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.n == w_rook.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_queen.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28    San Luis Potosi\n",
+       "Name: NAME, dtype: object"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf['NAME'][c9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 6, 7, 27, 29, 30, 31]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.neighbors[28]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[3, 5, 6, 7, 24, 27, 29, 30, 31]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.neighbors[28]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-105.,  -95.,   21.,   26.])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n",
+    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n",
+    "w_rook.plot(gdf, ax=ax[0], \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax[0].set_title('Rook')\n",
+    "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n",
+    "\n",
+    "ax[0].axis('off')\n",
+    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n",
+    "w_queen.plot(gdf, ax=ax[1], \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax[1].set_title('Queen')\n",
+    "ax[1].axis('off')\n",
+    "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_knn = KNN.from_dataframe(gdf, k=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(4, 32)]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_knn.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_knn.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weights from shapefiles (without geopandas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
+    "from libpysal.weights import Queen, Rook, KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_queen = Queen.from_shapefile(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_rook = Rook.from_shapefile(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 2 components\n",
+      "  warnings.warn(\"The weights matrix is not fully connected. There are %d components\" % self.n_components)\n"
+     ]
+    }
+   ],
+   "source": [
+    "w_knn1 = KNN.from_shapefile(pth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_knn1.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_knn3 = KNN.from_shapefile(pth,k=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_knn3.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lattice Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.weights import lat2W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = lat2W(4,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.61111111111111"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: [3, 1],\n",
+       " 3: [0, 6, 4],\n",
+       " 1: [0, 4, 2],\n",
+       " 4: [1, 3, 7, 5],\n",
+       " 2: [1, 5],\n",
+       " 5: [2, 4, 8],\n",
+       " 6: [3, 9, 7],\n",
+       " 7: [4, 6, 10, 8],\n",
+       " 8: [5, 7, 11],\n",
+       " 9: [6, 10],\n",
+       " 10: [7, 9, 11],\n",
+       " 11: [8, 10]}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.neighbors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling nonplanar geometries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rs = libpysal.examples.get_path('map_RS_BR.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:168: UserWarning: There are 29 disconnected observations \n",
+      "  Island ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374\n",
+      "  \" Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n"
+     ]
+    }
+   ],
+   "source": [
+    "rs_df = gpd.read_file(rs)\n",
+    "wq = libpysal.weights.Queen.from_dataframe(rs_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(wq.islands)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wq[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = libpysal.weights.fuzzy_contiguity(rs_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wf.islands"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wf[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"figure.figsize\"] = (20,15)\n",
+    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = wq.plot(rs_df, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = wf.plot(rs_df, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n",
+    "ax.set_axis_off()\n",
+    "plt.savefig('rioGrandeDoSul.png')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/_sources/references.rst.txt b/_sources/references.rst.txt
new file mode 100644
index 000000000..09d2529e0
--- /dev/null
+++ b/_sources/references.rst.txt
@@ -0,0 +1,7 @@
+.. reference for the docs
+
+References
+==========
+
+.. bibliography:: _static/references.bib
+   :cited:
diff --git a/_sources/tutorial.rst.txt b/_sources/tutorial.rst.txt
new file mode 100644
index 000000000..74b88d723
--- /dev/null
+++ b/_sources/tutorial.rst.txt
@@ -0,0 +1,21 @@
+libpysal Tutorial
+=================
+
+
+Spatial Weights
+---------------
+
+.. toctree::
+    :glob:
+
+    Spatial Weights <notebooks/weights.ipynb>
+    Voronoi <notebooks/voronoi.ipynb>
+
+
+Example Datasets
+----------------
+
+.. toctree::
+    :glob:
+
+    Example Data <notebooks/examples.ipynb>
\ No newline at end of file
diff --git a/_static/basic.css b/_static/basic.css
index 01192852b..30fee9d0f 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -15,6 +15,12 @@ div.clearer {
     clear: both;
 }
 
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
 /* -- relbar ---------------------------------------------------------------- */
 
 div.related {
@@ -124,7 +130,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li div.context {
+ul.search li p.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -216,7 +222,7 @@ table.modindextable td {
 /* -- general body styles --------------------------------------------------- */
 
 div.body {
-    min-width: 450px;
+    min-width: 360px;
     max-width: 800px;
 }
 
@@ -231,14 +237,8 @@ a.headerlink {
     visibility: hidden;
 }
 
-a.brackets:before,
-span.brackets > a:before{
-    content: "[";
-}
-
-a.brackets:after,
-span.brackets > a:after {
-    content: "]";
+a:visited {
+    color: #551A8B;
 }
 
 h1:hover > a.headerlink,
@@ -271,25 +271,25 @@ p.rubric {
     font-weight: bold;
 }
 
-img.align-left, .figure.align-left, object.align-left {
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
     clear: left;
     float: left;
     margin-right: 1em;
 }
 
-img.align-right, .figure.align-right, object.align-right {
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
     clear: right;
     float: right;
     margin-left: 1em;
 }
 
-img.align-center, .figure.align-center, object.align-center {
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
   display: block;
   margin-left: auto;
   margin-right: auto;
 }
 
-img.align-default, .figure.align-default {
+img.align-default, figure.align-default, .figure.align-default {
   display: block;
   margin-left: auto;
   margin-right: auto;
@@ -313,24 +313,35 @@ img.align-default, .figure.align-default {
 
 /* -- sidebars -------------------------------------------------------------- */
 
-div.sidebar {
+div.sidebar,
+aside.sidebar {
     margin: 0 0 0.5em 1em;
     border: 1px solid #ddb;
-    padding: 7px 7px 0 7px;
+    padding: 7px;
     background-color: #ffe;
     width: 40%;
     float: right;
+    clear: right;
+    overflow-x: auto;
 }
 
 p.sidebar-title {
     font-weight: bold;
 }
 
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
 /* -- topics ---------------------------------------------------------------- */
 
+nav.contents,
+aside.topic,
 div.topic {
     border: 1px solid #ccc;
-    padding: 7px 7px 0 7px;
+    padding: 7px;
     margin: 10px 0 10px 0;
 }
 
@@ -352,10 +363,6 @@ div.admonition dt {
     font-weight: bold;
 }
 
-div.admonition dl {
-    margin-bottom: 0;
-}
-
 p.admonition-title {
     margin: 0px 10px 5px 0px;
     font-weight: bold;
@@ -366,9 +373,34 @@ div.body p.centered {
     margin-top: 25px;
 }
 
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
 /* -- tables ---------------------------------------------------------------- */
 
 table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
     border: 0;
     border-collapse: collapse;
 }
@@ -398,10 +430,6 @@ table.docutils td, table.docutils th {
     border-bottom: 1px solid #aaa;
 }
 
-table.footnote td, table.footnote th {
-    border: 0 !important;
-}
-
 th {
     text-align: left;
     padding-right: 5px;
@@ -416,32 +444,34 @@ table.citation td {
     border-bottom: none;
 }
 
-th > p:first-child,
-td > p:first-child {
+th > :first-child,
+td > :first-child {
     margin-top: 0px;
 }
 
-th > p:last-child,
-td > p:last-child {
+th > :last-child,
+td > :last-child {
     margin-bottom: 0px;
 }
 
 /* -- figures --------------------------------------------------------------- */
 
-div.figure {
+div.figure, figure {
     margin: 0.5em;
     padding: 0.5em;
 }
 
-div.figure p.caption {
+div.figure p.caption, figcaption {
     padding: 0.3em;
 }
 
-div.figure p.caption span.caption-number {
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
     font-style: italic;
 }
 
-div.figure p.caption span.caption-text {
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
 }
 
 /* -- field list styles ----------------------------------------------------- */
@@ -468,10 +498,71 @@ table.field-list td, table.field-list th {
 
 /* -- hlist styles ---------------------------------------------------------- */
 
+table.hlist {
+    margin: 1em 0;
+}
+
 table.hlist td {
     vertical-align: top;
 }
 
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
 
 /* -- other body styles ----------------------------------------------------- */
 
@@ -495,26 +586,53 @@ ol.upperroman {
     list-style: upper-roman;
 }
 
-li > p:first-child {
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
     margin-top: 0px;
 }
 
-li > p:last-child {
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
     margin-bottom: 0px;
 }
 
-dl.footnote > dt,
-dl.citation > dt {
-    float: left;
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
 }
 
-dl.footnote > dd,
-dl.citation > dd {
-    margin-bottom: 0em;
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
 }
 
-dl.footnote > dd:after,
-dl.citation > dd:after {
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
     content: "";
     clear: both;
 }
@@ -531,10 +649,6 @@ dl.field-list > dt {
     padding-right: 5px;
 }
 
-dl.field-list > dt:after {
-    content: ":";
-}
-
 dl.field-list > dd {
     padding-left: 0.5em;
     margin-top: 0em;
@@ -546,7 +660,7 @@ dl {
     margin-bottom: 15px;
 }
 
-dd > p:first-child {
+dd > :first-child {
     margin-top: 0px;
 }
 
@@ -560,6 +674,21 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
 dt:target, span.highlighted {
     background-color: #fbe54e;
 }
@@ -573,14 +702,6 @@ dl.glossary dt {
     font-size: 1.1em;
 }
 
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
 .versionmodified {
     font-style: italic;
 }
@@ -621,8 +742,9 @@ dl.glossary dt {
 
 .classifier:before {
     font-style: normal;
-    margin: 0.5em;
+    margin: 0 0.5em;
     content: ":";
+    display: inline-block;
 }
 
 abbr, acronym {
@@ -630,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
@@ -637,29 +767,69 @@ pre {
     overflow-y: hidden;  /* fixes display issues on Chrome browsers */
 }
 
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
 span.pre {
     -moz-hyphens: none;
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
 }
 
 td.linenos pre {
-    padding: 5px 0px;
     border: 0;
     background-color: transparent;
     color: #aaa;
 }
 
 table.highlighttable {
-    margin-left: 0.5em;
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
 }
 
 table.highlighttable td {
-    padding: 0 0.5em 0 0.5em;
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
 }
 
 div.code-block-caption {
+    margin-top: 1em;
     padding: 2px 5px;
     font-size: small;
 }
@@ -668,12 +838,14 @@ div.code-block-caption code {
     background-color: transparent;
 }
 
-div.code-block-caption + div > div.highlight > pre {
-    margin-top: 0;
-}
-
-div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
-    user-select: none;
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
 }
 
 div.code-block-caption span.caption-number {
@@ -685,21 +857,7 @@ div.code-block-caption span.caption-text {
 }
 
 div.literal-block-wrapper {
-    padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
-    margin: 0;
-}
-
-code.descname {
-    background-color: transparent;
-    font-weight: bold;
-    font-size: 1.2em;
-}
-
-code.descclassname {
-    background-color: transparent;
+    margin: 1em 0;
 }
 
 code.xref, a code {
@@ -740,8 +898,7 @@ span.eqno {
 }
 
 span.eqno a.headerlink {
-    position: relative;
-    left: 0px;
+    position: absolute;
     z-index: 1;
 }
 
diff --git a/_static/bootstrap-2.3.2/css/bootstrap-responsive.css b/_static/bootstrap-2.3.2/css/bootstrap-responsive.css
new file mode 100644
index 000000000..09e88ce3f
--- /dev/null
+++ b/_static/bootstrap-2.3.2/css/bootstrap-responsive.css
@@ -0,0 +1,1109 @@
+/*!
+ * Bootstrap Responsive v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
+ */
+
+.clearfix {
+  *zoom: 1;
+}
+
+.clearfix:before,
+.clearfix:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.clearfix:after {
+  clear: both;
+}
+
+.hide-text {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+
+.input-block-level {
+  display: block;
+  width: 100%;
+  min-height: 30px;
+  -webkit-box-sizing: border-box;
+     -moz-box-sizing: border-box;
+          box-sizing: border-box;
+}
+
+@-ms-viewport {
+  width: device-width;
+}
+
+.hidden {
+  display: none;
+  visibility: hidden;
+}
+
+.visible-phone {
+  display: none !important;
+}
+
+.visible-tablet {
+  display: none !important;
+}
+
+.hidden-desktop {
+  display: none !important;
+}
+
+.visible-desktop {
+  display: inherit !important;
+}
+
+@media (min-width: 768px) and (max-width: 979px) {
+  .hidden-desktop {
+    display: inherit !important;
+  }
+  .visible-desktop {
+    display: none !important ;
+  }
+  .visible-tablet {
+    display: inherit !important;
+  }
+  .hidden-tablet {
+    display: none !important;
+  }
+}
+
+@media (max-width: 767px) {
+  .hidden-desktop {
+    display: inherit !important;
+  }
+  .visible-desktop {
+    display: none !important;
+  }
+  .visible-phone {
+    display: inherit !important;
+  }
+  .hidden-phone {
+    display: none !important;
+  }
+}
+
+.visible-print {
+  display: none !important;
+}
+
+@media print {
+  .visible-print {
+    display: inherit !important;
+  }
+  .hidden-print {
+    display: none !important;
+  }
+}
+
+@media (min-width: 1200px) {
+  .row {
+    margin-left: -30px;
+    *zoom: 1;
+  }
+  .row:before,
+  .row:after {
+    display: table;
+    line-height: 0;
+    content: "";
+  }
+  .row:after {
+    clear: both;
+  }
+  [class*="span"] {
+    float: left;
+    min-height: 1px;
+    margin-left: 30px;
+  }
+  .container,
+  .navbar-static-top .container,
+  .navbar-fixed-top .container,
+  .navbar-fixed-bottom .container {
+    width: 1170px;
+  }
+  .span12 {
+    width: 1170px;
+  }
+  .span11 {
+    width: 1070px;
+  }
+  .span10 {
+    width: 970px;
+  }
+  .span9 {
+    width: 870px;
+  }
+  .span8 {
+    width: 770px;
+  }
+  .span7 {
+    width: 670px;
+  }
+  .span6 {
+    width: 570px;
+  }
+  .span5 {
+    width: 470px;
+  }
+  .span4 {
+    width: 370px;
+  }
+  .span3 {
+    width: 270px;
+  }
+  .span2 {
+    width: 170px;
+  }
+  .span1 {
+    width: 70px;
+  }
+  .offset12 {
+    margin-left: 1230px;
+  }
+  .offset11 {
+    margin-left: 1130px;
+  }
+  .offset10 {
+    margin-left: 1030px;
+  }
+  .offset9 {
+    margin-left: 930px;
+  }
+  .offset8 {
+    margin-left: 830px;
+  }
+  .offset7 {
+    margin-left: 730px;
+  }
+  .offset6 {
+    margin-left: 630px;
+  }
+  .offset5 {
+    margin-left: 530px;
+  }
+  .offset4 {
+    margin-left: 430px;
+  }
+  .offset3 {
+    margin-left: 330px;
+  }
+  .offset2 {
+    margin-left: 230px;
+  }
+  .offset1 {
+    margin-left: 130px;
+  }
+  .row-fluid {
+    width: 100%;
+    *zoom: 1;
+  }
+  .row-fluid:before,
+  .row-fluid:after {
+    display: table;
+    line-height: 0;
+    content: "";
+  }
+  .row-fluid:after {
+    clear: both;
+  }
+  .row-fluid [class*="span"] {
+    display: block;
+    float: left;
+    width: 100%;
+    min-height: 30px;
+    margin-left: 2.564102564102564%;
+    *margin-left: 2.5109110747408616%;
+    -webkit-box-sizing: border-box;
+       -moz-box-sizing: border-box;
+            box-sizing: border-box;
+  }
+  .row-fluid [class*="span"]:first-child {
+    margin-left: 0;
+  }
+  .row-fluid .controls-row [class*="span"] + [class*="span"] {
+    margin-left: 2.564102564102564%;
+  }
+  .row-fluid .span12 {
+    width: 100%;
+    *width: 99.94680851063829%;
+  }
+  .row-fluid .span11 {
+    width: 91.45299145299145%;
+    *width: 91.39979996362975%;
+  }
+  .row-fluid .span10 {
+    width: 82.90598290598291%;
+    *width: 82.8527914166212%;
+  }
+  .row-fluid .span9 {
+    width: 74.35897435897436%;
+    *width: 74.30578286961266%;
+  }
+  .row-fluid .span8 {
+    width: 65.81196581196582%;
+    *width: 65.75877432260411%;
+  }
+  .row-fluid .span7 {
+    width: 57.26495726495726%;
+    *width: 57.21176577559556%;
+  }
+  .row-fluid .span6 {
+    width: 48.717948717948715%;
+    *width: 48.664757228587014%;
+  }
+  .row-fluid .span5 {
+    width: 40.17094017094017%;
+    *width: 40.11774868157847%;
+  }
+  .row-fluid .span4 {
+    width: 31.623931623931625%;
+    *width: 31.570740134569924%;
+  }
+  .row-fluid .span3 {
+    width: 23.076923076923077%;
+    *width: 23.023731587561375%;
+  }
+  .row-fluid .span2 {
+    width: 14.52991452991453%;
+    *width: 14.476723040552828%;
+  }
+  .row-fluid .span1 {
+    width: 5.982905982905983%;
+    *width: 5.929714493544281%;
+  }
+  .row-fluid .offset12 {
+    margin-left: 105.12820512820512%;
+    *margin-left: 105.02182214948171%;
+  }
+  .row-fluid .offset12:first-child {
+    margin-left: 102.56410256410257%;
+    *margin-left: 102.45771958537915%;
+  }
+  .row-fluid .offset11 {
+    margin-left: 96.58119658119658%;
+    *margin-left: 96.47481360247316%;
+  }
+  .row-fluid .offset11:first-child {
+    margin-left: 94.01709401709402%;
+    *margin-left: 93.91071103837061%;
+  }
+  .row-fluid .offset10 {
+    margin-left: 88.03418803418803%;
+    *margin-left: 87.92780505546462%;
+  }
+  .row-fluid .offset10:first-child {
+    margin-left: 85.47008547008548%;
+    *margin-left: 85.36370249136206%;
+  }
+  .row-fluid .offset9 {
+    margin-left: 79.48717948717949%;
+    *margin-left: 79.38079650845607%;
+  }
+  .row-fluid .offset9:first-child {
+    margin-left: 76.92307692307693%;
+    *margin-left: 76.81669394435352%;
+  }
+  .row-fluid .offset8 {
+    margin-left: 70.94017094017094%;
+    *margin-left: 70.83378796144753%;
+  }
+  .row-fluid .offset8:first-child {
+    margin-left: 68.37606837606839%;
+    *margin-left: 68.26968539734497%;
+  }
+  .row-fluid .offset7 {
+    margin-left: 62.393162393162385%;
+    *margin-left: 62.28677941443899%;
+  }
+  .row-fluid .offset7:first-child {
+    margin-left: 59.82905982905982%;
+    *margin-left: 59.72267685033642%;
+  }
+  .row-fluid .offset6 {
+    margin-left: 53.84615384615384%;
+    *margin-left: 53.739770867430444%;
+  }
+  .row-fluid .offset6:first-child {
+    margin-left: 51.28205128205128%;
+    *margin-left: 51.175668303327875%;
+  }
+  .row-fluid .offset5 {
+    margin-left: 45.299145299145295%;
+    *margin-left: 45.1927623204219%;
+  }
+  .row-fluid .offset5:first-child {
+    margin-left: 42.73504273504273%;
+    *margin-left: 42.62865975631933%;
+  }
+  .row-fluid .offset4 {
+    margin-left: 36.75213675213675%;
+    *margin-left: 36.645753773413354%;
+  }
+  .row-fluid .offset4:first-child {
+    margin-left: 34.18803418803419%;
+    *margin-left: 34.081651209310785%;
+  }
+  .row-fluid .offset3 {
+    margin-left: 28.205128205128204%;
+    *margin-left: 28.0987452264048%;
+  }
+  .row-fluid .offset3:first-child {
+    margin-left: 25.641025641025642%;
+    *margin-left: 25.53464266230224%;
+  }
+  .row-fluid .offset2 {
+    margin-left: 19.65811965811966%;
+    *margin-left: 19.551736679396257%;
+  }
+  .row-fluid .offset2:first-child {
+    margin-left: 17.094017094017094%;
+    *margin-left: 16.98763411529369%;
+  }
+  .row-fluid .offset1 {
+    margin-left: 11.11111111111111%;
+    *margin-left: 11.004728132387708%;
+  }
+  .row-fluid .offset1:first-child {
+    margin-left: 8.547008547008547%;
+    *margin-left: 8.440625568285142%;
+  }
+  input,
+  textarea,
+  .uneditable-input {
+    margin-left: 0;
+  }
+  .controls-row [class*="span"] + [class*="span"] {
+    margin-left: 30px;
+  }
+  input.span12,
+  textarea.span12,
+  .uneditable-input.span12 {
+    width: 1156px;
+  }
+  input.span11,
+  textarea.span11,
+  .uneditable-input.span11 {
+    width: 1056px;
+  }
+  input.span10,
+  textarea.span10,
+  .uneditable-input.span10 {
+    width: 956px;
+  }
+  input.span9,
+  textarea.span9,
+  .uneditable-input.span9 {
+    width: 856px;
+  }
+  input.span8,
+  textarea.span8,
+  .uneditable-input.span8 {
+    width: 756px;
+  }
+  input.span7,
+  textarea.span7,
+  .uneditable-input.span7 {
+    width: 656px;
+  }
+  input.span6,
+  textarea.span6,
+  .uneditable-input.span6 {
+    width: 556px;
+  }
+  input.span5,
+  textarea.span5,
+  .uneditable-input.span5 {
+    width: 456px;
+  }
+  input.span4,
+  textarea.span4,
+  .uneditable-input.span4 {
+    width: 356px;
+  }
+  input.span3,
+  textarea.span3,
+  .uneditable-input.span3 {
+    width: 256px;
+  }
+  input.span2,
+  textarea.span2,
+  .uneditable-input.span2 {
+    width: 156px;
+  }
+  input.span1,
+  textarea.span1,
+  .uneditable-input.span1 {
+    width: 56px;
+  }
+  .thumbnails {
+    margin-left: -30px;
+  }
+  .thumbnails > li {
+    margin-left: 30px;
+  }
+  .row-fluid .thumbnails {
+    margin-left: 0;
+  }
+}
+
+@media (min-width: 768px) and (max-width: 979px) {
+  .row {
+    margin-left: -20px;
+    *zoom: 1;
+  }
+  .row:before,
+  .row:after {
+    display: table;
+    line-height: 0;
+    content: "";
+  }
+  .row:after {
+    clear: both;
+  }
+  [class*="span"] {
+    float: left;
+    min-height: 1px;
+    margin-left: 20px;
+  }
+  .container,
+  .navbar-static-top .container,
+  .navbar-fixed-top .container,
+  .navbar-fixed-bottom .container {
+    width: 724px;
+  }
+  .span12 {
+    width: 724px;
+  }
+  .span11 {
+    width: 662px;
+  }
+  .span10 {
+    width: 600px;
+  }
+  .span9 {
+    width: 538px;
+  }
+  .span8 {
+    width: 476px;
+  }
+  .span7 {
+    width: 414px;
+  }
+  .span6 {
+    width: 352px;
+  }
+  .span5 {
+    width: 290px;
+  }
+  .span4 {
+    width: 228px;
+  }
+  .span3 {
+    width: 166px;
+  }
+  .span2 {
+    width: 104px;
+  }
+  .span1 {
+    width: 42px;
+  }
+  .offset12 {
+    margin-left: 764px;
+  }
+  .offset11 {
+    margin-left: 702px;
+  }
+  .offset10 {
+    margin-left: 640px;
+  }
+  .offset9 {
+    margin-left: 578px;
+  }
+  .offset8 {
+    margin-left: 516px;
+  }
+  .offset7 {
+    margin-left: 454px;
+  }
+  .offset6 {
+    margin-left: 392px;
+  }
+  .offset5 {
+    margin-left: 330px;
+  }
+  .offset4 {
+    margin-left: 268px;
+  }
+  .offset3 {
+    margin-left: 206px;
+  }
+  .offset2 {
+    margin-left: 144px;
+  }
+  .offset1 {
+    margin-left: 82px;
+  }
+  .row-fluid {
+    width: 100%;
+    *zoom: 1;
+  }
+  .row-fluid:before,
+  .row-fluid:after {
+    display: table;
+    line-height: 0;
+    content: "";
+  }
+  .row-fluid:after {
+    clear: both;
+  }
+  .row-fluid [class*="span"] {
+    display: block;
+    float: left;
+    width: 100%;
+    min-height: 30px;
+    margin-left: 2.7624309392265194%;
+    *margin-left: 2.709239449864817%;
+    -webkit-box-sizing: border-box;
+       -moz-box-sizing: border-box;
+            box-sizing: border-box;
+  }
+  .row-fluid [class*="span"]:first-child {
+    margin-left: 0;
+  }
+  .row-fluid .controls-row [class*="span"] + [class*="span"] {
+    margin-left: 2.7624309392265194%;
+  }
+  .row-fluid .span12 {
+    width: 100%;
+    *width: 99.94680851063829%;
+  }
+  .row-fluid .span11 {
+    width: 91.43646408839778%;
+    *width: 91.38327259903608%;
+  }
+  .row-fluid .span10 {
+    width: 82.87292817679558%;
+    *width: 82.81973668743387%;
+  }
+  .row-fluid .span9 {
+    width: 74.30939226519337%;
+    *width: 74.25620077583166%;
+  }
+  .row-fluid .span8 {
+    width: 65.74585635359117%;
+    *width: 65.69266486422946%;
+  }
+  .row-fluid .span7 {
+    width: 57.18232044198895%;
+    *width: 57.12912895262725%;
+  }
+  .row-fluid .span6 {
+    width: 48.61878453038674%;
+    *width: 48.56559304102504%;
+  }
+  .row-fluid .span5 {
+    width: 40.05524861878453%;
+    *width: 40.00205712942283%;
+  }
+  .row-fluid .span4 {
+    width: 31.491712707182323%;
+    *width: 31.43852121782062%;
+  }
+  .row-fluid .span3 {
+    width: 22.92817679558011%;
+    *width: 22.87498530621841%;
+  }
+  .row-fluid .span2 {
+    width: 14.3646408839779%;
+    *width: 14.311449394616199%;
+  }
+  .row-fluid .span1 {
+    width: 5.801104972375691%;
+    *width: 5.747913483013988%;
+  }
+  .row-fluid .offset12 {
+    margin-left: 105.52486187845304%;
+    *margin-left: 105.41847889972962%;
+  }
+  .row-fluid .offset12:first-child {
+    margin-left: 102.76243093922652%;
+    *margin-left: 102.6560479605031%;
+  }
+  .row-fluid .offset11 {
+    margin-left: 96.96132596685082%;
+    *margin-left: 96.8549429881274%;
+  }
+  .row-fluid .offset11:first-child {
+    margin-left: 94.1988950276243%;
+    *margin-left: 94.09251204890089%;
+  }
+  .row-fluid .offset10 {
+    margin-left: 88.39779005524862%;
+    *margin-left: 88.2914070765252%;
+  }
+  .row-fluid .offset10:first-child {
+    margin-left: 85.6353591160221%;
+    *margin-left: 85.52897613729868%;
+  }
+  .row-fluid .offset9 {
+    margin-left: 79.8342541436464%;
+    *margin-left: 79.72787116492299%;
+  }
+  .row-fluid .offset9:first-child {
+    margin-left: 77.07182320441989%;
+    *margin-left: 76.96544022569647%;
+  }
+  .row-fluid .offset8 {
+    margin-left: 71.2707182320442%;
+    *margin-left: 71.16433525332079%;
+  }
+  .row-fluid .offset8:first-child {
+    margin-left: 68.50828729281768%;
+    *margin-left: 68.40190431409427%;
+  }
+  .row-fluid .offset7 {
+    margin-left: 62.70718232044199%;
+    *margin-left: 62.600799341718584%;
+  }
+  .row-fluid .offset7:first-child {
+    margin-left: 59.94475138121547%;
+    *margin-left: 59.838368402492065%;
+  }
+  .row-fluid .offset6 {
+    margin-left: 54.14364640883978%;
+    *margin-left: 54.037263430116376%;
+  }
+  .row-fluid .offset6:first-child {
+    margin-left: 51.38121546961326%;
+    *margin-left: 51.27483249088986%;
+  }
+  .row-fluid .offset5 {
+    margin-left: 45.58011049723757%;
+    *margin-left: 45.47372751851417%;
+  }
+  .row-fluid .offset5:first-child {
+    margin-left: 42.81767955801105%;
+    *margin-left: 42.71129657928765%;
+  }
+  .row-fluid .offset4 {
+    margin-left: 37.01657458563536%;
+    *margin-left: 36.91019160691196%;
+  }
+  .row-fluid .offset4:first-child {
+    margin-left: 34.25414364640884%;
+    *margin-left: 34.14776066768544%;
+  }
+  .row-fluid .offset3 {
+    margin-left: 28.45303867403315%;
+    *margin-left: 28.346655695309746%;
+  }
+  .row-fluid .offset3:first-child {
+    margin-left: 25.69060773480663%;
+    *margin-left: 25.584224756083227%;
+  }
+  .row-fluid .offset2 {
+    margin-left: 19.88950276243094%;
+    *margin-left: 19.783119783707537%;
+  }
+  .row-fluid .offset2:first-child {
+    margin-left: 17.12707182320442%;
+    *margin-left: 17.02068884448102%;
+  }
+  .row-fluid .offset1 {
+    margin-left: 11.32596685082873%;
+    *margin-left: 11.219583872105325%;
+  }
+  .row-fluid .offset1:first-child {
+    margin-left: 8.56353591160221%;
+    *margin-left: 8.457152932878806%;
+  }
+  input,
+  textarea,
+  .uneditable-input {
+    margin-left: 0;
+  }
+  .controls-row [class*="span"] + [class*="span"] {
+    margin-left: 20px;
+  }
+  input.span12,
+  textarea.span12,
+  .uneditable-input.span12 {
+    width: 710px;
+  }
+  input.span11,
+  textarea.span11,
+  .uneditable-input.span11 {
+    width: 648px;
+  }
+  input.span10,
+  textarea.span10,
+  .uneditable-input.span10 {
+    width: 586px;
+  }
+  input.span9,
+  textarea.span9,
+  .uneditable-input.span9 {
+    width: 524px;
+  }
+  input.span8,
+  textarea.span8,
+  .uneditable-input.span8 {
+    width: 462px;
+  }
+  input.span7,
+  textarea.span7,
+  .uneditable-input.span7 {
+    width: 400px;
+  }
+  input.span6,
+  textarea.span6,
+  .uneditable-input.span6 {
+    width: 338px;
+  }
+  input.span5,
+  textarea.span5,
+  .uneditable-input.span5 {
+    width: 276px;
+  }
+  input.span4,
+  textarea.span4,
+  .uneditable-input.span4 {
+    width: 214px;
+  }
+  input.span3,
+  textarea.span3,
+  .uneditable-input.span3 {
+    width: 152px;
+  }
+  input.span2,
+  textarea.span2,
+  .uneditable-input.span2 {
+    width: 90px;
+  }
+  input.span1,
+  textarea.span1,
+  .uneditable-input.span1 {
+    width: 28px;
+  }
+}
+
+@media (max-width: 767px) {
+  body {
+    padding-right: 20px;
+    padding-left: 20px;
+  }
+  .navbar-fixed-top,
+  .navbar-fixed-bottom,
+  .navbar-static-top {
+    margin-right: -20px;
+    margin-left: -20px;
+  }
+  .container-fluid {
+    padding: 0;
+  }
+  .dl-horizontal dt {
+    float: none;
+    width: auto;
+    clear: none;
+    text-align: left;
+  }
+  .dl-horizontal dd {
+    margin-left: 0;
+  }
+  .container {
+    width: auto;
+  }
+  .row-fluid {
+    width: 100%;
+  }
+  .row,
+  .thumbnails {
+    margin-left: 0;
+  }
+  .thumbnails > li {
+    float: none;
+    margin-left: 0;
+  }
+  [class*="span"],
+  .uneditable-input[class*="span"],
+  .row-fluid [class*="span"] {
+    display: block;
+    float: none;
+    width: 100%;
+    margin-left: 0;
+    -webkit-box-sizing: border-box;
+       -moz-box-sizing: border-box;
+            box-sizing: border-box;
+  }
+  .span12,
+  .row-fluid .span12 {
+    width: 100%;
+    -webkit-box-sizing: border-box;
+       -moz-box-sizing: border-box;
+            box-sizing: border-box;
+  }
+  .row-fluid [class*="offset"]:first-child {
+    margin-left: 0;
+  }
+  .input-large,
+  .input-xlarge,
+  .input-xxlarge,
+  input[class*="span"],
+  select[class*="span"],
+  textarea[class*="span"],
+  .uneditable-input {
+    display: block;
+    width: 100%;
+    min-height: 30px;
+    -webkit-box-sizing: border-box;
+       -moz-box-sizing: border-box;
+            box-sizing: border-box;
+  }
+  .input-prepend input,
+  .input-append input,
+  .input-prepend input[class*="span"],
+  .input-append input[class*="span"] {
+    display: inline-block;
+    width: auto;
+  }
+  .controls-row [class*="span"] + [class*="span"] {
+    margin-left: 0;
+  }
+  .modal {
+    position: fixed;
+    top: 20px;
+    right: 20px;
+    left: 20px;
+    width: auto;
+    margin: 0;
+  }
+  .modal.fade {
+    top: -100px;
+  }
+  .modal.fade.in {
+    top: 20px;
+  }
+}
+
+@media (max-width: 480px) {
+  .nav-collapse {
+    -webkit-transform: translate3d(0, 0, 0);
+  }
+  .page-header h1 small {
+    display: block;
+    line-height: 20px;
+  }
+  input[type="checkbox"],
+  input[type="radio"] {
+    border: 1px solid #ccc;
+  }
+  .form-horizontal .control-label {
+    float: none;
+    width: auto;
+    padding-top: 0;
+    text-align: left;
+  }
+  .form-horizontal .controls {
+    margin-left: 0;
+  }
+  .form-horizontal .control-list {
+    padding-top: 0;
+  }
+  .form-horizontal .form-actions {
+    padding-right: 10px;
+    padding-left: 10px;
+  }
+  .media .pull-left,
+  .media .pull-right {
+    display: block;
+    float: none;
+    margin-bottom: 10px;
+  }
+  .media-object {
+    margin-right: 0;
+    margin-left: 0;
+  }
+  .modal {
+    top: 10px;
+    right: 10px;
+    left: 10px;
+  }
+  .modal-header .close {
+    padding: 10px;
+    margin: -10px;
+  }
+  .carousel-caption {
+    position: static;
+  }
+}
+
+@media (max-width: 979px) {
+  body {
+    padding-top: 0;
+  }
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    position: static;
+  }
+  .navbar-fixed-top {
+    margin-bottom: 20px;
+  }
+  .navbar-fixed-bottom {
+    margin-top: 20px;
+  }
+  .navbar-fixed-top .navbar-inner,
+  .navbar-fixed-bottom .navbar-inner {
+    padding: 5px;
+  }
+  .navbar .container {
+    width: auto;
+    padding: 0;
+  }
+  .navbar .brand {
+    padding-right: 10px;
+    padding-left: 10px;
+    margin: 0 0 0 -5px;
+  }
+  .nav-collapse {
+    clear: both;
+  }
+  .nav-collapse .nav {
+    float: none;
+    margin: 0 0 10px;
+  }
+  .nav-collapse .nav > li {
+    float: none;
+  }
+  .nav-collapse .nav > li > a {
+    margin-bottom: 2px;
+  }
+  .nav-collapse .nav > .divider-vertical {
+    display: none;
+  }
+  .nav-collapse .nav .nav-header {
+    color: #777777;
+    text-shadow: none;
+  }
+  .nav-collapse .nav > li > a,
+  .nav-collapse .dropdown-menu a {
+    padding: 9px 15px;
+    font-weight: bold;
+    color: #777777;
+    -webkit-border-radius: 3px;
+       -moz-border-radius: 3px;
+            border-radius: 3px;
+  }
+  .nav-collapse .btn {
+    padding: 4px 10px 4px;
+    font-weight: normal;
+    -webkit-border-radius: 4px;
+       -moz-border-radius: 4px;
+            border-radius: 4px;
+  }
+  .nav-collapse .dropdown-menu li + li a {
+    margin-bottom: 2px;
+  }
+  .nav-collapse .nav > li > a:hover,
+  .nav-collapse .nav > li > a:focus,
+  .nav-collapse .dropdown-menu a:hover,
+  .nav-collapse .dropdown-menu a:focus {
+    background-color: #f2f2f2;
+  }
+  .navbar-inverse .nav-collapse .nav > li > a,
+  .navbar-inverse .nav-collapse .dropdown-menu a {
+    color: #999999;
+  }
+  .navbar-inverse .nav-collapse .nav > li > a:hover,
+  .navbar-inverse .nav-collapse .nav > li > a:focus,
+  .navbar-inverse .nav-collapse .dropdown-menu a:hover,
+  .navbar-inverse .nav-collapse .dropdown-menu a:focus {
+    background-color: #111111;
+  }
+  .nav-collapse.in .btn-group {
+    padding: 0;
+    margin-top: 5px;
+  }
+  .nav-collapse .dropdown-menu {
+    position: static;
+    top: auto;
+    left: auto;
+    display: none;
+    float: none;
+    max-width: none;
+    padding: 0;
+    margin: 0 15px;
+    background-color: transparent;
+    border: none;
+    -webkit-border-radius: 0;
+       -moz-border-radius: 0;
+            border-radius: 0;
+    -webkit-box-shadow: none;
+       -moz-box-shadow: none;
+            box-shadow: none;
+  }
+  .nav-collapse .open > .dropdown-menu {
+    display: block;
+  }
+  .nav-collapse .dropdown-menu:before,
+  .nav-collapse .dropdown-menu:after {
+    display: none;
+  }
+  .nav-collapse .dropdown-menu .divider {
+    display: none;
+  }
+  .nav-collapse .nav > li > .dropdown-menu:before,
+  .nav-collapse .nav > li > .dropdown-menu:after {
+    display: none;
+  }
+  .nav-collapse .navbar-form,
+  .nav-collapse .navbar-search {
+    float: none;
+    padding: 10px 15px;
+    margin: 10px 0;
+    border-top: 1px solid #f2f2f2;
+    border-bottom: 1px solid #f2f2f2;
+    -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+       -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+            box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  }
+  .navbar-inverse .nav-collapse .navbar-form,
+  .navbar-inverse .nav-collapse .navbar-search {
+    border-top-color: #111111;
+    border-bottom-color: #111111;
+  }
+  .navbar .nav-collapse .nav.pull-right {
+    float: none;
+    margin-left: 0;
+  }
+  .nav-collapse,
+  .nav-collapse.collapse {
+    height: 0;
+    overflow: hidden;
+  }
+  .navbar .btn-navbar {
+    display: block;
+  }
+  .navbar-static .navbar-inner {
+    padding-right: 10px;
+    padding-left: 10px;
+  }
+}
+
+@media (min-width: 980px) {
+  .nav-collapse.collapse {
+    height: auto !important;
+    overflow: visible !important;
+  }
+}
diff --git a/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css b/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css
new file mode 100644
index 000000000..f4ede63f3
--- /dev/null
+++ b/_static/bootstrap-2.3.2/css/bootstrap-responsive.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Bootstrap Responsive v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
+ */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}.visible-phone{display:none!important}.visible-tablet{display:none!important}.hidden-desktop{display:none!important}.visible-desktop{display:inherit!important}@media(min-width:768px) and 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diff --git a/_static/bootstrap-2.3.2/css/bootstrap.css b/_static/bootstrap-2.3.2/css/bootstrap.css
new file mode 100644
index 000000000..b725064aa
--- /dev/null
+++ b/_static/bootstrap-2.3.2/css/bootstrap.css
@@ -0,0 +1,6167 @@
+/*!
+ * Bootstrap v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
+ */
+
+.clearfix {
+  *zoom: 1;
+}
+
+.clearfix:before,
+.clearfix:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.clearfix:after {
+  clear: both;
+}
+
+.hide-text {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+
+.input-block-level {
+  display: block;
+  width: 100%;
+  min-height: 30px;
+  -webkit-box-sizing: border-box;
+     -moz-box-sizing: border-box;
+          box-sizing: border-box;
+}
+
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+nav,
+section {
+  display: block;
+}
+
+audio,
+canvas,
+video {
+  display: inline-block;
+  *display: inline;
+  *zoom: 1;
+}
+
+audio:not([controls]) {
+  display: none;
+}
+
+html {
+  font-size: 100%;
+  -webkit-text-size-adjust: 100%;
+      -ms-text-size-adjust: 100%;
+}
+
+a:focus {
+  outline: thin dotted #333;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+
+a:hover,
+a:active {
+  outline: 0;
+}
+
+sub,
+sup {
+  position: relative;
+  font-size: 75%;
+  line-height: 0;
+  vertical-align: baseline;
+}
+
+sup {
+  top: -0.5em;
+}
+
+sub {
+  bottom: -0.25em;
+}
+
+img {
+  width: auto\9;
+  height: auto;
+  max-width: 100%;
+  vertical-align: middle;
+  border: 0;
+  -ms-interpolation-mode: bicubic;
+}
+
+#map_canvas img,
+.google-maps img {
+  max-width: none;
+}
+
+button,
+input,
+select,
+textarea {
+  margin: 0;
+  font-size: 100%;
+  vertical-align: middle;
+}
+
+button,
+input {
+  *overflow: visible;
+  line-height: normal;
+}
+
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  padding: 0;
+  border: 0;
+}
+
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  cursor: pointer;
+  -webkit-appearance: button;
+}
+
+label,
+select,
+button,
+input[type="button"],
+input[type="reset"],
+input[type="submit"],
+input[type="radio"],
+input[type="checkbox"] {
+  cursor: pointer;
+}
+
+input[type="search"] {
+  -webkit-box-sizing: content-box;
+     -moz-box-sizing: content-box;
+          box-sizing: content-box;
+  -webkit-appearance: textfield;
+}
+
+input[type="search"]::-webkit-search-decoration,
+input[type="search"]::-webkit-search-cancel-button {
+  -webkit-appearance: none;
+}
+
+textarea {
+  overflow: auto;
+  vertical-align: top;
+}
+
+@media print {
+  * {
+    color: #000 !important;
+    text-shadow: none !important;
+    background: transparent !important;
+    box-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  .ir a:after,
+  a[href^="javascript:"]:after,
+  a[href^="#"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  @page  {
+    margin: 0.5cm;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+}
+
+body {
+  margin: 0;
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  /* In IE7, the height of the select element cannot be changed by height, only font-size */
+
+  *margin-top: 4px;
+  /* For IE7, add top margin to align select with labels */
+
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+}
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+}
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+}
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+}
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+  background-color: #fcfcfc;
+  border-color: #cccccc;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025);
+     -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025);
+          box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.025);
+}
+
+.uneditable-input {
+  overflow: hidden;
+  white-space: nowrap;
+}
+
+.uneditable-textarea {
+  width: auto;
+  height: auto;
+}
+
+input:-moz-placeholder,
+textarea:-moz-placeholder {
+  color: #999999;
+}
+
+input:-ms-input-placeholder,
+textarea:-ms-input-placeholder {
+  color: #999999;
+}
+
+input::-webkit-input-placeholder,
+textarea::-webkit-input-placeholder {
+  color: #999999;
+}
+
+.radio,
+.checkbox {
+  min-height: 20px;
+  padding-left: 20px;
+}
+
+.radio input[type="radio"],
+.checkbox input[type="checkbox"] {
+  float: left;
+  margin-left: -20px;
+}
+
+.controls > .radio:first-child,
+.controls > .checkbox:first-child {
+  padding-top: 5px;
+}
+
+.radio.inline,
+.checkbox.inline {
+  display: inline-block;
+  padding-top: 5px;
+  margin-bottom: 0;
+  vertical-align: middle;
+}
+
+.radio.inline + .radio.inline,
+.checkbox.inline + .checkbox.inline {
+  margin-left: 10px;
+}
+
+.input-mini {
+  width: 60px;
+}
+
+.input-small {
+  width: 90px;
+}
+
+.input-medium {
+  width: 150px;
+}
+
+.input-large {
+  width: 210px;
+}
+
+.input-xlarge {
+  width: 270px;
+}
+
+.input-xxlarge {
+  width: 530px;
+}
+
+input[class*="span"],
+select[class*="span"],
+textarea[class*="span"],
+.uneditable-input[class*="span"],
+.row-fluid input[class*="span"],
+.row-fluid select[class*="span"],
+.row-fluid textarea[class*="span"],
+.row-fluid .uneditable-input[class*="span"] {
+  float: none;
+  margin-left: 0;
+}
+
+.input-append input[class*="span"],
+.input-append .uneditable-input[class*="span"],
+.input-prepend input[class*="span"],
+.input-prepend .uneditable-input[class*="span"],
+.row-fluid input[class*="span"],
+.row-fluid select[class*="span"],
+.row-fluid textarea[class*="span"],
+.row-fluid .uneditable-input[class*="span"],
+.row-fluid .input-prepend [class*="span"],
+.row-fluid .input-append [class*="span"] {
+  display: inline-block;
+}
+
+input,
+textarea,
+.uneditable-input {
+  margin-left: 0;
+}
+
+.controls-row [class*="span"] + [class*="span"] {
+  margin-left: 20px;
+}
+
+input.span12,
+textarea.span12,
+.uneditable-input.span12 {
+  width: 926px;
+}
+
+input.span11,
+textarea.span11,
+.uneditable-input.span11 {
+  width: 846px;
+}
+
+input.span10,
+textarea.span10,
+.uneditable-input.span10 {
+  width: 766px;
+}
+
+input.span9,
+textarea.span9,
+.uneditable-input.span9 {
+  width: 686px;
+}
+
+input.span8,
+textarea.span8,
+.uneditable-input.span8 {
+  width: 606px;
+}
+
+input.span7,
+textarea.span7,
+.uneditable-input.span7 {
+  width: 526px;
+}
+
+input.span6,
+textarea.span6,
+.uneditable-input.span6 {
+  width: 446px;
+}
+
+input.span5,
+textarea.span5,
+.uneditable-input.span5 {
+  width: 366px;
+}
+
+input.span4,
+textarea.span4,
+.uneditable-input.span4 {
+  width: 286px;
+}
+
+input.span3,
+textarea.span3,
+.uneditable-input.span3 {
+  width: 206px;
+}
+
+input.span2,
+textarea.span2,
+.uneditable-input.span2 {
+  width: 126px;
+}
+
+input.span1,
+textarea.span1,
+.uneditable-input.span1 {
+  width: 46px;
+}
+
+.controls-row {
+  *zoom: 1;
+}
+
+.controls-row:before,
+.controls-row:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.controls-row:after {
+  clear: both;
+}
+
+.controls-row [class*="span"],
+.row-fluid .controls-row [class*="span"] {
+  float: left;
+}
+
+.controls-row .checkbox[class*="span"],
+.controls-row .radio[class*="span"] {
+  padding-top: 5px;
+}
+
+input[disabled],
+select[disabled],
+textarea[disabled],
+input[readonly],
+select[readonly],
+textarea[readonly] {
+  cursor: not-allowed;
+  background-color: #eeeeee;
+}
+
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"][readonly],
+input[type="checkbox"][readonly] {
+  background-color: transparent;
+}
+
+.control-group.warning .control-label,
+.control-group.warning .help-block,
+.control-group.warning .help-inline {
+  color: #c09853;
+}
+
+.control-group.warning .checkbox,
+.control-group.warning .radio,
+.control-group.warning input,
+.control-group.warning select,
+.control-group.warning textarea {
+  color: #c09853;
+}
+
+.control-group.warning input,
+.control-group.warning select,
+.control-group.warning textarea {
+  border-color: #c09853;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+
+.control-group.warning input:focus,
+.control-group.warning select:focus,
+.control-group.warning textarea:focus {
+  border-color: #a47e3c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e;
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e;
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #dbc59e;
+}
+
+.control-group.warning .input-prepend .add-on,
+.control-group.warning .input-append .add-on {
+  color: #c09853;
+  background-color: #fcf8e3;
+  border-color: #c09853;
+}
+
+.control-group.error .control-label,
+.control-group.error .help-block,
+.control-group.error .help-inline {
+  color: #b94a48;
+}
+
+.control-group.error .checkbox,
+.control-group.error .radio,
+.control-group.error input,
+.control-group.error select,
+.control-group.error textarea {
+  color: #b94a48;
+}
+
+.control-group.error input,
+.control-group.error select,
+.control-group.error textarea {
+  border-color: #b94a48;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+
+.control-group.error input:focus,
+.control-group.error select:focus,
+.control-group.error textarea:focus {
+  border-color: #953b39;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392;
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392;
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #d59392;
+}
+
+.control-group.error .input-prepend .add-on,
+.control-group.error .input-append .add-on {
+  color: #b94a48;
+  background-color: #f2dede;
+  border-color: #b94a48;
+}
+
+.control-group.success .control-label,
+.control-group.success .help-block,
+.control-group.success .help-inline {
+  color: #468847;
+}
+
+.control-group.success .checkbox,
+.control-group.success .radio,
+.control-group.success input,
+.control-group.success select,
+.control-group.success textarea {
+  color: #468847;
+}
+
+.control-group.success input,
+.control-group.success select,
+.control-group.success textarea {
+  border-color: #468847;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+
+.control-group.success input:focus,
+.control-group.success select:focus,
+.control-group.success textarea:focus {
+  border-color: #356635;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b;
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b;
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7aba7b;
+}
+
+.control-group.success .input-prepend .add-on,
+.control-group.success .input-append .add-on {
+  color: #468847;
+  background-color: #dff0d8;
+  border-color: #468847;
+}
+
+.control-group.info .control-label,
+.control-group.info .help-block,
+.control-group.info .help-inline {
+  color: #3a87ad;
+}
+
+.control-group.info .checkbox,
+.control-group.info .radio,
+.control-group.info input,
+.control-group.info select,
+.control-group.info textarea {
+  color: #3a87ad;
+}
+
+.control-group.info input,
+.control-group.info select,
+.control-group.info textarea {
+  border-color: #3a87ad;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+
+.control-group.info input:focus,
+.control-group.info select:focus,
+.control-group.info textarea:focus {
+  border-color: #2d6987;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3;
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3;
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #7ab5d3;
+}
+
+.control-group.info .input-prepend .add-on,
+.control-group.info .input-append .add-on {
+  color: #3a87ad;
+  background-color: #d9edf7;
+  border-color: #3a87ad;
+}
+
+input:focus:invalid,
+textarea:focus:invalid,
+select:focus:invalid {
+  color: #b94a48;
+  border-color: #ee5f5b;
+}
+
+input:focus:invalid:focus,
+textarea:focus:invalid:focus,
+select:focus:invalid:focus {
+  border-color: #e9322d;
+  -webkit-box-shadow: 0 0 6px #f8b9b7;
+     -moz-box-shadow: 0 0 6px #f8b9b7;
+          box-shadow: 0 0 6px #f8b9b7;
+}
+
+.form-actions {
+  padding: 19px 20px 20px;
+  margin-top: 20px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #e5e5e5;
+  *zoom: 1;
+}
+
+.form-actions:before,
+.form-actions:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.form-actions:after {
+  clear: both;
+}
+
+.help-block,
+.help-inline {
+  color: #595959;
+}
+
+.help-block {
+  display: block;
+  margin-bottom: 10px;
+}
+
+.help-inline {
+  display: inline-block;
+  *display: inline;
+  padding-left: 5px;
+  vertical-align: middle;
+  *zoom: 1;
+}
+
+.input-append,
+.input-prepend {
+  display: inline-block;
+  margin-bottom: 10px;
+  font-size: 0;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+
+.input-append input,
+.input-prepend input,
+.input-append select,
+.input-prepend select,
+.input-append .uneditable-input,
+.input-prepend .uneditable-input,
+.input-append .dropdown-menu,
+.input-prepend .dropdown-menu,
+.input-append .popover,
+.input-prepend .popover {
+  font-size: 14px;
+}
+
+.input-append input,
+.input-prepend input,
+.input-append select,
+.input-prepend select,
+.input-append .uneditable-input,
+.input-prepend .uneditable-input {
+  position: relative;
+  margin-bottom: 0;
+  *margin-left: 0;
+  vertical-align: top;
+  -webkit-border-radius: 0 4px 4px 0;
+     -moz-border-radius: 0 4px 4px 0;
+          border-radius: 0 4px 4px 0;
+}
+
+.input-append input:focus,
+.input-prepend input:focus,
+.input-append select:focus,
+.input-prepend select:focus,
+.input-append .uneditable-input:focus,
+.input-prepend .uneditable-input:focus {
+  z-index: 2;
+}
+
+.input-append .add-on,
+.input-prepend .add-on {
+  display: inline-block;
+  width: auto;
+  height: 20px;
+  min-width: 16px;
+  padding: 4px 5px;
+  font-size: 14px;
+  font-weight: normal;
+  line-height: 20px;
+  text-align: center;
+  text-shadow: 0 1px 0 #ffffff;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+}
+
+.input-append .add-on,
+.input-prepend .add-on,
+.input-append .btn,
+.input-prepend .btn,
+.input-append .btn-group > .dropdown-toggle,
+.input-prepend .btn-group > .dropdown-toggle {
+  vertical-align: top;
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.input-append .active,
+.input-prepend .active {
+  background-color: #a9dba9;
+  border-color: #46a546;
+}
+
+.input-prepend .add-on,
+.input-prepend .btn {
+  margin-right: -1px;
+}
+
+.input-prepend .add-on:first-child,
+.input-prepend .btn:first-child {
+  -webkit-border-radius: 4px 0 0 4px;
+     -moz-border-radius: 4px 0 0 4px;
+          border-radius: 4px 0 0 4px;
+}
+
+.input-append input,
+.input-append select,
+.input-append .uneditable-input {
+  -webkit-border-radius: 4px 0 0 4px;
+     -moz-border-radius: 4px 0 0 4px;
+          border-radius: 4px 0 0 4px;
+}
+
+.input-append input + .btn-group .btn:last-child,
+.input-append select + .btn-group .btn:last-child,
+.input-append .uneditable-input + .btn-group .btn:last-child {
+  -webkit-border-radius: 0 4px 4px 0;
+     -moz-border-radius: 0 4px 4px 0;
+          border-radius: 0 4px 4px 0;
+}
+
+.input-append .add-on,
+.input-append .btn,
+.input-append .btn-group {
+  margin-left: -1px;
+}
+
+.input-append .add-on:last-child,
+.input-append .btn:last-child,
+.input-append .btn-group:last-child > .dropdown-toggle {
+  -webkit-border-radius: 0 4px 4px 0;
+     -moz-border-radius: 0 4px 4px 0;
+          border-radius: 0 4px 4px 0;
+}
+
+.input-prepend.input-append input,
+.input-prepend.input-append select,
+.input-prepend.input-append .uneditable-input {
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.input-prepend.input-append input + .btn-group .btn,
+.input-prepend.input-append select + .btn-group .btn,
+.input-prepend.input-append .uneditable-input + .btn-group .btn {
+  -webkit-border-radius: 0 4px 4px 0;
+     -moz-border-radius: 0 4px 4px 0;
+          border-radius: 0 4px 4px 0;
+}
+
+.input-prepend.input-append .add-on:first-child,
+.input-prepend.input-append .btn:first-child {
+  margin-right: -1px;
+  -webkit-border-radius: 4px 0 0 4px;
+     -moz-border-radius: 4px 0 0 4px;
+          border-radius: 4px 0 0 4px;
+}
+
+.input-prepend.input-append .add-on:last-child,
+.input-prepend.input-append .btn:last-child {
+  margin-left: -1px;
+  -webkit-border-radius: 0 4px 4px 0;
+     -moz-border-radius: 0 4px 4px 0;
+          border-radius: 0 4px 4px 0;
+}
+
+.input-prepend.input-append .btn-group:first-child {
+  margin-left: 0;
+}
+
+input.search-query {
+  padding-right: 14px;
+  padding-right: 4px \9;
+  padding-left: 14px;
+  padding-left: 4px \9;
+  /* IE7-8 doesn't have border-radius, so don't indent the padding */
+
+  margin-bottom: 0;
+  -webkit-border-radius: 15px;
+     -moz-border-radius: 15px;
+          border-radius: 15px;
+}
+
+/* Allow for input prepend/append in search forms */
+
+.form-search .input-append .search-query,
+.form-search .input-prepend .search-query {
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.form-search .input-append .search-query {
+  -webkit-border-radius: 14px 0 0 14px;
+     -moz-border-radius: 14px 0 0 14px;
+          border-radius: 14px 0 0 14px;
+}
+
+.form-search .input-append .btn {
+  -webkit-border-radius: 0 14px 14px 0;
+     -moz-border-radius: 0 14px 14px 0;
+          border-radius: 0 14px 14px 0;
+}
+
+.form-search .input-prepend .search-query {
+  -webkit-border-radius: 0 14px 14px 0;
+     -moz-border-radius: 0 14px 14px 0;
+          border-radius: 0 14px 14px 0;
+}
+
+.form-search .input-prepend .btn {
+  -webkit-border-radius: 14px 0 0 14px;
+     -moz-border-radius: 14px 0 0 14px;
+          border-radius: 14px 0 0 14px;
+}
+
+.form-search input,
+.form-inline input,
+.form-horizontal input,
+.form-search textarea,
+.form-inline textarea,
+.form-horizontal textarea,
+.form-search select,
+.form-inline select,
+.form-horizontal select,
+.form-search .help-inline,
+.form-inline .help-inline,
+.form-horizontal .help-inline,
+.form-search .uneditable-input,
+.form-inline .uneditable-input,
+.form-horizontal .uneditable-input,
+.form-search .input-prepend,
+.form-inline .input-prepend,
+.form-horizontal .input-prepend,
+.form-search .input-append,
+.form-inline .input-append,
+.form-horizontal .input-append {
+  display: inline-block;
+  *display: inline;
+  margin-bottom: 0;
+  vertical-align: middle;
+  *zoom: 1;
+}
+
+.form-search .hide,
+.form-inline .hide,
+.form-horizontal .hide {
+  display: none;
+}
+
+.form-search label,
+.form-inline label,
+.form-search .btn-group,
+.form-inline .btn-group {
+  display: inline-block;
+}
+
+.form-search .input-append,
+.form-inline .input-append,
+.form-search .input-prepend,
+.form-inline .input-prepend {
+  margin-bottom: 0;
+}
+
+.form-search .radio,
+.form-search .checkbox,
+.form-inline .radio,
+.form-inline .checkbox {
+  padding-left: 0;
+  margin-bottom: 0;
+  vertical-align: middle;
+}
+
+.form-search .radio input[type="radio"],
+.form-search .checkbox input[type="checkbox"],
+.form-inline .radio input[type="radio"],
+.form-inline .checkbox input[type="checkbox"] {
+  float: left;
+  margin-right: 3px;
+  margin-left: 0;
+}
+
+.control-group {
+  margin-bottom: 10px;
+}
+
+legend + .control-group {
+  margin-top: 20px;
+  -webkit-margin-top-collapse: separate;
+}
+
+.form-horizontal .control-group {
+  margin-bottom: 20px;
+  *zoom: 1;
+}
+
+.form-horizontal .control-group:before,
+.form-horizontal .control-group:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.form-horizontal .control-group:after {
+  clear: both;
+}
+
+.form-horizontal .control-label {
+  float: left;
+  width: 160px;
+  padding-top: 5px;
+  text-align: right;
+}
+
+.form-horizontal .controls {
+  *display: inline-block;
+  *padding-left: 20px;
+  margin-left: 180px;
+  *margin-left: 0;
+}
+
+.form-horizontal .controls:first-child {
+  *padding-left: 180px;
+}
+
+.form-horizontal .help-block {
+  margin-bottom: 0;
+}
+
+.form-horizontal input + .help-block,
+.form-horizontal select + .help-block,
+.form-horizontal textarea + .help-block,
+.form-horizontal .uneditable-input + .help-block,
+.form-horizontal .input-prepend + .help-block,
+.form-horizontal .input-append + .help-block {
+  margin-top: 10px;
+}
+
+.form-horizontal .form-actions {
+  padding-left: 180px;
+}
+
+table {
+  max-width: 100%;
+  background-color: transparent;
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+
+.table {
+  width: 100%;
+  margin-bottom: 20px;
+}
+
+.table th,
+.table td {
+  padding: 8px;
+  line-height: 20px;
+  text-align: left;
+  vertical-align: top;
+  border-top: 1px solid #dddddd;
+}
+
+.table th {
+  font-weight: bold;
+}
+
+.table thead th {
+  vertical-align: bottom;
+}
+
+.table caption + thead tr:first-child th,
+.table caption + thead tr:first-child td,
+.table colgroup + thead tr:first-child th,
+.table colgroup + thead tr:first-child td,
+.table thead:first-child tr:first-child th,
+.table thead:first-child tr:first-child td {
+  border-top: 0;
+}
+
+.table tbody + tbody {
+  border-top: 2px solid #dddddd;
+}
+
+.table .table {
+  background-color: #ffffff;
+}
+
+.table-condensed th,
+.table-condensed td {
+  padding: 4px 5px;
+}
+
+.table-bordered {
+  border: 1px solid #dddddd;
+  border-collapse: separate;
+  *border-collapse: collapse;
+  border-left: 0;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+}
+
+.table-bordered th,
+.table-bordered td {
+  border-left: 1px solid #dddddd;
+}
+
+.table-bordered caption + thead tr:first-child th,
+.table-bordered caption + tbody tr:first-child th,
+.table-bordered caption + tbody tr:first-child td,
+.table-bordered colgroup + thead tr:first-child th,
+.table-bordered colgroup + tbody tr:first-child th,
+.table-bordered colgroup + tbody tr:first-child td,
+.table-bordered thead:first-child tr:first-child th,
+.table-bordered tbody:first-child tr:first-child th,
+.table-bordered tbody:first-child tr:first-child td {
+  border-top: 0;
+}
+
+.table-bordered thead:first-child tr:first-child > th:first-child,
+.table-bordered tbody:first-child tr:first-child > td:first-child,
+.table-bordered tbody:first-child tr:first-child > th:first-child {
+  -webkit-border-top-left-radius: 4px;
+          border-top-left-radius: 4px;
+  -moz-border-radius-topleft: 4px;
+}
+
+.table-bordered thead:first-child tr:first-child > th:last-child,
+.table-bordered tbody:first-child tr:first-child > td:last-child,
+.table-bordered tbody:first-child tr:first-child > th:last-child {
+  -webkit-border-top-right-radius: 4px;
+          border-top-right-radius: 4px;
+  -moz-border-radius-topright: 4px;
+}
+
+.table-bordered thead:last-child tr:last-child > th:first-child,
+.table-bordered tbody:last-child tr:last-child > td:first-child,
+.table-bordered tbody:last-child tr:last-child > th:first-child,
+.table-bordered tfoot:last-child tr:last-child > td:first-child,
+.table-bordered tfoot:last-child tr:last-child > th:first-child {
+  -webkit-border-bottom-left-radius: 4px;
+          border-bottom-left-radius: 4px;
+  -moz-border-radius-bottomleft: 4px;
+}
+
+.table-bordered thead:last-child tr:last-child > th:last-child,
+.table-bordered tbody:last-child tr:last-child > td:last-child,
+.table-bordered tbody:last-child tr:last-child > th:last-child,
+.table-bordered tfoot:last-child tr:last-child > td:last-child,
+.table-bordered tfoot:last-child tr:last-child > th:last-child {
+  -webkit-border-bottom-right-radius: 4px;
+          border-bottom-right-radius: 4px;
+  -moz-border-radius-bottomright: 4px;
+}
+
+.table-bordered tfoot + tbody:last-child tr:last-child td:first-child {
+  -webkit-border-bottom-left-radius: 0;
+          border-bottom-left-radius: 0;
+  -moz-border-radius-bottomleft: 0;
+}
+
+.table-bordered tfoot + tbody:last-child tr:last-child td:last-child {
+  -webkit-border-bottom-right-radius: 0;
+          border-bottom-right-radius: 0;
+  -moz-border-radius-bottomright: 0;
+}
+
+.table-bordered caption + thead tr:first-child th:first-child,
+.table-bordered caption + tbody tr:first-child td:first-child,
+.table-bordered colgroup + thead tr:first-child th:first-child,
+.table-bordered colgroup + tbody tr:first-child td:first-child {
+  -webkit-border-top-left-radius: 4px;
+          border-top-left-radius: 4px;
+  -moz-border-radius-topleft: 4px;
+}
+
+.table-bordered caption + thead tr:first-child th:last-child,
+.table-bordered caption + tbody tr:first-child td:last-child,
+.table-bordered colgroup + thead tr:first-child th:last-child,
+.table-bordered colgroup + tbody tr:first-child td:last-child {
+  -webkit-border-top-right-radius: 4px;
+          border-top-right-radius: 4px;
+  -moz-border-radius-topright: 4px;
+}
+
+.table-striped tbody > tr:nth-child(odd) > td,
+.table-striped tbody > tr:nth-child(odd) > th {
+  background-color: #f9f9f9;
+}
+
+.table-hover tbody tr:hover > td,
+.table-hover tbody tr:hover > th {
+  background-color: #f5f5f5;
+}
+
+table td[class*="span"],
+table th[class*="span"],
+.row-fluid table td[class*="span"],
+.row-fluid table th[class*="span"] {
+  display: table-cell;
+  float: none;
+  margin-left: 0;
+}
+
+.table td.span1,
+.table th.span1 {
+  float: none;
+  width: 44px;
+  margin-left: 0;
+}
+
+.table td.span2,
+.table th.span2 {
+  float: none;
+  width: 124px;
+  margin-left: 0;
+}
+
+.table td.span3,
+.table th.span3 {
+  float: none;
+  width: 204px;
+  margin-left: 0;
+}
+
+.table td.span4,
+.table th.span4 {
+  float: none;
+  width: 284px;
+  margin-left: 0;
+}
+
+.table td.span5,
+.table th.span5 {
+  float: none;
+  width: 364px;
+  margin-left: 0;
+}
+
+.table td.span6,
+.table th.span6 {
+  float: none;
+  width: 444px;
+  margin-left: 0;
+}
+
+.table td.span7,
+.table th.span7 {
+  float: none;
+  width: 524px;
+  margin-left: 0;
+}
+
+.table td.span8,
+.table th.span8 {
+  float: none;
+  width: 604px;
+  margin-left: 0;
+}
+
+.table td.span9,
+.table th.span9 {
+  float: none;
+  width: 684px;
+  margin-left: 0;
+}
+
+.table td.span10,
+.table th.span10 {
+  float: none;
+  width: 764px;
+  margin-left: 0;
+}
+
+.table td.span11,
+.table th.span11 {
+  float: none;
+  width: 844px;
+  margin-left: 0;
+}
+
+.table td.span12,
+.table th.span12 {
+  float: none;
+  width: 924px;
+  margin-left: 0;
+}
+
+.table tbody tr.success > td {
+  background-color: #dff0d8;
+}
+
+.table tbody tr.error > td {
+  background-color: #f2dede;
+}
+
+.table tbody tr.warning > td {
+  background-color: #fcf8e3;
+}
+
+.table tbody tr.info > td {
+  background-color: #d9edf7;
+}
+
+.table-hover tbody tr.success:hover > td {
+  background-color: #d0e9c6;
+}
+
+.table-hover tbody tr.error:hover > td {
+  background-color: #ebcccc;
+}
+
+.table-hover tbody tr.warning:hover > td {
+  background-color: #faf2cc;
+}
+
+.table-hover tbody tr.info:hover > td {
+  background-color: #c4e3f3;
+}
+
+[class^="icon-"],
+[class*=" icon-"] {
+  display: inline-block;
+  width: 14px;
+  height: 14px;
+  margin-top: 1px;
+  *margin-right: .3em;
+  line-height: 14px;
+  vertical-align: text-top;
+  background-image: url("../img/glyphicons-halflings.png");
+  background-position: 14px 14px;
+  background-repeat: no-repeat;
+}
+
+/* White icons with optional class, or on hover/focus/active states of certain elements */
+
+.icon-white,
+.nav-pills > .active > a > [class^="icon-"],
+.nav-pills > .active > a > [class*=" icon-"],
+.nav-list > .active > a > [class^="icon-"],
+.nav-list > .active > a > [class*=" icon-"],
+.navbar-inverse .nav > .active > a > [class^="icon-"],
+.navbar-inverse .nav > .active > a > [class*=" icon-"],
+.dropdown-menu > li > a:hover > [class^="icon-"],
+.dropdown-menu > li > a:focus > [class^="icon-"],
+.dropdown-menu > li > a:hover > [class*=" icon-"],
+.dropdown-menu > li > a:focus > [class*=" icon-"],
+.dropdown-menu > .active > a > [class^="icon-"],
+.dropdown-menu > .active > a > [class*=" icon-"],
+.dropdown-submenu:hover > a > [class^="icon-"],
+.dropdown-submenu:focus > a > [class^="icon-"],
+.dropdown-submenu:hover > a > [class*=" icon-"],
+.dropdown-submenu:focus > a > [class*=" icon-"] {
+  background-image: url("../img/glyphicons-halflings-white.png");
+}
+
+.icon-glass {
+  background-position: 0      0;
+}
+
+.icon-music {
+  background-position: -24px 0;
+}
+
+.icon-search {
+  background-position: -48px 0;
+}
+
+.icon-envelope {
+  background-position: -72px 0;
+}
+
+.icon-heart {
+  background-position: -96px 0;
+}
+
+.icon-star {
+  background-position: -120px 0;
+}
+
+.icon-star-empty {
+  background-position: -144px 0;
+}
+
+.icon-user {
+  background-position: -168px 0;
+}
+
+.icon-film {
+  background-position: -192px 0;
+}
+
+.icon-th-large {
+  background-position: -216px 0;
+}
+
+.icon-th {
+  background-position: -240px 0;
+}
+
+.icon-th-list {
+  background-position: -264px 0;
+}
+
+.icon-ok {
+  background-position: -288px 0;
+}
+
+.icon-remove {
+  background-position: -312px 0;
+}
+
+.icon-zoom-in {
+  background-position: -336px 0;
+}
+
+.icon-zoom-out {
+  background-position: -360px 0;
+}
+
+.icon-off {
+  background-position: -384px 0;
+}
+
+.icon-signal {
+  background-position: -408px 0;
+}
+
+.icon-cog {
+  background-position: -432px 0;
+}
+
+.icon-trash {
+  background-position: -456px 0;
+}
+
+.icon-home {
+  background-position: 0 -24px;
+}
+
+.icon-file {
+  background-position: -24px -24px;
+}
+
+.icon-time {
+  background-position: -48px -24px;
+}
+
+.icon-road {
+  background-position: -72px -24px;
+}
+
+.icon-download-alt {
+  background-position: -96px -24px;
+}
+
+.icon-download {
+  background-position: -120px -24px;
+}
+
+.icon-upload {
+  background-position: -144px -24px;
+}
+
+.icon-inbox {
+  background-position: -168px -24px;
+}
+
+.icon-play-circle {
+  background-position: -192px -24px;
+}
+
+.icon-repeat {
+  background-position: -216px -24px;
+}
+
+.icon-refresh {
+  background-position: -240px -24px;
+}
+
+.icon-list-alt {
+  background-position: -264px -24px;
+}
+
+.icon-lock {
+  background-position: -287px -24px;
+}
+
+.icon-flag {
+  background-position: -312px -24px;
+}
+
+.icon-headphones {
+  background-position: -336px -24px;
+}
+
+.icon-volume-off {
+  background-position: -360px -24px;
+}
+
+.icon-volume-down {
+  background-position: -384px -24px;
+}
+
+.icon-volume-up {
+  background-position: -408px -24px;
+}
+
+.icon-qrcode {
+  background-position: -432px -24px;
+}
+
+.icon-barcode {
+  background-position: -456px -24px;
+}
+
+.icon-tag {
+  background-position: 0 -48px;
+}
+
+.icon-tags {
+  background-position: -25px -48px;
+}
+
+.icon-book {
+  background-position: -48px -48px;
+}
+
+.icon-bookmark {
+  background-position: -72px -48px;
+}
+
+.icon-print {
+  background-position: -96px -48px;
+}
+
+.icon-camera {
+  background-position: -120px -48px;
+}
+
+.icon-font {
+  background-position: -144px -48px;
+}
+
+.icon-bold {
+  background-position: -167px -48px;
+}
+
+.icon-italic {
+  background-position: -192px -48px;
+}
+
+.icon-text-height {
+  background-position: -216px -48px;
+}
+
+.icon-text-width {
+  background-position: -240px -48px;
+}
+
+.icon-align-left {
+  background-position: -264px -48px;
+}
+
+.icon-align-center {
+  background-position: -288px -48px;
+}
+
+.icon-align-right {
+  background-position: -312px -48px;
+}
+
+.icon-align-justify {
+  background-position: -336px -48px;
+}
+
+.icon-list {
+  background-position: -360px -48px;
+}
+
+.icon-indent-left {
+  background-position: -384px -48px;
+}
+
+.icon-indent-right {
+  background-position: -408px -48px;
+}
+
+.icon-facetime-video {
+  background-position: -432px -48px;
+}
+
+.icon-picture {
+  background-position: -456px -48px;
+}
+
+.icon-pencil {
+  background-position: 0 -72px;
+}
+
+.icon-map-marker {
+  background-position: -24px -72px;
+}
+
+.icon-adjust {
+  background-position: -48px -72px;
+}
+
+.icon-tint {
+  background-position: -72px -72px;
+}
+
+.icon-edit {
+  background-position: -96px -72px;
+}
+
+.icon-share {
+  background-position: -120px -72px;
+}
+
+.icon-check {
+  background-position: -144px -72px;
+}
+
+.icon-move {
+  background-position: -168px -72px;
+}
+
+.icon-step-backward {
+  background-position: -192px -72px;
+}
+
+.icon-fast-backward {
+  background-position: -216px -72px;
+}
+
+.icon-backward {
+  background-position: -240px -72px;
+}
+
+.icon-play {
+  background-position: -264px -72px;
+}
+
+.icon-pause {
+  background-position: -288px -72px;
+}
+
+.icon-stop {
+  background-position: -312px -72px;
+}
+
+.icon-forward {
+  background-position: -336px -72px;
+}
+
+.icon-fast-forward {
+  background-position: -360px -72px;
+}
+
+.icon-step-forward {
+  background-position: -384px -72px;
+}
+
+.icon-eject {
+  background-position: -408px -72px;
+}
+
+.icon-chevron-left {
+  background-position: -432px -72px;
+}
+
+.icon-chevron-right {
+  background-position: -456px -72px;
+}
+
+.icon-plus-sign {
+  background-position: 0 -96px;
+}
+
+.icon-minus-sign {
+  background-position: -24px -96px;
+}
+
+.icon-remove-sign {
+  background-position: -48px -96px;
+}
+
+.icon-ok-sign {
+  background-position: -72px -96px;
+}
+
+.icon-question-sign {
+  background-position: -96px -96px;
+}
+
+.icon-info-sign {
+  background-position: -120px -96px;
+}
+
+.icon-screenshot {
+  background-position: -144px -96px;
+}
+
+.icon-remove-circle {
+  background-position: -168px -96px;
+}
+
+.icon-ok-circle {
+  background-position: -192px -96px;
+}
+
+.icon-ban-circle {
+  background-position: -216px -96px;
+}
+
+.icon-arrow-left {
+  background-position: -240px -96px;
+}
+
+.icon-arrow-right {
+  background-position: -264px -96px;
+}
+
+.icon-arrow-up {
+  background-position: -289px -96px;
+}
+
+.icon-arrow-down {
+  background-position: -312px -96px;
+}
+
+.icon-share-alt {
+  background-position: -336px -96px;
+}
+
+.icon-resize-full {
+  background-position: -360px -96px;
+}
+
+.icon-resize-small {
+  background-position: -384px -96px;
+}
+
+.icon-plus {
+  background-position: -408px -96px;
+}
+
+.icon-minus {
+  background-position: -433px -96px;
+}
+
+.icon-asterisk {
+  background-position: -456px -96px;
+}
+
+.icon-exclamation-sign {
+  background-position: 0 -120px;
+}
+
+.icon-gift {
+  background-position: -24px -120px;
+}
+
+.icon-leaf {
+  background-position: -48px -120px;
+}
+
+.icon-fire {
+  background-position: -72px -120px;
+}
+
+.icon-eye-open {
+  background-position: -96px -120px;
+}
+
+.icon-eye-close {
+  background-position: -120px -120px;
+}
+
+.icon-warning-sign {
+  background-position: -144px -120px;
+}
+
+.icon-plane {
+  background-position: -168px -120px;
+}
+
+.icon-calendar {
+  background-position: -192px -120px;
+}
+
+.icon-random {
+  width: 16px;
+  background-position: -216px -120px;
+}
+
+.icon-comment {
+  background-position: -240px -120px;
+}
+
+.icon-magnet {
+  background-position: -264px -120px;
+}
+
+.icon-chevron-up {
+  background-position: -288px -120px;
+}
+
+.icon-chevron-down {
+  background-position: -313px -119px;
+}
+
+.icon-retweet {
+  background-position: -336px -120px;
+}
+
+.icon-shopping-cart {
+  background-position: -360px -120px;
+}
+
+.icon-folder-close {
+  width: 16px;
+  background-position: -384px -120px;
+}
+
+.icon-folder-open {
+  width: 16px;
+  background-position: -408px -120px;
+}
+
+.icon-resize-vertical {
+  background-position: -432px -119px;
+}
+
+.icon-resize-horizontal {
+  background-position: -456px -118px;
+}
+
+.icon-hdd {
+  background-position: 0 -144px;
+}
+
+.icon-bullhorn {
+  background-position: -24px -144px;
+}
+
+.icon-bell {
+  background-position: -48px -144px;
+}
+
+.icon-certificate {
+  background-position: -72px -144px;
+}
+
+.icon-thumbs-up {
+  background-position: -96px -144px;
+}
+
+.icon-thumbs-down {
+  background-position: -120px -144px;
+}
+
+.icon-hand-right {
+  background-position: -144px -144px;
+}
+
+.icon-hand-left {
+  background-position: -168px -144px;
+}
+
+.icon-hand-up {
+  background-position: -192px -144px;
+}
+
+.icon-hand-down {
+  background-position: -216px -144px;
+}
+
+.icon-circle-arrow-right {
+  background-position: -240px -144px;
+}
+
+.icon-circle-arrow-left {
+  background-position: -264px -144px;
+}
+
+.icon-circle-arrow-up {
+  background-position: -288px -144px;
+}
+
+.icon-circle-arrow-down {
+  background-position: -312px -144px;
+}
+
+.icon-globe {
+  background-position: -336px -144px;
+}
+
+.icon-wrench {
+  background-position: -360px -144px;
+}
+
+.icon-tasks {
+  background-position: -384px -144px;
+}
+
+.icon-filter {
+  background-position: -408px -144px;
+}
+
+.icon-briefcase {
+  background-position: -432px -144px;
+}
+
+.icon-fullscreen {
+  background-position: -456px -144px;
+}
+
+.dropup,
+.dropdown {
+  position: relative;
+}
+
+.dropdown-toggle {
+  *margin-bottom: -3px;
+}
+
+.dropdown-toggle:active,
+.open .dropdown-toggle {
+  outline: 0;
+}
+
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  vertical-align: top;
+  border-top: 4px solid #000000;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+  content: "";
+}
+
+.dropdown .caret {
+  margin-top: 8px;
+  margin-left: 2px;
+}
+
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  list-style: none;
+  background-color: #ffffff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  *border-right-width: 2px;
+  *border-bottom-width: 2px;
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+     -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+          box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  -webkit-background-clip: padding-box;
+     -moz-background-clip: padding;
+          background-clip: padding-box;
+}
+
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+
+.dropdown-menu .divider {
+  *width: 100%;
+  height: 1px;
+  margin: 9px 1px;
+  *margin: -5px 0 5px;
+  overflow: hidden;
+  background-color: #e5e5e5;
+  border-bottom: 1px solid #ffffff;
+}
+
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: normal;
+  line-height: 20px;
+  color: #333333;
+  white-space: nowrap;
+}
+
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus,
+.dropdown-submenu:hover > a,
+.dropdown-submenu:focus > a {
+  color: #ffffff;
+  text-decoration: none;
+  background-color: #0081c2;
+  background-image: -moz-linear-gradient(top, #0088cc, #0077b3);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3));
+  background-image: -webkit-linear-gradient(top, #0088cc, #0077b3);
+  background-image: -o-linear-gradient(top, #0088cc, #0077b3);
+  background-image: linear-gradient(to bottom, #0088cc, #0077b3);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0);
+}
+
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #ffffff;
+  text-decoration: none;
+  background-color: #0081c2;
+  background-image: -moz-linear-gradient(top, #0088cc, #0077b3);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3));
+  background-image: -webkit-linear-gradient(top, #0088cc, #0077b3);
+  background-image: -o-linear-gradient(top, #0088cc, #0077b3);
+  background-image: linear-gradient(to bottom, #0088cc, #0077b3);
+  background-repeat: repeat-x;
+  outline: 0;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0);
+}
+
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #999999;
+}
+
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  cursor: default;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.open {
+  *z-index: 1000;
+}
+
+.open > .dropdown-menu {
+  display: block;
+}
+
+.dropdown-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 990;
+}
+
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  border-top: 0;
+  border-bottom: 4px solid #000000;
+  content: "";
+}
+
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 1px;
+}
+
+.dropdown-submenu {
+  position: relative;
+}
+
+.dropdown-submenu > .dropdown-menu {
+  top: 0;
+  left: 100%;
+  margin-top: -6px;
+  margin-left: -1px;
+  -webkit-border-radius: 0 6px 6px 6px;
+     -moz-border-radius: 0 6px 6px 6px;
+          border-radius: 0 6px 6px 6px;
+}
+
+.dropdown-submenu:hover > .dropdown-menu {
+  display: block;
+}
+
+.dropup .dropdown-submenu > .dropdown-menu {
+  top: auto;
+  bottom: 0;
+  margin-top: 0;
+  margin-bottom: -2px;
+  -webkit-border-radius: 5px 5px 5px 0;
+     -moz-border-radius: 5px 5px 5px 0;
+          border-radius: 5px 5px 5px 0;
+}
+
+.dropdown-submenu > a:after {
+  display: block;
+  float: right;
+  width: 0;
+  height: 0;
+  margin-top: 5px;
+  margin-right: -10px;
+  border-color: transparent;
+  border-left-color: #cccccc;
+  border-style: solid;
+  border-width: 5px 0 5px 5px;
+  content: " ";
+}
+
+.dropdown-submenu:hover > a:after {
+  border-left-color: #ffffff;
+}
+
+.dropdown-submenu.pull-left {
+  float: none;
+}
+
+.dropdown-submenu.pull-left > .dropdown-menu {
+  left: -100%;
+  margin-left: 10px;
+  -webkit-border-radius: 6px 0 6px 6px;
+     -moz-border-radius: 6px 0 6px 6px;
+          border-radius: 6px 0 6px 6px;
+}
+
+.dropdown .dropdown-menu .nav-header {
+  padding-right: 20px;
+  padding-left: 20px;
+}
+
+.typeahead {
+  z-index: 1051;
+  margin-top: 2px;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+}
+
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+     -moz-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+          box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+
+.well-large {
+  padding: 24px;
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+}
+
+.well-small {
+  padding: 9px;
+  -webkit-border-radius: 3px;
+     -moz-border-radius: 3px;
+          border-radius: 3px;
+}
+
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+     -moz-transition: opacity 0.15s linear;
+       -o-transition: opacity 0.15s linear;
+          transition: opacity 0.15s linear;
+}
+
+.fade.in {
+  opacity: 1;
+}
+
+.collapse {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition: height 0.35s ease;
+     -moz-transition: height 0.35s ease;
+       -o-transition: height 0.35s ease;
+          transition: height 0.35s ease;
+}
+
+.collapse.in {
+  height: auto;
+}
+
+.close {
+  float: right;
+  font-size: 20px;
+  font-weight: bold;
+  line-height: 20px;
+  color: #000000;
+  text-shadow: 0 1px 0 #ffffff;
+  opacity: 0.2;
+  filter: alpha(opacity=20);
+}
+
+.close:hover,
+.close:focus {
+  color: #000000;
+  text-decoration: none;
+  cursor: pointer;
+  opacity: 0.4;
+  filter: alpha(opacity=40);
+}
+
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+
+.btn {
+  display: inline-block;
+  *display: inline;
+  padding: 4px 12px;
+  margin-bottom: 0;
+  *margin-left: .3em;
+  font-size: 14px;
+  line-height: 20px;
+  color: #333333;
+  text-align: center;
+  text-shadow: 0 1px 1px rgba(255, 255, 255, 0.75);
+  vertical-align: middle;
+  cursor: pointer;
+  background-color: #f5f5f5;
+  *background-color: #e6e6e6;
+  background-image: -moz-linear-gradient(top, #ffffff, #e6e6e6);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6));
+  background-image: -webkit-linear-gradient(top, #ffffff, #e6e6e6);
+  background-image: -o-linear-gradient(top, #ffffff, #e6e6e6);
+  background-image: linear-gradient(to bottom, #ffffff, #e6e6e6);
+  background-repeat: repeat-x;
+  border: 1px solid #cccccc;
+  *border: 0;
+  border-color: #e6e6e6 #e6e6e6 #bfbfbf;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  border-bottom-color: #b3b3b3;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+  *zoom: 1;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05);
+     -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.btn:hover,
+.btn:focus,
+.btn:active,
+.btn.active,
+.btn.disabled,
+.btn[disabled] {
+  color: #333333;
+  background-color: #e6e6e6;
+  *background-color: #d9d9d9;
+}
+
+.btn:active,
+.btn.active {
+  background-color: #cccccc \9;
+}
+
+.btn:first-child {
+  *margin-left: 0;
+}
+
+.btn:hover,
+.btn:focus {
+  color: #333333;
+  text-decoration: none;
+  background-position: 0 -15px;
+  -webkit-transition: background-position 0.1s linear;
+     -moz-transition: background-position 0.1s linear;
+       -o-transition: background-position 0.1s linear;
+          transition: background-position 0.1s linear;
+}
+
+.btn:focus {
+  outline: thin dotted #333;
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+
+.btn.active,
+.btn:active {
+  background-image: none;
+  outline: 0;
+  -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05);
+     -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05);
+          box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.btn.disabled,
+.btn[disabled] {
+  cursor: default;
+  background-image: none;
+  opacity: 0.65;
+  filter: alpha(opacity=65);
+  -webkit-box-shadow: none;
+     -moz-box-shadow: none;
+          box-shadow: none;
+}
+
+.btn-large {
+  padding: 11px 19px;
+  font-size: 17.5px;
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+}
+
+.btn-large [class^="icon-"],
+.btn-large [class*=" icon-"] {
+  margin-top: 4px;
+}
+
+.btn-small {
+  padding: 2px 10px;
+  font-size: 11.9px;
+  -webkit-border-radius: 3px;
+     -moz-border-radius: 3px;
+          border-radius: 3px;
+}
+
+.btn-small [class^="icon-"],
+.btn-small [class*=" icon-"] {
+  margin-top: 0;
+}
+
+.btn-mini [class^="icon-"],
+.btn-mini [class*=" icon-"] {
+  margin-top: -1px;
+}
+
+.btn-mini {
+  padding: 0 6px;
+  font-size: 10.5px;
+  -webkit-border-radius: 3px;
+     -moz-border-radius: 3px;
+          border-radius: 3px;
+}
+
+.btn-block {
+  display: block;
+  width: 100%;
+  padding-right: 0;
+  padding-left: 0;
+  -webkit-box-sizing: border-box;
+     -moz-box-sizing: border-box;
+          box-sizing: border-box;
+}
+
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+
+.btn-primary.active,
+.btn-warning.active,
+.btn-danger.active,
+.btn-success.active,
+.btn-info.active,
+.btn-inverse.active {
+  color: rgba(255, 255, 255, 0.75);
+}
+
+.btn-primary {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #006dcc;
+  *background-color: #0044cc;
+  background-image: -moz-linear-gradient(top, #0088cc, #0044cc);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc));
+  background-image: -webkit-linear-gradient(top, #0088cc, #0044cc);
+  background-image: -o-linear-gradient(top, #0088cc, #0044cc);
+  background-image: linear-gradient(to bottom, #0088cc, #0044cc);
+  background-repeat: repeat-x;
+  border-color: #0044cc #0044cc #002a80;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.btn-primary:hover,
+.btn-primary:focus,
+.btn-primary:active,
+.btn-primary.active,
+.btn-primary.disabled,
+.btn-primary[disabled] {
+  color: #ffffff;
+  background-color: #0044cc;
+  *background-color: #003bb3;
+}
+
+.btn-primary:active,
+.btn-primary.active {
+  background-color: #003399 \9;
+}
+
+.btn-warning {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #faa732;
+  *background-color: #f89406;
+  background-image: -moz-linear-gradient(top, #fbb450, #f89406);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));
+  background-image: -webkit-linear-gradient(top, #fbb450, #f89406);
+  background-image: -o-linear-gradient(top, #fbb450, #f89406);
+  background-image: linear-gradient(to bottom, #fbb450, #f89406);
+  background-repeat: repeat-x;
+  border-color: #f89406 #f89406 #ad6704;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.btn-warning:hover,
+.btn-warning:focus,
+.btn-warning:active,
+.btn-warning.active,
+.btn-warning.disabled,
+.btn-warning[disabled] {
+  color: #ffffff;
+  background-color: #f89406;
+  *background-color: #df8505;
+}
+
+.btn-warning:active,
+.btn-warning.active {
+  background-color: #c67605 \9;
+}
+
+.btn-danger {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #da4f49;
+  *background-color: #bd362f;
+  background-image: -moz-linear-gradient(top, #ee5f5b, #bd362f);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));
+  background-image: -webkit-linear-gradient(top, #ee5f5b, #bd362f);
+  background-image: -o-linear-gradient(top, #ee5f5b, #bd362f);
+  background-image: linear-gradient(to bottom, #ee5f5b, #bd362f);
+  background-repeat: repeat-x;
+  border-color: #bd362f #bd362f #802420;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.btn-danger:hover,
+.btn-danger:focus,
+.btn-danger:active,
+.btn-danger.active,
+.btn-danger.disabled,
+.btn-danger[disabled] {
+  color: #ffffff;
+  background-color: #bd362f;
+  *background-color: #a9302a;
+}
+
+.btn-danger:active,
+.btn-danger.active {
+  background-color: #942a25 \9;
+}
+
+.btn-success {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #5bb75b;
+  *background-color: #51a351;
+  background-image: -moz-linear-gradient(top, #62c462, #51a351);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));
+  background-image: -webkit-linear-gradient(top, #62c462, #51a351);
+  background-image: -o-linear-gradient(top, #62c462, #51a351);
+  background-image: linear-gradient(to bottom, #62c462, #51a351);
+  background-repeat: repeat-x;
+  border-color: #51a351 #51a351 #387038;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.btn-success:hover,
+.btn-success:focus,
+.btn-success:active,
+.btn-success.active,
+.btn-success.disabled,
+.btn-success[disabled] {
+  color: #ffffff;
+  background-color: #51a351;
+  *background-color: #499249;
+}
+
+.btn-success:active,
+.btn-success.active {
+  background-color: #408140 \9;
+}
+
+.btn-info {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #49afcd;
+  *background-color: #2f96b4;
+  background-image: -moz-linear-gradient(top, #5bc0de, #2f96b4);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));
+  background-image: -webkit-linear-gradient(top, #5bc0de, #2f96b4);
+  background-image: -o-linear-gradient(top, #5bc0de, #2f96b4);
+  background-image: linear-gradient(to bottom, #5bc0de, #2f96b4);
+  background-repeat: repeat-x;
+  border-color: #2f96b4 #2f96b4 #1f6377;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.btn-info:hover,
+.btn-info:focus,
+.btn-info:active,
+.btn-info.active,
+.btn-info.disabled,
+.btn-info[disabled] {
+  color: #ffffff;
+  background-color: #2f96b4;
+  *background-color: #2a85a0;
+}
+
+.btn-info:active,
+.btn-info.active {
+  background-color: #24748c \9;
+}
+
+.btn-inverse {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #363636;
+  *background-color: #222222;
+  background-image: -moz-linear-gradient(top, #444444, #222222);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#444444), to(#222222));
+  background-image: -webkit-linear-gradient(top, #444444, #222222);
+  background-image: -o-linear-gradient(top, #444444, #222222);
+  background-image: linear-gradient(to bottom, #444444, #222222);
+  background-repeat: repeat-x;
+  border-color: #222222 #222222 #000000;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.btn-inverse:hover,
+.btn-inverse:focus,
+.btn-inverse:active,
+.btn-inverse.active,
+.btn-inverse.disabled,
+.btn-inverse[disabled] {
+  color: #ffffff;
+  background-color: #222222;
+  *background-color: #151515;
+}
+
+.btn-inverse:active,
+.btn-inverse.active {
+  background-color: #080808 \9;
+}
+
+button.btn,
+input[type="submit"].btn {
+  *padding-top: 3px;
+  *padding-bottom: 3px;
+}
+
+button.btn::-moz-focus-inner,
+input[type="submit"].btn::-moz-focus-inner {
+  padding: 0;
+  border: 0;
+}
+
+button.btn.btn-large,
+input[type="submit"].btn.btn-large {
+  *padding-top: 7px;
+  *padding-bottom: 7px;
+}
+
+button.btn.btn-small,
+input[type="submit"].btn.btn-small {
+  *padding-top: 3px;
+  *padding-bottom: 3px;
+}
+
+button.btn.btn-mini,
+input[type="submit"].btn.btn-mini {
+  *padding-top: 1px;
+  *padding-bottom: 1px;
+}
+
+.btn-link,
+.btn-link:active,
+.btn-link[disabled] {
+  background-color: transparent;
+  background-image: none;
+  -webkit-box-shadow: none;
+     -moz-box-shadow: none;
+          box-shadow: none;
+}
+
+.btn-link {
+  color: #0088cc;
+  cursor: pointer;
+  border-color: transparent;
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.btn-link:hover,
+.btn-link:focus {
+  color: #005580;
+  text-decoration: underline;
+  background-color: transparent;
+}
+
+.btn-link[disabled]:hover,
+.btn-link[disabled]:focus {
+  color: #333333;
+  text-decoration: none;
+}
+
+.btn-group {
+  position: relative;
+  display: inline-block;
+  *display: inline;
+  *margin-left: .3em;
+  font-size: 0;
+  white-space: nowrap;
+  vertical-align: middle;
+  *zoom: 1;
+}
+
+.btn-group:first-child {
+  *margin-left: 0;
+}
+
+.btn-group + .btn-group {
+  margin-left: 5px;
+}
+
+.btn-toolbar {
+  margin-top: 10px;
+  margin-bottom: 10px;
+  font-size: 0;
+}
+
+.btn-toolbar > .btn + .btn,
+.btn-toolbar > .btn-group + .btn,
+.btn-toolbar > .btn + .btn-group {
+  margin-left: 5px;
+}
+
+.btn-group > .btn {
+  position: relative;
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.btn-group > .btn + .btn {
+  margin-left: -1px;
+}
+
+.btn-group > .btn,
+.btn-group > .dropdown-menu,
+.btn-group > .popover {
+  font-size: 14px;
+}
+
+.btn-group > .btn-mini {
+  font-size: 10.5px;
+}
+
+.btn-group > .btn-small {
+  font-size: 11.9px;
+}
+
+.btn-group > .btn-large {
+  font-size: 17.5px;
+}
+
+.btn-group > .btn:first-child {
+  margin-left: 0;
+  -webkit-border-bottom-left-radius: 4px;
+          border-bottom-left-radius: 4px;
+  -webkit-border-top-left-radius: 4px;
+          border-top-left-radius: 4px;
+  -moz-border-radius-bottomleft: 4px;
+  -moz-border-radius-topleft: 4px;
+}
+
+.btn-group > .btn:last-child,
+.btn-group > .dropdown-toggle {
+  -webkit-border-top-right-radius: 4px;
+          border-top-right-radius: 4px;
+  -webkit-border-bottom-right-radius: 4px;
+          border-bottom-right-radius: 4px;
+  -moz-border-radius-topright: 4px;
+  -moz-border-radius-bottomright: 4px;
+}
+
+.btn-group > .btn.large:first-child {
+  margin-left: 0;
+  -webkit-border-bottom-left-radius: 6px;
+          border-bottom-left-radius: 6px;
+  -webkit-border-top-left-radius: 6px;
+          border-top-left-radius: 6px;
+  -moz-border-radius-bottomleft: 6px;
+  -moz-border-radius-topleft: 6px;
+}
+
+.btn-group > .btn.large:last-child,
+.btn-group > .large.dropdown-toggle {
+  -webkit-border-top-right-radius: 6px;
+          border-top-right-radius: 6px;
+  -webkit-border-bottom-right-radius: 6px;
+          border-bottom-right-radius: 6px;
+  -moz-border-radius-topright: 6px;
+  -moz-border-radius-bottomright: 6px;
+}
+
+.btn-group > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group > .btn:active,
+.btn-group > .btn.active {
+  z-index: 2;
+}
+
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+
+.btn-group > .btn + .dropdown-toggle {
+  *padding-top: 5px;
+  padding-right: 8px;
+  *padding-bottom: 5px;
+  padding-left: 8px;
+  -webkit-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05);
+     -moz-box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05);
+          box-shadow: inset 1px 0 0 rgba(255, 255, 255, 0.125), inset 0 1px 0 rgba(255, 255, 255, 0.2), 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.btn-group > .btn-mini + .dropdown-toggle {
+  *padding-top: 2px;
+  padding-right: 5px;
+  *padding-bottom: 2px;
+  padding-left: 5px;
+}
+
+.btn-group > .btn-small + .dropdown-toggle {
+  *padding-top: 5px;
+  *padding-bottom: 4px;
+}
+
+.btn-group > .btn-large + .dropdown-toggle {
+  *padding-top: 7px;
+  padding-right: 12px;
+  *padding-bottom: 7px;
+  padding-left: 12px;
+}
+
+.btn-group.open .dropdown-toggle {
+  background-image: none;
+  -webkit-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05);
+     -moz-box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05);
+          box-shadow: inset 0 2px 4px rgba(0, 0, 0, 0.15), 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.btn-group.open .btn.dropdown-toggle {
+  background-color: #e6e6e6;
+}
+
+.btn-group.open .btn-primary.dropdown-toggle {
+  background-color: #0044cc;
+}
+
+.btn-group.open .btn-warning.dropdown-toggle {
+  background-color: #f89406;
+}
+
+.btn-group.open .btn-danger.dropdown-toggle {
+  background-color: #bd362f;
+}
+
+.btn-group.open .btn-success.dropdown-toggle {
+  background-color: #51a351;
+}
+
+.btn-group.open .btn-info.dropdown-toggle {
+  background-color: #2f96b4;
+}
+
+.btn-group.open .btn-inverse.dropdown-toggle {
+  background-color: #222222;
+}
+
+.btn .caret {
+  margin-top: 8px;
+  margin-left: 0;
+}
+
+.btn-large .caret {
+  margin-top: 6px;
+}
+
+.btn-large .caret {
+  border-top-width: 5px;
+  border-right-width: 5px;
+  border-left-width: 5px;
+}
+
+.btn-mini .caret,
+.btn-small .caret {
+  margin-top: 8px;
+}
+
+.dropup .btn-large .caret {
+  border-bottom-width: 5px;
+}
+
+.btn-primary .caret,
+.btn-warning .caret,
+.btn-danger .caret,
+.btn-info .caret,
+.btn-success .caret,
+.btn-inverse .caret {
+  border-top-color: #ffffff;
+  border-bottom-color: #ffffff;
+}
+
+.btn-group-vertical {
+  display: inline-block;
+  *display: inline;
+  /* IE7 inline-block hack */
+
+  *zoom: 1;
+}
+
+.btn-group-vertical > .btn {
+  display: block;
+  float: none;
+  max-width: 100%;
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.btn-group-vertical > .btn + .btn {
+  margin-top: -1px;
+  margin-left: 0;
+}
+
+.btn-group-vertical > .btn:first-child {
+  -webkit-border-radius: 4px 4px 0 0;
+     -moz-border-radius: 4px 4px 0 0;
+          border-radius: 4px 4px 0 0;
+}
+
+.btn-group-vertical > .btn:last-child {
+  -webkit-border-radius: 0 0 4px 4px;
+     -moz-border-radius: 0 0 4px 4px;
+          border-radius: 0 0 4px 4px;
+}
+
+.btn-group-vertical > .btn-large:first-child {
+  -webkit-border-radius: 6px 6px 0 0;
+     -moz-border-radius: 6px 6px 0 0;
+          border-radius: 6px 6px 0 0;
+}
+
+.btn-group-vertical > .btn-large:last-child {
+  -webkit-border-radius: 0 0 6px 6px;
+     -moz-border-radius: 0 0 6px 6px;
+          border-radius: 0 0 6px 6px;
+}
+
+.alert {
+  padding: 8px 35px 8px 14px;
+  margin-bottom: 20px;
+  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5);
+  background-color: #fcf8e3;
+  border: 1px solid #fbeed5;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+}
+
+.alert,
+.alert h4 {
+  color: #c09853;
+}
+
+.alert h4 {
+  margin: 0;
+}
+
+.alert .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  line-height: 20px;
+}
+
+.alert-success {
+  color: #468847;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+
+.alert-success h4 {
+  color: #468847;
+}
+
+.alert-danger,
+.alert-error {
+  color: #b94a48;
+  background-color: #f2dede;
+  border-color: #eed3d7;
+}
+
+.alert-danger h4,
+.alert-error h4 {
+  color: #b94a48;
+}
+
+.alert-info {
+  color: #3a87ad;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+
+.alert-info h4 {
+  color: #3a87ad;
+}
+
+.alert-block {
+  padding-top: 14px;
+  padding-bottom: 14px;
+}
+
+.alert-block > p,
+.alert-block > ul {
+  margin-bottom: 0;
+}
+
+.alert-block p + p {
+  margin-top: 5px;
+}
+
+.nav {
+  margin-bottom: 20px;
+  margin-left: 0;
+  list-style: none;
+}
+
+.nav > li > a {
+  display: block;
+}
+
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+
+.nav > li > a > img {
+  max-width: none;
+}
+
+.nav > .pull-right {
+  float: right;
+}
+
+.nav-header {
+  display: block;
+  padding: 3px 15px;
+  font-size: 11px;
+  font-weight: bold;
+  line-height: 20px;
+  color: #999999;
+  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5);
+  text-transform: uppercase;
+}
+
+.nav li + .nav-header {
+  margin-top: 9px;
+}
+
+.nav-list {
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-bottom: 0;
+}
+
+.nav-list > li > a,
+.nav-list .nav-header {
+  margin-right: -15px;
+  margin-left: -15px;
+  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.5);
+}
+
+.nav-list > li > a {
+  padding: 3px 15px;
+}
+
+.nav-list > .active > a,
+.nav-list > .active > a:hover,
+.nav-list > .active > a:focus {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);
+  background-color: #0088cc;
+}
+
+.nav-list [class^="icon-"],
+.nav-list [class*=" icon-"] {
+  margin-right: 2px;
+}
+
+.nav-list .divider {
+  *width: 100%;
+  height: 1px;
+  margin: 9px 1px;
+  *margin: -5px 0 5px;
+  overflow: hidden;
+  background-color: #e5e5e5;
+  border-bottom: 1px solid #ffffff;
+}
+
+.nav-tabs,
+.nav-pills {
+  *zoom: 1;
+}
+
+.nav-tabs:before,
+.nav-pills:before,
+.nav-tabs:after,
+.nav-pills:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.nav-tabs:after,
+.nav-pills:after {
+  clear: both;
+}
+
+.nav-tabs > li,
+.nav-pills > li {
+  float: left;
+}
+
+.nav-tabs > li > a,
+.nav-pills > li > a {
+  padding-right: 12px;
+  padding-left: 12px;
+  margin-right: 2px;
+  line-height: 14px;
+}
+
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+
+.nav-tabs > li {
+  margin-bottom: -1px;
+}
+
+.nav-tabs > li > a {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  line-height: 20px;
+  border: 1px solid transparent;
+  -webkit-border-radius: 4px 4px 0 0;
+     -moz-border-radius: 4px 4px 0 0;
+          border-radius: 4px 4px 0 0;
+}
+
+.nav-tabs > li > a:hover,
+.nav-tabs > li > a:focus {
+  border-color: #eeeeee #eeeeee #dddddd;
+}
+
+.nav-tabs > .active > a,
+.nav-tabs > .active > a:hover,
+.nav-tabs > .active > a:focus {
+  color: #555555;
+  cursor: default;
+  background-color: #ffffff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+}
+
+.nav-pills > li > a {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  margin-top: 2px;
+  margin-bottom: 2px;
+  -webkit-border-radius: 5px;
+     -moz-border-radius: 5px;
+          border-radius: 5px;
+}
+
+.nav-pills > .active > a,
+.nav-pills > .active > a:hover,
+.nav-pills > .active > a:focus {
+  color: #ffffff;
+  background-color: #0088cc;
+}
+
+.nav-stacked > li {
+  float: none;
+}
+
+.nav-stacked > li > a {
+  margin-right: 0;
+}
+
+.nav-tabs.nav-stacked {
+  border-bottom: 0;
+}
+
+.nav-tabs.nav-stacked > li > a {
+  border: 1px solid #ddd;
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.nav-tabs.nav-stacked > li:first-child > a {
+  -webkit-border-top-right-radius: 4px;
+          border-top-right-radius: 4px;
+  -webkit-border-top-left-radius: 4px;
+          border-top-left-radius: 4px;
+  -moz-border-radius-topright: 4px;
+  -moz-border-radius-topleft: 4px;
+}
+
+.nav-tabs.nav-stacked > li:last-child > a {
+  -webkit-border-bottom-right-radius: 4px;
+          border-bottom-right-radius: 4px;
+  -webkit-border-bottom-left-radius: 4px;
+          border-bottom-left-radius: 4px;
+  -moz-border-radius-bottomright: 4px;
+  -moz-border-radius-bottomleft: 4px;
+}
+
+.nav-tabs.nav-stacked > li > a:hover,
+.nav-tabs.nav-stacked > li > a:focus {
+  z-index: 2;
+  border-color: #ddd;
+}
+
+.nav-pills.nav-stacked > li > a {
+  margin-bottom: 3px;
+}
+
+.nav-pills.nav-stacked > li:last-child > a {
+  margin-bottom: 1px;
+}
+
+.nav-tabs .dropdown-menu {
+  -webkit-border-radius: 0 0 6px 6px;
+     -moz-border-radius: 0 0 6px 6px;
+          border-radius: 0 0 6px 6px;
+}
+
+.nav-pills .dropdown-menu {
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+}
+
+.nav .dropdown-toggle .caret {
+  margin-top: 6px;
+  border-top-color: #0088cc;
+  border-bottom-color: #0088cc;
+}
+
+.nav .dropdown-toggle:hover .caret,
+.nav .dropdown-toggle:focus .caret {
+  border-top-color: #005580;
+  border-bottom-color: #005580;
+}
+
+/* move down carets for tabs */
+
+.nav-tabs .dropdown-toggle .caret {
+  margin-top: 8px;
+}
+
+.nav .active .dropdown-toggle .caret {
+  border-top-color: #fff;
+  border-bottom-color: #fff;
+}
+
+.nav-tabs .active .dropdown-toggle .caret {
+  border-top-color: #555555;
+  border-bottom-color: #555555;
+}
+
+.nav > .dropdown.active > a:hover,
+.nav > .dropdown.active > a:focus {
+  cursor: pointer;
+}
+
+.nav-tabs .open .dropdown-toggle,
+.nav-pills .open .dropdown-toggle,
+.nav > li.dropdown.open.active > a:hover,
+.nav > li.dropdown.open.active > a:focus {
+  color: #ffffff;
+  background-color: #999999;
+  border-color: #999999;
+}
+
+.nav li.dropdown.open .caret,
+.nav li.dropdown.open.active .caret,
+.nav li.dropdown.open a:hover .caret,
+.nav li.dropdown.open a:focus .caret {
+  border-top-color: #ffffff;
+  border-bottom-color: #ffffff;
+  opacity: 1;
+  filter: alpha(opacity=100);
+}
+
+.tabs-stacked .open > a:hover,
+.tabs-stacked .open > a:focus {
+  border-color: #999999;
+}
+
+.tabbable {
+  *zoom: 1;
+}
+
+.tabbable:before,
+.tabbable:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.tabbable:after {
+  clear: both;
+}
+
+.tab-content {
+  overflow: auto;
+}
+
+.tabs-below > .nav-tabs,
+.tabs-right > .nav-tabs,
+.tabs-left > .nav-tabs {
+  border-bottom: 0;
+}
+
+.tab-content > .tab-pane,
+.pill-content > .pill-pane {
+  display: none;
+}
+
+.tab-content > .active,
+.pill-content > .active {
+  display: block;
+}
+
+.tabs-below > .nav-tabs {
+  border-top: 1px solid #ddd;
+}
+
+.tabs-below > .nav-tabs > li {
+  margin-top: -1px;
+  margin-bottom: 0;
+}
+
+.tabs-below > .nav-tabs > li > a {
+  -webkit-border-radius: 0 0 4px 4px;
+     -moz-border-radius: 0 0 4px 4px;
+          border-radius: 0 0 4px 4px;
+}
+
+.tabs-below > .nav-tabs > li > a:hover,
+.tabs-below > .nav-tabs > li > a:focus {
+  border-top-color: #ddd;
+  border-bottom-color: transparent;
+}
+
+.tabs-below > .nav-tabs > .active > a,
+.tabs-below > .nav-tabs > .active > a:hover,
+.tabs-below > .nav-tabs > .active > a:focus {
+  border-color: transparent #ddd #ddd #ddd;
+}
+
+.tabs-left > .nav-tabs > li,
+.tabs-right > .nav-tabs > li {
+  float: none;
+}
+
+.tabs-left > .nav-tabs > li > a,
+.tabs-right > .nav-tabs > li > a {
+  min-width: 74px;
+  margin-right: 0;
+  margin-bottom: 3px;
+}
+
+.tabs-left > .nav-tabs {
+  float: left;
+  margin-right: 19px;
+  border-right: 1px solid #ddd;
+}
+
+.tabs-left > .nav-tabs > li > a {
+  margin-right: -1px;
+  -webkit-border-radius: 4px 0 0 4px;
+     -moz-border-radius: 4px 0 0 4px;
+          border-radius: 4px 0 0 4px;
+}
+
+.tabs-left > .nav-tabs > li > a:hover,
+.tabs-left > .nav-tabs > li > a:focus {
+  border-color: #eeeeee #dddddd #eeeeee #eeeeee;
+}
+
+.tabs-left > .nav-tabs .active > a,
+.tabs-left > .nav-tabs .active > a:hover,
+.tabs-left > .nav-tabs .active > a:focus {
+  border-color: #ddd transparent #ddd #ddd;
+  *border-right-color: #ffffff;
+}
+
+.tabs-right > .nav-tabs {
+  float: right;
+  margin-left: 19px;
+  border-left: 1px solid #ddd;
+}
+
+.tabs-right > .nav-tabs > li > a {
+  margin-left: -1px;
+  -webkit-border-radius: 0 4px 4px 0;
+     -moz-border-radius: 0 4px 4px 0;
+          border-radius: 0 4px 4px 0;
+}
+
+.tabs-right > .nav-tabs > li > a:hover,
+.tabs-right > .nav-tabs > li > a:focus {
+  border-color: #eeeeee #eeeeee #eeeeee #dddddd;
+}
+
+.tabs-right > .nav-tabs .active > a,
+.tabs-right > .nav-tabs .active > a:hover,
+.tabs-right > .nav-tabs .active > a:focus {
+  border-color: #ddd #ddd #ddd transparent;
+  *border-left-color: #ffffff;
+}
+
+.nav > .disabled > a {
+  color: #999999;
+}
+
+.nav > .disabled > a:hover,
+.nav > .disabled > a:focus {
+  text-decoration: none;
+  cursor: default;
+  background-color: transparent;
+}
+
+.navbar {
+  *position: relative;
+  *z-index: 2;
+  margin-bottom: 20px;
+  overflow: visible;
+}
+
+.navbar-inner {
+  min-height: 40px;
+  padding-right: 20px;
+  padding-left: 20px;
+  background-color: #fafafa;
+  background-image: -moz-linear-gradient(top, #ffffff, #f2f2f2);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2));
+  background-image: -webkit-linear-gradient(top, #ffffff, #f2f2f2);
+  background-image: -o-linear-gradient(top, #ffffff, #f2f2f2);
+  background-image: linear-gradient(to bottom, #ffffff, #f2f2f2);
+  background-repeat: repeat-x;
+  border: 1px solid #d4d4d4;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);
+  *zoom: 1;
+  -webkit-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065);
+     -moz-box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065);
+          box-shadow: 0 1px 4px rgba(0, 0, 0, 0.065);
+}
+
+.navbar-inner:before,
+.navbar-inner:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.navbar-inner:after {
+  clear: both;
+}
+
+.navbar .container {
+  width: auto;
+}
+
+.nav-collapse.collapse {
+  height: auto;
+  overflow: visible;
+}
+
+.navbar .brand {
+  display: block;
+  float: left;
+  padding: 10px 20px 10px;
+  margin-left: -20px;
+  font-size: 20px;
+  font-weight: 200;
+  color: #777777;
+  text-shadow: 0 1px 0 #ffffff;
+}
+
+.navbar .brand:hover,
+.navbar .brand:focus {
+  text-decoration: none;
+}
+
+.navbar-text {
+  margin-bottom: 0;
+  line-height: 40px;
+  color: #777777;
+}
+
+.navbar-link {
+  color: #777777;
+}
+
+.navbar-link:hover,
+.navbar-link:focus {
+  color: #333333;
+}
+
+.navbar .divider-vertical {
+  height: 40px;
+  margin: 0 9px;
+  border-right: 1px solid #ffffff;
+  border-left: 1px solid #f2f2f2;
+}
+
+.navbar .btn,
+.navbar .btn-group {
+  margin-top: 5px;
+}
+
+.navbar .btn-group .btn,
+.navbar .input-prepend .btn,
+.navbar .input-append .btn,
+.navbar .input-prepend .btn-group,
+.navbar .input-append .btn-group {
+  margin-top: 0;
+}
+
+.navbar-form {
+  margin-bottom: 0;
+  *zoom: 1;
+}
+
+.navbar-form:before,
+.navbar-form:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.navbar-form:after {
+  clear: both;
+}
+
+.navbar-form input,
+.navbar-form select,
+.navbar-form .radio,
+.navbar-form .checkbox {
+  margin-top: 5px;
+}
+
+.navbar-form input,
+.navbar-form select,
+.navbar-form .btn {
+  display: inline-block;
+  margin-bottom: 0;
+}
+
+.navbar-form input[type="image"],
+.navbar-form input[type="checkbox"],
+.navbar-form input[type="radio"] {
+  margin-top: 3px;
+}
+
+.navbar-form .input-append,
+.navbar-form .input-prepend {
+  margin-top: 5px;
+  white-space: nowrap;
+}
+
+.navbar-form .input-append input,
+.navbar-form .input-prepend input {
+  margin-top: 0;
+}
+
+.navbar-search {
+  position: relative;
+  float: left;
+  margin-top: 5px;
+  margin-bottom: 0;
+}
+
+.navbar-search .search-query {
+  padding: 4px 14px;
+  margin-bottom: 0;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 13px;
+  font-weight: normal;
+  line-height: 1;
+  -webkit-border-radius: 15px;
+     -moz-border-radius: 15px;
+          border-radius: 15px;
+}
+
+.navbar-static-top {
+  position: static;
+  margin-bottom: 0;
+}
+
+.navbar-static-top .navbar-inner {
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+  margin-bottom: 0;
+}
+
+.navbar-fixed-top .navbar-inner,
+.navbar-static-top .navbar-inner {
+  border-width: 0 0 1px;
+}
+
+.navbar-fixed-bottom .navbar-inner {
+  border-width: 1px 0 0;
+}
+
+.navbar-fixed-top .navbar-inner,
+.navbar-fixed-bottom .navbar-inner {
+  padding-right: 0;
+  padding-left: 0;
+  -webkit-border-radius: 0;
+     -moz-border-radius: 0;
+          border-radius: 0;
+}
+
+.navbar-static-top .container,
+.navbar-fixed-top .container,
+.navbar-fixed-bottom .container {
+  width: 940px;
+}
+
+.navbar-fixed-top {
+  top: 0;
+}
+
+.navbar-fixed-top .navbar-inner,
+.navbar-static-top .navbar-inner {
+  -webkit-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1);
+     -moz-box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1);
+          box-shadow: 0 1px 10px rgba(0, 0, 0, 0.1);
+}
+
+.navbar-fixed-bottom {
+  bottom: 0;
+}
+
+.navbar-fixed-bottom .navbar-inner {
+  -webkit-box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1);
+     -moz-box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1);
+          box-shadow: 0 -1px 10px rgba(0, 0, 0, 0.1);
+}
+
+.navbar .nav {
+  position: relative;
+  left: 0;
+  display: block;
+  float: left;
+  margin: 0 10px 0 0;
+}
+
+.navbar .nav.pull-right {
+  float: right;
+  margin-right: 0;
+}
+
+.navbar .nav > li {
+  float: left;
+}
+
+.navbar .nav > li > a {
+  float: none;
+  padding: 10px 15px 10px;
+  color: #777777;
+  text-decoration: none;
+  text-shadow: 0 1px 0 #ffffff;
+}
+
+.navbar .nav .dropdown-toggle .caret {
+  margin-top: 8px;
+}
+
+.navbar .nav > li > a:focus,
+.navbar .nav > li > a:hover {
+  color: #333333;
+  text-decoration: none;
+  background-color: transparent;
+}
+
+.navbar .nav > .active > a,
+.navbar .nav > .active > a:hover,
+.navbar .nav > .active > a:focus {
+  color: #555555;
+  text-decoration: none;
+  background-color: #e5e5e5;
+  -webkit-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125);
+     -moz-box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125);
+          box-shadow: inset 0 3px 8px rgba(0, 0, 0, 0.125);
+}
+
+.navbar .btn-navbar {
+  display: none;
+  float: right;
+  padding: 7px 10px;
+  margin-right: 5px;
+  margin-left: 5px;
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #ededed;
+  *background-color: #e5e5e5;
+  background-image: -moz-linear-gradient(top, #f2f2f2, #e5e5e5);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));
+  background-image: -webkit-linear-gradient(top, #f2f2f2, #e5e5e5);
+  background-image: -o-linear-gradient(top, #f2f2f2, #e5e5e5);
+  background-image: linear-gradient(to bottom, #f2f2f2, #e5e5e5);
+  background-repeat: repeat-x;
+  border-color: #e5e5e5 #e5e5e5 #bfbfbf;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075);
+     -moz-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.075);
+}
+
+.navbar .btn-navbar:hover,
+.navbar .btn-navbar:focus,
+.navbar .btn-navbar:active,
+.navbar .btn-navbar.active,
+.navbar .btn-navbar.disabled,
+.navbar .btn-navbar[disabled] {
+  color: #ffffff;
+  background-color: #e5e5e5;
+  *background-color: #d9d9d9;
+}
+
+.navbar .btn-navbar:active,
+.navbar .btn-navbar.active {
+  background-color: #cccccc \9;
+}
+
+.navbar .btn-navbar .icon-bar {
+  display: block;
+  width: 18px;
+  height: 2px;
+  background-color: #f5f5f5;
+  -webkit-border-radius: 1px;
+     -moz-border-radius: 1px;
+          border-radius: 1px;
+  -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25);
+     -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25);
+          box-shadow: 0 1px 0 rgba(0, 0, 0, 0.25);
+}
+
+.btn-navbar .icon-bar + .icon-bar {
+  margin-top: 3px;
+}
+
+.navbar .nav > li > .dropdown-menu:before {
+  position: absolute;
+  top: -7px;
+  left: 9px;
+  display: inline-block;
+  border-right: 7px solid transparent;
+  border-bottom: 7px solid #ccc;
+  border-left: 7px solid transparent;
+  border-bottom-color: rgba(0, 0, 0, 0.2);
+  content: '';
+}
+
+.navbar .nav > li > .dropdown-menu:after {
+  position: absolute;
+  top: -6px;
+  left: 10px;
+  display: inline-block;
+  border-right: 6px solid transparent;
+  border-bottom: 6px solid #ffffff;
+  border-left: 6px solid transparent;
+  content: '';
+}
+
+.navbar-fixed-bottom .nav > li > .dropdown-menu:before {
+  top: auto;
+  bottom: -7px;
+  border-top: 7px solid #ccc;
+  border-bottom: 0;
+  border-top-color: rgba(0, 0, 0, 0.2);
+}
+
+.navbar-fixed-bottom .nav > li > .dropdown-menu:after {
+  top: auto;
+  bottom: -6px;
+  border-top: 6px solid #ffffff;
+  border-bottom: 0;
+}
+
+.navbar .nav li.dropdown > a:hover .caret,
+.navbar .nav li.dropdown > a:focus .caret {
+  border-top-color: #333333;
+  border-bottom-color: #333333;
+}
+
+.navbar .nav li.dropdown.open > .dropdown-toggle,
+.navbar .nav li.dropdown.active > .dropdown-toggle,
+.navbar .nav li.dropdown.open.active > .dropdown-toggle {
+  color: #555555;
+  background-color: #e5e5e5;
+}
+
+.navbar .nav li.dropdown > .dropdown-toggle .caret {
+  border-top-color: #777777;
+  border-bottom-color: #777777;
+}
+
+.navbar .nav li.dropdown.open > .dropdown-toggle .caret,
+.navbar .nav li.dropdown.active > .dropdown-toggle .caret,
+.navbar .nav li.dropdown.open.active > .dropdown-toggle .caret {
+  border-top-color: #555555;
+  border-bottom-color: #555555;
+}
+
+.navbar .pull-right > li > .dropdown-menu,
+.navbar .nav > li > .dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+
+.navbar .pull-right > li > .dropdown-menu:before,
+.navbar .nav > li > .dropdown-menu.pull-right:before {
+  right: 12px;
+  left: auto;
+}
+
+.navbar .pull-right > li > .dropdown-menu:after,
+.navbar .nav > li > .dropdown-menu.pull-right:after {
+  right: 13px;
+  left: auto;
+}
+
+.navbar .pull-right > li > .dropdown-menu .dropdown-menu,
+.navbar .nav > li > .dropdown-menu.pull-right .dropdown-menu {
+  right: 100%;
+  left: auto;
+  margin-right: -1px;
+  margin-left: 0;
+  -webkit-border-radius: 6px 0 6px 6px;
+     -moz-border-radius: 6px 0 6px 6px;
+          border-radius: 6px 0 6px 6px;
+}
+
+.navbar-inverse .navbar-inner {
+  background-color: #1b1b1b;
+  background-image: -moz-linear-gradient(top, #222222, #111111);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111));
+  background-image: -webkit-linear-gradient(top, #222222, #111111);
+  background-image: -o-linear-gradient(top, #222222, #111111);
+  background-image: linear-gradient(to bottom, #222222, #111111);
+  background-repeat: repeat-x;
+  border-color: #252525;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0);
+}
+
+.navbar-inverse .brand,
+.navbar-inverse .nav > li > a {
+  color: #999999;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+
+.navbar-inverse .brand:hover,
+.navbar-inverse .nav > li > a:hover,
+.navbar-inverse .brand:focus,
+.navbar-inverse .nav > li > a:focus {
+  color: #ffffff;
+}
+
+.navbar-inverse .brand {
+  color: #999999;
+}
+
+.navbar-inverse .navbar-text {
+  color: #999999;
+}
+
+.navbar-inverse .nav > li > a:focus,
+.navbar-inverse .nav > li > a:hover {
+  color: #ffffff;
+  background-color: transparent;
+}
+
+.navbar-inverse .nav .active > a,
+.navbar-inverse .nav .active > a:hover,
+.navbar-inverse .nav .active > a:focus {
+  color: #ffffff;
+  background-color: #111111;
+}
+
+.navbar-inverse .navbar-link {
+  color: #999999;
+}
+
+.navbar-inverse .navbar-link:hover,
+.navbar-inverse .navbar-link:focus {
+  color: #ffffff;
+}
+
+.navbar-inverse .divider-vertical {
+  border-right-color: #222222;
+  border-left-color: #111111;
+}
+
+.navbar-inverse .nav li.dropdown.open > .dropdown-toggle,
+.navbar-inverse .nav li.dropdown.active > .dropdown-toggle,
+.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle {
+  color: #ffffff;
+  background-color: #111111;
+}
+
+.navbar-inverse .nav li.dropdown > a:hover .caret,
+.navbar-inverse .nav li.dropdown > a:focus .caret {
+  border-top-color: #ffffff;
+  border-bottom-color: #ffffff;
+}
+
+.navbar-inverse .nav li.dropdown > .dropdown-toggle .caret {
+  border-top-color: #999999;
+  border-bottom-color: #999999;
+}
+
+.navbar-inverse .nav li.dropdown.open > .dropdown-toggle .caret,
+.navbar-inverse .nav li.dropdown.active > .dropdown-toggle .caret,
+.navbar-inverse .nav li.dropdown.open.active > .dropdown-toggle .caret {
+  border-top-color: #ffffff;
+  border-bottom-color: #ffffff;
+}
+
+.navbar-inverse .navbar-search .search-query {
+  color: #ffffff;
+  background-color: #515151;
+  border-color: #111111;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15);
+     -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15);
+          box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1), 0 1px 0 rgba(255, 255, 255, 0.15);
+  -webkit-transition: none;
+     -moz-transition: none;
+       -o-transition: none;
+          transition: none;
+}
+
+.navbar-inverse .navbar-search .search-query:-moz-placeholder {
+  color: #cccccc;
+}
+
+.navbar-inverse .navbar-search .search-query:-ms-input-placeholder {
+  color: #cccccc;
+}
+
+.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder {
+  color: #cccccc;
+}
+
+.navbar-inverse .navbar-search .search-query:focus,
+.navbar-inverse .navbar-search .search-query.focused {
+  padding: 5px 15px;
+  color: #333333;
+  text-shadow: 0 1px 0 #ffffff;
+  background-color: #ffffff;
+  border: 0;
+  outline: 0;
+  -webkit-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15);
+     -moz-box-shadow: 0 0 3px rgba(0, 0, 0, 0.15);
+          box-shadow: 0 0 3px rgba(0, 0, 0, 0.15);
+}
+
+.navbar-inverse .btn-navbar {
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #0e0e0e;
+  *background-color: #040404;
+  background-image: -moz-linear-gradient(top, #151515, #040404);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));
+  background-image: -webkit-linear-gradient(top, #151515, #040404);
+  background-image: -o-linear-gradient(top, #151515, #040404);
+  background-image: linear-gradient(to bottom, #151515, #040404);
+  background-repeat: repeat-x;
+  border-color: #040404 #040404 #000000;
+  border-color: rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled=false);
+}
+
+.navbar-inverse .btn-navbar:hover,
+.navbar-inverse .btn-navbar:focus,
+.navbar-inverse .btn-navbar:active,
+.navbar-inverse .btn-navbar.active,
+.navbar-inverse .btn-navbar.disabled,
+.navbar-inverse .btn-navbar[disabled] {
+  color: #ffffff;
+  background-color: #040404;
+  *background-color: #000000;
+}
+
+.navbar-inverse .btn-navbar:active,
+.navbar-inverse .btn-navbar.active {
+  background-color: #000000 \9;
+}
+
+.breadcrumb {
+  padding: 8px 15px;
+  margin: 0 0 20px;
+  list-style: none;
+  background-color: #f5f5f5;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+}
+
+.breadcrumb > li {
+  display: inline-block;
+  *display: inline;
+  text-shadow: 0 1px 0 #ffffff;
+  *zoom: 1;
+}
+
+.breadcrumb > li > .divider {
+  padding: 0 5px;
+  color: #ccc;
+}
+
+.breadcrumb > .active {
+  color: #999999;
+}
+
+.pagination {
+  margin: 20px 0;
+}
+
+.pagination ul {
+  display: inline-block;
+  *display: inline;
+  margin-bottom: 0;
+  margin-left: 0;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  *zoom: 1;
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+     -moz-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+
+.pagination ul > li {
+  display: inline;
+}
+
+.pagination ul > li > a,
+.pagination ul > li > span {
+  float: left;
+  padding: 4px 12px;
+  line-height: 20px;
+  text-decoration: none;
+  background-color: #ffffff;
+  border: 1px solid #dddddd;
+  border-left-width: 0;
+}
+
+.pagination ul > li > a:hover,
+.pagination ul > li > a:focus,
+.pagination ul > .active > a,
+.pagination ul > .active > span {
+  background-color: #f5f5f5;
+}
+
+.pagination ul > .active > a,
+.pagination ul > .active > span {
+  color: #999999;
+  cursor: default;
+}
+
+.pagination ul > .disabled > span,
+.pagination ul > .disabled > a,
+.pagination ul > .disabled > a:hover,
+.pagination ul > .disabled > a:focus {
+  color: #999999;
+  cursor: default;
+  background-color: transparent;
+}
+
+.pagination ul > li:first-child > a,
+.pagination ul > li:first-child > span {
+  border-left-width: 1px;
+  -webkit-border-bottom-left-radius: 4px;
+          border-bottom-left-radius: 4px;
+  -webkit-border-top-left-radius: 4px;
+          border-top-left-radius: 4px;
+  -moz-border-radius-bottomleft: 4px;
+  -moz-border-radius-topleft: 4px;
+}
+
+.pagination ul > li:last-child > a,
+.pagination ul > li:last-child > span {
+  -webkit-border-top-right-radius: 4px;
+          border-top-right-radius: 4px;
+  -webkit-border-bottom-right-radius: 4px;
+          border-bottom-right-radius: 4px;
+  -moz-border-radius-topright: 4px;
+  -moz-border-radius-bottomright: 4px;
+}
+
+.pagination-centered {
+  text-align: center;
+}
+
+.pagination-right {
+  text-align: right;
+}
+
+.pagination-large ul > li > a,
+.pagination-large ul > li > span {
+  padding: 11px 19px;
+  font-size: 17.5px;
+}
+
+.pagination-large ul > li:first-child > a,
+.pagination-large ul > li:first-child > span {
+  -webkit-border-bottom-left-radius: 6px;
+          border-bottom-left-radius: 6px;
+  -webkit-border-top-left-radius: 6px;
+          border-top-left-radius: 6px;
+  -moz-border-radius-bottomleft: 6px;
+  -moz-border-radius-topleft: 6px;
+}
+
+.pagination-large ul > li:last-child > a,
+.pagination-large ul > li:last-child > span {
+  -webkit-border-top-right-radius: 6px;
+          border-top-right-radius: 6px;
+  -webkit-border-bottom-right-radius: 6px;
+          border-bottom-right-radius: 6px;
+  -moz-border-radius-topright: 6px;
+  -moz-border-radius-bottomright: 6px;
+}
+
+.pagination-mini ul > li:first-child > a,
+.pagination-small ul > li:first-child > a,
+.pagination-mini ul > li:first-child > span,
+.pagination-small ul > li:first-child > span {
+  -webkit-border-bottom-left-radius: 3px;
+          border-bottom-left-radius: 3px;
+  -webkit-border-top-left-radius: 3px;
+          border-top-left-radius: 3px;
+  -moz-border-radius-bottomleft: 3px;
+  -moz-border-radius-topleft: 3px;
+}
+
+.pagination-mini ul > li:last-child > a,
+.pagination-small ul > li:last-child > a,
+.pagination-mini ul > li:last-child > span,
+.pagination-small ul > li:last-child > span {
+  -webkit-border-top-right-radius: 3px;
+          border-top-right-radius: 3px;
+  -webkit-border-bottom-right-radius: 3px;
+          border-bottom-right-radius: 3px;
+  -moz-border-radius-topright: 3px;
+  -moz-border-radius-bottomright: 3px;
+}
+
+.pagination-small ul > li > a,
+.pagination-small ul > li > span {
+  padding: 2px 10px;
+  font-size: 11.9px;
+}
+
+.pagination-mini ul > li > a,
+.pagination-mini ul > li > span {
+  padding: 0 6px;
+  font-size: 10.5px;
+}
+
+.pager {
+  margin: 20px 0;
+  text-align: center;
+  list-style: none;
+  *zoom: 1;
+}
+
+.pager:before,
+.pager:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.pager:after {
+  clear: both;
+}
+
+.pager li {
+  display: inline;
+}
+
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  -webkit-border-radius: 15px;
+     -moz-border-radius: 15px;
+          border-radius: 15px;
+}
+
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #f5f5f5;
+}
+
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #999999;
+  cursor: default;
+  background-color: #fff;
+}
+
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000000;
+}
+
+.modal-backdrop.fade {
+  opacity: 0;
+}
+
+.modal-backdrop,
+.modal-backdrop.fade.in {
+  opacity: 0.8;
+  filter: alpha(opacity=80);
+}
+
+.modal {
+  position: fixed;
+  top: 10%;
+  left: 50%;
+  z-index: 1050;
+  width: 560px;
+  margin-left: -280px;
+  background-color: #ffffff;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.3);
+  *border: 1px solid #999;
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+  outline: none;
+  -webkit-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);
+     -moz-box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);
+          box-shadow: 0 3px 7px rgba(0, 0, 0, 0.3);
+  -webkit-background-clip: padding-box;
+     -moz-background-clip: padding-box;
+          background-clip: padding-box;
+}
+
+.modal.fade {
+  top: -25%;
+  -webkit-transition: opacity 0.3s linear, top 0.3s ease-out;
+     -moz-transition: opacity 0.3s linear, top 0.3s ease-out;
+       -o-transition: opacity 0.3s linear, top 0.3s ease-out;
+          transition: opacity 0.3s linear, top 0.3s ease-out;
+}
+
+.modal.fade.in {
+  top: 10%;
+}
+
+.modal-header {
+  padding: 9px 15px;
+  border-bottom: 1px solid #eee;
+}
+
+.modal-header .close {
+  margin-top: 2px;
+}
+
+.modal-header h3 {
+  margin: 0;
+  line-height: 30px;
+}
+
+.modal-body {
+  position: relative;
+  max-height: 400px;
+  padding: 15px;
+  overflow-y: auto;
+}
+
+.modal-form {
+  margin-bottom: 0;
+}
+
+.modal-footer {
+  padding: 14px 15px 15px;
+  margin-bottom: 0;
+  text-align: right;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  -webkit-border-radius: 0 0 6px 6px;
+     -moz-border-radius: 0 0 6px 6px;
+          border-radius: 0 0 6px 6px;
+  *zoom: 1;
+  -webkit-box-shadow: inset 0 1px 0 #ffffff;
+     -moz-box-shadow: inset 0 1px 0 #ffffff;
+          box-shadow: inset 0 1px 0 #ffffff;
+}
+
+.modal-footer:before,
+.modal-footer:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.modal-footer:after {
+  clear: both;
+}
+
+.modal-footer .btn + .btn {
+  margin-bottom: 0;
+  margin-left: 5px;
+}
+
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+
+.tooltip {
+  position: absolute;
+  z-index: 1030;
+  display: block;
+  font-size: 11px;
+  line-height: 1.4;
+  opacity: 0;
+  filter: alpha(opacity=0);
+  visibility: visible;
+}
+
+.tooltip.in {
+  opacity: 0.8;
+  filter: alpha(opacity=80);
+}
+
+.tooltip.top {
+  padding: 5px 0;
+  margin-top: -3px;
+}
+
+.tooltip.right {
+  padding: 0 5px;
+  margin-left: 3px;
+}
+
+.tooltip.bottom {
+  padding: 5px 0;
+  margin-top: 3px;
+}
+
+.tooltip.left {
+  padding: 0 5px;
+  margin-left: -3px;
+}
+
+.tooltip-inner {
+  max-width: 200px;
+  padding: 8px;
+  color: #ffffff;
+  text-align: center;
+  text-decoration: none;
+  background-color: #000000;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+}
+
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-top-color: #000000;
+  border-width: 5px 5px 0;
+}
+
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-right-color: #000000;
+  border-width: 5px 5px 5px 0;
+}
+
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-left-color: #000000;
+  border-width: 5px 0 5px 5px;
+}
+
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-bottom-color: #000000;
+  border-width: 0 5px 5px;
+}
+
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1010;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  text-align: left;
+  white-space: normal;
+  background-color: #ffffff;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+     -moz-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+          box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  -webkit-background-clip: padding-box;
+     -moz-background-clip: padding;
+          background-clip: padding-box;
+}
+
+.popover.top {
+  margin-top: -10px;
+}
+
+.popover.right {
+  margin-left: 10px;
+}
+
+.popover.bottom {
+  margin-top: 10px;
+}
+
+.popover.left {
+  margin-left: -10px;
+}
+
+.popover-title {
+  padding: 8px 14px;
+  margin: 0;
+  font-size: 14px;
+  font-weight: normal;
+  line-height: 18px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  -webkit-border-radius: 5px 5px 0 0;
+     -moz-border-radius: 5px 5px 0 0;
+          border-radius: 5px 5px 0 0;
+}
+
+.popover-title:empty {
+  display: none;
+}
+
+.popover-content {
+  padding: 9px 14px;
+}
+
+.popover .arrow,
+.popover .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+
+.popover .arrow {
+  border-width: 11px;
+}
+
+.popover .arrow:after {
+  border-width: 10px;
+  content: "";
+}
+
+.popover.top .arrow {
+  bottom: -11px;
+  left: 50%;
+  margin-left: -11px;
+  border-top-color: #999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  border-bottom-width: 0;
+}
+
+.popover.top .arrow:after {
+  bottom: 1px;
+  margin-left: -10px;
+  border-top-color: #ffffff;
+  border-bottom-width: 0;
+}
+
+.popover.right .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-right-color: #999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+  border-left-width: 0;
+}
+
+.popover.right .arrow:after {
+  bottom: -10px;
+  left: 1px;
+  border-right-color: #ffffff;
+  border-left-width: 0;
+}
+
+.popover.bottom .arrow {
+  top: -11px;
+  left: 50%;
+  margin-left: -11px;
+  border-bottom-color: #999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+  border-top-width: 0;
+}
+
+.popover.bottom .arrow:after {
+  top: 1px;
+  margin-left: -10px;
+  border-bottom-color: #ffffff;
+  border-top-width: 0;
+}
+
+.popover.left .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-left-color: #999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+  border-right-width: 0;
+}
+
+.popover.left .arrow:after {
+  right: 1px;
+  bottom: -10px;
+  border-left-color: #ffffff;
+  border-right-width: 0;
+}
+
+.thumbnails {
+  margin-left: -20px;
+  list-style: none;
+  *zoom: 1;
+}
+
+.thumbnails:before,
+.thumbnails:after {
+  display: table;
+  line-height: 0;
+  content: "";
+}
+
+.thumbnails:after {
+  clear: both;
+}
+
+.row-fluid .thumbnails {
+  margin-left: 0;
+}
+
+.thumbnails > li {
+  float: left;
+  margin-bottom: 20px;
+  margin-left: 20px;
+}
+
+.thumbnail {
+  display: block;
+  padding: 4px;
+  line-height: 20px;
+  border: 1px solid #ddd;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  -webkit-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055);
+     -moz-box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055);
+          box-shadow: 0 1px 3px rgba(0, 0, 0, 0.055);
+  -webkit-transition: all 0.2s ease-in-out;
+     -moz-transition: all 0.2s ease-in-out;
+       -o-transition: all 0.2s ease-in-out;
+          transition: all 0.2s ease-in-out;
+}
+
+a.thumbnail:hover,
+a.thumbnail:focus {
+  border-color: #0088cc;
+  -webkit-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);
+     -moz-box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);
+          box-shadow: 0 1px 4px rgba(0, 105, 214, 0.25);
+}
+
+.thumbnail > img {
+  display: block;
+  max-width: 100%;
+  margin-right: auto;
+  margin-left: auto;
+}
+
+.thumbnail .caption {
+  padding: 9px;
+  color: #555555;
+}
+
+.media,
+.media-body {
+  overflow: hidden;
+  *overflow: visible;
+  zoom: 1;
+}
+
+.media,
+.media .media {
+  margin-top: 15px;
+}
+
+.media:first-child {
+  margin-top: 0;
+}
+
+.media-object {
+  display: block;
+}
+
+.media-heading {
+  margin: 0 0 5px;
+}
+
+.media > .pull-left {
+  margin-right: 10px;
+}
+
+.media > .pull-right {
+  margin-left: 10px;
+}
+
+.media-list {
+  margin-left: 0;
+  list-style: none;
+}
+
+.label,
+.badge {
+  display: inline-block;
+  padding: 2px 4px;
+  font-size: 11.844px;
+  font-weight: bold;
+  line-height: 14px;
+  color: #ffffff;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  white-space: nowrap;
+  vertical-align: baseline;
+  background-color: #999999;
+}
+
+.label {
+  -webkit-border-radius: 3px;
+     -moz-border-radius: 3px;
+          border-radius: 3px;
+}
+
+.badge {
+  padding-right: 9px;
+  padding-left: 9px;
+  -webkit-border-radius: 9px;
+     -moz-border-radius: 9px;
+          border-radius: 9px;
+}
+
+.label:empty,
+.badge:empty {
+  display: none;
+}
+
+a.label:hover,
+a.label:focus,
+a.badge:hover,
+a.badge:focus {
+  color: #ffffff;
+  text-decoration: none;
+  cursor: pointer;
+}
+
+.label-important,
+.badge-important {
+  background-color: #b94a48;
+}
+
+.label-important[href],
+.badge-important[href] {
+  background-color: #953b39;
+}
+
+.label-warning,
+.badge-warning {
+  background-color: #f89406;
+}
+
+.label-warning[href],
+.badge-warning[href] {
+  background-color: #c67605;
+}
+
+.label-success,
+.badge-success {
+  background-color: #468847;
+}
+
+.label-success[href],
+.badge-success[href] {
+  background-color: #356635;
+}
+
+.label-info,
+.badge-info {
+  background-color: #3a87ad;
+}
+
+.label-info[href],
+.badge-info[href] {
+  background-color: #2d6987;
+}
+
+.label-inverse,
+.badge-inverse {
+  background-color: #333333;
+}
+
+.label-inverse[href],
+.badge-inverse[href] {
+  background-color: #1a1a1a;
+}
+
+.btn .label,
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+
+.btn-mini .label,
+.btn-mini .badge {
+  top: 0;
+}
+
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+
+@-moz-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+
+@-ms-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+
+@-o-keyframes progress-bar-stripes {
+  from {
+    background-position: 0 0;
+  }
+  to {
+    background-position: 40px 0;
+  }
+}
+
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+
+.progress {
+  height: 20px;
+  margin-bottom: 20px;
+  overflow: hidden;
+  background-color: #f7f7f7;
+  background-image: -moz-linear-gradient(top, #f5f5f5, #f9f9f9);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));
+  background-image: -webkit-linear-gradient(top, #f5f5f5, #f9f9f9);
+  background-image: -o-linear-gradient(top, #f5f5f5, #f9f9f9);
+  background-image: linear-gradient(to bottom, #f5f5f5, #f9f9f9);
+  background-repeat: repeat-x;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+     -moz-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+          box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+
+.progress .bar {
+  float: left;
+  width: 0;
+  height: 100%;
+  font-size: 12px;
+  color: #ffffff;
+  text-align: center;
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+  background-color: #0e90d2;
+  background-image: -moz-linear-gradient(top, #149bdf, #0480be);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));
+  background-image: -webkit-linear-gradient(top, #149bdf, #0480be);
+  background-image: -o-linear-gradient(top, #149bdf, #0480be);
+  background-image: linear-gradient(to bottom, #149bdf, #0480be);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+     -moz-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+          box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-box-sizing: border-box;
+     -moz-box-sizing: border-box;
+          box-sizing: border-box;
+  -webkit-transition: width 0.6s ease;
+     -moz-transition: width 0.6s ease;
+       -o-transition: width 0.6s ease;
+          transition: width 0.6s ease;
+}
+
+.progress .bar + .bar {
+  -webkit-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+     -moz-box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+          box-shadow: inset 1px 0 0 rgba(0, 0, 0, 0.15), inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+}
+
+.progress-striped .bar {
+  background-color: #149bdf;
+  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  -webkit-background-size: 40px 40px;
+     -moz-background-size: 40px 40px;
+       -o-background-size: 40px 40px;
+          background-size: 40px 40px;
+}
+
+.progress.active .bar {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+     -moz-animation: progress-bar-stripes 2s linear infinite;
+      -ms-animation: progress-bar-stripes 2s linear infinite;
+       -o-animation: progress-bar-stripes 2s linear infinite;
+          animation: progress-bar-stripes 2s linear infinite;
+}
+
+.progress-danger .bar,
+.progress .bar-danger {
+  background-color: #dd514c;
+  background-image: -moz-linear-gradient(top, #ee5f5b, #c43c35);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));
+  background-image: -webkit-linear-gradient(top, #ee5f5b, #c43c35);
+  background-image: -o-linear-gradient(top, #ee5f5b, #c43c35);
+  background-image: linear-gradient(to bottom, #ee5f5b, #c43c35);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0);
+}
+
+.progress-danger.progress-striped .bar,
+.progress-striped .bar-danger {
+  background-color: #ee5f5b;
+  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+
+.progress-success .bar,
+.progress .bar-success {
+  background-color: #5eb95e;
+  background-image: -moz-linear-gradient(top, #62c462, #57a957);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));
+  background-image: -webkit-linear-gradient(top, #62c462, #57a957);
+  background-image: -o-linear-gradient(top, #62c462, #57a957);
+  background-image: linear-gradient(to bottom, #62c462, #57a957);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0);
+}
+
+.progress-success.progress-striped .bar,
+.progress-striped .bar-success {
+  background-color: #62c462;
+  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+
+.progress-info .bar,
+.progress .bar-info {
+  background-color: #4bb1cf;
+  background-image: -moz-linear-gradient(top, #5bc0de, #339bb9);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));
+  background-image: -webkit-linear-gradient(top, #5bc0de, #339bb9);
+  background-image: -o-linear-gradient(top, #5bc0de, #339bb9);
+  background-image: linear-gradient(to bottom, #5bc0de, #339bb9);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0);
+}
+
+.progress-info.progress-striped .bar,
+.progress-striped .bar-info {
+  background-color: #5bc0de;
+  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+
+.progress-warning .bar,
+.progress .bar-warning {
+  background-color: #faa732;
+  background-image: -moz-linear-gradient(top, #fbb450, #f89406);
+  background-image: -webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));
+  background-image: -webkit-linear-gradient(top, #fbb450, #f89406);
+  background-image: -o-linear-gradient(top, #fbb450, #f89406);
+  background-image: linear-gradient(to bottom, #fbb450, #f89406);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);
+}
+
+.progress-warning.progress-striped .bar,
+.progress-striped .bar-warning {
+  background-color: #fbb450;
+  background-image: -webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+
+.accordion {
+  margin-bottom: 20px;
+}
+
+.accordion-group {
+  margin-bottom: 2px;
+  border: 1px solid #e5e5e5;
+  -webkit-border-radius: 4px;
+     -moz-border-radius: 4px;
+          border-radius: 4px;
+}
+
+.accordion-heading {
+  border-bottom: 0;
+}
+
+.accordion-heading .accordion-toggle {
+  display: block;
+  padding: 8px 15px;
+}
+
+.accordion-toggle {
+  cursor: pointer;
+}
+
+.accordion-inner {
+  padding: 9px 15px;
+  border-top: 1px solid #e5e5e5;
+}
+
+.carousel {
+  position: relative;
+  margin-bottom: 20px;
+  line-height: 1;
+}
+
+.carousel-inner {
+  position: relative;
+  width: 100%;
+  overflow: hidden;
+}
+
+.carousel-inner > .item {
+  position: relative;
+  display: none;
+  -webkit-transition: 0.6s ease-in-out left;
+     -moz-transition: 0.6s ease-in-out left;
+       -o-transition: 0.6s ease-in-out left;
+          transition: 0.6s ease-in-out left;
+}
+
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  line-height: 1;
+}
+
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+
+.carousel-inner > .active {
+  left: 0;
+}
+
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+
+.carousel-inner > .next {
+  left: 100%;
+}
+
+.carousel-inner > .prev {
+  left: -100%;
+}
+
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+
+.carousel-inner > .active.left {
+  left: -100%;
+}
+
+.carousel-inner > .active.right {
+  left: 100%;
+}
+
+.carousel-control {
+  position: absolute;
+  top: 40%;
+  left: 15px;
+  width: 40px;
+  height: 40px;
+  margin-top: -20px;
+  font-size: 60px;
+  font-weight: 100;
+  line-height: 30px;
+  color: #ffffff;
+  text-align: center;
+  background: #222222;
+  border: 3px solid #ffffff;
+  -webkit-border-radius: 23px;
+     -moz-border-radius: 23px;
+          border-radius: 23px;
+  opacity: 0.5;
+  filter: alpha(opacity=50);
+}
+
+.carousel-control.right {
+  right: 15px;
+  left: auto;
+}
+
+.carousel-control:hover,
+.carousel-control:focus {
+  color: #ffffff;
+  text-decoration: none;
+  opacity: 0.9;
+  filter: alpha(opacity=90);
+}
+
+.carousel-indicators {
+  position: absolute;
+  top: 15px;
+  right: 15px;
+  z-index: 5;
+  margin: 0;
+  list-style: none;
+}
+
+.carousel-indicators li {
+  display: block;
+  float: left;
+  width: 10px;
+  height: 10px;
+  margin-left: 5px;
+  text-indent: -999px;
+  background-color: #ccc;
+  background-color: rgba(255, 255, 255, 0.25);
+  border-radius: 5px;
+}
+
+.carousel-indicators .active {
+  background-color: #fff;
+}
+
+.carousel-caption {
+  position: absolute;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  padding: 15px;
+  background: #333333;
+  background: rgba(0, 0, 0, 0.75);
+}
+
+.carousel-caption h4,
+.carousel-caption p {
+  line-height: 20px;
+  color: #ffffff;
+}
+
+.carousel-caption h4 {
+  margin: 0 0 5px;
+}
+
+.carousel-caption p {
+  margin-bottom: 0;
+}
+
+.hero-unit {
+  padding: 60px;
+  margin-bottom: 30px;
+  font-size: 18px;
+  font-weight: 200;
+  line-height: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+  -webkit-border-radius: 6px;
+     -moz-border-radius: 6px;
+          border-radius: 6px;
+}
+
+.hero-unit h1 {
+  margin-bottom: 0;
+  font-size: 60px;
+  line-height: 1;
+  letter-spacing: -1px;
+  color: inherit;
+}
+
+.hero-unit li {
+  line-height: 30px;
+}
+
+.pull-right {
+  float: right;
+}
+
+.pull-left {
+  float: left;
+}
+
+.hide {
+  display: none;
+}
+
+.show {
+  display: block;
+}
+
+.invisible {
+  visibility: hidden;
+}
+
+.affix {
+  position: fixed;
+}
diff --git a/_static/bootstrap-2.3.2/css/bootstrap.min.css b/_static/bootstrap-2.3.2/css/bootstrap.min.css
new file mode 100644
index 000000000..b6428e695
--- /dev/null
+++ b/_static/bootstrap-2.3.2/css/bootstrap.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Bootstrap v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
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diff --git a/_static/bootstrap-2.3.2/js/bootstrap.js b/_static/bootstrap-2.3.2/js/bootstrap.js
new file mode 100644
index 000000000..638bb1877
--- /dev/null
+++ b/_static/bootstrap-2.3.2/js/bootstrap.js
@@ -0,0 +1,2287 @@
+/* ===================================================
+ * bootstrap-transition.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#transitions
+ * ===================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+  /* CSS TRANSITION SUPPORT (http://www.modernizr.com/)
+   * ======================================================= */
+
+  $(function () {
+
+    $.support.transition = (function () {
+
+      var transitionEnd = (function () {
+
+        var el = document.createElement('bootstrap')
+          , transEndEventNames = {
+               'WebkitTransition' : 'webkitTransitionEnd'
+            ,  'MozTransition'    : 'transitionend'
+            ,  'OTransition'      : 'oTransitionEnd otransitionend'
+            ,  'transition'       : 'transitionend'
+            }
+          , name
+
+        for (name in transEndEventNames){
+          if (el.style[name] !== undefined) {
+            return transEndEventNames[name]
+          }
+        }
+
+      }())
+
+      return transitionEnd && {
+        end: transitionEnd
+      }
+
+    })()
+
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* ==========================================================
+ * bootstrap-alert.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#alerts
+ * ==========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* ALERT CLASS DEFINITION
+  * ====================== */
+
+  var dismiss = '[data-dismiss="alert"]'
+    , Alert = function (el) {
+        $(el).on('click', dismiss, this.close)
+      }
+
+  Alert.prototype.close = function (e) {
+    var $this = $(this)
+      , selector = $this.attr('data-target')
+      , $parent
+
+    if (!selector) {
+      selector = $this.attr('href')
+      selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7
+    }
+
+    $parent = $(selector)
+
+    e && e.preventDefault()
+
+    $parent.length || ($parent = $this.hasClass('alert') ? $this : $this.parent())
+
+    $parent.trigger(e = $.Event('close'))
+
+    if (e.isDefaultPrevented()) return
+
+    $parent.removeClass('in')
+
+    function removeElement() {
+      $parent
+        .trigger('closed')
+        .remove()
+    }
+
+    $.support.transition && $parent.hasClass('fade') ?
+      $parent.on($.support.transition.end, removeElement) :
+      removeElement()
+  }
+
+
+ /* ALERT PLUGIN DEFINITION
+  * ======================= */
+
+  var old = $.fn.alert
+
+  $.fn.alert = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('alert')
+      if (!data) $this.data('alert', (data = new Alert(this)))
+      if (typeof option == 'string') data[option].call($this)
+    })
+  }
+
+  $.fn.alert.Constructor = Alert
+
+
+ /* ALERT NO CONFLICT
+  * ================= */
+
+  $.fn.alert.noConflict = function () {
+    $.fn.alert = old
+    return this
+  }
+
+
+ /* ALERT DATA-API
+  * ============== */
+
+  $(document).on('click.alert.data-api', dismiss, Alert.prototype.close)
+
+}(window.$jqTheme || window.jQuery);
+/* ============================================================
+ * bootstrap-button.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#buttons
+ * ============================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ============================================================ */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* BUTTON PUBLIC CLASS DEFINITION
+  * ============================== */
+
+  var Button = function (element, options) {
+    this.$element = $(element)
+    this.options = $.extend({}, $.fn.button.defaults, options)
+  }
+
+  Button.prototype.setState = function (state) {
+    var d = 'disabled'
+      , $el = this.$element
+      , data = $el.data()
+      , val = $el.is('input') ? 'val' : 'html'
+
+    state = state + 'Text'
+    data.resetText || $el.data('resetText', $el[val]())
+
+    $el[val](data[state] || this.options[state])
+
+    // push to event loop to allow forms to submit
+    setTimeout(function () {
+      state == 'loadingText' ?
+        $el.addClass(d).attr(d, d) :
+        $el.removeClass(d).removeAttr(d)
+    }, 0)
+  }
+
+  Button.prototype.toggle = function () {
+    var $parent = this.$element.closest('[data-toggle="buttons-radio"]')
+
+    $parent && $parent
+      .find('.active')
+      .removeClass('active')
+
+    this.$element.toggleClass('active')
+  }
+
+
+ /* BUTTON PLUGIN DEFINITION
+  * ======================== */
+
+  var old = $.fn.button
+
+  $.fn.button = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('button')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('button', (data = new Button(this, options)))
+      if (option == 'toggle') data.toggle()
+      else if (option) data.setState(option)
+    })
+  }
+
+  $.fn.button.defaults = {
+    loadingText: 'loading...'
+  }
+
+  $.fn.button.Constructor = Button
+
+
+ /* BUTTON NO CONFLICT
+  * ================== */
+
+  $.fn.button.noConflict = function () {
+    $.fn.button = old
+    return this
+  }
+
+
+ /* BUTTON DATA-API
+  * =============== */
+
+  $(document).on('click.button.data-api', '[data-toggle^=button]', function (e) {
+    var $btn = $(e.target)
+    if (!$btn.hasClass('btn')) $btn = $btn.closest('.btn')
+    $btn.button('toggle')
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* ==========================================================
+ * bootstrap-carousel.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#carousel
+ * ==========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* CAROUSEL CLASS DEFINITION
+  * ========================= */
+
+  var Carousel = function (element, options) {
+    this.$element = $(element)
+    this.$indicators = this.$element.find('.carousel-indicators')
+    this.options = options
+    this.options.pause == 'hover' && this.$element
+      .on('mouseenter', $.proxy(this.pause, this))
+      .on('mouseleave', $.proxy(this.cycle, this))
+  }
+
+  Carousel.prototype = {
+
+    cycle: function (e) {
+      if (!e) this.paused = false
+      if (this.interval) clearInterval(this.interval);
+      this.options.interval
+        && !this.paused
+        && (this.interval = setInterval($.proxy(this.next, this), this.options.interval))
+      return this
+    }
+
+  , getActiveIndex: function () {
+      this.$active = this.$element.find('.item.active')
+      this.$items = this.$active.parent().children()
+      return this.$items.index(this.$active)
+    }
+
+  , to: function (pos) {
+      var activeIndex = this.getActiveIndex()
+        , that = this
+
+      if (pos > (this.$items.length - 1) || pos < 0) return
+
+      if (this.sliding) {
+        return this.$element.one('slid', function () {
+          that.to(pos)
+        })
+      }
+
+      if (activeIndex == pos) {
+        return this.pause().cycle()
+      }
+
+      return this.slide(pos > activeIndex ? 'next' : 'prev', $(this.$items[pos]))
+    }
+
+  , pause: function (e) {
+      if (!e) this.paused = true
+      if (this.$element.find('.next, .prev').length && $.support.transition.end) {
+        this.$element.trigger($.support.transition.end)
+        this.cycle(true)
+      }
+      clearInterval(this.interval)
+      this.interval = null
+      return this
+    }
+
+  , next: function () {
+      if (this.sliding) return
+      return this.slide('next')
+    }
+
+  , prev: function () {
+      if (this.sliding) return
+      return this.slide('prev')
+    }
+
+  , slide: function (type, next) {
+      var $active = this.$element.find('.item.active')
+        , $next = next || $active[type]()
+        , isCycling = this.interval
+        , direction = type == 'next' ? 'left' : 'right'
+        , fallback  = type == 'next' ? 'first' : 'last'
+        , that = this
+        , e
+
+      this.sliding = true
+
+      isCycling && this.pause()
+
+      $next = $next.length ? $next : this.$element.find('.item')[fallback]()
+
+      e = $.Event('slide', {
+        relatedTarget: $next[0]
+      , direction: direction
+      })
+
+      if ($next.hasClass('active')) return
+
+      if (this.$indicators.length) {
+        this.$indicators.find('.active').removeClass('active')
+        this.$element.one('slid', function () {
+          var $nextIndicator = $(that.$indicators.children()[that.getActiveIndex()])
+          $nextIndicator && $nextIndicator.addClass('active')
+        })
+      }
+
+      if ($.support.transition && this.$element.hasClass('slide')) {
+        this.$element.trigger(e)
+        if (e.isDefaultPrevented()) return
+        $next.addClass(type)
+        $next[0].offsetWidth // force reflow
+        $active.addClass(direction)
+        $next.addClass(direction)
+        this.$element.one($.support.transition.end, function () {
+          $next.removeClass([type, direction].join(' ')).addClass('active')
+          $active.removeClass(['active', direction].join(' '))
+          that.sliding = false
+          setTimeout(function () { that.$element.trigger('slid') }, 0)
+        })
+      } else {
+        this.$element.trigger(e)
+        if (e.isDefaultPrevented()) return
+        $active.removeClass('active')
+        $next.addClass('active')
+        this.sliding = false
+        this.$element.trigger('slid')
+      }
+
+      isCycling && this.cycle()
+
+      return this
+    }
+
+  }
+
+
+ /* CAROUSEL PLUGIN DEFINITION
+  * ========================== */
+
+  var old = $.fn.carousel
+
+  $.fn.carousel = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('carousel')
+        , options = $.extend({}, $.fn.carousel.defaults, typeof option == 'object' && option)
+        , action = typeof option == 'string' ? option : options.slide
+      if (!data) $this.data('carousel', (data = new Carousel(this, options)))
+      if (typeof option == 'number') data.to(option)
+      else if (action) data[action]()
+      else if (options.interval) data.pause().cycle()
+    })
+  }
+
+  $.fn.carousel.defaults = {
+    interval: 5000
+  , pause: 'hover'
+  }
+
+  $.fn.carousel.Constructor = Carousel
+
+
+ /* CAROUSEL NO CONFLICT
+  * ==================== */
+
+  $.fn.carousel.noConflict = function () {
+    $.fn.carousel = old
+    return this
+  }
+
+ /* CAROUSEL DATA-API
+  * ================= */
+
+  $(document).on('click.carousel.data-api', '[data-slide], [data-slide-to]', function (e) {
+    var $this = $(this), href
+      , $target = $($this.attr('data-target') || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7
+      , options = $.extend({}, $target.data(), $this.data())
+      , slideIndex
+
+    $target.carousel(options)
+
+    if (slideIndex = $this.attr('data-slide-to')) {
+      $target.data('carousel').pause().to(slideIndex).cycle()
+    }
+
+    e.preventDefault()
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* =============================================================
+ * bootstrap-collapse.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#collapse
+ * =============================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ============================================================ */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* COLLAPSE PUBLIC CLASS DEFINITION
+  * ================================ */
+
+  var Collapse = function (element, options) {
+    this.$element = $(element)
+    this.options = $.extend({}, $.fn.collapse.defaults, options)
+
+    if (this.options.parent) {
+      this.$parent = $(this.options.parent)
+    }
+
+    this.options.toggle && this.toggle()
+  }
+
+  Collapse.prototype = {
+
+    constructor: Collapse
+
+  , dimension: function () {
+      var hasWidth = this.$element.hasClass('width')
+      return hasWidth ? 'width' : 'height'
+    }
+
+  , show: function () {
+      var dimension
+        , scroll
+        , actives
+        , hasData
+
+      if (this.transitioning || this.$element.hasClass('in')) return
+
+      dimension = this.dimension()
+      scroll = $.camelCase(['scroll', dimension].join('-'))
+      actives = this.$parent && this.$parent.find('> .accordion-group > .in')
+
+      if (actives && actives.length) {
+        hasData = actives.data('collapse')
+        if (hasData && hasData.transitioning) return
+        actives.collapse('hide')
+        hasData || actives.data('collapse', null)
+      }
+
+      this.$element[dimension](0)
+      this.transition('addClass', $.Event('show'), 'shown')
+      $.support.transition && this.$element[dimension](this.$element[0][scroll])
+    }
+
+  , hide: function () {
+      var dimension
+      if (this.transitioning || !this.$element.hasClass('in')) return
+      dimension = this.dimension()
+      this.reset(this.$element[dimension]())
+      this.transition('removeClass', $.Event('hide'), 'hidden')
+      this.$element[dimension](0)
+    }
+
+  , reset: function (size) {
+      var dimension = this.dimension()
+
+      this.$element
+        .removeClass('collapse')
+        [dimension](size || 'auto')
+        [0].offsetWidth
+
+      this.$element[size !== null ? 'addClass' : 'removeClass']('collapse')
+
+      return this
+    }
+
+  , transition: function (method, startEvent, completeEvent) {
+      var that = this
+        , complete = function () {
+            if (startEvent.type == 'show') that.reset()
+            that.transitioning = 0
+            that.$element.trigger(completeEvent)
+          }
+
+      this.$element.trigger(startEvent)
+
+      if (startEvent.isDefaultPrevented()) return
+
+      this.transitioning = 1
+
+      this.$element[method]('in')
+
+      $.support.transition && this.$element.hasClass('collapse') ?
+        this.$element.one($.support.transition.end, complete) :
+        complete()
+    }
+
+  , toggle: function () {
+      this[this.$element.hasClass('in') ? 'hide' : 'show']()
+    }
+
+  }
+
+
+ /* COLLAPSE PLUGIN DEFINITION
+  * ========================== */
+
+  var old = $.fn.collapse
+
+  $.fn.collapse = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('collapse')
+        , options = $.extend({}, $.fn.collapse.defaults, $this.data(), typeof option == 'object' && option)
+      if (!data) $this.data('collapse', (data = new Collapse(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.collapse.defaults = {
+    toggle: true
+  }
+
+  $.fn.collapse.Constructor = Collapse
+
+
+ /* COLLAPSE NO CONFLICT
+  * ==================== */
+
+  $.fn.collapse.noConflict = function () {
+    $.fn.collapse = old
+    return this
+  }
+
+
+ /* COLLAPSE DATA-API
+  * ================= */
+
+  $(document).on('click.collapse.data-api', '[data-toggle=collapse]', function (e) {
+    var $this = $(this), href
+      , target = $this.attr('data-target')
+        || e.preventDefault()
+        || (href = $this.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') //strip for ie7
+      , option = $(target).data('collapse') ? 'toggle' : $this.data()
+    $this[$(target).hasClass('in') ? 'addClass' : 'removeClass']('collapsed')
+    $(target).collapse(option)
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* ============================================================
+ * bootstrap-dropdown.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#dropdowns
+ * ============================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ============================================================ */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* DROPDOWN CLASS DEFINITION
+  * ========================= */
+
+  var toggle = '[data-toggle=dropdown]'
+    , Dropdown = function (element) {
+        var $el = $(element).on('click.dropdown.data-api', this.toggle)
+        $('html').on('click.dropdown.data-api', function () {
+          $el.parent().removeClass('open')
+        })
+      }
+
+  Dropdown.prototype = {
+
+    constructor: Dropdown
+
+  , toggle: function (e) {
+      var $this = $(this)
+        , $parent
+        , isActive
+
+      if ($this.is('.disabled, :disabled')) return
+
+      $parent = getParent($this)
+
+      isActive = $parent.hasClass('open')
+
+      clearMenus()
+
+      if (!isActive) {
+        if ('ontouchstart' in document.documentElement) {
+          // if mobile we we use a backdrop because click events don't delegate
+          $('<div class="dropdown-backdrop"/>').insertBefore($(this)).on('click', clearMenus)
+        }
+        $parent.toggleClass('open')
+      }
+
+      $this.focus()
+
+      return false
+    }
+
+  , keydown: function (e) {
+      var $this
+        , $items
+        , $active
+        , $parent
+        , isActive
+        , index
+
+      if (!/(38|40|27)/.test(e.keyCode)) return
+
+      $this = $(this)
+
+      e.preventDefault()
+      e.stopPropagation()
+
+      if ($this.is('.disabled, :disabled')) return
+
+      $parent = getParent($this)
+
+      isActive = $parent.hasClass('open')
+
+      if (!isActive || (isActive && e.keyCode == 27)) {
+        if (e.which == 27) $parent.find(toggle).focus()
+        return $this.click()
+      }
+
+      $items = $('[role=menu] li:not(.divider):visible a', $parent)
+
+      if (!$items.length) return
+
+      index = $items.index($items.filter(':focus'))
+
+      if (e.keyCode == 38 && index > 0) index--                                        // up
+      if (e.keyCode == 40 && index < $items.length - 1) index++                        // down
+      if (!~index) index = 0
+
+      $items
+        .eq(index)
+        .focus()
+    }
+
+  }
+
+  function clearMenus() {
+    $('.dropdown-backdrop').remove()
+    $(toggle).each(function () {
+      getParent($(this)).removeClass('open')
+    })
+  }
+
+  function getParent($this) {
+    var selector = $this.attr('data-target')
+      , $parent
+
+    if (!selector) {
+      selector = $this.attr('href')
+      selector = selector && /#/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7
+    }
+
+    $parent = selector && $(selector)
+
+    if (!$parent || !$parent.length) $parent = $this.parent()
+
+    return $parent
+  }
+
+
+  /* DROPDOWN PLUGIN DEFINITION
+   * ========================== */
+
+  var old = $.fn.dropdown
+
+  $.fn.dropdown = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('dropdown')
+      if (!data) $this.data('dropdown', (data = new Dropdown(this)))
+      if (typeof option == 'string') data[option].call($this)
+    })
+  }
+
+  $.fn.dropdown.Constructor = Dropdown
+
+
+ /* DROPDOWN NO CONFLICT
+  * ==================== */
+
+  $.fn.dropdown.noConflict = function () {
+    $.fn.dropdown = old
+    return this
+  }
+
+
+  /* APPLY TO STANDARD DROPDOWN ELEMENTS
+   * =================================== */
+
+  $(document)
+    .on('click.dropdown.data-api', clearMenus)
+    .on('click.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() })
+    .on('click.dropdown.data-api'  , toggle, Dropdown.prototype.toggle)
+    .on('keydown.dropdown.data-api', toggle + ', [role=menu]' , Dropdown.prototype.keydown)
+
+}(window.$jqTheme || window.jQuery);
+/* =========================================================
+ * bootstrap-modal.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#modals
+ * =========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================= */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* MODAL CLASS DEFINITION
+  * ====================== */
+
+  var Modal = function (element, options) {
+    this.options = options
+    this.$element = $(element)
+      .delegate('[data-dismiss="modal"]', 'click.dismiss.modal', $.proxy(this.hide, this))
+    this.options.remote && this.$element.find('.modal-body').load(this.options.remote)
+  }
+
+  Modal.prototype = {
+
+      constructor: Modal
+
+    , toggle: function () {
+        return this[!this.isShown ? 'show' : 'hide']()
+      }
+
+    , show: function () {
+        var that = this
+          , e = $.Event('show')
+
+        this.$element.trigger(e)
+
+        if (this.isShown || e.isDefaultPrevented()) return
+
+        this.isShown = true
+
+        this.escape()
+
+        this.backdrop(function () {
+          var transition = $.support.transition && that.$element.hasClass('fade')
+
+          if (!that.$element.parent().length) {
+            that.$element.appendTo(document.body) //don't move modals dom position
+          }
+
+          that.$element.show()
+
+          if (transition) {
+            that.$element[0].offsetWidth // force reflow
+          }
+
+          that.$element
+            .addClass('in')
+            .attr('aria-hidden', false)
+
+          that.enforceFocus()
+
+          transition ?
+            that.$element.one($.support.transition.end, function () { that.$element.focus().trigger('shown') }) :
+            that.$element.focus().trigger('shown')
+
+        })
+      }
+
+    , hide: function (e) {
+        e && e.preventDefault()
+
+        var that = this
+
+        e = $.Event('hide')
+
+        this.$element.trigger(e)
+
+        if (!this.isShown || e.isDefaultPrevented()) return
+
+        this.isShown = false
+
+        this.escape()
+
+        $(document).off('focusin.modal')
+
+        this.$element
+          .removeClass('in')
+          .attr('aria-hidden', true)
+
+        $.support.transition && this.$element.hasClass('fade') ?
+          this.hideWithTransition() :
+          this.hideModal()
+      }
+
+    , enforceFocus: function () {
+        var that = this
+        $(document).on('focusin.modal', function (e) {
+          if (that.$element[0] !== e.target && !that.$element.has(e.target).length) {
+            that.$element.focus()
+          }
+        })
+      }
+
+    , escape: function () {
+        var that = this
+        if (this.isShown && this.options.keyboard) {
+          this.$element.on('keyup.dismiss.modal', function ( e ) {
+            e.which == 27 && that.hide()
+          })
+        } else if (!this.isShown) {
+          this.$element.off('keyup.dismiss.modal')
+        }
+      }
+
+    , hideWithTransition: function () {
+        var that = this
+          , timeout = setTimeout(function () {
+              that.$element.off($.support.transition.end)
+              that.hideModal()
+            }, 500)
+
+        this.$element.one($.support.transition.end, function () {
+          clearTimeout(timeout)
+          that.hideModal()
+        })
+      }
+
+    , hideModal: function () {
+        var that = this
+        this.$element.hide()
+        this.backdrop(function () {
+          that.removeBackdrop()
+          that.$element.trigger('hidden')
+        })
+      }
+
+    , removeBackdrop: function () {
+        this.$backdrop && this.$backdrop.remove()
+        this.$backdrop = null
+      }
+
+    , backdrop: function (callback) {
+        var that = this
+          , animate = this.$element.hasClass('fade') ? 'fade' : ''
+
+        if (this.isShown && this.options.backdrop) {
+          var doAnimate = $.support.transition && animate
+
+          this.$backdrop = $('<div class="modal-backdrop ' + animate + '" />')
+            .appendTo(document.body)
+
+          this.$backdrop.click(
+            this.options.backdrop == 'static' ?
+              $.proxy(this.$element[0].focus, this.$element[0])
+            : $.proxy(this.hide, this)
+          )
+
+          if (doAnimate) this.$backdrop[0].offsetWidth // force reflow
+
+          this.$backdrop.addClass('in')
+
+          if (!callback) return
+
+          doAnimate ?
+            this.$backdrop.one($.support.transition.end, callback) :
+            callback()
+
+        } else if (!this.isShown && this.$backdrop) {
+          this.$backdrop.removeClass('in')
+
+          $.support.transition && this.$element.hasClass('fade')?
+            this.$backdrop.one($.support.transition.end, callback) :
+            callback()
+
+        } else if (callback) {
+          callback()
+        }
+      }
+  }
+
+
+ /* MODAL PLUGIN DEFINITION
+  * ======================= */
+
+  var old = $.fn.modal
+
+  $.fn.modal = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('modal')
+        , options = $.extend({}, $.fn.modal.defaults, $this.data(), typeof option == 'object' && option)
+      if (!data) $this.data('modal', (data = new Modal(this, options)))
+      if (typeof option == 'string') data[option]()
+      else if (options.show) data.show()
+    })
+  }
+
+  $.fn.modal.defaults = {
+      backdrop: true
+    , keyboard: true
+    , show: true
+  }
+
+  $.fn.modal.Constructor = Modal
+
+
+ /* MODAL NO CONFLICT
+  * ================= */
+
+  $.fn.modal.noConflict = function () {
+    $.fn.modal = old
+    return this
+  }
+
+
+ /* MODAL DATA-API
+  * ============== */
+
+  $(document).on('click.modal.data-api', '[data-toggle="modal"]', function (e) {
+    var $this = $(this)
+      , href = $this.attr('href')
+      , $target = $($this.attr('data-target') || (href && href.replace(/.*(?=#[^\s]+$)/, ''))) //strip for ie7
+      , option = $target.data('modal') ? 'toggle' : $.extend({ remote:!/#/.test(href) && href }, $target.data(), $this.data())
+
+    e.preventDefault()
+
+    $target
+      .modal(option)
+      .one('hide', function () {
+        $this.focus()
+      })
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* ===========================================================
+ * bootstrap-tooltip.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#tooltips
+ * Inspired by the original jQuery.tipsy by Jason Frame
+ * ===========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* TOOLTIP PUBLIC CLASS DEFINITION
+  * =============================== */
+
+  var Tooltip = function (element, options) {
+    this.init('tooltip', element, options)
+  }
+
+  Tooltip.prototype = {
+
+    constructor: Tooltip
+
+  , init: function (type, element, options) {
+      var eventIn
+        , eventOut
+        , triggers
+        , trigger
+        , i
+
+      this.type = type
+      this.$element = $(element)
+      this.options = this.getOptions(options)
+      this.enabled = true
+
+      triggers = this.options.trigger.split(' ')
+
+      for (i = triggers.length; i--;) {
+        trigger = triggers[i]
+        if (trigger == 'click') {
+          this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
+        } else if (trigger != 'manual') {
+          eventIn = trigger == 'hover' ? 'mouseenter' : 'focus'
+          eventOut = trigger == 'hover' ? 'mouseleave' : 'blur'
+          this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
+          this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
+        }
+      }
+
+      this.options.selector ?
+        (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) :
+        this.fixTitle()
+    }
+
+  , getOptions: function (options) {
+      options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options)
+
+      if (options.delay && typeof options.delay == 'number') {
+        options.delay = {
+          show: options.delay
+        , hide: options.delay
+        }
+      }
+
+      return options
+    }
+
+  , enter: function (e) {
+      var defaults = $.fn[this.type].defaults
+        , options = {}
+        , self
+
+      this._options && $.each(this._options, function (key, value) {
+        if (defaults[key] != value) options[key] = value
+      }, this)
+
+      self = $(e.currentTarget)[this.type](options).data(this.type)
+
+      if (!self.options.delay || !self.options.delay.show) return self.show()
+
+      clearTimeout(this.timeout)
+      self.hoverState = 'in'
+      this.timeout = setTimeout(function() {
+        if (self.hoverState == 'in') self.show()
+      }, self.options.delay.show)
+    }
+
+  , leave: function (e) {
+      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
+
+      if (this.timeout) clearTimeout(this.timeout)
+      if (!self.options.delay || !self.options.delay.hide) return self.hide()
+
+      self.hoverState = 'out'
+      this.timeout = setTimeout(function() {
+        if (self.hoverState == 'out') self.hide()
+      }, self.options.delay.hide)
+    }
+
+  , show: function () {
+      var $tip
+        , pos
+        , actualWidth
+        , actualHeight
+        , placement
+        , tp
+        , e = $.Event('show')
+
+      if (this.hasContent() && this.enabled) {
+        this.$element.trigger(e)
+        if (e.isDefaultPrevented()) return
+        $tip = this.tip()
+        this.setContent()
+
+        if (this.options.animation) {
+          $tip.addClass('fade')
+        }
+
+        placement = typeof this.options.placement == 'function' ?
+          this.options.placement.call(this, $tip[0], this.$element[0]) :
+          this.options.placement
+
+        $tip
+          .detach()
+          .css({ top: 0, left: 0, display: 'block' })
+
+        this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element)
+
+        pos = this.getPosition()
+
+        actualWidth = $tip[0].offsetWidth
+        actualHeight = $tip[0].offsetHeight
+
+        switch (placement) {
+          case 'bottom':
+            tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2}
+            break
+          case 'top':
+            tp = {top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2}
+            break
+          case 'left':
+            tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth}
+            break
+          case 'right':
+            tp = {top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width}
+            break
+        }
+
+        this.applyPlacement(tp, placement)
+        this.$element.trigger('shown')
+      }
+    }
+
+  , applyPlacement: function(offset, placement){
+      var $tip = this.tip()
+        , width = $tip[0].offsetWidth
+        , height = $tip[0].offsetHeight
+        , actualWidth
+        , actualHeight
+        , delta
+        , replace
+
+      $tip
+        .offset(offset)
+        .addClass(placement)
+        .addClass('in')
+
+      actualWidth = $tip[0].offsetWidth
+      actualHeight = $tip[0].offsetHeight
+
+      if (placement == 'top' && actualHeight != height) {
+        offset.top = offset.top + height - actualHeight
+        replace = true
+      }
+
+      if (placement == 'bottom' || placement == 'top') {
+        delta = 0
+
+        if (offset.left < 0){
+          delta = offset.left * -2
+          offset.left = 0
+          $tip.offset(offset)
+          actualWidth = $tip[0].offsetWidth
+          actualHeight = $tip[0].offsetHeight
+        }
+
+        this.replaceArrow(delta - width + actualWidth, actualWidth, 'left')
+      } else {
+        this.replaceArrow(actualHeight - height, actualHeight, 'top')
+      }
+
+      if (replace) $tip.offset(offset)
+    }
+
+  , replaceArrow: function(delta, dimension, position){
+      this
+        .arrow()
+        .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '')
+    }
+
+  , setContent: function () {
+      var $tip = this.tip()
+        , title = this.getTitle()
+
+      $tip.find('.tooltip-inner')[this.options.html ? 'html' : 'text'](title)
+      $tip.removeClass('fade in top bottom left right')
+    }
+
+  , hide: function () {
+      var that = this
+        , $tip = this.tip()
+        , e = $.Event('hide')
+
+      this.$element.trigger(e)
+      if (e.isDefaultPrevented()) return
+
+      $tip.removeClass('in')
+
+      function removeWithAnimation() {
+        var timeout = setTimeout(function () {
+          $tip.off($.support.transition.end).detach()
+        }, 500)
+
+        $tip.one($.support.transition.end, function () {
+          clearTimeout(timeout)
+          $tip.detach()
+        })
+      }
+
+      $.support.transition && this.$tip.hasClass('fade') ?
+        removeWithAnimation() :
+        $tip.detach()
+
+      this.$element.trigger('hidden')
+
+      return this
+    }
+
+  , fixTitle: function () {
+      var $e = this.$element
+      if ($e.attr('title') || typeof($e.attr('data-original-title')) != 'string') {
+        $e.attr('data-original-title', $e.attr('title') || '').attr('title', '')
+      }
+    }
+
+  , hasContent: function () {
+      return this.getTitle()
+    }
+
+  , getPosition: function () {
+      var el = this.$element[0]
+      return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : {
+        width: el.offsetWidth
+      , height: el.offsetHeight
+      }, this.$element.offset())
+    }
+
+  , getTitle: function () {
+      var title
+        , $e = this.$element
+        , o = this.options
+
+      title = $e.attr('data-original-title')
+        || (typeof o.title == 'function' ? o.title.call($e[0]) :  o.title)
+
+      return title
+    }
+
+  , tip: function () {
+      return this.$tip = this.$tip || $(this.options.template)
+    }
+
+  , arrow: function(){
+      return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow")
+    }
+
+  , validate: function () {
+      if (!this.$element[0].parentNode) {
+        this.hide()
+        this.$element = null
+        this.options = null
+      }
+    }
+
+  , enable: function () {
+      this.enabled = true
+    }
+
+  , disable: function () {
+      this.enabled = false
+    }
+
+  , toggleEnabled: function () {
+      this.enabled = !this.enabled
+    }
+
+  , toggle: function (e) {
+      var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this
+      self.tip().hasClass('in') ? self.hide() : self.show()
+    }
+
+  , destroy: function () {
+      this.hide().$element.off('.' + this.type).removeData(this.type)
+    }
+
+  }
+
+
+ /* TOOLTIP PLUGIN DEFINITION
+  * ========================= */
+
+  var old = $.fn.tooltip
+
+  $.fn.tooltip = function ( option ) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('tooltip')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('tooltip', (data = new Tooltip(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.tooltip.Constructor = Tooltip
+
+  $.fn.tooltip.defaults = {
+    animation: true
+  , placement: 'top'
+  , selector: false
+  , template: '<div class="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>'
+  , trigger: 'hover focus'
+  , title: ''
+  , delay: 0
+  , html: false
+  , container: false
+  }
+
+
+ /* TOOLTIP NO CONFLICT
+  * =================== */
+
+  $.fn.tooltip.noConflict = function () {
+    $.fn.tooltip = old
+    return this
+  }
+
+}(window.$jqTheme || window.jQuery);
+/* ===========================================================
+ * bootstrap-popover.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#popovers
+ * ===========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* POPOVER PUBLIC CLASS DEFINITION
+  * =============================== */
+
+  var Popover = function (element, options) {
+    this.init('popover', element, options)
+  }
+
+
+  /* NOTE: POPOVER EXTENDS BOOTSTRAP-TOOLTIP.js
+     ========================================== */
+
+  Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype, {
+
+    constructor: Popover
+
+  , setContent: function () {
+      var $tip = this.tip()
+        , title = this.getTitle()
+        , content = this.getContent()
+
+      $tip.find('.popover-title')[this.options.html ? 'html' : 'text'](title)
+      $tip.find('.popover-content')[this.options.html ? 'html' : 'text'](content)
+
+      $tip.removeClass('fade top bottom left right in')
+    }
+
+  , hasContent: function () {
+      return this.getTitle() || this.getContent()
+    }
+
+  , getContent: function () {
+      var content
+        , $e = this.$element
+        , o = this.options
+
+      content = (typeof o.content == 'function' ? o.content.call($e[0]) :  o.content)
+        || $e.attr('data-content')
+
+      return content
+    }
+
+  , tip: function () {
+      if (!this.$tip) {
+        this.$tip = $(this.options.template)
+      }
+      return this.$tip
+    }
+
+  , destroy: function () {
+      this.hide().$element.off('.' + this.type).removeData(this.type)
+    }
+
+  })
+
+
+ /* POPOVER PLUGIN DEFINITION
+  * ======================= */
+
+  var old = $.fn.popover
+
+  $.fn.popover = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('popover')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('popover', (data = new Popover(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.popover.Constructor = Popover
+
+  $.fn.popover.defaults = $.extend({} , $.fn.tooltip.defaults, {
+    placement: 'right'
+  , trigger: 'click'
+  , content: ''
+  , template: '<div class="popover"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'
+  })
+
+
+ /* POPOVER NO CONFLICT
+  * =================== */
+
+  $.fn.popover.noConflict = function () {
+    $.fn.popover = old
+    return this
+  }
+
+}(window.$jqTheme || window.jQuery);
+/* =============================================================
+ * bootstrap-scrollspy.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#scrollspy
+ * =============================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ============================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* SCROLLSPY CLASS DEFINITION
+  * ========================== */
+
+  function ScrollSpy(element, options) {
+    var process = $.proxy(this.process, this)
+      , $element = $(element).is('body') ? $(window) : $(element)
+      , href
+    this.options = $.extend({}, $.fn.scrollspy.defaults, options)
+    this.$scrollElement = $element.on('scroll.scroll-spy.data-api', process)
+    this.selector = (this.options.target
+      || ((href = $(element).attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '')) //strip for ie7
+      || '') + ' .nav li > a'
+    this.$body = $('body')
+    this.refresh()
+    this.process()
+  }
+
+  ScrollSpy.prototype = {
+
+      constructor: ScrollSpy
+
+    , refresh: function () {
+        var self = this
+          , $targets
+
+        this.offsets = $([])
+        this.targets = $([])
+
+        $targets = this.$body
+          .find(this.selector)
+          .map(function () {
+            var $el = $(this)
+              , href = $el.data('target') || $el.attr('href')
+              , $href = /^#\w/.test(href) && $(href)
+            return ( $href
+              && $href.length
+              && [[ $href.position().top + (!$.isWindow(self.$scrollElement.get(0)) && self.$scrollElement.scrollTop()), href ]] ) || null
+          })
+          .sort(function (a, b) { return a[0] - b[0] })
+          .each(function () {
+            self.offsets.push(this[0])
+            self.targets.push(this[1])
+          })
+      }
+
+    , process: function () {
+        var scrollTop = this.$scrollElement.scrollTop() + this.options.offset
+          , scrollHeight = this.$scrollElement[0].scrollHeight || this.$body[0].scrollHeight
+          , maxScroll = scrollHeight - this.$scrollElement.height()
+          , offsets = this.offsets
+          , targets = this.targets
+          , activeTarget = this.activeTarget
+          , i
+
+        if (scrollTop >= maxScroll) {
+          return activeTarget != (i = targets.last()[0])
+            && this.activate ( i )
+        }
+
+        for (i = offsets.length; i--;) {
+          activeTarget != targets[i]
+            && scrollTop >= offsets[i]
+            && (!offsets[i + 1] || scrollTop <= offsets[i + 1])
+            && this.activate( targets[i] )
+        }
+      }
+
+    , activate: function (target) {
+        var active
+          , selector
+
+        this.activeTarget = target
+
+        $(this.selector)
+          .parent('.active')
+          .removeClass('active')
+
+        selector = this.selector
+          + '[data-target="' + target + '"],'
+          + this.selector + '[href="' + target + '"]'
+
+        active = $(selector)
+          .parent('li')
+          .addClass('active')
+
+        if (active.parent('.dropdown-menu').length)  {
+          active = active.closest('li.dropdown').addClass('active')
+        }
+
+        active.trigger('activate')
+      }
+
+  }
+
+
+ /* SCROLLSPY PLUGIN DEFINITION
+  * =========================== */
+
+  var old = $.fn.scrollspy
+
+  $.fn.scrollspy = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('scrollspy')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('scrollspy', (data = new ScrollSpy(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.scrollspy.Constructor = ScrollSpy
+
+  $.fn.scrollspy.defaults = {
+    offset: 10
+  }
+
+
+ /* SCROLLSPY NO CONFLICT
+  * ===================== */
+
+  $.fn.scrollspy.noConflict = function () {
+    $.fn.scrollspy = old
+    return this
+  }
+
+
+ /* SCROLLSPY DATA-API
+  * ================== */
+
+  $(window).on('load', function () {
+    $('[data-spy="scroll"]').each(function () {
+      var $spy = $(this)
+      $spy.scrollspy($spy.data())
+    })
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* ========================================================
+ * bootstrap-tab.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#tabs
+ * ========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ======================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* TAB CLASS DEFINITION
+  * ==================== */
+
+  var Tab = function (element) {
+    this.element = $(element)
+  }
+
+  Tab.prototype = {
+
+    constructor: Tab
+
+  , show: function () {
+      var $this = this.element
+        , $ul = $this.closest('ul:not(.dropdown-menu)')
+        , selector = $this.attr('data-target')
+        , previous
+        , $target
+        , e
+
+      if (!selector) {
+        selector = $this.attr('href')
+        selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') //strip for ie7
+      }
+
+      if ( $this.parent('li').hasClass('active') ) return
+
+      previous = $ul.find('.active:last a')[0]
+
+      e = $.Event('show', {
+        relatedTarget: previous
+      })
+
+      $this.trigger(e)
+
+      if (e.isDefaultPrevented()) return
+
+      $target = $(selector)
+
+      this.activate($this.parent('li'), $ul)
+      this.activate($target, $target.parent(), function () {
+        $this.trigger({
+          type: 'shown'
+        , relatedTarget: previous
+        })
+      })
+    }
+
+  , activate: function ( element, container, callback) {
+      var $active = container.find('> .active')
+        , transition = callback
+            && $.support.transition
+            && $active.hasClass('fade')
+
+      function next() {
+        $active
+          .removeClass('active')
+          .find('> .dropdown-menu > .active')
+          .removeClass('active')
+
+        element.addClass('active')
+
+        if (transition) {
+          element[0].offsetWidth // reflow for transition
+          element.addClass('in')
+        } else {
+          element.removeClass('fade')
+        }
+
+        if ( element.parent('.dropdown-menu') ) {
+          element.closest('li.dropdown').addClass('active')
+        }
+
+        callback && callback()
+      }
+
+      transition ?
+        $active.one($.support.transition.end, next) :
+        next()
+
+      $active.removeClass('in')
+    }
+  }
+
+
+ /* TAB PLUGIN DEFINITION
+  * ===================== */
+
+  var old = $.fn.tab
+
+  $.fn.tab = function ( option ) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('tab')
+      if (!data) $this.data('tab', (data = new Tab(this)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.tab.Constructor = Tab
+
+
+ /* TAB NO CONFLICT
+  * =============== */
+
+  $.fn.tab.noConflict = function () {
+    $.fn.tab = old
+    return this
+  }
+
+
+ /* TAB DATA-API
+  * ============ */
+
+  $(document).on('click.tab.data-api', '[data-toggle="tab"], [data-toggle="pill"]', function (e) {
+    e.preventDefault()
+    $(this).tab('show')
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* =============================================================
+ * bootstrap-typeahead.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#typeahead
+ * =============================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ============================================================ */
+
+
+!function($){
+
+  "use strict"; // jshint ;_;
+
+
+ /* TYPEAHEAD PUBLIC CLASS DEFINITION
+  * ================================= */
+
+  var Typeahead = function (element, options) {
+    this.$element = $(element)
+    this.options = $.extend({}, $.fn.typeahead.defaults, options)
+    this.matcher = this.options.matcher || this.matcher
+    this.sorter = this.options.sorter || this.sorter
+    this.highlighter = this.options.highlighter || this.highlighter
+    this.updater = this.options.updater || this.updater
+    this.source = this.options.source
+    this.$menu = $(this.options.menu)
+    this.shown = false
+    this.listen()
+  }
+
+  Typeahead.prototype = {
+
+    constructor: Typeahead
+
+  , select: function () {
+      var val = this.$menu.find('.active').attr('data-value')
+      this.$element
+        .val(this.updater(val))
+        .change()
+      return this.hide()
+    }
+
+  , updater: function (item) {
+      return item
+    }
+
+  , show: function () {
+      var pos = $.extend({}, this.$element.position(), {
+        height: this.$element[0].offsetHeight
+      })
+
+      this.$menu
+        .insertAfter(this.$element)
+        .css({
+          top: pos.top + pos.height
+        , left: pos.left
+        })
+        .show()
+
+      this.shown = true
+      return this
+    }
+
+  , hide: function () {
+      this.$menu.hide()
+      this.shown = false
+      return this
+    }
+
+  , lookup: function (event) {
+      var items
+
+      this.query = this.$element.val()
+
+      if (!this.query || this.query.length < this.options.minLength) {
+        return this.shown ? this.hide() : this
+      }
+
+      items = $.isFunction(this.source) ? this.source(this.query, $.proxy(this.process, this)) : this.source
+
+      return items ? this.process(items) : this
+    }
+
+  , process: function (items) {
+      var that = this
+
+      items = $.grep(items, function (item) {
+        return that.matcher(item)
+      })
+
+      items = this.sorter(items)
+
+      if (!items.length) {
+        return this.shown ? this.hide() : this
+      }
+
+      return this.render(items.slice(0, this.options.items)).show()
+    }
+
+  , matcher: function (item) {
+      return ~item.toLowerCase().indexOf(this.query.toLowerCase())
+    }
+
+  , sorter: function (items) {
+      var beginswith = []
+        , caseSensitive = []
+        , caseInsensitive = []
+        , item
+
+      while (item = items.shift()) {
+        if (!item.toLowerCase().indexOf(this.query.toLowerCase())) beginswith.push(item)
+        else if (~item.indexOf(this.query)) caseSensitive.push(item)
+        else caseInsensitive.push(item)
+      }
+
+      return beginswith.concat(caseSensitive, caseInsensitive)
+    }
+
+  , highlighter: function (item) {
+      var query = this.query.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g, '\\$&')
+      return item.replace(new RegExp('(' + query + ')', 'ig'), function ($1, match) {
+        return '<strong>' + match + '</strong>'
+      })
+    }
+
+  , render: function (items) {
+      var that = this
+
+      items = $(items).map(function (i, item) {
+        i = $(that.options.item).attr('data-value', item)
+        i.find('a').html(that.highlighter(item))
+        return i[0]
+      })
+
+      items.first().addClass('active')
+      this.$menu.html(items)
+      return this
+    }
+
+  , next: function (event) {
+      var active = this.$menu.find('.active').removeClass('active')
+        , next = active.next()
+
+      if (!next.length) {
+        next = $(this.$menu.find('li')[0])
+      }
+
+      next.addClass('active')
+    }
+
+  , prev: function (event) {
+      var active = this.$menu.find('.active').removeClass('active')
+        , prev = active.prev()
+
+      if (!prev.length) {
+        prev = this.$menu.find('li').last()
+      }
+
+      prev.addClass('active')
+    }
+
+  , listen: function () {
+      this.$element
+        .on('focus',    $.proxy(this.focus, this))
+        .on('blur',     $.proxy(this.blur, this))
+        .on('keypress', $.proxy(this.keypress, this))
+        .on('keyup',    $.proxy(this.keyup, this))
+
+      if (this.eventSupported('keydown')) {
+        this.$element.on('keydown', $.proxy(this.keydown, this))
+      }
+
+      this.$menu
+        .on('click', $.proxy(this.click, this))
+        .on('mouseenter', 'li', $.proxy(this.mouseenter, this))
+        .on('mouseleave', 'li', $.proxy(this.mouseleave, this))
+    }
+
+  , eventSupported: function(eventName) {
+      var isSupported = eventName in this.$element
+      if (!isSupported) {
+        this.$element.setAttribute(eventName, 'return;')
+        isSupported = typeof this.$element[eventName] === 'function'
+      }
+      return isSupported
+    }
+
+  , move: function (e) {
+      if (!this.shown) return
+
+      switch(e.keyCode) {
+        case 9: // tab
+        case 13: // enter
+        case 27: // escape
+          e.preventDefault()
+          break
+
+        case 38: // up arrow
+          e.preventDefault()
+          this.prev()
+          break
+
+        case 40: // down arrow
+          e.preventDefault()
+          this.next()
+          break
+      }
+
+      e.stopPropagation()
+    }
+
+  , keydown: function (e) {
+      this.suppressKeyPressRepeat = ~$.inArray(e.keyCode, [40,38,9,13,27])
+      this.move(e)
+    }
+
+  , keypress: function (e) {
+      if (this.suppressKeyPressRepeat) return
+      this.move(e)
+    }
+
+  , keyup: function (e) {
+      switch(e.keyCode) {
+        case 40: // down arrow
+        case 38: // up arrow
+        case 16: // shift
+        case 17: // ctrl
+        case 18: // alt
+          break
+
+        case 9: // tab
+        case 13: // enter
+          if (!this.shown) return
+          this.select()
+          break
+
+        case 27: // escape
+          if (!this.shown) return
+          this.hide()
+          break
+
+        default:
+          this.lookup()
+      }
+
+      e.stopPropagation()
+      e.preventDefault()
+  }
+
+  , focus: function (e) {
+      this.focused = true
+    }
+
+  , blur: function (e) {
+      this.focused = false
+      if (!this.mousedover && this.shown) this.hide()
+    }
+
+  , click: function (e) {
+      e.stopPropagation()
+      e.preventDefault()
+      this.select()
+      this.$element.focus()
+    }
+
+  , mouseenter: function (e) {
+      this.mousedover = true
+      this.$menu.find('.active').removeClass('active')
+      $(e.currentTarget).addClass('active')
+    }
+
+  , mouseleave: function (e) {
+      this.mousedover = false
+      if (!this.focused && this.shown) this.hide()
+    }
+
+  }
+
+
+  /* TYPEAHEAD PLUGIN DEFINITION
+   * =========================== */
+
+  var old = $.fn.typeahead
+
+  $.fn.typeahead = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('typeahead')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('typeahead', (data = new Typeahead(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.typeahead.defaults = {
+    source: []
+  , items: 8
+  , menu: '<ul class="typeahead dropdown-menu"></ul>'
+  , item: '<li><a href="#"></a></li>'
+  , minLength: 1
+  }
+
+  $.fn.typeahead.Constructor = Typeahead
+
+
+ /* TYPEAHEAD NO CONFLICT
+  * =================== */
+
+  $.fn.typeahead.noConflict = function () {
+    $.fn.typeahead = old
+    return this
+  }
+
+
+ /* TYPEAHEAD DATA-API
+  * ================== */
+
+  $(document).on('focus.typeahead.data-api', '[data-provide="typeahead"]', function (e) {
+    var $this = $(this)
+    if ($this.data('typeahead')) return
+    $this.typeahead($this.data())
+  })
+
+}(window.$jqTheme || window.jQuery);
+/* ==========================================================
+ * bootstrap-affix.js v2.3.2
+ * http://twitter.github.com/bootstrap/javascript.html#affix
+ * ==========================================================
+ * Copyright 2012 Twitter, Inc.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * ========================================================== */
+
+
+!function ($) {
+
+  "use strict"; // jshint ;_;
+
+
+ /* AFFIX CLASS DEFINITION
+  * ====================== */
+
+  var Affix = function (element, options) {
+    this.options = $.extend({}, $.fn.affix.defaults, options)
+    this.$window = $(window)
+      .on('scroll.affix.data-api', $.proxy(this.checkPosition, this))
+      .on('click.affix.data-api',  $.proxy(function () { setTimeout($.proxy(this.checkPosition, this), 1) }, this))
+    this.$element = $(element)
+    this.checkPosition()
+  }
+
+  Affix.prototype.checkPosition = function () {
+    if (!this.$element.is(':visible')) return
+
+    var scrollHeight = $(document).height()
+      , scrollTop = this.$window.scrollTop()
+      , position = this.$element.offset()
+      , offset = this.options.offset
+      , offsetBottom = offset.bottom
+      , offsetTop = offset.top
+      , reset = 'affix affix-top affix-bottom'
+      , affix
+
+    if (typeof offset != 'object') offsetBottom = offsetTop = offset
+    if (typeof offsetTop == 'function') offsetTop = offset.top()
+    if (typeof offsetBottom == 'function') offsetBottom = offset.bottom()
+
+    affix = this.unpin != null && (scrollTop + this.unpin <= position.top) ?
+      false    : offsetBottom != null && (position.top + this.$element.height() >= scrollHeight - offsetBottom) ?
+      'bottom' : offsetTop != null && scrollTop <= offsetTop ?
+      'top'    : false
+
+    if (this.affixed === affix) return
+
+    this.affixed = affix
+    this.unpin = affix == 'bottom' ? position.top - scrollTop : null
+
+    this.$element.removeClass(reset).addClass('affix' + (affix ? '-' + affix : ''))
+  }
+
+
+ /* AFFIX PLUGIN DEFINITION
+  * ======================= */
+
+  var old = $.fn.affix
+
+  $.fn.affix = function (option) {
+    return this.each(function () {
+      var $this = $(this)
+        , data = $this.data('affix')
+        , options = typeof option == 'object' && option
+      if (!data) $this.data('affix', (data = new Affix(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  $.fn.affix.Constructor = Affix
+
+  $.fn.affix.defaults = {
+    offset: 0
+  }
+
+
+ /* AFFIX NO CONFLICT
+  * ================= */
+
+  $.fn.affix.noConflict = function () {
+    $.fn.affix = old
+    return this
+  }
+
+
+ /* AFFIX DATA-API
+  * ============== */
+
+  $(window).on('load', function () {
+    $('[data-spy="affix"]').each(function () {
+      var $spy = $(this)
+        , data = $spy.data()
+
+      data.offset = data.offset || {}
+
+      data.offsetBottom && (data.offset.bottom = data.offsetBottom)
+      data.offsetTop && (data.offset.top = data.offsetTop)
+
+      $spy.affix(data)
+    })
+  })
+
+
+}(window.$jqTheme || window.jQuery);
diff --git a/_static/bootstrap-2.3.2/js/bootstrap.min.js b/_static/bootstrap-2.3.2/js/bootstrap.min.js
new file mode 100644
index 000000000..3a3394bd4
--- /dev/null
+++ b/_static/bootstrap-2.3.2/js/bootstrap.min.js
@@ -0,0 +1,6 @@
+/**
+* Bootstrap.js v2.3.2 by @fat & @mdo
+* Copyright 2012 Twitter, Inc.
+* http://www.apache.org/licenses/LICENSE-2.0.txt
+*/
+!function(e){"use strict";e(function(){e.support.transition=function(){var e=function(){var e=document.createElement("bootstrap"),t={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"},n;for(n in t)if(e.style[n]!==undefined)return t[n]}();return e&&{end:e}}()})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t='[data-dismiss="alert"]',n=function(n){e(n).on("click",t,this.close)};n.prototype.close=function(t){function s(){i.trigger("closed").remove()}var n=e(this),r=n.attr("data-target"),i;r||(r=n.attr("href"),r=r&&r.replace(/.*(?=#[^\s]*$)/,"")),i=e(r),t&&t.preventDefault(),i.length||(i=n.hasClass("alert")?n:n.parent()),i.trigger(t=e.Event("close"));if(t.isDefaultPrevented())return;i.removeClass("in"),e.support.transition&&i.hasClass("fade")?i.on(e.support.transition.end,s):s()};var r=e.fn.alert;e.fn.alert=function(t){return this.each(function(){var r=e(this),i=r.data("alert");i||r.data("alert",i=new n(this)),typeof t=="string"&&i[t].call(r)})},e.fn.alert.Constructor=n,e.fn.alert.noConflict=function(){return e.fn.alert=r,this},e(document).on("click.alert.data-api",t,n.prototype.close)}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.options=e.extend({},e.fn.button.defaults,n)};t.prototype.setState=function(e){var t="disabled",n=this.$element,r=n.data(),i=n.is("input")?"val":"html";e+="Text",r.resetText||n.data("resetText",n[i]()),n[i](r[e]||this.options[e]),setTimeout(function(){e=="loadingText"?n.addClass(t).attr(t,t):n.removeClass(t).removeAttr(t)},0)},t.prototype.toggle=function(){var e=this.$element.closest('[data-toggle="buttons-radio"]');e&&e.find(".active").removeClass("active"),this.$element.toggleClass("active")};var n=e.fn.button;e.fn.button=function(n){return this.each(function(){var r=e(this),i=r.data("button"),s=typeof n=="object"&&n;i||r.data("button",i=new t(this,s)),n=="toggle"?i.toggle():n&&i.setState(n)})},e.fn.button.defaults={loadingText:"loading..."},e.fn.button.Constructor=t,e.fn.button.noConflict=function(){return e.fn.button=n,this},e(document).on("click.button.data-api","[data-toggle^=button]",function(t){var n=e(t.target);n.hasClass("btn")||(n=n.closest(".btn")),n.button("toggle")})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.$indicators=this.$element.find(".carousel-indicators"),this.options=n,this.options.pause=="hover"&&this.$element.on("mouseenter",e.proxy(this.pause,this)).on("mouseleave",e.proxy(this.cycle,this))};t.prototype={cycle:function(t){return t||(this.paused=!1),this.interval&&clearInterval(this.interval),this.options.interval&&!this.paused&&(this.interval=setInterval(e.proxy(this.next,this),this.options.interval)),this},getActiveIndex:function(){return this.$active=this.$element.find(".item.active"),this.$items=this.$active.parent().children(),this.$items.index(this.$active)},to:function(t){var n=this.getActiveIndex(),r=this;if(t>this.$items.length-1||t<0)return;return this.sliding?this.$element.one("slid",function(){r.to(t)}):n==t?this.pause().cycle():this.slide(t>n?"next":"prev",e(this.$items[t]))},pause:function(t){return t||(this.paused=!0),this.$element.find(".next, .prev").length&&e.support.transition.end&&(this.$element.trigger(e.support.transition.end),this.cycle(!0)),clearInterval(this.interval),this.interval=null,this},next:function(){if(this.sliding)return;return this.slide("next")},prev:function(){if(this.sliding)return;return this.slide("prev")},slide:function(t,n){var r=this.$element.find(".item.active"),i=n||r[t](),s=this.interval,o=t=="next"?"left":"right",u=t=="next"?"first":"last",a=this,f;this.sliding=!0,s&&this.pause(),i=i.length?i:this.$element.find(".item")[u](),f=e.Event("slide",{relatedTarget:i[0],direction:o});if(i.hasClass("active"))return;this.$indicators.length&&(this.$indicators.find(".active").removeClass("active"),this.$element.one("slid",function(){var t=e(a.$indicators.children()[a.getActiveIndex()]);t&&t.addClass("active")}));if(e.support.transition&&this.$element.hasClass("slide")){this.$element.trigger(f);if(f.isDefaultPrevented())return;i.addClass(t),i[0].offsetWidth,r.addClass(o),i.addClass(o),this.$element.one(e.support.transition.end,function(){i.removeClass([t,o].join(" ")).addClass("active"),r.removeClass(["active",o].join(" 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n=e(this),r,i=e(n.attr("data-target")||(r=n.attr("href"))&&r.replace(/.*(?=#[^\s]+$)/,"")),s=e.extend({},i.data(),n.data()),o;i.carousel(s),(o=n.attr("data-slide-to"))&&i.data("carousel").pause().to(o).cycle(),t.preventDefault()})}(window.$jqTheme||window.jQuery),!function(e){"use strict";var t=function(t,n){this.$element=e(t),this.options=e.extend({},e.fn.collapse.defaults,n),this.options.parent&&(this.$parent=e(this.options.parent)),this.options.toggle&&this.toggle()};t.prototype={constructor:t,dimension:function(){var e=this.$element.hasClass("width");return e?"width":"height"},show:function(){var t,n,r,i;if(this.transitioning||this.$element.hasClass("in"))return;t=this.dimension(),n=e.camelCase(["scroll",t].join("-")),r=this.$parent&&this.$parent.find("> .accordion-group > 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\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/css/bootstrap-theme.css b/_static/bootstrap-3.4.1/css/bootstrap-theme.css
new file mode 100644
index 000000000..ea33f76a7
--- /dev/null
+++ b/_static/bootstrap-3.4.1/css/bootstrap-theme.css
@@ -0,0 +1,587 @@
+/*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+.btn-default,
+.btn-primary,
+.btn-success,
+.btn-info,
+.btn-warning,
+.btn-danger {
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.btn-default:active,
+.btn-primary:active,
+.btn-success:active,
+.btn-info:active,
+.btn-warning:active,
+.btn-danger:active,
+.btn-default.active,
+.btn-primary.active,
+.btn-success.active,
+.btn-info.active,
+.btn-warning.active,
+.btn-danger.active {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-default.disabled,
+.btn-primary.disabled,
+.btn-success.disabled,
+.btn-info.disabled,
+.btn-warning.disabled,
+.btn-danger.disabled,
+.btn-default[disabled],
+.btn-primary[disabled],
+.btn-success[disabled],
+.btn-info[disabled],
+.btn-warning[disabled],
+.btn-danger[disabled],
+fieldset[disabled] .btn-default,
+fieldset[disabled] .btn-primary,
+fieldset[disabled] .btn-success,
+fieldset[disabled] .btn-info,
+fieldset[disabled] .btn-warning,
+fieldset[disabled] .btn-danger {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-default .badge,
+.btn-primary .badge,
+.btn-success .badge,
+.btn-info .badge,
+.btn-warning .badge,
+.btn-danger .badge {
+  text-shadow: none;
+}
+.btn:active,
+.btn.active {
+  background-image: none;
+}
+.btn-default {
+  background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);
+  background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));
+  background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #dbdbdb;
+  text-shadow: 0 1px 0 #fff;
+  border-color: #ccc;
+}
+.btn-default:hover,
+.btn-default:focus {
+  background-color: #e0e0e0;
+  background-position: 0 -15px;
+}
+.btn-default:active,
+.btn-default.active {
+  background-color: #e0e0e0;
+  border-color: #dbdbdb;
+}
+.btn-default.disabled,
+.btn-default[disabled],
+fieldset[disabled] .btn-default,
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus,
+.btn-default.disabled:active,
+.btn-default[disabled]:active,
+fieldset[disabled] .btn-default:active,
+.btn-default.disabled.active,
+.btn-default[disabled].active,
+fieldset[disabled] .btn-default.active {
+  background-color: #e0e0e0;
+  background-image: none;
+}
+.btn-primary {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);
+  background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));
+  background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #245580;
+}
+.btn-primary:hover,
+.btn-primary:focus {
+  background-color: #265a88;
+  background-position: 0 -15px;
+}
+.btn-primary:active,
+.btn-primary.active {
+  background-color: #265a88;
+  border-color: #245580;
+}
+.btn-primary.disabled,
+.btn-primary[disabled],
+fieldset[disabled] .btn-primary,
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus,
+.btn-primary.disabled:active,
+.btn-primary[disabled]:active,
+fieldset[disabled] .btn-primary:active,
+.btn-primary.disabled.active,
+.btn-primary[disabled].active,
+fieldset[disabled] .btn-primary.active {
+  background-color: #265a88;
+  background-image: none;
+}
+.btn-success {
+  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);
+  background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));
+  background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #3e8f3e;
+}
+.btn-success:hover,
+.btn-success:focus {
+  background-color: #419641;
+  background-position: 0 -15px;
+}
+.btn-success:active,
+.btn-success.active {
+  background-color: #419641;
+  border-color: #3e8f3e;
+}
+.btn-success.disabled,
+.btn-success[disabled],
+fieldset[disabled] .btn-success,
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus,
+.btn-success.disabled:active,
+.btn-success[disabled]:active,
+fieldset[disabled] .btn-success:active,
+.btn-success.disabled.active,
+.btn-success[disabled].active,
+fieldset[disabled] .btn-success.active {
+  background-color: #419641;
+  background-image: none;
+}
+.btn-info {
+  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
+  background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));
+  background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #28a4c9;
+}
+.btn-info:hover,
+.btn-info:focus {
+  background-color: #2aabd2;
+  background-position: 0 -15px;
+}
+.btn-info:active,
+.btn-info.active {
+  background-color: #2aabd2;
+  border-color: #28a4c9;
+}
+.btn-info.disabled,
+.btn-info[disabled],
+fieldset[disabled] .btn-info,
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus,
+.btn-info.disabled:active,
+.btn-info[disabled]:active,
+fieldset[disabled] .btn-info:active,
+.btn-info.disabled.active,
+.btn-info[disabled].active,
+fieldset[disabled] .btn-info.active {
+  background-color: #2aabd2;
+  background-image: none;
+}
+.btn-warning {
+  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
+  background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));
+  background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #e38d13;
+}
+.btn-warning:hover,
+.btn-warning:focus {
+  background-color: #eb9316;
+  background-position: 0 -15px;
+}
+.btn-warning:active,
+.btn-warning.active {
+  background-color: #eb9316;
+  border-color: #e38d13;
+}
+.btn-warning.disabled,
+.btn-warning[disabled],
+fieldset[disabled] .btn-warning,
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus,
+.btn-warning.disabled:active,
+.btn-warning[disabled]:active,
+fieldset[disabled] .btn-warning:active,
+.btn-warning.disabled.active,
+.btn-warning[disabled].active,
+fieldset[disabled] .btn-warning.active {
+  background-color: #eb9316;
+  background-image: none;
+}
+.btn-danger {
+  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
+  background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));
+  background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #b92c28;
+}
+.btn-danger:hover,
+.btn-danger:focus {
+  background-color: #c12e2a;
+  background-position: 0 -15px;
+}
+.btn-danger:active,
+.btn-danger.active {
+  background-color: #c12e2a;
+  border-color: #b92c28;
+}
+.btn-danger.disabled,
+.btn-danger[disabled],
+fieldset[disabled] .btn-danger,
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus,
+.btn-danger.disabled:active,
+.btn-danger[disabled]:active,
+fieldset[disabled] .btn-danger:active,
+.btn-danger.disabled.active,
+.btn-danger[disabled].active,
+fieldset[disabled] .btn-danger.active {
+  background-color: #c12e2a;
+  background-image: none;
+}
+.thumbnail,
+.img-thumbnail {
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);
+  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
+  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
+  background-repeat: repeat-x;
+  background-color: #e8e8e8;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+  background-repeat: repeat-x;
+  background-color: #2e6da4;
+}
+.navbar-default {
+  background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);
+  background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#ffffff), to(#f8f8f8));
+  background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .active > a {
+  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
+  background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));
+  background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);
+  background-repeat: repeat-x;
+  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);
+}
+.navbar-brand,
+.navbar-nav > li > a {
+  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);
+}
+.navbar-inverse {
+  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);
+  background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));
+  background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);
+  background-repeat: repeat-x;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  border-radius: 4px;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .active > a {
+  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);
+  background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));
+  background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);
+  background-repeat: repeat-x;
+  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);
+  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);
+}
+.navbar-inverse .navbar-brand,
+.navbar-inverse .navbar-nav > li > a {
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+.navbar-static-top,
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  border-radius: 0;
+}
+@media (max-width: 767px) {
+  .navbar .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+    background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+    background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+    background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+    background-repeat: repeat-x;
+  }
+}
+.alert {
+  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+.alert-success {
+  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
+  background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));
+  background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #b2dba1;
+}
+.alert-info {
+  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
+  background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));
+  background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #9acfea;
+}
+.alert-warning {
+  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
+  background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));
+  background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #f5e79e;
+}
+.alert-danger {
+  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
+  background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));
+  background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #dca7a7;
+}
+.progress {
+  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
+  background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));
+  background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);
+  background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));
+  background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-success {
+  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);
+  background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));
+  background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-info {
+  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
+  background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));
+  background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-warning {
+  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
+  background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));
+  background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-danger {
+  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);
+  background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));
+  background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.list-group {
+  border-radius: 4px;
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);
+  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  text-shadow: 0 -1px 0 #286090;
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);
+  background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));
+  background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #2b669a;
+}
+.list-group-item.active .badge,
+.list-group-item.active:hover .badge,
+.list-group-item.active:focus .badge {
+  text-shadow: none;
+}
+.panel {
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);
+}
+.panel-default > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
+  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-primary > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-success > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
+  background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));
+  background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-info > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
+  background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));
+  background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-warning > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
+  background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));
+  background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-danger > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
+  background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));
+  background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);
+  background-repeat: repeat-x;
+}
+.well {
+  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
+  background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));
+  background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #dcdcdc;
+  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);
+}
+/*# sourceMappingURL=bootstrap-theme.css.map */
\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/css/bootstrap-theme.css.map b/_static/bootstrap-3.4.1/css/bootstrap-theme.css.map
new file mode 100644
index 000000000..949d09738
--- /dev/null
+++ b/_static/bootstrap-3.4.1/css/bootstrap-theme.css.map
@@ -0,0 +1 @@
+{"version":3,"sources":["bootstrap-theme.css","less/theme.less","less/mixins/vendor-prefixes.less","less/mixins/gradients.less","less/mixins/reset-filter.less"],"names":[],"mappings":"AAAA;;;;GAIG;ACiBH;;;;;;EAME,yCAAA;EC2CA,4FAAA;EACQ,oFAAA;CFzDT;ACkBC;;;;;;;;;;;;ECsCA,yDAAA;EACQ,iDAAA;CF1CT;ACQC;;;;;;;;;;;;;;;;;;ECiCA,yBAAA;EACQ,iBAAA;CFrBT;AC7BD;;;;;;EAuBI,kBAAA;CDcH;AC2BC;;EAEE,uBAAA;CDzBH;AC8BD;EEvEI,sEAAA;EACA,iEAAA;EACA,2FAAA;EAAA,oEAAA;EACA,uHAAA;EClBF,oEAAA;EH8CA,4BAAA;EACA,sBAAA;EAyCA,0BAAA;EACA,mBAAA;CDtBD;AClBC;;EAEE,0BAAA;EACA,6BAAA;CDoBH;ACjBC;;EAEE,0BAAA;EACA,sBAAA;CDmBH;ACbG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CD2BL;ACPD;EE5EI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EClBF,oEAAA;EH8CA,4BAAA;EACA,sBAAA;CD4DD;AC1DC;;EAEE,0BAAA;EACA,6BAAA;CD4DH;ACzDC;;EAEE,0BAAA;EACA,sBAAA;CD2DH;ACrDG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CDmEL;AC9CD;EE7EI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EClBF,oEAAA;EH8CA,4BAAA;EACA,sBAAA;CDoGD;AClGC;;EAEE,0BAAA;EACA,6BAAA;CDoGH;ACjGC;;EAEE,0BAAA;EACA,sBAAA;CDmGH;AC7FG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CD2GL;ACrFD;EE9EI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EClBF,oEAAA;EH8CA,4BAAA;EACA,sBAAA;CD4ID;AC1IC;;EAEE,0BAAA;EACA,6BAAA;CD4IH;ACzIC;;EAEE,0BAAA;EACA,sBAAA;CD2IH;ACrIG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CDmJL;AC5HD;EE/EI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EClBF,oEAAA;EH8CA,4BAAA;EACA,sBAAA;CDoLD;AClLC;;EAEE,0BAAA;EACA,6BAAA;CDoLH;ACjLC;;EAEE,0BAAA;EACA,sBAAA;CDmLH;AC7KG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CD2LL;ACnKD;EEhFI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EClBF,oEAAA;EH8CA,4BAAA;EACA,sBAAA;CD4ND;AC1NC;;EAEE,0BAAA;EACA,6BAAA;CD4NH;ACzNC;;EAEE,0BAAA;EACA,sBAAA;CD2NH;ACrNG;;;;;;;;;;;;;;;;;;EAME,0BAAA;EACA,uBAAA;CDmOL;ACpMD;;ECtCE,mDAAA;EACQ,2CAAA;CF8OT;AC/LD;;EEjGI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFgGF,0BAAA;CDqMD;ACnMD;;;EEtGI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFsGF,0BAAA;CDyMD;AChMD;EEnHI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;ECnBF,oEAAA;EHqIA,mBAAA;ECrEA,4FAAA;EACQ,oFAAA;CF4QT;AC3MD;;EEnHI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;ED6CF,yDAAA;EACQ,iDAAA;CFsRT;ACxMD;;EAEE,+CAAA;CD0MD;ACtMD;EEtII,sEAAA;EACA,iEAAA;EACA,2FAAA;EAAA,oEAAA;EACA,uHAAA;EACA,4BAAA;ECnBF,oEAAA;EHwJA,mBAAA;CD4MD;AC/MD;;EEtII,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;ED6CF,wDAAA;EACQ,gDAAA;CF6ST;ACzND;;EAYI,0CAAA;CDiNH;AC5MD;;;EAGE,iBAAA;CD8MD;AC1MD;EAEI;;;IAGE,YAAA;IEnKF,yEAAA;IACA,oEAAA;IACA,8FAAA;IAAA,uEAAA;IACA,uHAAA;IACA,4BAAA;GH+WD;CACF;ACrMD;EACE,8CAAA;EC/HA,2FAAA;EACQ,mFAAA;CFuUT;AC7LD;EE5LI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFoLF,sBAAA;CDyMD;ACpMD;EE7LI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFoLF,sBAAA;CDiND;AC3MD;EE9LI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFoLF,sBAAA;CDyND;AClND;EE/LI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFoLF,sBAAA;CDiOD;AClND;EEvMI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CH4ZH;AC/MD;EEjNI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHmaH;ACrND;EElNI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CH0aH;AC3ND;EEnNI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHibH;ACjOD;EEpNI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHwbH;ACvOD;EErNI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CH+bH;AC1OD;EExLI,8MAAA;EACA,yMAAA;EACA,sMAAA;CHqaH;ACtOD;EACE,mBAAA;EClLA,mDAAA;EACQ,2CAAA;CF2ZT;ACvOD;;;EAGE,8BAAA;EEzOE,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EFuOF,sBAAA;CD6OD;AClPD;;;EAQI,kBAAA;CD+OH;ACrOD;ECvME,kDAAA;EACQ,0CAAA;CF+aT;AC/ND;EElQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHoeH;ACrOD;EEnQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CH2eH;AC3OD;EEpQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHkfH;ACjPD;EErQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHyfH;ACvPD;EEtQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHggBH;AC7PD;EEvQI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;CHugBH;AC7PD;EE9QI,yEAAA;EACA,oEAAA;EACA,8FAAA;EAAA,uEAAA;EACA,uHAAA;EACA,4BAAA;EF4QF,sBAAA;EC/NA,0FAAA;EACQ,kFAAA;CFmeT","file":"bootstrap-theme.css","sourcesContent":["/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.btn-default:active,\n.btn-primary:active,\n.btn-success:active,\n.btn-info:active,\n.btn-warning:active,\n.btn-danger:active,\n.btn-default.active,\n.btn-primary.active,\n.btn-success.active,\n.btn-info.active,\n.btn-warning.active,\n.btn-danger.active {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-default.disabled,\n.btn-primary.disabled,\n.btn-success.disabled,\n.btn-info.disabled,\n.btn-warning.disabled,\n.btn-danger.disabled,\n.btn-default[disabled],\n.btn-primary[disabled],\n.btn-success[disabled],\n.btn-info[disabled],\n.btn-warning[disabled],\n.btn-danger[disabled],\nfieldset[disabled] .btn-default,\nfieldset[disabled] .btn-primary,\nfieldset[disabled] .btn-success,\nfieldset[disabled] .btn-info,\nfieldset[disabled] .btn-warning,\nfieldset[disabled] .btn-danger {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-default .badge,\n.btn-primary .badge,\n.btn-success .badge,\n.btn-info .badge,\n.btn-warning .badge,\n.btn-danger .badge {\n  text-shadow: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n}\n.btn-default {\n  background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n  background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n  background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #dbdbdb;\n  text-shadow: 0 1px 0 #fff;\n  border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n  background-color: #e0e0e0;\n  background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n  background-color: #e0e0e0;\n  border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n  background-color: #e0e0e0;\n  background-image: none;\n}\n.btn-primary {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n  background-color: #265a88;\n  background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n  background-color: #265a88;\n  border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n  background-color: #265a88;\n  background-image: none;\n}\n.btn-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n  background-color: #419641;\n  background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n  background-color: #419641;\n  border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n  background-color: #419641;\n  background-image: none;\n}\n.btn-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n  background-color: #2aabd2;\n  background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n  background-color: #2aabd2;\n  border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n  background-color: #2aabd2;\n  background-image: none;\n}\n.btn-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n  background-color: #eb9316;\n  background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n  background-color: #eb9316;\n  border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n  background-color: #eb9316;\n  background-image: none;\n}\n.btn-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n  background-color: #c12e2a;\n  background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n  background-color: #c12e2a;\n  border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n  background-color: #c12e2a;\n  background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-repeat: repeat-x;\n  background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-repeat: repeat-x;\n  background-color: #2e6da4;\n}\n.navbar-default {\n  background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n  background-repeat: repeat-x;\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n  background-repeat: repeat-x;\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n@media (max-width: 767px) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n    background-repeat: repeat-x;\n  }\n}\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #b2dba1;\n}\n.alert-info {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #9acfea;\n}\n.alert-warning {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #f5e79e;\n}\n.alert-danger {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #dca7a7;\n}\n.progress {\n  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 #286090;\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n  text-shadow: none;\n}\n.panel {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n  background-repeat: repeat-x;\n}\n.well {\n  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #dcdcdc;\n  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// stylelint-disable selector-no-qualifying-type, selector-max-compound-selectors\n\n/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);\n  .box-shadow(@shadow);\n\n  // Reset the shadow\n  &:active,\n  &.active {\n    .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    .box-shadow(none);\n  }\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n  #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n  .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n  background-repeat: repeat-x;\n  border-color: darken(@btn-color, 14%);\n\n  &:hover,\n  &:focus  {\n    background-color: darken(@btn-color, 12%);\n    background-position: 0 -15px;\n  }\n\n  &:active,\n  &.active {\n    background-color: darken(@btn-color, 12%);\n    border-color: darken(@btn-color, 14%);\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &,\n    &:hover,\n    &:focus,\n    &.focus,\n    &:active,\n    &.active {\n      background-color: darken(@btn-color, 12%);\n      background-image: none;\n    }\n  }\n}\n\n// Common styles\n.btn {\n  // Remove the gradient for the pressed/active state\n  &:active,\n  &.active {\n    background-image: none;\n  }\n}\n\n// Apply the mixin to the buttons\n.btn-default {\n  .btn-styles(@btn-default-bg);\n  text-shadow: 0 1px 0 #fff;\n  border-color: #ccc;\n}\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info    { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger  { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n  .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n  background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n  background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n  border-radius: @navbar-border-radius;\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);\n  .box-shadow(@shadow);\n\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n    .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .075));\n  }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, .25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n  #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n  border-radius: @navbar-border-radius;\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n    .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .25));\n  }\n\n  .navbar-brand,\n  .navbar-nav > li > a {\n    text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);\n  }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a {\n    &,\n    &:hover,\n    &:focus {\n      color: #fff;\n      #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n    }\n  }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, .2);\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);\n  .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n  border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success    { .alert-styles(@alert-success-bg); }\n.alert-info       { .alert-styles(@alert-info-bg); }\n.alert-warning    { .alert-styles(@alert-warning-bg); }\n.alert-danger     { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n  #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar            { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success    { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info       { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning    { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger     { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n  #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n  border-radius: @border-radius-base;\n  .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n  #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n  border-color: darken(@list-group-active-border, 7.5%);\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n  .box-shadow(0 1px 2px rgba(0, 0, 0, .05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading   { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading   { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading   { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading      { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading   { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading    { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n  #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n  border-color: darken(@well-bg, 10%);\n  @shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);\n  .box-shadow(@shadow);\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n  word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]}
\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css b/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css
new file mode 100644
index 000000000..2a69f48c7
--- /dev/null
+++ b/_static/bootstrap-3.4.1/css/bootstrap-theme.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */.btn-danger,.btn-default,.btn-info,.btn-primary,.btn-success,.btn-warning{text-shadow:0 -1px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.15),0 1px 1px rgba(0,0,0,.075)}.btn-danger.active,.btn-danger:active,.btn-default.active,.btn-default:active,.btn-info.active,.btn-info:active,.btn-primary.active,.btn-primary:active,.btn-success.active,.btn-success:active,.btn-warning.active,.btn-warning:active{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-danger.disabled,.btn-danger[disabled],.btn-default.disabled,.btn-default[disabled],.btn-info.disabled,.btn-info[disabled],.btn-primary.disabled,.btn-primary[disabled],.btn-success.disabled,.btn-success[disabled],.btn-warning.disabled,.btn-warning[disabled],fieldset[disabled] .btn-danger,fieldset[disabled] .btn-default,fieldset[disabled] .btn-info,fieldset[disabled] 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100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#245580}.btn-primary:focus,.btn-primary:hover{background-color:#265a88;background-position:0 -15px}.btn-primary.active,.btn-primary:active{background-color:#265a88;border-color:#245580}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] 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100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);filter:progid:DXImageTransform.Microsoft.gradient(enabled=false);background-repeat:repeat-x;border-color:#28a4c9}.btn-info:focus,.btn-info:hover{background-color:#2aabd2;background-position:0 -15px}.btn-info.active,.btn-info:active{background-color:#2aabd2;border-color:#28a4c9}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] 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progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n  background-color: #419641;\n  background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n  background-color: #419641;\n  border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n  background-color: #419641;\n  background-image: none;\n}\n.btn-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n  background-color: #2aabd2;\n  background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n  background-color: #2aabd2;\n  border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] 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.btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n  background-color: #c12e2a;\n  background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-repeat: repeat-x;\n  background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-repeat: repeat-x;\n  background-color: #2e6da4;\n}\n.navbar-default {\n  background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n  background-repeat: repeat-x;\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n  background-repeat: repeat-x;\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n@media (max-width: 767px) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n    background-repeat: repeat-x;\n  }\n}\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #b2dba1;\n}\n.alert-info {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #9acfea;\n}\n.alert-warning {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #f5e79e;\n}\n.alert-danger {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #dca7a7;\n}\n.progress {\n  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 #286090;\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n  text-shadow: none;\n}\n.panel {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n  background-repeat: repeat-x;\n}\n.well {\n  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #dcdcdc;\n  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.btn-default:active,\n.btn-primary:active,\n.btn-success:active,\n.btn-info:active,\n.btn-warning:active,\n.btn-danger:active,\n.btn-default.active,\n.btn-primary.active,\n.btn-success.active,\n.btn-info.active,\n.btn-warning.active,\n.btn-danger.active {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-default.disabled,\n.btn-primary.disabled,\n.btn-success.disabled,\n.btn-info.disabled,\n.btn-warning.disabled,\n.btn-danger.disabled,\n.btn-default[disabled],\n.btn-primary[disabled],\n.btn-success[disabled],\n.btn-info[disabled],\n.btn-warning[disabled],\n.btn-danger[disabled],\nfieldset[disabled] .btn-default,\nfieldset[disabled] .btn-primary,\nfieldset[disabled] .btn-success,\nfieldset[disabled] .btn-info,\nfieldset[disabled] .btn-warning,\nfieldset[disabled] .btn-danger {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-default .badge,\n.btn-primary .badge,\n.btn-success .badge,\n.btn-info .badge,\n.btn-warning .badge,\n.btn-danger .badge {\n  text-shadow: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n}\n.btn-default {\n  background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n  background-image: -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));\n  background-image: linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #dbdbdb;\n  text-shadow: 0 1px 0 #fff;\n  border-color: #ccc;\n}\n.btn-default:hover,\n.btn-default:focus {\n  background-color: #e0e0e0;\n  background-position: 0 -15px;\n}\n.btn-default:active,\n.btn-default.active {\n  background-color: #e0e0e0;\n  border-color: #dbdbdb;\n}\n.btn-default.disabled,\n.btn-default[disabled],\nfieldset[disabled] .btn-default,\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus,\n.btn-default.disabled:active,\n.btn-default[disabled]:active,\nfieldset[disabled] .btn-default:active,\n.btn-default.disabled.active,\n.btn-default[disabled].active,\nfieldset[disabled] .btn-default.active {\n  background-color: #e0e0e0;\n  background-image: none;\n}\n.btn-primary {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #265a88 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));\n  background-image: linear-gradient(to bottom, #337ab7 0%, #265a88 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #245580;\n}\n.btn-primary:hover,\n.btn-primary:focus {\n  background-color: #265a88;\n  background-position: 0 -15px;\n}\n.btn-primary:active,\n.btn-primary.active {\n  background-color: #265a88;\n  border-color: #245580;\n}\n.btn-primary.disabled,\n.btn-primary[disabled],\nfieldset[disabled] .btn-primary,\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus,\n.btn-primary.disabled:active,\n.btn-primary[disabled]:active,\nfieldset[disabled] .btn-primary:active,\n.btn-primary.disabled.active,\n.btn-primary[disabled].active,\nfieldset[disabled] .btn-primary.active {\n  background-color: #265a88;\n  background-image: none;\n}\n.btn-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #419641 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #419641 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #3e8f3e;\n}\n.btn-success:hover,\n.btn-success:focus {\n  background-color: #419641;\n  background-position: 0 -15px;\n}\n.btn-success:active,\n.btn-success.active {\n  background-color: #419641;\n  border-color: #3e8f3e;\n}\n.btn-success.disabled,\n.btn-success[disabled],\nfieldset[disabled] .btn-success,\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus,\n.btn-success.disabled:active,\n.btn-success[disabled]:active,\nfieldset[disabled] .btn-success:active,\n.btn-success.disabled.active,\n.btn-success[disabled].active,\nfieldset[disabled] .btn-success.active {\n  background-color: #419641;\n  background-image: none;\n}\n.btn-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #28a4c9;\n}\n.btn-info:hover,\n.btn-info:focus {\n  background-color: #2aabd2;\n  background-position: 0 -15px;\n}\n.btn-info:active,\n.btn-info.active {\n  background-color: #2aabd2;\n  border-color: #28a4c9;\n}\n.btn-info.disabled,\n.btn-info[disabled],\nfieldset[disabled] .btn-info,\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus,\n.btn-info.disabled:active,\n.btn-info[disabled]:active,\nfieldset[disabled] .btn-info:active,\n.btn-info.disabled.active,\n.btn-info[disabled].active,\nfieldset[disabled] .btn-info.active {\n  background-color: #2aabd2;\n  background-image: none;\n}\n.btn-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #e38d13;\n}\n.btn-warning:hover,\n.btn-warning:focus {\n  background-color: #eb9316;\n  background-position: 0 -15px;\n}\n.btn-warning:active,\n.btn-warning.active {\n  background-color: #eb9316;\n  border-color: #e38d13;\n}\n.btn-warning.disabled,\n.btn-warning[disabled],\nfieldset[disabled] .btn-warning,\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus,\n.btn-warning.disabled:active,\n.btn-warning[disabled]:active,\nfieldset[disabled] .btn-warning:active,\n.btn-warning.disabled.active,\n.btn-warning[disabled].active,\nfieldset[disabled] .btn-warning.active {\n  background-color: #eb9316;\n  background-image: none;\n}\n.btn-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  background-repeat: repeat-x;\n  border-color: #b92c28;\n}\n.btn-danger:hover,\n.btn-danger:focus {\n  background-color: #c12e2a;\n  background-position: 0 -15px;\n}\n.btn-danger:active,\n.btn-danger.active {\n  background-color: #c12e2a;\n  border-color: #b92c28;\n}\n.btn-danger.disabled,\n.btn-danger[disabled],\nfieldset[disabled] .btn-danger,\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus,\n.btn-danger.disabled:active,\n.btn-danger[disabled]:active,\nfieldset[disabled] .btn-danger:active,\n.btn-danger.disabled.active,\n.btn-danger[disabled].active,\nfieldset[disabled] .btn-danger.active {\n  background-color: #c12e2a;\n  background-image: none;\n}\n.thumbnail,\n.img-thumbnail {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-repeat: repeat-x;\n  background-color: #e8e8e8;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-repeat: repeat-x;\n  background-color: #2e6da4;\n}\n.navbar-default {\n  background-image: -webkit-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: -o-linear-gradient(top, #ffffff 0%, #f8f8f8 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#ffffff), to(#f8f8f8));\n  background-image: linear-gradient(to bottom, #ffffff 0%, #f8f8f8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.15), 0 1px 5px rgba(0, 0, 0, 0.075);\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));\n  background-image: linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);\n  background-repeat: repeat-x;\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.075);\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);\n}\n.navbar-inverse {\n  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: -o-linear-gradient(top, #3c3c3c 0%, #222 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));\n  background-image: linear-gradient(to bottom, #3c3c3c 0%, #222 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);\n  background-repeat: repeat-x;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n  border-radius: 4px;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .active > a {\n  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));\n  background-image: linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);\n  background-repeat: repeat-x;\n  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n  box-shadow: inset 0 3px 9px rgba(0, 0, 0, 0.25);\n}\n.navbar-inverse .navbar-brand,\n.navbar-inverse .navbar-nav > li > a {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n@media (max-width: 767px) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n    background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n    background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n    background-repeat: repeat-x;\n  }\n}\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.2);\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.25), 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.alert-success {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #b2dba1;\n}\n.alert-info {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #9acfea;\n}\n.alert-warning {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #f5e79e;\n}\n.alert-danger {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));\n  background-image: linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #dca7a7;\n}\n.progress {\n  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));\n  background-image: linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #286090 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));\n  background-image: linear-gradient(to bottom, #337ab7 0%, #286090 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-success {\n  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));\n  background-image: linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-info {\n  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));\n  background-image: linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-warning {\n  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));\n  background-image: linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-danger {\n  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));\n  background-image: linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);\n  background-repeat: repeat-x;\n}\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.list-group {\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.075);\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 #286090;\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #2b669a;\n}\n.list-group-item.active .badge,\n.list-group-item.active:hover .badge,\n.list-group-item.active:focus .badge {\n  text-shadow: none;\n}\n.panel {\n  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05);\n}\n.panel-default > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));\n  background-image: linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-primary > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));\n  background-image: linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-success > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));\n  background-image: linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-info > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));\n  background-image: linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-warning > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));\n  background-image: linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);\n  background-repeat: repeat-x;\n}\n.panel-danger > .panel-heading {\n  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));\n  background-image: linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);\n  background-repeat: repeat-x;\n}\n.well {\n  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);\n  background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));\n  background-image: linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);\n  background-repeat: repeat-x;\n  border-color: #dcdcdc;\n  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05), 0 1px 0 rgba(255, 255, 255, 0.1);\n}\n/*# sourceMappingURL=bootstrap-theme.css.map */","// stylelint-disable selector-no-qualifying-type, selector-max-compound-selectors\n\n/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n\n//\n// Load core variables and mixins\n// --------------------------------------------------\n\n@import \"variables.less\";\n@import \"mixins.less\";\n\n\n//\n// Buttons\n// --------------------------------------------------\n\n// Common styles\n.btn-default,\n.btn-primary,\n.btn-success,\n.btn-info,\n.btn-warning,\n.btn-danger {\n  text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);\n  .box-shadow(@shadow);\n\n  // Reset the shadow\n  &:active,\n  &.active {\n    .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    .box-shadow(none);\n  }\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n// Mixin for generating new styles\n.btn-styles(@btn-color: #555) {\n  #gradient > .vertical(@start-color: @btn-color; @end-color: darken(@btn-color, 12%));\n  .reset-filter(); // Disable gradients for IE9 because filter bleeds through rounded corners; see https://github.com/twbs/bootstrap/issues/10620\n  background-repeat: repeat-x;\n  border-color: darken(@btn-color, 14%);\n\n  &:hover,\n  &:focus  {\n    background-color: darken(@btn-color, 12%);\n    background-position: 0 -15px;\n  }\n\n  &:active,\n  &.active {\n    background-color: darken(@btn-color, 12%);\n    border-color: darken(@btn-color, 14%);\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &,\n    &:hover,\n    &:focus,\n    &.focus,\n    &:active,\n    &.active {\n      background-color: darken(@btn-color, 12%);\n      background-image: none;\n    }\n  }\n}\n\n// Common styles\n.btn {\n  // Remove the gradient for the pressed/active state\n  &:active,\n  &.active {\n    background-image: none;\n  }\n}\n\n// Apply the mixin to the buttons\n.btn-default {\n  .btn-styles(@btn-default-bg);\n  text-shadow: 0 1px 0 #fff;\n  border-color: #ccc;\n}\n.btn-primary { .btn-styles(@btn-primary-bg); }\n.btn-success { .btn-styles(@btn-success-bg); }\n.btn-info    { .btn-styles(@btn-info-bg); }\n.btn-warning { .btn-styles(@btn-warning-bg); }\n.btn-danger  { .btn-styles(@btn-danger-bg); }\n\n\n//\n// Images\n// --------------------------------------------------\n\n.thumbnail,\n.img-thumbnail {\n  .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n\n\n//\n// Dropdowns\n// --------------------------------------------------\n\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-hover-bg; @end-color: darken(@dropdown-link-hover-bg, 5%));\n  background-color: darken(@dropdown-link-hover-bg, 5%);\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n  background-color: darken(@dropdown-link-active-bg, 5%);\n}\n\n\n//\n// Navbar\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  #gradient > .vertical(@start-color: lighten(@navbar-default-bg, 10%); @end-color: @navbar-default-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered\n  border-radius: @navbar-border-radius;\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);\n  .box-shadow(@shadow);\n\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: darken(@navbar-default-link-active-bg, 5%); @end-color: darken(@navbar-default-link-active-bg, 2%));\n    .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .075));\n  }\n}\n.navbar-brand,\n.navbar-nav > li > a {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, .25);\n}\n\n// Inverted navbar\n.navbar-inverse {\n  #gradient > .vertical(@start-color: lighten(@navbar-inverse-bg, 10%); @end-color: @navbar-inverse-bg);\n  .reset-filter(); // Remove gradient in IE<10 to fix bug where dropdowns don't get triggered; see https://github.com/twbs/bootstrap/issues/10257\n  border-radius: @navbar-border-radius;\n  .navbar-nav > .open > a,\n  .navbar-nav > .active > a {\n    #gradient > .vertical(@start-color: @navbar-inverse-link-active-bg; @end-color: lighten(@navbar-inverse-link-active-bg, 2.5%));\n    .box-shadow(inset 0 3px 9px rgba(0, 0, 0, .25));\n  }\n\n  .navbar-brand,\n  .navbar-nav > li > a {\n    text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);\n  }\n}\n\n// Undo rounded corners in static and fixed navbars\n.navbar-static-top,\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  border-radius: 0;\n}\n\n// Fix active state of dropdown items in collapsed mode\n@media (max-width: @grid-float-breakpoint-max) {\n  .navbar .navbar-nav .open .dropdown-menu > .active > a {\n    &,\n    &:hover,\n    &:focus {\n      color: #fff;\n      #gradient > .vertical(@start-color: @dropdown-link-active-bg; @end-color: darken(@dropdown-link-active-bg, 5%));\n    }\n  }\n}\n\n\n//\n// Alerts\n// --------------------------------------------------\n\n// Common styles\n.alert {\n  text-shadow: 0 1px 0 rgba(255, 255, 255, .2);\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);\n  .box-shadow(@shadow);\n}\n\n// Mixin for generating new styles\n.alert-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 7.5%));\n  border-color: darken(@color, 15%);\n}\n\n// Apply the mixin to the alerts\n.alert-success    { .alert-styles(@alert-success-bg); }\n.alert-info       { .alert-styles(@alert-info-bg); }\n.alert-warning    { .alert-styles(@alert-warning-bg); }\n.alert-danger     { .alert-styles(@alert-danger-bg); }\n\n\n//\n// Progress bars\n// --------------------------------------------------\n\n// Give the progress background some depth\n.progress {\n  #gradient > .vertical(@start-color: darken(@progress-bg, 4%); @end-color: @progress-bg)\n}\n\n// Mixin for generating new styles\n.progress-bar-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 10%));\n}\n\n// Apply the mixin to the progress bars\n.progress-bar            { .progress-bar-styles(@progress-bar-bg); }\n.progress-bar-success    { .progress-bar-styles(@progress-bar-success-bg); }\n.progress-bar-info       { .progress-bar-styles(@progress-bar-info-bg); }\n.progress-bar-warning    { .progress-bar-styles(@progress-bar-warning-bg); }\n.progress-bar-danger     { .progress-bar-styles(@progress-bar-danger-bg); }\n\n// Reset the striped class because our mixins don't do multiple gradients and\n// the above custom styles override the new `.progress-bar-striped` in v3.2.0.\n.progress-bar-striped {\n  #gradient > .striped();\n}\n\n\n//\n// List groups\n// --------------------------------------------------\n\n.list-group {\n  border-radius: @border-radius-base;\n  .box-shadow(0 1px 2px rgba(0, 0, 0, .075));\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  text-shadow: 0 -1px 0 darken(@list-group-active-bg, 10%);\n  #gradient > .vertical(@start-color: @list-group-active-bg; @end-color: darken(@list-group-active-bg, 7.5%));\n  border-color: darken(@list-group-active-border, 7.5%);\n\n  .badge {\n    text-shadow: none;\n  }\n}\n\n\n//\n// Panels\n// --------------------------------------------------\n\n// Common styles\n.panel {\n  .box-shadow(0 1px 2px rgba(0, 0, 0, .05));\n}\n\n// Mixin for generating new styles\n.panel-heading-styles(@color) {\n  #gradient > .vertical(@start-color: @color; @end-color: darken(@color, 5%));\n}\n\n// Apply the mixin to the panel headings only\n.panel-default > .panel-heading   { .panel-heading-styles(@panel-default-heading-bg); }\n.panel-primary > .panel-heading   { .panel-heading-styles(@panel-primary-heading-bg); }\n.panel-success > .panel-heading   { .panel-heading-styles(@panel-success-heading-bg); }\n.panel-info > .panel-heading      { .panel-heading-styles(@panel-info-heading-bg); }\n.panel-warning > .panel-heading   { .panel-heading-styles(@panel-warning-heading-bg); }\n.panel-danger > .panel-heading    { .panel-heading-styles(@panel-danger-heading-bg); }\n\n\n//\n// Wells\n// --------------------------------------------------\n\n.well {\n  #gradient > .vertical(@start-color: darken(@well-bg, 5%); @end-color: @well-bg);\n  border-color: darken(@well-bg, 10%);\n  @shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);\n  .box-shadow(@shadow);\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n  word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n"]}
\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/css/bootstrap.css b/_static/bootstrap-3.4.1/css/bootstrap.css
new file mode 100644
index 000000000..fcab41554
--- /dev/null
+++ b/_static/bootstrap-3.4.1/css/bootstrap.css
@@ -0,0 +1,6834 @@
+/*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
+html {
+  font-family: sans-serif;
+  -ms-text-size-adjust: 100%;
+  -webkit-text-size-adjust: 100%;
+}
+body {
+  margin: 0;
+}
+article,
+aside,
+details,
+figcaption,
+figure,
+footer,
+header,
+hgroup,
+main,
+menu,
+nav,
+section,
+summary {
+  display: block;
+}
+audio,
+canvas,
+progress,
+video {
+  display: inline-block;
+  vertical-align: baseline;
+}
+audio:not([controls]) {
+  display: none;
+  height: 0;
+}
+[hidden],
+template {
+  display: none;
+}
+a {
+  background-color: transparent;
+}
+a:active,
+a:hover {
+  outline: 0;
+}
+abbr[title] {
+  border-bottom: none;
+  text-decoration: underline;
+  -webkit-text-decoration: underline dotted;
+  -moz-text-decoration: underline dotted;
+  text-decoration: underline dotted;
+}
+b,
+strong {
+  font-weight: bold;
+}
+dfn {
+  font-style: italic;
+}
+h1 {
+  font-size: 2em;
+  margin: 0.67em 0;
+}
+mark {
+  background: #ff0;
+  color: #000;
+}
+small {
+  font-size: 80%;
+}
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+sup {
+  top: -0.5em;
+}
+sub {
+  bottom: -0.25em;
+}
+img {
+  border: 0;
+}
+svg:not(:root) {
+  overflow: hidden;
+}
+figure {
+  margin: 1em 40px;
+}
+hr {
+  -webkit-box-sizing: content-box;
+  -moz-box-sizing: content-box;
+  box-sizing: content-box;
+  height: 0;
+}
+pre {
+  overflow: auto;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+button,
+input,
+optgroup,
+select,
+textarea {
+  color: inherit;
+  font: inherit;
+  margin: 0;
+}
+button {
+  overflow: visible;
+}
+button,
+select {
+  text-transform: none;
+}
+button,
+html input[type="button"],
+input[type="reset"],
+input[type="submit"] {
+  -webkit-appearance: button;
+  cursor: pointer;
+}
+button[disabled],
+html input[disabled] {
+  cursor: default;
+}
+button::-moz-focus-inner,
+input::-moz-focus-inner {
+  border: 0;
+  padding: 0;
+}
+input {
+  line-height: normal;
+}
+input[type="checkbox"],
+input[type="radio"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+  padding: 0;
+}
+input[type="number"]::-webkit-inner-spin-button,
+input[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+input[type="search"] {
+  -webkit-appearance: textfield;
+  -webkit-box-sizing: content-box;
+  -moz-box-sizing: content-box;
+  box-sizing: content-box;
+}
+input[type="search"]::-webkit-search-cancel-button,
+input[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+fieldset {
+  border: 1px solid #c0c0c0;
+  margin: 0 2px;
+  padding: 0.35em 0.625em 0.75em;
+}
+legend {
+  border: 0;
+  padding: 0;
+}
+textarea {
+  overflow: auto;
+}
+optgroup {
+  font-weight: bold;
+}
+table {
+  border-collapse: collapse;
+  border-spacing: 0;
+}
+td,
+th {
+  padding: 0;
+}
+/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
+@media print {
+  *,
+  *:before,
+  *:after {
+    color: #000 !important;
+    text-shadow: none !important;
+    background: transparent !important;
+    -webkit-box-shadow: none !important;
+    box-shadow: none !important;
+  }
+  a,
+  a:visited {
+    text-decoration: underline;
+  }
+  a[href]:after {
+    content: " (" attr(href) ")";
+  }
+  abbr[title]:after {
+    content: " (" attr(title) ")";
+  }
+  a[href^="#"]:after,
+  a[href^="javascript:"]:after {
+    content: "";
+  }
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+  thead {
+    display: table-header-group;
+  }
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+  img {
+    max-width: 100% !important;
+  }
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+  .navbar {
+    display: none;
+  }
+  .btn > .caret,
+  .dropup > .btn > .caret {
+    border-top-color: #000 !important;
+  }
+  .label {
+    border: 1px solid #000;
+  }
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
+@font-face {
+  font-family: "Glyphicons Halflings";
+  src: url("../fonts/glyphicons-halflings-regular.eot");
+  src: url("../fonts/glyphicons-halflings-regular.eot?#iefix") format("embedded-opentype"), url("../fonts/glyphicons-halflings-regular.woff2") format("woff2"), url("../fonts/glyphicons-halflings-regular.woff") format("woff"), url("../fonts/glyphicons-halflings-regular.ttf") format("truetype"), url("../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular") format("svg");
+}
+.glyphicon {
+  position: relative;
+  top: 1px;
+  display: inline-block;
+  font-family: "Glyphicons Halflings";
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+.glyphicon-asterisk:before {
+  content: "\002a";
+}
+.glyphicon-plus:before {
+  content: "\002b";
+}
+.glyphicon-euro:before,
+.glyphicon-eur:before {
+  content: "\20ac";
+}
+.glyphicon-minus:before {
+  content: "\2212";
+}
+.glyphicon-cloud:before {
+  content: "\2601";
+}
+.glyphicon-envelope:before {
+  content: "\2709";
+}
+.glyphicon-pencil:before {
+  content: "\270f";
+}
+.glyphicon-glass:before {
+  content: "\e001";
+}
+.glyphicon-music:before {
+  content: "\e002";
+}
+.glyphicon-search:before {
+  content: "\e003";
+}
+.glyphicon-heart:before {
+  content: "\e005";
+}
+.glyphicon-star:before {
+  content: "\e006";
+}
+.glyphicon-star-empty:before {
+  content: "\e007";
+}
+.glyphicon-user:before {
+  content: "\e008";
+}
+.glyphicon-film:before {
+  content: "\e009";
+}
+.glyphicon-th-large:before {
+  content: "\e010";
+}
+.glyphicon-th:before {
+  content: "\e011";
+}
+.glyphicon-th-list:before {
+  content: "\e012";
+}
+.glyphicon-ok:before {
+  content: "\e013";
+}
+.glyphicon-remove:before {
+  content: "\e014";
+}
+.glyphicon-zoom-in:before {
+  content: "\e015";
+}
+.glyphicon-zoom-out:before {
+  content: "\e016";
+}
+.glyphicon-off:before {
+  content: "\e017";
+}
+.glyphicon-signal:before {
+  content: "\e018";
+}
+.glyphicon-cog:before {
+  content: "\e019";
+}
+.glyphicon-trash:before {
+  content: "\e020";
+}
+.glyphicon-home:before {
+  content: "\e021";
+}
+.glyphicon-file:before {
+  content: "\e022";
+}
+.glyphicon-time:before {
+  content: "\e023";
+}
+.glyphicon-road:before {
+  content: "\e024";
+}
+.glyphicon-download-alt:before {
+  content: "\e025";
+}
+.glyphicon-download:before {
+  content: "\e026";
+}
+.glyphicon-upload:before {
+  content: "\e027";
+}
+.glyphicon-inbox:before {
+  content: "\e028";
+}
+.glyphicon-play-circle:before {
+  content: "\e029";
+}
+.glyphicon-repeat:before {
+  content: "\e030";
+}
+.glyphicon-refresh:before {
+  content: "\e031";
+}
+.glyphicon-list-alt:before {
+  content: "\e032";
+}
+.glyphicon-lock:before {
+  content: "\e033";
+}
+.glyphicon-flag:before {
+  content: "\e034";
+}
+.glyphicon-headphones:before {
+  content: "\e035";
+}
+.glyphicon-volume-off:before {
+  content: "\e036";
+}
+.glyphicon-volume-down:before {
+  content: "\e037";
+}
+.glyphicon-volume-up:before {
+  content: "\e038";
+}
+.glyphicon-qrcode:before {
+  content: "\e039";
+}
+.glyphicon-barcode:before {
+  content: "\e040";
+}
+.glyphicon-tag:before {
+  content: "\e041";
+}
+.glyphicon-tags:before {
+  content: "\e042";
+}
+.glyphicon-book:before {
+  content: "\e043";
+}
+.glyphicon-bookmark:before {
+  content: "\e044";
+}
+.glyphicon-print:before {
+  content: "\e045";
+}
+.glyphicon-camera:before {
+  content: "\e046";
+}
+.glyphicon-font:before {
+  content: "\e047";
+}
+.glyphicon-bold:before {
+  content: "\e048";
+}
+.glyphicon-italic:before {
+  content: "\e049";
+}
+.glyphicon-text-height:before {
+  content: "\e050";
+}
+.glyphicon-text-width:before {
+  content: "\e051";
+}
+.glyphicon-align-left:before {
+  content: "\e052";
+}
+.glyphicon-align-center:before {
+  content: "\e053";
+}
+.glyphicon-align-right:before {
+  content: "\e054";
+}
+.glyphicon-align-justify:before {
+  content: "\e055";
+}
+.glyphicon-list:before {
+  content: "\e056";
+}
+.glyphicon-indent-left:before {
+  content: "\e057";
+}
+.glyphicon-indent-right:before {
+  content: "\e058";
+}
+.glyphicon-facetime-video:before {
+  content: "\e059";
+}
+.glyphicon-picture:before {
+  content: "\e060";
+}
+.glyphicon-map-marker:before {
+  content: "\e062";
+}
+.glyphicon-adjust:before {
+  content: "\e063";
+}
+.glyphicon-tint:before {
+  content: "\e064";
+}
+.glyphicon-edit:before {
+  content: "\e065";
+}
+.glyphicon-share:before {
+  content: "\e066";
+}
+.glyphicon-check:before {
+  content: "\e067";
+}
+.glyphicon-move:before {
+  content: "\e068";
+}
+.glyphicon-step-backward:before {
+  content: "\e069";
+}
+.glyphicon-fast-backward:before {
+  content: "\e070";
+}
+.glyphicon-backward:before {
+  content: "\e071";
+}
+.glyphicon-play:before {
+  content: "\e072";
+}
+.glyphicon-pause:before {
+  content: "\e073";
+}
+.glyphicon-stop:before {
+  content: "\e074";
+}
+.glyphicon-forward:before {
+  content: "\e075";
+}
+.glyphicon-fast-forward:before {
+  content: "\e076";
+}
+.glyphicon-step-forward:before {
+  content: "\e077";
+}
+.glyphicon-eject:before {
+  content: "\e078";
+}
+.glyphicon-chevron-left:before {
+  content: "\e079";
+}
+.glyphicon-chevron-right:before {
+  content: "\e080";
+}
+.glyphicon-plus-sign:before {
+  content: "\e081";
+}
+.glyphicon-minus-sign:before {
+  content: "\e082";
+}
+.glyphicon-remove-sign:before {
+  content: "\e083";
+}
+.glyphicon-ok-sign:before {
+  content: "\e084";
+}
+.glyphicon-question-sign:before {
+  content: "\e085";
+}
+.glyphicon-info-sign:before {
+  content: "\e086";
+}
+.glyphicon-screenshot:before {
+  content: "\e087";
+}
+.glyphicon-remove-circle:before {
+  content: "\e088";
+}
+.glyphicon-ok-circle:before {
+  content: "\e089";
+}
+.glyphicon-ban-circle:before {
+  content: "\e090";
+}
+.glyphicon-arrow-left:before {
+  content: "\e091";
+}
+.glyphicon-arrow-right:before {
+  content: "\e092";
+}
+.glyphicon-arrow-up:before {
+  content: "\e093";
+}
+.glyphicon-arrow-down:before {
+  content: "\e094";
+}
+.glyphicon-share-alt:before {
+  content: "\e095";
+}
+.glyphicon-resize-full:before {
+  content: "\e096";
+}
+.glyphicon-resize-small:before {
+  content: "\e097";
+}
+.glyphicon-exclamation-sign:before {
+  content: "\e101";
+}
+.glyphicon-gift:before {
+  content: "\e102";
+}
+.glyphicon-leaf:before {
+  content: "\e103";
+}
+.glyphicon-fire:before {
+  content: "\e104";
+}
+.glyphicon-eye-open:before {
+  content: "\e105";
+}
+.glyphicon-eye-close:before {
+  content: "\e106";
+}
+.glyphicon-warning-sign:before {
+  content: "\e107";
+}
+.glyphicon-plane:before {
+  content: "\e108";
+}
+.glyphicon-calendar:before {
+  content: "\e109";
+}
+.glyphicon-random:before {
+  content: "\e110";
+}
+.glyphicon-comment:before {
+  content: "\e111";
+}
+.glyphicon-magnet:before {
+  content: "\e112";
+}
+.glyphicon-chevron-up:before {
+  content: "\e113";
+}
+.glyphicon-chevron-down:before {
+  content: "\e114";
+}
+.glyphicon-retweet:before {
+  content: "\e115";
+}
+.glyphicon-shopping-cart:before {
+  content: "\e116";
+}
+.glyphicon-folder-close:before {
+  content: "\e117";
+}
+.glyphicon-folder-open:before {
+  content: "\e118";
+}
+.glyphicon-resize-vertical:before {
+  content: "\e119";
+}
+.glyphicon-resize-horizontal:before {
+  content: "\e120";
+}
+.glyphicon-hdd:before {
+  content: "\e121";
+}
+.glyphicon-bullhorn:before {
+  content: "\e122";
+}
+.glyphicon-bell:before {
+  content: "\e123";
+}
+.glyphicon-certificate:before {
+  content: "\e124";
+}
+.glyphicon-thumbs-up:before {
+  content: "\e125";
+}
+.glyphicon-thumbs-down:before {
+  content: "\e126";
+}
+.glyphicon-hand-right:before {
+  content: "\e127";
+}
+.glyphicon-hand-left:before {
+  content: "\e128";
+}
+.glyphicon-hand-up:before {
+  content: "\e129";
+}
+.glyphicon-hand-down:before {
+  content: "\e130";
+}
+.glyphicon-circle-arrow-right:before {
+  content: "\e131";
+}
+.glyphicon-circle-arrow-left:before {
+  content: "\e132";
+}
+.glyphicon-circle-arrow-up:before {
+  content: "\e133";
+}
+.glyphicon-circle-arrow-down:before {
+  content: "\e134";
+}
+.glyphicon-globe:before {
+  content: "\e135";
+}
+.glyphicon-wrench:before {
+  content: "\e136";
+}
+.glyphicon-tasks:before {
+  content: "\e137";
+}
+.glyphicon-filter:before {
+  content: "\e138";
+}
+.glyphicon-briefcase:before {
+  content: "\e139";
+}
+.glyphicon-fullscreen:before {
+  content: "\e140";
+}
+.glyphicon-dashboard:before {
+  content: "\e141";
+}
+.glyphicon-paperclip:before {
+  content: "\e142";
+}
+.glyphicon-heart-empty:before {
+  content: "\e143";
+}
+.glyphicon-link:before {
+  content: "\e144";
+}
+.glyphicon-phone:before {
+  content: "\e145";
+}
+.glyphicon-pushpin:before {
+  content: "\e146";
+}
+.glyphicon-usd:before {
+  content: "\e148";
+}
+.glyphicon-gbp:before {
+  content: "\e149";
+}
+.glyphicon-sort:before {
+  content: "\e150";
+}
+.glyphicon-sort-by-alphabet:before {
+  content: "\e151";
+}
+.glyphicon-sort-by-alphabet-alt:before {
+  content: "\e152";
+}
+.glyphicon-sort-by-order:before {
+  content: "\e153";
+}
+.glyphicon-sort-by-order-alt:before {
+  content: "\e154";
+}
+.glyphicon-sort-by-attributes:before {
+  content: "\e155";
+}
+.glyphicon-sort-by-attributes-alt:before {
+  content: "\e156";
+}
+.glyphicon-unchecked:before {
+  content: "\e157";
+}
+.glyphicon-expand:before {
+  content: "\e158";
+}
+.glyphicon-collapse-down:before {
+  content: "\e159";
+}
+.glyphicon-collapse-up:before {
+  content: "\e160";
+}
+.glyphicon-log-in:before {
+  content: "\e161";
+}
+.glyphicon-flash:before {
+  content: "\e162";
+}
+.glyphicon-log-out:before {
+  content: "\e163";
+}
+.glyphicon-new-window:before {
+  content: "\e164";
+}
+.glyphicon-record:before {
+  content: "\e165";
+}
+.glyphicon-save:before {
+  content: "\e166";
+}
+.glyphicon-open:before {
+  content: "\e167";
+}
+.glyphicon-saved:before {
+  content: "\e168";
+}
+.glyphicon-import:before {
+  content: "\e169";
+}
+.glyphicon-export:before {
+  content: "\e170";
+}
+.glyphicon-send:before {
+  content: "\e171";
+}
+.glyphicon-floppy-disk:before {
+  content: "\e172";
+}
+.glyphicon-floppy-saved:before {
+  content: "\e173";
+}
+.glyphicon-floppy-remove:before {
+  content: "\e174";
+}
+.glyphicon-floppy-save:before {
+  content: "\e175";
+}
+.glyphicon-floppy-open:before {
+  content: "\e176";
+}
+.glyphicon-credit-card:before {
+  content: "\e177";
+}
+.glyphicon-transfer:before {
+  content: "\e178";
+}
+.glyphicon-cutlery:before {
+  content: "\e179";
+}
+.glyphicon-header:before {
+  content: "\e180";
+}
+.glyphicon-compressed:before {
+  content: "\e181";
+}
+.glyphicon-earphone:before {
+  content: "\e182";
+}
+.glyphicon-phone-alt:before {
+  content: "\e183";
+}
+.glyphicon-tower:before {
+  content: "\e184";
+}
+.glyphicon-stats:before {
+  content: "\e185";
+}
+.glyphicon-sd-video:before {
+  content: "\e186";
+}
+.glyphicon-hd-video:before {
+  content: "\e187";
+}
+.glyphicon-subtitles:before {
+  content: "\e188";
+}
+.glyphicon-sound-stereo:before {
+  content: "\e189";
+}
+.glyphicon-sound-dolby:before {
+  content: "\e190";
+}
+.glyphicon-sound-5-1:before {
+  content: "\e191";
+}
+.glyphicon-sound-6-1:before {
+  content: "\e192";
+}
+.glyphicon-sound-7-1:before {
+  content: "\e193";
+}
+.glyphicon-copyright-mark:before {
+  content: "\e194";
+}
+.glyphicon-registration-mark:before {
+  content: "\e195";
+}
+.glyphicon-cloud-download:before {
+  content: "\e197";
+}
+.glyphicon-cloud-upload:before {
+  content: "\e198";
+}
+.glyphicon-tree-conifer:before {
+  content: "\e199";
+}
+.glyphicon-tree-deciduous:before {
+  content: "\e200";
+}
+.glyphicon-cd:before {
+  content: "\e201";
+}
+.glyphicon-save-file:before {
+  content: "\e202";
+}
+.glyphicon-open-file:before {
+  content: "\e203";
+}
+.glyphicon-level-up:before {
+  content: "\e204";
+}
+.glyphicon-copy:before {
+  content: "\e205";
+}
+.glyphicon-paste:before {
+  content: "\e206";
+}
+.glyphicon-alert:before {
+  content: "\e209";
+}
+.glyphicon-equalizer:before {
+  content: "\e210";
+}
+.glyphicon-king:before {
+  content: "\e211";
+}
+.glyphicon-queen:before {
+  content: "\e212";
+}
+.glyphicon-pawn:before {
+  content: "\e213";
+}
+.glyphicon-bishop:before {
+  content: "\e214";
+}
+.glyphicon-knight:before {
+  content: "\e215";
+}
+.glyphicon-baby-formula:before {
+  content: "\e216";
+}
+.glyphicon-tent:before {
+  content: "\26fa";
+}
+.glyphicon-blackboard:before {
+  content: "\e218";
+}
+.glyphicon-bed:before {
+  content: "\e219";
+}
+.glyphicon-apple:before {
+  content: "\f8ff";
+}
+.glyphicon-erase:before {
+  content: "\e221";
+}
+.glyphicon-hourglass:before {
+  content: "\231b";
+}
+.glyphicon-lamp:before {
+  content: "\e223";
+}
+.glyphicon-duplicate:before {
+  content: "\e224";
+}
+.glyphicon-piggy-bank:before {
+  content: "\e225";
+}
+.glyphicon-scissors:before {
+  content: "\e226";
+}
+.glyphicon-bitcoin:before {
+  content: "\e227";
+}
+.glyphicon-btc:before {
+  content: "\e227";
+}
+.glyphicon-xbt:before {
+  content: "\e227";
+}
+.glyphicon-yen:before {
+  content: "\00a5";
+}
+.glyphicon-jpy:before {
+  content: "\00a5";
+}
+.glyphicon-ruble:before {
+  content: "\20bd";
+}
+.glyphicon-rub:before {
+  content: "\20bd";
+}
+.glyphicon-scale:before {
+  content: "\e230";
+}
+.glyphicon-ice-lolly:before {
+  content: "\e231";
+}
+.glyphicon-ice-lolly-tasted:before {
+  content: "\e232";
+}
+.glyphicon-education:before {
+  content: "\e233";
+}
+.glyphicon-option-horizontal:before {
+  content: "\e234";
+}
+.glyphicon-option-vertical:before {
+  content: "\e235";
+}
+.glyphicon-menu-hamburger:before {
+  content: "\e236";
+}
+.glyphicon-modal-window:before {
+  content: "\e237";
+}
+.glyphicon-oil:before {
+  content: "\e238";
+}
+.glyphicon-grain:before {
+  content: "\e239";
+}
+.glyphicon-sunglasses:before {
+  content: "\e240";
+}
+.glyphicon-text-size:before {
+  content: "\e241";
+}
+.glyphicon-text-color:before {
+  content: "\e242";
+}
+.glyphicon-text-background:before {
+  content: "\e243";
+}
+.glyphicon-object-align-top:before {
+  content: "\e244";
+}
+.glyphicon-object-align-bottom:before {
+  content: "\e245";
+}
+.glyphicon-object-align-horizontal:before {
+  content: "\e246";
+}
+.glyphicon-object-align-left:before {
+  content: "\e247";
+}
+.glyphicon-object-align-vertical:before {
+  content: "\e248";
+}
+.glyphicon-object-align-right:before {
+  content: "\e249";
+}
+.glyphicon-triangle-right:before {
+  content: "\e250";
+}
+.glyphicon-triangle-left:before {
+  content: "\e251";
+}
+.glyphicon-triangle-bottom:before {
+  content: "\e252";
+}
+.glyphicon-triangle-top:before {
+  content: "\e253";
+}
+.glyphicon-console:before {
+  content: "\e254";
+}
+.glyphicon-superscript:before {
+  content: "\e255";
+}
+.glyphicon-subscript:before {
+  content: "\e256";
+}
+.glyphicon-menu-left:before {
+  content: "\e257";
+}
+.glyphicon-menu-right:before {
+  content: "\e258";
+}
+.glyphicon-menu-down:before {
+  content: "\e259";
+}
+.glyphicon-menu-up:before {
+  content: "\e260";
+}
+* {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+*:before,
+*:after {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+}
+html {
+  font-size: 10px;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+body {
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 14px;
+  line-height: 1.42857143;
+  color: #333333;
+  background-color: #fff;
+}
+input,
+button,
+select,
+textarea {
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+a {
+  color: #337ab7;
+  text-decoration: none;
+}
+a:hover,
+a:focus {
+  color: #23527c;
+  text-decoration: underline;
+}
+a:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+figure {
+  margin: 0;
+}
+img {
+  vertical-align: middle;
+}
+.img-responsive,
+.thumbnail > img,
+.thumbnail a > img,
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  display: block;
+  max-width: 100%;
+  height: auto;
+}
+.img-rounded {
+  border-radius: 6px;
+}
+.img-thumbnail {
+  padding: 4px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  -webkit-transition: all 0.2s ease-in-out;
+  -o-transition: all 0.2s ease-in-out;
+  transition: all 0.2s ease-in-out;
+  display: inline-block;
+  max-width: 100%;
+  height: auto;
+}
+.img-circle {
+  border-radius: 50%;
+}
+hr {
+  margin-top: 20px;
+  margin-bottom: 20px;
+  border: 0;
+  border-top: 1px solid #eeeeee;
+}
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  border: 0;
+}
+.sr-only-focusable:active,
+.sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  margin: 0;
+  overflow: visible;
+  clip: auto;
+}
+[role="button"] {
+  cursor: pointer;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+.h1,
+.h2,
+.h3,
+.h4,
+.h5,
+.h6 {
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.1;
+  color: inherit;
+}
+h1 small,
+h2 small,
+h3 small,
+h4 small,
+h5 small,
+h6 small,
+.h1 small,
+.h2 small,
+.h3 small,
+.h4 small,
+.h5 small,
+.h6 small,
+h1 .small,
+h2 .small,
+h3 .small,
+h4 .small,
+h5 .small,
+h6 .small,
+.h1 .small,
+.h2 .small,
+.h3 .small,
+.h4 .small,
+.h5 .small,
+.h6 .small {
+  font-weight: 400;
+  line-height: 1;
+  color: #777777;
+}
+h1,
+.h1,
+h2,
+.h2,
+h3,
+.h3 {
+  margin-top: 20px;
+  margin-bottom: 10px;
+}
+h1 small,
+.h1 small,
+h2 small,
+.h2 small,
+h3 small,
+.h3 small,
+h1 .small,
+.h1 .small,
+h2 .small,
+.h2 .small,
+h3 .small,
+.h3 .small {
+  font-size: 65%;
+}
+h4,
+.h4,
+h5,
+.h5,
+h6,
+.h6 {
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+h4 small,
+.h4 small,
+h5 small,
+.h5 small,
+h6 small,
+.h6 small,
+h4 .small,
+.h4 .small,
+h5 .small,
+.h5 .small,
+h6 .small,
+.h6 .small {
+  font-size: 75%;
+}
+h1,
+.h1 {
+  font-size: 36px;
+}
+h2,
+.h2 {
+  font-size: 30px;
+}
+h3,
+.h3 {
+  font-size: 24px;
+}
+h4,
+.h4 {
+  font-size: 18px;
+}
+h5,
+.h5 {
+  font-size: 14px;
+}
+h6,
+.h6 {
+  font-size: 12px;
+}
+p {
+  margin: 0 0 10px;
+}
+.lead {
+  margin-bottom: 20px;
+  font-size: 16px;
+  font-weight: 300;
+  line-height: 1.4;
+}
+@media (min-width: 768px) {
+  .lead {
+    font-size: 21px;
+  }
+}
+small,
+.small {
+  font-size: 85%;
+}
+mark,
+.mark {
+  padding: 0.2em;
+  background-color: #fcf8e3;
+}
+.text-left {
+  text-align: left;
+}
+.text-right {
+  text-align: right;
+}
+.text-center {
+  text-align: center;
+}
+.text-justify {
+  text-align: justify;
+}
+.text-nowrap {
+  white-space: nowrap;
+}
+.text-lowercase {
+  text-transform: lowercase;
+}
+.text-uppercase {
+  text-transform: uppercase;
+}
+.text-capitalize {
+  text-transform: capitalize;
+}
+.text-muted {
+  color: #777777;
+}
+.text-primary {
+  color: #337ab7;
+}
+a.text-primary:hover,
+a.text-primary:focus {
+  color: #286090;
+}
+.text-success {
+  color: #3c763d;
+}
+a.text-success:hover,
+a.text-success:focus {
+  color: #2b542c;
+}
+.text-info {
+  color: #31708f;
+}
+a.text-info:hover,
+a.text-info:focus {
+  color: #245269;
+}
+.text-warning {
+  color: #8a6d3b;
+}
+a.text-warning:hover,
+a.text-warning:focus {
+  color: #66512c;
+}
+.text-danger {
+  color: #a94442;
+}
+a.text-danger:hover,
+a.text-danger:focus {
+  color: #843534;
+}
+.bg-primary {
+  color: #fff;
+  background-color: #337ab7;
+}
+a.bg-primary:hover,
+a.bg-primary:focus {
+  background-color: #286090;
+}
+.bg-success {
+  background-color: #dff0d8;
+}
+a.bg-success:hover,
+a.bg-success:focus {
+  background-color: #c1e2b3;
+}
+.bg-info {
+  background-color: #d9edf7;
+}
+a.bg-info:hover,
+a.bg-info:focus {
+  background-color: #afd9ee;
+}
+.bg-warning {
+  background-color: #fcf8e3;
+}
+a.bg-warning:hover,
+a.bg-warning:focus {
+  background-color: #f7ecb5;
+}
+.bg-danger {
+  background-color: #f2dede;
+}
+a.bg-danger:hover,
+a.bg-danger:focus {
+  background-color: #e4b9b9;
+}
+.page-header {
+  padding-bottom: 9px;
+  margin: 40px 0 20px;
+  border-bottom: 1px solid #eeeeee;
+}
+ul,
+ol {
+  margin-top: 0;
+  margin-bottom: 10px;
+}
+ul ul,
+ol ul,
+ul ol,
+ol ol {
+  margin-bottom: 0;
+}
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+  margin-left: -5px;
+}
+.list-inline > li {
+  display: inline-block;
+  padding-right: 5px;
+  padding-left: 5px;
+}
+dl {
+  margin-top: 0;
+  margin-bottom: 20px;
+}
+dt,
+dd {
+  line-height: 1.42857143;
+}
+dt {
+  font-weight: 700;
+}
+dd {
+  margin-left: 0;
+}
+@media (min-width: 768px) {
+  .dl-horizontal dt {
+    float: left;
+    width: 160px;
+    clear: left;
+    text-align: right;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    white-space: nowrap;
+  }
+  .dl-horizontal dd {
+    margin-left: 180px;
+  }
+}
+abbr[title],
+abbr[data-original-title] {
+  cursor: help;
+}
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+blockquote {
+  padding: 10px 20px;
+  margin: 0 0 20px;
+  font-size: 17.5px;
+  border-left: 5px solid #eeeeee;
+}
+blockquote p:last-child,
+blockquote ul:last-child,
+blockquote ol:last-child {
+  margin-bottom: 0;
+}
+blockquote footer,
+blockquote small,
+blockquote .small {
+  display: block;
+  font-size: 80%;
+  line-height: 1.42857143;
+  color: #777777;
+}
+blockquote footer:before,
+blockquote small:before,
+blockquote .small:before {
+  content: "\2014 \00A0";
+}
+.blockquote-reverse,
+blockquote.pull-right {
+  padding-right: 15px;
+  padding-left: 0;
+  text-align: right;
+  border-right: 5px solid #eeeeee;
+  border-left: 0;
+}
+.blockquote-reverse footer:before,
+blockquote.pull-right footer:before,
+.blockquote-reverse small:before,
+blockquote.pull-right small:before,
+.blockquote-reverse .small:before,
+blockquote.pull-right .small:before {
+  content: "";
+}
+.blockquote-reverse footer:after,
+blockquote.pull-right footer:after,
+.blockquote-reverse small:after,
+blockquote.pull-right small:after,
+.blockquote-reverse .small:after,
+blockquote.pull-right .small:after {
+  content: "\00A0 \2014";
+}
+address {
+  margin-bottom: 20px;
+  font-style: normal;
+  line-height: 1.42857143;
+}
+code,
+kbd,
+pre,
+samp {
+  font-family: Menlo, Monaco, Consolas, "Courier New", monospace;
+}
+code {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #c7254e;
+  background-color: #f9f2f4;
+  border-radius: 4px;
+}
+kbd {
+  padding: 2px 4px;
+  font-size: 90%;
+  color: #fff;
+  background-color: #333;
+  border-radius: 3px;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: 700;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+pre {
+  display: block;
+  padding: 9.5px;
+  margin: 0 0 10px;
+  font-size: 13px;
+  line-height: 1.42857143;
+  color: #333333;
+  word-break: break-all;
+  word-wrap: break-word;
+  background-color: #f5f5f5;
+  border: 1px solid #ccc;
+  border-radius: 4px;
+}
+pre code {
+  padding: 0;
+  font-size: inherit;
+  color: inherit;
+  white-space: pre-wrap;
+  background-color: transparent;
+  border-radius: 0;
+}
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+.container {
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-right: auto;
+  margin-left: auto;
+}
+@media (min-width: 768px) {
+  .container {
+    width: 750px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    width: 970px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    width: 1170px;
+  }
+}
+.container-fluid {
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-right: auto;
+  margin-left: auto;
+}
+.row {
+  margin-right: -15px;
+  margin-left: -15px;
+}
+.row-no-gutters {
+  margin-right: 0;
+  margin-left: 0;
+}
+.row-no-gutters [class*="col-"] {
+  padding-right: 0;
+  padding-left: 0;
+}
+.col-xs-1,
+.col-sm-1,
+.col-md-1,
+.col-lg-1,
+.col-xs-2,
+.col-sm-2,
+.col-md-2,
+.col-lg-2,
+.col-xs-3,
+.col-sm-3,
+.col-md-3,
+.col-lg-3,
+.col-xs-4,
+.col-sm-4,
+.col-md-4,
+.col-lg-4,
+.col-xs-5,
+.col-sm-5,
+.col-md-5,
+.col-lg-5,
+.col-xs-6,
+.col-sm-6,
+.col-md-6,
+.col-lg-6,
+.col-xs-7,
+.col-sm-7,
+.col-md-7,
+.col-lg-7,
+.col-xs-8,
+.col-sm-8,
+.col-md-8,
+.col-lg-8,
+.col-xs-9,
+.col-sm-9,
+.col-md-9,
+.col-lg-9,
+.col-xs-10,
+.col-sm-10,
+.col-md-10,
+.col-lg-10,
+.col-xs-11,
+.col-sm-11,
+.col-md-11,
+.col-lg-11,
+.col-xs-12,
+.col-sm-12,
+.col-md-12,
+.col-lg-12 {
+  position: relative;
+  min-height: 1px;
+  padding-right: 15px;
+  padding-left: 15px;
+}
+.col-xs-1,
+.col-xs-2,
+.col-xs-3,
+.col-xs-4,
+.col-xs-5,
+.col-xs-6,
+.col-xs-7,
+.col-xs-8,
+.col-xs-9,
+.col-xs-10,
+.col-xs-11,
+.col-xs-12 {
+  float: left;
+}
+.col-xs-12 {
+  width: 100%;
+}
+.col-xs-11 {
+  width: 91.66666667%;
+}
+.col-xs-10 {
+  width: 83.33333333%;
+}
+.col-xs-9 {
+  width: 75%;
+}
+.col-xs-8 {
+  width: 66.66666667%;
+}
+.col-xs-7 {
+  width: 58.33333333%;
+}
+.col-xs-6 {
+  width: 50%;
+}
+.col-xs-5 {
+  width: 41.66666667%;
+}
+.col-xs-4 {
+  width: 33.33333333%;
+}
+.col-xs-3 {
+  width: 25%;
+}
+.col-xs-2 {
+  width: 16.66666667%;
+}
+.col-xs-1 {
+  width: 8.33333333%;
+}
+.col-xs-pull-12 {
+  right: 100%;
+}
+.col-xs-pull-11 {
+  right: 91.66666667%;
+}
+.col-xs-pull-10 {
+  right: 83.33333333%;
+}
+.col-xs-pull-9 {
+  right: 75%;
+}
+.col-xs-pull-8 {
+  right: 66.66666667%;
+}
+.col-xs-pull-7 {
+  right: 58.33333333%;
+}
+.col-xs-pull-6 {
+  right: 50%;
+}
+.col-xs-pull-5 {
+  right: 41.66666667%;
+}
+.col-xs-pull-4 {
+  right: 33.33333333%;
+}
+.col-xs-pull-3 {
+  right: 25%;
+}
+.col-xs-pull-2 {
+  right: 16.66666667%;
+}
+.col-xs-pull-1 {
+  right: 8.33333333%;
+}
+.col-xs-pull-0 {
+  right: auto;
+}
+.col-xs-push-12 {
+  left: 100%;
+}
+.col-xs-push-11 {
+  left: 91.66666667%;
+}
+.col-xs-push-10 {
+  left: 83.33333333%;
+}
+.col-xs-push-9 {
+  left: 75%;
+}
+.col-xs-push-8 {
+  left: 66.66666667%;
+}
+.col-xs-push-7 {
+  left: 58.33333333%;
+}
+.col-xs-push-6 {
+  left: 50%;
+}
+.col-xs-push-5 {
+  left: 41.66666667%;
+}
+.col-xs-push-4 {
+  left: 33.33333333%;
+}
+.col-xs-push-3 {
+  left: 25%;
+}
+.col-xs-push-2 {
+  left: 16.66666667%;
+}
+.col-xs-push-1 {
+  left: 8.33333333%;
+}
+.col-xs-push-0 {
+  left: auto;
+}
+.col-xs-offset-12 {
+  margin-left: 100%;
+}
+.col-xs-offset-11 {
+  margin-left: 91.66666667%;
+}
+.col-xs-offset-10 {
+  margin-left: 83.33333333%;
+}
+.col-xs-offset-9 {
+  margin-left: 75%;
+}
+.col-xs-offset-8 {
+  margin-left: 66.66666667%;
+}
+.col-xs-offset-7 {
+  margin-left: 58.33333333%;
+}
+.col-xs-offset-6 {
+  margin-left: 50%;
+}
+.col-xs-offset-5 {
+  margin-left: 41.66666667%;
+}
+.col-xs-offset-4 {
+  margin-left: 33.33333333%;
+}
+.col-xs-offset-3 {
+  margin-left: 25%;
+}
+.col-xs-offset-2 {
+  margin-left: 16.66666667%;
+}
+.col-xs-offset-1 {
+  margin-left: 8.33333333%;
+}
+.col-xs-offset-0 {
+  margin-left: 0%;
+}
+@media (min-width: 768px) {
+  .col-sm-1,
+  .col-sm-2,
+  .col-sm-3,
+  .col-sm-4,
+  .col-sm-5,
+  .col-sm-6,
+  .col-sm-7,
+  .col-sm-8,
+  .col-sm-9,
+  .col-sm-10,
+  .col-sm-11,
+  .col-sm-12 {
+    float: left;
+  }
+  .col-sm-12 {
+    width: 100%;
+  }
+  .col-sm-11 {
+    width: 91.66666667%;
+  }
+  .col-sm-10 {
+    width: 83.33333333%;
+  }
+  .col-sm-9 {
+    width: 75%;
+  }
+  .col-sm-8 {
+    width: 66.66666667%;
+  }
+  .col-sm-7 {
+    width: 58.33333333%;
+  }
+  .col-sm-6 {
+    width: 50%;
+  }
+  .col-sm-5 {
+    width: 41.66666667%;
+  }
+  .col-sm-4 {
+    width: 33.33333333%;
+  }
+  .col-sm-3 {
+    width: 25%;
+  }
+  .col-sm-2 {
+    width: 16.66666667%;
+  }
+  .col-sm-1 {
+    width: 8.33333333%;
+  }
+  .col-sm-pull-12 {
+    right: 100%;
+  }
+  .col-sm-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-sm-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-sm-pull-9 {
+    right: 75%;
+  }
+  .col-sm-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-sm-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-sm-pull-6 {
+    right: 50%;
+  }
+  .col-sm-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-sm-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-sm-pull-3 {
+    right: 25%;
+  }
+  .col-sm-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-sm-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-sm-pull-0 {
+    right: auto;
+  }
+  .col-sm-push-12 {
+    left: 100%;
+  }
+  .col-sm-push-11 {
+    left: 91.66666667%;
+  }
+  .col-sm-push-10 {
+    left: 83.33333333%;
+  }
+  .col-sm-push-9 {
+    left: 75%;
+  }
+  .col-sm-push-8 {
+    left: 66.66666667%;
+  }
+  .col-sm-push-7 {
+    left: 58.33333333%;
+  }
+  .col-sm-push-6 {
+    left: 50%;
+  }
+  .col-sm-push-5 {
+    left: 41.66666667%;
+  }
+  .col-sm-push-4 {
+    left: 33.33333333%;
+  }
+  .col-sm-push-3 {
+    left: 25%;
+  }
+  .col-sm-push-2 {
+    left: 16.66666667%;
+  }
+  .col-sm-push-1 {
+    left: 8.33333333%;
+  }
+  .col-sm-push-0 {
+    left: auto;
+  }
+  .col-sm-offset-12 {
+    margin-left: 100%;
+  }
+  .col-sm-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-sm-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-sm-offset-9 {
+    margin-left: 75%;
+  }
+  .col-sm-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-sm-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-sm-offset-6 {
+    margin-left: 50%;
+  }
+  .col-sm-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-sm-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-sm-offset-3 {
+    margin-left: 25%;
+  }
+  .col-sm-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-sm-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-sm-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 992px) {
+  .col-md-1,
+  .col-md-2,
+  .col-md-3,
+  .col-md-4,
+  .col-md-5,
+  .col-md-6,
+  .col-md-7,
+  .col-md-8,
+  .col-md-9,
+  .col-md-10,
+  .col-md-11,
+  .col-md-12 {
+    float: left;
+  }
+  .col-md-12 {
+    width: 100%;
+  }
+  .col-md-11 {
+    width: 91.66666667%;
+  }
+  .col-md-10 {
+    width: 83.33333333%;
+  }
+  .col-md-9 {
+    width: 75%;
+  }
+  .col-md-8 {
+    width: 66.66666667%;
+  }
+  .col-md-7 {
+    width: 58.33333333%;
+  }
+  .col-md-6 {
+    width: 50%;
+  }
+  .col-md-5 {
+    width: 41.66666667%;
+  }
+  .col-md-4 {
+    width: 33.33333333%;
+  }
+  .col-md-3 {
+    width: 25%;
+  }
+  .col-md-2 {
+    width: 16.66666667%;
+  }
+  .col-md-1 {
+    width: 8.33333333%;
+  }
+  .col-md-pull-12 {
+    right: 100%;
+  }
+  .col-md-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-md-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-md-pull-9 {
+    right: 75%;
+  }
+  .col-md-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-md-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-md-pull-6 {
+    right: 50%;
+  }
+  .col-md-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-md-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-md-pull-3 {
+    right: 25%;
+  }
+  .col-md-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-md-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-md-pull-0 {
+    right: auto;
+  }
+  .col-md-push-12 {
+    left: 100%;
+  }
+  .col-md-push-11 {
+    left: 91.66666667%;
+  }
+  .col-md-push-10 {
+    left: 83.33333333%;
+  }
+  .col-md-push-9 {
+    left: 75%;
+  }
+  .col-md-push-8 {
+    left: 66.66666667%;
+  }
+  .col-md-push-7 {
+    left: 58.33333333%;
+  }
+  .col-md-push-6 {
+    left: 50%;
+  }
+  .col-md-push-5 {
+    left: 41.66666667%;
+  }
+  .col-md-push-4 {
+    left: 33.33333333%;
+  }
+  .col-md-push-3 {
+    left: 25%;
+  }
+  .col-md-push-2 {
+    left: 16.66666667%;
+  }
+  .col-md-push-1 {
+    left: 8.33333333%;
+  }
+  .col-md-push-0 {
+    left: auto;
+  }
+  .col-md-offset-12 {
+    margin-left: 100%;
+  }
+  .col-md-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-md-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-md-offset-9 {
+    margin-left: 75%;
+  }
+  .col-md-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-md-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-md-offset-6 {
+    margin-left: 50%;
+  }
+  .col-md-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-md-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-md-offset-3 {
+    margin-left: 25%;
+  }
+  .col-md-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-md-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-md-offset-0 {
+    margin-left: 0%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-lg-1,
+  .col-lg-2,
+  .col-lg-3,
+  .col-lg-4,
+  .col-lg-5,
+  .col-lg-6,
+  .col-lg-7,
+  .col-lg-8,
+  .col-lg-9,
+  .col-lg-10,
+  .col-lg-11,
+  .col-lg-12 {
+    float: left;
+  }
+  .col-lg-12 {
+    width: 100%;
+  }
+  .col-lg-11 {
+    width: 91.66666667%;
+  }
+  .col-lg-10 {
+    width: 83.33333333%;
+  }
+  .col-lg-9 {
+    width: 75%;
+  }
+  .col-lg-8 {
+    width: 66.66666667%;
+  }
+  .col-lg-7 {
+    width: 58.33333333%;
+  }
+  .col-lg-6 {
+    width: 50%;
+  }
+  .col-lg-5 {
+    width: 41.66666667%;
+  }
+  .col-lg-4 {
+    width: 33.33333333%;
+  }
+  .col-lg-3 {
+    width: 25%;
+  }
+  .col-lg-2 {
+    width: 16.66666667%;
+  }
+  .col-lg-1 {
+    width: 8.33333333%;
+  }
+  .col-lg-pull-12 {
+    right: 100%;
+  }
+  .col-lg-pull-11 {
+    right: 91.66666667%;
+  }
+  .col-lg-pull-10 {
+    right: 83.33333333%;
+  }
+  .col-lg-pull-9 {
+    right: 75%;
+  }
+  .col-lg-pull-8 {
+    right: 66.66666667%;
+  }
+  .col-lg-pull-7 {
+    right: 58.33333333%;
+  }
+  .col-lg-pull-6 {
+    right: 50%;
+  }
+  .col-lg-pull-5 {
+    right: 41.66666667%;
+  }
+  .col-lg-pull-4 {
+    right: 33.33333333%;
+  }
+  .col-lg-pull-3 {
+    right: 25%;
+  }
+  .col-lg-pull-2 {
+    right: 16.66666667%;
+  }
+  .col-lg-pull-1 {
+    right: 8.33333333%;
+  }
+  .col-lg-pull-0 {
+    right: auto;
+  }
+  .col-lg-push-12 {
+    left: 100%;
+  }
+  .col-lg-push-11 {
+    left: 91.66666667%;
+  }
+  .col-lg-push-10 {
+    left: 83.33333333%;
+  }
+  .col-lg-push-9 {
+    left: 75%;
+  }
+  .col-lg-push-8 {
+    left: 66.66666667%;
+  }
+  .col-lg-push-7 {
+    left: 58.33333333%;
+  }
+  .col-lg-push-6 {
+    left: 50%;
+  }
+  .col-lg-push-5 {
+    left: 41.66666667%;
+  }
+  .col-lg-push-4 {
+    left: 33.33333333%;
+  }
+  .col-lg-push-3 {
+    left: 25%;
+  }
+  .col-lg-push-2 {
+    left: 16.66666667%;
+  }
+  .col-lg-push-1 {
+    left: 8.33333333%;
+  }
+  .col-lg-push-0 {
+    left: auto;
+  }
+  .col-lg-offset-12 {
+    margin-left: 100%;
+  }
+  .col-lg-offset-11 {
+    margin-left: 91.66666667%;
+  }
+  .col-lg-offset-10 {
+    margin-left: 83.33333333%;
+  }
+  .col-lg-offset-9 {
+    margin-left: 75%;
+  }
+  .col-lg-offset-8 {
+    margin-left: 66.66666667%;
+  }
+  .col-lg-offset-7 {
+    margin-left: 58.33333333%;
+  }
+  .col-lg-offset-6 {
+    margin-left: 50%;
+  }
+  .col-lg-offset-5 {
+    margin-left: 41.66666667%;
+  }
+  .col-lg-offset-4 {
+    margin-left: 33.33333333%;
+  }
+  .col-lg-offset-3 {
+    margin-left: 25%;
+  }
+  .col-lg-offset-2 {
+    margin-left: 16.66666667%;
+  }
+  .col-lg-offset-1 {
+    margin-left: 8.33333333%;
+  }
+  .col-lg-offset-0 {
+    margin-left: 0%;
+  }
+}
+table {
+  background-color: transparent;
+}
+table col[class*="col-"] {
+  position: static;
+  display: table-column;
+  float: none;
+}
+table td[class*="col-"],
+table th[class*="col-"] {
+  position: static;
+  display: table-cell;
+  float: none;
+}
+caption {
+  padding-top: 8px;
+  padding-bottom: 8px;
+  color: #777777;
+  text-align: left;
+}
+th {
+  text-align: left;
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 20px;
+}
+.table > thead > tr > th,
+.table > tbody > tr > th,
+.table > tfoot > tr > th,
+.table > thead > tr > td,
+.table > tbody > tr > td,
+.table > tfoot > tr > td {
+  padding: 8px;
+  line-height: 1.42857143;
+  vertical-align: top;
+  border-top: 1px solid #ddd;
+}
+.table > thead > tr > th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #ddd;
+}
+.table > caption + thead > tr:first-child > th,
+.table > colgroup + thead > tr:first-child > th,
+.table > thead:first-child > tr:first-child > th,
+.table > caption + thead > tr:first-child > td,
+.table > colgroup + thead > tr:first-child > td,
+.table > thead:first-child > tr:first-child > td {
+  border-top: 0;
+}
+.table > tbody + tbody {
+  border-top: 2px solid #ddd;
+}
+.table .table {
+  background-color: #fff;
+}
+.table-condensed > thead > tr > th,
+.table-condensed > tbody > tr > th,
+.table-condensed > tfoot > tr > th,
+.table-condensed > thead > tr > td,
+.table-condensed > tbody > tr > td,
+.table-condensed > tfoot > tr > td {
+  padding: 5px;
+}
+.table-bordered {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > tbody > tr > th,
+.table-bordered > tfoot > tr > th,
+.table-bordered > thead > tr > td,
+.table-bordered > tbody > tr > td,
+.table-bordered > tfoot > tr > td {
+  border: 1px solid #ddd;
+}
+.table-bordered > thead > tr > th,
+.table-bordered > thead > tr > td {
+  border-bottom-width: 2px;
+}
+.table-striped > tbody > tr:nth-of-type(odd) {
+  background-color: #f9f9f9;
+}
+.table-hover > tbody > tr:hover {
+  background-color: #f5f5f5;
+}
+.table > thead > tr > td.active,
+.table > tbody > tr > td.active,
+.table > tfoot > tr > td.active,
+.table > thead > tr > th.active,
+.table > tbody > tr > th.active,
+.table > tfoot > tr > th.active,
+.table > thead > tr.active > td,
+.table > tbody > tr.active > td,
+.table > tfoot > tr.active > td,
+.table > thead > tr.active > th,
+.table > tbody > tr.active > th,
+.table > tfoot > tr.active > th {
+  background-color: #f5f5f5;
+}
+.table-hover > tbody > tr > td.active:hover,
+.table-hover > tbody > tr > th.active:hover,
+.table-hover > tbody > tr.active:hover > td,
+.table-hover > tbody > tr:hover > .active,
+.table-hover > tbody > tr.active:hover > th {
+  background-color: #e8e8e8;
+}
+.table > thead > tr > td.success,
+.table > tbody > tr > td.success,
+.table > tfoot > tr > td.success,
+.table > thead > tr > th.success,
+.table > tbody > tr > th.success,
+.table > tfoot > tr > th.success,
+.table > thead > tr.success > td,
+.table > tbody > tr.success > td,
+.table > tfoot > tr.success > td,
+.table > thead > tr.success > th,
+.table > tbody > tr.success > th,
+.table > tfoot > tr.success > th {
+  background-color: #dff0d8;
+}
+.table-hover > tbody > tr > td.success:hover,
+.table-hover > tbody > tr > th.success:hover,
+.table-hover > tbody > tr.success:hover > td,
+.table-hover > tbody > tr:hover > .success,
+.table-hover > tbody > tr.success:hover > th {
+  background-color: #d0e9c6;
+}
+.table > thead > tr > td.info,
+.table > tbody > tr > td.info,
+.table > tfoot > tr > td.info,
+.table > thead > tr > th.info,
+.table > tbody > tr > th.info,
+.table > tfoot > tr > th.info,
+.table > thead > tr.info > td,
+.table > tbody > tr.info > td,
+.table > tfoot > tr.info > td,
+.table > thead > tr.info > th,
+.table > tbody > tr.info > th,
+.table > tfoot > tr.info > th {
+  background-color: #d9edf7;
+}
+.table-hover > tbody > tr > td.info:hover,
+.table-hover > tbody > tr > th.info:hover,
+.table-hover > tbody > tr.info:hover > td,
+.table-hover > tbody > tr:hover > .info,
+.table-hover > tbody > tr.info:hover > th {
+  background-color: #c4e3f3;
+}
+.table > thead > tr > td.warning,
+.table > tbody > tr > td.warning,
+.table > tfoot > tr > td.warning,
+.table > thead > tr > th.warning,
+.table > tbody > tr > th.warning,
+.table > tfoot > tr > th.warning,
+.table > thead > tr.warning > td,
+.table > tbody > tr.warning > td,
+.table > tfoot > tr.warning > td,
+.table > thead > tr.warning > th,
+.table > tbody > tr.warning > th,
+.table > tfoot > tr.warning > th {
+  background-color: #fcf8e3;
+}
+.table-hover > tbody > tr > td.warning:hover,
+.table-hover > tbody > tr > th.warning:hover,
+.table-hover > tbody > tr.warning:hover > td,
+.table-hover > tbody > tr:hover > .warning,
+.table-hover > tbody > tr.warning:hover > th {
+  background-color: #faf2cc;
+}
+.table > thead > tr > td.danger,
+.table > tbody > tr > td.danger,
+.table > tfoot > tr > td.danger,
+.table > thead > tr > th.danger,
+.table > tbody > tr > th.danger,
+.table > tfoot > tr > th.danger,
+.table > thead > tr.danger > td,
+.table > tbody > tr.danger > td,
+.table > tfoot > tr.danger > td,
+.table > thead > tr.danger > th,
+.table > tbody > tr.danger > th,
+.table > tfoot > tr.danger > th {
+  background-color: #f2dede;
+}
+.table-hover > tbody > tr > td.danger:hover,
+.table-hover > tbody > tr > th.danger:hover,
+.table-hover > tbody > tr.danger:hover > td,
+.table-hover > tbody > tr:hover > .danger,
+.table-hover > tbody > tr.danger:hover > th {
+  background-color: #ebcccc;
+}
+.table-responsive {
+  min-height: 0.01%;
+  overflow-x: auto;
+}
+@media screen and (max-width: 767px) {
+  .table-responsive {
+    width: 100%;
+    margin-bottom: 15px;
+    overflow-y: hidden;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+    border: 1px solid #ddd;
+  }
+  .table-responsive > .table {
+    margin-bottom: 0;
+  }
+  .table-responsive > .table > thead > tr > th,
+  .table-responsive > .table > tbody > tr > th,
+  .table-responsive > .table > tfoot > tr > th,
+  .table-responsive > .table > thead > tr > td,
+  .table-responsive > .table > tbody > tr > td,
+  .table-responsive > .table > tfoot > tr > td {
+    white-space: nowrap;
+  }
+  .table-responsive > .table-bordered {
+    border: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:first-child,
+  .table-responsive > .table-bordered > tbody > tr > th:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+  .table-responsive > .table-bordered > thead > tr > td:first-child,
+  .table-responsive > .table-bordered > tbody > tr > td:first-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+    border-left: 0;
+  }
+  .table-responsive > .table-bordered > thead > tr > th:last-child,
+  .table-responsive > .table-bordered > tbody > tr > th:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+  .table-responsive > .table-bordered > thead > tr > td:last-child,
+  .table-responsive > .table-bordered > tbody > tr > td:last-child,
+  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+    border-right: 0;
+  }
+  .table-responsive > .table-bordered > tbody > tr:last-child > th,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
+  .table-responsive > .table-bordered > tbody > tr:last-child > td,
+  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
+    border-bottom: 0;
+  }
+}
+fieldset {
+  min-width: 0;
+  padding: 0;
+  margin: 0;
+  border: 0;
+}
+legend {
+  display: block;
+  width: 100%;
+  padding: 0;
+  margin-bottom: 20px;
+  font-size: 21px;
+  line-height: inherit;
+  color: #333333;
+  border: 0;
+  border-bottom: 1px solid #e5e5e5;
+}
+label {
+  display: inline-block;
+  max-width: 100%;
+  margin-bottom: 5px;
+  font-weight: 700;
+}
+input[type="search"] {
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  box-sizing: border-box;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+}
+input[type="radio"],
+input[type="checkbox"] {
+  margin: 4px 0 0;
+  margin-top: 1px \9;
+  line-height: normal;
+}
+input[type="radio"][disabled],
+input[type="checkbox"][disabled],
+input[type="radio"].disabled,
+input[type="checkbox"].disabled,
+fieldset[disabled] input[type="radio"],
+fieldset[disabled] input[type="checkbox"] {
+  cursor: not-allowed;
+}
+input[type="file"] {
+  display: block;
+}
+input[type="range"] {
+  display: block;
+  width: 100%;
+}
+select[multiple],
+select[size] {
+  height: auto;
+}
+input[type="file"]:focus,
+input[type="radio"]:focus,
+input[type="checkbox"]:focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+output {
+  display: block;
+  padding-top: 7px;
+  font-size: 14px;
+  line-height: 1.42857143;
+  color: #555555;
+}
+.form-control {
+  display: block;
+  width: 100%;
+  height: 34px;
+  padding: 6px 12px;
+  font-size: 14px;
+  line-height: 1.42857143;
+  color: #555555;
+  background-color: #fff;
+  background-image: none;
+  border: 1px solid #ccc;
+  border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
+  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;
+}
+.form-control:focus {
+  border-color: #66afe9;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);
+}
+.form-control::-moz-placeholder {
+  color: #999;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #999;
+}
+.form-control::-webkit-input-placeholder {
+  color: #999;
+}
+.form-control::-ms-expand {
+  background-color: transparent;
+  border: 0;
+}
+.form-control[disabled],
+.form-control[readonly],
+fieldset[disabled] .form-control {
+  background-color: #eeeeee;
+  opacity: 1;
+}
+.form-control[disabled],
+fieldset[disabled] .form-control {
+  cursor: not-allowed;
+}
+textarea.form-control {
+  height: auto;
+}
+@media screen and (-webkit-min-device-pixel-ratio: 0) {
+  input[type="date"].form-control,
+  input[type="time"].form-control,
+  input[type="datetime-local"].form-control,
+  input[type="month"].form-control {
+    line-height: 34px;
+  }
+  input[type="date"].input-sm,
+  input[type="time"].input-sm,
+  input[type="datetime-local"].input-sm,
+  input[type="month"].input-sm,
+  .input-group-sm input[type="date"],
+  .input-group-sm input[type="time"],
+  .input-group-sm input[type="datetime-local"],
+  .input-group-sm input[type="month"] {
+    line-height: 30px;
+  }
+  input[type="date"].input-lg,
+  input[type="time"].input-lg,
+  input[type="datetime-local"].input-lg,
+  input[type="month"].input-lg,
+  .input-group-lg input[type="date"],
+  .input-group-lg input[type="time"],
+  .input-group-lg input[type="datetime-local"],
+  .input-group-lg input[type="month"] {
+    line-height: 46px;
+  }
+}
+.form-group {
+  margin-bottom: 15px;
+}
+.radio,
+.checkbox {
+  position: relative;
+  display: block;
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.radio.disabled label,
+.checkbox.disabled label,
+fieldset[disabled] .radio label,
+fieldset[disabled] .checkbox label {
+  cursor: not-allowed;
+}
+.radio label,
+.checkbox label {
+  min-height: 20px;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: 400;
+  cursor: pointer;
+}
+.radio input[type="radio"],
+.radio-inline input[type="radio"],
+.checkbox input[type="checkbox"],
+.checkbox-inline input[type="checkbox"] {
+  position: absolute;
+  margin-top: 4px \9;
+  margin-left: -20px;
+}
+.radio + .radio,
+.checkbox + .checkbox {
+  margin-top: -5px;
+}
+.radio-inline,
+.checkbox-inline {
+  position: relative;
+  display: inline-block;
+  padding-left: 20px;
+  margin-bottom: 0;
+  font-weight: 400;
+  vertical-align: middle;
+  cursor: pointer;
+}
+.radio-inline.disabled,
+.checkbox-inline.disabled,
+fieldset[disabled] .radio-inline,
+fieldset[disabled] .checkbox-inline {
+  cursor: not-allowed;
+}
+.radio-inline + .radio-inline,
+.checkbox-inline + .checkbox-inline {
+  margin-top: 0;
+  margin-left: 10px;
+}
+.form-control-static {
+  min-height: 34px;
+  padding-top: 7px;
+  padding-bottom: 7px;
+  margin-bottom: 0;
+}
+.form-control-static.input-lg,
+.form-control-static.input-sm {
+  padding-right: 0;
+  padding-left: 0;
+}
+.input-sm {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 3px;
+}
+select.input-sm {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-sm,
+select[multiple].input-sm {
+  height: auto;
+}
+.form-group-sm .form-control {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 3px;
+}
+.form-group-sm select.form-control {
+  height: 30px;
+  line-height: 30px;
+}
+.form-group-sm textarea.form-control,
+.form-group-sm select[multiple].form-control {
+  height: auto;
+}
+.form-group-sm .form-control-static {
+  height: 30px;
+  min-height: 32px;
+  padding: 6px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.input-lg {
+  height: 46px;
+  padding: 10px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+  border-radius: 6px;
+}
+select.input-lg {
+  height: 46px;
+  line-height: 46px;
+}
+textarea.input-lg,
+select[multiple].input-lg {
+  height: auto;
+}
+.form-group-lg .form-control {
+  height: 46px;
+  padding: 10px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+  border-radius: 6px;
+}
+.form-group-lg select.form-control {
+  height: 46px;
+  line-height: 46px;
+}
+.form-group-lg textarea.form-control,
+.form-group-lg select[multiple].form-control {
+  height: auto;
+}
+.form-group-lg .form-control-static {
+  height: 46px;
+  min-height: 38px;
+  padding: 11px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+}
+.has-feedback {
+  position: relative;
+}
+.has-feedback .form-control {
+  padding-right: 42.5px;
+}
+.form-control-feedback {
+  position: absolute;
+  top: 0;
+  right: 0;
+  z-index: 2;
+  display: block;
+  width: 34px;
+  height: 34px;
+  line-height: 34px;
+  text-align: center;
+  pointer-events: none;
+}
+.input-lg + .form-control-feedback,
+.input-group-lg + .form-control-feedback,
+.form-group-lg .form-control + .form-control-feedback {
+  width: 46px;
+  height: 46px;
+  line-height: 46px;
+}
+.input-sm + .form-control-feedback,
+.input-group-sm + .form-control-feedback,
+.form-group-sm .form-control + .form-control-feedback {
+  width: 30px;
+  height: 30px;
+  line-height: 30px;
+}
+.has-success .help-block,
+.has-success .control-label,
+.has-success .radio,
+.has-success .checkbox,
+.has-success .radio-inline,
+.has-success .checkbox-inline,
+.has-success.radio label,
+.has-success.checkbox label,
+.has-success.radio-inline label,
+.has-success.checkbox-inline label {
+  color: #3c763d;
+}
+.has-success .form-control {
+  border-color: #3c763d;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-success .form-control:focus {
+  border-color: #2b542c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
+}
+.has-success .input-group-addon {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #3c763d;
+}
+.has-success .form-control-feedback {
+  color: #3c763d;
+}
+.has-warning .help-block,
+.has-warning .control-label,
+.has-warning .radio,
+.has-warning .checkbox,
+.has-warning .radio-inline,
+.has-warning .checkbox-inline,
+.has-warning.radio label,
+.has-warning.checkbox label,
+.has-warning.radio-inline label,
+.has-warning.checkbox-inline label {
+  color: #8a6d3b;
+}
+.has-warning .form-control {
+  border-color: #8a6d3b;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-warning .form-control:focus {
+  border-color: #66512c;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
+}
+.has-warning .input-group-addon {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #8a6d3b;
+}
+.has-warning .form-control-feedback {
+  color: #8a6d3b;
+}
+.has-error .help-block,
+.has-error .control-label,
+.has-error .radio,
+.has-error .checkbox,
+.has-error .radio-inline,
+.has-error .checkbox-inline,
+.has-error.radio label,
+.has-error.checkbox label,
+.has-error.radio-inline label,
+.has-error.checkbox-inline label {
+  color: #a94442;
+}
+.has-error .form-control {
+  border-color: #a94442;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
+}
+.has-error .form-control:focus {
+  border-color: #843534;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
+}
+.has-error .input-group-addon {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #a94442;
+}
+.has-error .form-control-feedback {
+  color: #a94442;
+}
+.has-feedback label ~ .form-control-feedback {
+  top: 25px;
+}
+.has-feedback label.sr-only ~ .form-control-feedback {
+  top: 0;
+}
+.help-block {
+  display: block;
+  margin-top: 5px;
+  margin-bottom: 10px;
+  color: #737373;
+}
+@media (min-width: 768px) {
+  .form-inline .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-static {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .form-inline .input-group .input-group-addon,
+  .form-inline .input-group .input-group-btn,
+  .form-inline .input-group .form-control {
+    width: auto;
+  }
+  .form-inline .input-group > .form-control {
+    width: 100%;
+  }
+  .form-inline .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio,
+  .form-inline .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .form-inline .radio label,
+  .form-inline .checkbox label {
+    padding-left: 0;
+  }
+  .form-inline .radio input[type="radio"],
+  .form-inline .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .form-inline .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox,
+.form-horizontal .radio-inline,
+.form-horizontal .checkbox-inline {
+  padding-top: 7px;
+  margin-top: 0;
+  margin-bottom: 0;
+}
+.form-horizontal .radio,
+.form-horizontal .checkbox {
+  min-height: 27px;
+}
+.form-horizontal .form-group {
+  margin-right: -15px;
+  margin-left: -15px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .control-label {
+    padding-top: 7px;
+    margin-bottom: 0;
+    text-align: right;
+  }
+}
+.form-horizontal .has-feedback .form-control-feedback {
+  right: 15px;
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-lg .control-label {
+    padding-top: 11px;
+    font-size: 18px;
+  }
+}
+@media (min-width: 768px) {
+  .form-horizontal .form-group-sm .control-label {
+    padding-top: 6px;
+    font-size: 12px;
+  }
+}
+.btn {
+  display: inline-block;
+  margin-bottom: 0;
+  font-weight: normal;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: middle;
+  -ms-touch-action: manipulation;
+  touch-action: manipulation;
+  cursor: pointer;
+  background-image: none;
+  border: 1px solid transparent;
+  padding: 6px 12px;
+  font-size: 14px;
+  line-height: 1.42857143;
+  border-radius: 4px;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+}
+.btn:focus,
+.btn:active:focus,
+.btn.active:focus,
+.btn.focus,
+.btn:active.focus,
+.btn.active.focus {
+  outline: 5px auto -webkit-focus-ring-color;
+  outline-offset: -2px;
+}
+.btn:hover,
+.btn:focus,
+.btn.focus {
+  color: #333;
+  text-decoration: none;
+}
+.btn:active,
+.btn.active {
+  background-image: none;
+  outline: 0;
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn.disabled,
+.btn[disabled],
+fieldset[disabled] .btn {
+  cursor: not-allowed;
+  filter: alpha(opacity=65);
+  opacity: 0.65;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+a.btn.disabled,
+fieldset[disabled] a.btn {
+  pointer-events: none;
+}
+.btn-default {
+  color: #333;
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default:focus,
+.btn-default.focus {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #8c8c8c;
+}
+.btn-default:hover {
+  color: #333;
+  background-color: #e6e6e6;
+  border-color: #adadad;
+}
+.btn-default:active,
+.btn-default.active,
+.open > .dropdown-toggle.btn-default {
+  color: #333;
+  background-color: #e6e6e6;
+  background-image: none;
+  border-color: #adadad;
+}
+.btn-default:active:hover,
+.btn-default.active:hover,
+.open > .dropdown-toggle.btn-default:hover,
+.btn-default:active:focus,
+.btn-default.active:focus,
+.open > .dropdown-toggle.btn-default:focus,
+.btn-default:active.focus,
+.btn-default.active.focus,
+.open > .dropdown-toggle.btn-default.focus {
+  color: #333;
+  background-color: #d4d4d4;
+  border-color: #8c8c8c;
+}
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus {
+  background-color: #fff;
+  border-color: #ccc;
+}
+.btn-default .badge {
+  color: #fff;
+  background-color: #333;
+}
+.btn-primary {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary:focus,
+.btn-primary.focus {
+  color: #fff;
+  background-color: #286090;
+  border-color: #122b40;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #286090;
+  border-color: #204d74;
+}
+.btn-primary:active,
+.btn-primary.active,
+.open > .dropdown-toggle.btn-primary {
+  color: #fff;
+  background-color: #286090;
+  background-image: none;
+  border-color: #204d74;
+}
+.btn-primary:active:hover,
+.btn-primary.active:hover,
+.open > .dropdown-toggle.btn-primary:hover,
+.btn-primary:active:focus,
+.btn-primary.active:focus,
+.open > .dropdown-toggle.btn-primary:focus,
+.btn-primary:active.focus,
+.btn-primary.active.focus,
+.open > .dropdown-toggle.btn-primary.focus {
+  color: #fff;
+  background-color: #204d74;
+  border-color: #122b40;
+}
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus {
+  background-color: #337ab7;
+  border-color: #2e6da4;
+}
+.btn-primary .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.btn-success {
+  color: #fff;
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success:focus,
+.btn-success.focus {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #255625;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #449d44;
+  border-color: #398439;
+}
+.btn-success:active,
+.btn-success.active,
+.open > .dropdown-toggle.btn-success {
+  color: #fff;
+  background-color: #449d44;
+  background-image: none;
+  border-color: #398439;
+}
+.btn-success:active:hover,
+.btn-success.active:hover,
+.open > .dropdown-toggle.btn-success:hover,
+.btn-success:active:focus,
+.btn-success.active:focus,
+.open > .dropdown-toggle.btn-success:focus,
+.btn-success:active.focus,
+.btn-success.active.focus,
+.open > .dropdown-toggle.btn-success.focus {
+  color: #fff;
+  background-color: #398439;
+  border-color: #255625;
+}
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus {
+  background-color: #5cb85c;
+  border-color: #4cae4c;
+}
+.btn-success .badge {
+  color: #5cb85c;
+  background-color: #fff;
+}
+.btn-info {
+  color: #fff;
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info:focus,
+.btn-info.focus {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #1b6d85;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #31b0d5;
+  border-color: #269abc;
+}
+.btn-info:active,
+.btn-info.active,
+.open > .dropdown-toggle.btn-info {
+  color: #fff;
+  background-color: #31b0d5;
+  background-image: none;
+  border-color: #269abc;
+}
+.btn-info:active:hover,
+.btn-info.active:hover,
+.open > .dropdown-toggle.btn-info:hover,
+.btn-info:active:focus,
+.btn-info.active:focus,
+.open > .dropdown-toggle.btn-info:focus,
+.btn-info:active.focus,
+.btn-info.active.focus,
+.open > .dropdown-toggle.btn-info.focus {
+  color: #fff;
+  background-color: #269abc;
+  border-color: #1b6d85;
+}
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus {
+  background-color: #5bc0de;
+  border-color: #46b8da;
+}
+.btn-info .badge {
+  color: #5bc0de;
+  background-color: #fff;
+}
+.btn-warning {
+  color: #fff;
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning:focus,
+.btn-warning.focus {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #985f0d;
+}
+.btn-warning:hover {
+  color: #fff;
+  background-color: #ec971f;
+  border-color: #d58512;
+}
+.btn-warning:active,
+.btn-warning.active,
+.open > .dropdown-toggle.btn-warning {
+  color: #fff;
+  background-color: #ec971f;
+  background-image: none;
+  border-color: #d58512;
+}
+.btn-warning:active:hover,
+.btn-warning.active:hover,
+.open > .dropdown-toggle.btn-warning:hover,
+.btn-warning:active:focus,
+.btn-warning.active:focus,
+.open > .dropdown-toggle.btn-warning:focus,
+.btn-warning:active.focus,
+.btn-warning.active.focus,
+.open > .dropdown-toggle.btn-warning.focus {
+  color: #fff;
+  background-color: #d58512;
+  border-color: #985f0d;
+}
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus {
+  background-color: #f0ad4e;
+  border-color: #eea236;
+}
+.btn-warning .badge {
+  color: #f0ad4e;
+  background-color: #fff;
+}
+.btn-danger {
+  color: #fff;
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger:focus,
+.btn-danger.focus {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #761c19;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c9302c;
+  border-color: #ac2925;
+}
+.btn-danger:active,
+.btn-danger.active,
+.open > .dropdown-toggle.btn-danger {
+  color: #fff;
+  background-color: #c9302c;
+  background-image: none;
+  border-color: #ac2925;
+}
+.btn-danger:active:hover,
+.btn-danger.active:hover,
+.open > .dropdown-toggle.btn-danger:hover,
+.btn-danger:active:focus,
+.btn-danger.active:focus,
+.open > .dropdown-toggle.btn-danger:focus,
+.btn-danger:active.focus,
+.btn-danger.active.focus,
+.open > .dropdown-toggle.btn-danger.focus {
+  color: #fff;
+  background-color: #ac2925;
+  border-color: #761c19;
+}
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus {
+  background-color: #d9534f;
+  border-color: #d43f3a;
+}
+.btn-danger .badge {
+  color: #d9534f;
+  background-color: #fff;
+}
+.btn-link {
+  font-weight: 400;
+  color: #337ab7;
+  border-radius: 0;
+}
+.btn-link,
+.btn-link:active,
+.btn-link.active,
+.btn-link[disabled],
+fieldset[disabled] .btn-link {
+  background-color: transparent;
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn-link,
+.btn-link:hover,
+.btn-link:focus,
+.btn-link:active {
+  border-color: transparent;
+}
+.btn-link:hover,
+.btn-link:focus {
+  color: #23527c;
+  text-decoration: underline;
+  background-color: transparent;
+}
+.btn-link[disabled]:hover,
+fieldset[disabled] .btn-link:hover,
+.btn-link[disabled]:focus,
+fieldset[disabled] .btn-link:focus {
+  color: #777777;
+  text-decoration: none;
+}
+.btn-lg,
+.btn-group-lg > .btn {
+  padding: 10px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+  border-radius: 6px;
+}
+.btn-sm,
+.btn-group-sm > .btn {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 3px;
+}
+.btn-xs,
+.btn-group-xs > .btn {
+  padding: 1px 5px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 3px;
+}
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 5px;
+}
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  -o-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.in {
+  opacity: 1;
+}
+.collapse {
+  display: none;
+}
+.collapse.in {
+  display: block;
+}
+tr.collapse.in {
+  display: table-row;
+}
+tbody.collapse.in {
+  display: table-row-group;
+}
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition-property: height, visibility;
+  -o-transition-property: height, visibility;
+  transition-property: height, visibility;
+  -webkit-transition-duration: 0.35s;
+  -o-transition-duration: 0.35s;
+  transition-duration: 0.35s;
+  -webkit-transition-timing-function: ease;
+  -o-transition-timing-function: ease;
+  transition-timing-function: ease;
+}
+.caret {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 2px;
+  vertical-align: middle;
+  border-top: 4px dashed;
+  border-top: 4px solid \9;
+  border-right: 4px solid transparent;
+  border-left: 4px solid transparent;
+}
+.dropup,
+.dropdown {
+  position: relative;
+}
+.dropdown-toggle:focus {
+  outline: 0;
+}
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 160px;
+  padding: 5px 0;
+  margin: 2px 0 0;
+  font-size: 14px;
+  text-align: left;
+  list-style: none;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 4px;
+  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
+}
+.dropdown-menu.pull-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu .divider {
+  height: 1px;
+  margin: 9px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.dropdown-menu > li > a {
+  display: block;
+  padding: 3px 20px;
+  clear: both;
+  font-weight: 400;
+  line-height: 1.42857143;
+  color: #333333;
+  white-space: nowrap;
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  color: #262626;
+  text-decoration: none;
+  background-color: #f5f5f5;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #337ab7;
+  outline: 0;
+}
+.dropdown-menu > .disabled > a,
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  color: #777777;
+}
+.dropdown-menu > .disabled > a:hover,
+.dropdown-menu > .disabled > a:focus {
+  text-decoration: none;
+  cursor: not-allowed;
+  background-color: transparent;
+  background-image: none;
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+}
+.open > .dropdown-menu {
+  display: block;
+}
+.open > a {
+  outline: 0;
+}
+.dropdown-menu-right {
+  right: 0;
+  left: auto;
+}
+.dropdown-menu-left {
+  right: auto;
+  left: 0;
+}
+.dropdown-header {
+  display: block;
+  padding: 3px 20px;
+  font-size: 12px;
+  line-height: 1.42857143;
+  color: #777777;
+  white-space: nowrap;
+}
+.dropdown-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 990;
+}
+.pull-right > .dropdown-menu {
+  right: 0;
+  left: auto;
+}
+.dropup .caret,
+.navbar-fixed-bottom .dropdown .caret {
+  content: "";
+  border-top: 0;
+  border-bottom: 4px dashed;
+  border-bottom: 4px solid \9;
+}
+.dropup .dropdown-menu,
+.navbar-fixed-bottom .dropdown .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+  margin-bottom: 2px;
+}
+@media (min-width: 768px) {
+  .navbar-right .dropdown-menu {
+    right: 0;
+    left: auto;
+  }
+  .navbar-right .dropdown-menu-left {
+    right: auto;
+    left: 0;
+  }
+}
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: inline-block;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  float: left;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover,
+.btn-group > .btn:focus,
+.btn-group-vertical > .btn:focus,
+.btn-group > .btn:active,
+.btn-group-vertical > .btn:active,
+.btn-group > .btn.active,
+.btn-group-vertical > .btn.active {
+  z-index: 2;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group {
+  margin-left: -1px;
+}
+.btn-toolbar {
+  margin-left: -5px;
+}
+.btn-toolbar .btn,
+.btn-toolbar .btn-group,
+.btn-toolbar .input-group {
+  float: left;
+}
+.btn-toolbar > .btn,
+.btn-toolbar > .btn-group,
+.btn-toolbar > .input-group {
+  margin-left: 5px;
+}
+.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
+  border-radius: 0;
+}
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.btn-group > .btn:last-child:not(:first-child),
+.btn-group > .dropdown-toggle:not(:first-child) {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group > .btn-group {
+  float: left;
+}
+.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group .dropdown-toggle:active,
+.btn-group.open .dropdown-toggle {
+  outline: 0;
+}
+.btn-group > .btn + .dropdown-toggle {
+  padding-right: 8px;
+  padding-left: 8px;
+}
+.btn-group > .btn-lg + .dropdown-toggle {
+  padding-right: 12px;
+  padding-left: 12px;
+}
+.btn-group.open .dropdown-toggle {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
+}
+.btn-group.open .dropdown-toggle.btn-link {
+  -webkit-box-shadow: none;
+  box-shadow: none;
+}
+.btn .caret {
+  margin-left: 0;
+}
+.btn-lg .caret {
+  border-width: 5px 5px 0;
+  border-bottom-width: 0;
+}
+.dropup .btn-lg .caret {
+  border-width: 0 5px 5px;
+}
+.btn-group-vertical > .btn,
+.btn-group-vertical > .btn-group,
+.btn-group-vertical > .btn-group > .btn {
+  display: block;
+  float: none;
+  width: 100%;
+  max-width: 100%;
+}
+.btn-group-vertical > .btn-group > .btn {
+  float: none;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn:first-child:not(:last-child) {
+  border-top-left-radius: 4px;
+  border-top-right-radius: 4px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:last-child:not(:first-child) {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 4px;
+  border-bottom-left-radius: 4px;
+}
+.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
+  border-radius: 0;
+}
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
+.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+.btn-group-justified {
+  display: table;
+  width: 100%;
+  table-layout: fixed;
+  border-collapse: separate;
+}
+.btn-group-justified > .btn,
+.btn-group-justified > .btn-group {
+  display: table-cell;
+  float: none;
+  width: 1%;
+}
+.btn-group-justified > .btn-group .btn {
+  width: 100%;
+}
+.btn-group-justified > .btn-group .dropdown-menu {
+  left: auto;
+}
+[data-toggle="buttons"] > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
+[data-toggle="buttons"] > .btn input[type="checkbox"],
+[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+.input-group {
+  position: relative;
+  display: table;
+  border-collapse: separate;
+}
+.input-group[class*="col-"] {
+  float: none;
+  padding-right: 0;
+  padding-left: 0;
+}
+.input-group .form-control {
+  position: relative;
+  z-index: 2;
+  float: left;
+  width: 100%;
+  margin-bottom: 0;
+}
+.input-group .form-control:focus {
+  z-index: 3;
+}
+.input-group-lg > .form-control,
+.input-group-lg > .input-group-addon,
+.input-group-lg > .input-group-btn > .btn {
+  height: 46px;
+  padding: 10px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+  border-radius: 6px;
+}
+select.input-group-lg > .form-control,
+select.input-group-lg > .input-group-addon,
+select.input-group-lg > .input-group-btn > .btn {
+  height: 46px;
+  line-height: 46px;
+}
+textarea.input-group-lg > .form-control,
+textarea.input-group-lg > .input-group-addon,
+textarea.input-group-lg > .input-group-btn > .btn,
+select[multiple].input-group-lg > .form-control,
+select[multiple].input-group-lg > .input-group-addon,
+select[multiple].input-group-lg > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-sm > .form-control,
+.input-group-sm > .input-group-addon,
+.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+  border-radius: 3px;
+}
+select.input-group-sm > .form-control,
+select.input-group-sm > .input-group-addon,
+select.input-group-sm > .input-group-btn > .btn {
+  height: 30px;
+  line-height: 30px;
+}
+textarea.input-group-sm > .form-control,
+textarea.input-group-sm > .input-group-addon,
+textarea.input-group-sm > .input-group-btn > .btn,
+select[multiple].input-group-sm > .form-control,
+select[multiple].input-group-sm > .input-group-addon,
+select[multiple].input-group-sm > .input-group-btn > .btn {
+  height: auto;
+}
+.input-group-addon,
+.input-group-btn,
+.input-group .form-control {
+  display: table-cell;
+}
+.input-group-addon:not(:first-child):not(:last-child),
+.input-group-btn:not(:first-child):not(:last-child),
+.input-group .form-control:not(:first-child):not(:last-child) {
+  border-radius: 0;
+}
+.input-group-addon,
+.input-group-btn {
+  width: 1%;
+  white-space: nowrap;
+  vertical-align: middle;
+}
+.input-group-addon {
+  padding: 6px 12px;
+  font-size: 14px;
+  font-weight: 400;
+  line-height: 1;
+  color: #555555;
+  text-align: center;
+  background-color: #eeeeee;
+  border: 1px solid #ccc;
+  border-radius: 4px;
+}
+.input-group-addon.input-sm {
+  padding: 5px 10px;
+  font-size: 12px;
+  border-radius: 3px;
+}
+.input-group-addon.input-lg {
+  padding: 10px 16px;
+  font-size: 18px;
+  border-radius: 6px;
+}
+.input-group-addon input[type="radio"],
+.input-group-addon input[type="checkbox"] {
+  margin-top: 0;
+}
+.input-group .form-control:first-child,
+.input-group-addon:first-child,
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group > .btn,
+.input-group-btn:first-child > .dropdown-toggle,
+.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.input-group-addon:first-child {
+  border-right: 0;
+}
+.input-group .form-control:last-child,
+.input-group-addon:last-child,
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group > .btn,
+.input-group-btn:last-child > .dropdown-toggle,
+.input-group-btn:first-child > .btn:not(:first-child),
+.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.input-group-addon:last-child {
+  border-left: 0;
+}
+.input-group-btn {
+  position: relative;
+  font-size: 0;
+  white-space: nowrap;
+}
+.input-group-btn > .btn {
+  position: relative;
+}
+.input-group-btn > .btn + .btn {
+  margin-left: -1px;
+}
+.input-group-btn > .btn:hover,
+.input-group-btn > .btn:focus,
+.input-group-btn > .btn:active {
+  z-index: 2;
+}
+.input-group-btn:first-child > .btn,
+.input-group-btn:first-child > .btn-group {
+  margin-right: -1px;
+}
+.input-group-btn:last-child > .btn,
+.input-group-btn:last-child > .btn-group {
+  z-index: 2;
+  margin-left: -1px;
+}
+.nav {
+  padding-left: 0;
+  margin-bottom: 0;
+  list-style: none;
+}
+.nav > li {
+  position: relative;
+  display: block;
+}
+.nav > li > a {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+}
+.nav > li > a:hover,
+.nav > li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.nav > li.disabled > a {
+  color: #777777;
+}
+.nav > li.disabled > a:hover,
+.nav > li.disabled > a:focus {
+  color: #777777;
+  text-decoration: none;
+  cursor: not-allowed;
+  background-color: transparent;
+}
+.nav .open > a,
+.nav .open > a:hover,
+.nav .open > a:focus {
+  background-color: #eeeeee;
+  border-color: #337ab7;
+}
+.nav .nav-divider {
+  height: 1px;
+  margin: 9px 0;
+  overflow: hidden;
+  background-color: #e5e5e5;
+}
+.nav > li > a > img {
+  max-width: none;
+}
+.nav-tabs {
+  border-bottom: 1px solid #ddd;
+}
+.nav-tabs > li {
+  float: left;
+  margin-bottom: -1px;
+}
+.nav-tabs > li > a {
+  margin-right: 2px;
+  line-height: 1.42857143;
+  border: 1px solid transparent;
+  border-radius: 4px 4px 0 0;
+}
+.nav-tabs > li > a:hover {
+  border-color: #eeeeee #eeeeee #ddd;
+}
+.nav-tabs > li.active > a,
+.nav-tabs > li.active > a:hover,
+.nav-tabs > li.active > a:focus {
+  color: #555555;
+  cursor: default;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-bottom-color: transparent;
+}
+.nav-tabs.nav-justified {
+  width: 100%;
+  border-bottom: 0;
+}
+.nav-tabs.nav-justified > li {
+  float: none;
+}
+.nav-tabs.nav-justified > li > a {
+  margin-bottom: 5px;
+  text-align: center;
+}
+.nav-tabs.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-tabs.nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs.nav-justified > li > a {
+  margin-right: 0;
+  border-radius: 4px;
+}
+.nav-tabs.nav-justified > .active > a,
+.nav-tabs.nav-justified > .active > a:hover,
+.nav-tabs.nav-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs.nav-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 4px 4px 0 0;
+  }
+  .nav-tabs.nav-justified > .active > a,
+  .nav-tabs.nav-justified > .active > a:hover,
+  .nav-tabs.nav-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.nav-pills > li {
+  float: left;
+}
+.nav-pills > li > a {
+  border-radius: 4px;
+}
+.nav-pills > li + li {
+  margin-left: 2px;
+}
+.nav-pills > li.active > a,
+.nav-pills > li.active > a:hover,
+.nav-pills > li.active > a:focus {
+  color: #fff;
+  background-color: #337ab7;
+}
+.nav-stacked > li {
+  float: none;
+}
+.nav-stacked > li + li {
+  margin-top: 2px;
+  margin-left: 0;
+}
+.nav-justified {
+  width: 100%;
+}
+.nav-justified > li {
+  float: none;
+}
+.nav-justified > li > a {
+  margin-bottom: 5px;
+  text-align: center;
+}
+.nav-justified > .dropdown .dropdown-menu {
+  top: auto;
+  left: auto;
+}
+@media (min-width: 768px) {
+  .nav-justified > li {
+    display: table-cell;
+    width: 1%;
+  }
+  .nav-justified > li > a {
+    margin-bottom: 0;
+  }
+}
+.nav-tabs-justified {
+  border-bottom: 0;
+}
+.nav-tabs-justified > li > a {
+  margin-right: 0;
+  border-radius: 4px;
+}
+.nav-tabs-justified > .active > a,
+.nav-tabs-justified > .active > a:hover,
+.nav-tabs-justified > .active > a:focus {
+  border: 1px solid #ddd;
+}
+@media (min-width: 768px) {
+  .nav-tabs-justified > li > a {
+    border-bottom: 1px solid #ddd;
+    border-radius: 4px 4px 0 0;
+  }
+  .nav-tabs-justified > .active > a,
+  .nav-tabs-justified > .active > a:hover,
+  .nav-tabs-justified > .active > a:focus {
+    border-bottom-color: #fff;
+  }
+}
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+.navbar {
+  position: relative;
+  min-height: 50px;
+  margin-bottom: 20px;
+  border: 1px solid transparent;
+}
+@media (min-width: 768px) {
+  .navbar {
+    border-radius: 4px;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-header {
+    float: left;
+  }
+}
+.navbar-collapse {
+  padding-right: 15px;
+  padding-left: 15px;
+  overflow-x: visible;
+  border-top: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
+  -webkit-overflow-scrolling: touch;
+}
+.navbar-collapse.in {
+  overflow-y: auto;
+}
+@media (min-width: 768px) {
+  .navbar-collapse {
+    width: auto;
+    border-top: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+  .navbar-collapse.collapse {
+    display: block !important;
+    height: auto !important;
+    padding-bottom: 0;
+    overflow: visible !important;
+  }
+  .navbar-collapse.in {
+    overflow-y: visible;
+  }
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-static-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  position: fixed;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+.navbar-fixed-top .navbar-collapse,
+.navbar-fixed-bottom .navbar-collapse {
+  max-height: 340px;
+}
+@media (max-device-width: 480px) and (orientation: landscape) {
+  .navbar-fixed-top .navbar-collapse,
+  .navbar-fixed-bottom .navbar-collapse {
+    max-height: 200px;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-fixed-top,
+  .navbar-fixed-bottom {
+    border-radius: 0;
+  }
+}
+.navbar-fixed-top {
+  top: 0;
+  border-width: 0 0 1px;
+}
+.navbar-fixed-bottom {
+  bottom: 0;
+  margin-bottom: 0;
+  border-width: 1px 0 0;
+}
+.container > .navbar-header,
+.container-fluid > .navbar-header,
+.container > .navbar-collapse,
+.container-fluid > .navbar-collapse {
+  margin-right: -15px;
+  margin-left: -15px;
+}
+@media (min-width: 768px) {
+  .container > .navbar-header,
+  .container-fluid > .navbar-header,
+  .container > .navbar-collapse,
+  .container-fluid > .navbar-collapse {
+    margin-right: 0;
+    margin-left: 0;
+  }
+}
+.navbar-static-top {
+  z-index: 1000;
+  border-width: 0 0 1px;
+}
+@media (min-width: 768px) {
+  .navbar-static-top {
+    border-radius: 0;
+  }
+}
+.navbar-brand {
+  float: left;
+  height: 50px;
+  padding: 15px 15px;
+  font-size: 18px;
+  line-height: 20px;
+}
+.navbar-brand:hover,
+.navbar-brand:focus {
+  text-decoration: none;
+}
+.navbar-brand > img {
+  display: block;
+}
+@media (min-width: 768px) {
+  .navbar > .container .navbar-brand,
+  .navbar > .container-fluid .navbar-brand {
+    margin-left: -15px;
+  }
+}
+.navbar-toggle {
+  position: relative;
+  float: right;
+  padding: 9px 10px;
+  margin-right: 15px;
+  margin-top: 8px;
+  margin-bottom: 8px;
+  background-color: transparent;
+  background-image: none;
+  border: 1px solid transparent;
+  border-radius: 4px;
+}
+.navbar-toggle:focus {
+  outline: 0;
+}
+.navbar-toggle .icon-bar {
+  display: block;
+  width: 22px;
+  height: 2px;
+  border-radius: 1px;
+}
+.navbar-toggle .icon-bar + .icon-bar {
+  margin-top: 4px;
+}
+@media (min-width: 768px) {
+  .navbar-toggle {
+    display: none;
+  }
+}
+.navbar-nav {
+  margin: 7.5px -15px;
+}
+.navbar-nav > li > a {
+  padding-top: 10px;
+  padding-bottom: 10px;
+  line-height: 20px;
+}
+@media (max-width: 767px) {
+  .navbar-nav .open .dropdown-menu {
+    position: static;
+    float: none;
+    width: auto;
+    margin-top: 0;
+    background-color: transparent;
+    border: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+  .navbar-nav .open .dropdown-menu > li > a,
+  .navbar-nav .open .dropdown-menu .dropdown-header {
+    padding: 5px 15px 5px 25px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a {
+    line-height: 20px;
+  }
+  .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-nav .open .dropdown-menu > li > a:focus {
+    background-image: none;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-nav {
+    float: left;
+    margin: 0;
+  }
+  .navbar-nav > li {
+    float: left;
+  }
+  .navbar-nav > li > a {
+    padding-top: 15px;
+    padding-bottom: 15px;
+  }
+}
+.navbar-form {
+  padding: 10px 15px;
+  margin-right: -15px;
+  margin-left: -15px;
+  border-top: 1px solid transparent;
+  border-bottom: 1px solid transparent;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
+  margin-top: 8px;
+  margin-bottom: 8px;
+}
+@media (min-width: 768px) {
+  .navbar-form .form-group {
+    display: inline-block;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .navbar-form .form-control-static {
+    display: inline-block;
+  }
+  .navbar-form .input-group {
+    display: inline-table;
+    vertical-align: middle;
+  }
+  .navbar-form .input-group .input-group-addon,
+  .navbar-form .input-group .input-group-btn,
+  .navbar-form .input-group .form-control {
+    width: auto;
+  }
+  .navbar-form .input-group > .form-control {
+    width: 100%;
+  }
+  .navbar-form .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio,
+  .navbar-form .checkbox {
+    display: inline-block;
+    margin-top: 0;
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  .navbar-form .radio label,
+  .navbar-form .checkbox label {
+    padding-left: 0;
+  }
+  .navbar-form .radio input[type="radio"],
+  .navbar-form .checkbox input[type="checkbox"] {
+    position: relative;
+    margin-left: 0;
+  }
+  .navbar-form .has-feedback .form-control-feedback {
+    top: 0;
+  }
+}
+@media (max-width: 767px) {
+  .navbar-form .form-group {
+    margin-bottom: 5px;
+  }
+  .navbar-form .form-group:last-child {
+    margin-bottom: 0;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-form {
+    width: auto;
+    padding-top: 0;
+    padding-bottom: 0;
+    margin-right: 0;
+    margin-left: 0;
+    border: 0;
+    -webkit-box-shadow: none;
+    box-shadow: none;
+  }
+}
+.navbar-nav > li > .dropdown-menu {
+  margin-top: 0;
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
+  margin-bottom: 0;
+  border-top-left-radius: 4px;
+  border-top-right-radius: 4px;
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.navbar-btn {
+  margin-top: 8px;
+  margin-bottom: 8px;
+}
+.navbar-btn.btn-sm {
+  margin-top: 10px;
+  margin-bottom: 10px;
+}
+.navbar-btn.btn-xs {
+  margin-top: 14px;
+  margin-bottom: 14px;
+}
+.navbar-text {
+  margin-top: 15px;
+  margin-bottom: 15px;
+}
+@media (min-width: 768px) {
+  .navbar-text {
+    float: left;
+    margin-right: 15px;
+    margin-left: 15px;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-left {
+    float: left !important;
+  }
+  .navbar-right {
+    float: right !important;
+    margin-right: -15px;
+  }
+  .navbar-right ~ .navbar-right {
+    margin-right: 0;
+  }
+}
+.navbar-default {
+  background-color: #f8f8f8;
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-brand {
+  color: #777;
+}
+.navbar-default .navbar-brand:hover,
+.navbar-default .navbar-brand:focus {
+  color: #5e5e5e;
+  background-color: transparent;
+}
+.navbar-default .navbar-text {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a {
+  color: #777;
+}
+.navbar-default .navbar-nav > li > a:hover,
+.navbar-default .navbar-nav > li > a:focus {
+  color: #333;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .active > a,
+.navbar-default .navbar-nav > .active > a:hover,
+.navbar-default .navbar-nav > .active > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+.navbar-default .navbar-nav > .disabled > a,
+.navbar-default .navbar-nav > .disabled > a:hover,
+.navbar-default .navbar-nav > .disabled > a:focus {
+  color: #ccc;
+  background-color: transparent;
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .open > a:hover,
+.navbar-default .navbar-nav > .open > a:focus {
+  color: #555;
+  background-color: #e7e7e7;
+}
+@media (max-width: 767px) {
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
+    color: #777;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #333;
+    background-color: transparent;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #555;
+    background-color: #e7e7e7;
+  }
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #ccc;
+    background-color: transparent;
+  }
+}
+.navbar-default .navbar-toggle {
+  border-color: #ddd;
+}
+.navbar-default .navbar-toggle:hover,
+.navbar-default .navbar-toggle:focus {
+  background-color: #ddd;
+}
+.navbar-default .navbar-toggle .icon-bar {
+  background-color: #888;
+}
+.navbar-default .navbar-collapse,
+.navbar-default .navbar-form {
+  border-color: #e7e7e7;
+}
+.navbar-default .navbar-link {
+  color: #777;
+}
+.navbar-default .navbar-link:hover {
+  color: #333;
+}
+.navbar-default .btn-link {
+  color: #777;
+}
+.navbar-default .btn-link:hover,
+.navbar-default .btn-link:focus {
+  color: #333;
+}
+.navbar-default .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-default .btn-link:hover,
+.navbar-default .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-default .btn-link:focus {
+  color: #ccc;
+}
+.navbar-inverse {
+  background-color: #222;
+  border-color: #080808;
+}
+.navbar-inverse .navbar-brand {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-brand:hover,
+.navbar-inverse .navbar-brand:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-text {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-nav > li > a:hover,
+.navbar-inverse .navbar-nav > li > a:focus {
+  color: #fff;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .active > a,
+.navbar-inverse .navbar-nav > .active > a:hover,
+.navbar-inverse .navbar-nav > .active > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+.navbar-inverse .navbar-nav > .disabled > a,
+.navbar-inverse .navbar-nav > .disabled > a:hover,
+.navbar-inverse .navbar-nav > .disabled > a:focus {
+  color: #444;
+  background-color: transparent;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .open > a:hover,
+.navbar-inverse .navbar-nav > .open > a:focus {
+  color: #fff;
+  background-color: #080808;
+}
+@media (max-width: 767px) {
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
+    border-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #9d9d9d;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
+    color: #fff;
+    background-color: transparent;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-color: #080808;
+  }
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
+  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
+    color: #444;
+    background-color: transparent;
+  }
+}
+.navbar-inverse .navbar-toggle {
+  border-color: #333;
+}
+.navbar-inverse .navbar-toggle:hover,
+.navbar-inverse .navbar-toggle:focus {
+  background-color: #333;
+}
+.navbar-inverse .navbar-toggle .icon-bar {
+  background-color: #fff;
+}
+.navbar-inverse .navbar-collapse,
+.navbar-inverse .navbar-form {
+  border-color: #101010;
+}
+.navbar-inverse .navbar-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .navbar-link:hover {
+  color: #fff;
+}
+.navbar-inverse .btn-link {
+  color: #9d9d9d;
+}
+.navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link:focus {
+  color: #fff;
+}
+.navbar-inverse .btn-link[disabled]:hover,
+fieldset[disabled] .navbar-inverse .btn-link:hover,
+.navbar-inverse .btn-link[disabled]:focus,
+fieldset[disabled] .navbar-inverse .btn-link:focus {
+  color: #444;
+}
+.breadcrumb {
+  padding: 8px 15px;
+  margin-bottom: 20px;
+  list-style: none;
+  background-color: #f5f5f5;
+  border-radius: 4px;
+}
+.breadcrumb > li {
+  display: inline-block;
+}
+.breadcrumb > li + li:before {
+  padding: 0 5px;
+  color: #ccc;
+  content: "/\00a0";
+}
+.breadcrumb > .active {
+  color: #777777;
+}
+.pagination {
+  display: inline-block;
+  padding-left: 0;
+  margin: 20px 0;
+  border-radius: 4px;
+}
+.pagination > li {
+  display: inline;
+}
+.pagination > li > a,
+.pagination > li > span {
+  position: relative;
+  float: left;
+  padding: 6px 12px;
+  margin-left: -1px;
+  line-height: 1.42857143;
+  color: #337ab7;
+  text-decoration: none;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.pagination > li > a:hover,
+.pagination > li > span:hover,
+.pagination > li > a:focus,
+.pagination > li > span:focus {
+  z-index: 2;
+  color: #23527c;
+  background-color: #eeeeee;
+  border-color: #ddd;
+}
+.pagination > li:first-child > a,
+.pagination > li:first-child > span {
+  margin-left: 0;
+  border-top-left-radius: 4px;
+  border-bottom-left-radius: 4px;
+}
+.pagination > li:last-child > a,
+.pagination > li:last-child > span {
+  border-top-right-radius: 4px;
+  border-bottom-right-radius: 4px;
+}
+.pagination > .active > a,
+.pagination > .active > span,
+.pagination > .active > a:hover,
+.pagination > .active > span:hover,
+.pagination > .active > a:focus,
+.pagination > .active > span:focus {
+  z-index: 3;
+  color: #fff;
+  cursor: default;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.pagination > .disabled > span,
+.pagination > .disabled > span:hover,
+.pagination > .disabled > span:focus,
+.pagination > .disabled > a,
+.pagination > .disabled > a:hover,
+.pagination > .disabled > a:focus {
+  color: #777777;
+  cursor: not-allowed;
+  background-color: #fff;
+  border-color: #ddd;
+}
+.pagination-lg > li > a,
+.pagination-lg > li > span {
+  padding: 10px 16px;
+  font-size: 18px;
+  line-height: 1.3333333;
+}
+.pagination-lg > li:first-child > a,
+.pagination-lg > li:first-child > span {
+  border-top-left-radius: 6px;
+  border-bottom-left-radius: 6px;
+}
+.pagination-lg > li:last-child > a,
+.pagination-lg > li:last-child > span {
+  border-top-right-radius: 6px;
+  border-bottom-right-radius: 6px;
+}
+.pagination-sm > li > a,
+.pagination-sm > li > span {
+  padding: 5px 10px;
+  font-size: 12px;
+  line-height: 1.5;
+}
+.pagination-sm > li:first-child > a,
+.pagination-sm > li:first-child > span {
+  border-top-left-radius: 3px;
+  border-bottom-left-radius: 3px;
+}
+.pagination-sm > li:last-child > a,
+.pagination-sm > li:last-child > span {
+  border-top-right-radius: 3px;
+  border-bottom-right-radius: 3px;
+}
+.pager {
+  padding-left: 0;
+  margin: 20px 0;
+  text-align: center;
+  list-style: none;
+}
+.pager li {
+  display: inline;
+}
+.pager li > a,
+.pager li > span {
+  display: inline-block;
+  padding: 5px 14px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 15px;
+}
+.pager li > a:hover,
+.pager li > a:focus {
+  text-decoration: none;
+  background-color: #eeeeee;
+}
+.pager .next > a,
+.pager .next > span {
+  float: right;
+}
+.pager .previous > a,
+.pager .previous > span {
+  float: left;
+}
+.pager .disabled > a,
+.pager .disabled > a:hover,
+.pager .disabled > a:focus,
+.pager .disabled > span {
+  color: #777777;
+  cursor: not-allowed;
+  background-color: #fff;
+}
+.label {
+  display: inline;
+  padding: 0.2em 0.6em 0.3em;
+  font-size: 75%;
+  font-weight: 700;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: 0.25em;
+}
+a.label:hover,
+a.label:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.label:empty {
+  display: none;
+}
+.btn .label {
+  position: relative;
+  top: -1px;
+}
+.label-default {
+  background-color: #777777;
+}
+.label-default[href]:hover,
+.label-default[href]:focus {
+  background-color: #5e5e5e;
+}
+.label-primary {
+  background-color: #337ab7;
+}
+.label-primary[href]:hover,
+.label-primary[href]:focus {
+  background-color: #286090;
+}
+.label-success {
+  background-color: #5cb85c;
+}
+.label-success[href]:hover,
+.label-success[href]:focus {
+  background-color: #449d44;
+}
+.label-info {
+  background-color: #5bc0de;
+}
+.label-info[href]:hover,
+.label-info[href]:focus {
+  background-color: #31b0d5;
+}
+.label-warning {
+  background-color: #f0ad4e;
+}
+.label-warning[href]:hover,
+.label-warning[href]:focus {
+  background-color: #ec971f;
+}
+.label-danger {
+  background-color: #d9534f;
+}
+.label-danger[href]:hover,
+.label-danger[href]:focus {
+  background-color: #c9302c;
+}
+.badge {
+  display: inline-block;
+  min-width: 10px;
+  padding: 3px 7px;
+  font-size: 12px;
+  font-weight: bold;
+  line-height: 1;
+  color: #fff;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: middle;
+  background-color: #777777;
+  border-radius: 10px;
+}
+.badge:empty {
+  display: none;
+}
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+.btn-xs .badge,
+.btn-group-xs > .btn .badge {
+  top: 0;
+  padding: 1px 5px;
+}
+a.badge:hover,
+a.badge:focus {
+  color: #fff;
+  text-decoration: none;
+  cursor: pointer;
+}
+.list-group-item.active > .badge,
+.nav-pills > .active > a > .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.list-group-item > .badge {
+  float: right;
+}
+.list-group-item > .badge + .badge {
+  margin-right: 5px;
+}
+.nav-pills > li > a > .badge {
+  margin-left: 3px;
+}
+.jumbotron {
+  padding-top: 30px;
+  padding-bottom: 30px;
+  margin-bottom: 30px;
+  color: inherit;
+  background-color: #eeeeee;
+}
+.jumbotron h1,
+.jumbotron .h1 {
+  color: inherit;
+}
+.jumbotron p {
+  margin-bottom: 15px;
+  font-size: 21px;
+  font-weight: 200;
+}
+.jumbotron > hr {
+  border-top-color: #d5d5d5;
+}
+.container .jumbotron,
+.container-fluid .jumbotron {
+  padding-right: 15px;
+  padding-left: 15px;
+  border-radius: 6px;
+}
+.jumbotron .container {
+  max-width: 100%;
+}
+@media screen and (min-width: 768px) {
+  .jumbotron {
+    padding-top: 48px;
+    padding-bottom: 48px;
+  }
+  .container .jumbotron,
+  .container-fluid .jumbotron {
+    padding-right: 60px;
+    padding-left: 60px;
+  }
+  .jumbotron h1,
+  .jumbotron .h1 {
+    font-size: 63px;
+  }
+}
+.thumbnail {
+  display: block;
+  padding: 4px;
+  margin-bottom: 20px;
+  line-height: 1.42857143;
+  background-color: #fff;
+  border: 1px solid #ddd;
+  border-radius: 4px;
+  -webkit-transition: border 0.2s ease-in-out;
+  -o-transition: border 0.2s ease-in-out;
+  transition: border 0.2s ease-in-out;
+}
+.thumbnail > img,
+.thumbnail a > img {
+  margin-right: auto;
+  margin-left: auto;
+}
+a.thumbnail:hover,
+a.thumbnail:focus,
+a.thumbnail.active {
+  border-color: #337ab7;
+}
+.thumbnail .caption {
+  padding: 9px;
+  color: #333333;
+}
+.alert {
+  padding: 15px;
+  margin-bottom: 20px;
+  border: 1px solid transparent;
+  border-radius: 4px;
+}
+.alert h4 {
+  margin-top: 0;
+  color: inherit;
+}
+.alert .alert-link {
+  font-weight: bold;
+}
+.alert > p,
+.alert > ul {
+  margin-bottom: 0;
+}
+.alert > p + p {
+  margin-top: 5px;
+}
+.alert-dismissable,
+.alert-dismissible {
+  padding-right: 35px;
+}
+.alert-dismissable .close,
+.alert-dismissible .close {
+  position: relative;
+  top: -2px;
+  right: -21px;
+  color: inherit;
+}
+.alert-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.alert-success hr {
+  border-top-color: #c9e2b3;
+}
+.alert-success .alert-link {
+  color: #2b542c;
+}
+.alert-info {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.alert-info hr {
+  border-top-color: #a6e1ec;
+}
+.alert-info .alert-link {
+  color: #245269;
+}
+.alert-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.alert-warning hr {
+  border-top-color: #f7e1b5;
+}
+.alert-warning .alert-link {
+  color: #66512c;
+}
+.alert-danger {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.alert-danger hr {
+  border-top-color: #e4b9c0;
+}
+.alert-danger .alert-link {
+  color: #843534;
+}
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@-o-keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 40px 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  height: 20px;
+  margin-bottom: 20px;
+  overflow: hidden;
+  background-color: #f5f5f5;
+  border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
+}
+.progress-bar {
+  float: left;
+  width: 0%;
+  height: 100%;
+  font-size: 12px;
+  line-height: 20px;
+  color: #fff;
+  text-align: center;
+  background-color: #337ab7;
+  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
+  -webkit-transition: width 0.6s ease;
+  -o-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+.progress-striped .progress-bar,
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  -webkit-background-size: 40px 40px;
+  background-size: 40px 40px;
+}
+.progress.active .progress-bar,
+.progress-bar.active {
+  -webkit-animation: progress-bar-stripes 2s linear infinite;
+  -o-animation: progress-bar-stripes 2s linear infinite;
+  animation: progress-bar-stripes 2s linear infinite;
+}
+.progress-bar-success {
+  background-color: #5cb85c;
+}
+.progress-striped .progress-bar-success {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-info {
+  background-color: #5bc0de;
+}
+.progress-striped .progress-bar-info {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-warning {
+  background-color: #f0ad4e;
+}
+.progress-striped .progress-bar-warning {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.progress-bar-danger {
+  background-color: #d9534f;
+}
+.progress-striped .progress-bar-danger {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+}
+.media {
+  margin-top: 15px;
+}
+.media:first-child {
+  margin-top: 0;
+}
+.media,
+.media-body {
+  overflow: hidden;
+  zoom: 1;
+}
+.media-body {
+  width: 10000px;
+}
+.media-object {
+  display: block;
+}
+.media-object.img-thumbnail {
+  max-width: none;
+}
+.media-right,
+.media > .pull-right {
+  padding-left: 10px;
+}
+.media-left,
+.media > .pull-left {
+  padding-right: 10px;
+}
+.media-left,
+.media-right,
+.media-body {
+  display: table-cell;
+  vertical-align: top;
+}
+.media-middle {
+  vertical-align: middle;
+}
+.media-bottom {
+  vertical-align: bottom;
+}
+.media-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.media-list {
+  padding-left: 0;
+  list-style: none;
+}
+.list-group {
+  padding-left: 0;
+  margin-bottom: 20px;
+}
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 10px 15px;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid #ddd;
+}
+.list-group-item:first-child {
+  border-top-left-radius: 4px;
+  border-top-right-radius: 4px;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 4px;
+  border-bottom-left-radius: 4px;
+}
+.list-group-item.disabled,
+.list-group-item.disabled:hover,
+.list-group-item.disabled:focus {
+  color: #777777;
+  cursor: not-allowed;
+  background-color: #eeeeee;
+}
+.list-group-item.disabled .list-group-item-heading,
+.list-group-item.disabled:hover .list-group-item-heading,
+.list-group-item.disabled:focus .list-group-item-heading {
+  color: inherit;
+}
+.list-group-item.disabled .list-group-item-text,
+.list-group-item.disabled:hover .list-group-item-text,
+.list-group-item.disabled:focus .list-group-item-text {
+  color: #777777;
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  z-index: 2;
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.list-group-item.active .list-group-item-heading,
+.list-group-item.active:hover .list-group-item-heading,
+.list-group-item.active:focus .list-group-item-heading,
+.list-group-item.active .list-group-item-heading > small,
+.list-group-item.active:hover .list-group-item-heading > small,
+.list-group-item.active:focus .list-group-item-heading > small,
+.list-group-item.active .list-group-item-heading > .small,
+.list-group-item.active:hover .list-group-item-heading > .small,
+.list-group-item.active:focus .list-group-item-heading > .small {
+  color: inherit;
+}
+.list-group-item.active .list-group-item-text,
+.list-group-item.active:hover .list-group-item-text,
+.list-group-item.active:focus .list-group-item-text {
+  color: #c7ddef;
+}
+a.list-group-item,
+button.list-group-item {
+  color: #555;
+}
+a.list-group-item .list-group-item-heading,
+button.list-group-item .list-group-item-heading {
+  color: #333;
+}
+a.list-group-item:hover,
+button.list-group-item:hover,
+a.list-group-item:focus,
+button.list-group-item:focus {
+  color: #555;
+  text-decoration: none;
+  background-color: #f5f5f5;
+}
+button.list-group-item {
+  width: 100%;
+  text-align: left;
+}
+.list-group-item-success {
+  color: #3c763d;
+  background-color: #dff0d8;
+}
+a.list-group-item-success,
+button.list-group-item-success {
+  color: #3c763d;
+}
+a.list-group-item-success .list-group-item-heading,
+button.list-group-item-success .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-success:hover,
+button.list-group-item-success:hover,
+a.list-group-item-success:focus,
+button.list-group-item-success:focus {
+  color: #3c763d;
+  background-color: #d0e9c6;
+}
+a.list-group-item-success.active,
+button.list-group-item-success.active,
+a.list-group-item-success.active:hover,
+button.list-group-item-success.active:hover,
+a.list-group-item-success.active:focus,
+button.list-group-item-success.active:focus {
+  color: #fff;
+  background-color: #3c763d;
+  border-color: #3c763d;
+}
+.list-group-item-info {
+  color: #31708f;
+  background-color: #d9edf7;
+}
+a.list-group-item-info,
+button.list-group-item-info {
+  color: #31708f;
+}
+a.list-group-item-info .list-group-item-heading,
+button.list-group-item-info .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-info:hover,
+button.list-group-item-info:hover,
+a.list-group-item-info:focus,
+button.list-group-item-info:focus {
+  color: #31708f;
+  background-color: #c4e3f3;
+}
+a.list-group-item-info.active,
+button.list-group-item-info.active,
+a.list-group-item-info.active:hover,
+button.list-group-item-info.active:hover,
+a.list-group-item-info.active:focus,
+button.list-group-item-info.active:focus {
+  color: #fff;
+  background-color: #31708f;
+  border-color: #31708f;
+}
+.list-group-item-warning {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+}
+a.list-group-item-warning,
+button.list-group-item-warning {
+  color: #8a6d3b;
+}
+a.list-group-item-warning .list-group-item-heading,
+button.list-group-item-warning .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-warning:hover,
+button.list-group-item-warning:hover,
+a.list-group-item-warning:focus,
+button.list-group-item-warning:focus {
+  color: #8a6d3b;
+  background-color: #faf2cc;
+}
+a.list-group-item-warning.active,
+button.list-group-item-warning.active,
+a.list-group-item-warning.active:hover,
+button.list-group-item-warning.active:hover,
+a.list-group-item-warning.active:focus,
+button.list-group-item-warning.active:focus {
+  color: #fff;
+  background-color: #8a6d3b;
+  border-color: #8a6d3b;
+}
+.list-group-item-danger {
+  color: #a94442;
+  background-color: #f2dede;
+}
+a.list-group-item-danger,
+button.list-group-item-danger {
+  color: #a94442;
+}
+a.list-group-item-danger .list-group-item-heading,
+button.list-group-item-danger .list-group-item-heading {
+  color: inherit;
+}
+a.list-group-item-danger:hover,
+button.list-group-item-danger:hover,
+a.list-group-item-danger:focus,
+button.list-group-item-danger:focus {
+  color: #a94442;
+  background-color: #ebcccc;
+}
+a.list-group-item-danger.active,
+button.list-group-item-danger.active,
+a.list-group-item-danger.active:hover,
+button.list-group-item-danger.active:hover,
+a.list-group-item-danger.active:focus,
+button.list-group-item-danger.active:focus {
+  color: #fff;
+  background-color: #a94442;
+  border-color: #a94442;
+}
+.list-group-item-heading {
+  margin-top: 0;
+  margin-bottom: 5px;
+}
+.list-group-item-text {
+  margin-bottom: 0;
+  line-height: 1.3;
+}
+.panel {
+  margin-bottom: 20px;
+  background-color: #fff;
+  border: 1px solid transparent;
+  border-radius: 4px;
+  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.panel-body {
+  padding: 15px;
+}
+.panel-heading {
+  padding: 10px 15px;
+  border-bottom: 1px solid transparent;
+  border-top-left-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.panel-heading > .dropdown .dropdown-toggle {
+  color: inherit;
+}
+.panel-title {
+  margin-top: 0;
+  margin-bottom: 0;
+  font-size: 16px;
+  color: inherit;
+}
+.panel-title > a,
+.panel-title > small,
+.panel-title > .small,
+.panel-title > small > a,
+.panel-title > .small > a {
+  color: inherit;
+}
+.panel-footer {
+  padding: 10px 15px;
+  background-color: #f5f5f5;
+  border-top: 1px solid #ddd;
+  border-bottom-right-radius: 3px;
+  border-bottom-left-radius: 3px;
+}
+.panel > .list-group,
+.panel > .panel-collapse > .list-group {
+  margin-bottom: 0;
+}
+.panel > .list-group .list-group-item,
+.panel > .panel-collapse > .list-group .list-group-item {
+  border-width: 1px 0;
+  border-radius: 0;
+}
+.panel > .list-group:first-child .list-group-item:first-child,
+.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
+  border-top: 0;
+  border-top-left-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.panel > .list-group:last-child .list-group-item:last-child,
+.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
+  border-bottom: 0;
+  border-bottom-right-radius: 3px;
+  border-bottom-left-radius: 3px;
+}
+.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+.panel-heading + .list-group .list-group-item:first-child {
+  border-top-width: 0;
+}
+.list-group + .panel-footer {
+  border-top-width: 0;
+}
+.panel > .table,
+.panel > .table-responsive > .table,
+.panel > .panel-collapse > .table {
+  margin-bottom: 0;
+}
+.panel > .table caption,
+.panel > .table-responsive > .table caption,
+.panel > .panel-collapse > .table caption {
+  padding-right: 15px;
+  padding-left: 15px;
+}
+.panel > .table:first-child,
+.panel > .table-responsive:first-child > .table:first-child {
+  border-top-left-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
+  border-top-left-radius: 3px;
+  border-top-right-radius: 3px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
+  border-top-left-radius: 3px;
+}
+.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
+.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
+.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
+.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
+  border-top-right-radius: 3px;
+}
+.panel > .table:last-child,
+.panel > .table-responsive:last-child > .table:last-child {
+  border-bottom-right-radius: 3px;
+  border-bottom-left-radius: 3px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
+  border-bottom-right-radius: 3px;
+  border-bottom-left-radius: 3px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
+  border-bottom-left-radius: 3px;
+}
+.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
+.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
+.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
+.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
+  border-bottom-right-radius: 3px;
+}
+.panel > .panel-body + .table,
+.panel > .panel-body + .table-responsive,
+.panel > .table + .panel-body,
+.panel > .table-responsive + .panel-body {
+  border-top: 1px solid #ddd;
+}
+.panel > .table > tbody:first-child > tr:first-child th,
+.panel > .table > tbody:first-child > tr:first-child td {
+  border-top: 0;
+}
+.panel > .table-bordered,
+.panel > .table-responsive > .table-bordered {
+  border: 0;
+}
+.panel > .table-bordered > thead > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
+.panel > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
+.panel > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
+.panel > .table-bordered > thead > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
+.panel > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
+.panel > .table-bordered > tfoot > tr > td:first-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
+  border-left: 0;
+}
+.panel > .table-bordered > thead > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
+.panel > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
+.panel > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
+.panel > .table-bordered > thead > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
+.panel > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
+.panel > .table-bordered > tfoot > tr > td:last-child,
+.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
+  border-right: 0;
+}
+.panel > .table-bordered > thead > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
+.panel > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
+.panel > .table-bordered > thead > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
+.panel > .table-bordered > tbody > tr:first-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
+  border-bottom: 0;
+}
+.panel > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
+.panel > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
+.panel > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
+.panel > .table-bordered > tfoot > tr:last-child > th,
+.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
+  border-bottom: 0;
+}
+.panel > .table-responsive {
+  margin-bottom: 0;
+  border: 0;
+}
+.panel-group {
+  margin-bottom: 20px;
+}
+.panel-group .panel {
+  margin-bottom: 0;
+  border-radius: 4px;
+}
+.panel-group .panel + .panel {
+  margin-top: 5px;
+}
+.panel-group .panel-heading {
+  border-bottom: 0;
+}
+.panel-group .panel-heading + .panel-collapse > .panel-body,
+.panel-group .panel-heading + .panel-collapse > .list-group {
+  border-top: 1px solid #ddd;
+}
+.panel-group .panel-footer {
+  border-top: 0;
+}
+.panel-group .panel-footer + .panel-collapse .panel-body {
+  border-bottom: 1px solid #ddd;
+}
+.panel-default {
+  border-color: #ddd;
+}
+.panel-default > .panel-heading {
+  color: #333333;
+  background-color: #f5f5f5;
+  border-color: #ddd;
+}
+.panel-default > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ddd;
+}
+.panel-default > .panel-heading .badge {
+  color: #f5f5f5;
+  background-color: #333333;
+}
+.panel-default > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ddd;
+}
+.panel-primary {
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading {
+  color: #fff;
+  background-color: #337ab7;
+  border-color: #337ab7;
+}
+.panel-primary > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #337ab7;
+}
+.panel-primary > .panel-heading .badge {
+  color: #337ab7;
+  background-color: #fff;
+}
+.panel-primary > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #337ab7;
+}
+.panel-success {
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading {
+  color: #3c763d;
+  background-color: #dff0d8;
+  border-color: #d6e9c6;
+}
+.panel-success > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #d6e9c6;
+}
+.panel-success > .panel-heading .badge {
+  color: #dff0d8;
+  background-color: #3c763d;
+}
+.panel-success > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #d6e9c6;
+}
+.panel-info {
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading {
+  color: #31708f;
+  background-color: #d9edf7;
+  border-color: #bce8f1;
+}
+.panel-info > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #bce8f1;
+}
+.panel-info > .panel-heading .badge {
+  color: #d9edf7;
+  background-color: #31708f;
+}
+.panel-info > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #bce8f1;
+}
+.panel-warning {
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading {
+  color: #8a6d3b;
+  background-color: #fcf8e3;
+  border-color: #faebcc;
+}
+.panel-warning > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #faebcc;
+}
+.panel-warning > .panel-heading .badge {
+  color: #fcf8e3;
+  background-color: #8a6d3b;
+}
+.panel-warning > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #faebcc;
+}
+.panel-danger {
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading {
+  color: #a94442;
+  background-color: #f2dede;
+  border-color: #ebccd1;
+}
+.panel-danger > .panel-heading + .panel-collapse > .panel-body {
+  border-top-color: #ebccd1;
+}
+.panel-danger > .panel-heading .badge {
+  color: #f2dede;
+  background-color: #a94442;
+}
+.panel-danger > .panel-footer + .panel-collapse > .panel-body {
+  border-bottom-color: #ebccd1;
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  height: 0;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  border: 0;
+}
+.embed-responsive-16by9 {
+  padding-bottom: 56.25%;
+}
+.embed-responsive-4by3 {
+  padding-bottom: 75%;
+}
+.well {
+  min-height: 20px;
+  padding: 19px;
+  margin-bottom: 20px;
+  background-color: #f5f5f5;
+  border: 1px solid #e3e3e3;
+  border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.well blockquote {
+  border-color: #ddd;
+  border-color: rgba(0, 0, 0, 0.15);
+}
+.well-lg {
+  padding: 24px;
+  border-radius: 6px;
+}
+.well-sm {
+  padding: 9px;
+  border-radius: 3px;
+}
+.close {
+  float: right;
+  font-size: 21px;
+  font-weight: bold;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  filter: alpha(opacity=20);
+  opacity: 0.2;
+}
+.close:hover,
+.close:focus {
+  color: #000;
+  text-decoration: none;
+  cursor: pointer;
+  filter: alpha(opacity=50);
+  opacity: 0.5;
+}
+button.close {
+  padding: 0;
+  cursor: pointer;
+  background: transparent;
+  border: 0;
+  -webkit-appearance: none;
+  -moz-appearance: none;
+  appearance: none;
+}
+.modal-open {
+  overflow: hidden;
+}
+.modal {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  display: none;
+  overflow: hidden;
+  -webkit-overflow-scrolling: touch;
+  outline: 0;
+}
+.modal.fade .modal-dialog {
+  -webkit-transform: translate(0, -25%);
+  -ms-transform: translate(0, -25%);
+  -o-transform: translate(0, -25%);
+  transform: translate(0, -25%);
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  -o-transition: -o-transform 0.3s ease-out;
+  transition: -webkit-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+  transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out, -o-transform 0.3s ease-out;
+}
+.modal.in .modal-dialog {
+  -webkit-transform: translate(0, 0);
+  -ms-transform: translate(0, 0);
+  -o-transform: translate(0, 0);
+  transform: translate(0, 0);
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 10px;
+}
+.modal-content {
+  position: relative;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #999;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 6px;
+  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
+  outline: 0;
+}
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  filter: alpha(opacity=0);
+  opacity: 0;
+}
+.modal-backdrop.in {
+  filter: alpha(opacity=50);
+  opacity: 0.5;
+}
+.modal-header {
+  padding: 15px;
+  border-bottom: 1px solid #e5e5e5;
+}
+.modal-header .close {
+  margin-top: -2px;
+}
+.modal-title {
+  margin: 0;
+  line-height: 1.42857143;
+}
+.modal-body {
+  position: relative;
+  padding: 15px;
+}
+.modal-footer {
+  padding: 15px;
+  text-align: right;
+  border-top: 1px solid #e5e5e5;
+}
+.modal-footer .btn + .btn {
+  margin-bottom: 0;
+  margin-left: 5px;
+}
+.modal-footer .btn-group .btn + .btn {
+  margin-left: -1px;
+}
+.modal-footer .btn-block + .btn-block {
+  margin-left: 0;
+}
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+@media (min-width: 768px) {
+  .modal-dialog {
+    width: 600px;
+    margin: 30px auto;
+  }
+  .modal-content {
+    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
+  }
+  .modal-sm {
+    width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    width: 900px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1.42857143;
+  line-break: auto;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  letter-spacing: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  white-space: normal;
+  font-size: 12px;
+  filter: alpha(opacity=0);
+  opacity: 0;
+}
+.tooltip.in {
+  filter: alpha(opacity=90);
+  opacity: 0.9;
+}
+.tooltip.top {
+  padding: 5px 0;
+  margin-top: -3px;
+}
+.tooltip.right {
+  padding: 0 5px;
+  margin-left: 3px;
+}
+.tooltip.bottom {
+  padding: 5px 0;
+  margin-top: 3px;
+}
+.tooltip.left {
+  padding: 0 5px;
+  margin-left: -3px;
+}
+.tooltip.top .tooltip-arrow {
+  bottom: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-left .tooltip-arrow {
+  right: 5px;
+  bottom: 0;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.top-right .tooltip-arrow {
+  bottom: 0;
+  left: 5px;
+  margin-bottom: -5px;
+  border-width: 5px 5px 0;
+  border-top-color: #000;
+}
+.tooltip.right .tooltip-arrow {
+  top: 50%;
+  left: 0;
+  margin-top: -5px;
+  border-width: 5px 5px 5px 0;
+  border-right-color: #000;
+}
+.tooltip.left .tooltip-arrow {
+  top: 50%;
+  right: 0;
+  margin-top: -5px;
+  border-width: 5px 0 5px 5px;
+  border-left-color: #000;
+}
+.tooltip.bottom .tooltip-arrow {
+  top: 0;
+  left: 50%;
+  margin-left: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-left .tooltip-arrow {
+  top: 0;
+  right: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip.bottom-right .tooltip-arrow {
+  top: 0;
+  left: 5px;
+  margin-top: -5px;
+  border-width: 0 5px 5px;
+  border-bottom-color: #000;
+}
+.tooltip-inner {
+  max-width: 200px;
+  padding: 3px 8px;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 4px;
+}
+.tooltip-arrow {
+  position: absolute;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: none;
+  max-width: 276px;
+  padding: 1px;
+  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1.42857143;
+  line-break: auto;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  letter-spacing: normal;
+  word-break: normal;
+  word-spacing: normal;
+  word-wrap: normal;
+  white-space: normal;
+  font-size: 14px;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ccc;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 6px;
+  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
+}
+.popover.top {
+  margin-top: -10px;
+}
+.popover.right {
+  margin-left: 10px;
+}
+.popover.bottom {
+  margin-top: 10px;
+}
+.popover.left {
+  margin-left: -10px;
+}
+.popover > .arrow {
+  border-width: 11px;
+}
+.popover > .arrow,
+.popover > .arrow:after {
+  position: absolute;
+  display: block;
+  width: 0;
+  height: 0;
+  border-color: transparent;
+  border-style: solid;
+}
+.popover > .arrow:after {
+  content: "";
+  border-width: 10px;
+}
+.popover.top > .arrow {
+  bottom: -11px;
+  left: 50%;
+  margin-left: -11px;
+  border-top-color: #999999;
+  border-top-color: rgba(0, 0, 0, 0.25);
+  border-bottom-width: 0;
+}
+.popover.top > .arrow:after {
+  bottom: 1px;
+  margin-left: -10px;
+  content: " ";
+  border-top-color: #fff;
+  border-bottom-width: 0;
+}
+.popover.right > .arrow {
+  top: 50%;
+  left: -11px;
+  margin-top: -11px;
+  border-right-color: #999999;
+  border-right-color: rgba(0, 0, 0, 0.25);
+  border-left-width: 0;
+}
+.popover.right > .arrow:after {
+  bottom: -10px;
+  left: 1px;
+  content: " ";
+  border-right-color: #fff;
+  border-left-width: 0;
+}
+.popover.bottom > .arrow {
+  top: -11px;
+  left: 50%;
+  margin-left: -11px;
+  border-top-width: 0;
+  border-bottom-color: #999999;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+}
+.popover.bottom > .arrow:after {
+  top: 1px;
+  margin-left: -10px;
+  content: " ";
+  border-top-width: 0;
+  border-bottom-color: #fff;
+}
+.popover.left > .arrow {
+  top: 50%;
+  right: -11px;
+  margin-top: -11px;
+  border-right-width: 0;
+  border-left-color: #999999;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.popover.left > .arrow:after {
+  right: 1px;
+  bottom: -10px;
+  content: " ";
+  border-right-width: 0;
+  border-left-color: #fff;
+}
+.popover-title {
+  padding: 8px 14px;
+  margin: 0;
+  font-size: 14px;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-radius: 5px 5px 0 0;
+}
+.popover-content {
+  padding: 9px 14px;
+}
+.carousel {
+  position: relative;
+}
+.carousel-inner {
+  position: relative;
+  width: 100%;
+  overflow: hidden;
+}
+.carousel-inner > .item {
+  position: relative;
+  display: none;
+  -webkit-transition: 0.6s ease-in-out left;
+  -o-transition: 0.6s ease-in-out left;
+  transition: 0.6s ease-in-out left;
+}
+.carousel-inner > .item > img,
+.carousel-inner > .item > a > img {
+  line-height: 1;
+}
+@media all and (transform-3d), (-webkit-transform-3d) {
+  .carousel-inner > .item {
+    -webkit-transition: -webkit-transform 0.6s ease-in-out;
+    -o-transition: -o-transform 0.6s ease-in-out;
+    transition: -webkit-transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out;
+    transition: transform 0.6s ease-in-out, -webkit-transform 0.6s ease-in-out, -o-transform 0.6s ease-in-out;
+    -webkit-backface-visibility: hidden;
+    backface-visibility: hidden;
+    -webkit-perspective: 1000px;
+    perspective: 1000px;
+  }
+  .carousel-inner > .item.next,
+  .carousel-inner > .item.active.right {
+    -webkit-transform: translate3d(100%, 0, 0);
+    transform: translate3d(100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.prev,
+  .carousel-inner > .item.active.left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+    transform: translate3d(-100%, 0, 0);
+    left: 0;
+  }
+  .carousel-inner > .item.next.left,
+  .carousel-inner > .item.prev.right,
+  .carousel-inner > .item.active {
+    -webkit-transform: translate3d(0, 0, 0);
+    transform: translate3d(0, 0, 0);
+    left: 0;
+  }
+}
+.carousel-inner > .active,
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  display: block;
+}
+.carousel-inner > .active {
+  left: 0;
+}
+.carousel-inner > .next,
+.carousel-inner > .prev {
+  position: absolute;
+  top: 0;
+  width: 100%;
+}
+.carousel-inner > .next {
+  left: 100%;
+}
+.carousel-inner > .prev {
+  left: -100%;
+}
+.carousel-inner > .next.left,
+.carousel-inner > .prev.right {
+  left: 0;
+}
+.carousel-inner > .active.left {
+  left: -100%;
+}
+.carousel-inner > .active.right {
+  left: 100%;
+}
+.carousel-control {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  left: 0;
+  width: 15%;
+  font-size: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+  background-color: rgba(0, 0, 0, 0);
+  filter: alpha(opacity=50);
+  opacity: 0.5;
+}
+.carousel-control.left {
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.5)), to(rgba(0, 0, 0, 0.0001)));
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
+  background-repeat: repeat-x;
+}
+.carousel-control.right {
+  right: 0;
+  left: auto;
+  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.0001)), to(rgba(0, 0, 0, 0.5)));
+  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
+  background-repeat: repeat-x;
+}
+.carousel-control:hover,
+.carousel-control:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  filter: alpha(opacity=90);
+  opacity: 0.9;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-left,
+.carousel-control .glyphicon-chevron-right {
+  position: absolute;
+  top: 50%;
+  z-index: 5;
+  display: inline-block;
+  margin-top: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .glyphicon-chevron-left {
+  left: 50%;
+  margin-left: -10px;
+}
+.carousel-control .icon-next,
+.carousel-control .glyphicon-chevron-right {
+  right: 50%;
+  margin-right: -10px;
+}
+.carousel-control .icon-prev,
+.carousel-control .icon-next {
+  width: 20px;
+  height: 20px;
+  font-family: serif;
+  line-height: 1;
+}
+.carousel-control .icon-prev:before {
+  content: "\2039";
+}
+.carousel-control .icon-next:before {
+  content: "\203a";
+}
+.carousel-indicators {
+  position: absolute;
+  bottom: 10px;
+  left: 50%;
+  z-index: 15;
+  width: 60%;
+  padding-left: 0;
+  margin-left: -30%;
+  text-align: center;
+  list-style: none;
+}
+.carousel-indicators li {
+  display: inline-block;
+  width: 10px;
+  height: 10px;
+  margin: 1px;
+  text-indent: -999px;
+  cursor: pointer;
+  background-color: #000 \9;
+  background-color: rgba(0, 0, 0, 0);
+  border: 1px solid #fff;
+  border-radius: 10px;
+}
+.carousel-indicators .active {
+  width: 12px;
+  height: 12px;
+  margin: 0;
+  background-color: #fff;
+}
+.carousel-caption {
+  position: absolute;
+  right: 15%;
+  bottom: 20px;
+  left: 15%;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
+}
+.carousel-caption .btn {
+  text-shadow: none;
+}
+@media screen and (min-width: 768px) {
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-prev,
+  .carousel-control .icon-next {
+    width: 30px;
+    height: 30px;
+    margin-top: -10px;
+    font-size: 30px;
+  }
+  .carousel-control .glyphicon-chevron-left,
+  .carousel-control .icon-prev {
+    margin-left: -10px;
+  }
+  .carousel-control .glyphicon-chevron-right,
+  .carousel-control .icon-next {
+    margin-right: -10px;
+  }
+  .carousel-caption {
+    right: 20%;
+    left: 20%;
+    padding-bottom: 30px;
+  }
+  .carousel-indicators {
+    bottom: 20px;
+  }
+}
+.clearfix:before,
+.clearfix:after,
+.dl-horizontal dd:before,
+.dl-horizontal dd:after,
+.container:before,
+.container:after,
+.container-fluid:before,
+.container-fluid:after,
+.row:before,
+.row:after,
+.form-horizontal .form-group:before,
+.form-horizontal .form-group:after,
+.btn-toolbar:before,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:before,
+.btn-group-vertical > .btn-group:after,
+.nav:before,
+.nav:after,
+.navbar:before,
+.navbar:after,
+.navbar-header:before,
+.navbar-header:after,
+.navbar-collapse:before,
+.navbar-collapse:after,
+.pager:before,
+.pager:after,
+.panel-body:before,
+.panel-body:after,
+.modal-header:before,
+.modal-header:after,
+.modal-footer:before,
+.modal-footer:after {
+  display: table;
+  content: " ";
+}
+.clearfix:after,
+.dl-horizontal dd:after,
+.container:after,
+.container-fluid:after,
+.row:after,
+.form-horizontal .form-group:after,
+.btn-toolbar:after,
+.btn-group-vertical > .btn-group:after,
+.nav:after,
+.navbar:after,
+.navbar-header:after,
+.navbar-collapse:after,
+.pager:after,
+.panel-body:after,
+.modal-header:after,
+.modal-footer:after {
+  clear: both;
+}
+.center-block {
+  display: block;
+  margin-right: auto;
+  margin-left: auto;
+}
+.pull-right {
+  float: right !important;
+}
+.pull-left {
+  float: left !important;
+}
+.hide {
+  display: none !important;
+}
+.show {
+  display: block !important;
+}
+.invisible {
+  visibility: hidden;
+}
+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+.hidden {
+  display: none !important;
+}
+.affix {
+  position: fixed;
+}
+@-ms-viewport {
+  width: device-width;
+}
+.visible-xs,
+.visible-sm,
+.visible-md,
+.visible-lg {
+  display: none !important;
+}
+.visible-xs-block,
+.visible-xs-inline,
+.visible-xs-inline-block,
+.visible-sm-block,
+.visible-sm-inline,
+.visible-sm-inline-block,
+.visible-md-block,
+.visible-md-inline,
+.visible-md-inline-block,
+.visible-lg-block,
+.visible-lg-inline,
+.visible-lg-inline-block {
+  display: none !important;
+}
+@media (max-width: 767px) {
+  .visible-xs {
+    display: block !important;
+  }
+  table.visible-xs {
+    display: table !important;
+  }
+  tr.visible-xs {
+    display: table-row !important;
+  }
+  th.visible-xs,
+  td.visible-xs {
+    display: table-cell !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-block {
+    display: block !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline {
+    display: inline !important;
+  }
+}
+@media (max-width: 767px) {
+  .visible-xs-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm {
+    display: block !important;
+  }
+  table.visible-sm {
+    display: table !important;
+  }
+  tr.visible-sm {
+    display: table-row !important;
+  }
+  th.visible-sm,
+  td.visible-sm {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-block {
+    display: block !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .visible-sm-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md {
+    display: block !important;
+  }
+  table.visible-md {
+    display: table !important;
+  }
+  tr.visible-md {
+    display: table-row !important;
+  }
+  th.visible-md,
+  td.visible-md {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-block {
+    display: block !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .visible-md-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg {
+    display: block !important;
+  }
+  table.visible-lg {
+    display: table !important;
+  }
+  tr.visible-lg {
+    display: table-row !important;
+  }
+  th.visible-lg,
+  td.visible-lg {
+    display: table-cell !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-block {
+    display: block !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline {
+    display: inline !important;
+  }
+}
+@media (min-width: 1200px) {
+  .visible-lg-inline-block {
+    display: inline-block !important;
+  }
+}
+@media (max-width: 767px) {
+  .hidden-xs {
+    display: none !important;
+  }
+}
+@media (min-width: 768px) and (max-width: 991px) {
+  .hidden-sm {
+    display: none !important;
+  }
+}
+@media (min-width: 992px) and (max-width: 1199px) {
+  .hidden-md {
+    display: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .hidden-lg {
+    display: none !important;
+  }
+}
+.visible-print {
+  display: none !important;
+}
+@media print {
+  .visible-print {
+    display: block !important;
+  }
+  table.visible-print {
+    display: table !important;
+  }
+  tr.visible-print {
+    display: table-row !important;
+  }
+  th.visible-print,
+  td.visible-print {
+    display: table-cell !important;
+  }
+}
+.visible-print-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-block {
+    display: block !important;
+  }
+}
+.visible-print-inline {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline {
+    display: inline !important;
+  }
+}
+.visible-print-inline-block {
+  display: none !important;
+}
+@media print {
+  .visible-print-inline-block {
+    display: inline-block !important;
+  }
+}
+@media print {
+  .hidden-print {
+    display: none !important;
+  }
+}
+/*# sourceMappingURL=bootstrap.css.map */
\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/css/bootstrap.css.map b/_static/bootstrap-3.4.1/css/bootstrap.css.map
new file mode 100644
index 000000000..caac3e612
--- /dev/null
+++ b/_static/bootstrap-3.4.1/css/bootstrap.css.map
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2em;\n  margin: 0.67em 0;\n}\nmark {\n  background: #ff0;\n  color: #000;\n}\nsmall {\n  font-size: 80%;\n}\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\nsup {\n  top: -0.5em;\n}\nsub {\n  bottom: -0.25em;\n}\nimg {\n  border: 0;\n}\nsvg:not(:root) {\n  overflow: hidden;\n}\nfigure {\n  margin: 1em 40px;\n}\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\npre {\n  overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit;\n  font: inherit;\n  margin: 0;\n}\nbutton {\n  overflow: visible;\n}\nbutton,\nselect {\n  text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button;\n  cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\ninput {\n  line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box;\n  padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: textfield;\n  box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n  border: 0;\n  padding: 0;\n}\ntextarea {\n  overflow: auto;\n}\noptgroup {\n  font-weight: bold;\n}\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\ntd,\nth {\n  padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n  *,\n  *:before,\n  *:after {\n    color: #000 !important;\n    text-shadow: none !important;\n    background: transparent !important;\n    box-shadow: none !important;\n  }\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n  thead {\n    display: table-header-group;\n  }\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n  img {\n    max-width: 100% !important;\n  }\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n  .navbar {\n    display: none;\n  }\n  .btn > .caret,\n  .dropup > .btn > .caret {\n    border-top-color: #000 !important;\n  }\n  .label {\n    border: 1px solid #000;\n  }\n  .table {\n    border-collapse: collapse !important;\n  }\n  .table td,\n  .table th {\n    background-color: #fff !important;\n  }\n  .table-bordered th,\n  .table-bordered td {\n    border: 1px solid #ddd !important;\n  }\n}\n@font-face {\n  font-family: \"Glyphicons Halflings\";\n  src: url(\"../fonts/glyphicons-halflings-regular.eot\");\n  src: url(\"../fonts/glyphicons-halflings-regular.eot?#iefix\") format(\"embedded-opentype\"), url(\"../fonts/glyphicons-halflings-regular.woff2\") format(\"woff2\"), url(\"../fonts/glyphicons-halflings-regular.woff\") format(\"woff\"), url(\"../fonts/glyphicons-halflings-regular.ttf\") format(\"truetype\"), url(\"../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular\") format(\"svg\");\n}\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: \"Glyphicons Halflings\";\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n  content: \"\\002a\";\n}\n.glyphicon-plus:before {\n  content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n  content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n  content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n  content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n  content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n  content: \"\\270f\";\n}\n.glyphicon-glass:before {\n  content: \"\\e001\";\n}\n.glyphicon-music:before {\n  content: \"\\e002\";\n}\n.glyphicon-search:before {\n  content: \"\\e003\";\n}\n.glyphicon-heart:before {\n  content: \"\\e005\";\n}\n.glyphicon-star:before {\n  content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n  content: \"\\e007\";\n}\n.glyphicon-user:before {\n  content: \"\\e008\";\n}\n.glyphicon-film:before {\n  content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n  content: \"\\e010\";\n}\n.glyphicon-th:before {\n  content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n  content: \"\\e012\";\n}\n.glyphicon-ok:before {\n  content: \"\\e013\";\n}\n.glyphicon-remove:before {\n  content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n  content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n  content: \"\\e016\";\n}\n.glyphicon-off:before {\n  content: \"\\e017\";\n}\n.glyphicon-signal:before {\n  content: \"\\e018\";\n}\n.glyphicon-cog:before {\n  content: \"\\e019\";\n}\n.glyphicon-trash:before {\n  content: \"\\e020\";\n}\n.glyphicon-home:before {\n  content: \"\\e021\";\n}\n.glyphicon-file:before {\n  content: \"\\e022\";\n}\n.glyphicon-time:before {\n  content: \"\\e023\";\n}\n.glyphicon-road:before {\n  content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n  content: \"\\e025\";\n}\n.glyphicon-download:before {\n  content: \"\\e026\";\n}\n.glyphicon-upload:before {\n  content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n  content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n  content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n  content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n  content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n  content: \"\\e032\";\n}\n.glyphicon-lock:before {\n  content: \"\\e033\";\n}\n.glyphicon-flag:before {\n  content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n  content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n  content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n  content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n  content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n  content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n  content: \"\\e040\";\n}\n.glyphicon-tag:before {\n  content: \"\\e041\";\n}\n.glyphicon-tags:before {\n  content: \"\\e042\";\n}\n.glyphicon-book:before {\n  content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n  content: \"\\e044\";\n}\n.glyphicon-print:before {\n  content: \"\\e045\";\n}\n.glyphicon-camera:before {\n  content: \"\\e046\";\n}\n.glyphicon-font:before {\n  content: \"\\e047\";\n}\n.glyphicon-bold:before {\n  content: \"\\e048\";\n}\n.glyphicon-italic:before {\n  content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n  content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n  content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n  content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n  content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n  content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n  content: \"\\e055\";\n}\n.glyphicon-list:before {\n  content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n  content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n  content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n  content: \"\\e059\";\n}\n.glyphicon-picture:before {\n  content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n  content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n  content: \"\\e063\";\n}\n.glyphicon-tint:before {\n  content: \"\\e064\";\n}\n.glyphicon-edit:before {\n  content: \"\\e065\";\n}\n.glyphicon-share:before {\n  content: \"\\e066\";\n}\n.glyphicon-check:before {\n  content: \"\\e067\";\n}\n.glyphicon-move:before {\n  content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n  content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n  content: \"\\e070\";\n}\n.glyphicon-backward:before {\n  content: \"\\e071\";\n}\n.glyphicon-play:before {\n  content: \"\\e072\";\n}\n.glyphicon-pause:before {\n  content: \"\\e073\";\n}\n.glyphicon-stop:before {\n  content: \"\\e074\";\n}\n.glyphicon-forward:before {\n  content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n  content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n  content: \"\\e077\";\n}\n.glyphicon-eject:before {\n  content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n  content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n  content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n  content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n  content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n  content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n  content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n  content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n  content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n  content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n  content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n  content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n  content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n  content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n  content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n  content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n  content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n  content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n  content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n  content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n  content: \"\\e101\";\n}\n.glyphicon-gift:before {\n  content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n  content: \"\\e103\";\n}\n.glyphicon-fire:before {\n  content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n  content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n  content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n  content: \"\\e107\";\n}\n.glyphicon-plane:before {\n  content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n  content: \"\\e109\";\n}\n.glyphicon-random:before {\n  content: \"\\e110\";\n}\n.glyphicon-comment:before {\n  content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n  content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n  content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n  content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n  content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n  content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n  content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n  content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n  content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n  content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n  content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n  content: \"\\e122\";\n}\n.glyphicon-bell:before {\n  content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n  content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n  content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n  content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n  content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n  content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n  content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n  content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n  content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n  content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n  content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n  content: \"\\e134\";\n}\n.glyphicon-globe:before {\n  content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n  content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n  content: \"\\e137\";\n}\n.glyphicon-filter:before {\n  content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n  content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n  content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n  content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n  content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n  content: \"\\e143\";\n}\n.glyphicon-link:before {\n  content: \"\\e144\";\n}\n.glyphicon-phone:before {\n  content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n  content: \"\\e146\";\n}\n.glyphicon-usd:before {\n  content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n  content: \"\\e149\";\n}\n.glyphicon-sort:before {\n  content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n  content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n  content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n  content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n  content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n  content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n  content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n  content: \"\\e157\";\n}\n.glyphicon-expand:before {\n  content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n  content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n  content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n  content: \"\\e161\";\n}\n.glyphicon-flash:before {\n  content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n  content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n  content: \"\\e164\";\n}\n.glyphicon-record:before {\n  content: \"\\e165\";\n}\n.glyphicon-save:before {\n  content: \"\\e166\";\n}\n.glyphicon-open:before {\n  content: \"\\e167\";\n}\n.glyphicon-saved:before {\n  content: \"\\e168\";\n}\n.glyphicon-import:before {\n  content: \"\\e169\";\n}\n.glyphicon-export:before {\n  content: \"\\e170\";\n}\n.glyphicon-send:before {\n  content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n  content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n  content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n  content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n  content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n  content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n  content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n  content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n  content: \"\\e179\";\n}\n.glyphicon-header:before {\n  content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n  content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n  content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n  content: \"\\e183\";\n}\n.glyphicon-tower:before {\n  content: \"\\e184\";\n}\n.glyphicon-stats:before {\n  content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n  content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n  content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n  content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n  content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n  content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n  content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n  content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n  content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n  content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n  content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n  content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n  content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n  content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n  content: \"\\e200\";\n}\n.glyphicon-cd:before {\n  content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n  content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n  content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n  content: \"\\e204\";\n}\n.glyphicon-copy:before {\n  content: \"\\e205\";\n}\n.glyphicon-paste:before {\n  content: \"\\e206\";\n}\n.glyphicon-alert:before {\n  content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n  content: \"\\e210\";\n}\n.glyphicon-king:before {\n  content: \"\\e211\";\n}\n.glyphicon-queen:before {\n  content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n  content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n  content: \"\\e214\";\n}\n.glyphicon-knight:before {\n  content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n  content: \"\\e216\";\n}\n.glyphicon-tent:before {\n  content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n  content: \"\\e218\";\n}\n.glyphicon-bed:before {\n  content: \"\\e219\";\n}\n.glyphicon-apple:before {\n  content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n  content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n  content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n  content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n  content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n  content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n  content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n  content: \"\\e227\";\n}\n.glyphicon-btc:before {\n  content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n  content: \"\\e227\";\n}\n.glyphicon-yen:before {\n  content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n  content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n  content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n  content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n  content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n  content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n  content: \"\\e232\";\n}\n.glyphicon-education:before {\n  content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n  content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n  content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n  content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n  content: \"\\e237\";\n}\n.glyphicon-oil:before {\n  content: \"\\e238\";\n}\n.glyphicon-grain:before {\n  content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n  content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n  content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n  content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n  content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n  content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n  content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n  content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n  content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n  content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n  content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n  content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n  content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n  content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n  content: \"\\e253\";\n}\n.glyphicon-console:before {\n  content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n  content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n  content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n  content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n  content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n  content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n  content: \"\\e260\";\n}\n* {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\n*:before,\n*:after {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #333333;\n  background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\na {\n  color: #337ab7;\n  text-decoration: none;\n}\na:hover,\na:focus {\n  color: #23527c;\n  text-decoration: underline;\n}\na:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\nfigure {\n  margin: 0;\n}\nimg {\n  vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  display: block;\n  max-width: 100%;\n  height: auto;\n}\n.img-rounded {\n  border-radius: 6px;\n}\n.img-thumbnail {\n  padding: 4px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: all 0.2s ease-in-out;\n  -o-transition: all 0.2s ease-in-out;\n  transition: all 0.2s ease-in-out;\n  display: inline-block;\n  max-width: 100%;\n  height: auto;\n}\n.img-circle {\n  border-radius: 50%;\n}\nhr {\n  margin-top: 20px;\n  margin-bottom: 20px;\n  border: 0;\n  border-top: 1px solid #eeeeee;\n}\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n  position: static;\n  width: auto;\n  height: auto;\n  margin: 0;\n  overflow: visible;\n  clip: auto;\n}\n[role=\"button\"] {\n  cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n  font-family: inherit;\n  font-weight: 500;\n  line-height: 1.1;\n  color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n  font-weight: 400;\n  line-height: 1;\n  color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n  margin-top: 20px;\n  margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n  font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n  font-size: 75%;\n}\nh1,\n.h1 {\n  font-size: 36px;\n}\nh2,\n.h2 {\n  font-size: 30px;\n}\nh3,\n.h3 {\n  font-size: 24px;\n}\nh4,\n.h4 {\n  font-size: 18px;\n}\nh5,\n.h5 {\n  font-size: 14px;\n}\nh6,\n.h6 {\n  font-size: 12px;\n}\np {\n  margin: 0 0 10px;\n}\n.lead {\n  margin-bottom: 20px;\n  font-size: 16px;\n  font-weight: 300;\n  line-height: 1.4;\n}\n@media (min-width: 768px) {\n  .lead {\n    font-size: 21px;\n  }\n}\nsmall,\n.small {\n  font-size: 85%;\n}\nmark,\n.mark {\n  padding: 0.2em;\n  background-color: #fcf8e3;\n}\n.text-left {\n  text-align: left;\n}\n.text-right {\n  text-align: right;\n}\n.text-center {\n  text-align: center;\n}\n.text-justify {\n  text-align: justify;\n}\n.text-nowrap {\n  white-space: nowrap;\n}\n.text-lowercase {\n  text-transform: lowercase;\n}\n.text-uppercase {\n  text-transform: uppercase;\n}\n.text-capitalize {\n  text-transform: capitalize;\n}\n.text-muted {\n  color: #777777;\n}\n.text-primary {\n  color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n  color: #286090;\n}\n.text-success {\n  color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n  color: #2b542c;\n}\n.text-info {\n  color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n  color: #245269;\n}\n.text-warning {\n  color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n  color: #66512c;\n}\n.text-danger {\n  color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n  color: #843534;\n}\n.bg-primary {\n  color: #fff;\n  background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n  background-color: #286090;\n}\n.bg-success {\n  background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n  background-color: #c1e2b3;\n}\n.bg-info {\n  background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n  background-color: #afd9ee;\n}\n.bg-warning {\n  background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n  background-color: #f7ecb5;\n}\n.bg-danger {\n  background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n  background-color: #e4b9b9;\n}\n.page-header {\n  padding-bottom: 9px;\n  margin: 40px 0 20px;\n  border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n  margin-top: 0;\n  margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n  margin-bottom: 0;\n}\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n  margin-left: -5px;\n}\n.list-inline > li {\n  display: inline-block;\n  padding-right: 5px;\n  padding-left: 5px;\n}\ndl {\n  margin-top: 0;\n  margin-bottom: 20px;\n}\ndt,\ndd {\n  line-height: 1.42857143;\n}\ndt {\n  font-weight: 700;\n}\ndd {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .dl-horizontal dt {\n    float: left;\n    width: 160px;\n    clear: left;\n    text-align: right;\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n  }\n  .dl-horizontal dd {\n    margin-left: 180px;\n  }\n}\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n}\n.initialism {\n  font-size: 90%;\n  text-transform: uppercase;\n}\nblockquote {\n  padding: 10px 20px;\n  margin: 0 0 20px;\n  font-size: 17.5px;\n  border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n  margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n  display: block;\n  font-size: 80%;\n  line-height: 1.42857143;\n  color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n  content: \"\\2014 \\00A0\";\n}\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  text-align: right;\n  border-right: 5px solid #eeeeee;\n  border-left: 0;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n  content: \"\";\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n  content: \"\\00A0 \\2014\";\n}\naddress {\n  margin-bottom: 20px;\n  font-style: normal;\n  line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #c7254e;\n  background-color: #f9f2f4;\n  border-radius: 4px;\n}\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #fff;\n  background-color: #333;\n  border-radius: 3px;\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n  padding: 0;\n  font-size: 100%;\n  font-weight: 700;\n  box-shadow: none;\n}\npre {\n  display: block;\n  padding: 9.5px;\n  margin: 0 0 10px;\n  font-size: 13px;\n  line-height: 1.42857143;\n  color: #333333;\n  word-break: break-all;\n  word-wrap: break-word;\n  background-color: #f5f5f5;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\npre code {\n  padding: 0;\n  font-size: inherit;\n  color: inherit;\n  white-space: pre-wrap;\n  background-color: transparent;\n  border-radius: 0;\n}\n.pre-scrollable {\n  max-height: 340px;\n  overflow-y: scroll;\n}\n.container {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n@media (min-width: 768px) {\n  .container {\n    width: 750px;\n  }\n}\n@media (min-width: 992px) {\n  .container {\n    width: 970px;\n  }\n}\n@media (min-width: 1200px) {\n  .container {\n    width: 1170px;\n  }\n}\n.container-fluid {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n.row {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n.row-no-gutters {\n  margin-right: 0;\n  margin-left: 0;\n}\n.row-no-gutters [class*=\"col-\"] {\n  padding-right: 0;\n  padding-left: 0;\n}\n.col-xs-1,\n.col-sm-1,\n.col-md-1,\n.col-lg-1,\n.col-xs-2,\n.col-sm-2,\n.col-md-2,\n.col-lg-2,\n.col-xs-3,\n.col-sm-3,\n.col-md-3,\n.col-lg-3,\n.col-xs-4,\n.col-sm-4,\n.col-md-4,\n.col-lg-4,\n.col-xs-5,\n.col-sm-5,\n.col-md-5,\n.col-lg-5,\n.col-xs-6,\n.col-sm-6,\n.col-md-6,\n.col-lg-6,\n.col-xs-7,\n.col-sm-7,\n.col-md-7,\n.col-lg-7,\n.col-xs-8,\n.col-sm-8,\n.col-md-8,\n.col-lg-8,\n.col-xs-9,\n.col-sm-9,\n.col-md-9,\n.col-lg-9,\n.col-xs-10,\n.col-sm-10,\n.col-md-10,\n.col-lg-10,\n.col-xs-11,\n.col-sm-11,\n.col-md-11,\n.col-lg-11,\n.col-xs-12,\n.col-sm-12,\n.col-md-12,\n.col-lg-12 {\n  position: relative;\n  min-height: 1px;\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.col-xs-1,\n.col-xs-2,\n.col-xs-3,\n.col-xs-4,\n.col-xs-5,\n.col-xs-6,\n.col-xs-7,\n.col-xs-8,\n.col-xs-9,\n.col-xs-10,\n.col-xs-11,\n.col-xs-12 {\n  float: left;\n}\n.col-xs-12 {\n  width: 100%;\n}\n.col-xs-11 {\n  width: 91.66666667%;\n}\n.col-xs-10 {\n  width: 83.33333333%;\n}\n.col-xs-9 {\n  width: 75%;\n}\n.col-xs-8 {\n  width: 66.66666667%;\n}\n.col-xs-7 {\n  width: 58.33333333%;\n}\n.col-xs-6 {\n  width: 50%;\n}\n.col-xs-5 {\n  width: 41.66666667%;\n}\n.col-xs-4 {\n  width: 33.33333333%;\n}\n.col-xs-3 {\n  width: 25%;\n}\n.col-xs-2 {\n  width: 16.66666667%;\n}\n.col-xs-1 {\n  width: 8.33333333%;\n}\n.col-xs-pull-12 {\n  right: 100%;\n}\n.col-xs-pull-11 {\n  right: 91.66666667%;\n}\n.col-xs-pull-10 {\n  right: 83.33333333%;\n}\n.col-xs-pull-9 {\n  right: 75%;\n}\n.col-xs-pull-8 {\n  right: 66.66666667%;\n}\n.col-xs-pull-7 {\n  right: 58.33333333%;\n}\n.col-xs-pull-6 {\n  right: 50%;\n}\n.col-xs-pull-5 {\n  right: 41.66666667%;\n}\n.col-xs-pull-4 {\n  right: 33.33333333%;\n}\n.col-xs-pull-3 {\n  right: 25%;\n}\n.col-xs-pull-2 {\n  right: 16.66666667%;\n}\n.col-xs-pull-1 {\n  right: 8.33333333%;\n}\n.col-xs-pull-0 {\n  right: auto;\n}\n.col-xs-push-12 {\n  left: 100%;\n}\n.col-xs-push-11 {\n  left: 91.66666667%;\n}\n.col-xs-push-10 {\n  left: 83.33333333%;\n}\n.col-xs-push-9 {\n  left: 75%;\n}\n.col-xs-push-8 {\n  left: 66.66666667%;\n}\n.col-xs-push-7 {\n  left: 58.33333333%;\n}\n.col-xs-push-6 {\n  left: 50%;\n}\n.col-xs-push-5 {\n  left: 41.66666667%;\n}\n.col-xs-push-4 {\n  left: 33.33333333%;\n}\n.col-xs-push-3 {\n  left: 25%;\n}\n.col-xs-push-2 {\n  left: 16.66666667%;\n}\n.col-xs-push-1 {\n  left: 8.33333333%;\n}\n.col-xs-push-0 {\n  left: auto;\n}\n.col-xs-offset-12 {\n  margin-left: 100%;\n}\n.col-xs-offset-11 {\n  margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n  margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n  margin-left: 75%;\n}\n.col-xs-offset-8 {\n  margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n  margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n  margin-left: 50%;\n}\n.col-xs-offset-5 {\n  margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n  margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n  margin-left: 25%;\n}\n.col-xs-offset-2 {\n  margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n  margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n  margin-left: 0%;\n}\n@media (min-width: 768px) {\n  .col-sm-1,\n  .col-sm-2,\n  .col-sm-3,\n  .col-sm-4,\n  .col-sm-5,\n  .col-sm-6,\n  .col-sm-7,\n  .col-sm-8,\n  .col-sm-9,\n  .col-sm-10,\n  .col-sm-11,\n  .col-sm-12 {\n    float: left;\n  }\n  .col-sm-12 {\n    width: 100%;\n  }\n  .col-sm-11 {\n    width: 91.66666667%;\n  }\n  .col-sm-10 {\n    width: 83.33333333%;\n  }\n  .col-sm-9 {\n    width: 75%;\n  }\n  .col-sm-8 {\n    width: 66.66666667%;\n  }\n  .col-sm-7 {\n    width: 58.33333333%;\n  }\n  .col-sm-6 {\n    width: 50%;\n  }\n  .col-sm-5 {\n    width: 41.66666667%;\n  }\n  .col-sm-4 {\n    width: 33.33333333%;\n  }\n  .col-sm-3 {\n    width: 25%;\n  }\n  .col-sm-2 {\n    width: 16.66666667%;\n  }\n  .col-sm-1 {\n    width: 8.33333333%;\n  }\n  .col-sm-pull-12 {\n    right: 100%;\n  }\n  .col-sm-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-sm-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-sm-pull-9 {\n    right: 75%;\n  }\n  .col-sm-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-sm-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-sm-pull-6 {\n    right: 50%;\n  }\n  .col-sm-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-sm-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-sm-pull-3 {\n    right: 25%;\n  }\n  .col-sm-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-sm-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-sm-pull-0 {\n    right: auto;\n  }\n  .col-sm-push-12 {\n    left: 100%;\n  }\n  .col-sm-push-11 {\n    left: 91.66666667%;\n  }\n  .col-sm-push-10 {\n    left: 83.33333333%;\n  }\n  .col-sm-push-9 {\n    left: 75%;\n  }\n  .col-sm-push-8 {\n    left: 66.66666667%;\n  }\n  .col-sm-push-7 {\n    left: 58.33333333%;\n  }\n  .col-sm-push-6 {\n    left: 50%;\n  }\n  .col-sm-push-5 {\n    left: 41.66666667%;\n  }\n  .col-sm-push-4 {\n    left: 33.33333333%;\n  }\n  .col-sm-push-3 {\n    left: 25%;\n  }\n  .col-sm-push-2 {\n    left: 16.66666667%;\n  }\n  .col-sm-push-1 {\n    left: 8.33333333%;\n  }\n  .col-sm-push-0 {\n    left: auto;\n  }\n  .col-sm-offset-12 {\n    margin-left: 100%;\n  }\n  .col-sm-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-sm-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-sm-offset-9 {\n    margin-left: 75%;\n  }\n  .col-sm-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-sm-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-sm-offset-6 {\n    margin-left: 50%;\n  }\n  .col-sm-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-sm-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-sm-offset-3 {\n    margin-left: 25%;\n  }\n  .col-sm-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-sm-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-sm-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 992px) {\n  .col-md-1,\n  .col-md-2,\n  .col-md-3,\n  .col-md-4,\n  .col-md-5,\n  .col-md-6,\n  .col-md-7,\n  .col-md-8,\n  .col-md-9,\n  .col-md-10,\n  .col-md-11,\n  .col-md-12 {\n    float: left;\n  }\n  .col-md-12 {\n    width: 100%;\n  }\n  .col-md-11 {\n    width: 91.66666667%;\n  }\n  .col-md-10 {\n    width: 83.33333333%;\n  }\n  .col-md-9 {\n    width: 75%;\n  }\n  .col-md-8 {\n    width: 66.66666667%;\n  }\n  .col-md-7 {\n    width: 58.33333333%;\n  }\n  .col-md-6 {\n    width: 50%;\n  }\n  .col-md-5 {\n    width: 41.66666667%;\n  }\n  .col-md-4 {\n    width: 33.33333333%;\n  }\n  .col-md-3 {\n    width: 25%;\n  }\n  .col-md-2 {\n    width: 16.66666667%;\n  }\n  .col-md-1 {\n    width: 8.33333333%;\n  }\n  .col-md-pull-12 {\n    right: 100%;\n  }\n  .col-md-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-md-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-md-pull-9 {\n    right: 75%;\n  }\n  .col-md-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-md-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-md-pull-6 {\n    right: 50%;\n  }\n  .col-md-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-md-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-md-pull-3 {\n    right: 25%;\n  }\n  .col-md-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-md-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-md-pull-0 {\n    right: auto;\n  }\n  .col-md-push-12 {\n    left: 100%;\n  }\n  .col-md-push-11 {\n    left: 91.66666667%;\n  }\n  .col-md-push-10 {\n    left: 83.33333333%;\n  }\n  .col-md-push-9 {\n    left: 75%;\n  }\n  .col-md-push-8 {\n    left: 66.66666667%;\n  }\n  .col-md-push-7 {\n    left: 58.33333333%;\n  }\n  .col-md-push-6 {\n    left: 50%;\n  }\n  .col-md-push-5 {\n    left: 41.66666667%;\n  }\n  .col-md-push-4 {\n    left: 33.33333333%;\n  }\n  .col-md-push-3 {\n    left: 25%;\n  }\n  .col-md-push-2 {\n    left: 16.66666667%;\n  }\n  .col-md-push-1 {\n    left: 8.33333333%;\n  }\n  .col-md-push-0 {\n    left: auto;\n  }\n  .col-md-offset-12 {\n    margin-left: 100%;\n  }\n  .col-md-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-md-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-md-offset-9 {\n    margin-left: 75%;\n  }\n  .col-md-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-md-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-md-offset-6 {\n    margin-left: 50%;\n  }\n  .col-md-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-md-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-md-offset-3 {\n    margin-left: 25%;\n  }\n  .col-md-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-md-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-md-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 1200px) {\n  .col-lg-1,\n  .col-lg-2,\n  .col-lg-3,\n  .col-lg-4,\n  .col-lg-5,\n  .col-lg-6,\n  .col-lg-7,\n  .col-lg-8,\n  .col-lg-9,\n  .col-lg-10,\n  .col-lg-11,\n  .col-lg-12 {\n    float: left;\n  }\n  .col-lg-12 {\n    width: 100%;\n  }\n  .col-lg-11 {\n    width: 91.66666667%;\n  }\n  .col-lg-10 {\n    width: 83.33333333%;\n  }\n  .col-lg-9 {\n    width: 75%;\n  }\n  .col-lg-8 {\n    width: 66.66666667%;\n  }\n  .col-lg-7 {\n    width: 58.33333333%;\n  }\n  .col-lg-6 {\n    width: 50%;\n  }\n  .col-lg-5 {\n    width: 41.66666667%;\n  }\n  .col-lg-4 {\n    width: 33.33333333%;\n  }\n  .col-lg-3 {\n    width: 25%;\n  }\n  .col-lg-2 {\n    width: 16.66666667%;\n  }\n  .col-lg-1 {\n    width: 8.33333333%;\n  }\n  .col-lg-pull-12 {\n    right: 100%;\n  }\n  .col-lg-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-lg-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-lg-pull-9 {\n    right: 75%;\n  }\n  .col-lg-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-lg-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-lg-pull-6 {\n    right: 50%;\n  }\n  .col-lg-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-lg-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-lg-pull-3 {\n    right: 25%;\n  }\n  .col-lg-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-lg-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-lg-pull-0 {\n    right: auto;\n  }\n  .col-lg-push-12 {\n    left: 100%;\n  }\n  .col-lg-push-11 {\n    left: 91.66666667%;\n  }\n  .col-lg-push-10 {\n    left: 83.33333333%;\n  }\n  .col-lg-push-9 {\n    left: 75%;\n  }\n  .col-lg-push-8 {\n    left: 66.66666667%;\n  }\n  .col-lg-push-7 {\n    left: 58.33333333%;\n  }\n  .col-lg-push-6 {\n    left: 50%;\n  }\n  .col-lg-push-5 {\n    left: 41.66666667%;\n  }\n  .col-lg-push-4 {\n    left: 33.33333333%;\n  }\n  .col-lg-push-3 {\n    left: 25%;\n  }\n  .col-lg-push-2 {\n    left: 16.66666667%;\n  }\n  .col-lg-push-1 {\n    left: 8.33333333%;\n  }\n  .col-lg-push-0 {\n    left: auto;\n  }\n  .col-lg-offset-12 {\n    margin-left: 100%;\n  }\n  .col-lg-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-lg-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-lg-offset-9 {\n    margin-left: 75%;\n  }\n  .col-lg-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-lg-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-lg-offset-6 {\n    margin-left: 50%;\n  }\n  .col-lg-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-lg-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-lg-offset-3 {\n    margin-left: 25%;\n  }\n  .col-lg-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-lg-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-lg-offset-0 {\n    margin-left: 0%;\n  }\n}\ntable {\n  background-color: transparent;\n}\ntable col[class*=\"col-\"] {\n  position: static;\n  display: table-column;\n  float: none;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n  position: static;\n  display: table-cell;\n  float: none;\n}\ncaption {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  color: #777777;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n  padding: 8px;\n  line-height: 1.42857143;\n  vertical-align: top;\n  border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n  vertical-align: bottom;\n  border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n  border-top: 0;\n}\n.table > tbody + tbody {\n  border-top: 2px solid #ddd;\n}\n.table .table {\n  background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n  padding: 5px;\n}\n.table-bordered {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n  border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n  background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n  background-color: #f5f5f5;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n  background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n  background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n  background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n  background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n  background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n  background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n  background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n  background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n  background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n  background-color: #ebcccc;\n}\n.table-responsive {\n  min-height: 0.01%;\n  overflow-x: auto;\n}\n@media screen and (max-width: 767px) {\n  .table-responsive {\n    width: 100%;\n    margin-bottom: 15px;\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid #ddd;\n  }\n  .table-responsive > .table {\n    margin-bottom: 0;\n  }\n  .table-responsive > .table > thead > tr > th,\n  .table-responsive > .table > tbody > tr > th,\n  .table-responsive > .table > tfoot > tr > th,\n  .table-responsive > .table > thead > tr > td,\n  .table-responsive > .table > tbody > tr > td,\n  .table-responsive > .table > tfoot > tr > td {\n    white-space: nowrap;\n  }\n  .table-responsive > .table-bordered {\n    border: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:first-child,\n  .table-responsive > .table-bordered > tbody > tr > th:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n  .table-responsive > .table-bordered > thead > tr > td:first-child,\n  .table-responsive > .table-bordered > tbody > tr > td:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n    border-left: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:last-child,\n  .table-responsive > .table-bordered > tbody > tr > th:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n  .table-responsive > .table-bordered > thead > tr > td:last-child,\n  .table-responsive > .table-bordered > tbody > tr > td:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n    border-right: 0;\n  }\n  .table-responsive > .table-bordered > tbody > tr:last-child > th,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n  .table-responsive > .table-bordered > tbody > tr:last-child > td,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n    border-bottom: 0;\n  }\n}\nfieldset {\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 20px;\n  font-size: 21px;\n  line-height: inherit;\n  color: #333333;\n  border: 0;\n  border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n  display: inline-block;\n  max-width: 100%;\n  margin-bottom: 5px;\n  font-weight: 700;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n  -webkit-appearance: none;\n  appearance: none;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9;\n  line-height: normal;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n  cursor: not-allowed;\n}\ninput[type=\"file\"] {\n  display: block;\n}\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\nselect[multiple],\nselect[size] {\n  height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\noutput {\n  display: block;\n  padding-top: 7px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n}\n.form-control {\n  display: block;\n  width: 100%;\n  height: 34px;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n  background-color: #fff;\n  background-image: none;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n  border-color: #66afe9;\n  outline: 0;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n  color: #999;\n  opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n  color: #999;\n}\n.form-control::-webkit-input-placeholder {\n  color: #999;\n}\n.form-control::-ms-expand {\n  background-color: transparent;\n  border: 0;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n  background-color: #eeeeee;\n  opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n  cursor: not-allowed;\n}\ntextarea.form-control {\n  height: auto;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"].form-control,\n  input[type=\"time\"].form-control,\n  input[type=\"datetime-local\"].form-control,\n  input[type=\"month\"].form-control {\n    line-height: 34px;\n  }\n  input[type=\"date\"].input-sm,\n  input[type=\"time\"].input-sm,\n  input[type=\"datetime-local\"].input-sm,\n  input[type=\"month\"].input-sm,\n  .input-group-sm input[type=\"date\"],\n  .input-group-sm input[type=\"time\"],\n  .input-group-sm input[type=\"datetime-local\"],\n  .input-group-sm input[type=\"month\"] {\n    line-height: 30px;\n  }\n  input[type=\"date\"].input-lg,\n  input[type=\"time\"].input-lg,\n  input[type=\"datetime-local\"].input-lg,\n  input[type=\"month\"].input-lg,\n  .input-group-lg input[type=\"date\"],\n  .input-group-lg input[type=\"time\"],\n  .input-group-lg input[type=\"datetime-local\"],\n  .input-group-lg input[type=\"month\"] {\n    line-height: 46px;\n  }\n}\n.form-group {\n  margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n  cursor: not-allowed;\n}\n.radio label,\n.checkbox label {\n  min-height: 20px;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-top: 4px \\9;\n  margin-left: -20px;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  vertical-align: middle;\n  cursor: pointer;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n  cursor: not-allowed;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px;\n}\n.form-control-static {\n  min-height: 34px;\n  padding-top: 7px;\n  padding-bottom: 7px;\n  margin-bottom: 0;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-sm {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-sm {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n  height: auto;\n}\n.form-group-sm .form-control {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.form-group-sm select.form-control {\n  height: 30px;\n  line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n  height: auto;\n}\n.form-group-sm .form-control-static {\n  height: 30px;\n  min-height: 32px;\n  padding: 6px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.input-lg {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-lg {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n  height: auto;\n}\n.form-group-lg .form-control {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.form-group-lg select.form-control {\n  height: 46px;\n  line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n  height: auto;\n}\n.form-group-lg .form-control-static {\n  height: 46px;\n  min-height: 38px;\n  padding: 11px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.has-feedback {\n  position: relative;\n}\n.has-feedback .form-control {\n  padding-right: 42.5px;\n}\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  display: block;\n  width: 34px;\n  height: 34px;\n  line-height: 34px;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: 46px;\n  height: 46px;\n  line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: 30px;\n  height: 30px;\n  line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n  color: #3c763d;\n}\n.has-success .form-control {\n  border-color: #3c763d;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n  border-color: #2b542c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #3c763d;\n}\n.has-success .form-control-feedback {\n  color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n  color: #8a6d3b;\n}\n.has-warning .form-control {\n  border-color: #8a6d3b;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n  border-color: #66512c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #8a6d3b;\n}\n.has-warning .form-control-feedback {\n  color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n  color: #a94442;\n}\n.has-error .form-control {\n  border-color: #a94442;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n  border-color: #843534;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #a94442;\n}\n.has-error .form-control-feedback {\n  color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n  top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n  top: 0;\n}\n.help-block {\n  display: block;\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: #737373;\n}\n@media (min-width: 768px) {\n  .form-inline .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .form-inline .form-control-static {\n    display: inline-block;\n  }\n  .form-inline .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .form-inline .input-group .input-group-addon,\n  .form-inline .input-group .input-group-btn,\n  .form-inline .input-group .form-control {\n    width: auto;\n  }\n  .form-inline .input-group > .form-control {\n    width: 100%;\n  }\n  .form-inline .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio,\n  .form-inline .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio label,\n  .form-inline .checkbox label {\n    padding-left: 0;\n  }\n  .form-inline .radio input[type=\"radio\"],\n  .form-inline .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .form-inline .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n  padding-top: 7px;\n  margin-top: 0;\n  margin-bottom: 0;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n  min-height: 27px;\n}\n.form-horizontal .form-group {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .control-label {\n    padding-top: 7px;\n    margin-bottom: 0;\n    text-align: right;\n  }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n  right: 15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-lg .control-label {\n    padding-top: 11px;\n    font-size: 18px;\n  }\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-sm .control-label {\n    padding-top: 6px;\n    font-size: 12px;\n  }\n}\n.btn {\n  display: inline-block;\n  margin-bottom: 0;\n  font-weight: normal;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none;\n  border: 1px solid transparent;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  border-radius: 4px;\n  -webkit-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n  color: #333;\n  text-decoration: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n  outline: 0;\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n  cursor: not-allowed;\n  filter: alpha(opacity=65);\n  opacity: 0.65;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n  pointer-events: none;\n}\n.btn-default {\n  color: #333;\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #8c8c8c;\n}\n.btn-default:hover {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  color: #333;\n  background-color: #e6e6e6;\n  background-image: none;\n  border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n  color: #333;\n  background-color: #d4d4d4;\n  border-color: #8c8c8c;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default .badge {\n  color: #fff;\n  background-color: #333;\n}\n.btn-primary {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n  color: #fff;\n  background-color: #286090;\n  border-color: #122b40;\n}\n.btn-primary:hover {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  color: #fff;\n  background-color: #286090;\n  background-image: none;\n  border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n  color: #fff;\n  background-color: #204d74;\n  border-color: #122b40;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.btn-success {\n  color: #fff;\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #255625;\n}\n.btn-success:hover {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  color: #fff;\n  background-color: #449d44;\n  background-image: none;\n  border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n  color: #fff;\n  background-color: #398439;\n  border-color: #255625;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success .badge {\n  color: #5cb85c;\n  background-color: #fff;\n}\n.btn-info {\n  color: #fff;\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #1b6d85;\n}\n.btn-info:hover {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  color: #fff;\n  background-color: #31b0d5;\n  background-image: none;\n  border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n  color: #fff;\n  background-color: #269abc;\n  border-color: #1b6d85;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info .badge {\n  color: #5bc0de;\n  background-color: #fff;\n}\n.btn-warning {\n  color: #fff;\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #985f0d;\n}\n.btn-warning:hover {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  color: #fff;\n  background-color: #ec971f;\n  background-image: none;\n  border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n  color: #fff;\n  background-color: #d58512;\n  border-color: #985f0d;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning .badge {\n  color: #f0ad4e;\n  background-color: #fff;\n}\n.btn-danger {\n  color: #fff;\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #761c19;\n}\n.btn-danger:hover {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  color: #fff;\n  background-color: #c9302c;\n  background-image: none;\n  border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n  color: #fff;\n  background-color: #ac2925;\n  border-color: #761c19;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger .badge {\n  color: #d9534f;\n  background-color: #fff;\n}\n.btn-link {\n  font-weight: 400;\n  color: #337ab7;\n  border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n  background-color: transparent;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n  border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n  color: #23527c;\n  text-decoration: underline;\n  background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n  color: #777777;\n  text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n  padding: 1px 5px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-block {\n  display: block;\n  width: 100%;\n}\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n  width: 100%;\n}\n.fade {\n  opacity: 0;\n  -webkit-transition: opacity 0.15s linear;\n  -o-transition: opacity 0.15s linear;\n  transition: opacity 0.15s linear;\n}\n.fade.in {\n  opacity: 1;\n}\n.collapse {\n  display: none;\n}\n.collapse.in {\n  display: block;\n}\ntr.collapse.in {\n  display: table-row;\n}\ntbody.collapse.in {\n  display: table-row-group;\n}\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  -webkit-transition-property: height, visibility;\n  transition-property: height, visibility;\n  -webkit-transition-duration: 0.35s;\n  transition-duration: 0.35s;\n  -webkit-transition-timing-function: ease;\n  transition-timing-function: ease;\n}\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: 4px dashed;\n  border-top: 4px solid \\9;\n  border-right: 4px solid transparent;\n  border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n  position: relative;\n}\n.dropdown-toggle:focus {\n  outline: 0;\n}\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: 1000;\n  display: none;\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0;\n  font-size: 14px;\n  text-align: left;\n  list-style: none;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.15);\n  border-radius: 4px;\n  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n}\n.dropdown-menu.pull-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu .divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n  display: block;\n  padding: 3px 20px;\n  clear: both;\n  font-weight: 400;\n  line-height: 1.42857143;\n  color: #333333;\n  white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  color: #262626;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  color: #fff;\n  text-decoration: none;\n  background-color: #337ab7;\n  outline: 0;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n  background-image: none;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.open > .dropdown-menu {\n  display: block;\n}\n.open > a {\n  outline: 0;\n}\n.dropdown-menu-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu-left {\n  right: auto;\n  left: 0;\n}\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: 12px;\n  line-height: 1.42857143;\n  color: #777777;\n  white-space: nowrap;\n}\n.dropdown-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 990;\n}\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n  content: \"\";\n  border-top: 0;\n  border-bottom: 4px dashed;\n  border-bottom: 4px solid \\9;\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n  top: auto;\n  bottom: 100%;\n  margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n  .navbar-right .dropdown-menu {\n    right: 0;\n    left: auto;\n  }\n  .navbar-right .dropdown-menu-left {\n    right: auto;\n    left: 0;\n  }\n}\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n  z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n  margin-left: -1px;\n}\n.btn-toolbar {\n  margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n  float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n  margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n.btn-group > .btn:first-child {\n  margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n  padding-right: 8px;\n  padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-right: 12px;\n  padding-left: 12px;\n}\n.btn-group.open .dropdown-toggle {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn .caret {\n  margin-left: 0;\n}\n.btn-lg .caret {\n  border-width: 5px 5px 0;\n  border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n  border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n  display: block;\n  float: none;\n  width: 100%;\n  max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n  float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n  margin-top: -1px;\n  margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n  display: table-cell;\n  float: none;\n  width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n  width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n  left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.input-group {\n  position: relative;\n  display: table;\n  border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n  float: none;\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-group .form-control {\n  position: relative;\n  z-index: 2;\n  float: left;\n  width: 100%;\n  margin-bottom: 0;\n}\n.input-group .form-control:focus {\n  z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle;\n}\n.input-group-addon {\n  padding: 6px 12px;\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 1;\n  color: #555555;\n  text-align: center;\n  background-color: #eeeeee;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\n.input-group-addon.input-sm {\n  padding: 5px 10px;\n  font-size: 12px;\n  border-radius: 3px;\n}\n.input-group-addon.input-lg {\n  padding: 10px 16px;\n  font-size: 18px;\n  border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n  margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n.input-group-btn {\n  position: relative;\n  font-size: 0;\n  white-space: nowrap;\n}\n.input-group-btn > .btn {\n  position: relative;\n}\n.input-group-btn > .btn + .btn {\n  margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n  z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n  margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n  z-index: 2;\n  margin-left: -1px;\n}\n.nav {\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n.nav > li {\n  position: relative;\n  display: block;\n}\n.nav > li > a {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n  color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n  color: #777777;\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n  background-color: #eeeeee;\n  border-color: #337ab7;\n}\n.nav .nav-divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.nav > li > a > img {\n  max-width: none;\n}\n.nav-tabs {\n  border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n  float: left;\n  margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n  margin-right: 2px;\n  line-height: 1.42857143;\n  border: 1px solid transparent;\n  border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n  border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n  color: #555555;\n  cursor: default;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-bottom-color: transparent;\n}\n.nav-tabs.nav-justified {\n  width: 100%;\n  border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n  float: none;\n}\n.nav-tabs.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-tabs.nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs.nav-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs.nav-justified > .active > a,\n  .nav-tabs.nav-justified > .active > a:hover,\n  .nav-tabs.nav-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.nav-pills > li {\n  float: left;\n}\n.nav-pills > li > a {\n  border-radius: 4px;\n}\n.nav-pills > li + li {\n  margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n  color: #fff;\n  background-color: #337ab7;\n}\n.nav-stacked > li {\n  float: none;\n}\n.nav-stacked > li + li {\n  margin-top: 2px;\n  margin-left: 0;\n}\n.nav-justified {\n  width: 100%;\n}\n.nav-justified > li {\n  float: none;\n}\n.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs-justified {\n  border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs-justified > .active > a,\n  .nav-tabs-justified > .active > a:hover,\n  .nav-tabs-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n.nav-tabs .dropdown-menu {\n  margin-top: -1px;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar {\n  position: relative;\n  min-height: 50px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n  .navbar {\n    border-radius: 4px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-header {\n    float: left;\n  }\n}\n.navbar-collapse {\n  padding-right: 15px;\n  padding-left: 15px;\n  overflow-x: visible;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n  -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n  overflow-y: auto;\n}\n@media (min-width: 768px) {\n  .navbar-collapse {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n  }\n  .navbar-collapse.collapse {\n    display: block !important;\n    height: auto !important;\n    padding-bottom: 0;\n    overflow: visible !important;\n  }\n  .navbar-collapse.in {\n    overflow-y: visible;\n  }\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-static-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n  max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    max-height: 200px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-fixed-top,\n  .navbar-fixed-bottom {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0;\n  border-width: 1px 0 0;\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .container > .navbar-header,\n  .container-fluid > .navbar-header,\n  .container > .navbar-collapse,\n  .container-fluid > .navbar-collapse {\n    margin-right: 0;\n    margin-left: 0;\n  }\n}\n.navbar-static-top {\n  z-index: 1000;\n  border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n  .navbar-static-top {\n    border-radius: 0;\n  }\n}\n.navbar-brand {\n  float: left;\n  height: 50px;\n  padding: 15px 15px;\n  font-size: 18px;\n  line-height: 20px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n  text-decoration: none;\n}\n.navbar-brand > img {\n  display: block;\n}\n@media (min-width: 768px) {\n  .navbar > .container .navbar-brand,\n  .navbar > .container-fluid .navbar-brand {\n    margin-left: -15px;\n  }\n}\n.navbar-toggle {\n  position: relative;\n  float: right;\n  padding: 9px 10px;\n  margin-right: 15px;\n  margin-top: 8px;\n  margin-bottom: 8px;\n  background-color: transparent;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.navbar-toggle:focus {\n  outline: 0;\n}\n.navbar-toggle .icon-bar {\n  display: block;\n  width: 22px;\n  height: 2px;\n  border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n  margin-top: 4px;\n}\n@media (min-width: 768px) {\n  .navbar-toggle {\n    display: none;\n  }\n}\n.navbar-nav {\n  margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n  padding-top: 10px;\n  padding-bottom: 10px;\n  line-height: 20px;\n}\n@media (max-width: 767px) {\n  .navbar-nav .open .dropdown-menu {\n    position: static;\n    float: none;\n    width: auto;\n    margin-top: 0;\n    background-color: transparent;\n    border: 0;\n    box-shadow: none;\n  }\n  .navbar-nav .open .dropdown-menu > li > a,\n  .navbar-nav .open .dropdown-menu .dropdown-header {\n    padding: 5px 15px 5px 25px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a {\n    line-height: 20px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-nav .open .dropdown-menu > li > a:focus {\n    background-image: none;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-nav {\n    float: left;\n    margin: 0;\n  }\n  .navbar-nav > li {\n    float: left;\n  }\n  .navbar-nav > li > a {\n    padding-top: 15px;\n    padding-bottom: 15px;\n  }\n}\n.navbar-form {\n  padding: 10px 15px;\n  margin-right: -15px;\n  margin-left: -15px;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n  .navbar-form .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control-static {\n    display: inline-block;\n  }\n  .navbar-form .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .navbar-form .input-group .input-group-addon,\n  .navbar-form .input-group .input-group-btn,\n  .navbar-form .input-group .form-control {\n    width: auto;\n  }\n  .navbar-form .input-group > .form-control {\n    width: 100%;\n  }\n  .navbar-form .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio,\n  .navbar-form .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio label,\n  .navbar-form .checkbox label {\n    padding-left: 0;\n  }\n  .navbar-form .radio input[type=\"radio\"],\n  .navbar-form .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .navbar-form .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n@media (max-width: 767px) {\n  .navbar-form .form-group {\n    margin-bottom: 5px;\n  }\n  .navbar-form .form-group:last-child {\n    margin-bottom: 0;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-form {\n    width: auto;\n    padding-top: 0;\n    padding-bottom: 0;\n    margin-right: 0;\n    margin-left: 0;\n    border: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n}\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.navbar-btn {\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n  margin-top: 14px;\n  margin-bottom: 14px;\n}\n.navbar-text {\n  margin-top: 15px;\n  margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n  .navbar-text {\n    float: left;\n    margin-right: 15px;\n    margin-left: 15px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-left {\n    float: left !important;\n  }\n  .navbar-right {\n    float: right !important;\n    margin-right: -15px;\n  }\n  .navbar-right ~ .navbar-right {\n    margin-right: 0;\n  }\n}\n.navbar-default {\n  background-color: #f8f8f8;\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n  color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n  color: #5e5e5e;\n  background-color: transparent;\n}\n.navbar-default .navbar-text {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n  color: #333;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n  color: #ccc;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n@media (max-width: 767px) {\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n    color: #777;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #333;\n    background-color: transparent;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #555;\n    background-color: #e7e7e7;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #ccc;\n    background-color: transparent;\n  }\n}\n.navbar-default .navbar-toggle {\n  border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n  background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n  background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-link {\n  color: #777;\n}\n.navbar-default .navbar-link:hover {\n  color: #333;\n}\n.navbar-default .btn-link {\n  color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n  color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n  color: #ccc;\n}\n.navbar-inverse {\n  background-color: #222;\n  border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n  color: #444;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n@media (max-width: 767px) {\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n    border-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n    color: #9d9d9d;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #fff;\n    background-color: transparent;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #444;\n    background-color: transparent;\n  }\n}\n.navbar-inverse .navbar-toggle {\n  border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n  background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n  background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n  border-color: #101010;\n}\n.navbar-inverse .navbar-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n  color: #fff;\n}\n.navbar-inverse .btn-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n  color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n  color: #444;\n}\n.breadcrumb {\n  padding: 8px 15px;\n  margin-bottom: 20px;\n  list-style: none;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n}\n.breadcrumb > li {\n  display: inline-block;\n}\n.breadcrumb > li + li:before {\n  padding: 0 5px;\n  color: #ccc;\n  content: \"/\\00a0\";\n}\n.breadcrumb > .active {\n  color: #777777;\n}\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: 20px 0;\n  border-radius: 4px;\n}\n.pagination > li {\n  display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n  position: relative;\n  float: left;\n  padding: 6px 12px;\n  margin-left: -1px;\n  line-height: 1.42857143;\n  color: #337ab7;\n  text-decoration: none;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n  z-index: 2;\n  color: #23527c;\n  background-color: #eeeeee;\n  border-color: #ddd;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n  margin-left: 0;\n  border-top-left-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 4px;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n  z-index: 3;\n  color: #fff;\n  cursor: default;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #fff;\n  border-color: #ddd;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n  border-top-left-radius: 6px;\n  border-bottom-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n  border-top-right-radius: 6px;\n  border-bottom-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n  border-top-left-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n  border-top-right-radius: 3px;\n  border-bottom-right-radius: 3px;\n}\n.pager {\n  padding-left: 0;\n  margin: 20px 0;\n  text-align: center;\n  list-style: none;\n}\n.pager li {\n  display: inline;\n}\n.pager li > a,\n.pager li > span {\n  display: inline-block;\n  padding: 5px 14px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n  float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n  float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #fff;\n}\n.label {\n  display: inline;\n  padding: 0.2em 0.6em 0.3em;\n  font-size: 75%;\n  font-weight: 700;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: 0.25em;\n}\na.label:hover,\na.label:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.label:empty {\n  display: none;\n}\n.btn .label {\n  position: relative;\n  top: -1px;\n}\n.label-default {\n  background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n  background-color: #5e5e5e;\n}\n.label-primary {\n  background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n  background-color: #286090;\n}\n.label-success {\n  background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n  background-color: #449d44;\n}\n.label-info {\n  background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n  background-color: #31b0d5;\n}\n.label-warning {\n  background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n  background-color: #ec971f;\n}\n.label-danger {\n  background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n  background-color: #c9302c;\n}\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: 12px;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  background-color: #777777;\n  border-radius: 10px;\n}\n.badge:empty {\n  display: none;\n}\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n  top: 0;\n  padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.list-group-item > .badge {\n  float: right;\n}\n.list-group-item > .badge + .badge {\n  margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n  margin-left: 3px;\n}\n.jumbotron {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  margin-bottom: 30px;\n  color: inherit;\n  background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n  color: inherit;\n}\n.jumbotron p {\n  margin-bottom: 15px;\n  font-size: 21px;\n  font-weight: 200;\n}\n.jumbotron > hr {\n  border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n  padding-right: 15px;\n  padding-left: 15px;\n  border-radius: 6px;\n}\n.jumbotron .container {\n  max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n  .jumbotron {\n    padding-top: 48px;\n    padding-bottom: 48px;\n  }\n  .container .jumbotron,\n  .container-fluid .jumbotron {\n    padding-right: 60px;\n    padding-left: 60px;\n  }\n  .jumbotron h1,\n  .jumbotron .h1 {\n    font-size: 63px;\n  }\n}\n.thumbnail {\n  display: block;\n  padding: 4px;\n  margin-bottom: 20px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: border 0.2s ease-in-out;\n  -o-transition: border 0.2s ease-in-out;\n  transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n  margin-right: auto;\n  margin-left: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n  border-color: #337ab7;\n}\n.thumbnail .caption {\n  padding: 9px;\n  color: #333333;\n}\n.alert {\n  padding: 15px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.alert h4 {\n  margin-top: 0;\n  color: inherit;\n}\n.alert .alert-link {\n  font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n  margin-bottom: 0;\n}\n.alert > p + p {\n  margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n  position: relative;\n  top: -2px;\n  right: -21px;\n  color: inherit;\n}\n.alert-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.alert-success hr {\n  border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n  color: #2b542c;\n}\n.alert-info {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.alert-info hr {\n  border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n  color: #245269;\n}\n.alert-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.alert-warning hr {\n  border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n  color: #66512c;\n}\n.alert-danger {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.alert-danger hr {\n  border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n  color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n.progress {\n  height: 20px;\n  margin-bottom: 20px;\n  overflow: hidden;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: 12px;\n  line-height: 20px;\n  color: #fff;\n  text-align: center;\n  background-color: #337ab7;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  -webkit-transition: width 0.6s ease;\n  -o-transition: width 0.6s ease;\n  transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n  -webkit-animation: progress-bar-stripes 2s linear infinite;\n  -o-animation: progress-bar-stripes 2s linear infinite;\n  animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n  background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n  background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n  background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n  background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n  margin-top: 15px;\n}\n.media:first-child {\n  margin-top: 0;\n}\n.media,\n.media-body {\n  overflow: hidden;\n  zoom: 1;\n}\n.media-body {\n  width: 10000px;\n}\n.media-object {\n  display: block;\n}\n.media-object.img-thumbnail {\n  max-width: none;\n}\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n.media-middle {\n  vertical-align: middle;\n}\n.media-bottom {\n  vertical-align: bottom;\n}\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n.list-group {\n  padding-left: 0;\n  margin-bottom: 20px;\n}\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  margin-bottom: -1px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n}\n.list-group-item:last-child {\n  margin-bottom: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #eeeeee;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n  color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n  color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  z-index: 2;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n  color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n  color: #c7ddef;\n}\na.list-group-item,\nbutton.list-group-item {\n  color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n  color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n  color: #555;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n.list-group-item-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n  color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n  color: #3c763d;\n  background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n  color: #fff;\n  background-color: #3c763d;\n  border-color: #3c763d;\n}\n.list-group-item-info {\n  color: #31708f;\n  background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n  color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n  color: #31708f;\n  background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n  color: #fff;\n  background-color: #31708f;\n  border-color: #31708f;\n}\n.list-group-item-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n  color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n  color: #8a6d3b;\n  background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n  color: #fff;\n  background-color: #8a6d3b;\n  border-color: #8a6d3b;\n}\n.list-group-item-danger {\n  color: #a94442;\n  background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n  color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n  color: #a94442;\n  background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n  color: #fff;\n  background-color: #a94442;\n  border-color: #a94442;\n}\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n.panel {\n  margin-bottom: 20px;\n  background-color: #fff;\n  border: 1px solid transparent;\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n  padding: 15px;\n}\n.panel-heading {\n  padding: 10px 15px;\n  border-bottom: 1px solid transparent;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n  color: inherit;\n}\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: 16px;\n  color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n  color: inherit;\n}\n.panel-footer {\n  padding: 10px 15px;\n  background-color: #f5f5f5;\n  border-top: 1px solid #ddd;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n  margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n  border-width: 1px 0;\n  border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n  border-top: 0;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n  border-bottom: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n  border-top-width: 0;\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n  margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n  border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n  border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n  border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n  border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n  border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n  border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n  border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n  border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n  border-bottom: 0;\n}\n.panel > .table-responsive {\n  margin-bottom: 0;\n  border: 0;\n}\n.panel-group {\n  margin-bottom: 20px;\n}\n.panel-group .panel {\n  margin-bottom: 0;\n  border-radius: 4px;\n}\n.panel-group .panel + .panel {\n  margin-top: 5px;\n}\n.panel-group .panel-heading {\n  border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n  border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n  border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n  border-bottom: 1px solid #ddd;\n}\n.panel-default {\n  border-color: #ddd;\n}\n.panel-default > .panel-heading {\n  color: #333333;\n  background-color: #f5f5f5;\n  border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n  color: #f5f5f5;\n  background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ddd;\n}\n.panel-primary {\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #337ab7;\n}\n.panel-success {\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n  color: #dff0d8;\n  background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #d6e9c6;\n}\n.panel-info {\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n  color: #d9edf7;\n  background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #bce8f1;\n}\n.panel-warning {\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n  color: #fcf8e3;\n  background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #faebcc;\n}\n.panel-danger {\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n  color: #f2dede;\n  background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  border: 0;\n}\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border: 1px solid #e3e3e3;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n  border-color: #ddd;\n  border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n  padding: 24px;\n  border-radius: 6px;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: 3px;\n}\n.close {\n  float: right;\n  font-size: 21px;\n  font-weight: bold;\n  line-height: 1;\n  color: #000;\n  text-shadow: 0 1px 0 #fff;\n  filter: alpha(opacity=20);\n  opacity: 0.2;\n}\n.close:hover,\n.close:focus {\n  color: #000;\n  text-decoration: none;\n  cursor: pointer;\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\nbutton.close {\n  padding: 0;\n  cursor: pointer;\n  background: transparent;\n  border: 0;\n  -webkit-appearance: none;\n  appearance: none;\n}\n.modal-open {\n  overflow: hidden;\n}\n.modal {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1050;\n  display: none;\n  overflow: hidden;\n  -webkit-overflow-scrolling: touch;\n  outline: 0;\n}\n.modal.fade .modal-dialog {\n  -webkit-transform: translate(0, -25%);\n  -ms-transform: translate(0, -25%);\n  -o-transform: translate(0, -25%);\n  transform: translate(0, -25%);\n  -webkit-transition: -webkit-transform 0.3s ease-out;\n  -moz-transition: -moz-transform 0.3s ease-out;\n  -o-transition: -o-transform 0.3s ease-out;\n  transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n  -webkit-transform: translate(0, 0);\n  -ms-transform: translate(0, 0);\n  -o-transform: translate(0, 0);\n  transform: translate(0, 0);\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n.modal-content {\n  position: relative;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #999;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  outline: 0;\n}\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1040;\n  background-color: #000;\n}\n.modal-backdrop.fade {\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.modal-backdrop.in {\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\n.modal-header {\n  padding: 15px;\n  border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n  margin-top: -2px;\n}\n.modal-title {\n  margin: 0;\n  line-height: 1.42857143;\n}\n.modal-body {\n  position: relative;\n  padding: 15px;\n}\n.modal-footer {\n  padding: 15px;\n  text-align: right;\n  border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n  margin-bottom: 0;\n  margin-left: 5px;\n}\n.modal-footer .btn-group .btn + .btn {\n  margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n  margin-left: 0;\n}\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n@media (min-width: 768px) {\n  .modal-dialog {\n    width: 600px;\n    margin: 30px auto;\n  }\n  .modal-content {\n    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n  }\n  .modal-sm {\n    width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg {\n    width: 900px;\n  }\n}\n.tooltip {\n  position: absolute;\n  z-index: 1070;\n  display: block;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.42857143;\n  line-break: auto;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  font-size: 12px;\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.tooltip.in {\n  filter: alpha(opacity=90);\n  opacity: 0.9;\n}\n.tooltip.top {\n  padding: 5px 0;\n  margin-top: -3px;\n}\n.tooltip.right {\n  padding: 0 5px;\n  margin-left: 3px;\n}\n.tooltip.bottom {\n  padding: 5px 0;\n  margin-top: 3px;\n}\n.tooltip.left {\n  padding: 0 5px;\n  margin-left: -3px;\n}\n.tooltip.top .tooltip-arrow {\n  bottom: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n  right: 5px;\n  bottom: 0;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n  bottom: 0;\n  left: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n  top: 50%;\n  left: 0;\n  margin-top: -5px;\n  border-width: 5px 5px 5px 0;\n  border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n  top: 50%;\n  right: 0;\n  margin-top: -5px;\n  border-width: 5px 0 5px 5px;\n  border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n  top: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n  top: 0;\n  right: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n  top: 0;\n  left: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip-inner {\n  max-width: 200px;\n  padding: 3px 8px;\n  color: #fff;\n  text-align: center;\n  background-color: #000;\n  border-radius: 4px;\n}\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 1060;\n  display: none;\n  max-width: 276px;\n  padding: 1px;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.42857143;\n  line-break: auto;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  font-size: 14px;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n  margin-top: -10px;\n}\n.popover.right {\n  margin-left: 10px;\n}\n.popover.bottom {\n  margin-top: 10px;\n}\n.popover.left {\n  margin-left: -10px;\n}\n.popover > .arrow {\n  border-width: 11px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n  position: absolute;\n  display: block;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover > .arrow:after {\n  content: \"\";\n  border-width: 10px;\n}\n.popover.top > .arrow {\n  bottom: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-color: #999999;\n  border-top-color: rgba(0, 0, 0, 0.25);\n  border-bottom-width: 0;\n}\n.popover.top > .arrow:after {\n  bottom: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-color: #fff;\n  border-bottom-width: 0;\n}\n.popover.right > .arrow {\n  top: 50%;\n  left: -11px;\n  margin-top: -11px;\n  border-right-color: #999999;\n  border-right-color: rgba(0, 0, 0, 0.25);\n  border-left-width: 0;\n}\n.popover.right > .arrow:after {\n  bottom: -10px;\n  left: 1px;\n  content: \" \";\n  border-right-color: #fff;\n  border-left-width: 0;\n}\n.popover.bottom > .arrow {\n  top: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-width: 0;\n  border-bottom-color: #999999;\n  border-bottom-color: rgba(0, 0, 0, 0.25);\n}\n.popover.bottom > .arrow:after {\n  top: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-width: 0;\n  border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n  top: 50%;\n  right: -11px;\n  margin-top: -11px;\n  border-right-width: 0;\n  border-left-color: #999999;\n  border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n  right: 1px;\n  bottom: -10px;\n  content: \" \";\n  border-right-width: 0;\n  border-left-color: #fff;\n}\n.popover-title {\n  padding: 8px 14px;\n  margin: 0;\n  font-size: 14px;\n  background-color: #f7f7f7;\n  border-bottom: 1px solid #ebebeb;\n  border-radius: 5px 5px 0 0;\n}\n.popover-content {\n  padding: 9px 14px;\n}\n.carousel {\n  position: relative;\n}\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n}\n.carousel-inner > .item {\n  position: relative;\n  display: none;\n  -webkit-transition: 0.6s ease-in-out left;\n  -o-transition: 0.6s ease-in-out left;\n  transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n  .carousel-inner > .item {\n    -webkit-transition: -webkit-transform 0.6s ease-in-out;\n    -moz-transition: -moz-transform 0.6s ease-in-out;\n    -o-transition: -o-transform 0.6s ease-in-out;\n    transition: transform 0.6s ease-in-out;\n    -webkit-backface-visibility: hidden;\n    -moz-backface-visibility: hidden;\n    backface-visibility: hidden;\n    -webkit-perspective: 1000px;\n    -moz-perspective: 1000px;\n    perspective: 1000px;\n  }\n  .carousel-inner > .item.next,\n  .carousel-inner > .item.active.right {\n    -webkit-transform: translate3d(100%, 0, 0);\n    transform: translate3d(100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.prev,\n  .carousel-inner > .item.active.left {\n    -webkit-transform: translate3d(-100%, 0, 0);\n    transform: translate3d(-100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.next.left,\n  .carousel-inner > .item.prev.right,\n  .carousel-inner > .item.active {\n    -webkit-transform: translate3d(0, 0, 0);\n    transform: translate3d(0, 0, 0);\n    left: 0;\n  }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  display: block;\n}\n.carousel-inner > .active {\n  left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  position: absolute;\n  top: 0;\n  width: 100%;\n}\n.carousel-inner > .next {\n  left: 100%;\n}\n.carousel-inner > .prev {\n  left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n  left: 0;\n}\n.carousel-inner > .active.left {\n  left: -100%;\n}\n.carousel-inner > .active.right {\n  left: 100%;\n}\n.carousel-control {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 15%;\n  font-size: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n  background-color: rgba(0, 0, 0, 0);\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\n.carousel-control.left {\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control.right {\n  right: 0;\n  left: auto;\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control:hover,\n.carousel-control:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  filter: alpha(opacity=90);\n  opacity: 0.9;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n  position: absolute;\n  top: 50%;\n  z-index: 5;\n  display: inline-block;\n  margin-top: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n  left: 50%;\n  margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n  right: 50%;\n  margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n  width: 20px;\n  height: 20px;\n  font-family: serif;\n  line-height: 1;\n}\n.carousel-control .icon-prev:before {\n  content: \"\\2039\";\n}\n.carousel-control .icon-next:before {\n  content: \"\\203a\";\n}\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  padding-left: 0;\n  margin-left: -30%;\n  text-align: center;\n  list-style: none;\n}\n.carousel-indicators li {\n  display: inline-block;\n  width: 10px;\n  height: 10px;\n  margin: 1px;\n  text-indent: -999px;\n  cursor: pointer;\n  background-color: #000 \\9;\n  background-color: rgba(0, 0, 0, 0);\n  border: 1px solid #fff;\n  border-radius: 10px;\n}\n.carousel-indicators .active {\n  width: 12px;\n  height: 12px;\n  margin: 0;\n  background-color: #fff;\n}\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 20px;\n  left: 15%;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n  text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-prev,\n  .carousel-control .icon-next {\n    width: 30px;\n    height: 30px;\n    margin-top: -10px;\n    font-size: 30px;\n  }\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .icon-prev {\n    margin-left: -10px;\n  }\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-next {\n    margin-right: -10px;\n  }\n  .carousel-caption {\n    right: 20%;\n    left: 20%;\n    padding-bottom: 30px;\n  }\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n  display: table;\n  content: \" \";\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n  clear: both;\n}\n.center-block {\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  font: 0/0 a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n.hidden {\n  display: none !important;\n}\n.affix {\n  position: fixed;\n}\n@-ms-viewport {\n  width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n@media (max-width: 767px) {\n  .visible-xs {\n    display: block !important;\n  }\n  table.visible-xs {\n    display: table !important;\n  }\n  tr.visible-xs {\n    display: table-row !important;\n  }\n  th.visible-xs,\n  td.visible-xs {\n    display: table-cell !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-block {\n    display: block !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline {\n    display: inline !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm {\n    display: block !important;\n  }\n  table.visible-sm {\n    display: table !important;\n  }\n  tr.visible-sm {\n    display: table-row !important;\n  }\n  th.visible-sm,\n  td.visible-sm {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-block {\n    display: block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md {\n    display: block !important;\n  }\n  table.visible-md {\n    display: table !important;\n  }\n  tr.visible-md {\n    display: table-row !important;\n  }\n  th.visible-md,\n  td.visible-md {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-block {\n    display: block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg {\n    display: block !important;\n  }\n  table.visible-lg {\n    display: table !important;\n  }\n  tr.visible-lg {\n    display: table-row !important;\n  }\n  th.visible-lg,\n  td.visible-lg {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-block {\n    display: block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (max-width: 767px) {\n  .hidden-xs {\n    display: none !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .hidden-sm {\n    display: none !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .hidden-md {\n    display: none !important;\n  }\n}\n@media (min-width: 1200px) {\n  .hidden-lg {\n    display: none !important;\n  }\n}\n.visible-print {\n  display: none !important;\n}\n@media print {\n  .visible-print {\n    display: block !important;\n  }\n  table.visible-print {\n    display: table !important;\n  }\n  tr.visible-print {\n    display: table-row !important;\n  }\n  th.visible-print,\n  td.visible-print {\n    display: table-cell !important;\n  }\n}\n.visible-print-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-block {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline-block {\n    display: inline-block !important;\n  }\n}\n@media print {\n  .hidden-print {\n    display: none !important;\n  }\n}\n/*# sourceMappingURL=bootstrap.css.map */","// stylelint-disable\n\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n//    without disabling user zoom.\n//\n\nhtml {\n  font-family: sans-serif; // 1\n  -ms-text-size-adjust: 100%; // 2\n  -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n  margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block; // 1\n  vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n  display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n  background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n  outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// 1. Remove the bottom border in Chrome 57- and Firefox 39-.\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n//\n\nabbr[title] {\n  border-bottom: none; // 1\n  text-decoration: underline; // 2\n  text-decoration: underline dotted; // 2\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n  font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n  font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n  background: #ff0;\n  color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n  font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsup {\n  top: -0.5em;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n  border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n  margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n  overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n//    Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit; // 1\n  font: inherit; // 2\n  margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n  overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n//    and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n//    `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n  cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n  line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box; // 1\n  padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n  border: 0; // 1\n  padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n  overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n  font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\n\ntd,\nth {\n  padding: 0;\n}\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type\n\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n  *,\n  *:before,\n  *:after {\n    color: #000 !important; // Black prints faster: h5bp.com/s\n    text-shadow: none !important;\n    background: transparent !important;\n    box-shadow: none !important;\n  }\n\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n\n  // Don't show links that are fragment identifiers,\n  // or use the `javascript:` pseudo protocol\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n\n  thead {\n    display: table-header-group; // h5bp.com/t\n  }\n\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n\n  img {\n    max-width: 100% !important;\n  }\n\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n\n  // Bootstrap specific changes start\n\n  // Bootstrap components\n  .navbar {\n    display: none;\n  }\n  .btn,\n  .dropup > .btn {\n    > .caret {\n      border-top-color: #000 !important;\n    }\n  }\n  .label {\n    border: 1px solid #000;\n  }\n\n  .table {\n    border-collapse: collapse !important;\n\n    td,\n    th {\n      background-color: #fff !important;\n    }\n  }\n  .table-bordered {\n    th,\n    td {\n      border: 1px solid #ddd !important;\n    }\n  }\n}\n","// stylelint-disable value-list-comma-newline-after, value-list-comma-space-after, indentation, declaration-colon-newline-after, font-family-no-missing-generic-family-keyword\n\n//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// <a href=\"#\"><span class=\"glyphicon glyphicon-star\"></span> Star</a>\n\n// Import the fonts\n@font-face {\n  font-family: \"Glyphicons Halflings\";\n  src: url(\"@{icon-font-path}@{icon-font-name}.eot\");\n  src: url(\"@{icon-font-path}@{icon-font-name}.eot?#iefix\") format(\"embedded-opentype\"),\n       url(\"@{icon-font-path}@{icon-font-name}.woff2\") format(\"woff2\"),\n       url(\"@{icon-font-path}@{icon-font-name}.woff\") format(\"woff\"),\n       url(\"@{icon-font-path}@{icon-font-name}.ttf\") format(\"truetype\"),\n       url(\"@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}\") format(\"svg\");\n}\n\n// Catchall baseclass\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: \"Glyphicons Halflings\";\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk               { &:before { content: \"\\002a\"; } }\n.glyphicon-plus                   { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur                    { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus                  { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud                  { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope               { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil                 { &:before { content: \"\\270f\"; } }\n.glyphicon-glass                  { &:before { content: \"\\e001\"; } }\n.glyphicon-music                  { &:before { content: \"\\e002\"; } }\n.glyphicon-search                 { &:before { content: \"\\e003\"; } }\n.glyphicon-heart                  { &:before { content: \"\\e005\"; } }\n.glyphicon-star                   { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty             { &:before { content: \"\\e007\"; } }\n.glyphicon-user                   { &:before { content: \"\\e008\"; } }\n.glyphicon-film                   { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large               { &:before { content: \"\\e010\"; } }\n.glyphicon-th                     { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list                { &:before { content: \"\\e012\"; } }\n.glyphicon-ok                     { &:before { content: \"\\e013\"; } }\n.glyphicon-remove                 { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in                { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out               { &:before { content: \"\\e016\"; } }\n.glyphicon-off                    { &:before { content: \"\\e017\"; } }\n.glyphicon-signal                 { &:before { content: \"\\e018\"; } }\n.glyphicon-cog                    { &:before { content: \"\\e019\"; } }\n.glyphicon-trash                  { &:before { content: \"\\e020\"; } }\n.glyphicon-home                   { &:before { content: \"\\e021\"; } }\n.glyphicon-file                   { &:before { content: \"\\e022\"; } }\n.glyphicon-time                   { &:before { content: \"\\e023\"; } }\n.glyphicon-road                   { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt           { &:before { content: \"\\e025\"; } }\n.glyphicon-download               { &:before { content: \"\\e026\"; } }\n.glyphicon-upload                 { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox                  { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle            { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat                 { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh                { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt               { &:before { content: \"\\e032\"; } }\n.glyphicon-lock                   { &:before { content: \"\\e033\"; } }\n.glyphicon-flag                   { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones             { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off             { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down            { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up              { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode                 { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode                { &:before { content: \"\\e040\"; } }\n.glyphicon-tag                    { &:before { content: \"\\e041\"; } }\n.glyphicon-tags                   { &:before { content: \"\\e042\"; } }\n.glyphicon-book                   { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark               { &:before { content: \"\\e044\"; } }\n.glyphicon-print                  { &:before { content: \"\\e045\"; } }\n.glyphicon-camera                 { &:before { content: \"\\e046\"; } }\n.glyphicon-font                   { &:before { content: \"\\e047\"; } }\n.glyphicon-bold                   { &:before { content: \"\\e048\"; } }\n.glyphicon-italic                 { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height            { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width             { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left             { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center           { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right            { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify          { &:before { content: \"\\e055\"; } }\n.glyphicon-list                   { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left            { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right           { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video         { &:before { content: \"\\e059\"; } }\n.glyphicon-picture                { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker             { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust                 { &:before { content: \"\\e063\"; } }\n.glyphicon-tint                   { &:before { content: \"\\e064\"; } }\n.glyphicon-edit                   { &:before { content: \"\\e065\"; } }\n.glyphicon-share                  { &:before { content: \"\\e066\"; } }\n.glyphicon-check                  { &:before { content: \"\\e067\"; } }\n.glyphicon-move                   { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward          { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward          { &:before { content: \"\\e070\"; } }\n.glyphicon-backward               { &:before { content: \"\\e071\"; } }\n.glyphicon-play                   { &:before { content: \"\\e072\"; } }\n.glyphicon-pause                  { &:before { content: \"\\e073\"; } }\n.glyphicon-stop                   { &:before { content: \"\\e074\"; } }\n.glyphicon-forward                { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward           { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward           { &:before { content: \"\\e077\"; } }\n.glyphicon-eject                  { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left           { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right          { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign              { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign             { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign            { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign                { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign          { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign              { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot             { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle          { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle              { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle             { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left             { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right            { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up               { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down             { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt              { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full            { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small           { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign       { &:before { content: \"\\e101\"; } }\n.glyphicon-gift                   { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf                   { &:before { content: \"\\e103\"; } }\n.glyphicon-fire                   { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open               { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close              { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign           { &:before { content: \"\\e107\"; } }\n.glyphicon-plane                  { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar               { &:before { content: \"\\e109\"; } }\n.glyphicon-random                 { &:before { content: \"\\e110\"; } }\n.glyphicon-comment                { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet                 { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up             { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down           { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet                { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart          { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close           { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open            { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical        { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal      { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd                    { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn               { &:before { content: \"\\e122\"; } }\n.glyphicon-bell                   { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate            { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up              { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down            { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right             { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left              { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up                { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down              { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right     { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left      { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up        { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down      { &:before { content: \"\\e134\"; } }\n.glyphicon-globe                  { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench                 { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks                  { &:before { content: \"\\e137\"; } }\n.glyphicon-filter                 { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase              { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen             { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard              { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip              { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty            { &:before { content: \"\\e143\"; } }\n.glyphicon-link                   { &:before { content: \"\\e144\"; } }\n.glyphicon-phone                  { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin                { &:before { content: \"\\e146\"; } }\n.glyphicon-usd                    { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp                    { &:before { content: \"\\e149\"; } }\n.glyphicon-sort                   { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet       { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt   { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order          { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt      { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes     { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked              { &:before { content: \"\\e157\"; } }\n.glyphicon-expand                 { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down          { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up            { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in                 { &:before { content: \"\\e161\"; } }\n.glyphicon-flash                  { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out                { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window             { &:before { content: \"\\e164\"; } }\n.glyphicon-record                 { &:before { content: \"\\e165\"; } }\n.glyphicon-save                   { &:before { content: \"\\e166\"; } }\n.glyphicon-open                   { &:before { content: \"\\e167\"; } }\n.glyphicon-saved                  { &:before { content: \"\\e168\"; } }\n.glyphicon-import                 { &:before { content: \"\\e169\"; } }\n.glyphicon-export                 { &:before { content: \"\\e170\"; } }\n.glyphicon-send                   { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk            { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved           { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove          { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save            { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open            { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card            { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer               { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery                { &:before { content: \"\\e179\"; } }\n.glyphicon-header                 { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed             { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone               { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt              { &:before { content: \"\\e183\"; } }\n.glyphicon-tower                  { &:before { content: \"\\e184\"; } }\n.glyphicon-stats                  { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video               { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video               { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles              { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo           { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby            { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1              { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1              { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1              { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark         { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark      { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download         { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload           { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer           { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous         { &:before { content: \"\\e200\"; } }\n.glyphicon-cd                     { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file              { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file              { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up               { &:before { content: \"\\e204\"; } }\n.glyphicon-copy                   { &:before { content: \"\\e205\"; } }\n.glyphicon-paste                  { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door                   { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key                    { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert                  { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer              { &:before { content: \"\\e210\"; } }\n.glyphicon-king                   { &:before { content: \"\\e211\"; } }\n.glyphicon-queen                  { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn                   { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop                 { &:before { content: \"\\e214\"; } }\n.glyphicon-knight                 { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula           { &:before { content: \"\\e216\"; } }\n.glyphicon-tent                   { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard             { &:before { content: \"\\e218\"; } }\n.glyphicon-bed                    { &:before { content: \"\\e219\"; } }\n.glyphicon-apple                  { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase                  { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass              { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp                   { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate              { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank             { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors               { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin                { &:before { content: \"\\e227\"; } }\n.glyphicon-btc                    { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt                    { &:before { content: \"\\e227\"; } }\n.glyphicon-yen                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble                  { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub                    { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale                  { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly              { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted       { &:before { content: \"\\e232\"; } }\n.glyphicon-education              { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal      { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical        { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger         { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window           { &:before { content: \"\\e237\"; } }\n.glyphicon-oil                    { &:before { content: \"\\e238\"; } }\n.glyphicon-grain                  { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses             { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size              { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color             { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background        { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top       { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom    { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left      { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical  { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right     { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right         { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left          { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom        { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top           { &:before { content: \"\\e253\"; } }\n.glyphicon-console                { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript            { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript              { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left              { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right             { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down              { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up                { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// https://getbootstrap.com/docs/3.4/getting-started/#third-box-sizing\n* {\n  .box-sizing(border-box);\n}\n*:before,\n*:after {\n  .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n\nbody {\n  font-family: @font-family-base;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @text-color;\n  background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\n\n\n// Links\n\na {\n  color: @link-color;\n  text-decoration: none;\n\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n  }\n\n  &:focus {\n    .tab-focus();\n  }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n  margin: 0;\n}\n\n\n// Images\n\nimg {\n  vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n  .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n  border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n  padding: @thumbnail-padding;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(all .2s ease-in-out);\n\n  // Keep them at most 100% wide\n  .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n  border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n  margin-top: @line-height-computed;\n  margin-bottom: @line-height-computed;\n  border: 0;\n  border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: https://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n  &:active,\n  &:focus {\n    position: static;\n    width: auto;\n    height: auto;\n    margin: 0;\n    overflow: visible;\n    clip: auto;\n  }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n  cursor: pointer;\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n  word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n  // WebKit-specific. Other browsers will keep their default outline style.\n  // (Initially tried to also force default via `outline: initial`,\n  // but that seems to erroneously remove the outline in Firefox altogether.)\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n","// stylelint-disable media-feature-name-no-vendor-prefix, media-feature-parentheses-space-inside, media-feature-name-no-unknown, indentation, at-rule-name-space-after\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n  display: @display;\n  max-width: 100%; // Part 1: Set a maximum relative to the parent\n  height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n  background-image: url(\"@{file-1x}\");\n\n  @media\n  only screen and (-webkit-min-device-pixel-ratio: 2),\n  only screen and ( min--moz-device-pixel-ratio: 2),\n  only screen and ( -o-min-device-pixel-ratio: 2/1),\n  only screen and ( min-device-pixel-ratio: 2),\n  only screen and ( min-resolution: 192dpi),\n  only screen and ( min-resolution: 2dppx) {\n    background-image: url(\"@{file-2x}\");\n    background-size: @width-1x @height-1x;\n  }\n}\n","// stylelint-disable selector-list-comma-newline-after, selector-no-qualifying-type\n\n//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n  font-family: @headings-font-family;\n  font-weight: @headings-font-weight;\n  line-height: @headings-line-height;\n  color: @headings-color;\n\n  small,\n  .small {\n    font-weight: 400;\n    line-height: 1;\n    color: @headings-small-color;\n  }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n  margin-top: @line-height-computed;\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 65%;\n  }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n  margin-top: (@line-height-computed / 2);\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 75%;\n  }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n  margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n  margin-bottom: @line-height-computed;\n  font-size: floor((@font-size-base * 1.15));\n  font-weight: 300;\n  line-height: 1.4;\n\n  @media (min-width: @screen-sm-min) {\n    font-size: (@font-size-base * 1.5);\n  }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n  font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n  padding: .2em;\n  background-color: @state-warning-bg;\n}\n\n// Alignment\n.text-left           { text-align: left; }\n.text-right          { text-align: right; }\n.text-center         { text-align: center; }\n.text-justify        { text-align: justify; }\n.text-nowrap         { white-space: nowrap; }\n\n// Transformation\n.text-lowercase      { text-transform: lowercase; }\n.text-uppercase      { text-transform: uppercase; }\n.text-capitalize     { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n  color: @text-muted;\n}\n.text-primary {\n  .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n  .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n  .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n  .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n  .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n  // Given the contrast here, this is the only class to have its color inverted\n  // automatically.\n  color: #fff;\n  .bg-variant(@brand-primary);\n}\n.bg-success {\n  .bg-variant(@state-success-bg);\n}\n.bg-info {\n  .bg-variant(@state-info-bg);\n}\n.bg-warning {\n  .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n  .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n  padding-bottom: ((@line-height-computed / 2) - 1);\n  margin: (@line-height-computed * 2) 0 @line-height-computed;\n  border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n  margin-top: 0;\n  margin-bottom: (@line-height-computed / 2);\n  ul,\n  ol {\n    margin-bottom: 0;\n  }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  .list-unstyled();\n  margin-left: -5px;\n\n  > li {\n    display: inline-block;\n    padding-right: 5px;\n    padding-left: 5px;\n  }\n}\n\n// Description Lists\ndl {\n  margin-top: 0; // Remove browser default\n  margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n  line-height: @line-height-base;\n}\ndt {\n  font-weight: 700;\n}\ndd {\n  margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n  dd {\n    &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n  }\n\n  @media (min-width: @dl-horizontal-breakpoint) {\n    dt {\n      float: left;\n      width: (@dl-horizontal-offset - 20);\n      clear: left;\n      text-align: right;\n      .text-overflow();\n    }\n    dd {\n      margin-left: @dl-horizontal-offset;\n    }\n  }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n}\n\n.initialism {\n  font-size: 90%;\n  .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n  padding: (@line-height-computed / 2) @line-height-computed;\n  margin: 0 0 @line-height-computed;\n  font-size: @blockquote-font-size;\n  border-left: 5px solid @blockquote-border-color;\n\n  p,\n  ul,\n  ol {\n    &:last-child {\n      margin-bottom: 0;\n    }\n  }\n\n  // Note: Deprecated small and .small as of v3.1.0\n  // Context: https://github.com/twbs/bootstrap/issues/11660\n  footer,\n  small,\n  .small {\n    display: block;\n    font-size: 80%; // back to default font-size\n    line-height: @line-height-base;\n    color: @blockquote-small-color;\n\n    &:before {\n      content: \"\\2014 \\00A0\"; // em dash, nbsp\n    }\n  }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  text-align: right;\n  border-right: 5px solid @blockquote-border-color;\n  border-left: 0;\n\n  // Account for citation\n  footer,\n  small,\n  .small {\n    &:before { content: \"\"; }\n    &:after {\n      content: \"\\00A0 \\2014\"; // nbsp, em dash\n    }\n  }\n}\n\n// Addresses\naddress {\n  margin-bottom: @line-height-computed;\n  font-style: normal;\n  line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n  color: @color;\n  a&:hover,\n  a&:focus {\n    color: darken(@color, 10%);\n  }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n  background-color: @color;\n  a&:hover,\n  a&:focus {\n    background-color: darken(@color, 10%);\n  }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n  font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @code-color;\n  background-color: @code-bg;\n  border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @kbd-color;\n  background-color: @kbd-bg;\n  border-radius: @border-radius-small;\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25);\n\n  kbd {\n    padding: 0;\n    font-size: 100%;\n    font-weight: 700;\n    box-shadow: none;\n  }\n}\n\n// Blocks of code\npre {\n  display: block;\n  padding: ((@line-height-computed - 1) / 2);\n  margin: 0 0 (@line-height-computed / 2);\n  font-size: (@font-size-base - 1); // 14px to 13px\n  line-height: @line-height-base;\n  color: @pre-color;\n  word-break: break-all;\n  word-wrap: break-word;\n  background-color: @pre-bg;\n  border: 1px solid @pre-border-color;\n  border-radius: @border-radius-base;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    padding: 0;\n    font-size: inherit;\n    color: inherit;\n    white-space: pre-wrap;\n    background-color: transparent;\n    border-radius: 0;\n  }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n  max-height: @pre-scrollable-max-height;\n  overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n  .container-fixed();\n\n  @media (min-width: @screen-sm-min) {\n    width: @container-sm;\n  }\n  @media (min-width: @screen-md-min) {\n    width: @container-md;\n  }\n  @media (min-width: @screen-lg-min) {\n    width: @container-lg;\n  }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n  .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n  .make-row();\n}\n\n.row-no-gutters {\n  margin-right: 0;\n  margin-left: 0;\n\n  [class*=\"col-\"] {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n  .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n  .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n  .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n  padding-right: ceil((@gutter / 2));\n  padding-left: floor((@gutter / 2));\n  margin-right: auto;\n  margin-left: auto;\n  &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n  margin-right: floor((@gutter / -2));\n  margin-left: ceil((@gutter / -2));\n  &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  float: left;\n  width: percentage((@columns / @grid-columns));\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n  margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n  left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n  right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n\n  @media (min-width: @screen-sm-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-offset(@columns) {\n  @media (min-width: @screen-sm-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-push(@columns) {\n  @media (min-width: @screen-sm-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-pull(@columns) {\n  @media (min-width: @screen-sm-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n\n  @media (min-width: @screen-md-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-offset(@columns) {\n  @media (min-width: @screen-md-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-push(@columns) {\n  @media (min-width: @screen-md-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-pull(@columns) {\n  @media (min-width: @screen-md-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n\n  @media (min-width: @screen-lg-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-offset(@columns) {\n  @media (min-width: @screen-lg-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-push(@columns) {\n  @media (min-width: @screen-lg-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-pull(@columns) {\n  @media (min-width: @screen-lg-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n  // Common styles for all sizes of grid columns, widths 1-12\n  .col(@index) { // initial\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      position: relative;\n      // Prevent columns from collapsing when empty\n      min-height: 1px;\n      // Inner gutter via padding\n      padding-right: floor((@grid-gutter-width / 2));\n      padding-left: ceil((@grid-gutter-width / 2));\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n  .col(@index) { // initial\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      float: left;\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n  .col-@{class}-@{index} {\n    width: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n  .col-@{class}-push-@{index} {\n    left: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n  .col-@{class}-push-0 {\n    left: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n  .col-@{class}-pull-@{index} {\n    right: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n  .col-@{class}-pull-0 {\n    right: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n  .col-@{class}-offset-@{index} {\n    margin-left: percentage((@index / @grid-columns));\n  }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n  .calc-grid-column(@index, @class, @type);\n  // next iteration\n  .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n  .float-grid-columns(@class);\n  .loop-grid-columns(@grid-columns, @class, width);\n  .loop-grid-columns(@grid-columns, @class, pull);\n  .loop-grid-columns(@grid-columns, @class, push);\n  .loop-grid-columns(@grid-columns, @class, offset);\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-no-qualifying-type\n\n//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n  background-color: @table-bg;\n\n  // Table cell sizing\n  //\n  // Reset default table behavior\n\n  col[class*=\"col-\"] {\n    position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n    display: table-column;\n    float: none;\n  }\n\n  td,\n  th {\n    &[class*=\"col-\"] {\n      position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n      display: table-cell;\n      float: none;\n    }\n  }\n}\n\ncaption {\n  padding-top: @table-cell-padding;\n  padding-bottom: @table-cell-padding;\n  color: @text-muted;\n  text-align: left;\n}\n\nth {\n  text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: @line-height-computed;\n  // Cells\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-cell-padding;\n        line-height: @line-height-base;\n        vertical-align: top;\n        border-top: 1px solid @table-border-color;\n      }\n    }\n  }\n  // Bottom align for column headings\n  > thead > tr > th {\n    vertical-align: bottom;\n    border-bottom: 2px solid @table-border-color;\n  }\n  // Remove top border from thead by default\n  > caption + thead,\n  > colgroup + thead,\n  > thead:first-child {\n    > tr:first-child {\n      > th,\n      > td {\n        border-top: 0;\n      }\n    }\n  }\n  // Account for multiple tbody instances\n  > tbody + tbody {\n    border-top: 2px solid @table-border-color;\n  }\n\n  // Nesting\n  .table {\n    background-color: @body-bg;\n  }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-condensed-cell-padding;\n      }\n    }\n  }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n  border: 1px solid @table-border-color;\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        border: 1px solid @table-border-color;\n      }\n    }\n  }\n  > thead > tr {\n    > th,\n    > td {\n      border-bottom-width: 2px;\n    }\n  }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n  > tbody > tr:nth-of-type(odd) {\n    background-color: @table-bg-accent;\n  }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover {\n    background-color: @table-bg-hover;\n  }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n  min-height: .01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n  overflow-x: auto;\n\n  @media screen and (max-width: @screen-xs-max) {\n    width: 100%;\n    margin-bottom: (@line-height-computed * .75);\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid @table-border-color;\n\n    // Tighten up spacing\n    > .table {\n      margin-bottom: 0;\n\n      // Ensure the content doesn't wrap\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th,\n          > td {\n            white-space: nowrap;\n          }\n        }\n      }\n    }\n\n    // Special overrides for the bordered tables\n    > .table-bordered {\n      border: 0;\n\n      // Nuke the appropriate borders so that the parent can handle them\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th:first-child,\n          > td:first-child {\n            border-left: 0;\n          }\n          > th:last-child,\n          > td:last-child {\n            border-right: 0;\n          }\n        }\n      }\n\n      // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n      // chances are there will be only one `tr` in a `thead` and that would\n      // remove the border altogether.\n      > tbody,\n      > tfoot {\n        > tr:last-child {\n          > th,\n          > td {\n            border-bottom: 0;\n          }\n        }\n      }\n\n    }\n  }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n  // Exact selectors below required to override `.table-striped` and prevent\n  // inheritance to nested tables.\n  .table > thead > tr,\n  .table > tbody > tr,\n  .table > tfoot > tr {\n    > td.@{state},\n    > th.@{state},\n    &.@{state} > td,\n    &.@{state} > th {\n      background-color: @background;\n    }\n  }\n\n  // Hover states for `.table-hover`\n  // Note: this is not available for cells or rows within `thead` or `tfoot`.\n  .table-hover > tbody > tr {\n    > td.@{state}:hover,\n    > th.@{state}:hover,\n    &.@{state}:hover > td,\n    &:hover > .@{state},\n    &.@{state}:hover > th {\n      background-color: darken(@background, 5%);\n    }\n  }\n}\n","// stylelint-disable selector-no-qualifying-type, property-no-vendor-prefix, media-feature-name-no-vendor-prefix\n\n//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n  // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n  // so we reset that to ensure it behaves more like a standard block element.\n  // See https://github.com/twbs/bootstrap/issues/12359.\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\n\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: @line-height-computed;\n  font-size: (@font-size-base * 1.5);\n  line-height: inherit;\n  color: @legend-color;\n  border: 0;\n  border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n  display: inline-block;\n  max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n  margin-bottom: 5px;\n  font-weight: 700;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\ninput[type=\"search\"] {\n  // Override content-box in Normalize (* isn't specific enough)\n  .box-sizing(border-box);\n\n  // Search inputs in iOS\n  //\n  // This overrides the extra rounded corners on search inputs in iOS so that our\n  // `.form-control` class can properly style them. Note that this cannot simply\n  // be added to `.form-control` as it's not specific enough. For details, see\n  // https://github.com/twbs/bootstrap/issues/11586.\n  -webkit-appearance: none;\n  appearance: none;\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9; // IE8-9\n  line-height: normal;\n\n  // Apply same disabled cursor tweak as for inputs\n  // Some special care is needed because <label>s don't inherit their parent's `cursor`.\n  //\n  // Note: Neither radios nor checkboxes can be readonly.\n  &[disabled],\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n\ninput[type=\"file\"] {\n  display: block;\n}\n\n// Make range inputs behave like textual form controls\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\n\n// Make multiple select elements height not fixed\nselect[multiple],\nselect[size] {\n  height: auto;\n}\n\n// Focus for file, radio, and checkbox\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  .tab-focus();\n}\n\n// Adjust output element\noutput {\n  display: block;\n  padding-top: (@padding-base-vertical + 1);\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n}\n\n\n// Common form controls\n//\n// Shared size and type resets for form controls. Apply `.form-control` to any\n// of the following form controls:\n//\n// select\n// textarea\n// input[type=\"text\"]\n// input[type=\"password\"]\n// input[type=\"datetime\"]\n// input[type=\"datetime-local\"]\n// input[type=\"date\"]\n// input[type=\"month\"]\n// input[type=\"time\"]\n// input[type=\"week\"]\n// input[type=\"number\"]\n// input[type=\"email\"]\n// input[type=\"url\"]\n// input[type=\"search\"]\n// input[type=\"tel\"]\n// input[type=\"color\"]\n\n.form-control {\n  display: block;\n  width: 100%;\n  height: @input-height-base; // Make inputs at least the height of their button counterpart (base line-height + padding + border)\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n  background-color: @input-bg;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid @input-border;\n  border-radius: @input-border-radius; // Note: This has no effect on <select>s in some browsers, due to the limited stylability of <select>s in CSS.\n  .box-shadow(inset 0 1px 1px rgba(0, 0, 0, .075));\n  .transition(~\"border-color ease-in-out .15s, box-shadow ease-in-out .15s\");\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  .form-control-focus();\n\n  // Placeholder\n  .placeholder();\n\n  // Unstyle the caret on `<select>`s in IE10+.\n  &::-ms-expand {\n    background-color: transparent;\n    border: 0;\n  }\n\n  // Disabled and read-only inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &[disabled],\n  &[readonly],\n  fieldset[disabled] & {\n    background-color: @input-bg-disabled;\n    opacity: 1; // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655\n  }\n\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n\n  // Reset height for `textarea`s\n  textarea& {\n    height: auto;\n  }\n}\n\n\n// Special styles for iOS temporal inputs\n//\n// In Mobile Safari, setting `display: block` on temporal inputs causes the\n// text within the input to become vertically misaligned. As a workaround, we\n// set a pixel line-height that matches the given height of the input, but only\n// for Safari. See https://bugs.webkit.org/show_bug.cgi?id=139848\n//\n// Note that as of 9.3, iOS doesn't support `week`.\n\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"],\n  input[type=\"time\"],\n  input[type=\"datetime-local\"],\n  input[type=\"month\"] {\n    &.form-control {\n      line-height: @input-height-base;\n    }\n\n    &.input-sm,\n    .input-group-sm & {\n      line-height: @input-height-small;\n    }\n\n    &.input-lg,\n    .input-group-lg & {\n      line-height: @input-height-large;\n    }\n  }\n}\n\n\n// Form groups\n//\n// Designed to help with the organization and spacing of vertical forms. For\n// horizontal forms, use the predefined grid classes.\n\n.form-group {\n  margin-bottom: @form-group-margin-bottom;\n}\n\n\n// Checkboxes and radios\n//\n// Indent the labels to position radios/checkboxes as hanging controls.\n\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n\n  // These are used on elements with <label> descendants\n  &.disabled,\n  fieldset[disabled] & {\n    label {\n      cursor: @cursor-disabled;\n    }\n  }\n\n  label {\n    min-height: @line-height-computed; // Ensure the input doesn't jump when there is no text\n    padding-left: 20px;\n    margin-bottom: 0;\n    font-weight: 400;\n    cursor: pointer;\n  }\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-top: 4px \\9;\n  margin-left: -20px;\n}\n\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px; // Move up sibling radios or checkboxes for tighter spacing\n}\n\n// Radios and checkboxes on same line\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  vertical-align: middle;\n  cursor: pointer;\n\n  // These are used directly on <label>s\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px; // space out consecutive inline controls\n}\n\n\n// Static form control text\n//\n// Apply class to a `p` element to make any string of text align with labels in\n// a horizontal form layout.\n\n.form-control-static {\n  min-height: (@line-height-computed + @font-size-base);\n  // Size it appropriately next to real form controls\n  padding-top: (@padding-base-vertical + 1);\n  padding-bottom: (@padding-base-vertical + 1);\n  // Remove default margin from `p`\n  margin-bottom: 0;\n\n  &.input-lg,\n  &.input-sm {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// The `.form-group-* form-control` variations are sadly duplicated to avoid the\n// issue documented in https://github.com/twbs/bootstrap/issues/15074.\n\n.input-sm {\n  .input-size(@input-height-small; @padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @input-border-radius-small);\n}\n.form-group-sm {\n  .form-control {\n    height: @input-height-small;\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n    border-radius: @input-border-radius-small;\n  }\n  select.form-control {\n    height: @input-height-small;\n    line-height: @input-height-small;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-small;\n    min-height: (@line-height-computed + @font-size-small);\n    padding: (@padding-small-vertical + 1) @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n  }\n}\n\n.input-lg {\n  .input-size(@input-height-large; @padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @input-border-radius-large);\n}\n.form-group-lg {\n  .form-control {\n    height: @input-height-large;\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n    border-radius: @input-border-radius-large;\n  }\n  select.form-control {\n    height: @input-height-large;\n    line-height: @input-height-large;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-large;\n    min-height: (@line-height-computed + @font-size-large);\n    padding: (@padding-large-vertical + 1) @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n  }\n}\n\n\n// Form control feedback states\n//\n// Apply contextual and semantic states to individual form controls.\n\n.has-feedback {\n  // Enable absolute positioning\n  position: relative;\n\n  // Ensure icons don't overlap text\n  .form-control {\n    padding-right: (@input-height-base * 1.25);\n  }\n}\n// Feedback icon (requires .glyphicon classes)\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2; // Ensure icon is above input groups\n  display: block;\n  width: @input-height-base;\n  height: @input-height-base;\n  line-height: @input-height-base;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: @input-height-large;\n  height: @input-height-large;\n  line-height: @input-height-large;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: @input-height-small;\n  height: @input-height-small;\n  line-height: @input-height-small;\n}\n\n// Feedback states\n.has-success {\n  .form-control-validation(@state-success-text; @state-success-text; @state-success-bg);\n}\n.has-warning {\n  .form-control-validation(@state-warning-text; @state-warning-text; @state-warning-bg);\n}\n.has-error {\n  .form-control-validation(@state-danger-text; @state-danger-text; @state-danger-bg);\n}\n\n// Reposition feedback icon if input has visible label above\n.has-feedback label {\n\n  & ~ .form-control-feedback {\n    top: (@line-height-computed + 5); // Height of the `label` and its margin\n  }\n  &.sr-only ~ .form-control-feedback {\n    top: 0;\n  }\n}\n\n\n// Help text\n//\n// Apply to any element you wish to create light text for placement immediately\n// below a form control. Use for general help, formatting, or instructional text.\n\n.help-block {\n  display: block; // account for any element using help-block\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: lighten(@text-color, 25%); // lighten the text some for contrast\n}\n\n\n// Inline forms\n//\n// Make forms appear inline(-block) by adding the `.form-inline` class. Inline\n// forms begin stacked on extra small (mobile) devices and then go inline when\n// viewports reach <768px.\n//\n// Requires wrapping inputs and labels with `.form-group` for proper display of\n// default HTML form controls and our custom form controls (e.g., input groups).\n//\n// Heads up! This is mixin-ed into `.navbar-form` in navbars.less.\n\n.form-inline {\n\n  // Kick in the inline\n  @media (min-width: @screen-sm-min) {\n    // Inline-block all the things for \"inline\"\n    .form-group {\n      display: inline-block;\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // In navbar-form, allow folks to *not* use `.form-group`\n    .form-control {\n      display: inline-block;\n      width: auto; // Prevent labels from stacking above inputs in `.form-group`\n      vertical-align: middle;\n    }\n\n    // Make static controls behave like regular ones\n    .form-control-static {\n      display: inline-block;\n    }\n\n    .input-group {\n      display: inline-table;\n      vertical-align: middle;\n\n      .input-group-addon,\n      .input-group-btn,\n      .form-control {\n        width: auto;\n      }\n    }\n\n    // Input groups need that 100% width though\n    .input-group > .form-control {\n      width: 100%;\n    }\n\n    .control-label {\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // Remove default margin on radios/checkboxes that were used for stacking, and\n    // then undo the floating of radios and checkboxes to match.\n    .radio,\n    .checkbox {\n      display: inline-block;\n      margin-top: 0;\n      margin-bottom: 0;\n      vertical-align: middle;\n\n      label {\n        padding-left: 0;\n      }\n    }\n    .radio input[type=\"radio\"],\n    .checkbox input[type=\"checkbox\"] {\n      position: relative;\n      margin-left: 0;\n    }\n\n    // Re-override the feedback icon.\n    .has-feedback .form-control-feedback {\n      top: 0;\n    }\n  }\n}\n\n\n// Horizontal forms\n//\n// Horizontal forms are built on grid classes and allow you to create forms with\n// labels on the left and inputs on the right.\n\n.form-horizontal {\n\n  // Consistent vertical alignment of radios and checkboxes\n  //\n  // Labels also get some reset styles, but that is scoped to a media query below.\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline {\n    padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n    margin-top: 0;\n    margin-bottom: 0;\n  }\n  // Account for padding we're adding to ensure the alignment and of help text\n  // and other content below items\n  .radio,\n  .checkbox {\n    min-height: (@line-height-computed + (@padding-base-vertical + 1));\n  }\n\n  // Make form groups behave like rows\n  .form-group {\n    .make-row();\n  }\n\n  // Reset spacing and right align labels, but scope to media queries so that\n  // labels on narrow viewports stack the same as a default form example.\n  @media (min-width: @screen-sm-min) {\n    .control-label {\n      padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n      margin-bottom: 0;\n      text-align: right;\n    }\n  }\n\n  // Validation states\n  //\n  // Reposition the icon because it's now within a grid column and columns have\n  // `position: relative;` on them. Also accounts for the grid gutter padding.\n  .has-feedback .form-control-feedback {\n    right: floor((@grid-gutter-width / 2));\n  }\n\n  // Form group sizes\n  //\n  // Quick utility class for applying `.input-lg` and `.input-sm` styles to the\n  // inputs and labels within a `.form-group`.\n  .form-group-lg {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-large-vertical + 1);\n        font-size: @font-size-large;\n      }\n    }\n  }\n  .form-group-sm {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-small-vertical + 1);\n        font-size: @font-size-small;\n      }\n    }\n  }\n}\n","// Form validation states\n//\n// Used in forms.less to generate the form validation CSS for warnings, errors,\n// and successes.\n\n.form-control-validation(@text-color: #555; @border-color: #ccc; @background-color: #f5f5f5) {\n  // Color the label and help text\n  .help-block,\n  .control-label,\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline,\n  &.radio label,\n  &.checkbox label,\n  &.radio-inline label,\n  &.checkbox-inline label  {\n    color: @text-color;\n  }\n  // Set the border and box shadow on specific inputs to match\n  .form-control {\n    border-color: @border-color;\n    .box-shadow(inset 0 1px 1px rgba(0, 0, 0, .075)); // Redeclare so transitions work\n    &:focus {\n      border-color: darken(@border-color, 10%);\n      @shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px lighten(@border-color, 20%);\n      .box-shadow(@shadow);\n    }\n  }\n  // Set validation states also for addons\n  .input-group-addon {\n    color: @text-color;\n    background-color: @background-color;\n    border-color: @border-color;\n  }\n  // Optional feedback icon\n  .form-control-feedback {\n    color: @text-color;\n  }\n}\n\n\n// Form control focus state\n//\n// Generate a customized focus state and for any input with the specified color,\n// which defaults to the `@input-border-focus` variable.\n//\n// We highly encourage you to not customize the default value, but instead use\n// this to tweak colors on an as-needed basis. This aesthetic change is based on\n// WebKit's default styles, but applicable to a wider range of browsers. Its\n// usability and accessibility should be taken into account with any change.\n//\n// Example usage: change the default blue border and shadow to white for better\n// contrast against a dark gray background.\n.form-control-focus(@color: @input-border-focus) {\n  @color-rgba: rgba(red(@color), green(@color), blue(@color), .6);\n  &:focus {\n    border-color: @color;\n    outline: 0;\n    .box-shadow(~\"inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px @{color-rgba}\");\n  }\n}\n\n// Form control sizing\n//\n// Relative text size, padding, and border-radii changes for form controls. For\n// horizontal sizing, wrap controls in the predefined grid classes. `<select>`\n// element gets special love because it's special, and that's a fact!\n.input-size(@input-height; @padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  height: @input-height;\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n\n  select& {\n    height: @input-height;\n    line-height: @input-height;\n  }\n\n  textarea&,\n  select[multiple]& {\n    height: auto;\n  }\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Buttons\n// --------------------------------------------------\n\n\n// Base styles\n// --------------------------------------------------\n\n.btn {\n  display: inline-block;\n  margin-bottom: 0; // For input.btn\n  font-weight: @btn-font-weight;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  .button-size(@padding-base-vertical; @padding-base-horizontal; @font-size-base; @line-height-base; @btn-border-radius-base);\n  .user-select(none);\n\n  &,\n  &:active,\n  &.active {\n    &:focus,\n    &.focus {\n      .tab-focus();\n    }\n  }\n\n  &:hover,\n  &:focus,\n  &.focus {\n    color: @btn-default-color;\n    text-decoration: none;\n  }\n\n  &:active,\n  &.active {\n    background-image: none;\n    outline: 0;\n    .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n    .opacity(.65);\n    .box-shadow(none);\n  }\n\n  a& {\n    &.disabled,\n    fieldset[disabled] & {\n      pointer-events: none; // Future-proof disabling of clicks on `<a>` elements\n    }\n  }\n}\n\n\n// Alternate buttons\n// --------------------------------------------------\n\n.btn-default {\n  .button-variant(@btn-default-color; @btn-default-bg; @btn-default-border);\n}\n.btn-primary {\n  .button-variant(@btn-primary-color; @btn-primary-bg; @btn-primary-border);\n}\n// Success appears as green\n.btn-success {\n  .button-variant(@btn-success-color; @btn-success-bg; @btn-success-border);\n}\n// Info appears as blue-green\n.btn-info {\n  .button-variant(@btn-info-color; @btn-info-bg; @btn-info-border);\n}\n// Warning appears as orange\n.btn-warning {\n  .button-variant(@btn-warning-color; @btn-warning-bg; @btn-warning-border);\n}\n// Danger and error appear as red\n.btn-danger {\n  .button-variant(@btn-danger-color; @btn-danger-bg; @btn-danger-border);\n}\n\n\n// Link buttons\n// -------------------------\n\n// Make a button look and behave like a link\n.btn-link {\n  font-weight: 400;\n  color: @link-color;\n  border-radius: 0;\n\n  &,\n  &:active,\n  &.active,\n  &[disabled],\n  fieldset[disabled] & {\n    background-color: transparent;\n    .box-shadow(none);\n  }\n  &,\n  &:hover,\n  &:focus,\n  &:active {\n    border-color: transparent;\n  }\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n    background-color: transparent;\n  }\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus {\n      color: @btn-link-disabled-color;\n      text-decoration: none;\n    }\n  }\n}\n\n\n// Button Sizes\n// --------------------------------------------------\n\n.btn-lg {\n  // line-height: ensure even-numbered height of button next to large input\n  .button-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @btn-border-radius-large);\n}\n.btn-sm {\n  // line-height: ensure proper height of button next to small input\n  .button-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n.btn-xs {\n  .button-size(@padding-xs-vertical; @padding-xs-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n\n\n// Block button\n// --------------------------------------------------\n\n.btn-block {\n  display: block;\n  width: 100%;\n}\n\n// Vertically space out multiple block buttons\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\n\n// Specificity overrides\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n  &.btn-block {\n    width: 100%;\n  }\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n.button-variant(@color; @background; @border) {\n  color: @color;\n  background-color: @background;\n  border-color: @border;\n\n  &:focus,\n  &.focus {\n    color: @color;\n    background-color: darken(@background, 10%);\n    border-color: darken(@border, 25%);\n  }\n  &:hover {\n    color: @color;\n    background-color: darken(@background, 10%);\n    border-color: darken(@border, 12%);\n  }\n  &:active,\n  &.active,\n  .open > .dropdown-toggle& {\n    color: @color;\n    background-color: darken(@background, 10%);\n    background-image: none;\n    border-color: darken(@border, 12%);\n\n    &:hover,\n    &:focus,\n    &.focus {\n      color: @color;\n      background-color: darken(@background, 17%);\n      border-color: darken(@border, 25%);\n    }\n  }\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus,\n    &.focus {\n      background-color: @background;\n      border-color: @border;\n    }\n  }\n\n  .badge {\n    color: @background;\n    background-color: @color;\n  }\n}\n\n// Button sizes\n.button-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n}\n","// Opacity\n\n.opacity(@opacity) {\n  @opacity-ie: (@opacity * 100);  // IE8 filter\n  filter: ~\"alpha(opacity=@{opacity-ie})\";\n  opacity: @opacity;\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Component animations\n// --------------------------------------------------\n\n// Heads up!\n//\n// We don't use the `.opacity()` mixin here since it causes a bug with text\n// fields in IE7-8. Source: https://github.com/twbs/bootstrap/pull/3552.\n\n.fade {\n  opacity: 0;\n  .transition(opacity .15s linear);\n\n  &.in {\n    opacity: 1;\n  }\n}\n\n.collapse {\n  display: none;\n\n  &.in      { display: block; }\n  tr&.in    { display: table-row; }\n  tbody&.in { display: table-row-group; }\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  .transition-property(~\"height, visibility\");\n  .transition-duration(.35s);\n  .transition-timing-function(ease);\n}\n","//\n// Dropdown menus\n// --------------------------------------------------\n\n\n// Dropdown arrow/caret\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: @caret-width-base dashed;\n  border-top: @caret-width-base solid ~\"\\9\"; // IE8\n  border-right: @caret-width-base solid transparent;\n  border-left: @caret-width-base solid transparent;\n}\n\n// The dropdown wrapper (div)\n.dropup,\n.dropdown {\n  position: relative;\n}\n\n// Prevent the focus on the dropdown toggle when closing dropdowns\n.dropdown-toggle:focus {\n  outline: 0;\n}\n\n// The dropdown menu (ul)\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: @zindex-dropdown;\n  display: none; // none by default, but block on \"open\" of the menu\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0; // override default ul\n  font-size: @font-size-base;\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: @dropdown-bg;\n  background-clip: padding-box;\n  border: 1px solid @dropdown-fallback-border; // IE8 fallback\n  border: 1px solid @dropdown-border;\n  border-radius: @border-radius-base;\n  .box-shadow(0 6px 12px rgba(0, 0, 0, .175));\n\n  // Aligns the dropdown menu to right\n  //\n  // Deprecated as of 3.1.0 in favor of `.dropdown-menu-[dir]`\n  &.pull-right {\n    right: 0;\n    left: auto;\n  }\n\n  // Dividers (basically an hr) within the dropdown\n  .divider {\n    .nav-divider(@dropdown-divider-bg);\n  }\n\n  // Links within the dropdown menu\n  > li > a {\n    display: block;\n    padding: 3px 20px;\n    clear: both;\n    font-weight: 400;\n    line-height: @line-height-base;\n    color: @dropdown-link-color;\n    white-space: nowrap; // prevent links from randomly breaking onto new lines\n\n    &:hover,\n    &:focus {\n      color: @dropdown-link-hover-color;\n      text-decoration: none;\n      background-color: @dropdown-link-hover-bg;\n    }\n  }\n}\n\n// Active state\n.dropdown-menu > .active > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-active-color;\n    text-decoration: none;\n    background-color: @dropdown-link-active-bg;\n    outline: 0;\n  }\n}\n\n// Disabled state\n//\n// Gray out text and ensure the hover/focus state remains gray\n\n.dropdown-menu > .disabled > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-disabled-color;\n  }\n\n  // Nuke hover/focus effects\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    cursor: @cursor-disabled;\n    background-color: transparent;\n    background-image: none; // Remove CSS gradient\n    .reset-filter();\n  }\n}\n\n// Open state for the dropdown\n.open {\n  // Show the menu\n  > .dropdown-menu {\n    display: block;\n  }\n\n  // Remove the outline when :focus is triggered\n  > a {\n    outline: 0;\n  }\n}\n\n// Menu positioning\n//\n// Add extra class to `.dropdown-menu` to flip the alignment of the dropdown\n// menu with the parent.\n.dropdown-menu-right {\n  right: 0;\n  left: auto; // Reset the default from `.dropdown-menu`\n}\n// With v3, we enabled auto-flipping if you have a dropdown within a right\n// aligned nav component. To enable the undoing of that, we provide an override\n// to restore the default dropdown menu alignment.\n//\n// This is only for left-aligning a dropdown menu within a `.navbar-right` or\n// `.pull-right` nav component.\n.dropdown-menu-left {\n  right: auto;\n  left: 0;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: @font-size-small;\n  line-height: @line-height-base;\n  color: @dropdown-header-color;\n  white-space: nowrap; // as with > li > a\n}\n\n// Backdrop to catch body clicks on mobile, etc.\n.dropdown-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: (@zindex-dropdown - 10);\n}\n\n// Right aligned dropdowns\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n//\n// Just add .dropup after the standard .dropdown class and you're set, bro.\n// TODO: abstract this so that the navbar fixed styles are not placed here?\n\n.dropup,\n.navbar-fixed-bottom .dropdown {\n  // Reverse the caret\n  .caret {\n    content: \"\";\n    border-top: 0;\n    border-bottom: @caret-width-base dashed;\n    border-bottom: @caret-width-base solid ~\"\\9\"; // IE8\n  }\n  // Different positioning for bottom up menu\n  .dropdown-menu {\n    top: auto;\n    bottom: 100%;\n    margin-bottom: 2px;\n  }\n}\n\n\n// Component alignment\n//\n// Reiterate per navbar.less and the modified component alignment there.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-right {\n    .dropdown-menu {\n      .dropdown-menu-right();\n    }\n    // Necessary for overrides of the default right aligned menu.\n    // Will remove come v4 in all likelihood.\n    .dropdown-menu-left {\n      .dropdown-menu-left();\n    }\n  }\n}\n","// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n.nav-divider(@color: #e5e5e5) {\n  height: 1px;\n  margin: ((@line-height-computed / 2) - 1) 0;\n  overflow: hidden;\n  background-color: @color;\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n","// stylelint-disable selector-no-qualifying-type */\n\n//\n// Button groups\n// --------------------------------------------------\n\n// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n  > .btn {\n    position: relative;\n    float: left;\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active,\n    &.active {\n      z-index: 2;\n    }\n  }\n}\n\n// Prevent double borders when buttons are next to each other\n.btn-group {\n  .btn + .btn,\n  .btn + .btn-group,\n  .btn-group + .btn,\n  .btn-group + .btn-group {\n    margin-left: -1px;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  margin-left: -5px; // Offset the first child's margin\n  &:extend(.clearfix all);\n\n  .btn,\n  .btn-group,\n  .input-group {\n    float: left;\n  }\n  > .btn,\n  > .btn-group,\n  > .input-group {\n    margin-left: 5px;\n  }\n}\n\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n\n// Set corners individual because sometimes a single button can be in a .btn-group and we need :first-child and :last-child to both match\n.btn-group > .btn:first-child {\n  margin-left: 0;\n  &:not(:last-child):not(.dropdown-toggle) {\n    .border-right-radius(0);\n  }\n}\n// Need .dropdown-toggle since :last-child doesn't apply, given that a .dropdown-menu is used immediately after it\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  .border-left-radius(0);\n}\n\n// Custom edits for including btn-groups within btn-groups (useful for including dropdown buttons within a btn-group)\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-right-radius(0);\n  }\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-left-radius(0);\n}\n\n// On active and open, don't show outline\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-xs > .btn { &:extend(.btn-xs); }\n.btn-group-sm > .btn { &:extend(.btn-sm); }\n.btn-group-lg > .btn { &:extend(.btn-lg); }\n\n\n// Split button dropdowns\n// ----------------------\n\n// Give the line between buttons some depth\n.btn-group > .btn + .dropdown-toggle {\n  padding-right: 8px;\n  padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-right: 12px;\n  padding-left: 12px;\n}\n\n// The clickable button for toggling the menu\n// Remove the gradient and set the same inset shadow as the :active state\n.btn-group.open .dropdown-toggle {\n  .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    .box-shadow(none);\n  }\n}\n\n\n// Reposition the caret\n.btn .caret {\n  margin-left: 0;\n}\n// Carets in other button sizes\n.btn-lg .caret {\n  border-width: @caret-width-large @caret-width-large 0;\n  border-bottom-width: 0;\n}\n// Upside down carets for .dropup\n.dropup .btn-lg .caret {\n  border-width: 0 @caret-width-large @caret-width-large;\n}\n\n\n// Vertical button groups\n// ----------------------\n\n.btn-group-vertical {\n  > .btn,\n  > .btn-group,\n  > .btn-group > .btn {\n    display: block;\n    float: none;\n    width: 100%;\n    max-width: 100%;\n  }\n\n  // Clear floats so dropdown menus can be properly placed\n  > .btn-group {\n    &:extend(.clearfix all);\n    > .btn {\n      float: none;\n    }\n  }\n\n  > .btn + .btn,\n  > .btn + .btn-group,\n  > .btn-group + .btn,\n  > .btn-group + .btn-group {\n    margin-top: -1px;\n    margin-left: 0;\n  }\n}\n\n.btn-group-vertical > .btn {\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n  &:first-child:not(:last-child) {\n    .border-top-radius(@btn-border-radius-base);\n    .border-bottom-radius(0);\n  }\n  &:last-child:not(:first-child) {\n    .border-top-radius(0);\n    .border-bottom-radius(@btn-border-radius-base);\n  }\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-bottom-radius(0);\n  }\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-top-radius(0);\n}\n\n\n// Justified button groups\n// ----------------------\n\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n  > .btn,\n  > .btn-group {\n    display: table-cell;\n    float: none;\n    width: 1%;\n  }\n  > .btn-group .btn {\n    width: 100%;\n  }\n\n  > .btn-group .dropdown-menu {\n    left: auto;\n  }\n}\n\n\n// Checkbox and radio options\n//\n// In order to support the browser's form validation feedback, powered by the\n// `required` attribute, we have to \"hide\" the inputs via `clip`. We cannot use\n// `display: none;` or `visibility: hidden;` as that also hides the popover.\n// Simply visually hiding the inputs via `opacity` would leave them clickable in\n// certain cases which is prevented by using `clip` and `pointer-events`.\n// This way, we ensure a DOM element is visible to position the popover from.\n//\n// See https://github.com/twbs/bootstrap/pull/12794 and\n// https://github.com/twbs/bootstrap/pull/14559 for more information.\n\n[data-toggle=\"buttons\"] {\n  > .btn,\n  > .btn-group > .btn {\n    input[type=\"radio\"],\n    input[type=\"checkbox\"] {\n      position: absolute;\n      clip: rect(0, 0, 0, 0);\n      pointer-events: none;\n    }\n  }\n}\n","// Single side border-radius\n\n.border-top-radius(@radius) {\n  border-top-left-radius: @radius;\n  border-top-right-radius: @radius;\n}\n.border-right-radius(@radius) {\n  border-top-right-radius: @radius;\n  border-bottom-right-radius: @radius;\n}\n.border-bottom-radius(@radius) {\n  border-bottom-right-radius: @radius;\n  border-bottom-left-radius: @radius;\n}\n.border-left-radius(@radius) {\n  border-top-left-radius: @radius;\n  border-bottom-left-radius: @radius;\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Input groups\n// --------------------------------------------------\n\n// Base styles\n// -------------------------\n.input-group {\n  position: relative; // For dropdowns\n  display: table;\n  border-collapse: separate; // prevent input groups from inheriting border styles from table cells when placed within a table\n\n  // Undo padding and float of grid classes\n  &[class*=\"col-\"] {\n    float: none;\n    padding-right: 0;\n    padding-left: 0;\n  }\n\n  .form-control {\n    // Ensure that the input is always above the *appended* addon button for\n    // proper border colors.\n    position: relative;\n    z-index: 2;\n\n    // IE9 fubars the placeholder attribute in text inputs and the arrows on\n    // select elements in input groups. To fix it, we float the input. Details:\n    // https://github.com/twbs/bootstrap/issues/11561#issuecomment-28936855\n    float: left;\n\n    width: 100%;\n    margin-bottom: 0;\n\n    &:focus {\n      z-index: 3;\n    }\n  }\n}\n\n// Sizing options\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  .input-lg();\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  .input-sm();\n}\n\n\n// Display as table-cell\n// -------------------------\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n}\n// Addon and addon wrapper for buttons\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle; // Match the inputs\n}\n\n// Text input groups\n// -------------------------\n.input-group-addon {\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  font-weight: 400;\n  line-height: 1;\n  color: @input-color;\n  text-align: center;\n  background-color: @input-group-addon-bg;\n  border: 1px solid @input-group-addon-border-color;\n  border-radius: @input-border-radius;\n\n  // Sizing\n  &.input-sm {\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    border-radius: @input-border-radius-small;\n  }\n  &.input-lg {\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    border-radius: @input-border-radius-large;\n  }\n\n  // Nuke default margins from checkboxes and radios to vertically center within.\n  input[type=\"radio\"],\n  input[type=\"checkbox\"] {\n    margin-top: 0;\n  }\n}\n\n// Reset rounded corners\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  .border-right-radius(0);\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  .border-left-radius(0);\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n\n// Button input groups\n// -------------------------\n.input-group-btn {\n  position: relative;\n  // Jankily prevent input button groups from wrapping with `white-space` and\n  // `font-size` in combination with `inline-block` on buttons.\n  font-size: 0;\n  white-space: nowrap;\n\n  // Negative margin for spacing, position for bringing hovered/focused/actived\n  // element above the siblings.\n  > .btn {\n    position: relative;\n    + .btn {\n      margin-left: -1px;\n    }\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active {\n      z-index: 2;\n    }\n  }\n\n  // Negative margin to only have a 1px border between the two\n  &:first-child {\n    > .btn,\n    > .btn-group {\n      margin-right: -1px;\n    }\n  }\n  &:last-child {\n    > .btn,\n    > .btn-group {\n      z-index: 2;\n      margin-left: -1px;\n    }\n  }\n}\n","// stylelint-disable selector-no-qualifying-type, selector-max-type\n\n//\n// Navs\n// --------------------------------------------------\n\n\n// Base class\n// --------------------------------------------------\n\n.nav {\n  padding-left: 0; // Override default ul/ol\n  margin-bottom: 0;\n  list-style: none;\n  &:extend(.clearfix all);\n\n  > li {\n    position: relative;\n    display: block;\n\n    > a {\n      position: relative;\n      display: block;\n      padding: @nav-link-padding;\n      &:hover,\n      &:focus {\n        text-decoration: none;\n        background-color: @nav-link-hover-bg;\n      }\n    }\n\n    // Disabled state sets text to gray and nukes hover/tab effects\n    &.disabled > a {\n      color: @nav-disabled-link-color;\n\n      &:hover,\n      &:focus {\n        color: @nav-disabled-link-hover-color;\n        text-decoration: none;\n        cursor: @cursor-disabled;\n        background-color: transparent;\n      }\n    }\n  }\n\n  // Open dropdowns\n  .open > a {\n    &,\n    &:hover,\n    &:focus {\n      background-color: @nav-link-hover-bg;\n      border-color: @link-color;\n    }\n  }\n\n  // Nav dividers (deprecated with v3.0.1)\n  //\n  // This should have been removed in v3 with the dropping of `.nav-list`, but\n  // we missed it. We don't currently support this anywhere, but in the interest\n  // of maintaining backward compatibility in case you use it, it's deprecated.\n  .nav-divider {\n    .nav-divider();\n  }\n\n  // Prevent IE8 from misplacing imgs\n  //\n  // See https://github.com/h5bp/html5-boilerplate/issues/984#issuecomment-3985989\n  > li > a > img {\n    max-width: none;\n  }\n}\n\n\n// Tabs\n// -------------------------\n\n// Give the tabs something to sit on\n.nav-tabs {\n  border-bottom: 1px solid @nav-tabs-border-color;\n  > li {\n    float: left;\n    // Make the list-items overlay the bottom border\n    margin-bottom: -1px;\n\n    // Actual tabs (as links)\n    > a {\n      margin-right: 2px;\n      line-height: @line-height-base;\n      border: 1px solid transparent;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n      &:hover {\n        border-color: @nav-tabs-link-hover-border-color @nav-tabs-link-hover-border-color @nav-tabs-border-color;\n      }\n    }\n\n    // Active state, and its :hover to override normal :hover\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-tabs-active-link-hover-color;\n        cursor: default;\n        background-color: @nav-tabs-active-link-hover-bg;\n        border: 1px solid @nav-tabs-active-link-hover-border-color;\n        border-bottom-color: transparent;\n      }\n    }\n  }\n  // pulling this in mainly for less shorthand\n  &.nav-justified {\n    .nav-justified();\n    .nav-tabs-justified();\n  }\n}\n\n\n// Pills\n// -------------------------\n.nav-pills {\n  > li {\n    float: left;\n\n    // Links rendered as pills\n    > a {\n      border-radius: @nav-pills-border-radius;\n    }\n    + li {\n      margin-left: 2px;\n    }\n\n    // Active state\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-pills-active-link-hover-color;\n        background-color: @nav-pills-active-link-hover-bg;\n      }\n    }\n  }\n}\n\n\n// Stacked pills\n.nav-stacked {\n  > li {\n    float: none;\n    + li {\n      margin-top: 2px;\n      margin-left: 0; // no need for this gap between nav items\n    }\n  }\n}\n\n\n// Nav variations\n// --------------------------------------------------\n\n// Justified nav links\n// -------------------------\n\n.nav-justified {\n  width: 100%;\n\n  > li {\n    float: none;\n    > a {\n      margin-bottom: 5px;\n      text-align: center;\n    }\n  }\n\n  > .dropdown .dropdown-menu {\n    top: auto;\n    left: auto;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li {\n      display: table-cell;\n      width: 1%;\n      > a {\n        margin-bottom: 0;\n      }\n    }\n  }\n}\n\n// Move borders to anchors instead of bottom of list\n//\n// Mixin for adding on top the shared `.nav-justified` styles for our tabs\n.nav-tabs-justified {\n  border-bottom: 0;\n\n  > li > a {\n    // Override margin from .nav-tabs\n    margin-right: 0;\n    border-radius: @border-radius-base;\n  }\n\n  > .active > a,\n  > .active > a:hover,\n  > .active > a:focus {\n    border: 1px solid @nav-tabs-justified-link-border-color;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li > a {\n      border-bottom: 1px solid @nav-tabs-justified-link-border-color;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n    }\n    > .active > a,\n    > .active > a:hover,\n    > .active > a:focus {\n      border-bottom-color: @nav-tabs-justified-active-link-border-color;\n    }\n  }\n}\n\n\n// Tabbable tabs\n// -------------------------\n\n// Hide tabbable panes to start, show them when `.active`\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n\n\n// Dropdowns\n// -------------------------\n\n// Specific dropdowns\n.nav-tabs .dropdown-menu {\n  // make dropdown border overlap tab border\n  margin-top: -1px;\n  // Remove the top rounded corners here since there is a hard edge above the menu\n  .border-top-radius(0);\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-max-combinators, selector-max-class, declaration-no-important, selector-no-qualifying-type\n\n//\n// Navbars\n// --------------------------------------------------\n\n\n// Wrapper and base class\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  position: relative;\n  min-height: @navbar-height; // Ensure a navbar always shows (e.g., without a .navbar-brand in collapsed mode)\n  margin-bottom: @navbar-margin-bottom;\n  border: 1px solid transparent;\n\n  // Prevent floats from breaking the navbar\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: @navbar-border-radius;\n  }\n}\n\n\n// Navbar heading\n//\n// Groups `.navbar-brand` and `.navbar-toggle` into a single component for easy\n// styling of responsive aspects.\n\n.navbar-header {\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n  }\n}\n\n\n// Navbar collapse (body)\n//\n// Group your navbar content into this for easy collapsing and expanding across\n// various device sizes. By default, this content is collapsed when <768px, but\n// will expand past that for a horizontal display.\n//\n// To start (on mobile devices) the navbar links, forms, and buttons are stacked\n// vertically and include a `max-height` to overflow in case you have too much\n// content for the user's viewport.\n\n.navbar-collapse {\n  padding-right: @navbar-padding-horizontal;\n  padding-left: @navbar-padding-horizontal;\n  overflow-x: visible;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1);\n  &:extend(.clearfix all);\n  -webkit-overflow-scrolling: touch;\n\n  &.in {\n    overflow-y: auto;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n\n    &.collapse {\n      display: block !important;\n      height: auto !important;\n      padding-bottom: 0; // Override default setting\n      overflow: visible !important;\n    }\n\n    &.in {\n      overflow-y: visible;\n    }\n\n    // Undo the collapse side padding for navbars with containers to ensure\n    // alignment of right-aligned contents.\n    .navbar-fixed-top &,\n    .navbar-static-top &,\n    .navbar-fixed-bottom & {\n      padding-right: 0;\n      padding-left: 0;\n    }\n  }\n}\n\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  .navbar-collapse {\n    max-height: @navbar-collapse-max-height;\n\n    @media (max-device-width: @screen-xs-min) and (orientation: landscape) {\n      max-height: 200px;\n    }\n  }\n\n  // Fix the top/bottom navbars when screen real estate supports it\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: @zindex-navbar-fixed;\n\n  // Undo the rounded corners\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0; // override .navbar defaults\n  border-width: 1px 0 0;\n}\n\n\n// Both navbar header and collapse\n//\n// When a container is present, change the behavior of the header and collapse.\n\n.container,\n.container-fluid {\n  > .navbar-header,\n  > .navbar-collapse {\n    margin-right: -@navbar-padding-horizontal;\n    margin-left: -@navbar-padding-horizontal;\n\n    @media (min-width: @grid-float-breakpoint) {\n      margin-right: 0;\n      margin-left: 0;\n    }\n  }\n}\n\n\n//\n// Navbar alignment options\n//\n// Display the navbar across the entirety of the page or fixed it to the top or\n// bottom of the page.\n\n// Static top (unfixed, but 100% wide) navbar\n.navbar-static-top {\n  z-index: @zindex-navbar;\n  border-width: 0 0 1px;\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n\n\n// Brand/project name\n\n.navbar-brand {\n  float: left;\n  height: @navbar-height;\n  padding: @navbar-padding-vertical @navbar-padding-horizontal;\n  font-size: @font-size-large;\n  line-height: @line-height-computed;\n\n  &:hover,\n  &:focus {\n    text-decoration: none;\n  }\n\n  > img {\n    display: block;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    .navbar > .container &,\n    .navbar > .container-fluid & {\n      margin-left: -@navbar-padding-horizontal;\n    }\n  }\n}\n\n\n// Navbar toggle\n//\n// Custom button for toggling the `.navbar-collapse`, powered by the collapse\n// JavaScript plugin.\n\n.navbar-toggle {\n  position: relative;\n  float: right;\n  padding: 9px 10px;\n  margin-right: @navbar-padding-horizontal;\n  .navbar-vertical-align(34px);\n  background-color: transparent;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  border-radius: @border-radius-base;\n\n  // We remove the `outline` here, but later compensate by attaching `:hover`\n  // styles to `:focus`.\n  &:focus {\n    outline: 0;\n  }\n\n  // Bars\n  .icon-bar {\n    display: block;\n    width: 22px;\n    height: 2px;\n    border-radius: 1px;\n  }\n  .icon-bar + .icon-bar {\n    margin-top: 4px;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    display: none;\n  }\n}\n\n\n// Navbar nav links\n//\n// Builds on top of the `.nav` components with its own modifier class to make\n// the nav the full height of the horizontal nav (above 768px).\n\n.navbar-nav {\n  margin: (@navbar-padding-vertical / 2) -@navbar-padding-horizontal;\n\n  > li > a {\n    padding-top: 10px;\n    padding-bottom: 10px;\n    line-height: @line-height-computed;\n  }\n\n  @media (max-width: @grid-float-breakpoint-max) {\n    // Dropdowns get custom display when collapsed\n    .open .dropdown-menu {\n      position: static;\n      float: none;\n      width: auto;\n      margin-top: 0;\n      background-color: transparent;\n      border: 0;\n      box-shadow: none;\n      > li > a,\n      .dropdown-header {\n        padding: 5px 15px 5px 25px;\n      }\n      > li > a {\n        line-height: @line-height-computed;\n        &:hover,\n        &:focus {\n          background-image: none;\n        }\n      }\n    }\n  }\n\n  // Uncollapse the nav\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin: 0;\n\n    > li {\n      float: left;\n      > a {\n        padding-top: @navbar-padding-vertical;\n        padding-bottom: @navbar-padding-vertical;\n      }\n    }\n  }\n}\n\n\n// Navbar form\n//\n// Extension of the `.form-inline` with some extra flavor for optimum display in\n// our navbars.\n\n.navbar-form {\n  padding: 10px @navbar-padding-horizontal;\n  margin-right: -@navbar-padding-horizontal;\n  margin-left: -@navbar-padding-horizontal;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1);\n  .box-shadow(@shadow);\n\n  // Mixin behavior for optimum display\n  .form-inline();\n\n  .form-group {\n    @media (max-width: @grid-float-breakpoint-max) {\n      margin-bottom: 5px;\n\n      &:last-child {\n        margin-bottom: 0;\n      }\n    }\n  }\n\n  // Vertically center in expanded, horizontal navbar\n  .navbar-vertical-align(@input-height-base);\n\n  // Undo 100% width for pull classes\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    padding-top: 0;\n    padding-bottom: 0;\n    margin-right: 0;\n    margin-left: 0;\n    border: 0;\n    .box-shadow(none);\n  }\n}\n\n\n// Dropdown menus\n\n// Menu position and menu carets\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  .border-top-radius(0);\n}\n// Menu position and menu caret support for dropups via extra dropup class\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  .border-top-radius(@navbar-border-radius);\n  .border-bottom-radius(0);\n}\n\n\n// Buttons in navbars\n//\n// Vertically center a button within a navbar (when *not* in a form).\n\n.navbar-btn {\n  .navbar-vertical-align(@input-height-base);\n\n  &.btn-sm {\n    .navbar-vertical-align(@input-height-small);\n  }\n  &.btn-xs {\n    .navbar-vertical-align(22);\n  }\n}\n\n\n// Text in navbars\n//\n// Add a class to make any element properly align itself vertically within the navbars.\n\n.navbar-text {\n  .navbar-vertical-align(@line-height-computed);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin-right: @navbar-padding-horizontal;\n    margin-left: @navbar-padding-horizontal;\n  }\n}\n\n\n// Component alignment\n//\n// Repurpose the pull utilities as their own navbar utilities to avoid specificity\n// issues with parents and chaining. Only do this when the navbar is uncollapsed\n// though so that navbar contents properly stack and align in mobile.\n//\n// Declared after the navbar components to ensure more specificity on the margins.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-left  { .pull-left(); }\n  .navbar-right {\n    .pull-right();\n    margin-right: -@navbar-padding-horizontal;\n\n    ~ .navbar-right {\n      margin-right: 0;\n    }\n  }\n}\n\n\n// Alternate navbars\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  background-color: @navbar-default-bg;\n  border-color: @navbar-default-border;\n\n  .navbar-brand {\n    color: @navbar-default-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-brand-hover-color;\n      background-color: @navbar-default-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-default-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-default-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-hover-color;\n        background-color: @navbar-default-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-active-color;\n        background-color: @navbar-default-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n        background-color: @navbar-default-link-disabled-bg;\n      }\n    }\n\n    // Dropdown menu items\n    // Remove background color from open dropdown\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-active-color;\n        background-color: @navbar-default-link-active-bg;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display when collapsed\n      .open .dropdown-menu {\n        > li > a {\n          color: @navbar-default-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-hover-color;\n            background-color: @navbar-default-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-active-color;\n            background-color: @navbar-default-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-disabled-color;\n            background-color: @navbar-default-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n  .navbar-toggle {\n    border-color: @navbar-default-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-default-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-default-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: @navbar-default-border;\n  }\n\n\n  // Links in navbars\n  //\n  // Add a class to ensure links outside the navbar nav are colored correctly.\n\n  .navbar-link {\n    color: @navbar-default-link-color;\n    &:hover {\n      color: @navbar-default-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-default-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n      }\n    }\n  }\n}\n\n// Inverse navbar\n\n.navbar-inverse {\n  background-color: @navbar-inverse-bg;\n  border-color: @navbar-inverse-border;\n\n  .navbar-brand {\n    color: @navbar-inverse-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-brand-hover-color;\n      background-color: @navbar-inverse-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-inverse-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-inverse-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-hover-color;\n        background-color: @navbar-inverse-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-active-color;\n        background-color: @navbar-inverse-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n        background-color: @navbar-inverse-link-disabled-bg;\n      }\n    }\n\n    // Dropdowns\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-active-color;\n        background-color: @navbar-inverse-link-active-bg;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display\n      .open .dropdown-menu {\n        > .dropdown-header {\n          border-color: @navbar-inverse-border;\n        }\n        .divider {\n          background-color: @navbar-inverse-border;\n        }\n        > li > a {\n          color: @navbar-inverse-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-hover-color;\n            background-color: @navbar-inverse-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-active-color;\n            background-color: @navbar-inverse-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-disabled-color;\n            background-color: @navbar-inverse-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n  // Darken the responsive nav toggle\n  .navbar-toggle {\n    border-color: @navbar-inverse-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-inverse-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-inverse-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: darken(@navbar-inverse-bg, 7%);\n  }\n\n  .navbar-link {\n    color: @navbar-inverse-link-color;\n    &:hover {\n      color: @navbar-inverse-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-inverse-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n      }\n    }\n  }\n}\n","// Navbar vertical align\n//\n// Vertically center elements in the navbar.\n// Example: an element has a height of 30px, so write out `.navbar-vertical-align(30px);` to calculate the appropriate top margin.\n\n.navbar-vertical-align(@element-height) {\n  margin-top: ((@navbar-height - @element-height) / 2);\n  margin-bottom: ((@navbar-height - @element-height) / 2);\n}\n","// stylelint-disable declaration-no-important\n\n//\n// Utility classes\n// --------------------------------------------------\n\n\n// Floats\n// -------------------------\n\n.clearfix {\n  .clearfix();\n}\n.center-block {\n  .center-block();\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n\n\n// Toggling content\n// -------------------------\n\n// Note: Deprecated .hide in favor of .hidden or .sr-only (as appropriate) in v3.0.1\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  .text-hide();\n}\n\n\n// Hide from screenreaders and browsers\n//\n// Credit: HTML5 Boilerplate\n\n.hidden {\n  display: none !important;\n}\n\n\n// For Affix plugin\n// -------------------------\n\n.affix {\n  position: fixed;\n}\n","//\n// Breadcrumbs\n// --------------------------------------------------\n\n\n.breadcrumb {\n  padding: @breadcrumb-padding-vertical @breadcrumb-padding-horizontal;\n  margin-bottom: @line-height-computed;\n  list-style: none;\n  background-color: @breadcrumb-bg;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline-block;\n\n    + li:before {\n      padding: 0 5px;\n      color: @breadcrumb-color;\n      content: \"@{breadcrumb-separator}\\00a0\"; // Unicode space added since inline-block means non-collapsing white-space\n    }\n  }\n\n  > .active {\n    color: @breadcrumb-active-color;\n  }\n}\n","//\n// Pagination (multiple pages)\n// --------------------------------------------------\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline; // Remove list-style and block-level defaults\n    > a,\n    > span {\n      position: relative;\n      float: left; // Collapse white-space\n      padding: @padding-base-vertical @padding-base-horizontal;\n      margin-left: -1px;\n      line-height: @line-height-base;\n      color: @pagination-color;\n      text-decoration: none;\n      background-color: @pagination-bg;\n      border: 1px solid @pagination-border;\n\n      &:hover,\n      &:focus {\n        z-index: 2;\n        color: @pagination-hover-color;\n        background-color: @pagination-hover-bg;\n        border-color: @pagination-hover-border;\n      }\n    }\n    &:first-child {\n      > a,\n      > span {\n        margin-left: 0;\n        .border-left-radius(@border-radius-base);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius-base);\n      }\n    }\n  }\n\n  > .active > a,\n  > .active > span {\n    &,\n    &:hover,\n    &:focus {\n      z-index: 3;\n      color: @pagination-active-color;\n      cursor: default;\n      background-color: @pagination-active-bg;\n      border-color: @pagination-active-border;\n    }\n  }\n\n  > .disabled {\n    > span,\n    > span:hover,\n    > span:focus,\n    > a,\n    > a:hover,\n    > a:focus {\n      color: @pagination-disabled-color;\n      cursor: @cursor-disabled;\n      background-color: @pagination-disabled-bg;\n      border-color: @pagination-disabled-border;\n    }\n  }\n}\n\n// Sizing\n// --------------------------------------------------\n\n// Large\n.pagination-lg {\n  .pagination-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @border-radius-large);\n}\n\n// Small\n.pagination-sm {\n  .pagination-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @border-radius-small);\n}\n","// Pagination\n\n.pagination-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  > li {\n    > a,\n    > span {\n      padding: @padding-vertical @padding-horizontal;\n      font-size: @font-size;\n      line-height: @line-height;\n    }\n    &:first-child {\n      > a,\n      > span {\n        .border-left-radius(@border-radius);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius);\n      }\n    }\n  }\n}\n","//\n// Pager pagination\n// --------------------------------------------------\n\n\n.pager {\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  text-align: center;\n  list-style: none;\n  &:extend(.clearfix all);\n  li {\n    display: inline;\n    > a,\n    > span {\n      display: inline-block;\n      padding: 5px 14px;\n      background-color: @pager-bg;\n      border: 1px solid @pager-border;\n      border-radius: @pager-border-radius;\n    }\n\n    > a:hover,\n    > a:focus {\n      text-decoration: none;\n      background-color: @pager-hover-bg;\n    }\n  }\n\n  .next {\n    > a,\n    > span {\n      float: right;\n    }\n  }\n\n  .previous {\n    > a,\n    > span {\n      float: left;\n    }\n  }\n\n  .disabled {\n    > a,\n    > a:hover,\n    > a:focus,\n    > span {\n      color: @pager-disabled-color;\n      cursor: @cursor-disabled;\n      background-color: @pager-bg;\n    }\n  }\n}\n","//\n// Labels\n// --------------------------------------------------\n\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: 700;\n  line-height: 1;\n  color: @label-color;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n\n  // Add hover effects, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @label-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Empty labels collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for labels in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n}\n\n// Colors\n// Contextual variations (linked labels get darker on :hover)\n\n.label-default {\n  .label-variant(@label-default-bg);\n}\n\n.label-primary {\n  .label-variant(@label-primary-bg);\n}\n\n.label-success {\n  .label-variant(@label-success-bg);\n}\n\n.label-info {\n  .label-variant(@label-info-bg);\n}\n\n.label-warning {\n  .label-variant(@label-warning-bg);\n}\n\n.label-danger {\n  .label-variant(@label-danger-bg);\n}\n","// Labels\n\n.label-variant(@color) {\n  background-color: @color;\n\n  &[href] {\n    &:hover,\n    &:focus {\n      background-color: darken(@color, 10%);\n    }\n  }\n}\n","//\n// Badges\n// --------------------------------------------------\n\n\n// Base class\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: @font-size-small;\n  font-weight: @badge-font-weight;\n  line-height: @badge-line-height;\n  color: @badge-color;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  background-color: @badge-bg;\n  border-radius: @badge-border-radius;\n\n  // Empty badges collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for badges in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n\n  .btn-xs &,\n  .btn-group-xs > .btn & {\n    top: 0;\n    padding: 1px 5px;\n  }\n\n  // Hover state, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @badge-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Account for badges in navs\n  .list-group-item.active > &,\n  .nav-pills > .active > a > & {\n    color: @badge-active-color;\n    background-color: @badge-active-bg;\n  }\n\n  .list-group-item > & {\n    float: right;\n  }\n\n  .list-group-item > & + & {\n    margin-right: 5px;\n  }\n\n  .nav-pills > li > a > & {\n    margin-left: 3px;\n  }\n}\n","//\n// Jumbotron\n// --------------------------------------------------\n\n\n.jumbotron {\n  padding-top: @jumbotron-padding;\n  padding-bottom: @jumbotron-padding;\n  margin-bottom: @jumbotron-padding;\n  color: @jumbotron-color;\n  background-color: @jumbotron-bg;\n\n  h1,\n  .h1 {\n    color: @jumbotron-heading-color;\n  }\n\n  p {\n    margin-bottom: (@jumbotron-padding / 2);\n    font-size: @jumbotron-font-size;\n    font-weight: 200;\n  }\n\n  > hr {\n    border-top-color: darken(@jumbotron-bg, 10%);\n  }\n\n  .container &,\n  .container-fluid & {\n    padding-right: (@grid-gutter-width / 2);\n    padding-left: (@grid-gutter-width / 2);\n    border-radius: @border-radius-large; // Only round corners at higher resolutions if contained in a container\n  }\n\n  .container {\n    max-width: 100%;\n  }\n\n  @media screen and (min-width: @screen-sm-min) {\n    padding-top: (@jumbotron-padding * 1.6);\n    padding-bottom: (@jumbotron-padding * 1.6);\n\n    .container &,\n    .container-fluid & {\n      padding-right: (@jumbotron-padding * 2);\n      padding-left: (@jumbotron-padding * 2);\n    }\n\n    h1,\n    .h1 {\n      font-size: @jumbotron-heading-font-size;\n    }\n  }\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Thumbnails\n// --------------------------------------------------\n\n\n// Mixin and adjust the regular image class\n.thumbnail {\n  display: block;\n  padding: @thumbnail-padding;\n  margin-bottom: @line-height-computed;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(border .2s ease-in-out);\n\n  > img,\n  a > img {\n    &:extend(.img-responsive);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  // Add a hover state for linked versions only\n  a&:hover,\n  a&:focus,\n  a&.active {\n    border-color: @link-color;\n  }\n\n  // Image captions\n  .caption {\n    padding: @thumbnail-caption-padding;\n    color: @thumbnail-caption-color;\n  }\n}\n","//\n// Alerts\n// --------------------------------------------------\n\n\n// Base styles\n// -------------------------\n\n.alert {\n  padding: @alert-padding;\n  margin-bottom: @line-height-computed;\n  border: 1px solid transparent;\n  border-radius: @alert-border-radius;\n\n  // Headings for larger alerts\n  h4 {\n    margin-top: 0;\n    color: inherit; // Specified for the h4 to prevent conflicts of changing @headings-color\n  }\n\n  // Provide class for links that match alerts\n  .alert-link {\n    font-weight: @alert-link-font-weight;\n  }\n\n  // Improve alignment and spacing of inner content\n  > p,\n  > ul {\n    margin-bottom: 0;\n  }\n\n  > p + p {\n    margin-top: 5px;\n  }\n}\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n// The misspelled .alert-dismissable was deprecated in 3.2.0.\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: (@alert-padding + 20);\n\n  // Adjust close link position\n  .close {\n    position: relative;\n    top: -2px;\n    right: -21px;\n    color: inherit;\n  }\n}\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n.alert-success {\n  .alert-variant(@alert-success-bg; @alert-success-border; @alert-success-text);\n}\n\n.alert-info {\n  .alert-variant(@alert-info-bg; @alert-info-border; @alert-info-text);\n}\n\n.alert-warning {\n  .alert-variant(@alert-warning-bg; @alert-warning-border; @alert-warning-text);\n}\n\n.alert-danger {\n  .alert-variant(@alert-danger-bg; @alert-danger-border; @alert-danger-text);\n}\n","// Alerts\n\n.alert-variant(@background; @border; @text-color) {\n  color: @text-color;\n  background-color: @background;\n  border-color: @border;\n\n  hr {\n    border-top-color: darken(@border, 5%);\n  }\n\n  .alert-link {\n    color: darken(@text-color, 10%);\n  }\n}\n","// stylelint-disable at-rule-no-vendor-prefix\n\n//\n// Progress bars\n// --------------------------------------------------\n\n\n// Bar animations\n// -------------------------\n\n// WebKit\n@-webkit-keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n// Spec and IE10+\n@keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n\n// Bar itself\n// -------------------------\n\n// Outer container\n.progress {\n  height: @line-height-computed;\n  margin-bottom: @line-height-computed;\n  overflow: hidden;\n  background-color: @progress-bg;\n  border-radius: @progress-border-radius;\n  .box-shadow(inset 0 1px 2px rgba(0, 0, 0, .1));\n}\n\n// Bar of progress\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: @font-size-small;\n  line-height: @line-height-computed;\n  color: @progress-bar-color;\n  text-align: center;\n  background-color: @progress-bar-bg;\n  .box-shadow(inset 0 -1px 0 rgba(0, 0, 0, .15));\n  .transition(width .6s ease);\n}\n\n// Striped bars\n//\n// `.progress-striped .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar-striped` class, which you just add to an existing\n// `.progress-bar`.\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  #gradient > .striped();\n  background-size: 40px 40px;\n}\n\n// Call animation for the active one\n//\n// `.progress.active .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar.active` approach.\n.progress.active .progress-bar,\n.progress-bar.active {\n  .animation(progress-bar-stripes 2s linear infinite);\n}\n\n\n// Variations\n// -------------------------\n\n.progress-bar-success {\n  .progress-bar-variant(@progress-bar-success-bg);\n}\n\n.progress-bar-info {\n  .progress-bar-variant(@progress-bar-info-bg);\n}\n\n.progress-bar-warning {\n  .progress-bar-variant(@progress-bar-warning-bg);\n}\n\n.progress-bar-danger {\n  .progress-bar-variant(@progress-bar-danger-bg);\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Progress bars\n\n.progress-bar-variant(@color) {\n  background-color: @color;\n\n  // Deprecated parent class requirement as of v3.2.0\n  .progress-striped & {\n    #gradient > .striped();\n  }\n}\n",".media {\n  // Proper spacing between instances of .media\n  margin-top: 15px;\n\n  &:first-child {\n    margin-top: 0;\n  }\n}\n\n.media,\n.media-body {\n  overflow: hidden;\n  zoom: 1;\n}\n\n.media-body {\n  width: 10000px;\n}\n\n.media-object {\n  display: block;\n\n  // Fix collapse in webkit from max-width: 100% and display: table-cell.\n  &.img-thumbnail {\n    max-width: none;\n  }\n}\n\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n\n.media-middle {\n  vertical-align: middle;\n}\n\n.media-bottom {\n  vertical-align: bottom;\n}\n\n// Reset margins on headings for tighter default spacing\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n\n// Media list variation\n//\n// Undo default ul/ol styles\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// List groups\n// --------------------------------------------------\n\n\n// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 20px;\n}\n\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  // Place the border on the list items and negative margin up for better styling\n  margin-bottom: -1px;\n  background-color: @list-group-bg;\n  border: 1px solid @list-group-border;\n\n  // Round the first and last items\n  &:first-child {\n    .border-top-radius(@list-group-border-radius);\n  }\n  &:last-child {\n    margin-bottom: 0;\n    .border-bottom-radius(@list-group-border-radius);\n  }\n\n  // Disabled state\n  &.disabled,\n  &.disabled:hover,\n  &.disabled:focus {\n    color: @list-group-disabled-color;\n    cursor: @cursor-disabled;\n    background-color: @list-group-disabled-bg;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-disabled-text-color;\n    }\n  }\n\n  // Active class on item itself, not parent\n  &.active,\n  &.active:hover,\n  &.active:focus {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: @list-group-active-color;\n    background-color: @list-group-active-bg;\n    border-color: @list-group-active-border;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading,\n    .list-group-item-heading > small,\n    .list-group-item-heading > .small {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-active-text-color;\n    }\n  }\n}\n\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive items.\n// Includes an extra `.active` modifier class for showing selected items.\n\na.list-group-item,\nbutton.list-group-item {\n  color: @list-group-link-color;\n\n  .list-group-item-heading {\n    color: @list-group-link-heading-color;\n  }\n\n  // Hover state\n  &:hover,\n  &:focus {\n    color: @list-group-link-hover-color;\n    text-decoration: none;\n    background-color: @list-group-hover-bg;\n  }\n}\n\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n\n\n// Contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n.list-group-item-variant(success; @state-success-bg; @state-success-text);\n.list-group-item-variant(info; @state-info-bg; @state-info-text);\n.list-group-item-variant(warning; @state-warning-bg; @state-warning-text);\n.list-group-item-variant(danger; @state-danger-bg; @state-danger-text);\n\n\n// Custom content options\n//\n// Extra classes for creating well-formatted content within `.list-group-item`s.\n\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n","// List Groups\n\n.list-group-item-variant(@state; @background; @color) {\n  .list-group-item-@{state} {\n    color: @color;\n    background-color: @background;\n\n    a&,\n    button& {\n      color: @color;\n\n      .list-group-item-heading {\n        color: inherit;\n      }\n\n      &:hover,\n      &:focus {\n        color: @color;\n        background-color: darken(@background, 5%);\n      }\n      &.active,\n      &.active:hover,\n      &.active:focus {\n        color: #fff;\n        background-color: @color;\n        border-color: @color;\n      }\n    }\n  }\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-max-combinators, no-duplicate-selectors\n\n//\n// Panels\n// --------------------------------------------------\n\n\n// Base class\n.panel {\n  margin-bottom: @line-height-computed;\n  background-color: @panel-bg;\n  border: 1px solid transparent;\n  border-radius: @panel-border-radius;\n  .box-shadow(0 1px 1px rgba(0, 0, 0, .05));\n}\n\n// Panel contents\n.panel-body {\n  padding: @panel-body-padding;\n  &:extend(.clearfix all);\n}\n\n// Optional heading\n.panel-heading {\n  padding: @panel-heading-padding;\n  border-bottom: 1px solid transparent;\n  .border-top-radius((@panel-border-radius - 1));\n\n  > .dropdown .dropdown-toggle {\n    color: inherit;\n  }\n}\n\n// Within heading, strip any `h*` tag of its default margins for spacing.\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: ceil((@font-size-base * 1.125));\n  color: inherit;\n\n  > a,\n  > small,\n  > .small,\n  > small > a,\n  > .small > a {\n    color: inherit;\n  }\n}\n\n// Optional footer (stays gray in every modifier class)\n.panel-footer {\n  padding: @panel-footer-padding;\n  background-color: @panel-footer-bg;\n  border-top: 1px solid @panel-inner-border;\n  .border-bottom-radius((@panel-border-radius - 1));\n}\n\n\n// List groups in panels\n//\n// By default, space out list group content from panel headings to account for\n// any kind of custom content between the two.\n\n.panel {\n  > .list-group,\n  > .panel-collapse > .list-group {\n    margin-bottom: 0;\n\n    .list-group-item {\n      border-width: 1px 0;\n      border-radius: 0;\n    }\n\n    // Add border top radius for first one\n    &:first-child {\n      .list-group-item:first-child {\n        border-top: 0;\n        .border-top-radius((@panel-border-radius - 1));\n      }\n    }\n\n    // Add border bottom radius for last one\n    &:last-child {\n      .list-group-item:last-child {\n        border-bottom: 0;\n        .border-bottom-radius((@panel-border-radius - 1));\n      }\n    }\n  }\n  > .panel-heading + .panel-collapse > .list-group {\n    .list-group-item:first-child {\n      .border-top-radius(0);\n    }\n  }\n}\n// Collapse space between when there's no additional content.\n.panel-heading + .list-group {\n  .list-group-item:first-child {\n    border-top-width: 0;\n  }\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n\n// Tables in panels\n//\n// Place a non-bordered `.table` within a panel (not within a `.panel-body`) and\n// watch it go full width.\n\n.panel {\n  > .table,\n  > .table-responsive > .table,\n  > .panel-collapse > .table {\n    margin-bottom: 0;\n\n    caption {\n      padding-right: @panel-body-padding;\n      padding-left: @panel-body-padding;\n    }\n  }\n  // Add border top radius for first one\n  > .table:first-child,\n  > .table-responsive:first-child > .table:first-child {\n    .border-top-radius((@panel-border-radius - 1));\n\n    > thead:first-child,\n    > tbody:first-child {\n      > tr:first-child {\n        border-top-left-radius: (@panel-border-radius - 1);\n        border-top-right-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-top-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-top-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  // Add border bottom radius for last one\n  > .table:last-child,\n  > .table-responsive:last-child > .table:last-child {\n    .border-bottom-radius((@panel-border-radius - 1));\n\n    > tbody:last-child,\n    > tfoot:last-child {\n      > tr:last-child {\n        border-bottom-right-radius: (@panel-border-radius - 1);\n        border-bottom-left-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-bottom-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-bottom-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  > .panel-body + .table,\n  > .panel-body + .table-responsive,\n  > .table + .panel-body,\n  > .table-responsive + .panel-body {\n    border-top: 1px solid @table-border-color;\n  }\n  > .table > tbody:first-child > tr:first-child th,\n  > .table > tbody:first-child > tr:first-child td {\n    border-top: 0;\n  }\n  > .table-bordered,\n  > .table-responsive > .table-bordered {\n    border: 0;\n    > thead,\n    > tbody,\n    > tfoot {\n      > tr {\n        > th:first-child,\n        > td:first-child {\n          border-left: 0;\n        }\n        > th:last-child,\n        > td:last-child {\n          border-right: 0;\n        }\n      }\n    }\n    > thead,\n    > tbody {\n      > tr:first-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n    > tbody,\n    > tfoot {\n      > tr:last-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n  }\n  > .table-responsive {\n    margin-bottom: 0;\n    border: 0;\n  }\n}\n\n\n// Collapsible panels (aka, accordion)\n//\n// Wrap a series of panels in `.panel-group` to turn them into an accordion with\n// the help of our collapse JavaScript plugin.\n\n.panel-group {\n  margin-bottom: @line-height-computed;\n\n  // Tighten up margin so it's only between panels\n  .panel {\n    margin-bottom: 0;\n    border-radius: @panel-border-radius;\n\n    + .panel {\n      margin-top: 5px;\n    }\n  }\n\n  .panel-heading {\n    border-bottom: 0;\n\n    + .panel-collapse > .panel-body,\n    + .panel-collapse > .list-group {\n      border-top: 1px solid @panel-inner-border;\n    }\n  }\n\n  .panel-footer {\n    border-top: 0;\n    + .panel-collapse .panel-body {\n      border-bottom: 1px solid @panel-inner-border;\n    }\n  }\n}\n\n\n// Contextual variations\n.panel-default {\n  .panel-variant(@panel-default-border; @panel-default-text; @panel-default-heading-bg; @panel-default-border);\n}\n.panel-primary {\n  .panel-variant(@panel-primary-border; @panel-primary-text; @panel-primary-heading-bg; @panel-primary-border);\n}\n.panel-success {\n  .panel-variant(@panel-success-border; @panel-success-text; @panel-success-heading-bg; @panel-success-border);\n}\n.panel-info {\n  .panel-variant(@panel-info-border; @panel-info-text; @panel-info-heading-bg; @panel-info-border);\n}\n.panel-warning {\n  .panel-variant(@panel-warning-border; @panel-warning-text; @panel-warning-heading-bg; @panel-warning-border);\n}\n.panel-danger {\n  .panel-variant(@panel-danger-border; @panel-danger-text; @panel-danger-heading-bg; @panel-danger-border);\n}\n","// Panels\n\n.panel-variant(@border; @heading-text-color; @heading-bg-color; @heading-border) {\n  border-color: @border;\n\n  & > .panel-heading {\n    color: @heading-text-color;\n    background-color: @heading-bg-color;\n    border-color: @heading-border;\n\n    + .panel-collapse > .panel-body {\n      border-top-color: @border;\n    }\n    .badge {\n      color: @heading-bg-color;\n      background-color: @heading-text-color;\n    }\n  }\n  & > .panel-footer {\n    + .panel-collapse > .panel-body {\n      border-bottom-color: @border;\n    }\n  }\n}\n","// Embeds responsive\n//\n// Credit: Nicolas Gallagher and SUIT CSS.\n\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n\n  .embed-responsive-item,\n  iframe,\n  embed,\n  object,\n  video {\n    position: absolute;\n    top: 0;\n    bottom: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n    border: 0;\n  }\n}\n\n// Modifier class for 16:9 aspect ratio\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n\n// Modifier class for 4:3 aspect ratio\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n","//\n// Wells\n// --------------------------------------------------\n\n\n// Base class\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: @well-bg;\n  border: 1px solid @well-border;\n  border-radius: @border-radius-base;\n  .box-shadow(inset 0 1px 1px rgba(0, 0, 0, .05));\n  blockquote {\n    border-color: #ddd;\n    border-color: rgba(0, 0, 0, .15);\n  }\n}\n\n// Sizes\n.well-lg {\n  padding: 24px;\n  border-radius: @border-radius-large;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: @border-radius-small;\n}\n","// stylelint-disable property-no-vendor-prefix\n\n//\n// Close icons\n// --------------------------------------------------\n\n\n.close {\n  float: right;\n  font-size: (@font-size-base * 1.5);\n  font-weight: @close-font-weight;\n  line-height: 1;\n  color: @close-color;\n  text-shadow: @close-text-shadow;\n  .opacity(.2);\n\n  &:hover,\n  &:focus {\n    color: @close-color;\n    text-decoration: none;\n    cursor: pointer;\n    .opacity(.5);\n  }\n\n  // Additional properties for button version\n  // iOS requires the button element instead of an anchor tag.\n  // If you want the anchor version, it requires `href=\"#\"`.\n  // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n  button& {\n    padding: 0;\n    cursor: pointer;\n    background: transparent;\n    border: 0;\n    -webkit-appearance: none;\n    appearance: none;\n  }\n}\n","//\n// Modals\n// --------------------------------------------------\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and shit\n\n// Kill the scroll on the body\n.modal-open {\n  overflow: hidden;\n}\n\n// Container that the modal scrolls within\n.modal {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal;\n  display: none;\n  overflow: hidden;\n  -webkit-overflow-scrolling: touch;\n\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n\n  // When fading in the modal, animate it to slide down\n  &.fade .modal-dialog {\n    .translate(0, -25%);\n    .transition-transform(~\"0.3s ease-out\");\n  }\n  &.in .modal-dialog { .translate(0, 0); }\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  background-color: @modal-content-bg;\n  background-clip: padding-box;\n  border: 1px solid @modal-content-fallback-border-color; //old browsers fallback (ie8 etc)\n  border: 1px solid @modal-content-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 3px 9px rgba(0, 0, 0, .5));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal-background;\n  background-color: @modal-backdrop-bg;\n  // Fade for backdrop\n  &.fade { .opacity(0); }\n  &.in { .opacity(@modal-backdrop-opacity); }\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  padding: @modal-title-padding;\n  border-bottom: 1px solid @modal-header-border-color;\n  &:extend(.clearfix all);\n}\n// Close icon\n.modal-header .close {\n  margin-top: -2px;\n}\n\n// Title text within header\n.modal-title {\n  margin: 0;\n  line-height: @modal-title-line-height;\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  padding: @modal-inner-padding;\n}\n\n// Footer (for actions)\n.modal-footer {\n  padding: @modal-inner-padding;\n  text-align: right; // right align buttons\n  border-top: 1px solid @modal-footer-border-color;\n  &:extend(.clearfix all); // clear it in case folks use .pull-* classes on buttons\n\n  // Properly space out buttons\n  .btn + .btn {\n    margin-bottom: 0; // account for input[type=\"submit\"] which gets the bottom margin like all other inputs\n    margin-left: 5px;\n  }\n  // but override that for button groups\n  .btn-group .btn + .btn {\n    margin-left: -1px;\n  }\n  // and override it for block buttons as well\n  .btn-block + .btn-block {\n    margin-left: 0;\n  }\n}\n\n// Measure scrollbar width for padding body during modal show/hide\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n\n// Scale up the modal\n@media (min-width: @screen-sm-min) {\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    width: @modal-md;\n    margin: 30px auto;\n  }\n  .modal-content {\n    .box-shadow(0 5px 15px rgba(0, 0, 0, .5));\n  }\n\n  // Modal sizes\n  .modal-sm { width: @modal-sm; }\n}\n\n@media (min-width: @screen-md-min) {\n  .modal-lg { width: @modal-lg; }\n}\n","//\n// Tooltips\n// --------------------------------------------------\n\n\n// Base class\n.tooltip {\n  position: absolute;\n  z-index: @zindex-tooltip;\n  display: block;\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-small;\n\n  .opacity(0);\n\n  &.in { .opacity(@tooltip-opacity); }\n  &.top {\n    padding: @tooltip-arrow-width 0;\n    margin-top: -3px;\n  }\n  &.right {\n    padding: 0 @tooltip-arrow-width;\n    margin-left: 3px;\n  }\n  &.bottom {\n    padding: @tooltip-arrow-width 0;\n    margin-top: 3px;\n  }\n  &.left {\n    padding: 0 @tooltip-arrow-width;\n    margin-left: -3px;\n  }\n\n  // Note: Deprecated .top-left, .top-right, .bottom-left, and .bottom-right as of v3.3.1\n  &.top .tooltip-arrow {\n    bottom: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-left .tooltip-arrow {\n    right: @tooltip-arrow-width;\n    bottom: 0;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-right .tooltip-arrow {\n    bottom: 0;\n    left: @tooltip-arrow-width;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.right .tooltip-arrow {\n    top: 50%;\n    left: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-right-color: @tooltip-arrow-color;\n  }\n  &.left .tooltip-arrow {\n    top: 50%;\n    right: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-left-color: @tooltip-arrow-color;\n  }\n  &.bottom .tooltip-arrow {\n    top: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-left .tooltip-arrow {\n    top: 0;\n    right: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-right .tooltip-arrow {\n    top: 0;\n    left: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: @tooltip-max-width;\n  padding: 3px 8px;\n  color: @tooltip-color;\n  text-align: center;\n  background-color: @tooltip-bg;\n  border-radius: @border-radius-base;\n}\n\n// Arrows\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n",".reset-text() {\n  font-family: @font-family-base;\n  // We deliberately do NOT reset font-size.\n  font-style: normal;\n  font-weight: 400;\n  line-height: @line-height-base;\n  line-break: auto;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n}\n","//\n// Popovers\n// --------------------------------------------------\n\n\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: @zindex-popover;\n  display: none;\n  max-width: @popover-max-width;\n  padding: 1px;\n  // Our parent element can be arbitrary since popovers are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-base;\n  background-color: @popover-bg;\n  background-clip: padding-box;\n  border: 1px solid @popover-fallback-border-color;\n  border: 1px solid @popover-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 5px 10px rgba(0, 0, 0, .2));\n\n  // Offset the popover to account for the popover arrow\n  &.top { margin-top: -@popover-arrow-width; }\n  &.right { margin-left: @popover-arrow-width; }\n  &.bottom { margin-top: @popover-arrow-width; }\n  &.left { margin-left: -@popover-arrow-width; }\n\n  // Arrows\n  // .arrow is outer, .arrow:after is inner\n  > .arrow {\n    border-width: @popover-arrow-outer-width;\n\n    &,\n    &:after {\n      position: absolute;\n      display: block;\n      width: 0;\n      height: 0;\n      border-color: transparent;\n      border-style: solid;\n    }\n\n    &:after {\n      content: \"\";\n      border-width: @popover-arrow-width;\n    }\n  }\n\n  &.top > .arrow {\n    bottom: -@popover-arrow-outer-width;\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-top-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-top-color: @popover-arrow-outer-color;\n    border-bottom-width: 0;\n    &:after {\n      bottom: 1px;\n      margin-left: -@popover-arrow-width;\n      content: \" \";\n      border-top-color: @popover-arrow-color;\n      border-bottom-width: 0;\n    }\n  }\n  &.right > .arrow {\n    top: 50%;\n    left: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-right-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-right-color: @popover-arrow-outer-color;\n    border-left-width: 0;\n    &:after {\n      bottom: -@popover-arrow-width;\n      left: 1px;\n      content: \" \";\n      border-right-color: @popover-arrow-color;\n      border-left-width: 0;\n    }\n  }\n  &.bottom > .arrow {\n    top: -@popover-arrow-outer-width;\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-top-width: 0;\n    border-bottom-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-bottom-color: @popover-arrow-outer-color;\n    &:after {\n      top: 1px;\n      margin-left: -@popover-arrow-width;\n      content: \" \";\n      border-top-width: 0;\n      border-bottom-color: @popover-arrow-color;\n    }\n  }\n\n  &.left > .arrow {\n    top: 50%;\n    right: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-right-width: 0;\n    border-left-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-left-color: @popover-arrow-outer-color;\n    &:after {\n      right: 1px;\n      bottom: -@popover-arrow-width;\n      content: \" \";\n      border-right-width: 0;\n      border-left-color: @popover-arrow-color;\n    }\n  }\n}\n\n.popover-title {\n  padding: 8px 14px;\n  margin: 0; // reset heading margin\n  font-size: @font-size-base;\n  background-color: @popover-title-bg;\n  border-bottom: 1px solid darken(@popover-title-bg, 5%);\n  border-radius: (@border-radius-large - 1) (@border-radius-large - 1) 0 0;\n}\n\n.popover-content {\n  padding: 9px 14px;\n}\n","// stylelint-disable media-feature-name-no-unknown\n\n//\n// Carousel\n// --------------------------------------------------\n\n\n// Wrapper for the slide container and indicators\n.carousel {\n  position: relative;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n\n  > .item {\n    position: relative;\n    display: none;\n    .transition(.6s ease-in-out left);\n\n    // Account for jankitude on images\n    > img,\n    > a > img {\n      &:extend(.img-responsive);\n      line-height: 1;\n    }\n\n    // WebKit CSS3 transforms for supported devices\n    @media all and (transform-3d), (-webkit-transform-3d) {\n      .transition-transform(~\"0.6s ease-in-out\");\n      .backface-visibility(~\"hidden\");\n      .perspective(1000px);\n\n      &.next,\n      &.active.right {\n        .translate3d(100%, 0, 0);\n        left: 0;\n      }\n      &.prev,\n      &.active.left {\n        .translate3d(-100%, 0, 0);\n        left: 0;\n      }\n      &.next.left,\n      &.prev.right,\n      &.active {\n        .translate3d(0, 0, 0);\n        left: 0;\n      }\n    }\n  }\n\n  > .active,\n  > .next,\n  > .prev {\n    display: block;\n  }\n\n  > .active {\n    left: 0;\n  }\n\n  > .next,\n  > .prev {\n    position: absolute;\n    top: 0;\n    width: 100%;\n  }\n\n  > .next {\n    left: 100%;\n  }\n  > .prev {\n    left: -100%;\n  }\n  > .next.left,\n  > .prev.right {\n    left: 0;\n  }\n\n  > .active.left {\n    left: -100%;\n  }\n  > .active.right {\n    left: 100%;\n  }\n\n}\n\n// Left/right controls for nav\n// ---------------------------\n\n.carousel-control {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: @carousel-control-width;\n  font-size: @carousel-control-font-size;\n  color: @carousel-control-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n  background-color: rgba(0, 0, 0, 0); // Fix IE9 click-thru bug\n  .opacity(@carousel-control-opacity);\n  // We can't have this transition here because WebKit cancels the carousel\n  // animation if you trip this while in the middle of another animation.\n\n  // Set gradients for backgrounds\n  &.left {\n    #gradient > .horizontal(@start-color: rgba(0, 0, 0, .5); @end-color: rgba(0, 0, 0, .0001));\n  }\n  &.right {\n    right: 0;\n    left: auto;\n    #gradient > .horizontal(@start-color: rgba(0, 0, 0, .0001); @end-color: rgba(0, 0, 0, .5));\n  }\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: @carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    .opacity(.9);\n  }\n\n  // Toggles\n  .icon-prev,\n  .icon-next,\n  .glyphicon-chevron-left,\n  .glyphicon-chevron-right {\n    position: absolute;\n    top: 50%;\n    z-index: 5;\n    display: inline-block;\n    margin-top: -10px;\n  }\n  .icon-prev,\n  .glyphicon-chevron-left {\n    left: 50%;\n    margin-left: -10px;\n  }\n  .icon-next,\n  .glyphicon-chevron-right {\n    right: 50%;\n    margin-right: -10px;\n  }\n  .icon-prev,\n  .icon-next {\n    width: 20px;\n    height: 20px;\n    font-family: serif;\n    line-height: 1;\n  }\n\n  .icon-prev {\n    &:before {\n      content: \"\\2039\";// SINGLE LEFT-POINTING ANGLE QUOTATION MARK (U+2039)\n    }\n  }\n  .icon-next {\n    &:before {\n      content: \"\\203a\";// SINGLE RIGHT-POINTING ANGLE QUOTATION MARK (U+203A)\n    }\n  }\n}\n\n// Optional indicator pips\n//\n// Add an unordered list with the following class and add a list item for each\n// slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  padding-left: 0;\n  margin-left: -30%;\n  text-align: center;\n  list-style: none;\n\n  li {\n    display: inline-block;\n    width: 10px;\n    height: 10px;\n    margin: 1px;\n    text-indent: -999px;\n    cursor: pointer;\n    // IE8-9 hack for event handling\n    //\n    // Internet Explorer 8-9 does not support clicks on elements without a set\n    // `background-color`. We cannot use `filter` since that's not viewed as a\n    // background color by the browser. Thus, a hack is needed.\n    // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Internet_Explorer\n    //\n    // For IE8, we set solid black as it doesn't support `rgba()`. For IE9, we\n    // set alpha transparency for the best results possible.\n    background-color: #000 \\9; // IE8\n    background-color: rgba(0, 0, 0, 0); // IE9\n\n    border: 1px solid @carousel-indicator-border-color;\n    border-radius: 10px;\n  }\n\n  .active {\n    width: 12px;\n    height: 12px;\n    margin: 0;\n    background-color: @carousel-indicator-active-bg;\n  }\n}\n\n// Optional captions\n// -----------------------------\n// Hidden by default for smaller viewports\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 20px;\n  left: 15%;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: @carousel-caption-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n\n  & .btn {\n    text-shadow: none; // No shadow for button elements in carousel-caption\n  }\n}\n\n\n// Scale up controls for tablets and up\n@media screen and (min-width: @screen-sm-min) {\n\n  // Scale up the controls a smidge\n  .carousel-control {\n    .glyphicon-chevron-left,\n    .glyphicon-chevron-right,\n    .icon-prev,\n    .icon-next {\n      width: (@carousel-control-font-size * 1.5);\n      height: (@carousel-control-font-size * 1.5);\n      margin-top: (@carousel-control-font-size / -2);\n      font-size: (@carousel-control-font-size * 1.5);\n    }\n    .glyphicon-chevron-left,\n    .icon-prev {\n      margin-left: (@carousel-control-font-size / -2);\n    }\n    .glyphicon-chevron-right,\n    .icon-next {\n      margin-right: (@carousel-control-font-size / -2);\n    }\n  }\n\n  // Show and left align the captions\n  .carousel-caption {\n    right: 20%;\n    left: 20%;\n    padding-bottom: 30px;\n  }\n\n  // Move up the indicators\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n","// Clearfix\n//\n// For modern browsers\n// 1. The space content is one way to avoid an Opera bug when the\n//    contenteditable attribute is included anywhere else in the document.\n//    Otherwise it causes space to appear at the top and bottom of elements\n//    that are clearfixed.\n// 2. The use of `table` rather than `block` is only necessary if using\n//    `:before` to contain the top-margins of child elements.\n//\n// Source: http://nicolasgallagher.com/micro-clearfix-hack/\n\n.clearfix() {\n  &:before,\n  &:after {\n    display: table; // 2\n    content: \" \"; // 1\n  }\n  &:after {\n    clear: both;\n  }\n}\n","// Center-align a block level element\n\n.center-block() {\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n","// stylelint-disable font-family-name-quotes, font-family-no-missing-generic-family-keyword\n\n// CSS image replacement\n//\n// Heads up! v3 launched with only `.hide-text()`, but per our pattern for\n// mixins being reused as classes with the same name, this doesn't hold up. As\n// of v3.0.1 we have added `.text-hide()` and deprecated `.hide-text()`.\n//\n// Source: https://github.com/h5bp/html5-boilerplate/commit/aa0396eae757\n\n// Deprecated as of v3.0.1 (has been removed in v4)\n.hide-text() {\n  font: ~\"0/0\" a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n\n// New mixin to use as of v3.0.1\n.text-hide() {\n  .hide-text();\n}\n","// stylelint-disable declaration-no-important, at-rule-no-vendor-prefix\n\n//\n// Responsive: Utility classes\n// --------------------------------------------------\n\n\n// IE10 in Windows (Phone) 8\n//\n// Support for responsive views via media queries is kind of borked in IE10, for\n// Surface/desktop in split view and for Windows Phone 8. This particular fix\n// must be accompanied by a snippet of JavaScript to sniff the user agent and\n// apply some conditional CSS to *only* the Surface/desktop Windows 8. Look at\n// our Getting Started page for more information on this bug.\n//\n// For more information, see the following:\n//\n// Issue: https://github.com/twbs/bootstrap/issues/10497\n// Docs: https://getbootstrap.com/docs/3.4/getting-started/#support-ie10-width\n// Source: https://timkadlec.com/2013/01/windows-phone-8-and-device-width/\n// Source: https://timkadlec.com/2012/10/ie10-snap-mode-and-responsive-design/\n\n@-ms-viewport {\n  width: device-width;\n}\n\n\n// Visibility utilities\n// Note: Deprecated .visible-xs, .visible-sm, .visible-md, and .visible-lg as of v3.2.0\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  .responsive-invisibility();\n}\n\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n\n.visible-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-visibility();\n  }\n}\n.visible-xs-block {\n  @media (max-width: @screen-xs-max) {\n    display: block !important;\n  }\n}\n.visible-xs-inline {\n  @media (max-width: @screen-xs-max) {\n    display: inline !important;\n  }\n}\n.visible-xs-inline-block {\n  @media (max-width: @screen-xs-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-visibility();\n  }\n}\n.visible-sm-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: block !important;\n  }\n}\n.visible-sm-inline {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline !important;\n  }\n}\n.visible-sm-inline-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-visibility();\n  }\n}\n.visible-md-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: block !important;\n  }\n}\n.visible-md-inline {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline !important;\n  }\n}\n.visible-md-inline-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-visibility();\n  }\n}\n.visible-lg-block {\n  @media (min-width: @screen-lg-min) {\n    display: block !important;\n  }\n}\n.visible-lg-inline {\n  @media (min-width: @screen-lg-min) {\n    display: inline !important;\n  }\n}\n.visible-lg-inline-block {\n  @media (min-width: @screen-lg-min) {\n    display: inline-block !important;\n  }\n}\n\n.hidden-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-invisibility();\n  }\n}\n\n\n// Print utilities\n//\n// Media queries are placed on the inside to be mixin-friendly.\n\n// Note: Deprecated .visible-print as of v3.2.0\n.visible-print {\n  .responsive-invisibility();\n\n  @media print {\n    .responsive-visibility();\n  }\n}\n.visible-print-block {\n  display: none !important;\n\n  @media print {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n\n  @media print {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n\n  @media print {\n    display: inline-block !important;\n  }\n}\n\n.hidden-print {\n  @media print {\n    .responsive-invisibility();\n  }\n}\n","// stylelint-disable declaration-no-important\n\n.responsive-visibility() {\n  display: block !important;\n  table&  { display: table !important; }\n  tr&     { display: table-row !important; }\n  th&,\n  td&     { display: table-cell !important; }\n}\n\n.responsive-invisibility() {\n  display: none !important;\n}\n"]}
\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/css/bootstrap.min.css b/_static/bootstrap-3.4.1/css/bootstrap.min.css
new file mode 100644
index 000000000..5b96335ff
--- /dev/null
+++ b/_static/bootstrap-3.4.1/css/bootstrap.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ *//*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */html{font-family:sans-serif;-ms-text-size-adjust:100%;-webkit-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:none;text-decoration:underline;-webkit-text-decoration:underline dotted;-moz-text-decoration:underline dotted;text-decoration:underline dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{font-size:2em;margin:.67em 0}mark{background:#ff0;color:#000}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;height:0}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{color:inherit;font:inherit;margin:0}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{border:0;padding:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-appearance:textfield;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{border:1px solid silver;margin:0 2px;padding:.35em .625em .75em}legend{border:0;padding:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-collapse:collapse;border-spacing:0}td,th{padding:0}/*! 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2em;\n  margin: 0.67em 0;\n}\nmark {\n  background: #ff0;\n  color: #000;\n}\nsmall {\n  font-size: 80%;\n}\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\nsup {\n  top: -0.5em;\n}\nsub {\n  bottom: -0.25em;\n}\nimg {\n  border: 0;\n}\nsvg:not(:root) {\n  overflow: hidden;\n}\nfigure {\n  margin: 1em 40px;\n}\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\npre {\n  overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit;\n  font: inherit;\n  margin: 0;\n}\nbutton {\n  overflow: visible;\n}\nbutton,\nselect {\n  text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button;\n  cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\ninput {\n  line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box;\n  padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: textfield;\n  box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n  border: 0;\n  padding: 0;\n}\ntextarea {\n  overflow: auto;\n}\noptgroup {\n  font-weight: bold;\n}\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\ntd,\nth {\n  padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n  *,\n  *:before,\n  *:after {\n    color: #000 !important;\n    text-shadow: none !important;\n    background: transparent !important;\n    box-shadow: none !important;\n  }\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n  thead {\n    display: table-header-group;\n  }\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n  img {\n    max-width: 100% !important;\n  }\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n  .navbar {\n    display: none;\n  }\n  .btn > .caret,\n  .dropup > .btn > .caret {\n    border-top-color: #000 !important;\n  }\n  .label {\n    border: 1px solid #000;\n  }\n  .table {\n    border-collapse: collapse !important;\n  }\n  .table td,\n  .table th {\n    background-color: #fff !important;\n  }\n  .table-bordered th,\n  .table-bordered td {\n    border: 1px solid #ddd !important;\n  }\n}\n@font-face {\n  font-family: \"Glyphicons Halflings\";\n  src: url(\"../fonts/glyphicons-halflings-regular.eot\");\n  src: url(\"../fonts/glyphicons-halflings-regular.eot?#iefix\") format(\"embedded-opentype\"), url(\"../fonts/glyphicons-halflings-regular.woff2\") format(\"woff2\"), url(\"../fonts/glyphicons-halflings-regular.woff\") format(\"woff\"), url(\"../fonts/glyphicons-halflings-regular.ttf\") format(\"truetype\"), url(\"../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular\") format(\"svg\");\n}\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: \"Glyphicons Halflings\";\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n  content: \"\\002a\";\n}\n.glyphicon-plus:before {\n  content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n  content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n  content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n  content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n  content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n  content: \"\\270f\";\n}\n.glyphicon-glass:before {\n  content: \"\\e001\";\n}\n.glyphicon-music:before {\n  content: \"\\e002\";\n}\n.glyphicon-search:before {\n  content: \"\\e003\";\n}\n.glyphicon-heart:before {\n  content: \"\\e005\";\n}\n.glyphicon-star:before {\n  content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n  content: \"\\e007\";\n}\n.glyphicon-user:before {\n  content: \"\\e008\";\n}\n.glyphicon-film:before {\n  content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n  content: \"\\e010\";\n}\n.glyphicon-th:before {\n  content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n  content: \"\\e012\";\n}\n.glyphicon-ok:before {\n  content: \"\\e013\";\n}\n.glyphicon-remove:before {\n  content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n  content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n  content: \"\\e016\";\n}\n.glyphicon-off:before {\n  content: \"\\e017\";\n}\n.glyphicon-signal:before {\n  content: \"\\e018\";\n}\n.glyphicon-cog:before {\n  content: \"\\e019\";\n}\n.glyphicon-trash:before {\n  content: \"\\e020\";\n}\n.glyphicon-home:before {\n  content: \"\\e021\";\n}\n.glyphicon-file:before {\n  content: \"\\e022\";\n}\n.glyphicon-time:before {\n  content: \"\\e023\";\n}\n.glyphicon-road:before {\n  content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n  content: \"\\e025\";\n}\n.glyphicon-download:before {\n  content: \"\\e026\";\n}\n.glyphicon-upload:before {\n  content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n  content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n  content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n  content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n  content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n  content: \"\\e032\";\n}\n.glyphicon-lock:before {\n  content: \"\\e033\";\n}\n.glyphicon-flag:before {\n  content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n  content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n  content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n  content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n  content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n  content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n  content: \"\\e040\";\n}\n.glyphicon-tag:before {\n  content: \"\\e041\";\n}\n.glyphicon-tags:before {\n  content: \"\\e042\";\n}\n.glyphicon-book:before {\n  content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n  content: \"\\e044\";\n}\n.glyphicon-print:before {\n  content: \"\\e045\";\n}\n.glyphicon-camera:before {\n  content: \"\\e046\";\n}\n.glyphicon-font:before {\n  content: \"\\e047\";\n}\n.glyphicon-bold:before {\n  content: \"\\e048\";\n}\n.glyphicon-italic:before {\n  content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n  content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n  content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n  content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n  content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n  content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n  content: \"\\e055\";\n}\n.glyphicon-list:before {\n  content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n  content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n  content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n  content: \"\\e059\";\n}\n.glyphicon-picture:before {\n  content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n  content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n  content: \"\\e063\";\n}\n.glyphicon-tint:before {\n  content: \"\\e064\";\n}\n.glyphicon-edit:before {\n  content: \"\\e065\";\n}\n.glyphicon-share:before {\n  content: \"\\e066\";\n}\n.glyphicon-check:before {\n  content: \"\\e067\";\n}\n.glyphicon-move:before {\n  content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n  content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n  content: \"\\e070\";\n}\n.glyphicon-backward:before {\n  content: \"\\e071\";\n}\n.glyphicon-play:before {\n  content: \"\\e072\";\n}\n.glyphicon-pause:before {\n  content: \"\\e073\";\n}\n.glyphicon-stop:before {\n  content: \"\\e074\";\n}\n.glyphicon-forward:before {\n  content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n  content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n  content: \"\\e077\";\n}\n.glyphicon-eject:before {\n  content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n  content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n  content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n  content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n  content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n  content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n  content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n  content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n  content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n  content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n  content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n  content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n  content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n  content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n  content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n  content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n  content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n  content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n  content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n  content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n  content: \"\\e101\";\n}\n.glyphicon-gift:before {\n  content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n  content: \"\\e103\";\n}\n.glyphicon-fire:before {\n  content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n  content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n  content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n  content: \"\\e107\";\n}\n.glyphicon-plane:before {\n  content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n  content: \"\\e109\";\n}\n.glyphicon-random:before {\n  content: \"\\e110\";\n}\n.glyphicon-comment:before {\n  content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n  content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n  content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n  content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n  content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n  content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n  content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n  content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n  content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n  content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n  content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n  content: \"\\e122\";\n}\n.glyphicon-bell:before {\n  content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n  content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n  content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n  content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n  content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n  content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n  content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n  content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n  content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n  content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n  content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n  content: \"\\e134\";\n}\n.glyphicon-globe:before {\n  content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n  content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n  content: \"\\e137\";\n}\n.glyphicon-filter:before {\n  content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n  content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n  content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n  content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n  content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n  content: \"\\e143\";\n}\n.glyphicon-link:before {\n  content: \"\\e144\";\n}\n.glyphicon-phone:before {\n  content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n  content: \"\\e146\";\n}\n.glyphicon-usd:before {\n  content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n  content: \"\\e149\";\n}\n.glyphicon-sort:before {\n  content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n  content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n  content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n  content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n  content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n  content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n  content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n  content: \"\\e157\";\n}\n.glyphicon-expand:before {\n  content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n  content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n  content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n  content: \"\\e161\";\n}\n.glyphicon-flash:before {\n  content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n  content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n  content: \"\\e164\";\n}\n.glyphicon-record:before {\n  content: \"\\e165\";\n}\n.glyphicon-save:before {\n  content: \"\\e166\";\n}\n.glyphicon-open:before {\n  content: \"\\e167\";\n}\n.glyphicon-saved:before {\n  content: \"\\e168\";\n}\n.glyphicon-import:before {\n  content: \"\\e169\";\n}\n.glyphicon-export:before {\n  content: \"\\e170\";\n}\n.glyphicon-send:before {\n  content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n  content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n  content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n  content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n  content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n  content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n  content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n  content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n  content: \"\\e179\";\n}\n.glyphicon-header:before {\n  content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n  content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n  content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n  content: \"\\e183\";\n}\n.glyphicon-tower:before {\n  content: \"\\e184\";\n}\n.glyphicon-stats:before {\n  content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n  content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n  content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n  content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n  content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n  content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n  content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n  content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n  content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n  content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n  content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n  content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n  content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n  content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n  content: \"\\e200\";\n}\n.glyphicon-cd:before {\n  content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n  content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n  content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n  content: \"\\e204\";\n}\n.glyphicon-copy:before {\n  content: \"\\e205\";\n}\n.glyphicon-paste:before {\n  content: \"\\e206\";\n}\n.glyphicon-alert:before {\n  content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n  content: \"\\e210\";\n}\n.glyphicon-king:before {\n  content: \"\\e211\";\n}\n.glyphicon-queen:before {\n  content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n  content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n  content: \"\\e214\";\n}\n.glyphicon-knight:before {\n  content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n  content: \"\\e216\";\n}\n.glyphicon-tent:before {\n  content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n  content: \"\\e218\";\n}\n.glyphicon-bed:before {\n  content: \"\\e219\";\n}\n.glyphicon-apple:before {\n  content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n  content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n  content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n  content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n  content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n  content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n  content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n  content: \"\\e227\";\n}\n.glyphicon-btc:before {\n  content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n  content: \"\\e227\";\n}\n.glyphicon-yen:before {\n  content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n  content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n  content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n  content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n  content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n  content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n  content: \"\\e232\";\n}\n.glyphicon-education:before {\n  content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n  content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n  content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n  content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n  content: \"\\e237\";\n}\n.glyphicon-oil:before {\n  content: \"\\e238\";\n}\n.glyphicon-grain:before {\n  content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n  content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n  content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n  content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n  content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n  content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n  content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n  content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n  content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n  content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n  content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n  content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n  content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n  content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n  content: \"\\e253\";\n}\n.glyphicon-console:before {\n  content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n  content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n  content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n  content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n  content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n  content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n  content: \"\\e260\";\n}\n* {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\n*:before,\n*:after {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #333333;\n  background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\na {\n  color: #337ab7;\n  text-decoration: none;\n}\na:hover,\na:focus {\n  color: #23527c;\n  text-decoration: underline;\n}\na:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\nfigure {\n  margin: 0;\n}\nimg {\n  vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  display: block;\n  max-width: 100%;\n  height: auto;\n}\n.img-rounded {\n  border-radius: 6px;\n}\n.img-thumbnail {\n  padding: 4px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: all 0.2s ease-in-out;\n  -o-transition: all 0.2s ease-in-out;\n  transition: all 0.2s ease-in-out;\n  display: inline-block;\n  max-width: 100%;\n  height: auto;\n}\n.img-circle {\n  border-radius: 50%;\n}\nhr {\n  margin-top: 20px;\n  margin-bottom: 20px;\n  border: 0;\n  border-top: 1px solid #eeeeee;\n}\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n  position: static;\n  width: auto;\n  height: auto;\n  margin: 0;\n  overflow: visible;\n  clip: auto;\n}\n[role=\"button\"] {\n  cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n  font-family: inherit;\n  font-weight: 500;\n  line-height: 1.1;\n  color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n  font-weight: 400;\n  line-height: 1;\n  color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n  margin-top: 20px;\n  margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n  font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n  font-size: 75%;\n}\nh1,\n.h1 {\n  font-size: 36px;\n}\nh2,\n.h2 {\n  font-size: 30px;\n}\nh3,\n.h3 {\n  font-size: 24px;\n}\nh4,\n.h4 {\n  font-size: 18px;\n}\nh5,\n.h5 {\n  font-size: 14px;\n}\nh6,\n.h6 {\n  font-size: 12px;\n}\np {\n  margin: 0 0 10px;\n}\n.lead {\n  margin-bottom: 20px;\n  font-size: 16px;\n  font-weight: 300;\n  line-height: 1.4;\n}\n@media (min-width: 768px) {\n  .lead {\n    font-size: 21px;\n  }\n}\nsmall,\n.small {\n  font-size: 85%;\n}\nmark,\n.mark {\n  padding: 0.2em;\n  background-color: #fcf8e3;\n}\n.text-left {\n  text-align: left;\n}\n.text-right {\n  text-align: right;\n}\n.text-center {\n  text-align: center;\n}\n.text-justify {\n  text-align: justify;\n}\n.text-nowrap {\n  white-space: nowrap;\n}\n.text-lowercase {\n  text-transform: lowercase;\n}\n.text-uppercase {\n  text-transform: uppercase;\n}\n.text-capitalize {\n  text-transform: capitalize;\n}\n.text-muted {\n  color: #777777;\n}\n.text-primary {\n  color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n  color: #286090;\n}\n.text-success {\n  color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n  color: #2b542c;\n}\n.text-info {\n  color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n  color: #245269;\n}\n.text-warning {\n  color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n  color: #66512c;\n}\n.text-danger {\n  color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n  color: #843534;\n}\n.bg-primary {\n  color: #fff;\n  background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n  background-color: #286090;\n}\n.bg-success {\n  background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n  background-color: #c1e2b3;\n}\n.bg-info {\n  background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n  background-color: #afd9ee;\n}\n.bg-warning {\n  background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n  background-color: #f7ecb5;\n}\n.bg-danger {\n  background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n  background-color: #e4b9b9;\n}\n.page-header {\n  padding-bottom: 9px;\n  margin: 40px 0 20px;\n  border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n  margin-top: 0;\n  margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n  margin-bottom: 0;\n}\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n  margin-left: -5px;\n}\n.list-inline > li {\n  display: inline-block;\n  padding-right: 5px;\n  padding-left: 5px;\n}\ndl {\n  margin-top: 0;\n  margin-bottom: 20px;\n}\ndt,\ndd {\n  line-height: 1.42857143;\n}\ndt {\n  font-weight: 700;\n}\ndd {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .dl-horizontal dt {\n    float: left;\n    width: 160px;\n    clear: left;\n    text-align: right;\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n  }\n  .dl-horizontal dd {\n    margin-left: 180px;\n  }\n}\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n}\n.initialism {\n  font-size: 90%;\n  text-transform: uppercase;\n}\nblockquote {\n  padding: 10px 20px;\n  margin: 0 0 20px;\n  font-size: 17.5px;\n  border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n  margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n  display: block;\n  font-size: 80%;\n  line-height: 1.42857143;\n  color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n  content: \"\\2014 \\00A0\";\n}\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  text-align: right;\n  border-right: 5px solid #eeeeee;\n  border-left: 0;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n  content: \"\";\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n  content: \"\\00A0 \\2014\";\n}\naddress {\n  margin-bottom: 20px;\n  font-style: normal;\n  line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #c7254e;\n  background-color: #f9f2f4;\n  border-radius: 4px;\n}\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #fff;\n  background-color: #333;\n  border-radius: 3px;\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n  padding: 0;\n  font-size: 100%;\n  font-weight: 700;\n  box-shadow: none;\n}\npre {\n  display: block;\n  padding: 9.5px;\n  margin: 0 0 10px;\n  font-size: 13px;\n  line-height: 1.42857143;\n  color: #333333;\n  word-break: break-all;\n  word-wrap: break-word;\n  background-color: #f5f5f5;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\npre code {\n  padding: 0;\n  font-size: inherit;\n  color: inherit;\n  white-space: pre-wrap;\n  background-color: transparent;\n  border-radius: 0;\n}\n.pre-scrollable {\n  max-height: 340px;\n  overflow-y: scroll;\n}\n.container {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n@media (min-width: 768px) {\n  .container {\n    width: 750px;\n  }\n}\n@media (min-width: 992px) {\n  .container {\n    width: 970px;\n  }\n}\n@media (min-width: 1200px) {\n  .container {\n    width: 1170px;\n  }\n}\n.container-fluid {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n.row {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n.row-no-gutters {\n  margin-right: 0;\n  margin-left: 0;\n}\n.row-no-gutters [class*=\"col-\"] {\n  padding-right: 0;\n  padding-left: 0;\n}\n.col-xs-1,\n.col-sm-1,\n.col-md-1,\n.col-lg-1,\n.col-xs-2,\n.col-sm-2,\n.col-md-2,\n.col-lg-2,\n.col-xs-3,\n.col-sm-3,\n.col-md-3,\n.col-lg-3,\n.col-xs-4,\n.col-sm-4,\n.col-md-4,\n.col-lg-4,\n.col-xs-5,\n.col-sm-5,\n.col-md-5,\n.col-lg-5,\n.col-xs-6,\n.col-sm-6,\n.col-md-6,\n.col-lg-6,\n.col-xs-7,\n.col-sm-7,\n.col-md-7,\n.col-lg-7,\n.col-xs-8,\n.col-sm-8,\n.col-md-8,\n.col-lg-8,\n.col-xs-9,\n.col-sm-9,\n.col-md-9,\n.col-lg-9,\n.col-xs-10,\n.col-sm-10,\n.col-md-10,\n.col-lg-10,\n.col-xs-11,\n.col-sm-11,\n.col-md-11,\n.col-lg-11,\n.col-xs-12,\n.col-sm-12,\n.col-md-12,\n.col-lg-12 {\n  position: relative;\n  min-height: 1px;\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.col-xs-1,\n.col-xs-2,\n.col-xs-3,\n.col-xs-4,\n.col-xs-5,\n.col-xs-6,\n.col-xs-7,\n.col-xs-8,\n.col-xs-9,\n.col-xs-10,\n.col-xs-11,\n.col-xs-12 {\n  float: left;\n}\n.col-xs-12 {\n  width: 100%;\n}\n.col-xs-11 {\n  width: 91.66666667%;\n}\n.col-xs-10 {\n  width: 83.33333333%;\n}\n.col-xs-9 {\n  width: 75%;\n}\n.col-xs-8 {\n  width: 66.66666667%;\n}\n.col-xs-7 {\n  width: 58.33333333%;\n}\n.col-xs-6 {\n  width: 50%;\n}\n.col-xs-5 {\n  width: 41.66666667%;\n}\n.col-xs-4 {\n  width: 33.33333333%;\n}\n.col-xs-3 {\n  width: 25%;\n}\n.col-xs-2 {\n  width: 16.66666667%;\n}\n.col-xs-1 {\n  width: 8.33333333%;\n}\n.col-xs-pull-12 {\n  right: 100%;\n}\n.col-xs-pull-11 {\n  right: 91.66666667%;\n}\n.col-xs-pull-10 {\n  right: 83.33333333%;\n}\n.col-xs-pull-9 {\n  right: 75%;\n}\n.col-xs-pull-8 {\n  right: 66.66666667%;\n}\n.col-xs-pull-7 {\n  right: 58.33333333%;\n}\n.col-xs-pull-6 {\n  right: 50%;\n}\n.col-xs-pull-5 {\n  right: 41.66666667%;\n}\n.col-xs-pull-4 {\n  right: 33.33333333%;\n}\n.col-xs-pull-3 {\n  right: 25%;\n}\n.col-xs-pull-2 {\n  right: 16.66666667%;\n}\n.col-xs-pull-1 {\n  right: 8.33333333%;\n}\n.col-xs-pull-0 {\n  right: auto;\n}\n.col-xs-push-12 {\n  left: 100%;\n}\n.col-xs-push-11 {\n  left: 91.66666667%;\n}\n.col-xs-push-10 {\n  left: 83.33333333%;\n}\n.col-xs-push-9 {\n  left: 75%;\n}\n.col-xs-push-8 {\n  left: 66.66666667%;\n}\n.col-xs-push-7 {\n  left: 58.33333333%;\n}\n.col-xs-push-6 {\n  left: 50%;\n}\n.col-xs-push-5 {\n  left: 41.66666667%;\n}\n.col-xs-push-4 {\n  left: 33.33333333%;\n}\n.col-xs-push-3 {\n  left: 25%;\n}\n.col-xs-push-2 {\n  left: 16.66666667%;\n}\n.col-xs-push-1 {\n  left: 8.33333333%;\n}\n.col-xs-push-0 {\n  left: auto;\n}\n.col-xs-offset-12 {\n  margin-left: 100%;\n}\n.col-xs-offset-11 {\n  margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n  margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n  margin-left: 75%;\n}\n.col-xs-offset-8 {\n  margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n  margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n  margin-left: 50%;\n}\n.col-xs-offset-5 {\n  margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n  margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n  margin-left: 25%;\n}\n.col-xs-offset-2 {\n  margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n  margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n  margin-left: 0%;\n}\n@media (min-width: 768px) {\n  .col-sm-1,\n  .col-sm-2,\n  .col-sm-3,\n  .col-sm-4,\n  .col-sm-5,\n  .col-sm-6,\n  .col-sm-7,\n  .col-sm-8,\n  .col-sm-9,\n  .col-sm-10,\n  .col-sm-11,\n  .col-sm-12 {\n    float: left;\n  }\n  .col-sm-12 {\n    width: 100%;\n  }\n  .col-sm-11 {\n    width: 91.66666667%;\n  }\n  .col-sm-10 {\n    width: 83.33333333%;\n  }\n  .col-sm-9 {\n    width: 75%;\n  }\n  .col-sm-8 {\n    width: 66.66666667%;\n  }\n  .col-sm-7 {\n    width: 58.33333333%;\n  }\n  .col-sm-6 {\n    width: 50%;\n  }\n  .col-sm-5 {\n    width: 41.66666667%;\n  }\n  .col-sm-4 {\n    width: 33.33333333%;\n  }\n  .col-sm-3 {\n    width: 25%;\n  }\n  .col-sm-2 {\n    width: 16.66666667%;\n  }\n  .col-sm-1 {\n    width: 8.33333333%;\n  }\n  .col-sm-pull-12 {\n    right: 100%;\n  }\n  .col-sm-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-sm-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-sm-pull-9 {\n    right: 75%;\n  }\n  .col-sm-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-sm-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-sm-pull-6 {\n    right: 50%;\n  }\n  .col-sm-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-sm-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-sm-pull-3 {\n    right: 25%;\n  }\n  .col-sm-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-sm-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-sm-pull-0 {\n    right: auto;\n  }\n  .col-sm-push-12 {\n    left: 100%;\n  }\n  .col-sm-push-11 {\n    left: 91.66666667%;\n  }\n  .col-sm-push-10 {\n    left: 83.33333333%;\n  }\n  .col-sm-push-9 {\n    left: 75%;\n  }\n  .col-sm-push-8 {\n    left: 66.66666667%;\n  }\n  .col-sm-push-7 {\n    left: 58.33333333%;\n  }\n  .col-sm-push-6 {\n    left: 50%;\n  }\n  .col-sm-push-5 {\n    left: 41.66666667%;\n  }\n  .col-sm-push-4 {\n    left: 33.33333333%;\n  }\n  .col-sm-push-3 {\n    left: 25%;\n  }\n  .col-sm-push-2 {\n    left: 16.66666667%;\n  }\n  .col-sm-push-1 {\n    left: 8.33333333%;\n  }\n  .col-sm-push-0 {\n    left: auto;\n  }\n  .col-sm-offset-12 {\n    margin-left: 100%;\n  }\n  .col-sm-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-sm-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-sm-offset-9 {\n    margin-left: 75%;\n  }\n  .col-sm-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-sm-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-sm-offset-6 {\n    margin-left: 50%;\n  }\n  .col-sm-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-sm-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-sm-offset-3 {\n    margin-left: 25%;\n  }\n  .col-sm-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-sm-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-sm-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 992px) {\n  .col-md-1,\n  .col-md-2,\n  .col-md-3,\n  .col-md-4,\n  .col-md-5,\n  .col-md-6,\n  .col-md-7,\n  .col-md-8,\n  .col-md-9,\n  .col-md-10,\n  .col-md-11,\n  .col-md-12 {\n    float: left;\n  }\n  .col-md-12 {\n    width: 100%;\n  }\n  .col-md-11 {\n    width: 91.66666667%;\n  }\n  .col-md-10 {\n    width: 83.33333333%;\n  }\n  .col-md-9 {\n    width: 75%;\n  }\n  .col-md-8 {\n    width: 66.66666667%;\n  }\n  .col-md-7 {\n    width: 58.33333333%;\n  }\n  .col-md-6 {\n    width: 50%;\n  }\n  .col-md-5 {\n    width: 41.66666667%;\n  }\n  .col-md-4 {\n    width: 33.33333333%;\n  }\n  .col-md-3 {\n    width: 25%;\n  }\n  .col-md-2 {\n    width: 16.66666667%;\n  }\n  .col-md-1 {\n    width: 8.33333333%;\n  }\n  .col-md-pull-12 {\n    right: 100%;\n  }\n  .col-md-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-md-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-md-pull-9 {\n    right: 75%;\n  }\n  .col-md-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-md-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-md-pull-6 {\n    right: 50%;\n  }\n  .col-md-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-md-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-md-pull-3 {\n    right: 25%;\n  }\n  .col-md-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-md-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-md-pull-0 {\n    right: auto;\n  }\n  .col-md-push-12 {\n    left: 100%;\n  }\n  .col-md-push-11 {\n    left: 91.66666667%;\n  }\n  .col-md-push-10 {\n    left: 83.33333333%;\n  }\n  .col-md-push-9 {\n    left: 75%;\n  }\n  .col-md-push-8 {\n    left: 66.66666667%;\n  }\n  .col-md-push-7 {\n    left: 58.33333333%;\n  }\n  .col-md-push-6 {\n    left: 50%;\n  }\n  .col-md-push-5 {\n    left: 41.66666667%;\n  }\n  .col-md-push-4 {\n    left: 33.33333333%;\n  }\n  .col-md-push-3 {\n    left: 25%;\n  }\n  .col-md-push-2 {\n    left: 16.66666667%;\n  }\n  .col-md-push-1 {\n    left: 8.33333333%;\n  }\n  .col-md-push-0 {\n    left: auto;\n  }\n  .col-md-offset-12 {\n    margin-left: 100%;\n  }\n  .col-md-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-md-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-md-offset-9 {\n    margin-left: 75%;\n  }\n  .col-md-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-md-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-md-offset-6 {\n    margin-left: 50%;\n  }\n  .col-md-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-md-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-md-offset-3 {\n    margin-left: 25%;\n  }\n  .col-md-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-md-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-md-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 1200px) {\n  .col-lg-1,\n  .col-lg-2,\n  .col-lg-3,\n  .col-lg-4,\n  .col-lg-5,\n  .col-lg-6,\n  .col-lg-7,\n  .col-lg-8,\n  .col-lg-9,\n  .col-lg-10,\n  .col-lg-11,\n  .col-lg-12 {\n    float: left;\n  }\n  .col-lg-12 {\n    width: 100%;\n  }\n  .col-lg-11 {\n    width: 91.66666667%;\n  }\n  .col-lg-10 {\n    width: 83.33333333%;\n  }\n  .col-lg-9 {\n    width: 75%;\n  }\n  .col-lg-8 {\n    width: 66.66666667%;\n  }\n  .col-lg-7 {\n    width: 58.33333333%;\n  }\n  .col-lg-6 {\n    width: 50%;\n  }\n  .col-lg-5 {\n    width: 41.66666667%;\n  }\n  .col-lg-4 {\n    width: 33.33333333%;\n  }\n  .col-lg-3 {\n    width: 25%;\n  }\n  .col-lg-2 {\n    width: 16.66666667%;\n  }\n  .col-lg-1 {\n    width: 8.33333333%;\n  }\n  .col-lg-pull-12 {\n    right: 100%;\n  }\n  .col-lg-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-lg-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-lg-pull-9 {\n    right: 75%;\n  }\n  .col-lg-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-lg-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-lg-pull-6 {\n    right: 50%;\n  }\n  .col-lg-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-lg-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-lg-pull-3 {\n    right: 25%;\n  }\n  .col-lg-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-lg-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-lg-pull-0 {\n    right: auto;\n  }\n  .col-lg-push-12 {\n    left: 100%;\n  }\n  .col-lg-push-11 {\n    left: 91.66666667%;\n  }\n  .col-lg-push-10 {\n    left: 83.33333333%;\n  }\n  .col-lg-push-9 {\n    left: 75%;\n  }\n  .col-lg-push-8 {\n    left: 66.66666667%;\n  }\n  .col-lg-push-7 {\n    left: 58.33333333%;\n  }\n  .col-lg-push-6 {\n    left: 50%;\n  }\n  .col-lg-push-5 {\n    left: 41.66666667%;\n  }\n  .col-lg-push-4 {\n    left: 33.33333333%;\n  }\n  .col-lg-push-3 {\n    left: 25%;\n  }\n  .col-lg-push-2 {\n    left: 16.66666667%;\n  }\n  .col-lg-push-1 {\n    left: 8.33333333%;\n  }\n  .col-lg-push-0 {\n    left: auto;\n  }\n  .col-lg-offset-12 {\n    margin-left: 100%;\n  }\n  .col-lg-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-lg-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-lg-offset-9 {\n    margin-left: 75%;\n  }\n  .col-lg-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-lg-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-lg-offset-6 {\n    margin-left: 50%;\n  }\n  .col-lg-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-lg-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-lg-offset-3 {\n    margin-left: 25%;\n  }\n  .col-lg-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-lg-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-lg-offset-0 {\n    margin-left: 0%;\n  }\n}\ntable {\n  background-color: transparent;\n}\ntable col[class*=\"col-\"] {\n  position: static;\n  display: table-column;\n  float: none;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n  position: static;\n  display: table-cell;\n  float: none;\n}\ncaption {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  color: #777777;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n  padding: 8px;\n  line-height: 1.42857143;\n  vertical-align: top;\n  border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n  vertical-align: bottom;\n  border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n  border-top: 0;\n}\n.table > tbody + tbody {\n  border-top: 2px solid #ddd;\n}\n.table .table {\n  background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n  padding: 5px;\n}\n.table-bordered {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n  border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n  background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n  background-color: #f5f5f5;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n  background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n  background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n  background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n  background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n  background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n  background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n  background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n  background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n  background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n  background-color: #ebcccc;\n}\n.table-responsive {\n  min-height: 0.01%;\n  overflow-x: auto;\n}\n@media screen and (max-width: 767px) {\n  .table-responsive {\n    width: 100%;\n    margin-bottom: 15px;\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid #ddd;\n  }\n  .table-responsive > .table {\n    margin-bottom: 0;\n  }\n  .table-responsive > .table > thead > tr > th,\n  .table-responsive > .table > tbody > tr > th,\n  .table-responsive > .table > tfoot > tr > th,\n  .table-responsive > .table > thead > tr > td,\n  .table-responsive > .table > tbody > tr > td,\n  .table-responsive > .table > tfoot > tr > td {\n    white-space: nowrap;\n  }\n  .table-responsive > .table-bordered {\n    border: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:first-child,\n  .table-responsive > .table-bordered > tbody > tr > th:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n  .table-responsive > .table-bordered > thead > tr > td:first-child,\n  .table-responsive > .table-bordered > tbody > tr > td:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n    border-left: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:last-child,\n  .table-responsive > .table-bordered > tbody > tr > th:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n  .table-responsive > .table-bordered > thead > tr > td:last-child,\n  .table-responsive > .table-bordered > tbody > tr > td:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n    border-right: 0;\n  }\n  .table-responsive > .table-bordered > tbody > tr:last-child > th,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n  .table-responsive > .table-bordered > tbody > tr:last-child > td,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n    border-bottom: 0;\n  }\n}\nfieldset {\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 20px;\n  font-size: 21px;\n  line-height: inherit;\n  color: #333333;\n  border: 0;\n  border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n  display: inline-block;\n  max-width: 100%;\n  margin-bottom: 5px;\n  font-weight: 700;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n  -webkit-appearance: none;\n  appearance: none;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9;\n  line-height: normal;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n  cursor: not-allowed;\n}\ninput[type=\"file\"] {\n  display: block;\n}\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\nselect[multiple],\nselect[size] {\n  height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\noutput {\n  display: block;\n  padding-top: 7px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n}\n.form-control {\n  display: block;\n  width: 100%;\n  height: 34px;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n  background-color: #fff;\n  background-image: none;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n  border-color: #66afe9;\n  outline: 0;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n  color: #999;\n  opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n  color: #999;\n}\n.form-control::-webkit-input-placeholder {\n  color: #999;\n}\n.form-control::-ms-expand {\n  background-color: transparent;\n  border: 0;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n  background-color: #eeeeee;\n  opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n  cursor: not-allowed;\n}\ntextarea.form-control {\n  height: auto;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"].form-control,\n  input[type=\"time\"].form-control,\n  input[type=\"datetime-local\"].form-control,\n  input[type=\"month\"].form-control {\n    line-height: 34px;\n  }\n  input[type=\"date\"].input-sm,\n  input[type=\"time\"].input-sm,\n  input[type=\"datetime-local\"].input-sm,\n  input[type=\"month\"].input-sm,\n  .input-group-sm input[type=\"date\"],\n  .input-group-sm input[type=\"time\"],\n  .input-group-sm input[type=\"datetime-local\"],\n  .input-group-sm input[type=\"month\"] {\n    line-height: 30px;\n  }\n  input[type=\"date\"].input-lg,\n  input[type=\"time\"].input-lg,\n  input[type=\"datetime-local\"].input-lg,\n  input[type=\"month\"].input-lg,\n  .input-group-lg input[type=\"date\"],\n  .input-group-lg input[type=\"time\"],\n  .input-group-lg input[type=\"datetime-local\"],\n  .input-group-lg input[type=\"month\"] {\n    line-height: 46px;\n  }\n}\n.form-group {\n  margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n  cursor: not-allowed;\n}\n.radio label,\n.checkbox label {\n  min-height: 20px;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-top: 4px \\9;\n  margin-left: -20px;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  vertical-align: middle;\n  cursor: pointer;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n  cursor: not-allowed;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px;\n}\n.form-control-static {\n  min-height: 34px;\n  padding-top: 7px;\n  padding-bottom: 7px;\n  margin-bottom: 0;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-sm {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-sm {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n  height: auto;\n}\n.form-group-sm .form-control {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.form-group-sm select.form-control {\n  height: 30px;\n  line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n  height: auto;\n}\n.form-group-sm .form-control-static {\n  height: 30px;\n  min-height: 32px;\n  padding: 6px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.input-lg {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-lg {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n  height: auto;\n}\n.form-group-lg .form-control {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.form-group-lg select.form-control {\n  height: 46px;\n  line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n  height: auto;\n}\n.form-group-lg .form-control-static {\n  height: 46px;\n  min-height: 38px;\n  padding: 11px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.has-feedback {\n  position: relative;\n}\n.has-feedback .form-control {\n  padding-right: 42.5px;\n}\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  display: block;\n  width: 34px;\n  height: 34px;\n  line-height: 34px;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: 46px;\n  height: 46px;\n  line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: 30px;\n  height: 30px;\n  line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n  color: #3c763d;\n}\n.has-success .form-control {\n  border-color: #3c763d;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n  border-color: #2b542c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #3c763d;\n}\n.has-success .form-control-feedback {\n  color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n  color: #8a6d3b;\n}\n.has-warning .form-control {\n  border-color: #8a6d3b;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n  border-color: #66512c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #8a6d3b;\n}\n.has-warning .form-control-feedback {\n  color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n  color: #a94442;\n}\n.has-error .form-control {\n  border-color: #a94442;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n  border-color: #843534;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #a94442;\n}\n.has-error .form-control-feedback {\n  color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n  top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n  top: 0;\n}\n.help-block {\n  display: block;\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: #737373;\n}\n@media (min-width: 768px) {\n  .form-inline .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .form-inline .form-control-static {\n    display: inline-block;\n  }\n  .form-inline .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .form-inline .input-group .input-group-addon,\n  .form-inline .input-group .input-group-btn,\n  .form-inline .input-group .form-control {\n    width: auto;\n  }\n  .form-inline .input-group > .form-control {\n    width: 100%;\n  }\n  .form-inline .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio,\n  .form-inline .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio label,\n  .form-inline .checkbox label {\n    padding-left: 0;\n  }\n  .form-inline .radio input[type=\"radio\"],\n  .form-inline .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .form-inline .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n  padding-top: 7px;\n  margin-top: 0;\n  margin-bottom: 0;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n  min-height: 27px;\n}\n.form-horizontal .form-group {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .control-label {\n    padding-top: 7px;\n    margin-bottom: 0;\n    text-align: right;\n  }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n  right: 15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-lg .control-label {\n    padding-top: 11px;\n    font-size: 18px;\n  }\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-sm .control-label {\n    padding-top: 6px;\n    font-size: 12px;\n  }\n}\n.btn {\n  display: inline-block;\n  margin-bottom: 0;\n  font-weight: normal;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none;\n  border: 1px solid transparent;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  border-radius: 4px;\n  -webkit-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n  color: #333;\n  text-decoration: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n  outline: 0;\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n  cursor: not-allowed;\n  filter: alpha(opacity=65);\n  opacity: 0.65;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n  pointer-events: none;\n}\n.btn-default {\n  color: #333;\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #8c8c8c;\n}\n.btn-default:hover {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  color: #333;\n  background-color: #e6e6e6;\n  background-image: none;\n  border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n  color: #333;\n  background-color: #d4d4d4;\n  border-color: #8c8c8c;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default .badge {\n  color: #fff;\n  background-color: #333;\n}\n.btn-primary {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n  color: #fff;\n  background-color: #286090;\n  border-color: #122b40;\n}\n.btn-primary:hover {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  color: #fff;\n  background-color: #286090;\n  background-image: none;\n  border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n  color: #fff;\n  background-color: #204d74;\n  border-color: #122b40;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.btn-success {\n  color: #fff;\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #255625;\n}\n.btn-success:hover {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  color: #fff;\n  background-color: #449d44;\n  background-image: none;\n  border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n  color: #fff;\n  background-color: #398439;\n  border-color: #255625;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success .badge {\n  color: #5cb85c;\n  background-color: #fff;\n}\n.btn-info {\n  color: #fff;\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #1b6d85;\n}\n.btn-info:hover {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  color: #fff;\n  background-color: #31b0d5;\n  background-image: none;\n  border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n  color: #fff;\n  background-color: #269abc;\n  border-color: #1b6d85;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info .badge {\n  color: #5bc0de;\n  background-color: #fff;\n}\n.btn-warning {\n  color: #fff;\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #985f0d;\n}\n.btn-warning:hover {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  color: #fff;\n  background-color: #ec971f;\n  background-image: none;\n  border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n  color: #fff;\n  background-color: #d58512;\n  border-color: #985f0d;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning .badge {\n  color: #f0ad4e;\n  background-color: #fff;\n}\n.btn-danger {\n  color: #fff;\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #761c19;\n}\n.btn-danger:hover {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  color: #fff;\n  background-color: #c9302c;\n  background-image: none;\n  border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n  color: #fff;\n  background-color: #ac2925;\n  border-color: #761c19;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger .badge {\n  color: #d9534f;\n  background-color: #fff;\n}\n.btn-link {\n  font-weight: 400;\n  color: #337ab7;\n  border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n  background-color: transparent;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n  border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n  color: #23527c;\n  text-decoration: underline;\n  background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n  color: #777777;\n  text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n  padding: 1px 5px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-block {\n  display: block;\n  width: 100%;\n}\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n  width: 100%;\n}\n.fade {\n  opacity: 0;\n  -webkit-transition: opacity 0.15s linear;\n  -o-transition: opacity 0.15s linear;\n  transition: opacity 0.15s linear;\n}\n.fade.in {\n  opacity: 1;\n}\n.collapse {\n  display: none;\n}\n.collapse.in {\n  display: block;\n}\ntr.collapse.in {\n  display: table-row;\n}\ntbody.collapse.in {\n  display: table-row-group;\n}\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  -webkit-transition-property: height, visibility;\n  transition-property: height, visibility;\n  -webkit-transition-duration: 0.35s;\n  transition-duration: 0.35s;\n  -webkit-transition-timing-function: ease;\n  transition-timing-function: ease;\n}\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: 4px dashed;\n  border-top: 4px solid \\9;\n  border-right: 4px solid transparent;\n  border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n  position: relative;\n}\n.dropdown-toggle:focus {\n  outline: 0;\n}\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: 1000;\n  display: none;\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0;\n  font-size: 14px;\n  text-align: left;\n  list-style: none;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.15);\n  border-radius: 4px;\n  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n}\n.dropdown-menu.pull-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu .divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n  display: block;\n  padding: 3px 20px;\n  clear: both;\n  font-weight: 400;\n  line-height: 1.42857143;\n  color: #333333;\n  white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  color: #262626;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  color: #fff;\n  text-decoration: none;\n  background-color: #337ab7;\n  outline: 0;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n  background-image: none;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.open > .dropdown-menu {\n  display: block;\n}\n.open > a {\n  outline: 0;\n}\n.dropdown-menu-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu-left {\n  right: auto;\n  left: 0;\n}\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: 12px;\n  line-height: 1.42857143;\n  color: #777777;\n  white-space: nowrap;\n}\n.dropdown-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 990;\n}\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n  content: \"\";\n  border-top: 0;\n  border-bottom: 4px dashed;\n  border-bottom: 4px solid \\9;\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n  top: auto;\n  bottom: 100%;\n  margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n  .navbar-right .dropdown-menu {\n    right: 0;\n    left: auto;\n  }\n  .navbar-right .dropdown-menu-left {\n    right: auto;\n    left: 0;\n  }\n}\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n  z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n  margin-left: -1px;\n}\n.btn-toolbar {\n  margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n  float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n  margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n.btn-group > .btn:first-child {\n  margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n  padding-right: 8px;\n  padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-right: 12px;\n  padding-left: 12px;\n}\n.btn-group.open .dropdown-toggle {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn .caret {\n  margin-left: 0;\n}\n.btn-lg .caret {\n  border-width: 5px 5px 0;\n  border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n  border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n  display: block;\n  float: none;\n  width: 100%;\n  max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n  float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n  margin-top: -1px;\n  margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n  display: table-cell;\n  float: none;\n  width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n  width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n  left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.input-group {\n  position: relative;\n  display: table;\n  border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n  float: none;\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-group .form-control {\n  position: relative;\n  z-index: 2;\n  float: left;\n  width: 100%;\n  margin-bottom: 0;\n}\n.input-group .form-control:focus {\n  z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle;\n}\n.input-group-addon {\n  padding: 6px 12px;\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 1;\n  color: #555555;\n  text-align: center;\n  background-color: #eeeeee;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\n.input-group-addon.input-sm {\n  padding: 5px 10px;\n  font-size: 12px;\n  border-radius: 3px;\n}\n.input-group-addon.input-lg {\n  padding: 10px 16px;\n  font-size: 18px;\n  border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n  margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n.input-group-btn {\n  position: relative;\n  font-size: 0;\n  white-space: nowrap;\n}\n.input-group-btn > .btn {\n  position: relative;\n}\n.input-group-btn > .btn + .btn {\n  margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n  z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n  margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n  z-index: 2;\n  margin-left: -1px;\n}\n.nav {\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n.nav > li {\n  position: relative;\n  display: block;\n}\n.nav > li > a {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n  color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n  color: #777777;\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n  background-color: #eeeeee;\n  border-color: #337ab7;\n}\n.nav .nav-divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.nav > li > a > img {\n  max-width: none;\n}\n.nav-tabs {\n  border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n  float: left;\n  margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n  margin-right: 2px;\n  line-height: 1.42857143;\n  border: 1px solid transparent;\n  border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n  border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n  color: #555555;\n  cursor: default;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-bottom-color: transparent;\n}\n.nav-tabs.nav-justified {\n  width: 100%;\n  border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n  float: none;\n}\n.nav-tabs.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-tabs.nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs.nav-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs.nav-justified > .active > a,\n  .nav-tabs.nav-justified > .active > a:hover,\n  .nav-tabs.nav-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.nav-pills > li {\n  float: left;\n}\n.nav-pills > li > a {\n  border-radius: 4px;\n}\n.nav-pills > li + li {\n  margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n  color: #fff;\n  background-color: #337ab7;\n}\n.nav-stacked > li {\n  float: none;\n}\n.nav-stacked > li + li {\n  margin-top: 2px;\n  margin-left: 0;\n}\n.nav-justified {\n  width: 100%;\n}\n.nav-justified > li {\n  float: none;\n}\n.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs-justified {\n  border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs-justified > .active > a,\n  .nav-tabs-justified > .active > a:hover,\n  .nav-tabs-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n.nav-tabs .dropdown-menu {\n  margin-top: -1px;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar {\n  position: relative;\n  min-height: 50px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n  .navbar {\n    border-radius: 4px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-header {\n    float: left;\n  }\n}\n.navbar-collapse {\n  padding-right: 15px;\n  padding-left: 15px;\n  overflow-x: visible;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n  -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n  overflow-y: auto;\n}\n@media (min-width: 768px) {\n  .navbar-collapse {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n  }\n  .navbar-collapse.collapse {\n    display: block !important;\n    height: auto !important;\n    padding-bottom: 0;\n    overflow: visible !important;\n  }\n  .navbar-collapse.in {\n    overflow-y: visible;\n  }\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-static-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n  max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    max-height: 200px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-fixed-top,\n  .navbar-fixed-bottom {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0;\n  border-width: 1px 0 0;\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .container > .navbar-header,\n  .container-fluid > .navbar-header,\n  .container > .navbar-collapse,\n  .container-fluid > .navbar-collapse {\n    margin-right: 0;\n    margin-left: 0;\n  }\n}\n.navbar-static-top {\n  z-index: 1000;\n  border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n  .navbar-static-top {\n    border-radius: 0;\n  }\n}\n.navbar-brand {\n  float: left;\n  height: 50px;\n  padding: 15px 15px;\n  font-size: 18px;\n  line-height: 20px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n  text-decoration: none;\n}\n.navbar-brand > img {\n  display: block;\n}\n@media (min-width: 768px) {\n  .navbar > .container .navbar-brand,\n  .navbar > .container-fluid .navbar-brand {\n    margin-left: -15px;\n  }\n}\n.navbar-toggle {\n  position: relative;\n  float: right;\n  padding: 9px 10px;\n  margin-right: 15px;\n  margin-top: 8px;\n  margin-bottom: 8px;\n  background-color: transparent;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.navbar-toggle:focus {\n  outline: 0;\n}\n.navbar-toggle .icon-bar {\n  display: block;\n  width: 22px;\n  height: 2px;\n  border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n  margin-top: 4px;\n}\n@media (min-width: 768px) {\n  .navbar-toggle {\n    display: none;\n  }\n}\n.navbar-nav {\n  margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n  padding-top: 10px;\n  padding-bottom: 10px;\n  line-height: 20px;\n}\n@media (max-width: 767px) {\n  .navbar-nav .open .dropdown-menu {\n    position: static;\n    float: none;\n    width: auto;\n    margin-top: 0;\n    background-color: transparent;\n    border: 0;\n    box-shadow: none;\n  }\n  .navbar-nav .open .dropdown-menu > li > a,\n  .navbar-nav .open .dropdown-menu .dropdown-header {\n    padding: 5px 15px 5px 25px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a {\n    line-height: 20px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-nav .open .dropdown-menu > li > a:focus {\n    background-image: none;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-nav {\n    float: left;\n    margin: 0;\n  }\n  .navbar-nav > li {\n    float: left;\n  }\n  .navbar-nav > li > a {\n    padding-top: 15px;\n    padding-bottom: 15px;\n  }\n}\n.navbar-form {\n  padding: 10px 15px;\n  margin-right: -15px;\n  margin-left: -15px;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n  .navbar-form .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control-static {\n    display: inline-block;\n  }\n  .navbar-form .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .navbar-form .input-group .input-group-addon,\n  .navbar-form .input-group .input-group-btn,\n  .navbar-form .input-group .form-control {\n    width: auto;\n  }\n  .navbar-form .input-group > .form-control {\n    width: 100%;\n  }\n  .navbar-form .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio,\n  .navbar-form .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio label,\n  .navbar-form .checkbox label {\n    padding-left: 0;\n  }\n  .navbar-form .radio input[type=\"radio\"],\n  .navbar-form .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .navbar-form .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n@media (max-width: 767px) {\n  .navbar-form .form-group {\n    margin-bottom: 5px;\n  }\n  .navbar-form .form-group:last-child {\n    margin-bottom: 0;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-form {\n    width: auto;\n    padding-top: 0;\n    padding-bottom: 0;\n    margin-right: 0;\n    margin-left: 0;\n    border: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n}\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.navbar-btn {\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n  margin-top: 14px;\n  margin-bottom: 14px;\n}\n.navbar-text {\n  margin-top: 15px;\n  margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n  .navbar-text {\n    float: left;\n    margin-right: 15px;\n    margin-left: 15px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-left {\n    float: left !important;\n  }\n  .navbar-right {\n    float: right !important;\n    margin-right: -15px;\n  }\n  .navbar-right ~ .navbar-right {\n    margin-right: 0;\n  }\n}\n.navbar-default {\n  background-color: #f8f8f8;\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n  color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n  color: #5e5e5e;\n  background-color: transparent;\n}\n.navbar-default .navbar-text {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n  color: #333;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n  color: #ccc;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n@media (max-width: 767px) {\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n    color: #777;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #333;\n    background-color: transparent;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #555;\n    background-color: #e7e7e7;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #ccc;\n    background-color: transparent;\n  }\n}\n.navbar-default .navbar-toggle {\n  border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n  background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n  background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-link {\n  color: #777;\n}\n.navbar-default .navbar-link:hover {\n  color: #333;\n}\n.navbar-default .btn-link {\n  color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n  color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n  color: #ccc;\n}\n.navbar-inverse {\n  background-color: #222;\n  border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n  color: #444;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n@media (max-width: 767px) {\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n    border-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n    color: #9d9d9d;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #fff;\n    background-color: transparent;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #444;\n    background-color: transparent;\n  }\n}\n.navbar-inverse .navbar-toggle {\n  border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n  background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n  background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n  border-color: #101010;\n}\n.navbar-inverse .navbar-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n  color: #fff;\n}\n.navbar-inverse .btn-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n  color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n  color: #444;\n}\n.breadcrumb {\n  padding: 8px 15px;\n  margin-bottom: 20px;\n  list-style: none;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n}\n.breadcrumb > li {\n  display: inline-block;\n}\n.breadcrumb > li + li:before {\n  padding: 0 5px;\n  color: #ccc;\n  content: \"/\\00a0\";\n}\n.breadcrumb > .active {\n  color: #777777;\n}\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: 20px 0;\n  border-radius: 4px;\n}\n.pagination > li {\n  display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n  position: relative;\n  float: left;\n  padding: 6px 12px;\n  margin-left: -1px;\n  line-height: 1.42857143;\n  color: #337ab7;\n  text-decoration: none;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n  z-index: 2;\n  color: #23527c;\n  background-color: #eeeeee;\n  border-color: #ddd;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n  margin-left: 0;\n  border-top-left-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 4px;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n  z-index: 3;\n  color: #fff;\n  cursor: default;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #fff;\n  border-color: #ddd;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n  border-top-left-radius: 6px;\n  border-bottom-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n  border-top-right-radius: 6px;\n  border-bottom-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n  border-top-left-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n  border-top-right-radius: 3px;\n  border-bottom-right-radius: 3px;\n}\n.pager {\n  padding-left: 0;\n  margin: 20px 0;\n  text-align: center;\n  list-style: none;\n}\n.pager li {\n  display: inline;\n}\n.pager li > a,\n.pager li > span {\n  display: inline-block;\n  padding: 5px 14px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n  float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n  float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #fff;\n}\n.label {\n  display: inline;\n  padding: 0.2em 0.6em 0.3em;\n  font-size: 75%;\n  font-weight: 700;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: 0.25em;\n}\na.label:hover,\na.label:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.label:empty {\n  display: none;\n}\n.btn .label {\n  position: relative;\n  top: -1px;\n}\n.label-default {\n  background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n  background-color: #5e5e5e;\n}\n.label-primary {\n  background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n  background-color: #286090;\n}\n.label-success {\n  background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n  background-color: #449d44;\n}\n.label-info {\n  background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n  background-color: #31b0d5;\n}\n.label-warning {\n  background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n  background-color: #ec971f;\n}\n.label-danger {\n  background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n  background-color: #c9302c;\n}\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: 12px;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  background-color: #777777;\n  border-radius: 10px;\n}\n.badge:empty {\n  display: none;\n}\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n  top: 0;\n  padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.list-group-item > .badge {\n  float: right;\n}\n.list-group-item > .badge + .badge {\n  margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n  margin-left: 3px;\n}\n.jumbotron {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  margin-bottom: 30px;\n  color: inherit;\n  background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n  color: inherit;\n}\n.jumbotron p {\n  margin-bottom: 15px;\n  font-size: 21px;\n  font-weight: 200;\n}\n.jumbotron > hr {\n  border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n  padding-right: 15px;\n  padding-left: 15px;\n  border-radius: 6px;\n}\n.jumbotron .container {\n  max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n  .jumbotron {\n    padding-top: 48px;\n    padding-bottom: 48px;\n  }\n  .container .jumbotron,\n  .container-fluid .jumbotron {\n    padding-right: 60px;\n    padding-left: 60px;\n  }\n  .jumbotron h1,\n  .jumbotron .h1 {\n    font-size: 63px;\n  }\n}\n.thumbnail {\n  display: block;\n  padding: 4px;\n  margin-bottom: 20px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: border 0.2s ease-in-out;\n  -o-transition: border 0.2s ease-in-out;\n  transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n  margin-right: auto;\n  margin-left: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n  border-color: #337ab7;\n}\n.thumbnail .caption {\n  padding: 9px;\n  color: #333333;\n}\n.alert {\n  padding: 15px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.alert h4 {\n  margin-top: 0;\n  color: inherit;\n}\n.alert .alert-link {\n  font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n  margin-bottom: 0;\n}\n.alert > p + p {\n  margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n  position: relative;\n  top: -2px;\n  right: -21px;\n  color: inherit;\n}\n.alert-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.alert-success hr {\n  border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n  color: #2b542c;\n}\n.alert-info {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.alert-info hr {\n  border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n  color: #245269;\n}\n.alert-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.alert-warning hr {\n  border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n  color: #66512c;\n}\n.alert-danger {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.alert-danger hr {\n  border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n  color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n.progress {\n  height: 20px;\n  margin-bottom: 20px;\n  overflow: hidden;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: 12px;\n  line-height: 20px;\n  color: #fff;\n  text-align: center;\n  background-color: #337ab7;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  -webkit-transition: width 0.6s ease;\n  -o-transition: width 0.6s ease;\n  transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n  -webkit-animation: progress-bar-stripes 2s linear infinite;\n  -o-animation: progress-bar-stripes 2s linear infinite;\n  animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n  background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n  background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n  background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n  background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n  margin-top: 15px;\n}\n.media:first-child {\n  margin-top: 0;\n}\n.media,\n.media-body {\n  overflow: hidden;\n  zoom: 1;\n}\n.media-body {\n  width: 10000px;\n}\n.media-object {\n  display: block;\n}\n.media-object.img-thumbnail {\n  max-width: none;\n}\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n.media-middle {\n  vertical-align: middle;\n}\n.media-bottom {\n  vertical-align: bottom;\n}\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n.list-group {\n  padding-left: 0;\n  margin-bottom: 20px;\n}\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  margin-bottom: -1px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n}\n.list-group-item:last-child {\n  margin-bottom: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #eeeeee;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n  color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n  color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  z-index: 2;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n  color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n  color: #c7ddef;\n}\na.list-group-item,\nbutton.list-group-item {\n  color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n  color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n  color: #555;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n.list-group-item-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n  color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n  color: #3c763d;\n  background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n  color: #fff;\n  background-color: #3c763d;\n  border-color: #3c763d;\n}\n.list-group-item-info {\n  color: #31708f;\n  background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n  color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n  color: #31708f;\n  background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n  color: #fff;\n  background-color: #31708f;\n  border-color: #31708f;\n}\n.list-group-item-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n  color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n  color: #8a6d3b;\n  background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n  color: #fff;\n  background-color: #8a6d3b;\n  border-color: #8a6d3b;\n}\n.list-group-item-danger {\n  color: #a94442;\n  background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n  color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n  color: #a94442;\n  background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n  color: #fff;\n  background-color: #a94442;\n  border-color: #a94442;\n}\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n.panel {\n  margin-bottom: 20px;\n  background-color: #fff;\n  border: 1px solid transparent;\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n  padding: 15px;\n}\n.panel-heading {\n  padding: 10px 15px;\n  border-bottom: 1px solid transparent;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n  color: inherit;\n}\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: 16px;\n  color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n  color: inherit;\n}\n.panel-footer {\n  padding: 10px 15px;\n  background-color: #f5f5f5;\n  border-top: 1px solid #ddd;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n  margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n  border-width: 1px 0;\n  border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n  border-top: 0;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n  border-bottom: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n  border-top-width: 0;\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n  margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n  border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n  border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n  border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n  border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n  border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n  border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n  border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n  border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n  border-bottom: 0;\n}\n.panel > .table-responsive {\n  margin-bottom: 0;\n  border: 0;\n}\n.panel-group {\n  margin-bottom: 20px;\n}\n.panel-group .panel {\n  margin-bottom: 0;\n  border-radius: 4px;\n}\n.panel-group .panel + .panel {\n  margin-top: 5px;\n}\n.panel-group .panel-heading {\n  border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n  border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n  border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n  border-bottom: 1px solid #ddd;\n}\n.panel-default {\n  border-color: #ddd;\n}\n.panel-default > .panel-heading {\n  color: #333333;\n  background-color: #f5f5f5;\n  border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n  color: #f5f5f5;\n  background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ddd;\n}\n.panel-primary {\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #337ab7;\n}\n.panel-success {\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n  color: #dff0d8;\n  background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #d6e9c6;\n}\n.panel-info {\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n  color: #d9edf7;\n  background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #bce8f1;\n}\n.panel-warning {\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n  color: #fcf8e3;\n  background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #faebcc;\n}\n.panel-danger {\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n  color: #f2dede;\n  background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  border: 0;\n}\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border: 1px solid #e3e3e3;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n  border-color: #ddd;\n  border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n  padding: 24px;\n  border-radius: 6px;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: 3px;\n}\n.close {\n  float: right;\n  font-size: 21px;\n  font-weight: bold;\n  line-height: 1;\n  color: #000;\n  text-shadow: 0 1px 0 #fff;\n  filter: alpha(opacity=20);\n  opacity: 0.2;\n}\n.close:hover,\n.close:focus {\n  color: #000;\n  text-decoration: none;\n  cursor: pointer;\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\nbutton.close {\n  padding: 0;\n  cursor: pointer;\n  background: transparent;\n  border: 0;\n  -webkit-appearance: none;\n  appearance: none;\n}\n.modal-open {\n  overflow: hidden;\n}\n.modal {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1050;\n  display: none;\n  overflow: hidden;\n  -webkit-overflow-scrolling: touch;\n  outline: 0;\n}\n.modal.fade .modal-dialog {\n  -webkit-transform: translate(0, -25%);\n  -ms-transform: translate(0, -25%);\n  -o-transform: translate(0, -25%);\n  transform: translate(0, -25%);\n  -webkit-transition: -webkit-transform 0.3s ease-out;\n  -moz-transition: -moz-transform 0.3s ease-out;\n  -o-transition: -o-transform 0.3s ease-out;\n  transition: transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n  -webkit-transform: translate(0, 0);\n  -ms-transform: translate(0, 0);\n  -o-transform: translate(0, 0);\n  transform: translate(0, 0);\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n.modal-content {\n  position: relative;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #999;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  outline: 0;\n}\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1040;\n  background-color: #000;\n}\n.modal-backdrop.fade {\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.modal-backdrop.in {\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\n.modal-header {\n  padding: 15px;\n  border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n  margin-top: -2px;\n}\n.modal-title {\n  margin: 0;\n  line-height: 1.42857143;\n}\n.modal-body {\n  position: relative;\n  padding: 15px;\n}\n.modal-footer {\n  padding: 15px;\n  text-align: right;\n  border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n  margin-bottom: 0;\n  margin-left: 5px;\n}\n.modal-footer .btn-group .btn + .btn {\n  margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n  margin-left: 0;\n}\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n@media (min-width: 768px) {\n  .modal-dialog {\n    width: 600px;\n    margin: 30px auto;\n  }\n  .modal-content {\n    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n  }\n  .modal-sm {\n    width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg {\n    width: 900px;\n  }\n}\n.tooltip {\n  position: absolute;\n  z-index: 1070;\n  display: block;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.42857143;\n  line-break: auto;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  font-size: 12px;\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.tooltip.in {\n  filter: alpha(opacity=90);\n  opacity: 0.9;\n}\n.tooltip.top {\n  padding: 5px 0;\n  margin-top: -3px;\n}\n.tooltip.right {\n  padding: 0 5px;\n  margin-left: 3px;\n}\n.tooltip.bottom {\n  padding: 5px 0;\n  margin-top: 3px;\n}\n.tooltip.left {\n  padding: 0 5px;\n  margin-left: -3px;\n}\n.tooltip.top .tooltip-arrow {\n  bottom: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n  right: 5px;\n  bottom: 0;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n  bottom: 0;\n  left: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n  top: 50%;\n  left: 0;\n  margin-top: -5px;\n  border-width: 5px 5px 5px 0;\n  border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n  top: 50%;\n  right: 0;\n  margin-top: -5px;\n  border-width: 5px 0 5px 5px;\n  border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n  top: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n  top: 0;\n  right: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n  top: 0;\n  left: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip-inner {\n  max-width: 200px;\n  padding: 3px 8px;\n  color: #fff;\n  text-align: center;\n  background-color: #000;\n  border-radius: 4px;\n}\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 1060;\n  display: none;\n  max-width: 276px;\n  padding: 1px;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.42857143;\n  line-break: auto;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  font-size: 14px;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n  margin-top: -10px;\n}\n.popover.right {\n  margin-left: 10px;\n}\n.popover.bottom {\n  margin-top: 10px;\n}\n.popover.left {\n  margin-left: -10px;\n}\n.popover > .arrow {\n  border-width: 11px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n  position: absolute;\n  display: block;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover > .arrow:after {\n  content: \"\";\n  border-width: 10px;\n}\n.popover.top > .arrow {\n  bottom: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-color: #999999;\n  border-top-color: rgba(0, 0, 0, 0.25);\n  border-bottom-width: 0;\n}\n.popover.top > .arrow:after {\n  bottom: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-color: #fff;\n  border-bottom-width: 0;\n}\n.popover.right > .arrow {\n  top: 50%;\n  left: -11px;\n  margin-top: -11px;\n  border-right-color: #999999;\n  border-right-color: rgba(0, 0, 0, 0.25);\n  border-left-width: 0;\n}\n.popover.right > .arrow:after {\n  bottom: -10px;\n  left: 1px;\n  content: \" \";\n  border-right-color: #fff;\n  border-left-width: 0;\n}\n.popover.bottom > .arrow {\n  top: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-width: 0;\n  border-bottom-color: #999999;\n  border-bottom-color: rgba(0, 0, 0, 0.25);\n}\n.popover.bottom > .arrow:after {\n  top: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-width: 0;\n  border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n  top: 50%;\n  right: -11px;\n  margin-top: -11px;\n  border-right-width: 0;\n  border-left-color: #999999;\n  border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n  right: 1px;\n  bottom: -10px;\n  content: \" \";\n  border-right-width: 0;\n  border-left-color: #fff;\n}\n.popover-title {\n  padding: 8px 14px;\n  margin: 0;\n  font-size: 14px;\n  background-color: #f7f7f7;\n  border-bottom: 1px solid #ebebeb;\n  border-radius: 5px 5px 0 0;\n}\n.popover-content {\n  padding: 9px 14px;\n}\n.carousel {\n  position: relative;\n}\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n}\n.carousel-inner > .item {\n  position: relative;\n  display: none;\n  -webkit-transition: 0.6s ease-in-out left;\n  -o-transition: 0.6s ease-in-out left;\n  transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n  .carousel-inner > .item {\n    -webkit-transition: -webkit-transform 0.6s ease-in-out;\n    -moz-transition: -moz-transform 0.6s ease-in-out;\n    -o-transition: -o-transform 0.6s ease-in-out;\n    transition: transform 0.6s ease-in-out;\n    -webkit-backface-visibility: hidden;\n    -moz-backface-visibility: hidden;\n    backface-visibility: hidden;\n    -webkit-perspective: 1000px;\n    -moz-perspective: 1000px;\n    perspective: 1000px;\n  }\n  .carousel-inner > .item.next,\n  .carousel-inner > .item.active.right {\n    -webkit-transform: translate3d(100%, 0, 0);\n    transform: translate3d(100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.prev,\n  .carousel-inner > .item.active.left {\n    -webkit-transform: translate3d(-100%, 0, 0);\n    transform: translate3d(-100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.next.left,\n  .carousel-inner > .item.prev.right,\n  .carousel-inner > .item.active {\n    -webkit-transform: translate3d(0, 0, 0);\n    transform: translate3d(0, 0, 0);\n    left: 0;\n  }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  display: block;\n}\n.carousel-inner > .active {\n  left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  position: absolute;\n  top: 0;\n  width: 100%;\n}\n.carousel-inner > .next {\n  left: 100%;\n}\n.carousel-inner > .prev {\n  left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n  left: 0;\n}\n.carousel-inner > .active.left {\n  left: -100%;\n}\n.carousel-inner > .active.right {\n  left: 100%;\n}\n.carousel-control {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 15%;\n  font-size: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n  background-color: rgba(0, 0, 0, 0);\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\n.carousel-control.left {\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control.right {\n  right: 0;\n  left: auto;\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control:hover,\n.carousel-control:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  filter: alpha(opacity=90);\n  opacity: 0.9;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n  position: absolute;\n  top: 50%;\n  z-index: 5;\n  display: inline-block;\n  margin-top: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n  left: 50%;\n  margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n  right: 50%;\n  margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n  width: 20px;\n  height: 20px;\n  font-family: serif;\n  line-height: 1;\n}\n.carousel-control .icon-prev:before {\n  content: \"\\2039\";\n}\n.carousel-control .icon-next:before {\n  content: \"\\203a\";\n}\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  padding-left: 0;\n  margin-left: -30%;\n  text-align: center;\n  list-style: none;\n}\n.carousel-indicators li {\n  display: inline-block;\n  width: 10px;\n  height: 10px;\n  margin: 1px;\n  text-indent: -999px;\n  cursor: pointer;\n  background-color: #000 \\9;\n  background-color: rgba(0, 0, 0, 0);\n  border: 1px solid #fff;\n  border-radius: 10px;\n}\n.carousel-indicators .active {\n  width: 12px;\n  height: 12px;\n  margin: 0;\n  background-color: #fff;\n}\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 20px;\n  left: 15%;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n  text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-prev,\n  .carousel-control .icon-next {\n    width: 30px;\n    height: 30px;\n    margin-top: -10px;\n    font-size: 30px;\n  }\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .icon-prev {\n    margin-left: -10px;\n  }\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-next {\n    margin-right: -10px;\n  }\n  .carousel-caption {\n    right: 20%;\n    left: 20%;\n    padding-bottom: 30px;\n  }\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n  display: table;\n  content: \" \";\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n  clear: both;\n}\n.center-block {\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  font: 0/0 a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n.hidden {\n  display: none !important;\n}\n.affix {\n  position: fixed;\n}\n@-ms-viewport {\n  width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n@media (max-width: 767px) {\n  .visible-xs {\n    display: block !important;\n  }\n  table.visible-xs {\n    display: table !important;\n  }\n  tr.visible-xs {\n    display: table-row !important;\n  }\n  th.visible-xs,\n  td.visible-xs {\n    display: table-cell !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-block {\n    display: block !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline {\n    display: inline !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm {\n    display: block !important;\n  }\n  table.visible-sm {\n    display: table !important;\n  }\n  tr.visible-sm {\n    display: table-row !important;\n  }\n  th.visible-sm,\n  td.visible-sm {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-block {\n    display: block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md {\n    display: block !important;\n  }\n  table.visible-md {\n    display: table !important;\n  }\n  tr.visible-md {\n    display: table-row !important;\n  }\n  th.visible-md,\n  td.visible-md {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-block {\n    display: block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg {\n    display: block !important;\n  }\n  table.visible-lg {\n    display: table !important;\n  }\n  tr.visible-lg {\n    display: table-row !important;\n  }\n  th.visible-lg,\n  td.visible-lg {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-block {\n    display: block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (max-width: 767px) {\n  .hidden-xs {\n    display: none !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .hidden-sm {\n    display: none !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .hidden-md {\n    display: none !important;\n  }\n}\n@media (min-width: 1200px) {\n  .hidden-lg {\n    display: none !important;\n  }\n}\n.visible-print {\n  display: none !important;\n}\n@media print {\n  .visible-print {\n    display: block !important;\n  }\n  table.visible-print {\n    display: table !important;\n  }\n  tr.visible-print {\n    display: table-row !important;\n  }\n  th.visible-print,\n  td.visible-print {\n    display: table-cell !important;\n  }\n}\n.visible-print-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-block {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline-block {\n    display: inline-block !important;\n  }\n}\n@media print {\n  .hidden-print {\n    display: none !important;\n  }\n}\n/*# sourceMappingURL=bootstrap.css.map */","// stylelint-disable\n\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\n\n//\n// 1. Set default font family to sans-serif.\n// 2. Prevent iOS and IE text size adjust after device orientation change,\n//    without disabling user zoom.\n//\n\nhtml {\n  font-family: sans-serif; // 1\n  -ms-text-size-adjust: 100%; // 2\n  -webkit-text-size-adjust: 100%; // 2\n}\n\n//\n// Remove default margin.\n//\n\nbody {\n  margin: 0;\n}\n\n// HTML5 display definitions\n// ==========================================================================\n\n//\n// Correct `block` display not defined for any HTML5 element in IE 8/9.\n// Correct `block` display not defined for `details` or `summary` in IE 10/11\n// and Firefox.\n// Correct `block` display not defined for `main` in IE 11.\n//\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\n\n//\n// 1. Correct `inline-block` display not defined in IE 8/9.\n// 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n//\n\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block; // 1\n  vertical-align: baseline; // 2\n}\n\n//\n// Prevent modern browsers from displaying `audio` without controls.\n// Remove excess height in iOS 5 devices.\n//\n\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n\n//\n// Address `[hidden]` styling not present in IE 8/9/10.\n// Hide the `template` element in IE 8/9/10/11, Safari, and Firefox < 22.\n//\n\n[hidden],\ntemplate {\n  display: none;\n}\n\n// Links\n// ==========================================================================\n\n//\n// Remove the gray background color from active links in IE 10.\n//\n\na {\n  background-color: transparent;\n}\n\n//\n// Improve readability of focused elements when they are also in an\n// active/hover state.\n//\n\na:active,\na:hover {\n  outline: 0;\n}\n\n// Text-level semantics\n// ==========================================================================\n\n//\n// 1. Remove the bottom border in Chrome 57- and Firefox 39-.\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n//\n\nabbr[title] {\n  border-bottom: none; // 1\n  text-decoration: underline; // 2\n  text-decoration: underline dotted; // 2\n}\n\n//\n// Address style set to `bolder` in Firefox 4+, Safari, and Chrome.\n//\n\nb,\nstrong {\n  font-weight: bold;\n}\n\n//\n// Address styling not present in Safari and Chrome.\n//\n\ndfn {\n  font-style: italic;\n}\n\n//\n// Address variable `h1` font-size and margin within `section` and `article`\n// contexts in Firefox 4+, Safari, and Chrome.\n//\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n//\n// Address styling not present in IE 8/9.\n//\n\nmark {\n  background: #ff0;\n  color: #000;\n}\n\n//\n// Address inconsistent and variable font size in all browsers.\n//\n\nsmall {\n  font-size: 80%;\n}\n\n//\n// Prevent `sub` and `sup` affecting `line-height` in all browsers.\n//\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsup {\n  top: -0.5em;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\n// Embedded content\n// ==========================================================================\n\n//\n// Remove border when inside `a` element in IE 8/9/10.\n//\n\nimg {\n  border: 0;\n}\n\n//\n// Correct overflow not hidden in IE 9/10/11.\n//\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n// Grouping content\n// ==========================================================================\n\n//\n// Address margin not present in IE 8/9 and Safari.\n//\n\nfigure {\n  margin: 1em 40px;\n}\n\n//\n// Address differences between Firefox and other browsers.\n//\n\nhr {\n  box-sizing: content-box;\n  height: 0;\n}\n\n//\n// Contain overflow in all browsers.\n//\n\npre {\n  overflow: auto;\n}\n\n//\n// Address odd `em`-unit font size rendering in all browsers.\n//\n\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\n\n// Forms\n// ==========================================================================\n\n//\n// Known limitation: by default, Chrome and Safari on OS X allow very limited\n// styling of `select`, unless a `border` property is set.\n//\n\n//\n// 1. Correct color not being inherited.\n//    Known issue: affects color of disabled elements.\n// 2. Correct font properties not being inherited.\n// 3. Address margins set differently in Firefox 4+, Safari, and Chrome.\n//\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit; // 1\n  font: inherit; // 2\n  margin: 0; // 3\n}\n\n//\n// Address `overflow` set to `hidden` in IE 8/9/10/11.\n//\n\nbutton {\n  overflow: visible;\n}\n\n//\n// Address inconsistent `text-transform` inheritance for `button` and `select`.\n// All other form control elements do not inherit `text-transform` values.\n// Correct `button` style inheritance in Firefox, IE 8/9/10/11, and Opera.\n// Correct `select` style inheritance in Firefox.\n//\n\nbutton,\nselect {\n  text-transform: none;\n}\n\n//\n// 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n//    and `video` controls.\n// 2. Correct inability to style clickable `input` types in iOS.\n// 3. Improve usability and consistency of cursor style between image-type\n//    `input` and others.\n//\n\nbutton,\nhtml input[type=\"button\"], // 1\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n  cursor: pointer; // 3\n}\n\n//\n// Re-set default cursor for disabled elements.\n//\n\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\n\n//\n// Remove inner padding and border in Firefox 4+.\n//\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\n\n//\n// Address Firefox 4+ setting `line-height` on `input` using `!important` in\n// the UA stylesheet.\n//\n\ninput {\n  line-height: normal;\n}\n\n//\n// It's recommended that you don't attempt to style these elements.\n// Firefox's implementation doesn't respect box-sizing, padding, or width.\n//\n// 1. Address box sizing set to `content-box` in IE 8/9/10.\n// 2. Remove excess padding in IE 8/9/10.\n//\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  box-sizing: border-box; // 1\n  padding: 0; // 2\n}\n\n//\n// Fix the cursor style for Chrome's increment/decrement buttons. For certain\n// `font-size` values of the `input`, it causes the cursor style of the\n// decrement button to change from `default` to `text`.\n//\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n//\n// 1. Address `appearance` set to `searchfield` in Safari and Chrome.\n// 2. Address `box-sizing` set to `border-box` in Safari and Chrome.\n//\n\ninput[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  box-sizing: content-box; //2\n}\n\n//\n// Remove inner padding and search cancel button in Safari and Chrome on OS X.\n// Safari (but not Chrome) clips the cancel button when the search input has\n// padding (and `textfield` appearance).\n//\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n//\n// Define consistent border, margin, and padding.\n//\n\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\n\n//\n// 1. Correct `color` not being inherited in IE 8/9/10/11.\n// 2. Remove padding so people aren't caught out if they zero out fieldsets.\n//\n\nlegend {\n  border: 0; // 1\n  padding: 0; // 2\n}\n\n//\n// Remove default vertical scrollbar in IE 8/9/10/11.\n//\n\ntextarea {\n  overflow: auto;\n}\n\n//\n// Don't inherit the `font-weight` (applied by a rule above).\n// NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n//\n\noptgroup {\n  font-weight: bold;\n}\n\n// Tables\n// ==========================================================================\n\n//\n// Remove most spacing between table cells.\n//\n\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\n\ntd,\nth {\n  padding: 0;\n}\n","/*!\n * Bootstrap v3.4.1 (https://getbootstrap.com/)\n * Copyright 2011-2019 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n */\n/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */\nhtml {\n  font-family: sans-serif;\n  -ms-text-size-adjust: 100%;\n  -webkit-text-size-adjust: 100%;\n}\nbody {\n  margin: 0;\n}\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nmenu,\nnav,\nsection,\nsummary {\n  display: block;\n}\naudio,\ncanvas,\nprogress,\nvideo {\n  display: inline-block;\n  vertical-align: baseline;\n}\naudio:not([controls]) {\n  display: none;\n  height: 0;\n}\n[hidden],\ntemplate {\n  display: none;\n}\na {\n  background-color: transparent;\n}\na:active,\na:hover {\n  outline: 0;\n}\nabbr[title] {\n  border-bottom: none;\n  text-decoration: underline;\n  -webkit-text-decoration: underline dotted;\n  -moz-text-decoration: underline dotted;\n  text-decoration: underline dotted;\n}\nb,\nstrong {\n  font-weight: bold;\n}\ndfn {\n  font-style: italic;\n}\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\nmark {\n  background: #ff0;\n  color: #000;\n}\nsmall {\n  font-size: 80%;\n}\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\nsup {\n  top: -0.5em;\n}\nsub {\n  bottom: -0.25em;\n}\nimg {\n  border: 0;\n}\nsvg:not(:root) {\n  overflow: hidden;\n}\nfigure {\n  margin: 1em 40px;\n}\nhr {\n  -webkit-box-sizing: content-box;\n  -moz-box-sizing: content-box;\n  box-sizing: content-box;\n  height: 0;\n}\npre {\n  overflow: auto;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: monospace, monospace;\n  font-size: 1em;\n}\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  color: inherit;\n  font: inherit;\n  margin: 0;\n}\nbutton {\n  overflow: visible;\n}\nbutton,\nselect {\n  text-transform: none;\n}\nbutton,\nhtml input[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n  -webkit-appearance: button;\n  cursor: pointer;\n}\nbutton[disabled],\nhtml input[disabled] {\n  cursor: default;\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n  border: 0;\n  padding: 0;\n}\ninput {\n  line-height: normal;\n}\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n  padding: 0;\n}\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\ninput[type=\"search\"] {\n  -webkit-appearance: textfield;\n  -webkit-box-sizing: content-box;\n  -moz-box-sizing: content-box;\n  box-sizing: content-box;\n}\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\nfieldset {\n  border: 1px solid #c0c0c0;\n  margin: 0 2px;\n  padding: 0.35em 0.625em 0.75em;\n}\nlegend {\n  border: 0;\n  padding: 0;\n}\ntextarea {\n  overflow: auto;\n}\noptgroup {\n  font-weight: bold;\n}\ntable {\n  border-collapse: collapse;\n  border-spacing: 0;\n}\ntd,\nth {\n  padding: 0;\n}\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n@media print {\n  *,\n  *:before,\n  *:after {\n    color: #000 !important;\n    text-shadow: none !important;\n    background: transparent !important;\n    -webkit-box-shadow: none !important;\n    box-shadow: none !important;\n  }\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n  thead {\n    display: table-header-group;\n  }\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n  img {\n    max-width: 100% !important;\n  }\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n  .navbar {\n    display: none;\n  }\n  .btn > .caret,\n  .dropup > .btn > .caret {\n    border-top-color: #000 !important;\n  }\n  .label {\n    border: 1px solid #000;\n  }\n  .table {\n    border-collapse: collapse !important;\n  }\n  .table td,\n  .table th {\n    background-color: #fff !important;\n  }\n  .table-bordered th,\n  .table-bordered td {\n    border: 1px solid #ddd !important;\n  }\n}\n@font-face {\n  font-family: \"Glyphicons Halflings\";\n  src: url(\"../fonts/glyphicons-halflings-regular.eot\");\n  src: url(\"../fonts/glyphicons-halflings-regular.eot?#iefix\") format(\"embedded-opentype\"), url(\"../fonts/glyphicons-halflings-regular.woff2\") format(\"woff2\"), url(\"../fonts/glyphicons-halflings-regular.woff\") format(\"woff\"), url(\"../fonts/glyphicons-halflings-regular.ttf\") format(\"truetype\"), url(\"../fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular\") format(\"svg\");\n}\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: \"Glyphicons Halflings\";\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n.glyphicon-asterisk:before {\n  content: \"\\002a\";\n}\n.glyphicon-plus:before {\n  content: \"\\002b\";\n}\n.glyphicon-euro:before,\n.glyphicon-eur:before {\n  content: \"\\20ac\";\n}\n.glyphicon-minus:before {\n  content: \"\\2212\";\n}\n.glyphicon-cloud:before {\n  content: \"\\2601\";\n}\n.glyphicon-envelope:before {\n  content: \"\\2709\";\n}\n.glyphicon-pencil:before {\n  content: \"\\270f\";\n}\n.glyphicon-glass:before {\n  content: \"\\e001\";\n}\n.glyphicon-music:before {\n  content: \"\\e002\";\n}\n.glyphicon-search:before {\n  content: \"\\e003\";\n}\n.glyphicon-heart:before {\n  content: \"\\e005\";\n}\n.glyphicon-star:before {\n  content: \"\\e006\";\n}\n.glyphicon-star-empty:before {\n  content: \"\\e007\";\n}\n.glyphicon-user:before {\n  content: \"\\e008\";\n}\n.glyphicon-film:before {\n  content: \"\\e009\";\n}\n.glyphicon-th-large:before {\n  content: \"\\e010\";\n}\n.glyphicon-th:before {\n  content: \"\\e011\";\n}\n.glyphicon-th-list:before {\n  content: \"\\e012\";\n}\n.glyphicon-ok:before {\n  content: \"\\e013\";\n}\n.glyphicon-remove:before {\n  content: \"\\e014\";\n}\n.glyphicon-zoom-in:before {\n  content: \"\\e015\";\n}\n.glyphicon-zoom-out:before {\n  content: \"\\e016\";\n}\n.glyphicon-off:before {\n  content: \"\\e017\";\n}\n.glyphicon-signal:before {\n  content: \"\\e018\";\n}\n.glyphicon-cog:before {\n  content: \"\\e019\";\n}\n.glyphicon-trash:before {\n  content: \"\\e020\";\n}\n.glyphicon-home:before {\n  content: \"\\e021\";\n}\n.glyphicon-file:before {\n  content: \"\\e022\";\n}\n.glyphicon-time:before {\n  content: \"\\e023\";\n}\n.glyphicon-road:before {\n  content: \"\\e024\";\n}\n.glyphicon-download-alt:before {\n  content: \"\\e025\";\n}\n.glyphicon-download:before {\n  content: \"\\e026\";\n}\n.glyphicon-upload:before {\n  content: \"\\e027\";\n}\n.glyphicon-inbox:before {\n  content: \"\\e028\";\n}\n.glyphicon-play-circle:before {\n  content: \"\\e029\";\n}\n.glyphicon-repeat:before {\n  content: \"\\e030\";\n}\n.glyphicon-refresh:before {\n  content: \"\\e031\";\n}\n.glyphicon-list-alt:before {\n  content: \"\\e032\";\n}\n.glyphicon-lock:before {\n  content: \"\\e033\";\n}\n.glyphicon-flag:before {\n  content: \"\\e034\";\n}\n.glyphicon-headphones:before {\n  content: \"\\e035\";\n}\n.glyphicon-volume-off:before {\n  content: \"\\e036\";\n}\n.glyphicon-volume-down:before {\n  content: \"\\e037\";\n}\n.glyphicon-volume-up:before {\n  content: \"\\e038\";\n}\n.glyphicon-qrcode:before {\n  content: \"\\e039\";\n}\n.glyphicon-barcode:before {\n  content: \"\\e040\";\n}\n.glyphicon-tag:before {\n  content: \"\\e041\";\n}\n.glyphicon-tags:before {\n  content: \"\\e042\";\n}\n.glyphicon-book:before {\n  content: \"\\e043\";\n}\n.glyphicon-bookmark:before {\n  content: \"\\e044\";\n}\n.glyphicon-print:before {\n  content: \"\\e045\";\n}\n.glyphicon-camera:before {\n  content: \"\\e046\";\n}\n.glyphicon-font:before {\n  content: \"\\e047\";\n}\n.glyphicon-bold:before {\n  content: \"\\e048\";\n}\n.glyphicon-italic:before {\n  content: \"\\e049\";\n}\n.glyphicon-text-height:before {\n  content: \"\\e050\";\n}\n.glyphicon-text-width:before {\n  content: \"\\e051\";\n}\n.glyphicon-align-left:before {\n  content: \"\\e052\";\n}\n.glyphicon-align-center:before {\n  content: \"\\e053\";\n}\n.glyphicon-align-right:before {\n  content: \"\\e054\";\n}\n.glyphicon-align-justify:before {\n  content: \"\\e055\";\n}\n.glyphicon-list:before {\n  content: \"\\e056\";\n}\n.glyphicon-indent-left:before {\n  content: \"\\e057\";\n}\n.glyphicon-indent-right:before {\n  content: \"\\e058\";\n}\n.glyphicon-facetime-video:before {\n  content: \"\\e059\";\n}\n.glyphicon-picture:before {\n  content: \"\\e060\";\n}\n.glyphicon-map-marker:before {\n  content: \"\\e062\";\n}\n.glyphicon-adjust:before {\n  content: \"\\e063\";\n}\n.glyphicon-tint:before {\n  content: \"\\e064\";\n}\n.glyphicon-edit:before {\n  content: \"\\e065\";\n}\n.glyphicon-share:before {\n  content: \"\\e066\";\n}\n.glyphicon-check:before {\n  content: \"\\e067\";\n}\n.glyphicon-move:before {\n  content: \"\\e068\";\n}\n.glyphicon-step-backward:before {\n  content: \"\\e069\";\n}\n.glyphicon-fast-backward:before {\n  content: \"\\e070\";\n}\n.glyphicon-backward:before {\n  content: \"\\e071\";\n}\n.glyphicon-play:before {\n  content: \"\\e072\";\n}\n.glyphicon-pause:before {\n  content: \"\\e073\";\n}\n.glyphicon-stop:before {\n  content: \"\\e074\";\n}\n.glyphicon-forward:before {\n  content: \"\\e075\";\n}\n.glyphicon-fast-forward:before {\n  content: \"\\e076\";\n}\n.glyphicon-step-forward:before {\n  content: \"\\e077\";\n}\n.glyphicon-eject:before {\n  content: \"\\e078\";\n}\n.glyphicon-chevron-left:before {\n  content: \"\\e079\";\n}\n.glyphicon-chevron-right:before {\n  content: \"\\e080\";\n}\n.glyphicon-plus-sign:before {\n  content: \"\\e081\";\n}\n.glyphicon-minus-sign:before {\n  content: \"\\e082\";\n}\n.glyphicon-remove-sign:before {\n  content: \"\\e083\";\n}\n.glyphicon-ok-sign:before {\n  content: \"\\e084\";\n}\n.glyphicon-question-sign:before {\n  content: \"\\e085\";\n}\n.glyphicon-info-sign:before {\n  content: \"\\e086\";\n}\n.glyphicon-screenshot:before {\n  content: \"\\e087\";\n}\n.glyphicon-remove-circle:before {\n  content: \"\\e088\";\n}\n.glyphicon-ok-circle:before {\n  content: \"\\e089\";\n}\n.glyphicon-ban-circle:before {\n  content: \"\\e090\";\n}\n.glyphicon-arrow-left:before {\n  content: \"\\e091\";\n}\n.glyphicon-arrow-right:before {\n  content: \"\\e092\";\n}\n.glyphicon-arrow-up:before {\n  content: \"\\e093\";\n}\n.glyphicon-arrow-down:before {\n  content: \"\\e094\";\n}\n.glyphicon-share-alt:before {\n  content: \"\\e095\";\n}\n.glyphicon-resize-full:before {\n  content: \"\\e096\";\n}\n.glyphicon-resize-small:before {\n  content: \"\\e097\";\n}\n.glyphicon-exclamation-sign:before {\n  content: \"\\e101\";\n}\n.glyphicon-gift:before {\n  content: \"\\e102\";\n}\n.glyphicon-leaf:before {\n  content: \"\\e103\";\n}\n.glyphicon-fire:before {\n  content: \"\\e104\";\n}\n.glyphicon-eye-open:before {\n  content: \"\\e105\";\n}\n.glyphicon-eye-close:before {\n  content: \"\\e106\";\n}\n.glyphicon-warning-sign:before {\n  content: \"\\e107\";\n}\n.glyphicon-plane:before {\n  content: \"\\e108\";\n}\n.glyphicon-calendar:before {\n  content: \"\\e109\";\n}\n.glyphicon-random:before {\n  content: \"\\e110\";\n}\n.glyphicon-comment:before {\n  content: \"\\e111\";\n}\n.glyphicon-magnet:before {\n  content: \"\\e112\";\n}\n.glyphicon-chevron-up:before {\n  content: \"\\e113\";\n}\n.glyphicon-chevron-down:before {\n  content: \"\\e114\";\n}\n.glyphicon-retweet:before {\n  content: \"\\e115\";\n}\n.glyphicon-shopping-cart:before {\n  content: \"\\e116\";\n}\n.glyphicon-folder-close:before {\n  content: \"\\e117\";\n}\n.glyphicon-folder-open:before {\n  content: \"\\e118\";\n}\n.glyphicon-resize-vertical:before {\n  content: \"\\e119\";\n}\n.glyphicon-resize-horizontal:before {\n  content: \"\\e120\";\n}\n.glyphicon-hdd:before {\n  content: \"\\e121\";\n}\n.glyphicon-bullhorn:before {\n  content: \"\\e122\";\n}\n.glyphicon-bell:before {\n  content: \"\\e123\";\n}\n.glyphicon-certificate:before {\n  content: \"\\e124\";\n}\n.glyphicon-thumbs-up:before {\n  content: \"\\e125\";\n}\n.glyphicon-thumbs-down:before {\n  content: \"\\e126\";\n}\n.glyphicon-hand-right:before {\n  content: \"\\e127\";\n}\n.glyphicon-hand-left:before {\n  content: \"\\e128\";\n}\n.glyphicon-hand-up:before {\n  content: \"\\e129\";\n}\n.glyphicon-hand-down:before {\n  content: \"\\e130\";\n}\n.glyphicon-circle-arrow-right:before {\n  content: \"\\e131\";\n}\n.glyphicon-circle-arrow-left:before {\n  content: \"\\e132\";\n}\n.glyphicon-circle-arrow-up:before {\n  content: \"\\e133\";\n}\n.glyphicon-circle-arrow-down:before {\n  content: \"\\e134\";\n}\n.glyphicon-globe:before {\n  content: \"\\e135\";\n}\n.glyphicon-wrench:before {\n  content: \"\\e136\";\n}\n.glyphicon-tasks:before {\n  content: \"\\e137\";\n}\n.glyphicon-filter:before {\n  content: \"\\e138\";\n}\n.glyphicon-briefcase:before {\n  content: \"\\e139\";\n}\n.glyphicon-fullscreen:before {\n  content: \"\\e140\";\n}\n.glyphicon-dashboard:before {\n  content: \"\\e141\";\n}\n.glyphicon-paperclip:before {\n  content: \"\\e142\";\n}\n.glyphicon-heart-empty:before {\n  content: \"\\e143\";\n}\n.glyphicon-link:before {\n  content: \"\\e144\";\n}\n.glyphicon-phone:before {\n  content: \"\\e145\";\n}\n.glyphicon-pushpin:before {\n  content: \"\\e146\";\n}\n.glyphicon-usd:before {\n  content: \"\\e148\";\n}\n.glyphicon-gbp:before {\n  content: \"\\e149\";\n}\n.glyphicon-sort:before {\n  content: \"\\e150\";\n}\n.glyphicon-sort-by-alphabet:before {\n  content: \"\\e151\";\n}\n.glyphicon-sort-by-alphabet-alt:before {\n  content: \"\\e152\";\n}\n.glyphicon-sort-by-order:before {\n  content: \"\\e153\";\n}\n.glyphicon-sort-by-order-alt:before {\n  content: \"\\e154\";\n}\n.glyphicon-sort-by-attributes:before {\n  content: \"\\e155\";\n}\n.glyphicon-sort-by-attributes-alt:before {\n  content: \"\\e156\";\n}\n.glyphicon-unchecked:before {\n  content: \"\\e157\";\n}\n.glyphicon-expand:before {\n  content: \"\\e158\";\n}\n.glyphicon-collapse-down:before {\n  content: \"\\e159\";\n}\n.glyphicon-collapse-up:before {\n  content: \"\\e160\";\n}\n.glyphicon-log-in:before {\n  content: \"\\e161\";\n}\n.glyphicon-flash:before {\n  content: \"\\e162\";\n}\n.glyphicon-log-out:before {\n  content: \"\\e163\";\n}\n.glyphicon-new-window:before {\n  content: \"\\e164\";\n}\n.glyphicon-record:before {\n  content: \"\\e165\";\n}\n.glyphicon-save:before {\n  content: \"\\e166\";\n}\n.glyphicon-open:before {\n  content: \"\\e167\";\n}\n.glyphicon-saved:before {\n  content: \"\\e168\";\n}\n.glyphicon-import:before {\n  content: \"\\e169\";\n}\n.glyphicon-export:before {\n  content: \"\\e170\";\n}\n.glyphicon-send:before {\n  content: \"\\e171\";\n}\n.glyphicon-floppy-disk:before {\n  content: \"\\e172\";\n}\n.glyphicon-floppy-saved:before {\n  content: \"\\e173\";\n}\n.glyphicon-floppy-remove:before {\n  content: \"\\e174\";\n}\n.glyphicon-floppy-save:before {\n  content: \"\\e175\";\n}\n.glyphicon-floppy-open:before {\n  content: \"\\e176\";\n}\n.glyphicon-credit-card:before {\n  content: \"\\e177\";\n}\n.glyphicon-transfer:before {\n  content: \"\\e178\";\n}\n.glyphicon-cutlery:before {\n  content: \"\\e179\";\n}\n.glyphicon-header:before {\n  content: \"\\e180\";\n}\n.glyphicon-compressed:before {\n  content: \"\\e181\";\n}\n.glyphicon-earphone:before {\n  content: \"\\e182\";\n}\n.glyphicon-phone-alt:before {\n  content: \"\\e183\";\n}\n.glyphicon-tower:before {\n  content: \"\\e184\";\n}\n.glyphicon-stats:before {\n  content: \"\\e185\";\n}\n.glyphicon-sd-video:before {\n  content: \"\\e186\";\n}\n.glyphicon-hd-video:before {\n  content: \"\\e187\";\n}\n.glyphicon-subtitles:before {\n  content: \"\\e188\";\n}\n.glyphicon-sound-stereo:before {\n  content: \"\\e189\";\n}\n.glyphicon-sound-dolby:before {\n  content: \"\\e190\";\n}\n.glyphicon-sound-5-1:before {\n  content: \"\\e191\";\n}\n.glyphicon-sound-6-1:before {\n  content: \"\\e192\";\n}\n.glyphicon-sound-7-1:before {\n  content: \"\\e193\";\n}\n.glyphicon-copyright-mark:before {\n  content: \"\\e194\";\n}\n.glyphicon-registration-mark:before {\n  content: \"\\e195\";\n}\n.glyphicon-cloud-download:before {\n  content: \"\\e197\";\n}\n.glyphicon-cloud-upload:before {\n  content: \"\\e198\";\n}\n.glyphicon-tree-conifer:before {\n  content: \"\\e199\";\n}\n.glyphicon-tree-deciduous:before {\n  content: \"\\e200\";\n}\n.glyphicon-cd:before {\n  content: \"\\e201\";\n}\n.glyphicon-save-file:before {\n  content: \"\\e202\";\n}\n.glyphicon-open-file:before {\n  content: \"\\e203\";\n}\n.glyphicon-level-up:before {\n  content: \"\\e204\";\n}\n.glyphicon-copy:before {\n  content: \"\\e205\";\n}\n.glyphicon-paste:before {\n  content: \"\\e206\";\n}\n.glyphicon-alert:before {\n  content: \"\\e209\";\n}\n.glyphicon-equalizer:before {\n  content: \"\\e210\";\n}\n.glyphicon-king:before {\n  content: \"\\e211\";\n}\n.glyphicon-queen:before {\n  content: \"\\e212\";\n}\n.glyphicon-pawn:before {\n  content: \"\\e213\";\n}\n.glyphicon-bishop:before {\n  content: \"\\e214\";\n}\n.glyphicon-knight:before {\n  content: \"\\e215\";\n}\n.glyphicon-baby-formula:before {\n  content: \"\\e216\";\n}\n.glyphicon-tent:before {\n  content: \"\\26fa\";\n}\n.glyphicon-blackboard:before {\n  content: \"\\e218\";\n}\n.glyphicon-bed:before {\n  content: \"\\e219\";\n}\n.glyphicon-apple:before {\n  content: \"\\f8ff\";\n}\n.glyphicon-erase:before {\n  content: \"\\e221\";\n}\n.glyphicon-hourglass:before {\n  content: \"\\231b\";\n}\n.glyphicon-lamp:before {\n  content: \"\\e223\";\n}\n.glyphicon-duplicate:before {\n  content: \"\\e224\";\n}\n.glyphicon-piggy-bank:before {\n  content: \"\\e225\";\n}\n.glyphicon-scissors:before {\n  content: \"\\e226\";\n}\n.glyphicon-bitcoin:before {\n  content: \"\\e227\";\n}\n.glyphicon-btc:before {\n  content: \"\\e227\";\n}\n.glyphicon-xbt:before {\n  content: \"\\e227\";\n}\n.glyphicon-yen:before {\n  content: \"\\00a5\";\n}\n.glyphicon-jpy:before {\n  content: \"\\00a5\";\n}\n.glyphicon-ruble:before {\n  content: \"\\20bd\";\n}\n.glyphicon-rub:before {\n  content: \"\\20bd\";\n}\n.glyphicon-scale:before {\n  content: \"\\e230\";\n}\n.glyphicon-ice-lolly:before {\n  content: \"\\e231\";\n}\n.glyphicon-ice-lolly-tasted:before {\n  content: \"\\e232\";\n}\n.glyphicon-education:before {\n  content: \"\\e233\";\n}\n.glyphicon-option-horizontal:before {\n  content: \"\\e234\";\n}\n.glyphicon-option-vertical:before {\n  content: \"\\e235\";\n}\n.glyphicon-menu-hamburger:before {\n  content: \"\\e236\";\n}\n.glyphicon-modal-window:before {\n  content: \"\\e237\";\n}\n.glyphicon-oil:before {\n  content: \"\\e238\";\n}\n.glyphicon-grain:before {\n  content: \"\\e239\";\n}\n.glyphicon-sunglasses:before {\n  content: \"\\e240\";\n}\n.glyphicon-text-size:before {\n  content: \"\\e241\";\n}\n.glyphicon-text-color:before {\n  content: \"\\e242\";\n}\n.glyphicon-text-background:before {\n  content: \"\\e243\";\n}\n.glyphicon-object-align-top:before {\n  content: \"\\e244\";\n}\n.glyphicon-object-align-bottom:before {\n  content: \"\\e245\";\n}\n.glyphicon-object-align-horizontal:before {\n  content: \"\\e246\";\n}\n.glyphicon-object-align-left:before {\n  content: \"\\e247\";\n}\n.glyphicon-object-align-vertical:before {\n  content: \"\\e248\";\n}\n.glyphicon-object-align-right:before {\n  content: \"\\e249\";\n}\n.glyphicon-triangle-right:before {\n  content: \"\\e250\";\n}\n.glyphicon-triangle-left:before {\n  content: \"\\e251\";\n}\n.glyphicon-triangle-bottom:before {\n  content: \"\\e252\";\n}\n.glyphicon-triangle-top:before {\n  content: \"\\e253\";\n}\n.glyphicon-console:before {\n  content: \"\\e254\";\n}\n.glyphicon-superscript:before {\n  content: \"\\e255\";\n}\n.glyphicon-subscript:before {\n  content: \"\\e256\";\n}\n.glyphicon-menu-left:before {\n  content: \"\\e257\";\n}\n.glyphicon-menu-right:before {\n  content: \"\\e258\";\n}\n.glyphicon-menu-down:before {\n  content: \"\\e259\";\n}\n.glyphicon-menu-up:before {\n  content: \"\\e260\";\n}\n* {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\n*:before,\n*:after {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\nbody {\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #333333;\n  background-color: #fff;\n}\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\na {\n  color: #337ab7;\n  text-decoration: none;\n}\na:hover,\na:focus {\n  color: #23527c;\n  text-decoration: underline;\n}\na:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\nfigure {\n  margin: 0;\n}\nimg {\n  vertical-align: middle;\n}\n.img-responsive,\n.thumbnail > img,\n.thumbnail a > img,\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  display: block;\n  max-width: 100%;\n  height: auto;\n}\n.img-rounded {\n  border-radius: 6px;\n}\n.img-thumbnail {\n  padding: 4px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: all 0.2s ease-in-out;\n  -o-transition: all 0.2s ease-in-out;\n  transition: all 0.2s ease-in-out;\n  display: inline-block;\n  max-width: 100%;\n  height: auto;\n}\n.img-circle {\n  border-radius: 50%;\n}\nhr {\n  margin-top: 20px;\n  margin-bottom: 20px;\n  border: 0;\n  border-top: 1px solid #eeeeee;\n}\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n.sr-only-focusable:active,\n.sr-only-focusable:focus {\n  position: static;\n  width: auto;\n  height: auto;\n  margin: 0;\n  overflow: visible;\n  clip: auto;\n}\n[role=\"button\"] {\n  cursor: pointer;\n}\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\n.h1,\n.h2,\n.h3,\n.h4,\n.h5,\n.h6 {\n  font-family: inherit;\n  font-weight: 500;\n  line-height: 1.1;\n  color: inherit;\n}\nh1 small,\nh2 small,\nh3 small,\nh4 small,\nh5 small,\nh6 small,\n.h1 small,\n.h2 small,\n.h3 small,\n.h4 small,\n.h5 small,\n.h6 small,\nh1 .small,\nh2 .small,\nh3 .small,\nh4 .small,\nh5 .small,\nh6 .small,\n.h1 .small,\n.h2 .small,\n.h3 .small,\n.h4 .small,\n.h5 .small,\n.h6 .small {\n  font-weight: 400;\n  line-height: 1;\n  color: #777777;\n}\nh1,\n.h1,\nh2,\n.h2,\nh3,\n.h3 {\n  margin-top: 20px;\n  margin-bottom: 10px;\n}\nh1 small,\n.h1 small,\nh2 small,\n.h2 small,\nh3 small,\n.h3 small,\nh1 .small,\n.h1 .small,\nh2 .small,\n.h2 .small,\nh3 .small,\n.h3 .small {\n  font-size: 65%;\n}\nh4,\n.h4,\nh5,\n.h5,\nh6,\n.h6 {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\nh4 small,\n.h4 small,\nh5 small,\n.h5 small,\nh6 small,\n.h6 small,\nh4 .small,\n.h4 .small,\nh5 .small,\n.h5 .small,\nh6 .small,\n.h6 .small {\n  font-size: 75%;\n}\nh1,\n.h1 {\n  font-size: 36px;\n}\nh2,\n.h2 {\n  font-size: 30px;\n}\nh3,\n.h3 {\n  font-size: 24px;\n}\nh4,\n.h4 {\n  font-size: 18px;\n}\nh5,\n.h5 {\n  font-size: 14px;\n}\nh6,\n.h6 {\n  font-size: 12px;\n}\np {\n  margin: 0 0 10px;\n}\n.lead {\n  margin-bottom: 20px;\n  font-size: 16px;\n  font-weight: 300;\n  line-height: 1.4;\n}\n@media (min-width: 768px) {\n  .lead {\n    font-size: 21px;\n  }\n}\nsmall,\n.small {\n  font-size: 85%;\n}\nmark,\n.mark {\n  padding: 0.2em;\n  background-color: #fcf8e3;\n}\n.text-left {\n  text-align: left;\n}\n.text-right {\n  text-align: right;\n}\n.text-center {\n  text-align: center;\n}\n.text-justify {\n  text-align: justify;\n}\n.text-nowrap {\n  white-space: nowrap;\n}\n.text-lowercase {\n  text-transform: lowercase;\n}\n.text-uppercase {\n  text-transform: uppercase;\n}\n.text-capitalize {\n  text-transform: capitalize;\n}\n.text-muted {\n  color: #777777;\n}\n.text-primary {\n  color: #337ab7;\n}\na.text-primary:hover,\na.text-primary:focus {\n  color: #286090;\n}\n.text-success {\n  color: #3c763d;\n}\na.text-success:hover,\na.text-success:focus {\n  color: #2b542c;\n}\n.text-info {\n  color: #31708f;\n}\na.text-info:hover,\na.text-info:focus {\n  color: #245269;\n}\n.text-warning {\n  color: #8a6d3b;\n}\na.text-warning:hover,\na.text-warning:focus {\n  color: #66512c;\n}\n.text-danger {\n  color: #a94442;\n}\na.text-danger:hover,\na.text-danger:focus {\n  color: #843534;\n}\n.bg-primary {\n  color: #fff;\n  background-color: #337ab7;\n}\na.bg-primary:hover,\na.bg-primary:focus {\n  background-color: #286090;\n}\n.bg-success {\n  background-color: #dff0d8;\n}\na.bg-success:hover,\na.bg-success:focus {\n  background-color: #c1e2b3;\n}\n.bg-info {\n  background-color: #d9edf7;\n}\na.bg-info:hover,\na.bg-info:focus {\n  background-color: #afd9ee;\n}\n.bg-warning {\n  background-color: #fcf8e3;\n}\na.bg-warning:hover,\na.bg-warning:focus {\n  background-color: #f7ecb5;\n}\n.bg-danger {\n  background-color: #f2dede;\n}\na.bg-danger:hover,\na.bg-danger:focus {\n  background-color: #e4b9b9;\n}\n.page-header {\n  padding-bottom: 9px;\n  margin: 40px 0 20px;\n  border-bottom: 1px solid #eeeeee;\n}\nul,\nol {\n  margin-top: 0;\n  margin-bottom: 10px;\n}\nul ul,\nol ul,\nul ol,\nol ol {\n  margin-bottom: 0;\n}\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n.list-inline {\n  padding-left: 0;\n  list-style: none;\n  margin-left: -5px;\n}\n.list-inline > li {\n  display: inline-block;\n  padding-right: 5px;\n  padding-left: 5px;\n}\ndl {\n  margin-top: 0;\n  margin-bottom: 20px;\n}\ndt,\ndd {\n  line-height: 1.42857143;\n}\ndt {\n  font-weight: 700;\n}\ndd {\n  margin-left: 0;\n}\n@media (min-width: 768px) {\n  .dl-horizontal dt {\n    float: left;\n    width: 160px;\n    clear: left;\n    text-align: right;\n    overflow: hidden;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n  }\n  .dl-horizontal dd {\n    margin-left: 180px;\n  }\n}\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n}\n.initialism {\n  font-size: 90%;\n  text-transform: uppercase;\n}\nblockquote {\n  padding: 10px 20px;\n  margin: 0 0 20px;\n  font-size: 17.5px;\n  border-left: 5px solid #eeeeee;\n}\nblockquote p:last-child,\nblockquote ul:last-child,\nblockquote ol:last-child {\n  margin-bottom: 0;\n}\nblockquote footer,\nblockquote small,\nblockquote .small {\n  display: block;\n  font-size: 80%;\n  line-height: 1.42857143;\n  color: #777777;\n}\nblockquote footer:before,\nblockquote small:before,\nblockquote .small:before {\n  content: \"\\2014 \\00A0\";\n}\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  text-align: right;\n  border-right: 5px solid #eeeeee;\n  border-left: 0;\n}\n.blockquote-reverse footer:before,\nblockquote.pull-right footer:before,\n.blockquote-reverse small:before,\nblockquote.pull-right small:before,\n.blockquote-reverse .small:before,\nblockquote.pull-right .small:before {\n  content: \"\";\n}\n.blockquote-reverse footer:after,\nblockquote.pull-right footer:after,\n.blockquote-reverse small:after,\nblockquote.pull-right small:after,\n.blockquote-reverse .small:after,\nblockquote.pull-right .small:after {\n  content: \"\\00A0 \\2014\";\n}\naddress {\n  margin-bottom: 20px;\n  font-style: normal;\n  line-height: 1.42857143;\n}\ncode,\nkbd,\npre,\nsamp {\n  font-family: Menlo, Monaco, Consolas, \"Courier New\", monospace;\n}\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #c7254e;\n  background-color: #f9f2f4;\n  border-radius: 4px;\n}\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: #fff;\n  background-color: #333;\n  border-radius: 3px;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);\n}\nkbd kbd {\n  padding: 0;\n  font-size: 100%;\n  font-weight: 700;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\npre {\n  display: block;\n  padding: 9.5px;\n  margin: 0 0 10px;\n  font-size: 13px;\n  line-height: 1.42857143;\n  color: #333333;\n  word-break: break-all;\n  word-wrap: break-word;\n  background-color: #f5f5f5;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\npre code {\n  padding: 0;\n  font-size: inherit;\n  color: inherit;\n  white-space: pre-wrap;\n  background-color: transparent;\n  border-radius: 0;\n}\n.pre-scrollable {\n  max-height: 340px;\n  overflow-y: scroll;\n}\n.container {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n@media (min-width: 768px) {\n  .container {\n    width: 750px;\n  }\n}\n@media (min-width: 992px) {\n  .container {\n    width: 970px;\n  }\n}\n@media (min-width: 1200px) {\n  .container {\n    width: 1170px;\n  }\n}\n.container-fluid {\n  padding-right: 15px;\n  padding-left: 15px;\n  margin-right: auto;\n  margin-left: auto;\n}\n.row {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n.row-no-gutters {\n  margin-right: 0;\n  margin-left: 0;\n}\n.row-no-gutters [class*=\"col-\"] {\n  padding-right: 0;\n  padding-left: 0;\n}\n.col-xs-1,\n.col-sm-1,\n.col-md-1,\n.col-lg-1,\n.col-xs-2,\n.col-sm-2,\n.col-md-2,\n.col-lg-2,\n.col-xs-3,\n.col-sm-3,\n.col-md-3,\n.col-lg-3,\n.col-xs-4,\n.col-sm-4,\n.col-md-4,\n.col-lg-4,\n.col-xs-5,\n.col-sm-5,\n.col-md-5,\n.col-lg-5,\n.col-xs-6,\n.col-sm-6,\n.col-md-6,\n.col-lg-6,\n.col-xs-7,\n.col-sm-7,\n.col-md-7,\n.col-lg-7,\n.col-xs-8,\n.col-sm-8,\n.col-md-8,\n.col-lg-8,\n.col-xs-9,\n.col-sm-9,\n.col-md-9,\n.col-lg-9,\n.col-xs-10,\n.col-sm-10,\n.col-md-10,\n.col-lg-10,\n.col-xs-11,\n.col-sm-11,\n.col-md-11,\n.col-lg-11,\n.col-xs-12,\n.col-sm-12,\n.col-md-12,\n.col-lg-12 {\n  position: relative;\n  min-height: 1px;\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.col-xs-1,\n.col-xs-2,\n.col-xs-3,\n.col-xs-4,\n.col-xs-5,\n.col-xs-6,\n.col-xs-7,\n.col-xs-8,\n.col-xs-9,\n.col-xs-10,\n.col-xs-11,\n.col-xs-12 {\n  float: left;\n}\n.col-xs-12 {\n  width: 100%;\n}\n.col-xs-11 {\n  width: 91.66666667%;\n}\n.col-xs-10 {\n  width: 83.33333333%;\n}\n.col-xs-9 {\n  width: 75%;\n}\n.col-xs-8 {\n  width: 66.66666667%;\n}\n.col-xs-7 {\n  width: 58.33333333%;\n}\n.col-xs-6 {\n  width: 50%;\n}\n.col-xs-5 {\n  width: 41.66666667%;\n}\n.col-xs-4 {\n  width: 33.33333333%;\n}\n.col-xs-3 {\n  width: 25%;\n}\n.col-xs-2 {\n  width: 16.66666667%;\n}\n.col-xs-1 {\n  width: 8.33333333%;\n}\n.col-xs-pull-12 {\n  right: 100%;\n}\n.col-xs-pull-11 {\n  right: 91.66666667%;\n}\n.col-xs-pull-10 {\n  right: 83.33333333%;\n}\n.col-xs-pull-9 {\n  right: 75%;\n}\n.col-xs-pull-8 {\n  right: 66.66666667%;\n}\n.col-xs-pull-7 {\n  right: 58.33333333%;\n}\n.col-xs-pull-6 {\n  right: 50%;\n}\n.col-xs-pull-5 {\n  right: 41.66666667%;\n}\n.col-xs-pull-4 {\n  right: 33.33333333%;\n}\n.col-xs-pull-3 {\n  right: 25%;\n}\n.col-xs-pull-2 {\n  right: 16.66666667%;\n}\n.col-xs-pull-1 {\n  right: 8.33333333%;\n}\n.col-xs-pull-0 {\n  right: auto;\n}\n.col-xs-push-12 {\n  left: 100%;\n}\n.col-xs-push-11 {\n  left: 91.66666667%;\n}\n.col-xs-push-10 {\n  left: 83.33333333%;\n}\n.col-xs-push-9 {\n  left: 75%;\n}\n.col-xs-push-8 {\n  left: 66.66666667%;\n}\n.col-xs-push-7 {\n  left: 58.33333333%;\n}\n.col-xs-push-6 {\n  left: 50%;\n}\n.col-xs-push-5 {\n  left: 41.66666667%;\n}\n.col-xs-push-4 {\n  left: 33.33333333%;\n}\n.col-xs-push-3 {\n  left: 25%;\n}\n.col-xs-push-2 {\n  left: 16.66666667%;\n}\n.col-xs-push-1 {\n  left: 8.33333333%;\n}\n.col-xs-push-0 {\n  left: auto;\n}\n.col-xs-offset-12 {\n  margin-left: 100%;\n}\n.col-xs-offset-11 {\n  margin-left: 91.66666667%;\n}\n.col-xs-offset-10 {\n  margin-left: 83.33333333%;\n}\n.col-xs-offset-9 {\n  margin-left: 75%;\n}\n.col-xs-offset-8 {\n  margin-left: 66.66666667%;\n}\n.col-xs-offset-7 {\n  margin-left: 58.33333333%;\n}\n.col-xs-offset-6 {\n  margin-left: 50%;\n}\n.col-xs-offset-5 {\n  margin-left: 41.66666667%;\n}\n.col-xs-offset-4 {\n  margin-left: 33.33333333%;\n}\n.col-xs-offset-3 {\n  margin-left: 25%;\n}\n.col-xs-offset-2 {\n  margin-left: 16.66666667%;\n}\n.col-xs-offset-1 {\n  margin-left: 8.33333333%;\n}\n.col-xs-offset-0 {\n  margin-left: 0%;\n}\n@media (min-width: 768px) {\n  .col-sm-1,\n  .col-sm-2,\n  .col-sm-3,\n  .col-sm-4,\n  .col-sm-5,\n  .col-sm-6,\n  .col-sm-7,\n  .col-sm-8,\n  .col-sm-9,\n  .col-sm-10,\n  .col-sm-11,\n  .col-sm-12 {\n    float: left;\n  }\n  .col-sm-12 {\n    width: 100%;\n  }\n  .col-sm-11 {\n    width: 91.66666667%;\n  }\n  .col-sm-10 {\n    width: 83.33333333%;\n  }\n  .col-sm-9 {\n    width: 75%;\n  }\n  .col-sm-8 {\n    width: 66.66666667%;\n  }\n  .col-sm-7 {\n    width: 58.33333333%;\n  }\n  .col-sm-6 {\n    width: 50%;\n  }\n  .col-sm-5 {\n    width: 41.66666667%;\n  }\n  .col-sm-4 {\n    width: 33.33333333%;\n  }\n  .col-sm-3 {\n    width: 25%;\n  }\n  .col-sm-2 {\n    width: 16.66666667%;\n  }\n  .col-sm-1 {\n    width: 8.33333333%;\n  }\n  .col-sm-pull-12 {\n    right: 100%;\n  }\n  .col-sm-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-sm-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-sm-pull-9 {\n    right: 75%;\n  }\n  .col-sm-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-sm-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-sm-pull-6 {\n    right: 50%;\n  }\n  .col-sm-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-sm-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-sm-pull-3 {\n    right: 25%;\n  }\n  .col-sm-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-sm-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-sm-pull-0 {\n    right: auto;\n  }\n  .col-sm-push-12 {\n    left: 100%;\n  }\n  .col-sm-push-11 {\n    left: 91.66666667%;\n  }\n  .col-sm-push-10 {\n    left: 83.33333333%;\n  }\n  .col-sm-push-9 {\n    left: 75%;\n  }\n  .col-sm-push-8 {\n    left: 66.66666667%;\n  }\n  .col-sm-push-7 {\n    left: 58.33333333%;\n  }\n  .col-sm-push-6 {\n    left: 50%;\n  }\n  .col-sm-push-5 {\n    left: 41.66666667%;\n  }\n  .col-sm-push-4 {\n    left: 33.33333333%;\n  }\n  .col-sm-push-3 {\n    left: 25%;\n  }\n  .col-sm-push-2 {\n    left: 16.66666667%;\n  }\n  .col-sm-push-1 {\n    left: 8.33333333%;\n  }\n  .col-sm-push-0 {\n    left: auto;\n  }\n  .col-sm-offset-12 {\n    margin-left: 100%;\n  }\n  .col-sm-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-sm-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-sm-offset-9 {\n    margin-left: 75%;\n  }\n  .col-sm-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-sm-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-sm-offset-6 {\n    margin-left: 50%;\n  }\n  .col-sm-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-sm-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-sm-offset-3 {\n    margin-left: 25%;\n  }\n  .col-sm-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-sm-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-sm-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 992px) {\n  .col-md-1,\n  .col-md-2,\n  .col-md-3,\n  .col-md-4,\n  .col-md-5,\n  .col-md-6,\n  .col-md-7,\n  .col-md-8,\n  .col-md-9,\n  .col-md-10,\n  .col-md-11,\n  .col-md-12 {\n    float: left;\n  }\n  .col-md-12 {\n    width: 100%;\n  }\n  .col-md-11 {\n    width: 91.66666667%;\n  }\n  .col-md-10 {\n    width: 83.33333333%;\n  }\n  .col-md-9 {\n    width: 75%;\n  }\n  .col-md-8 {\n    width: 66.66666667%;\n  }\n  .col-md-7 {\n    width: 58.33333333%;\n  }\n  .col-md-6 {\n    width: 50%;\n  }\n  .col-md-5 {\n    width: 41.66666667%;\n  }\n  .col-md-4 {\n    width: 33.33333333%;\n  }\n  .col-md-3 {\n    width: 25%;\n  }\n  .col-md-2 {\n    width: 16.66666667%;\n  }\n  .col-md-1 {\n    width: 8.33333333%;\n  }\n  .col-md-pull-12 {\n    right: 100%;\n  }\n  .col-md-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-md-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-md-pull-9 {\n    right: 75%;\n  }\n  .col-md-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-md-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-md-pull-6 {\n    right: 50%;\n  }\n  .col-md-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-md-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-md-pull-3 {\n    right: 25%;\n  }\n  .col-md-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-md-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-md-pull-0 {\n    right: auto;\n  }\n  .col-md-push-12 {\n    left: 100%;\n  }\n  .col-md-push-11 {\n    left: 91.66666667%;\n  }\n  .col-md-push-10 {\n    left: 83.33333333%;\n  }\n  .col-md-push-9 {\n    left: 75%;\n  }\n  .col-md-push-8 {\n    left: 66.66666667%;\n  }\n  .col-md-push-7 {\n    left: 58.33333333%;\n  }\n  .col-md-push-6 {\n    left: 50%;\n  }\n  .col-md-push-5 {\n    left: 41.66666667%;\n  }\n  .col-md-push-4 {\n    left: 33.33333333%;\n  }\n  .col-md-push-3 {\n    left: 25%;\n  }\n  .col-md-push-2 {\n    left: 16.66666667%;\n  }\n  .col-md-push-1 {\n    left: 8.33333333%;\n  }\n  .col-md-push-0 {\n    left: auto;\n  }\n  .col-md-offset-12 {\n    margin-left: 100%;\n  }\n  .col-md-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-md-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-md-offset-9 {\n    margin-left: 75%;\n  }\n  .col-md-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-md-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-md-offset-6 {\n    margin-left: 50%;\n  }\n  .col-md-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-md-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-md-offset-3 {\n    margin-left: 25%;\n  }\n  .col-md-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-md-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-md-offset-0 {\n    margin-left: 0%;\n  }\n}\n@media (min-width: 1200px) {\n  .col-lg-1,\n  .col-lg-2,\n  .col-lg-3,\n  .col-lg-4,\n  .col-lg-5,\n  .col-lg-6,\n  .col-lg-7,\n  .col-lg-8,\n  .col-lg-9,\n  .col-lg-10,\n  .col-lg-11,\n  .col-lg-12 {\n    float: left;\n  }\n  .col-lg-12 {\n    width: 100%;\n  }\n  .col-lg-11 {\n    width: 91.66666667%;\n  }\n  .col-lg-10 {\n    width: 83.33333333%;\n  }\n  .col-lg-9 {\n    width: 75%;\n  }\n  .col-lg-8 {\n    width: 66.66666667%;\n  }\n  .col-lg-7 {\n    width: 58.33333333%;\n  }\n  .col-lg-6 {\n    width: 50%;\n  }\n  .col-lg-5 {\n    width: 41.66666667%;\n  }\n  .col-lg-4 {\n    width: 33.33333333%;\n  }\n  .col-lg-3 {\n    width: 25%;\n  }\n  .col-lg-2 {\n    width: 16.66666667%;\n  }\n  .col-lg-1 {\n    width: 8.33333333%;\n  }\n  .col-lg-pull-12 {\n    right: 100%;\n  }\n  .col-lg-pull-11 {\n    right: 91.66666667%;\n  }\n  .col-lg-pull-10 {\n    right: 83.33333333%;\n  }\n  .col-lg-pull-9 {\n    right: 75%;\n  }\n  .col-lg-pull-8 {\n    right: 66.66666667%;\n  }\n  .col-lg-pull-7 {\n    right: 58.33333333%;\n  }\n  .col-lg-pull-6 {\n    right: 50%;\n  }\n  .col-lg-pull-5 {\n    right: 41.66666667%;\n  }\n  .col-lg-pull-4 {\n    right: 33.33333333%;\n  }\n  .col-lg-pull-3 {\n    right: 25%;\n  }\n  .col-lg-pull-2 {\n    right: 16.66666667%;\n  }\n  .col-lg-pull-1 {\n    right: 8.33333333%;\n  }\n  .col-lg-pull-0 {\n    right: auto;\n  }\n  .col-lg-push-12 {\n    left: 100%;\n  }\n  .col-lg-push-11 {\n    left: 91.66666667%;\n  }\n  .col-lg-push-10 {\n    left: 83.33333333%;\n  }\n  .col-lg-push-9 {\n    left: 75%;\n  }\n  .col-lg-push-8 {\n    left: 66.66666667%;\n  }\n  .col-lg-push-7 {\n    left: 58.33333333%;\n  }\n  .col-lg-push-6 {\n    left: 50%;\n  }\n  .col-lg-push-5 {\n    left: 41.66666667%;\n  }\n  .col-lg-push-4 {\n    left: 33.33333333%;\n  }\n  .col-lg-push-3 {\n    left: 25%;\n  }\n  .col-lg-push-2 {\n    left: 16.66666667%;\n  }\n  .col-lg-push-1 {\n    left: 8.33333333%;\n  }\n  .col-lg-push-0 {\n    left: auto;\n  }\n  .col-lg-offset-12 {\n    margin-left: 100%;\n  }\n  .col-lg-offset-11 {\n    margin-left: 91.66666667%;\n  }\n  .col-lg-offset-10 {\n    margin-left: 83.33333333%;\n  }\n  .col-lg-offset-9 {\n    margin-left: 75%;\n  }\n  .col-lg-offset-8 {\n    margin-left: 66.66666667%;\n  }\n  .col-lg-offset-7 {\n    margin-left: 58.33333333%;\n  }\n  .col-lg-offset-6 {\n    margin-left: 50%;\n  }\n  .col-lg-offset-5 {\n    margin-left: 41.66666667%;\n  }\n  .col-lg-offset-4 {\n    margin-left: 33.33333333%;\n  }\n  .col-lg-offset-3 {\n    margin-left: 25%;\n  }\n  .col-lg-offset-2 {\n    margin-left: 16.66666667%;\n  }\n  .col-lg-offset-1 {\n    margin-left: 8.33333333%;\n  }\n  .col-lg-offset-0 {\n    margin-left: 0%;\n  }\n}\ntable {\n  background-color: transparent;\n}\ntable col[class*=\"col-\"] {\n  position: static;\n  display: table-column;\n  float: none;\n}\ntable td[class*=\"col-\"],\ntable th[class*=\"col-\"] {\n  position: static;\n  display: table-cell;\n  float: none;\n}\ncaption {\n  padding-top: 8px;\n  padding-bottom: 8px;\n  color: #777777;\n  text-align: left;\n}\nth {\n  text-align: left;\n}\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: 20px;\n}\n.table > thead > tr > th,\n.table > tbody > tr > th,\n.table > tfoot > tr > th,\n.table > thead > tr > td,\n.table > tbody > tr > td,\n.table > tfoot > tr > td {\n  padding: 8px;\n  line-height: 1.42857143;\n  vertical-align: top;\n  border-top: 1px solid #ddd;\n}\n.table > thead > tr > th {\n  vertical-align: bottom;\n  border-bottom: 2px solid #ddd;\n}\n.table > caption + thead > tr:first-child > th,\n.table > colgroup + thead > tr:first-child > th,\n.table > thead:first-child > tr:first-child > th,\n.table > caption + thead > tr:first-child > td,\n.table > colgroup + thead > tr:first-child > td,\n.table > thead:first-child > tr:first-child > td {\n  border-top: 0;\n}\n.table > tbody + tbody {\n  border-top: 2px solid #ddd;\n}\n.table .table {\n  background-color: #fff;\n}\n.table-condensed > thead > tr > th,\n.table-condensed > tbody > tr > th,\n.table-condensed > tfoot > tr > th,\n.table-condensed > thead > tr > td,\n.table-condensed > tbody > tr > td,\n.table-condensed > tfoot > tr > td {\n  padding: 5px;\n}\n.table-bordered {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > tbody > tr > th,\n.table-bordered > tfoot > tr > th,\n.table-bordered > thead > tr > td,\n.table-bordered > tbody > tr > td,\n.table-bordered > tfoot > tr > td {\n  border: 1px solid #ddd;\n}\n.table-bordered > thead > tr > th,\n.table-bordered > thead > tr > td {\n  border-bottom-width: 2px;\n}\n.table-striped > tbody > tr:nth-of-type(odd) {\n  background-color: #f9f9f9;\n}\n.table-hover > tbody > tr:hover {\n  background-color: #f5f5f5;\n}\n.table > thead > tr > td.active,\n.table > tbody > tr > td.active,\n.table > tfoot > tr > td.active,\n.table > thead > tr > th.active,\n.table > tbody > tr > th.active,\n.table > tfoot > tr > th.active,\n.table > thead > tr.active > td,\n.table > tbody > tr.active > td,\n.table > tfoot > tr.active > td,\n.table > thead > tr.active > th,\n.table > tbody > tr.active > th,\n.table > tfoot > tr.active > th {\n  background-color: #f5f5f5;\n}\n.table-hover > tbody > tr > td.active:hover,\n.table-hover > tbody > tr > th.active:hover,\n.table-hover > tbody > tr.active:hover > td,\n.table-hover > tbody > tr:hover > .active,\n.table-hover > tbody > tr.active:hover > th {\n  background-color: #e8e8e8;\n}\n.table > thead > tr > td.success,\n.table > tbody > tr > td.success,\n.table > tfoot > tr > td.success,\n.table > thead > tr > th.success,\n.table > tbody > tr > th.success,\n.table > tfoot > tr > th.success,\n.table > thead > tr.success > td,\n.table > tbody > tr.success > td,\n.table > tfoot > tr.success > td,\n.table > thead > tr.success > th,\n.table > tbody > tr.success > th,\n.table > tfoot > tr.success > th {\n  background-color: #dff0d8;\n}\n.table-hover > tbody > tr > td.success:hover,\n.table-hover > tbody > tr > th.success:hover,\n.table-hover > tbody > tr.success:hover > td,\n.table-hover > tbody > tr:hover > .success,\n.table-hover > tbody > tr.success:hover > th {\n  background-color: #d0e9c6;\n}\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n  background-color: #d9edf7;\n}\n.table-hover > tbody > tr > td.info:hover,\n.table-hover > tbody > tr > th.info:hover,\n.table-hover > tbody > tr.info:hover > td,\n.table-hover > tbody > tr:hover > .info,\n.table-hover > tbody > tr.info:hover > th {\n  background-color: #c4e3f3;\n}\n.table > thead > tr > td.warning,\n.table > tbody > tr > td.warning,\n.table > tfoot > tr > td.warning,\n.table > thead > tr > th.warning,\n.table > tbody > tr > th.warning,\n.table > tfoot > tr > th.warning,\n.table > thead > tr.warning > td,\n.table > tbody > tr.warning > td,\n.table > tfoot > tr.warning > td,\n.table > thead > tr.warning > th,\n.table > tbody > tr.warning > th,\n.table > tfoot > tr.warning > th {\n  background-color: #fcf8e3;\n}\n.table-hover > tbody > tr > td.warning:hover,\n.table-hover > tbody > tr > th.warning:hover,\n.table-hover > tbody > tr.warning:hover > td,\n.table-hover > tbody > tr:hover > .warning,\n.table-hover > tbody > tr.warning:hover > th {\n  background-color: #faf2cc;\n}\n.table > thead > tr > td.danger,\n.table > tbody > tr > td.danger,\n.table > tfoot > tr > td.danger,\n.table > thead > tr > th.danger,\n.table > tbody > tr > th.danger,\n.table > tfoot > tr > th.danger,\n.table > thead > tr.danger > td,\n.table > tbody > tr.danger > td,\n.table > tfoot > tr.danger > td,\n.table > thead > tr.danger > th,\n.table > tbody > tr.danger > th,\n.table > tfoot > tr.danger > th {\n  background-color: #f2dede;\n}\n.table-hover > tbody > tr > td.danger:hover,\n.table-hover > tbody > tr > th.danger:hover,\n.table-hover > tbody > tr.danger:hover > td,\n.table-hover > tbody > tr:hover > .danger,\n.table-hover > tbody > tr.danger:hover > th {\n  background-color: #ebcccc;\n}\n.table-responsive {\n  min-height: 0.01%;\n  overflow-x: auto;\n}\n@media screen and (max-width: 767px) {\n  .table-responsive {\n    width: 100%;\n    margin-bottom: 15px;\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid #ddd;\n  }\n  .table-responsive > .table {\n    margin-bottom: 0;\n  }\n  .table-responsive > .table > thead > tr > th,\n  .table-responsive > .table > tbody > tr > th,\n  .table-responsive > .table > tfoot > tr > th,\n  .table-responsive > .table > thead > tr > td,\n  .table-responsive > .table > tbody > tr > td,\n  .table-responsive > .table > tfoot > tr > td {\n    white-space: nowrap;\n  }\n  .table-responsive > .table-bordered {\n    border: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:first-child,\n  .table-responsive > .table-bordered > tbody > tr > th:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n  .table-responsive > .table-bordered > thead > tr > td:first-child,\n  .table-responsive > .table-bordered > tbody > tr > td:first-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n    border-left: 0;\n  }\n  .table-responsive > .table-bordered > thead > tr > th:last-child,\n  .table-responsive > .table-bordered > tbody > tr > th:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n  .table-responsive > .table-bordered > thead > tr > td:last-child,\n  .table-responsive > .table-bordered > tbody > tr > td:last-child,\n  .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n    border-right: 0;\n  }\n  .table-responsive > .table-bordered > tbody > tr:last-child > th,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > th,\n  .table-responsive > .table-bordered > tbody > tr:last-child > td,\n  .table-responsive > .table-bordered > tfoot > tr:last-child > td {\n    border-bottom: 0;\n  }\n}\nfieldset {\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: 20px;\n  font-size: 21px;\n  line-height: inherit;\n  color: #333333;\n  border: 0;\n  border-bottom: 1px solid #e5e5e5;\n}\nlabel {\n  display: inline-block;\n  max-width: 100%;\n  margin-bottom: 5px;\n  font-weight: 700;\n}\ninput[type=\"search\"] {\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n  -webkit-appearance: none;\n  -moz-appearance: none;\n  appearance: none;\n}\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9;\n  line-height: normal;\n}\ninput[type=\"radio\"][disabled],\ninput[type=\"checkbox\"][disabled],\ninput[type=\"radio\"].disabled,\ninput[type=\"checkbox\"].disabled,\nfieldset[disabled] input[type=\"radio\"],\nfieldset[disabled] input[type=\"checkbox\"] {\n  cursor: not-allowed;\n}\ninput[type=\"file\"] {\n  display: block;\n}\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\nselect[multiple],\nselect[size] {\n  height: auto;\n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\noutput {\n  display: block;\n  padding-top: 7px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n}\n.form-control {\n  display: block;\n  width: 100%;\n  height: 34px;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  color: #555555;\n  background-color: #fff;\n  background-image: none;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  -webkit-transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;\n  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s, -webkit-box-shadow ease-in-out .15s;\n}\n.form-control:focus {\n  border-color: #66afe9;\n  outline: 0;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px rgba(102, 175, 233, 0.6);\n}\n.form-control::-moz-placeholder {\n  color: #999;\n  opacity: 1;\n}\n.form-control:-ms-input-placeholder {\n  color: #999;\n}\n.form-control::-webkit-input-placeholder {\n  color: #999;\n}\n.form-control::-ms-expand {\n  background-color: transparent;\n  border: 0;\n}\n.form-control[disabled],\n.form-control[readonly],\nfieldset[disabled] .form-control {\n  background-color: #eeeeee;\n  opacity: 1;\n}\n.form-control[disabled],\nfieldset[disabled] .form-control {\n  cursor: not-allowed;\n}\ntextarea.form-control {\n  height: auto;\n}\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"].form-control,\n  input[type=\"time\"].form-control,\n  input[type=\"datetime-local\"].form-control,\n  input[type=\"month\"].form-control {\n    line-height: 34px;\n  }\n  input[type=\"date\"].input-sm,\n  input[type=\"time\"].input-sm,\n  input[type=\"datetime-local\"].input-sm,\n  input[type=\"month\"].input-sm,\n  .input-group-sm input[type=\"date\"],\n  .input-group-sm input[type=\"time\"],\n  .input-group-sm input[type=\"datetime-local\"],\n  .input-group-sm input[type=\"month\"] {\n    line-height: 30px;\n  }\n  input[type=\"date\"].input-lg,\n  input[type=\"time\"].input-lg,\n  input[type=\"datetime-local\"].input-lg,\n  input[type=\"month\"].input-lg,\n  .input-group-lg input[type=\"date\"],\n  .input-group-lg input[type=\"time\"],\n  .input-group-lg input[type=\"datetime-local\"],\n  .input-group-lg input[type=\"month\"] {\n    line-height: 46px;\n  }\n}\n.form-group {\n  margin-bottom: 15px;\n}\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.radio.disabled label,\n.checkbox.disabled label,\nfieldset[disabled] .radio label,\nfieldset[disabled] .checkbox label {\n  cursor: not-allowed;\n}\n.radio label,\n.checkbox label {\n  min-height: 20px;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  cursor: pointer;\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-top: 4px \\9;\n  margin-left: -20px;\n}\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px;\n}\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  vertical-align: middle;\n  cursor: pointer;\n}\n.radio-inline.disabled,\n.checkbox-inline.disabled,\nfieldset[disabled] .radio-inline,\nfieldset[disabled] .checkbox-inline {\n  cursor: not-allowed;\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px;\n}\n.form-control-static {\n  min-height: 34px;\n  padding-top: 7px;\n  padding-bottom: 7px;\n  margin-bottom: 0;\n}\n.form-control-static.input-lg,\n.form-control-static.input-sm {\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-sm {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-sm {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-sm,\nselect[multiple].input-sm {\n  height: auto;\n}\n.form-group-sm .form-control {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.form-group-sm select.form-control {\n  height: 30px;\n  line-height: 30px;\n}\n.form-group-sm textarea.form-control,\n.form-group-sm select[multiple].form-control {\n  height: auto;\n}\n.form-group-sm .form-control-static {\n  height: 30px;\n  min-height: 32px;\n  padding: 6px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.input-lg {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-lg {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-lg,\nselect[multiple].input-lg {\n  height: auto;\n}\n.form-group-lg .form-control {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.form-group-lg select.form-control {\n  height: 46px;\n  line-height: 46px;\n}\n.form-group-lg textarea.form-control,\n.form-group-lg select[multiple].form-control {\n  height: auto;\n}\n.form-group-lg .form-control-static {\n  height: 46px;\n  min-height: 38px;\n  padding: 11px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.has-feedback {\n  position: relative;\n}\n.has-feedback .form-control {\n  padding-right: 42.5px;\n}\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2;\n  display: block;\n  width: 34px;\n  height: 34px;\n  line-height: 34px;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: 46px;\n  height: 46px;\n  line-height: 46px;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: 30px;\n  height: 30px;\n  line-height: 30px;\n}\n.has-success .help-block,\n.has-success .control-label,\n.has-success .radio,\n.has-success .checkbox,\n.has-success .radio-inline,\n.has-success .checkbox-inline,\n.has-success.radio label,\n.has-success.checkbox label,\n.has-success.radio-inline label,\n.has-success.checkbox-inline label {\n  color: #3c763d;\n}\n.has-success .form-control {\n  border-color: #3c763d;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-success .form-control:focus {\n  border-color: #2b542c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;\n}\n.has-success .input-group-addon {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #3c763d;\n}\n.has-success .form-control-feedback {\n  color: #3c763d;\n}\n.has-warning .help-block,\n.has-warning .control-label,\n.has-warning .radio,\n.has-warning .checkbox,\n.has-warning .radio-inline,\n.has-warning .checkbox-inline,\n.has-warning.radio label,\n.has-warning.checkbox label,\n.has-warning.radio-inline label,\n.has-warning.checkbox-inline label {\n  color: #8a6d3b;\n}\n.has-warning .form-control {\n  border-color: #8a6d3b;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-warning .form-control:focus {\n  border-color: #66512c;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;\n}\n.has-warning .input-group-addon {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #8a6d3b;\n}\n.has-warning .form-control-feedback {\n  color: #8a6d3b;\n}\n.has-error .help-block,\n.has-error .control-label,\n.has-error .radio,\n.has-error .checkbox,\n.has-error .radio-inline,\n.has-error .checkbox-inline,\n.has-error.radio label,\n.has-error.checkbox label,\n.has-error.radio-inline label,\n.has-error.checkbox-inline label {\n  color: #a94442;\n}\n.has-error .form-control {\n  border-color: #a94442;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);\n}\n.has-error .form-control:focus {\n  border-color: #843534;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;\n}\n.has-error .input-group-addon {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #a94442;\n}\n.has-error .form-control-feedback {\n  color: #a94442;\n}\n.has-feedback label ~ .form-control-feedback {\n  top: 25px;\n}\n.has-feedback label.sr-only ~ .form-control-feedback {\n  top: 0;\n}\n.help-block {\n  display: block;\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: #737373;\n}\n@media (min-width: 768px) {\n  .form-inline .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .form-inline .form-control-static {\n    display: inline-block;\n  }\n  .form-inline .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .form-inline .input-group .input-group-addon,\n  .form-inline .input-group .input-group-btn,\n  .form-inline .input-group .form-control {\n    width: auto;\n  }\n  .form-inline .input-group > .form-control {\n    width: 100%;\n  }\n  .form-inline .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio,\n  .form-inline .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .form-inline .radio label,\n  .form-inline .checkbox label {\n    padding-left: 0;\n  }\n  .form-inline .radio input[type=\"radio\"],\n  .form-inline .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .form-inline .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox,\n.form-horizontal .radio-inline,\n.form-horizontal .checkbox-inline {\n  padding-top: 7px;\n  margin-top: 0;\n  margin-bottom: 0;\n}\n.form-horizontal .radio,\n.form-horizontal .checkbox {\n  min-height: 27px;\n}\n.form-horizontal .form-group {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .control-label {\n    padding-top: 7px;\n    margin-bottom: 0;\n    text-align: right;\n  }\n}\n.form-horizontal .has-feedback .form-control-feedback {\n  right: 15px;\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-lg .control-label {\n    padding-top: 11px;\n    font-size: 18px;\n  }\n}\n@media (min-width: 768px) {\n  .form-horizontal .form-group-sm .control-label {\n    padding-top: 6px;\n    font-size: 12px;\n  }\n}\n.btn {\n  display: inline-block;\n  margin-bottom: 0;\n  font-weight: normal;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  -ms-touch-action: manipulation;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none;\n  border: 1px solid transparent;\n  padding: 6px 12px;\n  font-size: 14px;\n  line-height: 1.42857143;\n  border-radius: 4px;\n  -webkit-user-select: none;\n  -moz-user-select: none;\n  -ms-user-select: none;\n  user-select: none;\n}\n.btn:focus,\n.btn:active:focus,\n.btn.active:focus,\n.btn.focus,\n.btn:active.focus,\n.btn.active.focus {\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n.btn:hover,\n.btn:focus,\n.btn.focus {\n  color: #333;\n  text-decoration: none;\n}\n.btn:active,\n.btn.active {\n  background-image: none;\n  outline: 0;\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn.disabled,\n.btn[disabled],\nfieldset[disabled] .btn {\n  cursor: not-allowed;\n  filter: alpha(opacity=65);\n  opacity: 0.65;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\na.btn.disabled,\nfieldset[disabled] a.btn {\n  pointer-events: none;\n}\n.btn-default {\n  color: #333;\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default:focus,\n.btn-default.focus {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #8c8c8c;\n}\n.btn-default:hover {\n  color: #333;\n  background-color: #e6e6e6;\n  border-color: #adadad;\n}\n.btn-default:active,\n.btn-default.active,\n.open > .dropdown-toggle.btn-default {\n  color: #333;\n  background-color: #e6e6e6;\n  background-image: none;\n  border-color: #adadad;\n}\n.btn-default:active:hover,\n.btn-default.active:hover,\n.open > .dropdown-toggle.btn-default:hover,\n.btn-default:active:focus,\n.btn-default.active:focus,\n.open > .dropdown-toggle.btn-default:focus,\n.btn-default:active.focus,\n.btn-default.active.focus,\n.open > .dropdown-toggle.btn-default.focus {\n  color: #333;\n  background-color: #d4d4d4;\n  border-color: #8c8c8c;\n}\n.btn-default.disabled:hover,\n.btn-default[disabled]:hover,\nfieldset[disabled] .btn-default:hover,\n.btn-default.disabled:focus,\n.btn-default[disabled]:focus,\nfieldset[disabled] .btn-default:focus,\n.btn-default.disabled.focus,\n.btn-default[disabled].focus,\nfieldset[disabled] .btn-default.focus {\n  background-color: #fff;\n  border-color: #ccc;\n}\n.btn-default .badge {\n  color: #fff;\n  background-color: #333;\n}\n.btn-primary {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary:focus,\n.btn-primary.focus {\n  color: #fff;\n  background-color: #286090;\n  border-color: #122b40;\n}\n.btn-primary:hover {\n  color: #fff;\n  background-color: #286090;\n  border-color: #204d74;\n}\n.btn-primary:active,\n.btn-primary.active,\n.open > .dropdown-toggle.btn-primary {\n  color: #fff;\n  background-color: #286090;\n  background-image: none;\n  border-color: #204d74;\n}\n.btn-primary:active:hover,\n.btn-primary.active:hover,\n.open > .dropdown-toggle.btn-primary:hover,\n.btn-primary:active:focus,\n.btn-primary.active:focus,\n.open > .dropdown-toggle.btn-primary:focus,\n.btn-primary:active.focus,\n.btn-primary.active.focus,\n.open > .dropdown-toggle.btn-primary.focus {\n  color: #fff;\n  background-color: #204d74;\n  border-color: #122b40;\n}\n.btn-primary.disabled:hover,\n.btn-primary[disabled]:hover,\nfieldset[disabled] .btn-primary:hover,\n.btn-primary.disabled:focus,\n.btn-primary[disabled]:focus,\nfieldset[disabled] .btn-primary:focus,\n.btn-primary.disabled.focus,\n.btn-primary[disabled].focus,\nfieldset[disabled] .btn-primary.focus {\n  background-color: #337ab7;\n  border-color: #2e6da4;\n}\n.btn-primary .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.btn-success {\n  color: #fff;\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success:focus,\n.btn-success.focus {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #255625;\n}\n.btn-success:hover {\n  color: #fff;\n  background-color: #449d44;\n  border-color: #398439;\n}\n.btn-success:active,\n.btn-success.active,\n.open > .dropdown-toggle.btn-success {\n  color: #fff;\n  background-color: #449d44;\n  background-image: none;\n  border-color: #398439;\n}\n.btn-success:active:hover,\n.btn-success.active:hover,\n.open > .dropdown-toggle.btn-success:hover,\n.btn-success:active:focus,\n.btn-success.active:focus,\n.open > .dropdown-toggle.btn-success:focus,\n.btn-success:active.focus,\n.btn-success.active.focus,\n.open > .dropdown-toggle.btn-success.focus {\n  color: #fff;\n  background-color: #398439;\n  border-color: #255625;\n}\n.btn-success.disabled:hover,\n.btn-success[disabled]:hover,\nfieldset[disabled] .btn-success:hover,\n.btn-success.disabled:focus,\n.btn-success[disabled]:focus,\nfieldset[disabled] .btn-success:focus,\n.btn-success.disabled.focus,\n.btn-success[disabled].focus,\nfieldset[disabled] .btn-success.focus {\n  background-color: #5cb85c;\n  border-color: #4cae4c;\n}\n.btn-success .badge {\n  color: #5cb85c;\n  background-color: #fff;\n}\n.btn-info {\n  color: #fff;\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info:focus,\n.btn-info.focus {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #1b6d85;\n}\n.btn-info:hover {\n  color: #fff;\n  background-color: #31b0d5;\n  border-color: #269abc;\n}\n.btn-info:active,\n.btn-info.active,\n.open > .dropdown-toggle.btn-info {\n  color: #fff;\n  background-color: #31b0d5;\n  background-image: none;\n  border-color: #269abc;\n}\n.btn-info:active:hover,\n.btn-info.active:hover,\n.open > .dropdown-toggle.btn-info:hover,\n.btn-info:active:focus,\n.btn-info.active:focus,\n.open > .dropdown-toggle.btn-info:focus,\n.btn-info:active.focus,\n.btn-info.active.focus,\n.open > .dropdown-toggle.btn-info.focus {\n  color: #fff;\n  background-color: #269abc;\n  border-color: #1b6d85;\n}\n.btn-info.disabled:hover,\n.btn-info[disabled]:hover,\nfieldset[disabled] .btn-info:hover,\n.btn-info.disabled:focus,\n.btn-info[disabled]:focus,\nfieldset[disabled] .btn-info:focus,\n.btn-info.disabled.focus,\n.btn-info[disabled].focus,\nfieldset[disabled] .btn-info.focus {\n  background-color: #5bc0de;\n  border-color: #46b8da;\n}\n.btn-info .badge {\n  color: #5bc0de;\n  background-color: #fff;\n}\n.btn-warning {\n  color: #fff;\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning:focus,\n.btn-warning.focus {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #985f0d;\n}\n.btn-warning:hover {\n  color: #fff;\n  background-color: #ec971f;\n  border-color: #d58512;\n}\n.btn-warning:active,\n.btn-warning.active,\n.open > .dropdown-toggle.btn-warning {\n  color: #fff;\n  background-color: #ec971f;\n  background-image: none;\n  border-color: #d58512;\n}\n.btn-warning:active:hover,\n.btn-warning.active:hover,\n.open > .dropdown-toggle.btn-warning:hover,\n.btn-warning:active:focus,\n.btn-warning.active:focus,\n.open > .dropdown-toggle.btn-warning:focus,\n.btn-warning:active.focus,\n.btn-warning.active.focus,\n.open > .dropdown-toggle.btn-warning.focus {\n  color: #fff;\n  background-color: #d58512;\n  border-color: #985f0d;\n}\n.btn-warning.disabled:hover,\n.btn-warning[disabled]:hover,\nfieldset[disabled] .btn-warning:hover,\n.btn-warning.disabled:focus,\n.btn-warning[disabled]:focus,\nfieldset[disabled] .btn-warning:focus,\n.btn-warning.disabled.focus,\n.btn-warning[disabled].focus,\nfieldset[disabled] .btn-warning.focus {\n  background-color: #f0ad4e;\n  border-color: #eea236;\n}\n.btn-warning .badge {\n  color: #f0ad4e;\n  background-color: #fff;\n}\n.btn-danger {\n  color: #fff;\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger:focus,\n.btn-danger.focus {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #761c19;\n}\n.btn-danger:hover {\n  color: #fff;\n  background-color: #c9302c;\n  border-color: #ac2925;\n}\n.btn-danger:active,\n.btn-danger.active,\n.open > .dropdown-toggle.btn-danger {\n  color: #fff;\n  background-color: #c9302c;\n  background-image: none;\n  border-color: #ac2925;\n}\n.btn-danger:active:hover,\n.btn-danger.active:hover,\n.open > .dropdown-toggle.btn-danger:hover,\n.btn-danger:active:focus,\n.btn-danger.active:focus,\n.open > .dropdown-toggle.btn-danger:focus,\n.btn-danger:active.focus,\n.btn-danger.active.focus,\n.open > .dropdown-toggle.btn-danger.focus {\n  color: #fff;\n  background-color: #ac2925;\n  border-color: #761c19;\n}\n.btn-danger.disabled:hover,\n.btn-danger[disabled]:hover,\nfieldset[disabled] .btn-danger:hover,\n.btn-danger.disabled:focus,\n.btn-danger[disabled]:focus,\nfieldset[disabled] .btn-danger:focus,\n.btn-danger.disabled.focus,\n.btn-danger[disabled].focus,\nfieldset[disabled] .btn-danger.focus {\n  background-color: #d9534f;\n  border-color: #d43f3a;\n}\n.btn-danger .badge {\n  color: #d9534f;\n  background-color: #fff;\n}\n.btn-link {\n  font-weight: 400;\n  color: #337ab7;\n  border-radius: 0;\n}\n.btn-link,\n.btn-link:active,\n.btn-link.active,\n.btn-link[disabled],\nfieldset[disabled] .btn-link {\n  background-color: transparent;\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn-link,\n.btn-link:hover,\n.btn-link:focus,\n.btn-link:active {\n  border-color: transparent;\n}\n.btn-link:hover,\n.btn-link:focus {\n  color: #23527c;\n  text-decoration: underline;\n  background-color: transparent;\n}\n.btn-link[disabled]:hover,\nfieldset[disabled] .btn-link:hover,\n.btn-link[disabled]:focus,\nfieldset[disabled] .btn-link:focus {\n  color: #777777;\n  text-decoration: none;\n}\n.btn-lg,\n.btn-group-lg > .btn {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\n.btn-sm,\n.btn-group-sm > .btn {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-xs,\n.btn-group-xs > .btn {\n  padding: 1px 5px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\n.btn-block {\n  display: block;\n  width: 100%;\n}\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\ninput[type=\"submit\"].btn-block,\ninput[type=\"reset\"].btn-block,\ninput[type=\"button\"].btn-block {\n  width: 100%;\n}\n.fade {\n  opacity: 0;\n  -webkit-transition: opacity 0.15s linear;\n  -o-transition: opacity 0.15s linear;\n  transition: opacity 0.15s linear;\n}\n.fade.in {\n  opacity: 1;\n}\n.collapse {\n  display: none;\n}\n.collapse.in {\n  display: block;\n}\ntr.collapse.in {\n  display: table-row;\n}\ntbody.collapse.in {\n  display: table-row-group;\n}\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  -webkit-transition-property: height, visibility;\n  -o-transition-property: height, visibility;\n  transition-property: height, visibility;\n  -webkit-transition-duration: 0.35s;\n  -o-transition-duration: 0.35s;\n  transition-duration: 0.35s;\n  -webkit-transition-timing-function: ease;\n  -o-transition-timing-function: ease;\n  transition-timing-function: ease;\n}\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: 4px dashed;\n  border-top: 4px solid \\9;\n  border-right: 4px solid transparent;\n  border-left: 4px solid transparent;\n}\n.dropup,\n.dropdown {\n  position: relative;\n}\n.dropdown-toggle:focus {\n  outline: 0;\n}\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: 1000;\n  display: none;\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0;\n  font-size: 14px;\n  text-align: left;\n  list-style: none;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.15);\n  border-radius: 4px;\n  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);\n}\n.dropdown-menu.pull-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu .divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.dropdown-menu > li > a {\n  display: block;\n  padding: 3px 20px;\n  clear: both;\n  font-weight: 400;\n  line-height: 1.42857143;\n  color: #333333;\n  white-space: nowrap;\n}\n.dropdown-menu > li > a:hover,\n.dropdown-menu > li > a:focus {\n  color: #262626;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\n.dropdown-menu > .active > a,\n.dropdown-menu > .active > a:hover,\n.dropdown-menu > .active > a:focus {\n  color: #fff;\n  text-decoration: none;\n  background-color: #337ab7;\n  outline: 0;\n}\n.dropdown-menu > .disabled > a,\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  color: #777777;\n}\n.dropdown-menu > .disabled > a:hover,\n.dropdown-menu > .disabled > a:focus {\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n  background-image: none;\n  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);\n}\n.open > .dropdown-menu {\n  display: block;\n}\n.open > a {\n  outline: 0;\n}\n.dropdown-menu-right {\n  right: 0;\n  left: auto;\n}\n.dropdown-menu-left {\n  right: auto;\n  left: 0;\n}\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: 12px;\n  line-height: 1.42857143;\n  color: #777777;\n  white-space: nowrap;\n}\n.dropdown-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 990;\n}\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n.dropup .caret,\n.navbar-fixed-bottom .dropdown .caret {\n  content: \"\";\n  border-top: 0;\n  border-bottom: 4px dashed;\n  border-bottom: 4px solid \\9;\n}\n.dropup .dropdown-menu,\n.navbar-fixed-bottom .dropdown .dropdown-menu {\n  top: auto;\n  bottom: 100%;\n  margin-bottom: 2px;\n}\n@media (min-width: 768px) {\n  .navbar-right .dropdown-menu {\n    right: 0;\n    left: auto;\n  }\n  .navbar-right .dropdown-menu-left {\n    right: auto;\n    left: 0;\n  }\n}\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n}\n.btn-group > .btn,\n.btn-group-vertical > .btn {\n  position: relative;\n  float: left;\n}\n.btn-group > .btn:hover,\n.btn-group-vertical > .btn:hover,\n.btn-group > .btn:focus,\n.btn-group-vertical > .btn:focus,\n.btn-group > .btn:active,\n.btn-group-vertical > .btn:active,\n.btn-group > .btn.active,\n.btn-group-vertical > .btn.active {\n  z-index: 2;\n}\n.btn-group .btn + .btn,\n.btn-group .btn + .btn-group,\n.btn-group .btn-group + .btn,\n.btn-group .btn-group + .btn-group {\n  margin-left: -1px;\n}\n.btn-toolbar {\n  margin-left: -5px;\n}\n.btn-toolbar .btn,\n.btn-toolbar .btn-group,\n.btn-toolbar .input-group {\n  float: left;\n}\n.btn-toolbar > .btn,\n.btn-toolbar > .btn-group,\n.btn-toolbar > .input-group {\n  margin-left: 5px;\n}\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n.btn-group > .btn:first-child {\n  margin-left: 0;\n}\n.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n.btn-group > .btn + .dropdown-toggle {\n  padding-right: 8px;\n  padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-right: 12px;\n  padding-left: 12px;\n}\n.btn-group.open .dropdown-toggle {\n  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);\n}\n.btn-group.open .dropdown-toggle.btn-link {\n  -webkit-box-shadow: none;\n  box-shadow: none;\n}\n.btn .caret {\n  margin-left: 0;\n}\n.btn-lg .caret {\n  border-width: 5px 5px 0;\n  border-bottom-width: 0;\n}\n.dropup .btn-lg .caret {\n  border-width: 0 5px 5px;\n}\n.btn-group-vertical > .btn,\n.btn-group-vertical > .btn-group,\n.btn-group-vertical > .btn-group > .btn {\n  display: block;\n  float: none;\n  width: 100%;\n  max-width: 100%;\n}\n.btn-group-vertical > .btn-group > .btn {\n  float: none;\n}\n.btn-group-vertical > .btn + .btn,\n.btn-group-vertical > .btn + .btn-group,\n.btn-group-vertical > .btn-group + .btn,\n.btn-group-vertical > .btn-group + .btn-group {\n  margin-top: -1px;\n  margin-left: 0;\n}\n.btn-group-vertical > .btn:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn:first-child:not(:last-child) {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn:last-child:not(:first-child) {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,\n.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n}\n.btn-group-justified > .btn,\n.btn-group-justified > .btn-group {\n  display: table-cell;\n  float: none;\n  width: 1%;\n}\n.btn-group-justified > .btn-group .btn {\n  width: 100%;\n}\n.btn-group-justified > .btn-group .dropdown-menu {\n  left: auto;\n}\n[data-toggle=\"buttons\"] > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"radio\"],\n[data-toggle=\"buttons\"] > .btn input[type=\"checkbox\"],\n[data-toggle=\"buttons\"] > .btn-group > .btn input[type=\"checkbox\"] {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n}\n.input-group {\n  position: relative;\n  display: table;\n  border-collapse: separate;\n}\n.input-group[class*=\"col-\"] {\n  float: none;\n  padding-right: 0;\n  padding-left: 0;\n}\n.input-group .form-control {\n  position: relative;\n  z-index: 2;\n  float: left;\n  width: 100%;\n  margin-bottom: 0;\n}\n.input-group .form-control:focus {\n  z-index: 3;\n}\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n  border-radius: 6px;\n}\nselect.input-group-lg > .form-control,\nselect.input-group-lg > .input-group-addon,\nselect.input-group-lg > .input-group-btn > .btn {\n  height: 46px;\n  line-height: 46px;\n}\ntextarea.input-group-lg > .form-control,\ntextarea.input-group-lg > .input-group-addon,\ntextarea.input-group-lg > .input-group-btn > .btn,\nselect[multiple].input-group-lg > .form-control,\nselect[multiple].input-group-lg > .input-group-addon,\nselect[multiple].input-group-lg > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n  border-radius: 3px;\n}\nselect.input-group-sm > .form-control,\nselect.input-group-sm > .input-group-addon,\nselect.input-group-sm > .input-group-btn > .btn {\n  height: 30px;\n  line-height: 30px;\n}\ntextarea.input-group-sm > .form-control,\ntextarea.input-group-sm > .input-group-addon,\ntextarea.input-group-sm > .input-group-btn > .btn,\nselect[multiple].input-group-sm > .form-control,\nselect[multiple].input-group-sm > .input-group-addon,\nselect[multiple].input-group-sm > .input-group-btn > .btn {\n  height: auto;\n}\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n}\n.input-group-addon:not(:first-child):not(:last-child),\n.input-group-btn:not(:first-child):not(:last-child),\n.input-group .form-control:not(:first-child):not(:last-child) {\n  border-radius: 0;\n}\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle;\n}\n.input-group-addon {\n  padding: 6px 12px;\n  font-size: 14px;\n  font-weight: 400;\n  line-height: 1;\n  color: #555555;\n  text-align: center;\n  background-color: #eeeeee;\n  border: 1px solid #ccc;\n  border-radius: 4px;\n}\n.input-group-addon.input-sm {\n  padding: 5px 10px;\n  font-size: 12px;\n  border-radius: 3px;\n}\n.input-group-addon.input-lg {\n  padding: 10px 16px;\n  font-size: 18px;\n  border-radius: 6px;\n}\n.input-group-addon input[type=\"radio\"],\n.input-group-addon input[type=\"checkbox\"] {\n  margin-top: 0;\n}\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  border-top-right-radius: 0;\n  border-bottom-right-radius: 0;\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  border-top-left-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n.input-group-btn {\n  position: relative;\n  font-size: 0;\n  white-space: nowrap;\n}\n.input-group-btn > .btn {\n  position: relative;\n}\n.input-group-btn > .btn + .btn {\n  margin-left: -1px;\n}\n.input-group-btn > .btn:hover,\n.input-group-btn > .btn:focus,\n.input-group-btn > .btn:active {\n  z-index: 2;\n}\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group {\n  margin-right: -1px;\n}\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group {\n  z-index: 2;\n  margin-left: -1px;\n}\n.nav {\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n.nav > li {\n  position: relative;\n  display: block;\n}\n.nav > li > a {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n}\n.nav > li > a:hover,\n.nav > li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.nav > li.disabled > a {\n  color: #777777;\n}\n.nav > li.disabled > a:hover,\n.nav > li.disabled > a:focus {\n  color: #777777;\n  text-decoration: none;\n  cursor: not-allowed;\n  background-color: transparent;\n}\n.nav .open > a,\n.nav .open > a:hover,\n.nav .open > a:focus {\n  background-color: #eeeeee;\n  border-color: #337ab7;\n}\n.nav .nav-divider {\n  height: 1px;\n  margin: 9px 0;\n  overflow: hidden;\n  background-color: #e5e5e5;\n}\n.nav > li > a > img {\n  max-width: none;\n}\n.nav-tabs {\n  border-bottom: 1px solid #ddd;\n}\n.nav-tabs > li {\n  float: left;\n  margin-bottom: -1px;\n}\n.nav-tabs > li > a {\n  margin-right: 2px;\n  line-height: 1.42857143;\n  border: 1px solid transparent;\n  border-radius: 4px 4px 0 0;\n}\n.nav-tabs > li > a:hover {\n  border-color: #eeeeee #eeeeee #ddd;\n}\n.nav-tabs > li.active > a,\n.nav-tabs > li.active > a:hover,\n.nav-tabs > li.active > a:focus {\n  color: #555555;\n  cursor: default;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-bottom-color: transparent;\n}\n.nav-tabs.nav-justified {\n  width: 100%;\n  border-bottom: 0;\n}\n.nav-tabs.nav-justified > li {\n  float: none;\n}\n.nav-tabs.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-tabs.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-tabs.nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs.nav-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs.nav-justified > .active > a,\n.nav-tabs.nav-justified > .active > a:hover,\n.nav-tabs.nav-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs.nav-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs.nav-justified > .active > a,\n  .nav-tabs.nav-justified > .active > a:hover,\n  .nav-tabs.nav-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.nav-pills > li {\n  float: left;\n}\n.nav-pills > li > a {\n  border-radius: 4px;\n}\n.nav-pills > li + li {\n  margin-left: 2px;\n}\n.nav-pills > li.active > a,\n.nav-pills > li.active > a:hover,\n.nav-pills > li.active > a:focus {\n  color: #fff;\n  background-color: #337ab7;\n}\n.nav-stacked > li {\n  float: none;\n}\n.nav-stacked > li + li {\n  margin-top: 2px;\n  margin-left: 0;\n}\n.nav-justified {\n  width: 100%;\n}\n.nav-justified > li {\n  float: none;\n}\n.nav-justified > li > a {\n  margin-bottom: 5px;\n  text-align: center;\n}\n.nav-justified > .dropdown .dropdown-menu {\n  top: auto;\n  left: auto;\n}\n@media (min-width: 768px) {\n  .nav-justified > li {\n    display: table-cell;\n    width: 1%;\n  }\n  .nav-justified > li > a {\n    margin-bottom: 0;\n  }\n}\n.nav-tabs-justified {\n  border-bottom: 0;\n}\n.nav-tabs-justified > li > a {\n  margin-right: 0;\n  border-radius: 4px;\n}\n.nav-tabs-justified > .active > a,\n.nav-tabs-justified > .active > a:hover,\n.nav-tabs-justified > .active > a:focus {\n  border: 1px solid #ddd;\n}\n@media (min-width: 768px) {\n  .nav-tabs-justified > li > a {\n    border-bottom: 1px solid #ddd;\n    border-radius: 4px 4px 0 0;\n  }\n  .nav-tabs-justified > .active > a,\n  .nav-tabs-justified > .active > a:hover,\n  .nav-tabs-justified > .active > a:focus {\n    border-bottom-color: #fff;\n  }\n}\n.tab-content > .tab-pane {\n  display: none;\n}\n.tab-content > .active {\n  display: block;\n}\n.nav-tabs .dropdown-menu {\n  margin-top: -1px;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar {\n  position: relative;\n  min-height: 50px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n}\n@media (min-width: 768px) {\n  .navbar {\n    border-radius: 4px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-header {\n    float: left;\n  }\n}\n.navbar-collapse {\n  padding-right: 15px;\n  padding-left: 15px;\n  overflow-x: visible;\n  border-top: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);\n  -webkit-overflow-scrolling: touch;\n}\n.navbar-collapse.in {\n  overflow-y: auto;\n}\n@media (min-width: 768px) {\n  .navbar-collapse {\n    width: auto;\n    border-top: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n  .navbar-collapse.collapse {\n    display: block !important;\n    height: auto !important;\n    padding-bottom: 0;\n    overflow: visible !important;\n  }\n  .navbar-collapse.in {\n    overflow-y: visible;\n  }\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-static-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: 1030;\n}\n.navbar-fixed-top .navbar-collapse,\n.navbar-fixed-bottom .navbar-collapse {\n  max-height: 340px;\n}\n@media (max-device-width: 480px) and (orientation: landscape) {\n  .navbar-fixed-top .navbar-collapse,\n  .navbar-fixed-bottom .navbar-collapse {\n    max-height: 200px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-fixed-top,\n  .navbar-fixed-bottom {\n    border-radius: 0;\n  }\n}\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0;\n  border-width: 1px 0 0;\n}\n.container > .navbar-header,\n.container-fluid > .navbar-header,\n.container > .navbar-collapse,\n.container-fluid > .navbar-collapse {\n  margin-right: -15px;\n  margin-left: -15px;\n}\n@media (min-width: 768px) {\n  .container > .navbar-header,\n  .container-fluid > .navbar-header,\n  .container > .navbar-collapse,\n  .container-fluid > .navbar-collapse {\n    margin-right: 0;\n    margin-left: 0;\n  }\n}\n.navbar-static-top {\n  z-index: 1000;\n  border-width: 0 0 1px;\n}\n@media (min-width: 768px) {\n  .navbar-static-top {\n    border-radius: 0;\n  }\n}\n.navbar-brand {\n  float: left;\n  height: 50px;\n  padding: 15px 15px;\n  font-size: 18px;\n  line-height: 20px;\n}\n.navbar-brand:hover,\n.navbar-brand:focus {\n  text-decoration: none;\n}\n.navbar-brand > img {\n  display: block;\n}\n@media (min-width: 768px) {\n  .navbar > .container .navbar-brand,\n  .navbar > .container-fluid .navbar-brand {\n    margin-left: -15px;\n  }\n}\n.navbar-toggle {\n  position: relative;\n  float: right;\n  padding: 9px 10px;\n  margin-right: 15px;\n  margin-top: 8px;\n  margin-bottom: 8px;\n  background-color: transparent;\n  background-image: none;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.navbar-toggle:focus {\n  outline: 0;\n}\n.navbar-toggle .icon-bar {\n  display: block;\n  width: 22px;\n  height: 2px;\n  border-radius: 1px;\n}\n.navbar-toggle .icon-bar + .icon-bar {\n  margin-top: 4px;\n}\n@media (min-width: 768px) {\n  .navbar-toggle {\n    display: none;\n  }\n}\n.navbar-nav {\n  margin: 7.5px -15px;\n}\n.navbar-nav > li > a {\n  padding-top: 10px;\n  padding-bottom: 10px;\n  line-height: 20px;\n}\n@media (max-width: 767px) {\n  .navbar-nav .open .dropdown-menu {\n    position: static;\n    float: none;\n    width: auto;\n    margin-top: 0;\n    background-color: transparent;\n    border: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n  .navbar-nav .open .dropdown-menu > li > a,\n  .navbar-nav .open .dropdown-menu .dropdown-header {\n    padding: 5px 15px 5px 25px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a {\n    line-height: 20px;\n  }\n  .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-nav .open .dropdown-menu > li > a:focus {\n    background-image: none;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-nav {\n    float: left;\n    margin: 0;\n  }\n  .navbar-nav > li {\n    float: left;\n  }\n  .navbar-nav > li > a {\n    padding-top: 15px;\n    padding-bottom: 15px;\n  }\n}\n.navbar-form {\n  padding: 10px 15px;\n  margin-right: -15px;\n  margin-left: -15px;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n@media (min-width: 768px) {\n  .navbar-form .form-group {\n    display: inline-block;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control {\n    display: inline-block;\n    width: auto;\n    vertical-align: middle;\n  }\n  .navbar-form .form-control-static {\n    display: inline-block;\n  }\n  .navbar-form .input-group {\n    display: inline-table;\n    vertical-align: middle;\n  }\n  .navbar-form .input-group .input-group-addon,\n  .navbar-form .input-group .input-group-btn,\n  .navbar-form .input-group .form-control {\n    width: auto;\n  }\n  .navbar-form .input-group > .form-control {\n    width: 100%;\n  }\n  .navbar-form .control-label {\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio,\n  .navbar-form .checkbox {\n    display: inline-block;\n    margin-top: 0;\n    margin-bottom: 0;\n    vertical-align: middle;\n  }\n  .navbar-form .radio label,\n  .navbar-form .checkbox label {\n    padding-left: 0;\n  }\n  .navbar-form .radio input[type=\"radio\"],\n  .navbar-form .checkbox input[type=\"checkbox\"] {\n    position: relative;\n    margin-left: 0;\n  }\n  .navbar-form .has-feedback .form-control-feedback {\n    top: 0;\n  }\n}\n@media (max-width: 767px) {\n  .navbar-form .form-group {\n    margin-bottom: 5px;\n  }\n  .navbar-form .form-group:last-child {\n    margin-bottom: 0;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-form {\n    width: auto;\n    padding-top: 0;\n    padding-bottom: 0;\n    margin-right: 0;\n    margin-left: 0;\n    border: 0;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n}\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 0;\n  border-bottom-left-radius: 0;\n}\n.navbar-btn {\n  margin-top: 8px;\n  margin-bottom: 8px;\n}\n.navbar-btn.btn-sm {\n  margin-top: 10px;\n  margin-bottom: 10px;\n}\n.navbar-btn.btn-xs {\n  margin-top: 14px;\n  margin-bottom: 14px;\n}\n.navbar-text {\n  margin-top: 15px;\n  margin-bottom: 15px;\n}\n@media (min-width: 768px) {\n  .navbar-text {\n    float: left;\n    margin-right: 15px;\n    margin-left: 15px;\n  }\n}\n@media (min-width: 768px) {\n  .navbar-left {\n    float: left !important;\n  }\n  .navbar-right {\n    float: right !important;\n    margin-right: -15px;\n  }\n  .navbar-right ~ .navbar-right {\n    margin-right: 0;\n  }\n}\n.navbar-default {\n  background-color: #f8f8f8;\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-brand {\n  color: #777;\n}\n.navbar-default .navbar-brand:hover,\n.navbar-default .navbar-brand:focus {\n  color: #5e5e5e;\n  background-color: transparent;\n}\n.navbar-default .navbar-text {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a {\n  color: #777;\n}\n.navbar-default .navbar-nav > li > a:hover,\n.navbar-default .navbar-nav > li > a:focus {\n  color: #333;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .active > a,\n.navbar-default .navbar-nav > .active > a:hover,\n.navbar-default .navbar-nav > .active > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n.navbar-default .navbar-nav > .disabled > a,\n.navbar-default .navbar-nav > .disabled > a:hover,\n.navbar-default .navbar-nav > .disabled > a:focus {\n  color: #ccc;\n  background-color: transparent;\n}\n.navbar-default .navbar-nav > .open > a,\n.navbar-default .navbar-nav > .open > a:hover,\n.navbar-default .navbar-nav > .open > a:focus {\n  color: #555;\n  background-color: #e7e7e7;\n}\n@media (max-width: 767px) {\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a {\n    color: #777;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #333;\n    background-color: transparent;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #555;\n    background-color: #e7e7e7;\n  }\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #ccc;\n    background-color: transparent;\n  }\n}\n.navbar-default .navbar-toggle {\n  border-color: #ddd;\n}\n.navbar-default .navbar-toggle:hover,\n.navbar-default .navbar-toggle:focus {\n  background-color: #ddd;\n}\n.navbar-default .navbar-toggle .icon-bar {\n  background-color: #888;\n}\n.navbar-default .navbar-collapse,\n.navbar-default .navbar-form {\n  border-color: #e7e7e7;\n}\n.navbar-default .navbar-link {\n  color: #777;\n}\n.navbar-default .navbar-link:hover {\n  color: #333;\n}\n.navbar-default .btn-link {\n  color: #777;\n}\n.navbar-default .btn-link:hover,\n.navbar-default .btn-link:focus {\n  color: #333;\n}\n.navbar-default .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-default .btn-link:hover,\n.navbar-default .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-default .btn-link:focus {\n  color: #ccc;\n}\n.navbar-inverse {\n  background-color: #222;\n  border-color: #080808;\n}\n.navbar-inverse .navbar-brand {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-brand:hover,\n.navbar-inverse .navbar-brand:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-text {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-nav > li > a:hover,\n.navbar-inverse .navbar-nav > li > a:focus {\n  color: #fff;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .active > a,\n.navbar-inverse .navbar-nav > .active > a:hover,\n.navbar-inverse .navbar-nav > .active > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n.navbar-inverse .navbar-nav > .disabled > a,\n.navbar-inverse .navbar-nav > .disabled > a:hover,\n.navbar-inverse .navbar-nav > .disabled > a:focus {\n  color: #444;\n  background-color: transparent;\n}\n.navbar-inverse .navbar-nav > .open > a,\n.navbar-inverse .navbar-nav > .open > a:hover,\n.navbar-inverse .navbar-nav > .open > a:focus {\n  color: #fff;\n  background-color: #080808;\n}\n@media (max-width: 767px) {\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {\n    border-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {\n    color: #9d9d9d;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {\n    color: #fff;\n    background-color: transparent;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {\n    color: #fff;\n    background-color: #080808;\n  }\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,\n  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {\n    color: #444;\n    background-color: transparent;\n  }\n}\n.navbar-inverse .navbar-toggle {\n  border-color: #333;\n}\n.navbar-inverse .navbar-toggle:hover,\n.navbar-inverse .navbar-toggle:focus {\n  background-color: #333;\n}\n.navbar-inverse .navbar-toggle .icon-bar {\n  background-color: #fff;\n}\n.navbar-inverse .navbar-collapse,\n.navbar-inverse .navbar-form {\n  border-color: #101010;\n}\n.navbar-inverse .navbar-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .navbar-link:hover {\n  color: #fff;\n}\n.navbar-inverse .btn-link {\n  color: #9d9d9d;\n}\n.navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link:focus {\n  color: #fff;\n}\n.navbar-inverse .btn-link[disabled]:hover,\nfieldset[disabled] .navbar-inverse .btn-link:hover,\n.navbar-inverse .btn-link[disabled]:focus,\nfieldset[disabled] .navbar-inverse .btn-link:focus {\n  color: #444;\n}\n.breadcrumb {\n  padding: 8px 15px;\n  margin-bottom: 20px;\n  list-style: none;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n}\n.breadcrumb > li {\n  display: inline-block;\n}\n.breadcrumb > li + li:before {\n  padding: 0 5px;\n  color: #ccc;\n  content: \"/\\00a0\";\n}\n.breadcrumb > .active {\n  color: #777777;\n}\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: 20px 0;\n  border-radius: 4px;\n}\n.pagination > li {\n  display: inline;\n}\n.pagination > li > a,\n.pagination > li > span {\n  position: relative;\n  float: left;\n  padding: 6px 12px;\n  margin-left: -1px;\n  line-height: 1.42857143;\n  color: #337ab7;\n  text-decoration: none;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.pagination > li > a:hover,\n.pagination > li > span:hover,\n.pagination > li > a:focus,\n.pagination > li > span:focus {\n  z-index: 2;\n  color: #23527c;\n  background-color: #eeeeee;\n  border-color: #ddd;\n}\n.pagination > li:first-child > a,\n.pagination > li:first-child > span {\n  margin-left: 0;\n  border-top-left-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.pagination > li:last-child > a,\n.pagination > li:last-child > span {\n  border-top-right-radius: 4px;\n  border-bottom-right-radius: 4px;\n}\n.pagination > .active > a,\n.pagination > .active > span,\n.pagination > .active > a:hover,\n.pagination > .active > span:hover,\n.pagination > .active > a:focus,\n.pagination > .active > span:focus {\n  z-index: 3;\n  color: #fff;\n  cursor: default;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.pagination > .disabled > span,\n.pagination > .disabled > span:hover,\n.pagination > .disabled > span:focus,\n.pagination > .disabled > a,\n.pagination > .disabled > a:hover,\n.pagination > .disabled > a:focus {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #fff;\n  border-color: #ddd;\n}\n.pagination-lg > li > a,\n.pagination-lg > li > span {\n  padding: 10px 16px;\n  font-size: 18px;\n  line-height: 1.3333333;\n}\n.pagination-lg > li:first-child > a,\n.pagination-lg > li:first-child > span {\n  border-top-left-radius: 6px;\n  border-bottom-left-radius: 6px;\n}\n.pagination-lg > li:last-child > a,\n.pagination-lg > li:last-child > span {\n  border-top-right-radius: 6px;\n  border-bottom-right-radius: 6px;\n}\n.pagination-sm > li > a,\n.pagination-sm > li > span {\n  padding: 5px 10px;\n  font-size: 12px;\n  line-height: 1.5;\n}\n.pagination-sm > li:first-child > a,\n.pagination-sm > li:first-child > span {\n  border-top-left-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.pagination-sm > li:last-child > a,\n.pagination-sm > li:last-child > span {\n  border-top-right-radius: 3px;\n  border-bottom-right-radius: 3px;\n}\n.pager {\n  padding-left: 0;\n  margin: 20px 0;\n  text-align: center;\n  list-style: none;\n}\n.pager li {\n  display: inline;\n}\n.pager li > a,\n.pager li > span {\n  display: inline-block;\n  padding: 5px 14px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 15px;\n}\n.pager li > a:hover,\n.pager li > a:focus {\n  text-decoration: none;\n  background-color: #eeeeee;\n}\n.pager .next > a,\n.pager .next > span {\n  float: right;\n}\n.pager .previous > a,\n.pager .previous > span {\n  float: left;\n}\n.pager .disabled > a,\n.pager .disabled > a:hover,\n.pager .disabled > a:focus,\n.pager .disabled > span {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #fff;\n}\n.label {\n  display: inline;\n  padding: 0.2em 0.6em 0.3em;\n  font-size: 75%;\n  font-weight: 700;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: 0.25em;\n}\na.label:hover,\na.label:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.label:empty {\n  display: none;\n}\n.btn .label {\n  position: relative;\n  top: -1px;\n}\n.label-default {\n  background-color: #777777;\n}\n.label-default[href]:hover,\n.label-default[href]:focus {\n  background-color: #5e5e5e;\n}\n.label-primary {\n  background-color: #337ab7;\n}\n.label-primary[href]:hover,\n.label-primary[href]:focus {\n  background-color: #286090;\n}\n.label-success {\n  background-color: #5cb85c;\n}\n.label-success[href]:hover,\n.label-success[href]:focus {\n  background-color: #449d44;\n}\n.label-info {\n  background-color: #5bc0de;\n}\n.label-info[href]:hover,\n.label-info[href]:focus {\n  background-color: #31b0d5;\n}\n.label-warning {\n  background-color: #f0ad4e;\n}\n.label-warning[href]:hover,\n.label-warning[href]:focus {\n  background-color: #ec971f;\n}\n.label-danger {\n  background-color: #d9534f;\n}\n.label-danger[href]:hover,\n.label-danger[href]:focus {\n  background-color: #c9302c;\n}\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: 12px;\n  font-weight: bold;\n  line-height: 1;\n  color: #fff;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  background-color: #777777;\n  border-radius: 10px;\n}\n.badge:empty {\n  display: none;\n}\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n.btn-xs .badge,\n.btn-group-xs > .btn .badge {\n  top: 0;\n  padding: 1px 5px;\n}\na.badge:hover,\na.badge:focus {\n  color: #fff;\n  text-decoration: none;\n  cursor: pointer;\n}\n.list-group-item.active > .badge,\n.nav-pills > .active > a > .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.list-group-item > .badge {\n  float: right;\n}\n.list-group-item > .badge + .badge {\n  margin-right: 5px;\n}\n.nav-pills > li > a > .badge {\n  margin-left: 3px;\n}\n.jumbotron {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  margin-bottom: 30px;\n  color: inherit;\n  background-color: #eeeeee;\n}\n.jumbotron h1,\n.jumbotron .h1 {\n  color: inherit;\n}\n.jumbotron p {\n  margin-bottom: 15px;\n  font-size: 21px;\n  font-weight: 200;\n}\n.jumbotron > hr {\n  border-top-color: #d5d5d5;\n}\n.container .jumbotron,\n.container-fluid .jumbotron {\n  padding-right: 15px;\n  padding-left: 15px;\n  border-radius: 6px;\n}\n.jumbotron .container {\n  max-width: 100%;\n}\n@media screen and (min-width: 768px) {\n  .jumbotron {\n    padding-top: 48px;\n    padding-bottom: 48px;\n  }\n  .container .jumbotron,\n  .container-fluid .jumbotron {\n    padding-right: 60px;\n    padding-left: 60px;\n  }\n  .jumbotron h1,\n  .jumbotron .h1 {\n    font-size: 63px;\n  }\n}\n.thumbnail {\n  display: block;\n  padding: 4px;\n  margin-bottom: 20px;\n  line-height: 1.42857143;\n  background-color: #fff;\n  border: 1px solid #ddd;\n  border-radius: 4px;\n  -webkit-transition: border 0.2s ease-in-out;\n  -o-transition: border 0.2s ease-in-out;\n  transition: border 0.2s ease-in-out;\n}\n.thumbnail > img,\n.thumbnail a > img {\n  margin-right: auto;\n  margin-left: auto;\n}\na.thumbnail:hover,\na.thumbnail:focus,\na.thumbnail.active {\n  border-color: #337ab7;\n}\n.thumbnail .caption {\n  padding: 9px;\n  color: #333333;\n}\n.alert {\n  padding: 15px;\n  margin-bottom: 20px;\n  border: 1px solid transparent;\n  border-radius: 4px;\n}\n.alert h4 {\n  margin-top: 0;\n  color: inherit;\n}\n.alert .alert-link {\n  font-weight: bold;\n}\n.alert > p,\n.alert > ul {\n  margin-bottom: 0;\n}\n.alert > p + p {\n  margin-top: 5px;\n}\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: 35px;\n}\n.alert-dismissable .close,\n.alert-dismissible .close {\n  position: relative;\n  top: -2px;\n  right: -21px;\n  color: inherit;\n}\n.alert-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.alert-success hr {\n  border-top-color: #c9e2b3;\n}\n.alert-success .alert-link {\n  color: #2b542c;\n}\n.alert-info {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.alert-info hr {\n  border-top-color: #a6e1ec;\n}\n.alert-info .alert-link {\n  color: #245269;\n}\n.alert-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.alert-warning hr {\n  border-top-color: #f7e1b5;\n}\n.alert-warning .alert-link {\n  color: #66512c;\n}\n.alert-danger {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.alert-danger hr {\n  border-top-color: #e4b9c0;\n}\n.alert-danger .alert-link {\n  color: #843534;\n}\n@-webkit-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@-o-keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n@keyframes progress-bar-stripes {\n  from {\n    background-position: 40px 0;\n  }\n  to {\n    background-position: 0 0;\n  }\n}\n.progress {\n  height: 20px;\n  margin-bottom: 20px;\n  overflow: hidden;\n  background-color: #f5f5f5;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);\n}\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: 12px;\n  line-height: 20px;\n  color: #fff;\n  text-align: center;\n  background-color: #337ab7;\n  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);\n  -webkit-transition: width 0.6s ease;\n  -o-transition: width 0.6s ease;\n  transition: width 0.6s ease;\n}\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  -webkit-background-size: 40px 40px;\n  background-size: 40px 40px;\n}\n.progress.active .progress-bar,\n.progress-bar.active {\n  -webkit-animation: progress-bar-stripes 2s linear infinite;\n  -o-animation: progress-bar-stripes 2s linear infinite;\n  animation: progress-bar-stripes 2s linear infinite;\n}\n.progress-bar-success {\n  background-color: #5cb85c;\n}\n.progress-striped .progress-bar-success {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-info {\n  background-color: #5bc0de;\n}\n.progress-striped .progress-bar-info {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-warning {\n  background-color: #f0ad4e;\n}\n.progress-striped .progress-bar-warning {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.progress-bar-danger {\n  background-color: #d9534f;\n}\n.progress-striped .progress-bar-danger {\n  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);\n}\n.media {\n  margin-top: 15px;\n}\n.media:first-child {\n  margin-top: 0;\n}\n.media,\n.media-body {\n  overflow: hidden;\n  zoom: 1;\n}\n.media-body {\n  width: 10000px;\n}\n.media-object {\n  display: block;\n}\n.media-object.img-thumbnail {\n  max-width: none;\n}\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n.media-middle {\n  vertical-align: middle;\n}\n.media-bottom {\n  vertical-align: bottom;\n}\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n.list-group {\n  padding-left: 0;\n  margin-bottom: 20px;\n}\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  margin-bottom: -1px;\n  background-color: #fff;\n  border: 1px solid #ddd;\n}\n.list-group-item:first-child {\n  border-top-left-radius: 4px;\n  border-top-right-radius: 4px;\n}\n.list-group-item:last-child {\n  margin-bottom: 0;\n  border-bottom-right-radius: 4px;\n  border-bottom-left-radius: 4px;\n}\n.list-group-item.disabled,\n.list-group-item.disabled:hover,\n.list-group-item.disabled:focus {\n  color: #777777;\n  cursor: not-allowed;\n  background-color: #eeeeee;\n}\n.list-group-item.disabled .list-group-item-heading,\n.list-group-item.disabled:hover .list-group-item-heading,\n.list-group-item.disabled:focus .list-group-item-heading {\n  color: inherit;\n}\n.list-group-item.disabled .list-group-item-text,\n.list-group-item.disabled:hover .list-group-item-text,\n.list-group-item.disabled:focus .list-group-item-text {\n  color: #777777;\n}\n.list-group-item.active,\n.list-group-item.active:hover,\n.list-group-item.active:focus {\n  z-index: 2;\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.list-group-item.active .list-group-item-heading,\n.list-group-item.active:hover .list-group-item-heading,\n.list-group-item.active:focus .list-group-item-heading,\n.list-group-item.active .list-group-item-heading > small,\n.list-group-item.active:hover .list-group-item-heading > small,\n.list-group-item.active:focus .list-group-item-heading > small,\n.list-group-item.active .list-group-item-heading > .small,\n.list-group-item.active:hover .list-group-item-heading > .small,\n.list-group-item.active:focus .list-group-item-heading > .small {\n  color: inherit;\n}\n.list-group-item.active .list-group-item-text,\n.list-group-item.active:hover .list-group-item-text,\n.list-group-item.active:focus .list-group-item-text {\n  color: #c7ddef;\n}\na.list-group-item,\nbutton.list-group-item {\n  color: #555;\n}\na.list-group-item .list-group-item-heading,\nbutton.list-group-item .list-group-item-heading {\n  color: #333;\n}\na.list-group-item:hover,\nbutton.list-group-item:hover,\na.list-group-item:focus,\nbutton.list-group-item:focus {\n  color: #555;\n  text-decoration: none;\n  background-color: #f5f5f5;\n}\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n.list-group-item-success {\n  color: #3c763d;\n  background-color: #dff0d8;\n}\na.list-group-item-success,\nbutton.list-group-item-success {\n  color: #3c763d;\n}\na.list-group-item-success .list-group-item-heading,\nbutton.list-group-item-success .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-success:hover,\nbutton.list-group-item-success:hover,\na.list-group-item-success:focus,\nbutton.list-group-item-success:focus {\n  color: #3c763d;\n  background-color: #d0e9c6;\n}\na.list-group-item-success.active,\nbutton.list-group-item-success.active,\na.list-group-item-success.active:hover,\nbutton.list-group-item-success.active:hover,\na.list-group-item-success.active:focus,\nbutton.list-group-item-success.active:focus {\n  color: #fff;\n  background-color: #3c763d;\n  border-color: #3c763d;\n}\n.list-group-item-info {\n  color: #31708f;\n  background-color: #d9edf7;\n}\na.list-group-item-info,\nbutton.list-group-item-info {\n  color: #31708f;\n}\na.list-group-item-info .list-group-item-heading,\nbutton.list-group-item-info .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-info:hover,\nbutton.list-group-item-info:hover,\na.list-group-item-info:focus,\nbutton.list-group-item-info:focus {\n  color: #31708f;\n  background-color: #c4e3f3;\n}\na.list-group-item-info.active,\nbutton.list-group-item-info.active,\na.list-group-item-info.active:hover,\nbutton.list-group-item-info.active:hover,\na.list-group-item-info.active:focus,\nbutton.list-group-item-info.active:focus {\n  color: #fff;\n  background-color: #31708f;\n  border-color: #31708f;\n}\n.list-group-item-warning {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n}\na.list-group-item-warning,\nbutton.list-group-item-warning {\n  color: #8a6d3b;\n}\na.list-group-item-warning .list-group-item-heading,\nbutton.list-group-item-warning .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-warning:hover,\nbutton.list-group-item-warning:hover,\na.list-group-item-warning:focus,\nbutton.list-group-item-warning:focus {\n  color: #8a6d3b;\n  background-color: #faf2cc;\n}\na.list-group-item-warning.active,\nbutton.list-group-item-warning.active,\na.list-group-item-warning.active:hover,\nbutton.list-group-item-warning.active:hover,\na.list-group-item-warning.active:focus,\nbutton.list-group-item-warning.active:focus {\n  color: #fff;\n  background-color: #8a6d3b;\n  border-color: #8a6d3b;\n}\n.list-group-item-danger {\n  color: #a94442;\n  background-color: #f2dede;\n}\na.list-group-item-danger,\nbutton.list-group-item-danger {\n  color: #a94442;\n}\na.list-group-item-danger .list-group-item-heading,\nbutton.list-group-item-danger .list-group-item-heading {\n  color: inherit;\n}\na.list-group-item-danger:hover,\nbutton.list-group-item-danger:hover,\na.list-group-item-danger:focus,\nbutton.list-group-item-danger:focus {\n  color: #a94442;\n  background-color: #ebcccc;\n}\na.list-group-item-danger.active,\nbutton.list-group-item-danger.active,\na.list-group-item-danger.active:hover,\nbutton.list-group-item-danger.active:hover,\na.list-group-item-danger.active:focus,\nbutton.list-group-item-danger.active:focus {\n  color: #fff;\n  background-color: #a94442;\n  border-color: #a94442;\n}\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n.panel {\n  margin-bottom: 20px;\n  background-color: #fff;\n  border: 1px solid transparent;\n  border-radius: 4px;\n  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.panel-body {\n  padding: 15px;\n}\n.panel-heading {\n  padding: 10px 15px;\n  border-bottom: 1px solid transparent;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel-heading > .dropdown .dropdown-toggle {\n  color: inherit;\n}\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: 16px;\n  color: inherit;\n}\n.panel-title > a,\n.panel-title > small,\n.panel-title > .small,\n.panel-title > small > a,\n.panel-title > .small > a {\n  color: inherit;\n}\n.panel-footer {\n  padding: 10px 15px;\n  background-color: #f5f5f5;\n  border-top: 1px solid #ddd;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .list-group,\n.panel > .panel-collapse > .list-group {\n  margin-bottom: 0;\n}\n.panel > .list-group .list-group-item,\n.panel > .panel-collapse > .list-group .list-group-item {\n  border-width: 1px 0;\n  border-radius: 0;\n}\n.panel > .list-group:first-child .list-group-item:first-child,\n.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {\n  border-top: 0;\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .list-group:last-child .list-group-item:last-child,\n.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {\n  border-bottom: 0;\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {\n  border-top-left-radius: 0;\n  border-top-right-radius: 0;\n}\n.panel-heading + .list-group .list-group-item:first-child {\n  border-top-width: 0;\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n.panel > .table,\n.panel > .table-responsive > .table,\n.panel > .panel-collapse > .table {\n  margin-bottom: 0;\n}\n.panel > .table caption,\n.panel > .table-responsive > .table caption,\n.panel > .panel-collapse > .table caption {\n  padding-right: 15px;\n  padding-left: 15px;\n}\n.panel > .table:first-child,\n.panel > .table-responsive:first-child > .table:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {\n  border-top-left-radius: 3px;\n  border-top-right-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {\n  border-top-left-radius: 3px;\n}\n.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,\n.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,\n.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,\n.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {\n  border-top-right-radius: 3px;\n}\n.panel > .table:last-child,\n.panel > .table-responsive:last-child > .table:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {\n  border-bottom-right-radius: 3px;\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {\n  border-bottom-left-radius: 3px;\n}\n.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,\n.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,\n.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,\n.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {\n  border-bottom-right-radius: 3px;\n}\n.panel > .panel-body + .table,\n.panel > .panel-body + .table-responsive,\n.panel > .table + .panel-body,\n.panel > .table-responsive + .panel-body {\n  border-top: 1px solid #ddd;\n}\n.panel > .table > tbody:first-child > tr:first-child th,\n.panel > .table > tbody:first-child > tr:first-child td {\n  border-top: 0;\n}\n.panel > .table-bordered,\n.panel > .table-responsive > .table-bordered {\n  border: 0;\n}\n.panel > .table-bordered > thead > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,\n.panel > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,\n.panel > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,\n.panel > .table-bordered > thead > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,\n.panel > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,\n.panel > .table-bordered > tfoot > tr > td:first-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {\n  border-left: 0;\n}\n.panel > .table-bordered > thead > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,\n.panel > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,\n.panel > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,\n.panel > .table-bordered > thead > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,\n.panel > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,\n.panel > .table-bordered > tfoot > tr > td:last-child,\n.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {\n  border-right: 0;\n}\n.panel > .table-bordered > thead > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,\n.panel > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,\n.panel > .table-bordered > thead > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,\n.panel > .table-bordered > tbody > tr:first-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {\n  border-bottom: 0;\n}\n.panel > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,\n.panel > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,\n.panel > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,\n.panel > .table-bordered > tfoot > tr:last-child > th,\n.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {\n  border-bottom: 0;\n}\n.panel > .table-responsive {\n  margin-bottom: 0;\n  border: 0;\n}\n.panel-group {\n  margin-bottom: 20px;\n}\n.panel-group .panel {\n  margin-bottom: 0;\n  border-radius: 4px;\n}\n.panel-group .panel + .panel {\n  margin-top: 5px;\n}\n.panel-group .panel-heading {\n  border-bottom: 0;\n}\n.panel-group .panel-heading + .panel-collapse > .panel-body,\n.panel-group .panel-heading + .panel-collapse > .list-group {\n  border-top: 1px solid #ddd;\n}\n.panel-group .panel-footer {\n  border-top: 0;\n}\n.panel-group .panel-footer + .panel-collapse .panel-body {\n  border-bottom: 1px solid #ddd;\n}\n.panel-default {\n  border-color: #ddd;\n}\n.panel-default > .panel-heading {\n  color: #333333;\n  background-color: #f5f5f5;\n  border-color: #ddd;\n}\n.panel-default > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ddd;\n}\n.panel-default > .panel-heading .badge {\n  color: #f5f5f5;\n  background-color: #333333;\n}\n.panel-default > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ddd;\n}\n.panel-primary {\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading {\n  color: #fff;\n  background-color: #337ab7;\n  border-color: #337ab7;\n}\n.panel-primary > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #337ab7;\n}\n.panel-primary > .panel-heading .badge {\n  color: #337ab7;\n  background-color: #fff;\n}\n.panel-primary > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #337ab7;\n}\n.panel-success {\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading {\n  color: #3c763d;\n  background-color: #dff0d8;\n  border-color: #d6e9c6;\n}\n.panel-success > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #d6e9c6;\n}\n.panel-success > .panel-heading .badge {\n  color: #dff0d8;\n  background-color: #3c763d;\n}\n.panel-success > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #d6e9c6;\n}\n.panel-info {\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading {\n  color: #31708f;\n  background-color: #d9edf7;\n  border-color: #bce8f1;\n}\n.panel-info > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #bce8f1;\n}\n.panel-info > .panel-heading .badge {\n  color: #d9edf7;\n  background-color: #31708f;\n}\n.panel-info > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #bce8f1;\n}\n.panel-warning {\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading {\n  color: #8a6d3b;\n  background-color: #fcf8e3;\n  border-color: #faebcc;\n}\n.panel-warning > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #faebcc;\n}\n.panel-warning > .panel-heading .badge {\n  color: #fcf8e3;\n  background-color: #8a6d3b;\n}\n.panel-warning > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #faebcc;\n}\n.panel-danger {\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading {\n  color: #a94442;\n  background-color: #f2dede;\n  border-color: #ebccd1;\n}\n.panel-danger > .panel-heading + .panel-collapse > .panel-body {\n  border-top-color: #ebccd1;\n}\n.panel-danger > .panel-heading .badge {\n  color: #f2dede;\n  background-color: #a94442;\n}\n.panel-danger > .panel-footer + .panel-collapse > .panel-body {\n  border-bottom-color: #ebccd1;\n}\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n}\n.embed-responsive .embed-responsive-item,\n.embed-responsive iframe,\n.embed-responsive embed,\n.embed-responsive object,\n.embed-responsive video {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  border: 0;\n}\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: #f5f5f5;\n  border: 1px solid #e3e3e3;\n  border-radius: 4px;\n  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);\n}\n.well blockquote {\n  border-color: #ddd;\n  border-color: rgba(0, 0, 0, 0.15);\n}\n.well-lg {\n  padding: 24px;\n  border-radius: 6px;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: 3px;\n}\n.close {\n  float: right;\n  font-size: 21px;\n  font-weight: bold;\n  line-height: 1;\n  color: #000;\n  text-shadow: 0 1px 0 #fff;\n  filter: alpha(opacity=20);\n  opacity: 0.2;\n}\n.close:hover,\n.close:focus {\n  color: #000;\n  text-decoration: none;\n  cursor: pointer;\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\nbutton.close {\n  padding: 0;\n  cursor: pointer;\n  background: transparent;\n  border: 0;\n  -webkit-appearance: none;\n  -moz-appearance: none;\n  appearance: none;\n}\n.modal-open {\n  overflow: hidden;\n}\n.modal {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1050;\n  display: none;\n  overflow: hidden;\n  -webkit-overflow-scrolling: touch;\n  outline: 0;\n}\n.modal.fade .modal-dialog {\n  -webkit-transform: translate(0, -25%);\n  -ms-transform: translate(0, -25%);\n  -o-transform: translate(0, -25%);\n  transform: translate(0, -25%);\n  -webkit-transition: -webkit-transform 0.3s ease-out;\n  -o-transition: -o-transform 0.3s ease-out;\n  transition: -webkit-transform 0.3s ease-out;\n  transition: transform 0.3s ease-out;\n  transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out, -o-transform 0.3s ease-out;\n}\n.modal.in .modal-dialog {\n  -webkit-transform: translate(0, 0);\n  -ms-transform: translate(0, 0);\n  -o-transform: translate(0, 0);\n  transform: translate(0, 0);\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n.modal-content {\n  position: relative;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #999;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);\n  outline: 0;\n}\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 1040;\n  background-color: #000;\n}\n.modal-backdrop.fade {\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.modal-backdrop.in {\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\n.modal-header {\n  padding: 15px;\n  border-bottom: 1px solid #e5e5e5;\n}\n.modal-header .close {\n  margin-top: -2px;\n}\n.modal-title {\n  margin: 0;\n  line-height: 1.42857143;\n}\n.modal-body {\n  position: relative;\n  padding: 15px;\n}\n.modal-footer {\n  padding: 15px;\n  text-align: right;\n  border-top: 1px solid #e5e5e5;\n}\n.modal-footer .btn + .btn {\n  margin-bottom: 0;\n  margin-left: 5px;\n}\n.modal-footer .btn-group .btn + .btn {\n  margin-left: -1px;\n}\n.modal-footer .btn-block + .btn-block {\n  margin-left: 0;\n}\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n@media (min-width: 768px) {\n  .modal-dialog {\n    width: 600px;\n    margin: 30px auto;\n  }\n  .modal-content {\n    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);\n  }\n  .modal-sm {\n    width: 300px;\n  }\n}\n@media (min-width: 992px) {\n  .modal-lg {\n    width: 900px;\n  }\n}\n.tooltip {\n  position: absolute;\n  z-index: 1070;\n  display: block;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.42857143;\n  line-break: auto;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  font-size: 12px;\n  filter: alpha(opacity=0);\n  opacity: 0;\n}\n.tooltip.in {\n  filter: alpha(opacity=90);\n  opacity: 0.9;\n}\n.tooltip.top {\n  padding: 5px 0;\n  margin-top: -3px;\n}\n.tooltip.right {\n  padding: 0 5px;\n  margin-left: 3px;\n}\n.tooltip.bottom {\n  padding: 5px 0;\n  margin-top: 3px;\n}\n.tooltip.left {\n  padding: 0 5px;\n  margin-left: -3px;\n}\n.tooltip.top .tooltip-arrow {\n  bottom: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-left .tooltip-arrow {\n  right: 5px;\n  bottom: 0;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.top-right .tooltip-arrow {\n  bottom: 0;\n  left: 5px;\n  margin-bottom: -5px;\n  border-width: 5px 5px 0;\n  border-top-color: #000;\n}\n.tooltip.right .tooltip-arrow {\n  top: 50%;\n  left: 0;\n  margin-top: -5px;\n  border-width: 5px 5px 5px 0;\n  border-right-color: #000;\n}\n.tooltip.left .tooltip-arrow {\n  top: 50%;\n  right: 0;\n  margin-top: -5px;\n  border-width: 5px 0 5px 5px;\n  border-left-color: #000;\n}\n.tooltip.bottom .tooltip-arrow {\n  top: 0;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-left .tooltip-arrow {\n  top: 0;\n  right: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip.bottom-right .tooltip-arrow {\n  top: 0;\n  left: 5px;\n  margin-top: -5px;\n  border-width: 0 5px 5px;\n  border-bottom-color: #000;\n}\n.tooltip-inner {\n  max-width: 200px;\n  padding: 3px 8px;\n  color: #fff;\n  text-align: center;\n  background-color: #000;\n  border-radius: 4px;\n}\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: 1060;\n  display: none;\n  max-width: 276px;\n  padding: 1px;\n  font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1.42857143;\n  line-break: auto;\n  text-align: left;\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n  font-size: 14px;\n  background-color: #fff;\n  background-clip: padding-box;\n  border: 1px solid #ccc;\n  border: 1px solid rgba(0, 0, 0, 0.2);\n  border-radius: 6px;\n  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);\n}\n.popover.top {\n  margin-top: -10px;\n}\n.popover.right {\n  margin-left: 10px;\n}\n.popover.bottom {\n  margin-top: 10px;\n}\n.popover.left {\n  margin-left: -10px;\n}\n.popover > .arrow {\n  border-width: 11px;\n}\n.popover > .arrow,\n.popover > .arrow:after {\n  position: absolute;\n  display: block;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n.popover > .arrow:after {\n  content: \"\";\n  border-width: 10px;\n}\n.popover.top > .arrow {\n  bottom: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-color: #999999;\n  border-top-color: rgba(0, 0, 0, 0.25);\n  border-bottom-width: 0;\n}\n.popover.top > .arrow:after {\n  bottom: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-color: #fff;\n  border-bottom-width: 0;\n}\n.popover.right > .arrow {\n  top: 50%;\n  left: -11px;\n  margin-top: -11px;\n  border-right-color: #999999;\n  border-right-color: rgba(0, 0, 0, 0.25);\n  border-left-width: 0;\n}\n.popover.right > .arrow:after {\n  bottom: -10px;\n  left: 1px;\n  content: \" \";\n  border-right-color: #fff;\n  border-left-width: 0;\n}\n.popover.bottom > .arrow {\n  top: -11px;\n  left: 50%;\n  margin-left: -11px;\n  border-top-width: 0;\n  border-bottom-color: #999999;\n  border-bottom-color: rgba(0, 0, 0, 0.25);\n}\n.popover.bottom > .arrow:after {\n  top: 1px;\n  margin-left: -10px;\n  content: \" \";\n  border-top-width: 0;\n  border-bottom-color: #fff;\n}\n.popover.left > .arrow {\n  top: 50%;\n  right: -11px;\n  margin-top: -11px;\n  border-right-width: 0;\n  border-left-color: #999999;\n  border-left-color: rgba(0, 0, 0, 0.25);\n}\n.popover.left > .arrow:after {\n  right: 1px;\n  bottom: -10px;\n  content: \" \";\n  border-right-width: 0;\n  border-left-color: #fff;\n}\n.popover-title {\n  padding: 8px 14px;\n  margin: 0;\n  font-size: 14px;\n  background-color: #f7f7f7;\n  border-bottom: 1px solid #ebebeb;\n  border-radius: 5px 5px 0 0;\n}\n.popover-content {\n  padding: 9px 14px;\n}\n.carousel {\n  position: relative;\n}\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n}\n.carousel-inner > .item {\n  position: relative;\n  display: none;\n  -webkit-transition: 0.6s ease-in-out left;\n  -o-transition: 0.6s ease-in-out left;\n  transition: 0.6s ease-in-out left;\n}\n.carousel-inner > .item > img,\n.carousel-inner > .item > a > img {\n  line-height: 1;\n}\n@media all and (transform-3d), (-webkit-transform-3d) {\n  .carousel-inner > .item {\n    -webkit-transition: -webkit-transform 0.6s ease-in-out;\n    -o-transition: -o-transform 0.6s ease-in-out;\n    transition: -webkit-transform 0.6s ease-in-out;\n    transition: transform 0.6s ease-in-out;\n    transition: transform 0.6s ease-in-out, -webkit-transform 0.6s ease-in-out, -o-transform 0.6s ease-in-out;\n    -webkit-backface-visibility: hidden;\n    backface-visibility: hidden;\n    -webkit-perspective: 1000px;\n    perspective: 1000px;\n  }\n  .carousel-inner > .item.next,\n  .carousel-inner > .item.active.right {\n    -webkit-transform: translate3d(100%, 0, 0);\n    transform: translate3d(100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.prev,\n  .carousel-inner > .item.active.left {\n    -webkit-transform: translate3d(-100%, 0, 0);\n    transform: translate3d(-100%, 0, 0);\n    left: 0;\n  }\n  .carousel-inner > .item.next.left,\n  .carousel-inner > .item.prev.right,\n  .carousel-inner > .item.active {\n    -webkit-transform: translate3d(0, 0, 0);\n    transform: translate3d(0, 0, 0);\n    left: 0;\n  }\n}\n.carousel-inner > .active,\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  display: block;\n}\n.carousel-inner > .active {\n  left: 0;\n}\n.carousel-inner > .next,\n.carousel-inner > .prev {\n  position: absolute;\n  top: 0;\n  width: 100%;\n}\n.carousel-inner > .next {\n  left: 100%;\n}\n.carousel-inner > .prev {\n  left: -100%;\n}\n.carousel-inner > .next.left,\n.carousel-inner > .prev.right {\n  left: 0;\n}\n.carousel-inner > .active.left {\n  left: -100%;\n}\n.carousel-inner > .active.right {\n  left: 100%;\n}\n.carousel-control {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: 15%;\n  font-size: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n  background-color: rgba(0, 0, 0, 0);\n  filter: alpha(opacity=50);\n  opacity: 0.5;\n}\n.carousel-control.left {\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.5)), to(rgba(0, 0, 0, 0.0001)));\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control.right {\n  right: 0;\n  left: auto;\n  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  background-image: -webkit-gradient(linear, left top, right top, from(rgba(0, 0, 0, 0.0001)), to(rgba(0, 0, 0, 0.5)));\n  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);\n  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);\n  background-repeat: repeat-x;\n}\n.carousel-control:hover,\n.carousel-control:focus {\n  color: #fff;\n  text-decoration: none;\n  outline: 0;\n  filter: alpha(opacity=90);\n  opacity: 0.9;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-left,\n.carousel-control .glyphicon-chevron-right {\n  position: absolute;\n  top: 50%;\n  z-index: 5;\n  display: inline-block;\n  margin-top: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .glyphicon-chevron-left {\n  left: 50%;\n  margin-left: -10px;\n}\n.carousel-control .icon-next,\n.carousel-control .glyphicon-chevron-right {\n  right: 50%;\n  margin-right: -10px;\n}\n.carousel-control .icon-prev,\n.carousel-control .icon-next {\n  width: 20px;\n  height: 20px;\n  font-family: serif;\n  line-height: 1;\n}\n.carousel-control .icon-prev:before {\n  content: \"\\2039\";\n}\n.carousel-control .icon-next:before {\n  content: \"\\203a\";\n}\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  padding-left: 0;\n  margin-left: -30%;\n  text-align: center;\n  list-style: none;\n}\n.carousel-indicators li {\n  display: inline-block;\n  width: 10px;\n  height: 10px;\n  margin: 1px;\n  text-indent: -999px;\n  cursor: pointer;\n  background-color: #000 \\9;\n  background-color: rgba(0, 0, 0, 0);\n  border: 1px solid #fff;\n  border-radius: 10px;\n}\n.carousel-indicators .active {\n  width: 12px;\n  height: 12px;\n  margin: 0;\n  background-color: #fff;\n}\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 20px;\n  left: 15%;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: #fff;\n  text-align: center;\n  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);\n}\n.carousel-caption .btn {\n  text-shadow: none;\n}\n@media screen and (min-width: 768px) {\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-prev,\n  .carousel-control .icon-next {\n    width: 30px;\n    height: 30px;\n    margin-top: -10px;\n    font-size: 30px;\n  }\n  .carousel-control .glyphicon-chevron-left,\n  .carousel-control .icon-prev {\n    margin-left: -10px;\n  }\n  .carousel-control .glyphicon-chevron-right,\n  .carousel-control .icon-next {\n    margin-right: -10px;\n  }\n  .carousel-caption {\n    right: 20%;\n    left: 20%;\n    padding-bottom: 30px;\n  }\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n.clearfix:before,\n.clearfix:after,\n.dl-horizontal dd:before,\n.dl-horizontal dd:after,\n.container:before,\n.container:after,\n.container-fluid:before,\n.container-fluid:after,\n.row:before,\n.row:after,\n.form-horizontal .form-group:before,\n.form-horizontal .form-group:after,\n.btn-toolbar:before,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:before,\n.btn-group-vertical > .btn-group:after,\n.nav:before,\n.nav:after,\n.navbar:before,\n.navbar:after,\n.navbar-header:before,\n.navbar-header:after,\n.navbar-collapse:before,\n.navbar-collapse:after,\n.pager:before,\n.pager:after,\n.panel-body:before,\n.panel-body:after,\n.modal-header:before,\n.modal-header:after,\n.modal-footer:before,\n.modal-footer:after {\n  display: table;\n  content: \" \";\n}\n.clearfix:after,\n.dl-horizontal dd:after,\n.container:after,\n.container-fluid:after,\n.row:after,\n.form-horizontal .form-group:after,\n.btn-toolbar:after,\n.btn-group-vertical > .btn-group:after,\n.nav:after,\n.navbar:after,\n.navbar-header:after,\n.navbar-collapse:after,\n.pager:after,\n.panel-body:after,\n.modal-header:after,\n.modal-footer:after {\n  clear: both;\n}\n.center-block {\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  font: 0/0 a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n.hidden {\n  display: none !important;\n}\n.affix {\n  position: fixed;\n}\n@-ms-viewport {\n  width: device-width;\n}\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  display: none !important;\n}\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n@media (max-width: 767px) {\n  .visible-xs {\n    display: block !important;\n  }\n  table.visible-xs {\n    display: table !important;\n  }\n  tr.visible-xs {\n    display: table-row !important;\n  }\n  th.visible-xs,\n  td.visible-xs {\n    display: table-cell !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-block {\n    display: block !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline {\n    display: inline !important;\n  }\n}\n@media (max-width: 767px) {\n  .visible-xs-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm {\n    display: block !important;\n  }\n  table.visible-sm {\n    display: table !important;\n  }\n  tr.visible-sm {\n    display: table-row !important;\n  }\n  th.visible-sm,\n  td.visible-sm {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-block {\n    display: block !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .visible-sm-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md {\n    display: block !important;\n  }\n  table.visible-md {\n    display: table !important;\n  }\n  tr.visible-md {\n    display: table-row !important;\n  }\n  th.visible-md,\n  td.visible-md {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-block {\n    display: block !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .visible-md-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg {\n    display: block !important;\n  }\n  table.visible-lg {\n    display: table !important;\n  }\n  tr.visible-lg {\n    display: table-row !important;\n  }\n  th.visible-lg,\n  td.visible-lg {\n    display: table-cell !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-block {\n    display: block !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline {\n    display: inline !important;\n  }\n}\n@media (min-width: 1200px) {\n  .visible-lg-inline-block {\n    display: inline-block !important;\n  }\n}\n@media (max-width: 767px) {\n  .hidden-xs {\n    display: none !important;\n  }\n}\n@media (min-width: 768px) and (max-width: 991px) {\n  .hidden-sm {\n    display: none !important;\n  }\n}\n@media (min-width: 992px) and (max-width: 1199px) {\n  .hidden-md {\n    display: none !important;\n  }\n}\n@media (min-width: 1200px) {\n  .hidden-lg {\n    display: none !important;\n  }\n}\n.visible-print {\n  display: none !important;\n}\n@media print {\n  .visible-print {\n    display: block !important;\n  }\n  table.visible-print {\n    display: table !important;\n  }\n  tr.visible-print {\n    display: table-row !important;\n  }\n  th.visible-print,\n  td.visible-print {\n    display: table-cell !important;\n  }\n}\n.visible-print-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-block {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n}\n@media print {\n  .visible-print-inline-block {\n    display: inline-block !important;\n  }\n}\n@media print {\n  .hidden-print {\n    display: none !important;\n  }\n}\n/*# sourceMappingURL=bootstrap.css.map */","// stylelint-disable declaration-no-important, selector-no-qualifying-type\n\n/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */\n\n// ==========================================================================\n// Print styles.\n// Inlined to avoid the additional HTTP request: h5bp.com/r\n// ==========================================================================\n\n@media print {\n  *,\n  *:before,\n  *:after {\n    color: #000 !important; // Black prints faster: h5bp.com/s\n    text-shadow: none !important;\n    background: transparent !important;\n    box-shadow: none !important;\n  }\n\n  a,\n  a:visited {\n    text-decoration: underline;\n  }\n\n  a[href]:after {\n    content: \" (\" attr(href) \")\";\n  }\n\n  abbr[title]:after {\n    content: \" (\" attr(title) \")\";\n  }\n\n  // Don't show links that are fragment identifiers,\n  // or use the `javascript:` pseudo protocol\n  a[href^=\"#\"]:after,\n  a[href^=\"javascript:\"]:after {\n    content: \"\";\n  }\n\n  pre,\n  blockquote {\n    border: 1px solid #999;\n    page-break-inside: avoid;\n  }\n\n  thead {\n    display: table-header-group; // h5bp.com/t\n  }\n\n  tr,\n  img {\n    page-break-inside: avoid;\n  }\n\n  img {\n    max-width: 100% !important;\n  }\n\n  p,\n  h2,\n  h3 {\n    orphans: 3;\n    widows: 3;\n  }\n\n  h2,\n  h3 {\n    page-break-after: avoid;\n  }\n\n  // Bootstrap specific changes start\n\n  // Bootstrap components\n  .navbar {\n    display: none;\n  }\n  .btn,\n  .dropup > .btn {\n    > .caret {\n      border-top-color: #000 !important;\n    }\n  }\n  .label {\n    border: 1px solid #000;\n  }\n\n  .table {\n    border-collapse: collapse !important;\n\n    td,\n    th {\n      background-color: #fff !important;\n    }\n  }\n  .table-bordered {\n    th,\n    td {\n      border: 1px solid #ddd !important;\n    }\n  }\n}\n","// stylelint-disable value-list-comma-newline-after, value-list-comma-space-after, indentation, declaration-colon-newline-after, font-family-no-missing-generic-family-keyword\n\n//\n// Glyphicons for Bootstrap\n//\n// Since icons are fonts, they can be placed anywhere text is placed and are\n// thus automatically sized to match the surrounding child. To use, create an\n// inline element with the appropriate classes, like so:\n//\n// <a href=\"#\"><span class=\"glyphicon glyphicon-star\"></span> Star</a>\n\n// Import the fonts\n@font-face {\n  font-family: \"Glyphicons Halflings\";\n  src: url(\"@{icon-font-path}@{icon-font-name}.eot\");\n  src: url(\"@{icon-font-path}@{icon-font-name}.eot?#iefix\") format(\"embedded-opentype\"),\n       url(\"@{icon-font-path}@{icon-font-name}.woff2\") format(\"woff2\"),\n       url(\"@{icon-font-path}@{icon-font-name}.woff\") format(\"woff\"),\n       url(\"@{icon-font-path}@{icon-font-name}.ttf\") format(\"truetype\"),\n       url(\"@{icon-font-path}@{icon-font-name}.svg#@{icon-font-svg-id}\") format(\"svg\");\n}\n\n// Catchall baseclass\n.glyphicon {\n  position: relative;\n  top: 1px;\n  display: inline-block;\n  font-family: \"Glyphicons Halflings\";\n  font-style: normal;\n  font-weight: 400;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n\n// Individual icons\n.glyphicon-asterisk               { &:before { content: \"\\002a\"; } }\n.glyphicon-plus                   { &:before { content: \"\\002b\"; } }\n.glyphicon-euro,\n.glyphicon-eur                    { &:before { content: \"\\20ac\"; } }\n.glyphicon-minus                  { &:before { content: \"\\2212\"; } }\n.glyphicon-cloud                  { &:before { content: \"\\2601\"; } }\n.glyphicon-envelope               { &:before { content: \"\\2709\"; } }\n.glyphicon-pencil                 { &:before { content: \"\\270f\"; } }\n.glyphicon-glass                  { &:before { content: \"\\e001\"; } }\n.glyphicon-music                  { &:before { content: \"\\e002\"; } }\n.glyphicon-search                 { &:before { content: \"\\e003\"; } }\n.glyphicon-heart                  { &:before { content: \"\\e005\"; } }\n.glyphicon-star                   { &:before { content: \"\\e006\"; } }\n.glyphicon-star-empty             { &:before { content: \"\\e007\"; } }\n.glyphicon-user                   { &:before { content: \"\\e008\"; } }\n.glyphicon-film                   { &:before { content: \"\\e009\"; } }\n.glyphicon-th-large               { &:before { content: \"\\e010\"; } }\n.glyphicon-th                     { &:before { content: \"\\e011\"; } }\n.glyphicon-th-list                { &:before { content: \"\\e012\"; } }\n.glyphicon-ok                     { &:before { content: \"\\e013\"; } }\n.glyphicon-remove                 { &:before { content: \"\\e014\"; } }\n.glyphicon-zoom-in                { &:before { content: \"\\e015\"; } }\n.glyphicon-zoom-out               { &:before { content: \"\\e016\"; } }\n.glyphicon-off                    { &:before { content: \"\\e017\"; } }\n.glyphicon-signal                 { &:before { content: \"\\e018\"; } }\n.glyphicon-cog                    { &:before { content: \"\\e019\"; } }\n.glyphicon-trash                  { &:before { content: \"\\e020\"; } }\n.glyphicon-home                   { &:before { content: \"\\e021\"; } }\n.glyphicon-file                   { &:before { content: \"\\e022\"; } }\n.glyphicon-time                   { &:before { content: \"\\e023\"; } }\n.glyphicon-road                   { &:before { content: \"\\e024\"; } }\n.glyphicon-download-alt           { &:before { content: \"\\e025\"; } }\n.glyphicon-download               { &:before { content: \"\\e026\"; } }\n.glyphicon-upload                 { &:before { content: \"\\e027\"; } }\n.glyphicon-inbox                  { &:before { content: \"\\e028\"; } }\n.glyphicon-play-circle            { &:before { content: \"\\e029\"; } }\n.glyphicon-repeat                 { &:before { content: \"\\e030\"; } }\n.glyphicon-refresh                { &:before { content: \"\\e031\"; } }\n.glyphicon-list-alt               { &:before { content: \"\\e032\"; } }\n.glyphicon-lock                   { &:before { content: \"\\e033\"; } }\n.glyphicon-flag                   { &:before { content: \"\\e034\"; } }\n.glyphicon-headphones             { &:before { content: \"\\e035\"; } }\n.glyphicon-volume-off             { &:before { content: \"\\e036\"; } }\n.glyphicon-volume-down            { &:before { content: \"\\e037\"; } }\n.glyphicon-volume-up              { &:before { content: \"\\e038\"; } }\n.glyphicon-qrcode                 { &:before { content: \"\\e039\"; } }\n.glyphicon-barcode                { &:before { content: \"\\e040\"; } }\n.glyphicon-tag                    { &:before { content: \"\\e041\"; } }\n.glyphicon-tags                   { &:before { content: \"\\e042\"; } }\n.glyphicon-book                   { &:before { content: \"\\e043\"; } }\n.glyphicon-bookmark               { &:before { content: \"\\e044\"; } }\n.glyphicon-print                  { &:before { content: \"\\e045\"; } }\n.glyphicon-camera                 { &:before { content: \"\\e046\"; } }\n.glyphicon-font                   { &:before { content: \"\\e047\"; } }\n.glyphicon-bold                   { &:before { content: \"\\e048\"; } }\n.glyphicon-italic                 { &:before { content: \"\\e049\"; } }\n.glyphicon-text-height            { &:before { content: \"\\e050\"; } }\n.glyphicon-text-width             { &:before { content: \"\\e051\"; } }\n.glyphicon-align-left             { &:before { content: \"\\e052\"; } }\n.glyphicon-align-center           { &:before { content: \"\\e053\"; } }\n.glyphicon-align-right            { &:before { content: \"\\e054\"; } }\n.glyphicon-align-justify          { &:before { content: \"\\e055\"; } }\n.glyphicon-list                   { &:before { content: \"\\e056\"; } }\n.glyphicon-indent-left            { &:before { content: \"\\e057\"; } }\n.glyphicon-indent-right           { &:before { content: \"\\e058\"; } }\n.glyphicon-facetime-video         { &:before { content: \"\\e059\"; } }\n.glyphicon-picture                { &:before { content: \"\\e060\"; } }\n.glyphicon-map-marker             { &:before { content: \"\\e062\"; } }\n.glyphicon-adjust                 { &:before { content: \"\\e063\"; } }\n.glyphicon-tint                   { &:before { content: \"\\e064\"; } }\n.glyphicon-edit                   { &:before { content: \"\\e065\"; } }\n.glyphicon-share                  { &:before { content: \"\\e066\"; } }\n.glyphicon-check                  { &:before { content: \"\\e067\"; } }\n.glyphicon-move                   { &:before { content: \"\\e068\"; } }\n.glyphicon-step-backward          { &:before { content: \"\\e069\"; } }\n.glyphicon-fast-backward          { &:before { content: \"\\e070\"; } }\n.glyphicon-backward               { &:before { content: \"\\e071\"; } }\n.glyphicon-play                   { &:before { content: \"\\e072\"; } }\n.glyphicon-pause                  { &:before { content: \"\\e073\"; } }\n.glyphicon-stop                   { &:before { content: \"\\e074\"; } }\n.glyphicon-forward                { &:before { content: \"\\e075\"; } }\n.glyphicon-fast-forward           { &:before { content: \"\\e076\"; } }\n.glyphicon-step-forward           { &:before { content: \"\\e077\"; } }\n.glyphicon-eject                  { &:before { content: \"\\e078\"; } }\n.glyphicon-chevron-left           { &:before { content: \"\\e079\"; } }\n.glyphicon-chevron-right          { &:before { content: \"\\e080\"; } }\n.glyphicon-plus-sign              { &:before { content: \"\\e081\"; } }\n.glyphicon-minus-sign             { &:before { content: \"\\e082\"; } }\n.glyphicon-remove-sign            { &:before { content: \"\\e083\"; } }\n.glyphicon-ok-sign                { &:before { content: \"\\e084\"; } }\n.glyphicon-question-sign          { &:before { content: \"\\e085\"; } }\n.glyphicon-info-sign              { &:before { content: \"\\e086\"; } }\n.glyphicon-screenshot             { &:before { content: \"\\e087\"; } }\n.glyphicon-remove-circle          { &:before { content: \"\\e088\"; } }\n.glyphicon-ok-circle              { &:before { content: \"\\e089\"; } }\n.glyphicon-ban-circle             { &:before { content: \"\\e090\"; } }\n.glyphicon-arrow-left             { &:before { content: \"\\e091\"; } }\n.glyphicon-arrow-right            { &:before { content: \"\\e092\"; } }\n.glyphicon-arrow-up               { &:before { content: \"\\e093\"; } }\n.glyphicon-arrow-down             { &:before { content: \"\\e094\"; } }\n.glyphicon-share-alt              { &:before { content: \"\\e095\"; } }\n.glyphicon-resize-full            { &:before { content: \"\\e096\"; } }\n.glyphicon-resize-small           { &:before { content: \"\\e097\"; } }\n.glyphicon-exclamation-sign       { &:before { content: \"\\e101\"; } }\n.glyphicon-gift                   { &:before { content: \"\\e102\"; } }\n.glyphicon-leaf                   { &:before { content: \"\\e103\"; } }\n.glyphicon-fire                   { &:before { content: \"\\e104\"; } }\n.glyphicon-eye-open               { &:before { content: \"\\e105\"; } }\n.glyphicon-eye-close              { &:before { content: \"\\e106\"; } }\n.glyphicon-warning-sign           { &:before { content: \"\\e107\"; } }\n.glyphicon-plane                  { &:before { content: \"\\e108\"; } }\n.glyphicon-calendar               { &:before { content: \"\\e109\"; } }\n.glyphicon-random                 { &:before { content: \"\\e110\"; } }\n.glyphicon-comment                { &:before { content: \"\\e111\"; } }\n.glyphicon-magnet                 { &:before { content: \"\\e112\"; } }\n.glyphicon-chevron-up             { &:before { content: \"\\e113\"; } }\n.glyphicon-chevron-down           { &:before { content: \"\\e114\"; } }\n.glyphicon-retweet                { &:before { content: \"\\e115\"; } }\n.glyphicon-shopping-cart          { &:before { content: \"\\e116\"; } }\n.glyphicon-folder-close           { &:before { content: \"\\e117\"; } }\n.glyphicon-folder-open            { &:before { content: \"\\e118\"; } }\n.glyphicon-resize-vertical        { &:before { content: \"\\e119\"; } }\n.glyphicon-resize-horizontal      { &:before { content: \"\\e120\"; } }\n.glyphicon-hdd                    { &:before { content: \"\\e121\"; } }\n.glyphicon-bullhorn               { &:before { content: \"\\e122\"; } }\n.glyphicon-bell                   { &:before { content: \"\\e123\"; } }\n.glyphicon-certificate            { &:before { content: \"\\e124\"; } }\n.glyphicon-thumbs-up              { &:before { content: \"\\e125\"; } }\n.glyphicon-thumbs-down            { &:before { content: \"\\e126\"; } }\n.glyphicon-hand-right             { &:before { content: \"\\e127\"; } }\n.glyphicon-hand-left              { &:before { content: \"\\e128\"; } }\n.glyphicon-hand-up                { &:before { content: \"\\e129\"; } }\n.glyphicon-hand-down              { &:before { content: \"\\e130\"; } }\n.glyphicon-circle-arrow-right     { &:before { content: \"\\e131\"; } }\n.glyphicon-circle-arrow-left      { &:before { content: \"\\e132\"; } }\n.glyphicon-circle-arrow-up        { &:before { content: \"\\e133\"; } }\n.glyphicon-circle-arrow-down      { &:before { content: \"\\e134\"; } }\n.glyphicon-globe                  { &:before { content: \"\\e135\"; } }\n.glyphicon-wrench                 { &:before { content: \"\\e136\"; } }\n.glyphicon-tasks                  { &:before { content: \"\\e137\"; } }\n.glyphicon-filter                 { &:before { content: \"\\e138\"; } }\n.glyphicon-briefcase              { &:before { content: \"\\e139\"; } }\n.glyphicon-fullscreen             { &:before { content: \"\\e140\"; } }\n.glyphicon-dashboard              { &:before { content: \"\\e141\"; } }\n.glyphicon-paperclip              { &:before { content: \"\\e142\"; } }\n.glyphicon-heart-empty            { &:before { content: \"\\e143\"; } }\n.glyphicon-link                   { &:before { content: \"\\e144\"; } }\n.glyphicon-phone                  { &:before { content: \"\\e145\"; } }\n.glyphicon-pushpin                { &:before { content: \"\\e146\"; } }\n.glyphicon-usd                    { &:before { content: \"\\e148\"; } }\n.glyphicon-gbp                    { &:before { content: \"\\e149\"; } }\n.glyphicon-sort                   { &:before { content: \"\\e150\"; } }\n.glyphicon-sort-by-alphabet       { &:before { content: \"\\e151\"; } }\n.glyphicon-sort-by-alphabet-alt   { &:before { content: \"\\e152\"; } }\n.glyphicon-sort-by-order          { &:before { content: \"\\e153\"; } }\n.glyphicon-sort-by-order-alt      { &:before { content: \"\\e154\"; } }\n.glyphicon-sort-by-attributes     { &:before { content: \"\\e155\"; } }\n.glyphicon-sort-by-attributes-alt { &:before { content: \"\\e156\"; } }\n.glyphicon-unchecked              { &:before { content: \"\\e157\"; } }\n.glyphicon-expand                 { &:before { content: \"\\e158\"; } }\n.glyphicon-collapse-down          { &:before { content: \"\\e159\"; } }\n.glyphicon-collapse-up            { &:before { content: \"\\e160\"; } }\n.glyphicon-log-in                 { &:before { content: \"\\e161\"; } }\n.glyphicon-flash                  { &:before { content: \"\\e162\"; } }\n.glyphicon-log-out                { &:before { content: \"\\e163\"; } }\n.glyphicon-new-window             { &:before { content: \"\\e164\"; } }\n.glyphicon-record                 { &:before { content: \"\\e165\"; } }\n.glyphicon-save                   { &:before { content: \"\\e166\"; } }\n.glyphicon-open                   { &:before { content: \"\\e167\"; } }\n.glyphicon-saved                  { &:before { content: \"\\e168\"; } }\n.glyphicon-import                 { &:before { content: \"\\e169\"; } }\n.glyphicon-export                 { &:before { content: \"\\e170\"; } }\n.glyphicon-send                   { &:before { content: \"\\e171\"; } }\n.glyphicon-floppy-disk            { &:before { content: \"\\e172\"; } }\n.glyphicon-floppy-saved           { &:before { content: \"\\e173\"; } }\n.glyphicon-floppy-remove          { &:before { content: \"\\e174\"; } }\n.glyphicon-floppy-save            { &:before { content: \"\\e175\"; } }\n.glyphicon-floppy-open            { &:before { content: \"\\e176\"; } }\n.glyphicon-credit-card            { &:before { content: \"\\e177\"; } }\n.glyphicon-transfer               { &:before { content: \"\\e178\"; } }\n.glyphicon-cutlery                { &:before { content: \"\\e179\"; } }\n.glyphicon-header                 { &:before { content: \"\\e180\"; } }\n.glyphicon-compressed             { &:before { content: \"\\e181\"; } }\n.glyphicon-earphone               { &:before { content: \"\\e182\"; } }\n.glyphicon-phone-alt              { &:before { content: \"\\e183\"; } }\n.glyphicon-tower                  { &:before { content: \"\\e184\"; } }\n.glyphicon-stats                  { &:before { content: \"\\e185\"; } }\n.glyphicon-sd-video               { &:before { content: \"\\e186\"; } }\n.glyphicon-hd-video               { &:before { content: \"\\e187\"; } }\n.glyphicon-subtitles              { &:before { content: \"\\e188\"; } }\n.glyphicon-sound-stereo           { &:before { content: \"\\e189\"; } }\n.glyphicon-sound-dolby            { &:before { content: \"\\e190\"; } }\n.glyphicon-sound-5-1              { &:before { content: \"\\e191\"; } }\n.glyphicon-sound-6-1              { &:before { content: \"\\e192\"; } }\n.glyphicon-sound-7-1              { &:before { content: \"\\e193\"; } }\n.glyphicon-copyright-mark         { &:before { content: \"\\e194\"; } }\n.glyphicon-registration-mark      { &:before { content: \"\\e195\"; } }\n.glyphicon-cloud-download         { &:before { content: \"\\e197\"; } }\n.glyphicon-cloud-upload           { &:before { content: \"\\e198\"; } }\n.glyphicon-tree-conifer           { &:before { content: \"\\e199\"; } }\n.glyphicon-tree-deciduous         { &:before { content: \"\\e200\"; } }\n.glyphicon-cd                     { &:before { content: \"\\e201\"; } }\n.glyphicon-save-file              { &:before { content: \"\\e202\"; } }\n.glyphicon-open-file              { &:before { content: \"\\e203\"; } }\n.glyphicon-level-up               { &:before { content: \"\\e204\"; } }\n.glyphicon-copy                   { &:before { content: \"\\e205\"; } }\n.glyphicon-paste                  { &:before { content: \"\\e206\"; } }\n// The following 2 Glyphicons are omitted for the time being because\n// they currently use Unicode codepoints that are outside the\n// Basic Multilingual Plane (BMP). Older buggy versions of WebKit can't handle\n// non-BMP codepoints in CSS string escapes, and thus can't display these two icons.\n// Notably, the bug affects some older versions of the Android Browser.\n// More info: https://github.com/twbs/bootstrap/issues/10106\n// .glyphicon-door                   { &:before { content: \"\\1f6aa\"; } }\n// .glyphicon-key                    { &:before { content: \"\\1f511\"; } }\n.glyphicon-alert                  { &:before { content: \"\\e209\"; } }\n.glyphicon-equalizer              { &:before { content: \"\\e210\"; } }\n.glyphicon-king                   { &:before { content: \"\\e211\"; } }\n.glyphicon-queen                  { &:before { content: \"\\e212\"; } }\n.glyphicon-pawn                   { &:before { content: \"\\e213\"; } }\n.glyphicon-bishop                 { &:before { content: \"\\e214\"; } }\n.glyphicon-knight                 { &:before { content: \"\\e215\"; } }\n.glyphicon-baby-formula           { &:before { content: \"\\e216\"; } }\n.glyphicon-tent                   { &:before { content: \"\\26fa\"; } }\n.glyphicon-blackboard             { &:before { content: \"\\e218\"; } }\n.glyphicon-bed                    { &:before { content: \"\\e219\"; } }\n.glyphicon-apple                  { &:before { content: \"\\f8ff\"; } }\n.glyphicon-erase                  { &:before { content: \"\\e221\"; } }\n.glyphicon-hourglass              { &:before { content: \"\\231b\"; } }\n.glyphicon-lamp                   { &:before { content: \"\\e223\"; } }\n.glyphicon-duplicate              { &:before { content: \"\\e224\"; } }\n.glyphicon-piggy-bank             { &:before { content: \"\\e225\"; } }\n.glyphicon-scissors               { &:before { content: \"\\e226\"; } }\n.glyphicon-bitcoin                { &:before { content: \"\\e227\"; } }\n.glyphicon-btc                    { &:before { content: \"\\e227\"; } }\n.glyphicon-xbt                    { &:before { content: \"\\e227\"; } }\n.glyphicon-yen                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-jpy                    { &:before { content: \"\\00a5\"; } }\n.glyphicon-ruble                  { &:before { content: \"\\20bd\"; } }\n.glyphicon-rub                    { &:before { content: \"\\20bd\"; } }\n.glyphicon-scale                  { &:before { content: \"\\e230\"; } }\n.glyphicon-ice-lolly              { &:before { content: \"\\e231\"; } }\n.glyphicon-ice-lolly-tasted       { &:before { content: \"\\e232\"; } }\n.glyphicon-education              { &:before { content: \"\\e233\"; } }\n.glyphicon-option-horizontal      { &:before { content: \"\\e234\"; } }\n.glyphicon-option-vertical        { &:before { content: \"\\e235\"; } }\n.glyphicon-menu-hamburger         { &:before { content: \"\\e236\"; } }\n.glyphicon-modal-window           { &:before { content: \"\\e237\"; } }\n.glyphicon-oil                    { &:before { content: \"\\e238\"; } }\n.glyphicon-grain                  { &:before { content: \"\\e239\"; } }\n.glyphicon-sunglasses             { &:before { content: \"\\e240\"; } }\n.glyphicon-text-size              { &:before { content: \"\\e241\"; } }\n.glyphicon-text-color             { &:before { content: \"\\e242\"; } }\n.glyphicon-text-background        { &:before { content: \"\\e243\"; } }\n.glyphicon-object-align-top       { &:before { content: \"\\e244\"; } }\n.glyphicon-object-align-bottom    { &:before { content: \"\\e245\"; } }\n.glyphicon-object-align-horizontal{ &:before { content: \"\\e246\"; } }\n.glyphicon-object-align-left      { &:before { content: \"\\e247\"; } }\n.glyphicon-object-align-vertical  { &:before { content: \"\\e248\"; } }\n.glyphicon-object-align-right     { &:before { content: \"\\e249\"; } }\n.glyphicon-triangle-right         { &:before { content: \"\\e250\"; } }\n.glyphicon-triangle-left          { &:before { content: \"\\e251\"; } }\n.glyphicon-triangle-bottom        { &:before { content: \"\\e252\"; } }\n.glyphicon-triangle-top           { &:before { content: \"\\e253\"; } }\n.glyphicon-console                { &:before { content: \"\\e254\"; } }\n.glyphicon-superscript            { &:before { content: \"\\e255\"; } }\n.glyphicon-subscript              { &:before { content: \"\\e256\"; } }\n.glyphicon-menu-left              { &:before { content: \"\\e257\"; } }\n.glyphicon-menu-right             { &:before { content: \"\\e258\"; } }\n.glyphicon-menu-down              { &:before { content: \"\\e259\"; } }\n.glyphicon-menu-up                { &:before { content: \"\\e260\"; } }\n","//\n// Scaffolding\n// --------------------------------------------------\n\n\n// Reset the box-sizing\n//\n// Heads up! This reset may cause conflicts with some third-party widgets.\n// For recommendations on resolving such conflicts, see\n// https://getbootstrap.com/docs/3.4/getting-started/#third-box-sizing\n* {\n  .box-sizing(border-box);\n}\n*:before,\n*:after {\n  .box-sizing(border-box);\n}\n\n\n// Body reset\n\nhtml {\n  font-size: 10px;\n  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);\n}\n\nbody {\n  font-family: @font-family-base;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @text-color;\n  background-color: @body-bg;\n}\n\n// Reset fonts for relevant elements\ninput,\nbutton,\nselect,\ntextarea {\n  font-family: inherit;\n  font-size: inherit;\n  line-height: inherit;\n}\n\n\n// Links\n\na {\n  color: @link-color;\n  text-decoration: none;\n\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n  }\n\n  &:focus {\n    .tab-focus();\n  }\n}\n\n\n// Figures\n//\n// We reset this here because previously Normalize had no `figure` margins. This\n// ensures we don't break anyone's use of the element.\n\nfigure {\n  margin: 0;\n}\n\n\n// Images\n\nimg {\n  vertical-align: middle;\n}\n\n// Responsive images (ensure images don't scale beyond their parents)\n.img-responsive {\n  .img-responsive();\n}\n\n// Rounded corners\n.img-rounded {\n  border-radius: @border-radius-large;\n}\n\n// Image thumbnails\n//\n// Heads up! This is mixin-ed into thumbnails.less for `.thumbnail`.\n.img-thumbnail {\n  padding: @thumbnail-padding;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(all .2s ease-in-out);\n\n  // Keep them at most 100% wide\n  .img-responsive(inline-block);\n}\n\n// Perfect circle\n.img-circle {\n  border-radius: 50%; // set radius in percents\n}\n\n\n// Horizontal rules\n\nhr {\n  margin-top: @line-height-computed;\n  margin-bottom: @line-height-computed;\n  border: 0;\n  border-top: 1px solid @hr-border;\n}\n\n\n// Only display content to screen readers\n//\n// See: https://a11yproject.com/posts/how-to-hide-content\n\n.sr-only {\n  position: absolute;\n  width: 1px;\n  height: 1px;\n  padding: 0;\n  margin: -1px;\n  overflow: hidden;\n  clip: rect(0, 0, 0, 0);\n  border: 0;\n}\n\n// Use in conjunction with .sr-only to only display content when it's focused.\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n// Credit: HTML5 Boilerplate\n\n.sr-only-focusable {\n  &:active,\n  &:focus {\n    position: static;\n    width: auto;\n    height: auto;\n    margin: 0;\n    overflow: visible;\n    clip: auto;\n  }\n}\n\n\n// iOS \"clickable elements\" fix for role=\"button\"\n//\n// Fixes \"clickability\" issue (and more generally, the firing of events such as focus as well)\n// for traditionally non-focusable elements with role=\"button\"\n// see https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n[role=\"button\"] {\n  cursor: pointer;\n}\n","// stylelint-disable indentation, property-no-vendor-prefix, selector-no-vendor-prefix\n\n// Vendor Prefixes\n//\n// All vendor mixins are deprecated as of v3.2.0 due to the introduction of\n// Autoprefixer in our Gruntfile. They have been removed in v4.\n\n// - Animations\n// - Backface visibility\n// - Box shadow\n// - Box sizing\n// - Content columns\n// - Hyphens\n// - Placeholder text\n// - Transformations\n// - Transitions\n// - User Select\n\n\n// Animations\n.animation(@animation) {\n  -webkit-animation: @animation;\n       -o-animation: @animation;\n          animation: @animation;\n}\n.animation-name(@name) {\n  -webkit-animation-name: @name;\n          animation-name: @name;\n}\n.animation-duration(@duration) {\n  -webkit-animation-duration: @duration;\n          animation-duration: @duration;\n}\n.animation-timing-function(@timing-function) {\n  -webkit-animation-timing-function: @timing-function;\n          animation-timing-function: @timing-function;\n}\n.animation-delay(@delay) {\n  -webkit-animation-delay: @delay;\n          animation-delay: @delay;\n}\n.animation-iteration-count(@iteration-count) {\n  -webkit-animation-iteration-count: @iteration-count;\n          animation-iteration-count: @iteration-count;\n}\n.animation-direction(@direction) {\n  -webkit-animation-direction: @direction;\n          animation-direction: @direction;\n}\n.animation-fill-mode(@fill-mode) {\n  -webkit-animation-fill-mode: @fill-mode;\n          animation-fill-mode: @fill-mode;\n}\n\n// Backface visibility\n// Prevent browsers from flickering when using CSS 3D transforms.\n// Default value is `visible`, but can be changed to `hidden`\n\n.backface-visibility(@visibility) {\n  -webkit-backface-visibility: @visibility;\n     -moz-backface-visibility: @visibility;\n          backface-visibility: @visibility;\n}\n\n// Drop shadows\n//\n// Note: Deprecated `.box-shadow()` as of v3.1.0 since all of Bootstrap's\n// supported browsers that have box shadow capabilities now support it.\n\n.box-shadow(@shadow) {\n  -webkit-box-shadow: @shadow; // iOS <4.3 & Android <4.1\n          box-shadow: @shadow;\n}\n\n// Box sizing\n.box-sizing(@boxmodel) {\n  -webkit-box-sizing: @boxmodel;\n     -moz-box-sizing: @boxmodel;\n          box-sizing: @boxmodel;\n}\n\n// CSS3 Content Columns\n.content-columns(@column-count; @column-gap: @grid-gutter-width) {\n  -webkit-column-count: @column-count;\n     -moz-column-count: @column-count;\n          column-count: @column-count;\n  -webkit-column-gap: @column-gap;\n     -moz-column-gap: @column-gap;\n          column-gap: @column-gap;\n}\n\n// Optional hyphenation\n.hyphens(@mode: auto) {\n  -webkit-hyphens: @mode;\n     -moz-hyphens: @mode;\n      -ms-hyphens: @mode; // IE10+\n       -o-hyphens: @mode;\n          hyphens: @mode;\n  word-wrap: break-word;\n}\n\n// Placeholder text\n.placeholder(@color: @input-color-placeholder) {\n  // Firefox\n  &::-moz-placeholder {\n    color: @color;\n    opacity: 1; // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526\n  }\n  &:-ms-input-placeholder { color: @color; } // Internet Explorer 10+\n  &::-webkit-input-placeholder  { color: @color; } // Safari and Chrome\n}\n\n// Transformations\n.scale(@ratio) {\n  -webkit-transform: scale(@ratio);\n      -ms-transform: scale(@ratio); // IE9 only\n       -o-transform: scale(@ratio);\n          transform: scale(@ratio);\n}\n.scale(@ratioX; @ratioY) {\n  -webkit-transform: scale(@ratioX, @ratioY);\n      -ms-transform: scale(@ratioX, @ratioY); // IE9 only\n       -o-transform: scale(@ratioX, @ratioY);\n          transform: scale(@ratioX, @ratioY);\n}\n.scaleX(@ratio) {\n  -webkit-transform: scaleX(@ratio);\n      -ms-transform: scaleX(@ratio); // IE9 only\n       -o-transform: scaleX(@ratio);\n          transform: scaleX(@ratio);\n}\n.scaleY(@ratio) {\n  -webkit-transform: scaleY(@ratio);\n      -ms-transform: scaleY(@ratio); // IE9 only\n       -o-transform: scaleY(@ratio);\n          transform: scaleY(@ratio);\n}\n.skew(@x; @y) {\n  -webkit-transform: skewX(@x) skewY(@y);\n      -ms-transform: skewX(@x) skewY(@y); // See https://github.com/twbs/bootstrap/issues/4885; IE9+\n       -o-transform: skewX(@x) skewY(@y);\n          transform: skewX(@x) skewY(@y);\n}\n.translate(@x; @y) {\n  -webkit-transform: translate(@x, @y);\n      -ms-transform: translate(@x, @y); // IE9 only\n       -o-transform: translate(@x, @y);\n          transform: translate(@x, @y);\n}\n.translate3d(@x; @y; @z) {\n  -webkit-transform: translate3d(@x, @y, @z);\n          transform: translate3d(@x, @y, @z);\n}\n.rotate(@degrees) {\n  -webkit-transform: rotate(@degrees);\n      -ms-transform: rotate(@degrees); // IE9 only\n       -o-transform: rotate(@degrees);\n          transform: rotate(@degrees);\n}\n.rotateX(@degrees) {\n  -webkit-transform: rotateX(@degrees);\n      -ms-transform: rotateX(@degrees); // IE9 only\n       -o-transform: rotateX(@degrees);\n          transform: rotateX(@degrees);\n}\n.rotateY(@degrees) {\n  -webkit-transform: rotateY(@degrees);\n      -ms-transform: rotateY(@degrees); // IE9 only\n       -o-transform: rotateY(@degrees);\n          transform: rotateY(@degrees);\n}\n.perspective(@perspective) {\n  -webkit-perspective: @perspective;\n     -moz-perspective: @perspective;\n          perspective: @perspective;\n}\n.perspective-origin(@perspective) {\n  -webkit-perspective-origin: @perspective;\n     -moz-perspective-origin: @perspective;\n          perspective-origin: @perspective;\n}\n.transform-origin(@origin) {\n  -webkit-transform-origin: @origin;\n     -moz-transform-origin: @origin;\n      -ms-transform-origin: @origin; // IE9 only\n          transform-origin: @origin;\n}\n\n\n// Transitions\n\n.transition(@transition) {\n  -webkit-transition: @transition;\n       -o-transition: @transition;\n          transition: @transition;\n}\n.transition-property(@transition-property) {\n  -webkit-transition-property: @transition-property;\n          transition-property: @transition-property;\n}\n.transition-delay(@transition-delay) {\n  -webkit-transition-delay: @transition-delay;\n          transition-delay: @transition-delay;\n}\n.transition-duration(@transition-duration) {\n  -webkit-transition-duration: @transition-duration;\n          transition-duration: @transition-duration;\n}\n.transition-timing-function(@timing-function) {\n  -webkit-transition-timing-function: @timing-function;\n          transition-timing-function: @timing-function;\n}\n.transition-transform(@transition) {\n  -webkit-transition: -webkit-transform @transition;\n     -moz-transition: -moz-transform @transition;\n       -o-transition: -o-transform @transition;\n          transition: transform @transition;\n}\n\n\n// User select\n// For selecting text on the page\n\n.user-select(@select) {\n  -webkit-user-select: @select;\n     -moz-user-select: @select;\n      -ms-user-select: @select; // IE10+\n          user-select: @select;\n}\n","// WebKit-style focus\n\n.tab-focus() {\n  // WebKit-specific. Other browsers will keep their default outline style.\n  // (Initially tried to also force default via `outline: initial`,\n  // but that seems to erroneously remove the outline in Firefox altogether.)\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n","// stylelint-disable media-feature-name-no-vendor-prefix, media-feature-parentheses-space-inside, media-feature-name-no-unknown, indentation, at-rule-name-space-after\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n.img-responsive(@display: block) {\n  display: @display;\n  max-width: 100%; // Part 1: Set a maximum relative to the parent\n  height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n}\n\n\n// Retina image\n//\n// Short retina mixin for setting background-image and -size. Note that the\n// spelling of `min--moz-device-pixel-ratio` is intentional.\n.img-retina(@file-1x; @file-2x; @width-1x; @height-1x) {\n  background-image: url(\"@{file-1x}\");\n\n  @media\n  only screen and (-webkit-min-device-pixel-ratio: 2),\n  only screen and ( min--moz-device-pixel-ratio: 2),\n  only screen and ( -o-min-device-pixel-ratio: 2/1),\n  only screen and ( min-device-pixel-ratio: 2),\n  only screen and ( min-resolution: 192dpi),\n  only screen and ( min-resolution: 2dppx) {\n    background-image: url(\"@{file-2x}\");\n    background-size: @width-1x @height-1x;\n  }\n}\n","// stylelint-disable selector-list-comma-newline-after, selector-no-qualifying-type\n\n//\n// Typography\n// --------------------------------------------------\n\n\n// Headings\n// -------------------------\n\nh1, h2, h3, h4, h5, h6,\n.h1, .h2, .h3, .h4, .h5, .h6 {\n  font-family: @headings-font-family;\n  font-weight: @headings-font-weight;\n  line-height: @headings-line-height;\n  color: @headings-color;\n\n  small,\n  .small {\n    font-weight: 400;\n    line-height: 1;\n    color: @headings-small-color;\n  }\n}\n\nh1, .h1,\nh2, .h2,\nh3, .h3 {\n  margin-top: @line-height-computed;\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 65%;\n  }\n}\nh4, .h4,\nh5, .h5,\nh6, .h6 {\n  margin-top: (@line-height-computed / 2);\n  margin-bottom: (@line-height-computed / 2);\n\n  small,\n  .small {\n    font-size: 75%;\n  }\n}\n\nh1, .h1 { font-size: @font-size-h1; }\nh2, .h2 { font-size: @font-size-h2; }\nh3, .h3 { font-size: @font-size-h3; }\nh4, .h4 { font-size: @font-size-h4; }\nh5, .h5 { font-size: @font-size-h5; }\nh6, .h6 { font-size: @font-size-h6; }\n\n\n// Body text\n// -------------------------\n\np {\n  margin: 0 0 (@line-height-computed / 2);\n}\n\n.lead {\n  margin-bottom: @line-height-computed;\n  font-size: floor((@font-size-base * 1.15));\n  font-weight: 300;\n  line-height: 1.4;\n\n  @media (min-width: @screen-sm-min) {\n    font-size: (@font-size-base * 1.5);\n  }\n}\n\n\n// Emphasis & misc\n// -------------------------\n\n// Ex: (12px small font / 14px base font) * 100% = about 85%\nsmall,\n.small {\n  font-size: floor((100% * @font-size-small / @font-size-base));\n}\n\nmark,\n.mark {\n  padding: .2em;\n  background-color: @state-warning-bg;\n}\n\n// Alignment\n.text-left           { text-align: left; }\n.text-right          { text-align: right; }\n.text-center         { text-align: center; }\n.text-justify        { text-align: justify; }\n.text-nowrap         { white-space: nowrap; }\n\n// Transformation\n.text-lowercase      { text-transform: lowercase; }\n.text-uppercase      { text-transform: uppercase; }\n.text-capitalize     { text-transform: capitalize; }\n\n// Contextual colors\n.text-muted {\n  color: @text-muted;\n}\n.text-primary {\n  .text-emphasis-variant(@brand-primary);\n}\n.text-success {\n  .text-emphasis-variant(@state-success-text);\n}\n.text-info {\n  .text-emphasis-variant(@state-info-text);\n}\n.text-warning {\n  .text-emphasis-variant(@state-warning-text);\n}\n.text-danger {\n  .text-emphasis-variant(@state-danger-text);\n}\n\n// Contextual backgrounds\n// For now we'll leave these alongside the text classes until v4 when we can\n// safely shift things around (per SemVer rules).\n.bg-primary {\n  // Given the contrast here, this is the only class to have its color inverted\n  // automatically.\n  color: #fff;\n  .bg-variant(@brand-primary);\n}\n.bg-success {\n  .bg-variant(@state-success-bg);\n}\n.bg-info {\n  .bg-variant(@state-info-bg);\n}\n.bg-warning {\n  .bg-variant(@state-warning-bg);\n}\n.bg-danger {\n  .bg-variant(@state-danger-bg);\n}\n\n\n// Page header\n// -------------------------\n\n.page-header {\n  padding-bottom: ((@line-height-computed / 2) - 1);\n  margin: (@line-height-computed * 2) 0 @line-height-computed;\n  border-bottom: 1px solid @page-header-border-color;\n}\n\n\n// Lists\n// -------------------------\n\n// Unordered and Ordered lists\nul,\nol {\n  margin-top: 0;\n  margin-bottom: (@line-height-computed / 2);\n  ul,\n  ol {\n    margin-bottom: 0;\n  }\n}\n\n// List options\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n.list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  .list-unstyled();\n  margin-left: -5px;\n\n  > li {\n    display: inline-block;\n    padding-right: 5px;\n    padding-left: 5px;\n  }\n}\n\n// Description Lists\ndl {\n  margin-top: 0; // Remove browser default\n  margin-bottom: @line-height-computed;\n}\ndt,\ndd {\n  line-height: @line-height-base;\n}\ndt {\n  font-weight: 700;\n}\ndd {\n  margin-left: 0; // Undo browser default\n}\n\n// Horizontal description lists\n//\n// Defaults to being stacked without any of the below styles applied, until the\n// grid breakpoint is reached (default of ~768px).\n\n.dl-horizontal {\n  dd {\n    &:extend(.clearfix all); // Clear the floated `dt` if an empty `dd` is present\n  }\n\n  @media (min-width: @dl-horizontal-breakpoint) {\n    dt {\n      float: left;\n      width: (@dl-horizontal-offset - 20);\n      clear: left;\n      text-align: right;\n      .text-overflow();\n    }\n    dd {\n      margin-left: @dl-horizontal-offset;\n    }\n  }\n}\n\n\n// Misc\n// -------------------------\n\n// Abbreviations and acronyms\n// Add data-* attribute to help out our tooltip plugin, per https://github.com/twbs/bootstrap/issues/5257\nabbr[title],\nabbr[data-original-title] {\n  cursor: help;\n}\n\n.initialism {\n  font-size: 90%;\n  .text-uppercase();\n}\n\n// Blockquotes\nblockquote {\n  padding: (@line-height-computed / 2) @line-height-computed;\n  margin: 0 0 @line-height-computed;\n  font-size: @blockquote-font-size;\n  border-left: 5px solid @blockquote-border-color;\n\n  p,\n  ul,\n  ol {\n    &:last-child {\n      margin-bottom: 0;\n    }\n  }\n\n  // Note: Deprecated small and .small as of v3.1.0\n  // Context: https://github.com/twbs/bootstrap/issues/11660\n  footer,\n  small,\n  .small {\n    display: block;\n    font-size: 80%; // back to default font-size\n    line-height: @line-height-base;\n    color: @blockquote-small-color;\n\n    &:before {\n      content: \"\\2014 \\00A0\"; // em dash, nbsp\n    }\n  }\n}\n\n// Opposite alignment of blockquote\n//\n// Heads up: `blockquote.pull-right` has been deprecated as of v3.1.0.\n.blockquote-reverse,\nblockquote.pull-right {\n  padding-right: 15px;\n  padding-left: 0;\n  text-align: right;\n  border-right: 5px solid @blockquote-border-color;\n  border-left: 0;\n\n  // Account for citation\n  footer,\n  small,\n  .small {\n    &:before { content: \"\"; }\n    &:after {\n      content: \"\\00A0 \\2014\"; // nbsp, em dash\n    }\n  }\n}\n\n// Addresses\naddress {\n  margin-bottom: @line-height-computed;\n  font-style: normal;\n  line-height: @line-height-base;\n}\n","// Typography\n\n.text-emphasis-variant(@color) {\n  color: @color;\n  a&:hover,\n  a&:focus {\n    color: darken(@color, 10%);\n  }\n}\n","// Contextual backgrounds\n\n.bg-variant(@color) {\n  background-color: @color;\n  a&:hover,\n  a&:focus {\n    background-color: darken(@color, 10%);\n  }\n}\n","// Text overflow\n// Requires inline-block or block for proper styling\n\n.text-overflow() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n","//\n// Code (inline and block)\n// --------------------------------------------------\n\n\n// Inline and block code styles\ncode,\nkbd,\npre,\nsamp {\n  font-family: @font-family-monospace;\n}\n\n// Inline code\ncode {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @code-color;\n  background-color: @code-bg;\n  border-radius: @border-radius-base;\n}\n\n// User input typically entered via keyboard\nkbd {\n  padding: 2px 4px;\n  font-size: 90%;\n  color: @kbd-color;\n  background-color: @kbd-bg;\n  border-radius: @border-radius-small;\n  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, .25);\n\n  kbd {\n    padding: 0;\n    font-size: 100%;\n    font-weight: 700;\n    box-shadow: none;\n  }\n}\n\n// Blocks of code\npre {\n  display: block;\n  padding: ((@line-height-computed - 1) / 2);\n  margin: 0 0 (@line-height-computed / 2);\n  font-size: (@font-size-base - 1); // 14px to 13px\n  line-height: @line-height-base;\n  color: @pre-color;\n  word-break: break-all;\n  word-wrap: break-word;\n  background-color: @pre-bg;\n  border: 1px solid @pre-border-color;\n  border-radius: @border-radius-base;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    padding: 0;\n    font-size: inherit;\n    color: inherit;\n    white-space: pre-wrap;\n    background-color: transparent;\n    border-radius: 0;\n  }\n}\n\n// Enable scrollable blocks of code\n.pre-scrollable {\n  max-height: @pre-scrollable-max-height;\n  overflow-y: scroll;\n}\n","//\n// Grid system\n// --------------------------------------------------\n\n\n// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n.container {\n  .container-fixed();\n\n  @media (min-width: @screen-sm-min) {\n    width: @container-sm;\n  }\n  @media (min-width: @screen-md-min) {\n    width: @container-md;\n  }\n  @media (min-width: @screen-lg-min) {\n    width: @container-lg;\n  }\n}\n\n\n// Fluid container\n//\n// Utilizes the mixin meant for fixed width containers, but without any defined\n// width for fluid, full width layouts.\n\n.container-fluid {\n  .container-fixed();\n}\n\n\n// Row\n//\n// Rows contain and clear the floats of your columns.\n\n.row {\n  .make-row();\n}\n\n.row-no-gutters {\n  margin-right: 0;\n  margin-left: 0;\n\n  [class*=\"col-\"] {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n.make-grid-columns();\n\n\n// Extra small grid\n//\n// Columns, offsets, pushes, and pulls for extra small devices like\n// smartphones.\n\n.make-grid(xs);\n\n\n// Small grid\n//\n// Columns, offsets, pushes, and pulls for the small device range, from phones\n// to tablets.\n\n@media (min-width: @screen-sm-min) {\n  .make-grid(sm);\n}\n\n\n// Medium grid\n//\n// Columns, offsets, pushes, and pulls for the desktop device range.\n\n@media (min-width: @screen-md-min) {\n  .make-grid(md);\n}\n\n\n// Large grid\n//\n// Columns, offsets, pushes, and pulls for the large desktop device range.\n\n@media (min-width: @screen-lg-min) {\n  .make-grid(lg);\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n// Centered container element\n.container-fixed(@gutter: @grid-gutter-width) {\n  padding-right: ceil((@gutter / 2));\n  padding-left: floor((@gutter / 2));\n  margin-right: auto;\n  margin-left: auto;\n  &:extend(.clearfix all);\n}\n\n// Creates a wrapper for a series of columns\n.make-row(@gutter: @grid-gutter-width) {\n  margin-right: floor((@gutter / -2));\n  margin-left: ceil((@gutter / -2));\n  &:extend(.clearfix all);\n}\n\n// Generate the extra small columns\n.make-xs-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  float: left;\n  width: percentage((@columns / @grid-columns));\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n}\n.make-xs-column-offset(@columns) {\n  margin-left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-push(@columns) {\n  left: percentage((@columns / @grid-columns));\n}\n.make-xs-column-pull(@columns) {\n  right: percentage((@columns / @grid-columns));\n}\n\n// Generate the small columns\n.make-sm-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n\n  @media (min-width: @screen-sm-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-offset(@columns) {\n  @media (min-width: @screen-sm-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-push(@columns) {\n  @media (min-width: @screen-sm-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-sm-column-pull(@columns) {\n  @media (min-width: @screen-sm-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the medium columns\n.make-md-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n\n  @media (min-width: @screen-md-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-offset(@columns) {\n  @media (min-width: @screen-md-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-push(@columns) {\n  @media (min-width: @screen-md-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-md-column-pull(@columns) {\n  @media (min-width: @screen-md-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n\n// Generate the large columns\n.make-lg-column(@columns; @gutter: @grid-gutter-width) {\n  position: relative;\n  min-height: 1px;\n  padding-right: (@gutter / 2);\n  padding-left: (@gutter / 2);\n\n  @media (min-width: @screen-lg-min) {\n    float: left;\n    width: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-offset(@columns) {\n  @media (min-width: @screen-lg-min) {\n    margin-left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-push(@columns) {\n  @media (min-width: @screen-lg-min) {\n    left: percentage((@columns / @grid-columns));\n  }\n}\n.make-lg-column-pull(@columns) {\n  @media (min-width: @screen-lg-min) {\n    right: percentage((@columns / @grid-columns));\n  }\n}\n","// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `@grid-columns`.\n\n.make-grid-columns() {\n  // Common styles for all sizes of grid columns, widths 1-12\n  .col(@index) { // initial\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general; \"=<\" isn't a typo\n    @item: ~\".col-xs-@{index}, .col-sm-@{index}, .col-md-@{index}, .col-lg-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      position: relative;\n      // Prevent columns from collapsing when empty\n      min-height: 1px;\n      // Inner gutter via padding\n      padding-right: floor((@grid-gutter-width / 2));\n      padding-left: ceil((@grid-gutter-width / 2));\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.float-grid-columns(@class) {\n  .col(@index) { // initial\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), @item);\n  }\n  .col(@index, @list) when (@index =< @grid-columns) { // general\n    @item: ~\".col-@{class}-@{index}\";\n    .col((@index + 1), ~\"@{list}, @{item}\");\n  }\n  .col(@index, @list) when (@index > @grid-columns) { // terminal\n    @{list} {\n      float: left;\n    }\n  }\n  .col(1); // kickstart it\n}\n\n.calc-grid-column(@index, @class, @type) when (@type = width) and (@index > 0) {\n  .col-@{class}-@{index} {\n    width: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index > 0) {\n  .col-@{class}-push-@{index} {\n    left: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = push) and (@index = 0) {\n  .col-@{class}-push-0 {\n    left: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index > 0) {\n  .col-@{class}-pull-@{index} {\n    right: percentage((@index / @grid-columns));\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = pull) and (@index = 0) {\n  .col-@{class}-pull-0 {\n    right: auto;\n  }\n}\n.calc-grid-column(@index, @class, @type) when (@type = offset) {\n  .col-@{class}-offset-@{index} {\n    margin-left: percentage((@index / @grid-columns));\n  }\n}\n\n// Basic looping in LESS\n.loop-grid-columns(@index, @class, @type) when (@index >= 0) {\n  .calc-grid-column(@index, @class, @type);\n  // next iteration\n  .loop-grid-columns((@index - 1), @class, @type);\n}\n\n// Create grid for specific class\n.make-grid(@class) {\n  .float-grid-columns(@class);\n  .loop-grid-columns(@grid-columns, @class, width);\n  .loop-grid-columns(@grid-columns, @class, pull);\n  .loop-grid-columns(@grid-columns, @class, push);\n  .loop-grid-columns(@grid-columns, @class, offset);\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-no-qualifying-type\n\n//\n// Tables\n// --------------------------------------------------\n\n\ntable {\n  background-color: @table-bg;\n\n  // Table cell sizing\n  //\n  // Reset default table behavior\n\n  col[class*=\"col-\"] {\n    position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n    display: table-column;\n    float: none;\n  }\n\n  td,\n  th {\n    &[class*=\"col-\"] {\n      position: static; // Prevent border hiding in Firefox and IE9-11 (see https://github.com/twbs/bootstrap/issues/11623)\n      display: table-cell;\n      float: none;\n    }\n  }\n}\n\ncaption {\n  padding-top: @table-cell-padding;\n  padding-bottom: @table-cell-padding;\n  color: @text-muted;\n  text-align: left;\n}\n\nth {\n  text-align: left;\n}\n\n\n// Baseline styles\n\n.table {\n  width: 100%;\n  max-width: 100%;\n  margin-bottom: @line-height-computed;\n  // Cells\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-cell-padding;\n        line-height: @line-height-base;\n        vertical-align: top;\n        border-top: 1px solid @table-border-color;\n      }\n    }\n  }\n  // Bottom align for column headings\n  > thead > tr > th {\n    vertical-align: bottom;\n    border-bottom: 2px solid @table-border-color;\n  }\n  // Remove top border from thead by default\n  > caption + thead,\n  > colgroup + thead,\n  > thead:first-child {\n    > tr:first-child {\n      > th,\n      > td {\n        border-top: 0;\n      }\n    }\n  }\n  // Account for multiple tbody instances\n  > tbody + tbody {\n    border-top: 2px solid @table-border-color;\n  }\n\n  // Nesting\n  .table {\n    background-color: @body-bg;\n  }\n}\n\n\n// Condensed table w/ half padding\n\n.table-condensed {\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        padding: @table-condensed-cell-padding;\n      }\n    }\n  }\n}\n\n\n// Bordered version\n//\n// Add borders all around the table and between all the columns.\n\n.table-bordered {\n  border: 1px solid @table-border-color;\n  > thead,\n  > tbody,\n  > tfoot {\n    > tr {\n      > th,\n      > td {\n        border: 1px solid @table-border-color;\n      }\n    }\n  }\n  > thead > tr {\n    > th,\n    > td {\n      border-bottom-width: 2px;\n    }\n  }\n}\n\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n.table-striped {\n  > tbody > tr:nth-of-type(odd) {\n    background-color: @table-bg-accent;\n  }\n}\n\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover {\n    background-color: @table-bg-hover;\n  }\n}\n\n\n// Table backgrounds\n//\n// Exact selectors below required to override `.table-striped` and prevent\n// inheritance to nested tables.\n\n// Generate the contextual variants\n.table-row-variant(active; @table-bg-active);\n.table-row-variant(success; @state-success-bg);\n.table-row-variant(info; @state-info-bg);\n.table-row-variant(warning; @state-warning-bg);\n.table-row-variant(danger; @state-danger-bg);\n\n\n// Responsive tables\n//\n// Wrap your tables in `.table-responsive` and we'll make them mobile friendly\n// by enabling horizontal scrolling. Only applies <768px. Everything above that\n// will display normally.\n\n.table-responsive {\n  min-height: .01%; // Workaround for IE9 bug (see https://github.com/twbs/bootstrap/issues/14837)\n  overflow-x: auto;\n\n  @media screen and (max-width: @screen-xs-max) {\n    width: 100%;\n    margin-bottom: (@line-height-computed * .75);\n    overflow-y: hidden;\n    -ms-overflow-style: -ms-autohiding-scrollbar;\n    border: 1px solid @table-border-color;\n\n    // Tighten up spacing\n    > .table {\n      margin-bottom: 0;\n\n      // Ensure the content doesn't wrap\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th,\n          > td {\n            white-space: nowrap;\n          }\n        }\n      }\n    }\n\n    // Special overrides for the bordered tables\n    > .table-bordered {\n      border: 0;\n\n      // Nuke the appropriate borders so that the parent can handle them\n      > thead,\n      > tbody,\n      > tfoot {\n        > tr {\n          > th:first-child,\n          > td:first-child {\n            border-left: 0;\n          }\n          > th:last-child,\n          > td:last-child {\n            border-right: 0;\n          }\n        }\n      }\n\n      // Only nuke the last row's bottom-border in `tbody` and `tfoot` since\n      // chances are there will be only one `tr` in a `thead` and that would\n      // remove the border altogether.\n      > tbody,\n      > tfoot {\n        > tr:last-child {\n          > th,\n          > td {\n            border-bottom: 0;\n          }\n        }\n      }\n\n    }\n  }\n}\n","// Tables\n\n.table-row-variant(@state; @background) {\n  // Exact selectors below required to override `.table-striped` and prevent\n  // inheritance to nested tables.\n  .table > thead > tr,\n  .table > tbody > tr,\n  .table > tfoot > tr {\n    > td.@{state},\n    > th.@{state},\n    &.@{state} > td,\n    &.@{state} > th {\n      background-color: @background;\n    }\n  }\n\n  // Hover states for `.table-hover`\n  // Note: this is not available for cells or rows within `thead` or `tfoot`.\n  .table-hover > tbody > tr {\n    > td.@{state}:hover,\n    > th.@{state}:hover,\n    &.@{state}:hover > td,\n    &:hover > .@{state},\n    &.@{state}:hover > th {\n      background-color: darken(@background, 5%);\n    }\n  }\n}\n","// stylelint-disable selector-no-qualifying-type, property-no-vendor-prefix, media-feature-name-no-vendor-prefix\n\n//\n// Forms\n// --------------------------------------------------\n\n\n// Normalize non-controls\n//\n// Restyle and baseline non-control form elements.\n\nfieldset {\n  // Chrome and Firefox set a `min-width: min-content;` on fieldsets,\n  // so we reset that to ensure it behaves more like a standard block element.\n  // See https://github.com/twbs/bootstrap/issues/12359.\n  min-width: 0;\n  padding: 0;\n  margin: 0;\n  border: 0;\n}\n\nlegend {\n  display: block;\n  width: 100%;\n  padding: 0;\n  margin-bottom: @line-height-computed;\n  font-size: (@font-size-base * 1.5);\n  line-height: inherit;\n  color: @legend-color;\n  border: 0;\n  border-bottom: 1px solid @legend-border-color;\n}\n\nlabel {\n  display: inline-block;\n  max-width: 100%; // Force IE8 to wrap long content (see https://github.com/twbs/bootstrap/issues/13141)\n  margin-bottom: 5px;\n  font-weight: 700;\n}\n\n\n// Normalize form controls\n//\n// While most of our form styles require extra classes, some basic normalization\n// is required to ensure optimum display with or without those classes to better\n// address browser inconsistencies.\n\ninput[type=\"search\"] {\n  // Override content-box in Normalize (* isn't specific enough)\n  .box-sizing(border-box);\n\n  // Search inputs in iOS\n  //\n  // This overrides the extra rounded corners on search inputs in iOS so that our\n  // `.form-control` class can properly style them. Note that this cannot simply\n  // be added to `.form-control` as it's not specific enough. For details, see\n  // https://github.com/twbs/bootstrap/issues/11586.\n  -webkit-appearance: none;\n  appearance: none;\n}\n\n// Position radios and checkboxes better\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n  margin: 4px 0 0;\n  margin-top: 1px \\9; // IE8-9\n  line-height: normal;\n\n  // Apply same disabled cursor tweak as for inputs\n  // Some special care is needed because <label>s don't inherit their parent's `cursor`.\n  //\n  // Note: Neither radios nor checkboxes can be readonly.\n  &[disabled],\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n\ninput[type=\"file\"] {\n  display: block;\n}\n\n// Make range inputs behave like textual form controls\ninput[type=\"range\"] {\n  display: block;\n  width: 100%;\n}\n\n// Make multiple select elements height not fixed\nselect[multiple],\nselect[size] {\n  height: auto;\n}\n\n// Focus for file, radio, and checkbox\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus {\n  .tab-focus();\n}\n\n// Adjust output element\noutput {\n  display: block;\n  padding-top: (@padding-base-vertical + 1);\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n}\n\n\n// Common form controls\n//\n// Shared size and type resets for form controls. Apply `.form-control` to any\n// of the following form controls:\n//\n// select\n// textarea\n// input[type=\"text\"]\n// input[type=\"password\"]\n// input[type=\"datetime\"]\n// input[type=\"datetime-local\"]\n// input[type=\"date\"]\n// input[type=\"month\"]\n// input[type=\"time\"]\n// input[type=\"week\"]\n// input[type=\"number\"]\n// input[type=\"email\"]\n// input[type=\"url\"]\n// input[type=\"search\"]\n// input[type=\"tel\"]\n// input[type=\"color\"]\n\n.form-control {\n  display: block;\n  width: 100%;\n  height: @input-height-base; // Make inputs at least the height of their button counterpart (base line-height + padding + border)\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  line-height: @line-height-base;\n  color: @input-color;\n  background-color: @input-bg;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid @input-border;\n  border-radius: @input-border-radius; // Note: This has no effect on <select>s in some browsers, due to the limited stylability of <select>s in CSS.\n  .box-shadow(inset 0 1px 1px rgba(0, 0, 0, .075));\n  .transition(~\"border-color ease-in-out .15s, box-shadow ease-in-out .15s\");\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  .form-control-focus();\n\n  // Placeholder\n  .placeholder();\n\n  // Unstyle the caret on `<select>`s in IE10+.\n  &::-ms-expand {\n    background-color: transparent;\n    border: 0;\n  }\n\n  // Disabled and read-only inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &[disabled],\n  &[readonly],\n  fieldset[disabled] & {\n    background-color: @input-bg-disabled;\n    opacity: 1; // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655\n  }\n\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n\n  // Reset height for `textarea`s\n  textarea& {\n    height: auto;\n  }\n}\n\n\n// Special styles for iOS temporal inputs\n//\n// In Mobile Safari, setting `display: block` on temporal inputs causes the\n// text within the input to become vertically misaligned. As a workaround, we\n// set a pixel line-height that matches the given height of the input, but only\n// for Safari. See https://bugs.webkit.org/show_bug.cgi?id=139848\n//\n// Note that as of 9.3, iOS doesn't support `week`.\n\n@media screen and (-webkit-min-device-pixel-ratio: 0) {\n  input[type=\"date\"],\n  input[type=\"time\"],\n  input[type=\"datetime-local\"],\n  input[type=\"month\"] {\n    &.form-control {\n      line-height: @input-height-base;\n    }\n\n    &.input-sm,\n    .input-group-sm & {\n      line-height: @input-height-small;\n    }\n\n    &.input-lg,\n    .input-group-lg & {\n      line-height: @input-height-large;\n    }\n  }\n}\n\n\n// Form groups\n//\n// Designed to help with the organization and spacing of vertical forms. For\n// horizontal forms, use the predefined grid classes.\n\n.form-group {\n  margin-bottom: @form-group-margin-bottom;\n}\n\n\n// Checkboxes and radios\n//\n// Indent the labels to position radios/checkboxes as hanging controls.\n\n.radio,\n.checkbox {\n  position: relative;\n  display: block;\n  margin-top: 10px;\n  margin-bottom: 10px;\n\n  // These are used on elements with <label> descendants\n  &.disabled,\n  fieldset[disabled] & {\n    label {\n      cursor: @cursor-disabled;\n    }\n  }\n\n  label {\n    min-height: @line-height-computed; // Ensure the input doesn't jump when there is no text\n    padding-left: 20px;\n    margin-bottom: 0;\n    font-weight: 400;\n    cursor: pointer;\n  }\n}\n.radio input[type=\"radio\"],\n.radio-inline input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"],\n.checkbox-inline input[type=\"checkbox\"] {\n  position: absolute;\n  margin-top: 4px \\9;\n  margin-left: -20px;\n}\n\n.radio + .radio,\n.checkbox + .checkbox {\n  margin-top: -5px; // Move up sibling radios or checkboxes for tighter spacing\n}\n\n// Radios and checkboxes on same line\n.radio-inline,\n.checkbox-inline {\n  position: relative;\n  display: inline-block;\n  padding-left: 20px;\n  margin-bottom: 0;\n  font-weight: 400;\n  vertical-align: middle;\n  cursor: pointer;\n\n  // These are used directly on <label>s\n  &.disabled,\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n  }\n}\n.radio-inline + .radio-inline,\n.checkbox-inline + .checkbox-inline {\n  margin-top: 0;\n  margin-left: 10px; // space out consecutive inline controls\n}\n\n\n// Static form control text\n//\n// Apply class to a `p` element to make any string of text align with labels in\n// a horizontal form layout.\n\n.form-control-static {\n  min-height: (@line-height-computed + @font-size-base);\n  // Size it appropriately next to real form controls\n  padding-top: (@padding-base-vertical + 1);\n  padding-bottom: (@padding-base-vertical + 1);\n  // Remove default margin from `p`\n  margin-bottom: 0;\n\n  &.input-lg,\n  &.input-sm {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// The `.form-group-* form-control` variations are sadly duplicated to avoid the\n// issue documented in https://github.com/twbs/bootstrap/issues/15074.\n\n.input-sm {\n  .input-size(@input-height-small; @padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @input-border-radius-small);\n}\n.form-group-sm {\n  .form-control {\n    height: @input-height-small;\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n    border-radius: @input-border-radius-small;\n  }\n  select.form-control {\n    height: @input-height-small;\n    line-height: @input-height-small;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-small;\n    min-height: (@line-height-computed + @font-size-small);\n    padding: (@padding-small-vertical + 1) @padding-small-horizontal;\n    font-size: @font-size-small;\n    line-height: @line-height-small;\n  }\n}\n\n.input-lg {\n  .input-size(@input-height-large; @padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @input-border-radius-large);\n}\n.form-group-lg {\n  .form-control {\n    height: @input-height-large;\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n    border-radius: @input-border-radius-large;\n  }\n  select.form-control {\n    height: @input-height-large;\n    line-height: @input-height-large;\n  }\n  textarea.form-control,\n  select[multiple].form-control {\n    height: auto;\n  }\n  .form-control-static {\n    height: @input-height-large;\n    min-height: (@line-height-computed + @font-size-large);\n    padding: (@padding-large-vertical + 1) @padding-large-horizontal;\n    font-size: @font-size-large;\n    line-height: @line-height-large;\n  }\n}\n\n\n// Form control feedback states\n//\n// Apply contextual and semantic states to individual form controls.\n\n.has-feedback {\n  // Enable absolute positioning\n  position: relative;\n\n  // Ensure icons don't overlap text\n  .form-control {\n    padding-right: (@input-height-base * 1.25);\n  }\n}\n// Feedback icon (requires .glyphicon classes)\n.form-control-feedback {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 2; // Ensure icon is above input groups\n  display: block;\n  width: @input-height-base;\n  height: @input-height-base;\n  line-height: @input-height-base;\n  text-align: center;\n  pointer-events: none;\n}\n.input-lg + .form-control-feedback,\n.input-group-lg + .form-control-feedback,\n.form-group-lg .form-control + .form-control-feedback {\n  width: @input-height-large;\n  height: @input-height-large;\n  line-height: @input-height-large;\n}\n.input-sm + .form-control-feedback,\n.input-group-sm + .form-control-feedback,\n.form-group-sm .form-control + .form-control-feedback {\n  width: @input-height-small;\n  height: @input-height-small;\n  line-height: @input-height-small;\n}\n\n// Feedback states\n.has-success {\n  .form-control-validation(@state-success-text; @state-success-text; @state-success-bg);\n}\n.has-warning {\n  .form-control-validation(@state-warning-text; @state-warning-text; @state-warning-bg);\n}\n.has-error {\n  .form-control-validation(@state-danger-text; @state-danger-text; @state-danger-bg);\n}\n\n// Reposition feedback icon if input has visible label above\n.has-feedback label {\n\n  & ~ .form-control-feedback {\n    top: (@line-height-computed + 5); // Height of the `label` and its margin\n  }\n  &.sr-only ~ .form-control-feedback {\n    top: 0;\n  }\n}\n\n\n// Help text\n//\n// Apply to any element you wish to create light text for placement immediately\n// below a form control. Use for general help, formatting, or instructional text.\n\n.help-block {\n  display: block; // account for any element using help-block\n  margin-top: 5px;\n  margin-bottom: 10px;\n  color: lighten(@text-color, 25%); // lighten the text some for contrast\n}\n\n\n// Inline forms\n//\n// Make forms appear inline(-block) by adding the `.form-inline` class. Inline\n// forms begin stacked on extra small (mobile) devices and then go inline when\n// viewports reach <768px.\n//\n// Requires wrapping inputs and labels with `.form-group` for proper display of\n// default HTML form controls and our custom form controls (e.g., input groups).\n//\n// Heads up! This is mixin-ed into `.navbar-form` in navbars.less.\n\n.form-inline {\n\n  // Kick in the inline\n  @media (min-width: @screen-sm-min) {\n    // Inline-block all the things for \"inline\"\n    .form-group {\n      display: inline-block;\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // In navbar-form, allow folks to *not* use `.form-group`\n    .form-control {\n      display: inline-block;\n      width: auto; // Prevent labels from stacking above inputs in `.form-group`\n      vertical-align: middle;\n    }\n\n    // Make static controls behave like regular ones\n    .form-control-static {\n      display: inline-block;\n    }\n\n    .input-group {\n      display: inline-table;\n      vertical-align: middle;\n\n      .input-group-addon,\n      .input-group-btn,\n      .form-control {\n        width: auto;\n      }\n    }\n\n    // Input groups need that 100% width though\n    .input-group > .form-control {\n      width: 100%;\n    }\n\n    .control-label {\n      margin-bottom: 0;\n      vertical-align: middle;\n    }\n\n    // Remove default margin on radios/checkboxes that were used for stacking, and\n    // then undo the floating of radios and checkboxes to match.\n    .radio,\n    .checkbox {\n      display: inline-block;\n      margin-top: 0;\n      margin-bottom: 0;\n      vertical-align: middle;\n\n      label {\n        padding-left: 0;\n      }\n    }\n    .radio input[type=\"radio\"],\n    .checkbox input[type=\"checkbox\"] {\n      position: relative;\n      margin-left: 0;\n    }\n\n    // Re-override the feedback icon.\n    .has-feedback .form-control-feedback {\n      top: 0;\n    }\n  }\n}\n\n\n// Horizontal forms\n//\n// Horizontal forms are built on grid classes and allow you to create forms with\n// labels on the left and inputs on the right.\n\n.form-horizontal {\n\n  // Consistent vertical alignment of radios and checkboxes\n  //\n  // Labels also get some reset styles, but that is scoped to a media query below.\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline {\n    padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n    margin-top: 0;\n    margin-bottom: 0;\n  }\n  // Account for padding we're adding to ensure the alignment and of help text\n  // and other content below items\n  .radio,\n  .checkbox {\n    min-height: (@line-height-computed + (@padding-base-vertical + 1));\n  }\n\n  // Make form groups behave like rows\n  .form-group {\n    .make-row();\n  }\n\n  // Reset spacing and right align labels, but scope to media queries so that\n  // labels on narrow viewports stack the same as a default form example.\n  @media (min-width: @screen-sm-min) {\n    .control-label {\n      padding-top: (@padding-base-vertical + 1); // Default padding plus a border\n      margin-bottom: 0;\n      text-align: right;\n    }\n  }\n\n  // Validation states\n  //\n  // Reposition the icon because it's now within a grid column and columns have\n  // `position: relative;` on them. Also accounts for the grid gutter padding.\n  .has-feedback .form-control-feedback {\n    right: floor((@grid-gutter-width / 2));\n  }\n\n  // Form group sizes\n  //\n  // Quick utility class for applying `.input-lg` and `.input-sm` styles to the\n  // inputs and labels within a `.form-group`.\n  .form-group-lg {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-large-vertical + 1);\n        font-size: @font-size-large;\n      }\n    }\n  }\n  .form-group-sm {\n    @media (min-width: @screen-sm-min) {\n      .control-label {\n        padding-top: (@padding-small-vertical + 1);\n        font-size: @font-size-small;\n      }\n    }\n  }\n}\n","// Form validation states\n//\n// Used in forms.less to generate the form validation CSS for warnings, errors,\n// and successes.\n\n.form-control-validation(@text-color: #555; @border-color: #ccc; @background-color: #f5f5f5) {\n  // Color the label and help text\n  .help-block,\n  .control-label,\n  .radio,\n  .checkbox,\n  .radio-inline,\n  .checkbox-inline,\n  &.radio label,\n  &.checkbox label,\n  &.radio-inline label,\n  &.checkbox-inline label  {\n    color: @text-color;\n  }\n  // Set the border and box shadow on specific inputs to match\n  .form-control {\n    border-color: @border-color;\n    .box-shadow(inset 0 1px 1px rgba(0, 0, 0, .075)); // Redeclare so transitions work\n    &:focus {\n      border-color: darken(@border-color, 10%);\n      @shadow: inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 6px lighten(@border-color, 20%);\n      .box-shadow(@shadow);\n    }\n  }\n  // Set validation states also for addons\n  .input-group-addon {\n    color: @text-color;\n    background-color: @background-color;\n    border-color: @border-color;\n  }\n  // Optional feedback icon\n  .form-control-feedback {\n    color: @text-color;\n  }\n}\n\n\n// Form control focus state\n//\n// Generate a customized focus state and for any input with the specified color,\n// which defaults to the `@input-border-focus` variable.\n//\n// We highly encourage you to not customize the default value, but instead use\n// this to tweak colors on an as-needed basis. This aesthetic change is based on\n// WebKit's default styles, but applicable to a wider range of browsers. Its\n// usability and accessibility should be taken into account with any change.\n//\n// Example usage: change the default blue border and shadow to white for better\n// contrast against a dark gray background.\n.form-control-focus(@color: @input-border-focus) {\n  @color-rgba: rgba(red(@color), green(@color), blue(@color), .6);\n  &:focus {\n    border-color: @color;\n    outline: 0;\n    .box-shadow(~\"inset 0 1px 1px rgba(0, 0, 0, .075), 0 0 8px @{color-rgba}\");\n  }\n}\n\n// Form control sizing\n//\n// Relative text size, padding, and border-radii changes for form controls. For\n// horizontal sizing, wrap controls in the predefined grid classes. `<select>`\n// element gets special love because it's special, and that's a fact!\n.input-size(@input-height; @padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  height: @input-height;\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n\n  select& {\n    height: @input-height;\n    line-height: @input-height;\n  }\n\n  textarea&,\n  select[multiple]& {\n    height: auto;\n  }\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Buttons\n// --------------------------------------------------\n\n\n// Base styles\n// --------------------------------------------------\n\n.btn {\n  display: inline-block;\n  margin-bottom: 0; // For input.btn\n  font-weight: @btn-font-weight;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  touch-action: manipulation;\n  cursor: pointer;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  .button-size(@padding-base-vertical; @padding-base-horizontal; @font-size-base; @line-height-base; @btn-border-radius-base);\n  .user-select(none);\n\n  &,\n  &:active,\n  &.active {\n    &:focus,\n    &.focus {\n      .tab-focus();\n    }\n  }\n\n  &:hover,\n  &:focus,\n  &.focus {\n    color: @btn-default-color;\n    text-decoration: none;\n  }\n\n  &:active,\n  &.active {\n    background-image: none;\n    outline: 0;\n    .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n  }\n\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    cursor: @cursor-disabled;\n    .opacity(.65);\n    .box-shadow(none);\n  }\n\n  a& {\n    &.disabled,\n    fieldset[disabled] & {\n      pointer-events: none; // Future-proof disabling of clicks on `<a>` elements\n    }\n  }\n}\n\n\n// Alternate buttons\n// --------------------------------------------------\n\n.btn-default {\n  .button-variant(@btn-default-color; @btn-default-bg; @btn-default-border);\n}\n.btn-primary {\n  .button-variant(@btn-primary-color; @btn-primary-bg; @btn-primary-border);\n}\n// Success appears as green\n.btn-success {\n  .button-variant(@btn-success-color; @btn-success-bg; @btn-success-border);\n}\n// Info appears as blue-green\n.btn-info {\n  .button-variant(@btn-info-color; @btn-info-bg; @btn-info-border);\n}\n// Warning appears as orange\n.btn-warning {\n  .button-variant(@btn-warning-color; @btn-warning-bg; @btn-warning-border);\n}\n// Danger and error appear as red\n.btn-danger {\n  .button-variant(@btn-danger-color; @btn-danger-bg; @btn-danger-border);\n}\n\n\n// Link buttons\n// -------------------------\n\n// Make a button look and behave like a link\n.btn-link {\n  font-weight: 400;\n  color: @link-color;\n  border-radius: 0;\n\n  &,\n  &:active,\n  &.active,\n  &[disabled],\n  fieldset[disabled] & {\n    background-color: transparent;\n    .box-shadow(none);\n  }\n  &,\n  &:hover,\n  &:focus,\n  &:active {\n    border-color: transparent;\n  }\n  &:hover,\n  &:focus {\n    color: @link-hover-color;\n    text-decoration: @link-hover-decoration;\n    background-color: transparent;\n  }\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus {\n      color: @btn-link-disabled-color;\n      text-decoration: none;\n    }\n  }\n}\n\n\n// Button Sizes\n// --------------------------------------------------\n\n.btn-lg {\n  // line-height: ensure even-numbered height of button next to large input\n  .button-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @btn-border-radius-large);\n}\n.btn-sm {\n  // line-height: ensure proper height of button next to small input\n  .button-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n.btn-xs {\n  .button-size(@padding-xs-vertical; @padding-xs-horizontal; @font-size-small; @line-height-small; @btn-border-radius-small);\n}\n\n\n// Block button\n// --------------------------------------------------\n\n.btn-block {\n  display: block;\n  width: 100%;\n}\n\n// Vertically space out multiple block buttons\n.btn-block + .btn-block {\n  margin-top: 5px;\n}\n\n// Specificity overrides\ninput[type=\"submit\"],\ninput[type=\"reset\"],\ninput[type=\"button\"] {\n  &.btn-block {\n    width: 100%;\n  }\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n.button-variant(@color; @background; @border) {\n  color: @color;\n  background-color: @background;\n  border-color: @border;\n\n  &:focus,\n  &.focus {\n    color: @color;\n    background-color: darken(@background, 10%);\n    border-color: darken(@border, 25%);\n  }\n  &:hover {\n    color: @color;\n    background-color: darken(@background, 10%);\n    border-color: darken(@border, 12%);\n  }\n  &:active,\n  &.active,\n  .open > .dropdown-toggle& {\n    color: @color;\n    background-color: darken(@background, 10%);\n    background-image: none;\n    border-color: darken(@border, 12%);\n\n    &:hover,\n    &:focus,\n    &.focus {\n      color: @color;\n      background-color: darken(@background, 17%);\n      border-color: darken(@border, 25%);\n    }\n  }\n  &.disabled,\n  &[disabled],\n  fieldset[disabled] & {\n    &:hover,\n    &:focus,\n    &.focus {\n      background-color: @background;\n      border-color: @border;\n    }\n  }\n\n  .badge {\n    color: @background;\n    background-color: @color;\n  }\n}\n\n// Button sizes\n.button-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  padding: @padding-vertical @padding-horizontal;\n  font-size: @font-size;\n  line-height: @line-height;\n  border-radius: @border-radius;\n}\n","// Opacity\n\n.opacity(@opacity) {\n  @opacity-ie: (@opacity * 100);  // IE8 filter\n  filter: ~\"alpha(opacity=@{opacity-ie})\";\n  opacity: @opacity;\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Component animations\n// --------------------------------------------------\n\n// Heads up!\n//\n// We don't use the `.opacity()` mixin here since it causes a bug with text\n// fields in IE7-8. Source: https://github.com/twbs/bootstrap/pull/3552.\n\n.fade {\n  opacity: 0;\n  .transition(opacity .15s linear);\n\n  &.in {\n    opacity: 1;\n  }\n}\n\n.collapse {\n  display: none;\n\n  &.in      { display: block; }\n  tr&.in    { display: table-row; }\n  tbody&.in { display: table-row-group; }\n}\n\n.collapsing {\n  position: relative;\n  height: 0;\n  overflow: hidden;\n  .transition-property(~\"height, visibility\");\n  .transition-duration(.35s);\n  .transition-timing-function(ease);\n}\n","//\n// Dropdown menus\n// --------------------------------------------------\n\n\n// Dropdown arrow/caret\n.caret {\n  display: inline-block;\n  width: 0;\n  height: 0;\n  margin-left: 2px;\n  vertical-align: middle;\n  border-top: @caret-width-base dashed;\n  border-top: @caret-width-base solid ~\"\\9\"; // IE8\n  border-right: @caret-width-base solid transparent;\n  border-left: @caret-width-base solid transparent;\n}\n\n// The dropdown wrapper (div)\n.dropup,\n.dropdown {\n  position: relative;\n}\n\n// Prevent the focus on the dropdown toggle when closing dropdowns\n.dropdown-toggle:focus {\n  outline: 0;\n}\n\n// The dropdown menu (ul)\n.dropdown-menu {\n  position: absolute;\n  top: 100%;\n  left: 0;\n  z-index: @zindex-dropdown;\n  display: none; // none by default, but block on \"open\" of the menu\n  float: left;\n  min-width: 160px;\n  padding: 5px 0;\n  margin: 2px 0 0; // override default ul\n  font-size: @font-size-base;\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: @dropdown-bg;\n  background-clip: padding-box;\n  border: 1px solid @dropdown-fallback-border; // IE8 fallback\n  border: 1px solid @dropdown-border;\n  border-radius: @border-radius-base;\n  .box-shadow(0 6px 12px rgba(0, 0, 0, .175));\n\n  // Aligns the dropdown menu to right\n  //\n  // Deprecated as of 3.1.0 in favor of `.dropdown-menu-[dir]`\n  &.pull-right {\n    right: 0;\n    left: auto;\n  }\n\n  // Dividers (basically an hr) within the dropdown\n  .divider {\n    .nav-divider(@dropdown-divider-bg);\n  }\n\n  // Links within the dropdown menu\n  > li > a {\n    display: block;\n    padding: 3px 20px;\n    clear: both;\n    font-weight: 400;\n    line-height: @line-height-base;\n    color: @dropdown-link-color;\n    white-space: nowrap; // prevent links from randomly breaking onto new lines\n\n    &:hover,\n    &:focus {\n      color: @dropdown-link-hover-color;\n      text-decoration: none;\n      background-color: @dropdown-link-hover-bg;\n    }\n  }\n}\n\n// Active state\n.dropdown-menu > .active > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-active-color;\n    text-decoration: none;\n    background-color: @dropdown-link-active-bg;\n    outline: 0;\n  }\n}\n\n// Disabled state\n//\n// Gray out text and ensure the hover/focus state remains gray\n\n.dropdown-menu > .disabled > a {\n  &,\n  &:hover,\n  &:focus {\n    color: @dropdown-link-disabled-color;\n  }\n\n  // Nuke hover/focus effects\n  &:hover,\n  &:focus {\n    text-decoration: none;\n    cursor: @cursor-disabled;\n    background-color: transparent;\n    background-image: none; // Remove CSS gradient\n    .reset-filter();\n  }\n}\n\n// Open state for the dropdown\n.open {\n  // Show the menu\n  > .dropdown-menu {\n    display: block;\n  }\n\n  // Remove the outline when :focus is triggered\n  > a {\n    outline: 0;\n  }\n}\n\n// Menu positioning\n//\n// Add extra class to `.dropdown-menu` to flip the alignment of the dropdown\n// menu with the parent.\n.dropdown-menu-right {\n  right: 0;\n  left: auto; // Reset the default from `.dropdown-menu`\n}\n// With v3, we enabled auto-flipping if you have a dropdown within a right\n// aligned nav component. To enable the undoing of that, we provide an override\n// to restore the default dropdown menu alignment.\n//\n// This is only for left-aligning a dropdown menu within a `.navbar-right` or\n// `.pull-right` nav component.\n.dropdown-menu-left {\n  right: auto;\n  left: 0;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: 3px 20px;\n  font-size: @font-size-small;\n  line-height: @line-height-base;\n  color: @dropdown-header-color;\n  white-space: nowrap; // as with > li > a\n}\n\n// Backdrop to catch body clicks on mobile, etc.\n.dropdown-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: (@zindex-dropdown - 10);\n}\n\n// Right aligned dropdowns\n.pull-right > .dropdown-menu {\n  right: 0;\n  left: auto;\n}\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n//\n// Just add .dropup after the standard .dropdown class and you're set, bro.\n// TODO: abstract this so that the navbar fixed styles are not placed here?\n\n.dropup,\n.navbar-fixed-bottom .dropdown {\n  // Reverse the caret\n  .caret {\n    content: \"\";\n    border-top: 0;\n    border-bottom: @caret-width-base dashed;\n    border-bottom: @caret-width-base solid ~\"\\9\"; // IE8\n  }\n  // Different positioning for bottom up menu\n  .dropdown-menu {\n    top: auto;\n    bottom: 100%;\n    margin-bottom: 2px;\n  }\n}\n\n\n// Component alignment\n//\n// Reiterate per navbar.less and the modified component alignment there.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-right {\n    .dropdown-menu {\n      .dropdown-menu-right();\n    }\n    // Necessary for overrides of the default right aligned menu.\n    // Will remove come v4 in all likelihood.\n    .dropdown-menu-left {\n      .dropdown-menu-left();\n    }\n  }\n}\n","// Horizontal dividers\n//\n// Dividers (basically an hr) within dropdowns and nav lists\n\n.nav-divider(@color: #e5e5e5) {\n  height: 1px;\n  margin: ((@line-height-computed / 2) - 1) 0;\n  overflow: hidden;\n  background-color: @color;\n}\n","// Reset filters for IE\n//\n// When you need to remove a gradient background, do not forget to use this to reset\n// the IE filter for IE9 and below.\n\n.reset-filter() {\n  filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(enabled = false)\"));\n}\n","// stylelint-disable selector-no-qualifying-type */\n\n//\n// Button groups\n// --------------------------------------------------\n\n// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n  > .btn {\n    position: relative;\n    float: left;\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active,\n    &.active {\n      z-index: 2;\n    }\n  }\n}\n\n// Prevent double borders when buttons are next to each other\n.btn-group {\n  .btn + .btn,\n  .btn + .btn-group,\n  .btn-group + .btn,\n  .btn-group + .btn-group {\n    margin-left: -1px;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  margin-left: -5px; // Offset the first child's margin\n  &:extend(.clearfix all);\n\n  .btn,\n  .btn-group,\n  .input-group {\n    float: left;\n  }\n  > .btn,\n  > .btn-group,\n  > .input-group {\n    margin-left: 5px;\n  }\n}\n\n.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {\n  border-radius: 0;\n}\n\n// Set corners individual because sometimes a single button can be in a .btn-group and we need :first-child and :last-child to both match\n.btn-group > .btn:first-child {\n  margin-left: 0;\n  &:not(:last-child):not(.dropdown-toggle) {\n    .border-right-radius(0);\n  }\n}\n// Need .dropdown-toggle since :last-child doesn't apply, given that a .dropdown-menu is used immediately after it\n.btn-group > .btn:last-child:not(:first-child),\n.btn-group > .dropdown-toggle:not(:first-child) {\n  .border-left-radius(0);\n}\n\n// Custom edits for including btn-groups within btn-groups (useful for including dropdown buttons within a btn-group)\n.btn-group > .btn-group {\n  float: left;\n}\n.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-right-radius(0);\n  }\n}\n.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-left-radius(0);\n}\n\n// On active and open, don't show outline\n.btn-group .dropdown-toggle:active,\n.btn-group.open .dropdown-toggle {\n  outline: 0;\n}\n\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-xs > .btn { &:extend(.btn-xs); }\n.btn-group-sm > .btn { &:extend(.btn-sm); }\n.btn-group-lg > .btn { &:extend(.btn-lg); }\n\n\n// Split button dropdowns\n// ----------------------\n\n// Give the line between buttons some depth\n.btn-group > .btn + .dropdown-toggle {\n  padding-right: 8px;\n  padding-left: 8px;\n}\n.btn-group > .btn-lg + .dropdown-toggle {\n  padding-right: 12px;\n  padding-left: 12px;\n}\n\n// The clickable button for toggling the menu\n// Remove the gradient and set the same inset shadow as the :active state\n.btn-group.open .dropdown-toggle {\n  .box-shadow(inset 0 3px 5px rgba(0, 0, 0, .125));\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    .box-shadow(none);\n  }\n}\n\n\n// Reposition the caret\n.btn .caret {\n  margin-left: 0;\n}\n// Carets in other button sizes\n.btn-lg .caret {\n  border-width: @caret-width-large @caret-width-large 0;\n  border-bottom-width: 0;\n}\n// Upside down carets for .dropup\n.dropup .btn-lg .caret {\n  border-width: 0 @caret-width-large @caret-width-large;\n}\n\n\n// Vertical button groups\n// ----------------------\n\n.btn-group-vertical {\n  > .btn,\n  > .btn-group,\n  > .btn-group > .btn {\n    display: block;\n    float: none;\n    width: 100%;\n    max-width: 100%;\n  }\n\n  // Clear floats so dropdown menus can be properly placed\n  > .btn-group {\n    &:extend(.clearfix all);\n    > .btn {\n      float: none;\n    }\n  }\n\n  > .btn + .btn,\n  > .btn + .btn-group,\n  > .btn-group + .btn,\n  > .btn-group + .btn-group {\n    margin-top: -1px;\n    margin-left: 0;\n  }\n}\n\n.btn-group-vertical > .btn {\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n  &:first-child:not(:last-child) {\n    .border-top-radius(@btn-border-radius-base);\n    .border-bottom-radius(0);\n  }\n  &:last-child:not(:first-child) {\n    .border-top-radius(0);\n    .border-bottom-radius(@btn-border-radius-base);\n  }\n}\n.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {\n  border-radius: 0;\n}\n.btn-group-vertical > .btn-group:first-child:not(:last-child) {\n  > .btn:last-child,\n  > .dropdown-toggle {\n    .border-bottom-radius(0);\n  }\n}\n.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {\n  .border-top-radius(0);\n}\n\n\n// Justified button groups\n// ----------------------\n\n.btn-group-justified {\n  display: table;\n  width: 100%;\n  table-layout: fixed;\n  border-collapse: separate;\n  > .btn,\n  > .btn-group {\n    display: table-cell;\n    float: none;\n    width: 1%;\n  }\n  > .btn-group .btn {\n    width: 100%;\n  }\n\n  > .btn-group .dropdown-menu {\n    left: auto;\n  }\n}\n\n\n// Checkbox and radio options\n//\n// In order to support the browser's form validation feedback, powered by the\n// `required` attribute, we have to \"hide\" the inputs via `clip`. We cannot use\n// `display: none;` or `visibility: hidden;` as that also hides the popover.\n// Simply visually hiding the inputs via `opacity` would leave them clickable in\n// certain cases which is prevented by using `clip` and `pointer-events`.\n// This way, we ensure a DOM element is visible to position the popover from.\n//\n// See https://github.com/twbs/bootstrap/pull/12794 and\n// https://github.com/twbs/bootstrap/pull/14559 for more information.\n\n[data-toggle=\"buttons\"] {\n  > .btn,\n  > .btn-group > .btn {\n    input[type=\"radio\"],\n    input[type=\"checkbox\"] {\n      position: absolute;\n      clip: rect(0, 0, 0, 0);\n      pointer-events: none;\n    }\n  }\n}\n","// Single side border-radius\n\n.border-top-radius(@radius) {\n  border-top-left-radius: @radius;\n  border-top-right-radius: @radius;\n}\n.border-right-radius(@radius) {\n  border-top-right-radius: @radius;\n  border-bottom-right-radius: @radius;\n}\n.border-bottom-radius(@radius) {\n  border-bottom-right-radius: @radius;\n  border-bottom-left-radius: @radius;\n}\n.border-left-radius(@radius) {\n  border-top-left-radius: @radius;\n  border-bottom-left-radius: @radius;\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Input groups\n// --------------------------------------------------\n\n// Base styles\n// -------------------------\n.input-group {\n  position: relative; // For dropdowns\n  display: table;\n  border-collapse: separate; // prevent input groups from inheriting border styles from table cells when placed within a table\n\n  // Undo padding and float of grid classes\n  &[class*=\"col-\"] {\n    float: none;\n    padding-right: 0;\n    padding-left: 0;\n  }\n\n  .form-control {\n    // Ensure that the input is always above the *appended* addon button for\n    // proper border colors.\n    position: relative;\n    z-index: 2;\n\n    // IE9 fubars the placeholder attribute in text inputs and the arrows on\n    // select elements in input groups. To fix it, we float the input. Details:\n    // https://github.com/twbs/bootstrap/issues/11561#issuecomment-28936855\n    float: left;\n\n    width: 100%;\n    margin-bottom: 0;\n\n    &:focus {\n      z-index: 3;\n    }\n  }\n}\n\n// Sizing options\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .input-group-addon,\n.input-group-lg > .input-group-btn > .btn {\n  .input-lg();\n}\n.input-group-sm > .form-control,\n.input-group-sm > .input-group-addon,\n.input-group-sm > .input-group-btn > .btn {\n  .input-sm();\n}\n\n\n// Display as table-cell\n// -------------------------\n.input-group-addon,\n.input-group-btn,\n.input-group .form-control {\n  display: table-cell;\n\n  &:not(:first-child):not(:last-child) {\n    border-radius: 0;\n  }\n}\n// Addon and addon wrapper for buttons\n.input-group-addon,\n.input-group-btn {\n  width: 1%;\n  white-space: nowrap;\n  vertical-align: middle; // Match the inputs\n}\n\n// Text input groups\n// -------------------------\n.input-group-addon {\n  padding: @padding-base-vertical @padding-base-horizontal;\n  font-size: @font-size-base;\n  font-weight: 400;\n  line-height: 1;\n  color: @input-color;\n  text-align: center;\n  background-color: @input-group-addon-bg;\n  border: 1px solid @input-group-addon-border-color;\n  border-radius: @input-border-radius;\n\n  // Sizing\n  &.input-sm {\n    padding: @padding-small-vertical @padding-small-horizontal;\n    font-size: @font-size-small;\n    border-radius: @input-border-radius-small;\n  }\n  &.input-lg {\n    padding: @padding-large-vertical @padding-large-horizontal;\n    font-size: @font-size-large;\n    border-radius: @input-border-radius-large;\n  }\n\n  // Nuke default margins from checkboxes and radios to vertically center within.\n  input[type=\"radio\"],\n  input[type=\"checkbox\"] {\n    margin-top: 0;\n  }\n}\n\n// Reset rounded corners\n.input-group .form-control:first-child,\n.input-group-addon:first-child,\n.input-group-btn:first-child > .btn,\n.input-group-btn:first-child > .btn-group > .btn,\n.input-group-btn:first-child > .dropdown-toggle,\n.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),\n.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {\n  .border-right-radius(0);\n}\n.input-group-addon:first-child {\n  border-right: 0;\n}\n.input-group .form-control:last-child,\n.input-group-addon:last-child,\n.input-group-btn:last-child > .btn,\n.input-group-btn:last-child > .btn-group > .btn,\n.input-group-btn:last-child > .dropdown-toggle,\n.input-group-btn:first-child > .btn:not(:first-child),\n.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {\n  .border-left-radius(0);\n}\n.input-group-addon:last-child {\n  border-left: 0;\n}\n\n// Button input groups\n// -------------------------\n.input-group-btn {\n  position: relative;\n  // Jankily prevent input button groups from wrapping with `white-space` and\n  // `font-size` in combination with `inline-block` on buttons.\n  font-size: 0;\n  white-space: nowrap;\n\n  // Negative margin for spacing, position for bringing hovered/focused/actived\n  // element above the siblings.\n  > .btn {\n    position: relative;\n    + .btn {\n      margin-left: -1px;\n    }\n    // Bring the \"active\" button to the front\n    &:hover,\n    &:focus,\n    &:active {\n      z-index: 2;\n    }\n  }\n\n  // Negative margin to only have a 1px border between the two\n  &:first-child {\n    > .btn,\n    > .btn-group {\n      margin-right: -1px;\n    }\n  }\n  &:last-child {\n    > .btn,\n    > .btn-group {\n      z-index: 2;\n      margin-left: -1px;\n    }\n  }\n}\n","// stylelint-disable selector-no-qualifying-type, selector-max-type\n\n//\n// Navs\n// --------------------------------------------------\n\n\n// Base class\n// --------------------------------------------------\n\n.nav {\n  padding-left: 0; // Override default ul/ol\n  margin-bottom: 0;\n  list-style: none;\n  &:extend(.clearfix all);\n\n  > li {\n    position: relative;\n    display: block;\n\n    > a {\n      position: relative;\n      display: block;\n      padding: @nav-link-padding;\n      &:hover,\n      &:focus {\n        text-decoration: none;\n        background-color: @nav-link-hover-bg;\n      }\n    }\n\n    // Disabled state sets text to gray and nukes hover/tab effects\n    &.disabled > a {\n      color: @nav-disabled-link-color;\n\n      &:hover,\n      &:focus {\n        color: @nav-disabled-link-hover-color;\n        text-decoration: none;\n        cursor: @cursor-disabled;\n        background-color: transparent;\n      }\n    }\n  }\n\n  // Open dropdowns\n  .open > a {\n    &,\n    &:hover,\n    &:focus {\n      background-color: @nav-link-hover-bg;\n      border-color: @link-color;\n    }\n  }\n\n  // Nav dividers (deprecated with v3.0.1)\n  //\n  // This should have been removed in v3 with the dropping of `.nav-list`, but\n  // we missed it. We don't currently support this anywhere, but in the interest\n  // of maintaining backward compatibility in case you use it, it's deprecated.\n  .nav-divider {\n    .nav-divider();\n  }\n\n  // Prevent IE8 from misplacing imgs\n  //\n  // See https://github.com/h5bp/html5-boilerplate/issues/984#issuecomment-3985989\n  > li > a > img {\n    max-width: none;\n  }\n}\n\n\n// Tabs\n// -------------------------\n\n// Give the tabs something to sit on\n.nav-tabs {\n  border-bottom: 1px solid @nav-tabs-border-color;\n  > li {\n    float: left;\n    // Make the list-items overlay the bottom border\n    margin-bottom: -1px;\n\n    // Actual tabs (as links)\n    > a {\n      margin-right: 2px;\n      line-height: @line-height-base;\n      border: 1px solid transparent;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n      &:hover {\n        border-color: @nav-tabs-link-hover-border-color @nav-tabs-link-hover-border-color @nav-tabs-border-color;\n      }\n    }\n\n    // Active state, and its :hover to override normal :hover\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-tabs-active-link-hover-color;\n        cursor: default;\n        background-color: @nav-tabs-active-link-hover-bg;\n        border: 1px solid @nav-tabs-active-link-hover-border-color;\n        border-bottom-color: transparent;\n      }\n    }\n  }\n  // pulling this in mainly for less shorthand\n  &.nav-justified {\n    .nav-justified();\n    .nav-tabs-justified();\n  }\n}\n\n\n// Pills\n// -------------------------\n.nav-pills {\n  > li {\n    float: left;\n\n    // Links rendered as pills\n    > a {\n      border-radius: @nav-pills-border-radius;\n    }\n    + li {\n      margin-left: 2px;\n    }\n\n    // Active state\n    &.active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @nav-pills-active-link-hover-color;\n        background-color: @nav-pills-active-link-hover-bg;\n      }\n    }\n  }\n}\n\n\n// Stacked pills\n.nav-stacked {\n  > li {\n    float: none;\n    + li {\n      margin-top: 2px;\n      margin-left: 0; // no need for this gap between nav items\n    }\n  }\n}\n\n\n// Nav variations\n// --------------------------------------------------\n\n// Justified nav links\n// -------------------------\n\n.nav-justified {\n  width: 100%;\n\n  > li {\n    float: none;\n    > a {\n      margin-bottom: 5px;\n      text-align: center;\n    }\n  }\n\n  > .dropdown .dropdown-menu {\n    top: auto;\n    left: auto;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li {\n      display: table-cell;\n      width: 1%;\n      > a {\n        margin-bottom: 0;\n      }\n    }\n  }\n}\n\n// Move borders to anchors instead of bottom of list\n//\n// Mixin for adding on top the shared `.nav-justified` styles for our tabs\n.nav-tabs-justified {\n  border-bottom: 0;\n\n  > li > a {\n    // Override margin from .nav-tabs\n    margin-right: 0;\n    border-radius: @border-radius-base;\n  }\n\n  > .active > a,\n  > .active > a:hover,\n  > .active > a:focus {\n    border: 1px solid @nav-tabs-justified-link-border-color;\n  }\n\n  @media (min-width: @screen-sm-min) {\n    > li > a {\n      border-bottom: 1px solid @nav-tabs-justified-link-border-color;\n      border-radius: @border-radius-base @border-radius-base 0 0;\n    }\n    > .active > a,\n    > .active > a:hover,\n    > .active > a:focus {\n      border-bottom-color: @nav-tabs-justified-active-link-border-color;\n    }\n  }\n}\n\n\n// Tabbable tabs\n// -------------------------\n\n// Hide tabbable panes to start, show them when `.active`\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n\n\n// Dropdowns\n// -------------------------\n\n// Specific dropdowns\n.nav-tabs .dropdown-menu {\n  // make dropdown border overlap tab border\n  margin-top: -1px;\n  // Remove the top rounded corners here since there is a hard edge above the menu\n  .border-top-radius(0);\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-max-combinators, selector-max-class, declaration-no-important, selector-no-qualifying-type\n\n//\n// Navbars\n// --------------------------------------------------\n\n\n// Wrapper and base class\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  position: relative;\n  min-height: @navbar-height; // Ensure a navbar always shows (e.g., without a .navbar-brand in collapsed mode)\n  margin-bottom: @navbar-margin-bottom;\n  border: 1px solid transparent;\n\n  // Prevent floats from breaking the navbar\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: @navbar-border-radius;\n  }\n}\n\n\n// Navbar heading\n//\n// Groups `.navbar-brand` and `.navbar-toggle` into a single component for easy\n// styling of responsive aspects.\n\n.navbar-header {\n  &:extend(.clearfix all);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n  }\n}\n\n\n// Navbar collapse (body)\n//\n// Group your navbar content into this for easy collapsing and expanding across\n// various device sizes. By default, this content is collapsed when <768px, but\n// will expand past that for a horizontal display.\n//\n// To start (on mobile devices) the navbar links, forms, and buttons are stacked\n// vertically and include a `max-height` to overflow in case you have too much\n// content for the user's viewport.\n\n.navbar-collapse {\n  padding-right: @navbar-padding-horizontal;\n  padding-left: @navbar-padding-horizontal;\n  overflow-x: visible;\n  border-top: 1px solid transparent;\n  box-shadow: inset 0 1px 0 rgba(255, 255, 255, .1);\n  &:extend(.clearfix all);\n  -webkit-overflow-scrolling: touch;\n\n  &.in {\n    overflow-y: auto;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    border-top: 0;\n    box-shadow: none;\n\n    &.collapse {\n      display: block !important;\n      height: auto !important;\n      padding-bottom: 0; // Override default setting\n      overflow: visible !important;\n    }\n\n    &.in {\n      overflow-y: visible;\n    }\n\n    // Undo the collapse side padding for navbars with containers to ensure\n    // alignment of right-aligned contents.\n    .navbar-fixed-top &,\n    .navbar-static-top &,\n    .navbar-fixed-bottom & {\n      padding-right: 0;\n      padding-left: 0;\n    }\n  }\n}\n\n.navbar-fixed-top,\n.navbar-fixed-bottom {\n  .navbar-collapse {\n    max-height: @navbar-collapse-max-height;\n\n    @media (max-device-width: @screen-xs-min) and (orientation: landscape) {\n      max-height: 200px;\n    }\n  }\n\n  // Fix the top/bottom navbars when screen real estate supports it\n  position: fixed;\n  right: 0;\n  left: 0;\n  z-index: @zindex-navbar-fixed;\n\n  // Undo the rounded corners\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n\n.navbar-fixed-top {\n  top: 0;\n  border-width: 0 0 1px;\n}\n.navbar-fixed-bottom {\n  bottom: 0;\n  margin-bottom: 0; // override .navbar defaults\n  border-width: 1px 0 0;\n}\n\n\n// Both navbar header and collapse\n//\n// When a container is present, change the behavior of the header and collapse.\n\n.container,\n.container-fluid {\n  > .navbar-header,\n  > .navbar-collapse {\n    margin-right: -@navbar-padding-horizontal;\n    margin-left: -@navbar-padding-horizontal;\n\n    @media (min-width: @grid-float-breakpoint) {\n      margin-right: 0;\n      margin-left: 0;\n    }\n  }\n}\n\n\n//\n// Navbar alignment options\n//\n// Display the navbar across the entirety of the page or fixed it to the top or\n// bottom of the page.\n\n// Static top (unfixed, but 100% wide) navbar\n.navbar-static-top {\n  z-index: @zindex-navbar;\n  border-width: 0 0 1px;\n\n  @media (min-width: @grid-float-breakpoint) {\n    border-radius: 0;\n  }\n}\n\n\n// Brand/project name\n\n.navbar-brand {\n  float: left;\n  height: @navbar-height;\n  padding: @navbar-padding-vertical @navbar-padding-horizontal;\n  font-size: @font-size-large;\n  line-height: @line-height-computed;\n\n  &:hover,\n  &:focus {\n    text-decoration: none;\n  }\n\n  > img {\n    display: block;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    .navbar > .container &,\n    .navbar > .container-fluid & {\n      margin-left: -@navbar-padding-horizontal;\n    }\n  }\n}\n\n\n// Navbar toggle\n//\n// Custom button for toggling the `.navbar-collapse`, powered by the collapse\n// JavaScript plugin.\n\n.navbar-toggle {\n  position: relative;\n  float: right;\n  padding: 9px 10px;\n  margin-right: @navbar-padding-horizontal;\n  .navbar-vertical-align(34px);\n  background-color: transparent;\n  background-image: none; // Reset unusual Firefox-on-Android default style; see https://github.com/necolas/normalize.css/issues/214\n  border: 1px solid transparent;\n  border-radius: @border-radius-base;\n\n  // We remove the `outline` here, but later compensate by attaching `:hover`\n  // styles to `:focus`.\n  &:focus {\n    outline: 0;\n  }\n\n  // Bars\n  .icon-bar {\n    display: block;\n    width: 22px;\n    height: 2px;\n    border-radius: 1px;\n  }\n  .icon-bar + .icon-bar {\n    margin-top: 4px;\n  }\n\n  @media (min-width: @grid-float-breakpoint) {\n    display: none;\n  }\n}\n\n\n// Navbar nav links\n//\n// Builds on top of the `.nav` components with its own modifier class to make\n// the nav the full height of the horizontal nav (above 768px).\n\n.navbar-nav {\n  margin: (@navbar-padding-vertical / 2) -@navbar-padding-horizontal;\n\n  > li > a {\n    padding-top: 10px;\n    padding-bottom: 10px;\n    line-height: @line-height-computed;\n  }\n\n  @media (max-width: @grid-float-breakpoint-max) {\n    // Dropdowns get custom display when collapsed\n    .open .dropdown-menu {\n      position: static;\n      float: none;\n      width: auto;\n      margin-top: 0;\n      background-color: transparent;\n      border: 0;\n      box-shadow: none;\n      > li > a,\n      .dropdown-header {\n        padding: 5px 15px 5px 25px;\n      }\n      > li > a {\n        line-height: @line-height-computed;\n        &:hover,\n        &:focus {\n          background-image: none;\n        }\n      }\n    }\n  }\n\n  // Uncollapse the nav\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin: 0;\n\n    > li {\n      float: left;\n      > a {\n        padding-top: @navbar-padding-vertical;\n        padding-bottom: @navbar-padding-vertical;\n      }\n    }\n  }\n}\n\n\n// Navbar form\n//\n// Extension of the `.form-inline` with some extra flavor for optimum display in\n// our navbars.\n\n.navbar-form {\n  padding: 10px @navbar-padding-horizontal;\n  margin-right: -@navbar-padding-horizontal;\n  margin-left: -@navbar-padding-horizontal;\n  border-top: 1px solid transparent;\n  border-bottom: 1px solid transparent;\n  @shadow: inset 0 1px 0 rgba(255, 255, 255, .1), 0 1px 0 rgba(255, 255, 255, .1);\n  .box-shadow(@shadow);\n\n  // Mixin behavior for optimum display\n  .form-inline();\n\n  .form-group {\n    @media (max-width: @grid-float-breakpoint-max) {\n      margin-bottom: 5px;\n\n      &:last-child {\n        margin-bottom: 0;\n      }\n    }\n  }\n\n  // Vertically center in expanded, horizontal navbar\n  .navbar-vertical-align(@input-height-base);\n\n  // Undo 100% width for pull classes\n  @media (min-width: @grid-float-breakpoint) {\n    width: auto;\n    padding-top: 0;\n    padding-bottom: 0;\n    margin-right: 0;\n    margin-left: 0;\n    border: 0;\n    .box-shadow(none);\n  }\n}\n\n\n// Dropdown menus\n\n// Menu position and menu carets\n.navbar-nav > li > .dropdown-menu {\n  margin-top: 0;\n  .border-top-radius(0);\n}\n// Menu position and menu caret support for dropups via extra dropup class\n.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {\n  margin-bottom: 0;\n  .border-top-radius(@navbar-border-radius);\n  .border-bottom-radius(0);\n}\n\n\n// Buttons in navbars\n//\n// Vertically center a button within a navbar (when *not* in a form).\n\n.navbar-btn {\n  .navbar-vertical-align(@input-height-base);\n\n  &.btn-sm {\n    .navbar-vertical-align(@input-height-small);\n  }\n  &.btn-xs {\n    .navbar-vertical-align(22);\n  }\n}\n\n\n// Text in navbars\n//\n// Add a class to make any element properly align itself vertically within the navbars.\n\n.navbar-text {\n  .navbar-vertical-align(@line-height-computed);\n\n  @media (min-width: @grid-float-breakpoint) {\n    float: left;\n    margin-right: @navbar-padding-horizontal;\n    margin-left: @navbar-padding-horizontal;\n  }\n}\n\n\n// Component alignment\n//\n// Repurpose the pull utilities as their own navbar utilities to avoid specificity\n// issues with parents and chaining. Only do this when the navbar is uncollapsed\n// though so that navbar contents properly stack and align in mobile.\n//\n// Declared after the navbar components to ensure more specificity on the margins.\n\n@media (min-width: @grid-float-breakpoint) {\n  .navbar-left  { .pull-left(); }\n  .navbar-right {\n    .pull-right();\n    margin-right: -@navbar-padding-horizontal;\n\n    ~ .navbar-right {\n      margin-right: 0;\n    }\n  }\n}\n\n\n// Alternate navbars\n// --------------------------------------------------\n\n// Default navbar\n.navbar-default {\n  background-color: @navbar-default-bg;\n  border-color: @navbar-default-border;\n\n  .navbar-brand {\n    color: @navbar-default-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-brand-hover-color;\n      background-color: @navbar-default-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-default-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-default-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-hover-color;\n        background-color: @navbar-default-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-active-color;\n        background-color: @navbar-default-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n        background-color: @navbar-default-link-disabled-bg;\n      }\n    }\n\n    // Dropdown menu items\n    // Remove background color from open dropdown\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-active-color;\n        background-color: @navbar-default-link-active-bg;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display when collapsed\n      .open .dropdown-menu {\n        > li > a {\n          color: @navbar-default-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-hover-color;\n            background-color: @navbar-default-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-active-color;\n            background-color: @navbar-default-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-default-link-disabled-color;\n            background-color: @navbar-default-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n  .navbar-toggle {\n    border-color: @navbar-default-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-default-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-default-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: @navbar-default-border;\n  }\n\n\n  // Links in navbars\n  //\n  // Add a class to ensure links outside the navbar nav are colored correctly.\n\n  .navbar-link {\n    color: @navbar-default-link-color;\n    &:hover {\n      color: @navbar-default-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-default-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-default-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-default-link-disabled-color;\n      }\n    }\n  }\n}\n\n// Inverse navbar\n\n.navbar-inverse {\n  background-color: @navbar-inverse-bg;\n  border-color: @navbar-inverse-border;\n\n  .navbar-brand {\n    color: @navbar-inverse-brand-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-brand-hover-color;\n      background-color: @navbar-inverse-brand-hover-bg;\n    }\n  }\n\n  .navbar-text {\n    color: @navbar-inverse-color;\n  }\n\n  .navbar-nav {\n    > li > a {\n      color: @navbar-inverse-link-color;\n\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-hover-color;\n        background-color: @navbar-inverse-link-hover-bg;\n      }\n    }\n    > .active > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-active-color;\n        background-color: @navbar-inverse-link-active-bg;\n      }\n    }\n    > .disabled > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n        background-color: @navbar-inverse-link-disabled-bg;\n      }\n    }\n\n    // Dropdowns\n    > .open > a {\n      &,\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-active-color;\n        background-color: @navbar-inverse-link-active-bg;\n      }\n    }\n\n    @media (max-width: @grid-float-breakpoint-max) {\n      // Dropdowns get custom display\n      .open .dropdown-menu {\n        > .dropdown-header {\n          border-color: @navbar-inverse-border;\n        }\n        .divider {\n          background-color: @navbar-inverse-border;\n        }\n        > li > a {\n          color: @navbar-inverse-link-color;\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-hover-color;\n            background-color: @navbar-inverse-link-hover-bg;\n          }\n        }\n        > .active > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-active-color;\n            background-color: @navbar-inverse-link-active-bg;\n          }\n        }\n        > .disabled > a {\n          &,\n          &:hover,\n          &:focus {\n            color: @navbar-inverse-link-disabled-color;\n            background-color: @navbar-inverse-link-disabled-bg;\n          }\n        }\n      }\n    }\n  }\n\n  // Darken the responsive nav toggle\n  .navbar-toggle {\n    border-color: @navbar-inverse-toggle-border-color;\n    &:hover,\n    &:focus {\n      background-color: @navbar-inverse-toggle-hover-bg;\n    }\n    .icon-bar {\n      background-color: @navbar-inverse-toggle-icon-bar-bg;\n    }\n  }\n\n  .navbar-collapse,\n  .navbar-form {\n    border-color: darken(@navbar-inverse-bg, 7%);\n  }\n\n  .navbar-link {\n    color: @navbar-inverse-link-color;\n    &:hover {\n      color: @navbar-inverse-link-hover-color;\n    }\n  }\n\n  .btn-link {\n    color: @navbar-inverse-link-color;\n    &:hover,\n    &:focus {\n      color: @navbar-inverse-link-hover-color;\n    }\n    &[disabled],\n    fieldset[disabled] & {\n      &:hover,\n      &:focus {\n        color: @navbar-inverse-link-disabled-color;\n      }\n    }\n  }\n}\n","// Navbar vertical align\n//\n// Vertically center elements in the navbar.\n// Example: an element has a height of 30px, so write out `.navbar-vertical-align(30px);` to calculate the appropriate top margin.\n\n.navbar-vertical-align(@element-height) {\n  margin-top: ((@navbar-height - @element-height) / 2);\n  margin-bottom: ((@navbar-height - @element-height) / 2);\n}\n","// stylelint-disable declaration-no-important\n\n//\n// Utility classes\n// --------------------------------------------------\n\n\n// Floats\n// -------------------------\n\n.clearfix {\n  .clearfix();\n}\n.center-block {\n  .center-block();\n}\n.pull-right {\n  float: right !important;\n}\n.pull-left {\n  float: left !important;\n}\n\n\n// Toggling content\n// -------------------------\n\n// Note: Deprecated .hide in favor of .hidden or .sr-only (as appropriate) in v3.0.1\n.hide {\n  display: none !important;\n}\n.show {\n  display: block !important;\n}\n.invisible {\n  visibility: hidden;\n}\n.text-hide {\n  .text-hide();\n}\n\n\n// Hide from screenreaders and browsers\n//\n// Credit: HTML5 Boilerplate\n\n.hidden {\n  display: none !important;\n}\n\n\n// For Affix plugin\n// -------------------------\n\n.affix {\n  position: fixed;\n}\n","//\n// Breadcrumbs\n// --------------------------------------------------\n\n\n.breadcrumb {\n  padding: @breadcrumb-padding-vertical @breadcrumb-padding-horizontal;\n  margin-bottom: @line-height-computed;\n  list-style: none;\n  background-color: @breadcrumb-bg;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline-block;\n\n    + li:before {\n      padding: 0 5px;\n      color: @breadcrumb-color;\n      content: \"@{breadcrumb-separator}\\00a0\"; // Unicode space added since inline-block means non-collapsing white-space\n    }\n  }\n\n  > .active {\n    color: @breadcrumb-active-color;\n  }\n}\n","//\n// Pagination (multiple pages)\n// --------------------------------------------------\n.pagination {\n  display: inline-block;\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  border-radius: @border-radius-base;\n\n  > li {\n    display: inline; // Remove list-style and block-level defaults\n    > a,\n    > span {\n      position: relative;\n      float: left; // Collapse white-space\n      padding: @padding-base-vertical @padding-base-horizontal;\n      margin-left: -1px;\n      line-height: @line-height-base;\n      color: @pagination-color;\n      text-decoration: none;\n      background-color: @pagination-bg;\n      border: 1px solid @pagination-border;\n\n      &:hover,\n      &:focus {\n        z-index: 2;\n        color: @pagination-hover-color;\n        background-color: @pagination-hover-bg;\n        border-color: @pagination-hover-border;\n      }\n    }\n    &:first-child {\n      > a,\n      > span {\n        margin-left: 0;\n        .border-left-radius(@border-radius-base);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius-base);\n      }\n    }\n  }\n\n  > .active > a,\n  > .active > span {\n    &,\n    &:hover,\n    &:focus {\n      z-index: 3;\n      color: @pagination-active-color;\n      cursor: default;\n      background-color: @pagination-active-bg;\n      border-color: @pagination-active-border;\n    }\n  }\n\n  > .disabled {\n    > span,\n    > span:hover,\n    > span:focus,\n    > a,\n    > a:hover,\n    > a:focus {\n      color: @pagination-disabled-color;\n      cursor: @cursor-disabled;\n      background-color: @pagination-disabled-bg;\n      border-color: @pagination-disabled-border;\n    }\n  }\n}\n\n// Sizing\n// --------------------------------------------------\n\n// Large\n.pagination-lg {\n  .pagination-size(@padding-large-vertical; @padding-large-horizontal; @font-size-large; @line-height-large; @border-radius-large);\n}\n\n// Small\n.pagination-sm {\n  .pagination-size(@padding-small-vertical; @padding-small-horizontal; @font-size-small; @line-height-small; @border-radius-small);\n}\n","// Pagination\n\n.pagination-size(@padding-vertical; @padding-horizontal; @font-size; @line-height; @border-radius) {\n  > li {\n    > a,\n    > span {\n      padding: @padding-vertical @padding-horizontal;\n      font-size: @font-size;\n      line-height: @line-height;\n    }\n    &:first-child {\n      > a,\n      > span {\n        .border-left-radius(@border-radius);\n      }\n    }\n    &:last-child {\n      > a,\n      > span {\n        .border-right-radius(@border-radius);\n      }\n    }\n  }\n}\n","//\n// Pager pagination\n// --------------------------------------------------\n\n\n.pager {\n  padding-left: 0;\n  margin: @line-height-computed 0;\n  text-align: center;\n  list-style: none;\n  &:extend(.clearfix all);\n  li {\n    display: inline;\n    > a,\n    > span {\n      display: inline-block;\n      padding: 5px 14px;\n      background-color: @pager-bg;\n      border: 1px solid @pager-border;\n      border-radius: @pager-border-radius;\n    }\n\n    > a:hover,\n    > a:focus {\n      text-decoration: none;\n      background-color: @pager-hover-bg;\n    }\n  }\n\n  .next {\n    > a,\n    > span {\n      float: right;\n    }\n  }\n\n  .previous {\n    > a,\n    > span {\n      float: left;\n    }\n  }\n\n  .disabled {\n    > a,\n    > a:hover,\n    > a:focus,\n    > span {\n      color: @pager-disabled-color;\n      cursor: @cursor-disabled;\n      background-color: @pager-bg;\n    }\n  }\n}\n","//\n// Labels\n// --------------------------------------------------\n\n.label {\n  display: inline;\n  padding: .2em .6em .3em;\n  font-size: 75%;\n  font-weight: 700;\n  line-height: 1;\n  color: @label-color;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  border-radius: .25em;\n\n  // Add hover effects, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @label-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Empty labels collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for labels in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n}\n\n// Colors\n// Contextual variations (linked labels get darker on :hover)\n\n.label-default {\n  .label-variant(@label-default-bg);\n}\n\n.label-primary {\n  .label-variant(@label-primary-bg);\n}\n\n.label-success {\n  .label-variant(@label-success-bg);\n}\n\n.label-info {\n  .label-variant(@label-info-bg);\n}\n\n.label-warning {\n  .label-variant(@label-warning-bg);\n}\n\n.label-danger {\n  .label-variant(@label-danger-bg);\n}\n","// Labels\n\n.label-variant(@color) {\n  background-color: @color;\n\n  &[href] {\n    &:hover,\n    &:focus {\n      background-color: darken(@color, 10%);\n    }\n  }\n}\n","//\n// Badges\n// --------------------------------------------------\n\n\n// Base class\n.badge {\n  display: inline-block;\n  min-width: 10px;\n  padding: 3px 7px;\n  font-size: @font-size-small;\n  font-weight: @badge-font-weight;\n  line-height: @badge-line-height;\n  color: @badge-color;\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: middle;\n  background-color: @badge-bg;\n  border-radius: @badge-border-radius;\n\n  // Empty badges collapse automatically (not available in IE8)\n  &:empty {\n    display: none;\n  }\n\n  // Quick fix for badges in buttons\n  .btn & {\n    position: relative;\n    top: -1px;\n  }\n\n  .btn-xs &,\n  .btn-group-xs > .btn & {\n    top: 0;\n    padding: 1px 5px;\n  }\n\n  // Hover state, but only for links\n  a& {\n    &:hover,\n    &:focus {\n      color: @badge-link-hover-color;\n      text-decoration: none;\n      cursor: pointer;\n    }\n  }\n\n  // Account for badges in navs\n  .list-group-item.active > &,\n  .nav-pills > .active > a > & {\n    color: @badge-active-color;\n    background-color: @badge-active-bg;\n  }\n\n  .list-group-item > & {\n    float: right;\n  }\n\n  .list-group-item > & + & {\n    margin-right: 5px;\n  }\n\n  .nav-pills > li > a > & {\n    margin-left: 3px;\n  }\n}\n","//\n// Jumbotron\n// --------------------------------------------------\n\n\n.jumbotron {\n  padding-top: @jumbotron-padding;\n  padding-bottom: @jumbotron-padding;\n  margin-bottom: @jumbotron-padding;\n  color: @jumbotron-color;\n  background-color: @jumbotron-bg;\n\n  h1,\n  .h1 {\n    color: @jumbotron-heading-color;\n  }\n\n  p {\n    margin-bottom: (@jumbotron-padding / 2);\n    font-size: @jumbotron-font-size;\n    font-weight: 200;\n  }\n\n  > hr {\n    border-top-color: darken(@jumbotron-bg, 10%);\n  }\n\n  .container &,\n  .container-fluid & {\n    padding-right: (@grid-gutter-width / 2);\n    padding-left: (@grid-gutter-width / 2);\n    border-radius: @border-radius-large; // Only round corners at higher resolutions if contained in a container\n  }\n\n  .container {\n    max-width: 100%;\n  }\n\n  @media screen and (min-width: @screen-sm-min) {\n    padding-top: (@jumbotron-padding * 1.6);\n    padding-bottom: (@jumbotron-padding * 1.6);\n\n    .container &,\n    .container-fluid & {\n      padding-right: (@jumbotron-padding * 2);\n      padding-left: (@jumbotron-padding * 2);\n    }\n\n    h1,\n    .h1 {\n      font-size: @jumbotron-heading-font-size;\n    }\n  }\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// Thumbnails\n// --------------------------------------------------\n\n\n// Mixin and adjust the regular image class\n.thumbnail {\n  display: block;\n  padding: @thumbnail-padding;\n  margin-bottom: @line-height-computed;\n  line-height: @line-height-base;\n  background-color: @thumbnail-bg;\n  border: 1px solid @thumbnail-border;\n  border-radius: @thumbnail-border-radius;\n  .transition(border .2s ease-in-out);\n\n  > img,\n  a > img {\n    &:extend(.img-responsive);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  // Add a hover state for linked versions only\n  a&:hover,\n  a&:focus,\n  a&.active {\n    border-color: @link-color;\n  }\n\n  // Image captions\n  .caption {\n    padding: @thumbnail-caption-padding;\n    color: @thumbnail-caption-color;\n  }\n}\n","//\n// Alerts\n// --------------------------------------------------\n\n\n// Base styles\n// -------------------------\n\n.alert {\n  padding: @alert-padding;\n  margin-bottom: @line-height-computed;\n  border: 1px solid transparent;\n  border-radius: @alert-border-radius;\n\n  // Headings for larger alerts\n  h4 {\n    margin-top: 0;\n    color: inherit; // Specified for the h4 to prevent conflicts of changing @headings-color\n  }\n\n  // Provide class for links that match alerts\n  .alert-link {\n    font-weight: @alert-link-font-weight;\n  }\n\n  // Improve alignment and spacing of inner content\n  > p,\n  > ul {\n    margin-bottom: 0;\n  }\n\n  > p + p {\n    margin-top: 5px;\n  }\n}\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n// The misspelled .alert-dismissable was deprecated in 3.2.0.\n.alert-dismissable,\n.alert-dismissible {\n  padding-right: (@alert-padding + 20);\n\n  // Adjust close link position\n  .close {\n    position: relative;\n    top: -2px;\n    right: -21px;\n    color: inherit;\n  }\n}\n\n// Alternate styles\n//\n// Generate contextual modifier classes for colorizing the alert.\n\n.alert-success {\n  .alert-variant(@alert-success-bg; @alert-success-border; @alert-success-text);\n}\n\n.alert-info {\n  .alert-variant(@alert-info-bg; @alert-info-border; @alert-info-text);\n}\n\n.alert-warning {\n  .alert-variant(@alert-warning-bg; @alert-warning-border; @alert-warning-text);\n}\n\n.alert-danger {\n  .alert-variant(@alert-danger-bg; @alert-danger-border; @alert-danger-text);\n}\n","// Alerts\n\n.alert-variant(@background; @border; @text-color) {\n  color: @text-color;\n  background-color: @background;\n  border-color: @border;\n\n  hr {\n    border-top-color: darken(@border, 5%);\n  }\n\n  .alert-link {\n    color: darken(@text-color, 10%);\n  }\n}\n","// stylelint-disable at-rule-no-vendor-prefix\n\n//\n// Progress bars\n// --------------------------------------------------\n\n\n// Bar animations\n// -------------------------\n\n// WebKit\n@-webkit-keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n// Spec and IE10+\n@keyframes progress-bar-stripes {\n  from  { background-position: 40px 0; }\n  to    { background-position: 0 0; }\n}\n\n\n// Bar itself\n// -------------------------\n\n// Outer container\n.progress {\n  height: @line-height-computed;\n  margin-bottom: @line-height-computed;\n  overflow: hidden;\n  background-color: @progress-bg;\n  border-radius: @progress-border-radius;\n  .box-shadow(inset 0 1px 2px rgba(0, 0, 0, .1));\n}\n\n// Bar of progress\n.progress-bar {\n  float: left;\n  width: 0%;\n  height: 100%;\n  font-size: @font-size-small;\n  line-height: @line-height-computed;\n  color: @progress-bar-color;\n  text-align: center;\n  background-color: @progress-bar-bg;\n  .box-shadow(inset 0 -1px 0 rgba(0, 0, 0, .15));\n  .transition(width .6s ease);\n}\n\n// Striped bars\n//\n// `.progress-striped .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar-striped` class, which you just add to an existing\n// `.progress-bar`.\n.progress-striped .progress-bar,\n.progress-bar-striped {\n  #gradient > .striped();\n  background-size: 40px 40px;\n}\n\n// Call animation for the active one\n//\n// `.progress.active .progress-bar` is deprecated as of v3.2.0 in favor of the\n// `.progress-bar.active` approach.\n.progress.active .progress-bar,\n.progress-bar.active {\n  .animation(progress-bar-stripes 2s linear infinite);\n}\n\n\n// Variations\n// -------------------------\n\n.progress-bar-success {\n  .progress-bar-variant(@progress-bar-success-bg);\n}\n\n.progress-bar-info {\n  .progress-bar-variant(@progress-bar-info-bg);\n}\n\n.progress-bar-warning {\n  .progress-bar-variant(@progress-bar-warning-bg);\n}\n\n.progress-bar-danger {\n  .progress-bar-variant(@progress-bar-danger-bg);\n}\n","// stylelint-disable value-no-vendor-prefix, selector-max-id\n\n#gradient {\n\n  // Horizontal gradient, from left to right\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .horizontal(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(left, @start-color @start-percent, @end-color @end-percent); // Opera 12\n    background-image: linear-gradient(to right, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  // Vertical gradient, from top to bottom\n  //\n  // Creates two color stops, start and end, by specifying a color and position for each color stop.\n  // Color stops are not available in IE9 and below.\n  .vertical(@start-color: #555; @end-color: #333; @start-percent: 0%; @end-percent: 100%) {\n    background-image: -webkit-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(top, @start-color @start-percent, @end-color @end-percent);  // Opera 12\n    background-image: linear-gradient(to bottom, @start-color @start-percent, @end-color @end-percent); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down\n    background-repeat: repeat-x;\n  }\n\n  .directional(@start-color: #555; @end-color: #333; @deg: 45deg) {\n    background-image: -webkit-linear-gradient(@deg, @start-color, @end-color); // Safari 5.1-6, Chrome 10+\n    background-image: -o-linear-gradient(@deg, @start-color, @end-color); // Opera 12\n    background-image: linear-gradient(@deg, @start-color, @end-color); // Standard, IE10, Firefox 16+, Opera 12.10+, Safari 7+, Chrome 26+\n    background-repeat: repeat-x;\n  }\n  .horizontal-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(left, @start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(to right, @start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=1)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .vertical-three-colors(@start-color: #00b3ee; @mid-color: #7a43b6; @color-stop: 50%; @end-color: #c3325f) {\n    background-image: -webkit-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: -o-linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    background-image: linear-gradient(@start-color, @mid-color @color-stop, @end-color);\n    filter: e(%(\"progid:DXImageTransform.Microsoft.gradient(startColorstr='%d', endColorstr='%d', GradientType=0)\", argb(@start-color), argb(@end-color))); // IE9 and down, gets no color-stop at all for proper fallback\n    background-repeat: no-repeat;\n  }\n  .radial(@inner-color: #555; @outer-color: #333) {\n    background-image: -webkit-radial-gradient(circle, @inner-color, @outer-color);\n    background-image: radial-gradient(circle, @inner-color, @outer-color);\n    background-repeat: no-repeat;\n  }\n  .striped(@color: rgba(255, 255, 255, .15); @angle: 45deg) {\n    background-image: -webkit-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: -o-linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n    background-image: linear-gradient(@angle, @color 25%, transparent 25%, transparent 50%, @color 50%, @color 75%, transparent 75%, transparent);\n  }\n}\n","// Progress bars\n\n.progress-bar-variant(@color) {\n  background-color: @color;\n\n  // Deprecated parent class requirement as of v3.2.0\n  .progress-striped & {\n    #gradient > .striped();\n  }\n}\n",".media {\n  // Proper spacing between instances of .media\n  margin-top: 15px;\n\n  &:first-child {\n    margin-top: 0;\n  }\n}\n\n.media,\n.media-body {\n  overflow: hidden;\n  zoom: 1;\n}\n\n.media-body {\n  width: 10000px;\n}\n\n.media-object {\n  display: block;\n\n  // Fix collapse in webkit from max-width: 100% and display: table-cell.\n  &.img-thumbnail {\n    max-width: none;\n  }\n}\n\n.media-right,\n.media > .pull-right {\n  padding-left: 10px;\n}\n\n.media-left,\n.media > .pull-left {\n  padding-right: 10px;\n}\n\n.media-left,\n.media-right,\n.media-body {\n  display: table-cell;\n  vertical-align: top;\n}\n\n.media-middle {\n  vertical-align: middle;\n}\n\n.media-bottom {\n  vertical-align: bottom;\n}\n\n// Reset margins on headings for tighter default spacing\n.media-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n\n// Media list variation\n//\n// Undo default ul/ol styles\n.media-list {\n  padding-left: 0;\n  list-style: none;\n}\n","// stylelint-disable selector-no-qualifying-type\n\n//\n// List groups\n// --------------------------------------------------\n\n\n// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 20px;\n}\n\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: 10px 15px;\n  // Place the border on the list items and negative margin up for better styling\n  margin-bottom: -1px;\n  background-color: @list-group-bg;\n  border: 1px solid @list-group-border;\n\n  // Round the first and last items\n  &:first-child {\n    .border-top-radius(@list-group-border-radius);\n  }\n  &:last-child {\n    margin-bottom: 0;\n    .border-bottom-radius(@list-group-border-radius);\n  }\n\n  // Disabled state\n  &.disabled,\n  &.disabled:hover,\n  &.disabled:focus {\n    color: @list-group-disabled-color;\n    cursor: @cursor-disabled;\n    background-color: @list-group-disabled-bg;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-disabled-text-color;\n    }\n  }\n\n  // Active class on item itself, not parent\n  &.active,\n  &.active:hover,\n  &.active:focus {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: @list-group-active-color;\n    background-color: @list-group-active-bg;\n    border-color: @list-group-active-border;\n\n    // Force color to inherit for custom content\n    .list-group-item-heading,\n    .list-group-item-heading > small,\n    .list-group-item-heading > .small {\n      color: inherit;\n    }\n    .list-group-item-text {\n      color: @list-group-active-text-color;\n    }\n  }\n}\n\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive items.\n// Includes an extra `.active` modifier class for showing selected items.\n\na.list-group-item,\nbutton.list-group-item {\n  color: @list-group-link-color;\n\n  .list-group-item-heading {\n    color: @list-group-link-heading-color;\n  }\n\n  // Hover state\n  &:hover,\n  &:focus {\n    color: @list-group-link-hover-color;\n    text-decoration: none;\n    background-color: @list-group-hover-bg;\n  }\n}\n\nbutton.list-group-item {\n  width: 100%;\n  text-align: left;\n}\n\n\n// Contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n.list-group-item-variant(success; @state-success-bg; @state-success-text);\n.list-group-item-variant(info; @state-info-bg; @state-info-text);\n.list-group-item-variant(warning; @state-warning-bg; @state-warning-text);\n.list-group-item-variant(danger; @state-danger-bg; @state-danger-text);\n\n\n// Custom content options\n//\n// Extra classes for creating well-formatted content within `.list-group-item`s.\n\n.list-group-item-heading {\n  margin-top: 0;\n  margin-bottom: 5px;\n}\n.list-group-item-text {\n  margin-bottom: 0;\n  line-height: 1.3;\n}\n","// List Groups\n\n.list-group-item-variant(@state; @background; @color) {\n  .list-group-item-@{state} {\n    color: @color;\n    background-color: @background;\n\n    a&,\n    button& {\n      color: @color;\n\n      .list-group-item-heading {\n        color: inherit;\n      }\n\n      &:hover,\n      &:focus {\n        color: @color;\n        background-color: darken(@background, 5%);\n      }\n      &.active,\n      &.active:hover,\n      &.active:focus {\n        color: #fff;\n        background-color: @color;\n        border-color: @color;\n      }\n    }\n  }\n}\n","// stylelint-disable selector-max-type, selector-max-compound-selectors, selector-max-combinators, no-duplicate-selectors\n\n//\n// Panels\n// --------------------------------------------------\n\n\n// Base class\n.panel {\n  margin-bottom: @line-height-computed;\n  background-color: @panel-bg;\n  border: 1px solid transparent;\n  border-radius: @panel-border-radius;\n  .box-shadow(0 1px 1px rgba(0, 0, 0, .05));\n}\n\n// Panel contents\n.panel-body {\n  padding: @panel-body-padding;\n  &:extend(.clearfix all);\n}\n\n// Optional heading\n.panel-heading {\n  padding: @panel-heading-padding;\n  border-bottom: 1px solid transparent;\n  .border-top-radius((@panel-border-radius - 1));\n\n  > .dropdown .dropdown-toggle {\n    color: inherit;\n  }\n}\n\n// Within heading, strip any `h*` tag of its default margins for spacing.\n.panel-title {\n  margin-top: 0;\n  margin-bottom: 0;\n  font-size: ceil((@font-size-base * 1.125));\n  color: inherit;\n\n  > a,\n  > small,\n  > .small,\n  > small > a,\n  > .small > a {\n    color: inherit;\n  }\n}\n\n// Optional footer (stays gray in every modifier class)\n.panel-footer {\n  padding: @panel-footer-padding;\n  background-color: @panel-footer-bg;\n  border-top: 1px solid @panel-inner-border;\n  .border-bottom-radius((@panel-border-radius - 1));\n}\n\n\n// List groups in panels\n//\n// By default, space out list group content from panel headings to account for\n// any kind of custom content between the two.\n\n.panel {\n  > .list-group,\n  > .panel-collapse > .list-group {\n    margin-bottom: 0;\n\n    .list-group-item {\n      border-width: 1px 0;\n      border-radius: 0;\n    }\n\n    // Add border top radius for first one\n    &:first-child {\n      .list-group-item:first-child {\n        border-top: 0;\n        .border-top-radius((@panel-border-radius - 1));\n      }\n    }\n\n    // Add border bottom radius for last one\n    &:last-child {\n      .list-group-item:last-child {\n        border-bottom: 0;\n        .border-bottom-radius((@panel-border-radius - 1));\n      }\n    }\n  }\n  > .panel-heading + .panel-collapse > .list-group {\n    .list-group-item:first-child {\n      .border-top-radius(0);\n    }\n  }\n}\n// Collapse space between when there's no additional content.\n.panel-heading + .list-group {\n  .list-group-item:first-child {\n    border-top-width: 0;\n  }\n}\n.list-group + .panel-footer {\n  border-top-width: 0;\n}\n\n// Tables in panels\n//\n// Place a non-bordered `.table` within a panel (not within a `.panel-body`) and\n// watch it go full width.\n\n.panel {\n  > .table,\n  > .table-responsive > .table,\n  > .panel-collapse > .table {\n    margin-bottom: 0;\n\n    caption {\n      padding-right: @panel-body-padding;\n      padding-left: @panel-body-padding;\n    }\n  }\n  // Add border top radius for first one\n  > .table:first-child,\n  > .table-responsive:first-child > .table:first-child {\n    .border-top-radius((@panel-border-radius - 1));\n\n    > thead:first-child,\n    > tbody:first-child {\n      > tr:first-child {\n        border-top-left-radius: (@panel-border-radius - 1);\n        border-top-right-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-top-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-top-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  // Add border bottom radius for last one\n  > .table:last-child,\n  > .table-responsive:last-child > .table:last-child {\n    .border-bottom-radius((@panel-border-radius - 1));\n\n    > tbody:last-child,\n    > tfoot:last-child {\n      > tr:last-child {\n        border-bottom-right-radius: (@panel-border-radius - 1);\n        border-bottom-left-radius: (@panel-border-radius - 1);\n\n        td:first-child,\n        th:first-child {\n          border-bottom-left-radius: (@panel-border-radius - 1);\n        }\n        td:last-child,\n        th:last-child {\n          border-bottom-right-radius: (@panel-border-radius - 1);\n        }\n      }\n    }\n  }\n  > .panel-body + .table,\n  > .panel-body + .table-responsive,\n  > .table + .panel-body,\n  > .table-responsive + .panel-body {\n    border-top: 1px solid @table-border-color;\n  }\n  > .table > tbody:first-child > tr:first-child th,\n  > .table > tbody:first-child > tr:first-child td {\n    border-top: 0;\n  }\n  > .table-bordered,\n  > .table-responsive > .table-bordered {\n    border: 0;\n    > thead,\n    > tbody,\n    > tfoot {\n      > tr {\n        > th:first-child,\n        > td:first-child {\n          border-left: 0;\n        }\n        > th:last-child,\n        > td:last-child {\n          border-right: 0;\n        }\n      }\n    }\n    > thead,\n    > tbody {\n      > tr:first-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n    > tbody,\n    > tfoot {\n      > tr:last-child {\n        > td,\n        > th {\n          border-bottom: 0;\n        }\n      }\n    }\n  }\n  > .table-responsive {\n    margin-bottom: 0;\n    border: 0;\n  }\n}\n\n\n// Collapsible panels (aka, accordion)\n//\n// Wrap a series of panels in `.panel-group` to turn them into an accordion with\n// the help of our collapse JavaScript plugin.\n\n.panel-group {\n  margin-bottom: @line-height-computed;\n\n  // Tighten up margin so it's only between panels\n  .panel {\n    margin-bottom: 0;\n    border-radius: @panel-border-radius;\n\n    + .panel {\n      margin-top: 5px;\n    }\n  }\n\n  .panel-heading {\n    border-bottom: 0;\n\n    + .panel-collapse > .panel-body,\n    + .panel-collapse > .list-group {\n      border-top: 1px solid @panel-inner-border;\n    }\n  }\n\n  .panel-footer {\n    border-top: 0;\n    + .panel-collapse .panel-body {\n      border-bottom: 1px solid @panel-inner-border;\n    }\n  }\n}\n\n\n// Contextual variations\n.panel-default {\n  .panel-variant(@panel-default-border; @panel-default-text; @panel-default-heading-bg; @panel-default-border);\n}\n.panel-primary {\n  .panel-variant(@panel-primary-border; @panel-primary-text; @panel-primary-heading-bg; @panel-primary-border);\n}\n.panel-success {\n  .panel-variant(@panel-success-border; @panel-success-text; @panel-success-heading-bg; @panel-success-border);\n}\n.panel-info {\n  .panel-variant(@panel-info-border; @panel-info-text; @panel-info-heading-bg; @panel-info-border);\n}\n.panel-warning {\n  .panel-variant(@panel-warning-border; @panel-warning-text; @panel-warning-heading-bg; @panel-warning-border);\n}\n.panel-danger {\n  .panel-variant(@panel-danger-border; @panel-danger-text; @panel-danger-heading-bg; @panel-danger-border);\n}\n","// Panels\n\n.panel-variant(@border; @heading-text-color; @heading-bg-color; @heading-border) {\n  border-color: @border;\n\n  & > .panel-heading {\n    color: @heading-text-color;\n    background-color: @heading-bg-color;\n    border-color: @heading-border;\n\n    + .panel-collapse > .panel-body {\n      border-top-color: @border;\n    }\n    .badge {\n      color: @heading-bg-color;\n      background-color: @heading-text-color;\n    }\n  }\n  & > .panel-footer {\n    + .panel-collapse > .panel-body {\n      border-bottom-color: @border;\n    }\n  }\n}\n","// Embeds responsive\n//\n// Credit: Nicolas Gallagher and SUIT CSS.\n\n.embed-responsive {\n  position: relative;\n  display: block;\n  height: 0;\n  padding: 0;\n  overflow: hidden;\n\n  .embed-responsive-item,\n  iframe,\n  embed,\n  object,\n  video {\n    position: absolute;\n    top: 0;\n    bottom: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n    border: 0;\n  }\n}\n\n// Modifier class for 16:9 aspect ratio\n.embed-responsive-16by9 {\n  padding-bottom: 56.25%;\n}\n\n// Modifier class for 4:3 aspect ratio\n.embed-responsive-4by3 {\n  padding-bottom: 75%;\n}\n","//\n// Wells\n// --------------------------------------------------\n\n\n// Base class\n.well {\n  min-height: 20px;\n  padding: 19px;\n  margin-bottom: 20px;\n  background-color: @well-bg;\n  border: 1px solid @well-border;\n  border-radius: @border-radius-base;\n  .box-shadow(inset 0 1px 1px rgba(0, 0, 0, .05));\n  blockquote {\n    border-color: #ddd;\n    border-color: rgba(0, 0, 0, .15);\n  }\n}\n\n// Sizes\n.well-lg {\n  padding: 24px;\n  border-radius: @border-radius-large;\n}\n.well-sm {\n  padding: 9px;\n  border-radius: @border-radius-small;\n}\n","// stylelint-disable property-no-vendor-prefix\n\n//\n// Close icons\n// --------------------------------------------------\n\n\n.close {\n  float: right;\n  font-size: (@font-size-base * 1.5);\n  font-weight: @close-font-weight;\n  line-height: 1;\n  color: @close-color;\n  text-shadow: @close-text-shadow;\n  .opacity(.2);\n\n  &:hover,\n  &:focus {\n    color: @close-color;\n    text-decoration: none;\n    cursor: pointer;\n    .opacity(.5);\n  }\n\n  // Additional properties for button version\n  // iOS requires the button element instead of an anchor tag.\n  // If you want the anchor version, it requires `href=\"#\"`.\n  // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n  button& {\n    padding: 0;\n    cursor: pointer;\n    background: transparent;\n    border: 0;\n    -webkit-appearance: none;\n    appearance: none;\n  }\n}\n","//\n// Modals\n// --------------------------------------------------\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and shit\n\n// Kill the scroll on the body\n.modal-open {\n  overflow: hidden;\n}\n\n// Container that the modal scrolls within\n.modal {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal;\n  display: none;\n  overflow: hidden;\n  -webkit-overflow-scrolling: touch;\n\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n\n  // When fading in the modal, animate it to slide down\n  &.fade .modal-dialog {\n    .translate(0, -25%);\n    .transition-transform(~\"0.3s ease-out\");\n  }\n  &.in .modal-dialog { .translate(0, 0); }\n}\n.modal-open .modal {\n  overflow-x: hidden;\n  overflow-y: auto;\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: 10px;\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  background-color: @modal-content-bg;\n  background-clip: padding-box;\n  border: 1px solid @modal-content-fallback-border-color; //old browsers fallback (ie8 etc)\n  border: 1px solid @modal-content-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 3px 9px rgba(0, 0, 0, .5));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  position: fixed;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: @zindex-modal-background;\n  background-color: @modal-backdrop-bg;\n  // Fade for backdrop\n  &.fade { .opacity(0); }\n  &.in { .opacity(@modal-backdrop-opacity); }\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  padding: @modal-title-padding;\n  border-bottom: 1px solid @modal-header-border-color;\n  &:extend(.clearfix all);\n}\n// Close icon\n.modal-header .close {\n  margin-top: -2px;\n}\n\n// Title text within header\n.modal-title {\n  margin: 0;\n  line-height: @modal-title-line-height;\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  padding: @modal-inner-padding;\n}\n\n// Footer (for actions)\n.modal-footer {\n  padding: @modal-inner-padding;\n  text-align: right; // right align buttons\n  border-top: 1px solid @modal-footer-border-color;\n  &:extend(.clearfix all); // clear it in case folks use .pull-* classes on buttons\n\n  // Properly space out buttons\n  .btn + .btn {\n    margin-bottom: 0; // account for input[type=\"submit\"] which gets the bottom margin like all other inputs\n    margin-left: 5px;\n  }\n  // but override that for button groups\n  .btn-group .btn + .btn {\n    margin-left: -1px;\n  }\n  // and override it for block buttons as well\n  .btn-block + .btn-block {\n    margin-left: 0;\n  }\n}\n\n// Measure scrollbar width for padding body during modal show/hide\n.modal-scrollbar-measure {\n  position: absolute;\n  top: -9999px;\n  width: 50px;\n  height: 50px;\n  overflow: scroll;\n}\n\n// Scale up the modal\n@media (min-width: @screen-sm-min) {\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    width: @modal-md;\n    margin: 30px auto;\n  }\n  .modal-content {\n    .box-shadow(0 5px 15px rgba(0, 0, 0, .5));\n  }\n\n  // Modal sizes\n  .modal-sm { width: @modal-sm; }\n}\n\n@media (min-width: @screen-md-min) {\n  .modal-lg { width: @modal-lg; }\n}\n","//\n// Tooltips\n// --------------------------------------------------\n\n\n// Base class\n.tooltip {\n  position: absolute;\n  z-index: @zindex-tooltip;\n  display: block;\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-small;\n\n  .opacity(0);\n\n  &.in { .opacity(@tooltip-opacity); }\n  &.top {\n    padding: @tooltip-arrow-width 0;\n    margin-top: -3px;\n  }\n  &.right {\n    padding: 0 @tooltip-arrow-width;\n    margin-left: 3px;\n  }\n  &.bottom {\n    padding: @tooltip-arrow-width 0;\n    margin-top: 3px;\n  }\n  &.left {\n    padding: 0 @tooltip-arrow-width;\n    margin-left: -3px;\n  }\n\n  // Note: Deprecated .top-left, .top-right, .bottom-left, and .bottom-right as of v3.3.1\n  &.top .tooltip-arrow {\n    bottom: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-left .tooltip-arrow {\n    right: @tooltip-arrow-width;\n    bottom: 0;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.top-right .tooltip-arrow {\n    bottom: 0;\n    left: @tooltip-arrow-width;\n    margin-bottom: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-top-color: @tooltip-arrow-color;\n  }\n  &.right .tooltip-arrow {\n    top: 50%;\n    left: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width @tooltip-arrow-width @tooltip-arrow-width 0;\n    border-right-color: @tooltip-arrow-color;\n  }\n  &.left .tooltip-arrow {\n    top: 50%;\n    right: 0;\n    margin-top: -@tooltip-arrow-width;\n    border-width: @tooltip-arrow-width 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-left-color: @tooltip-arrow-color;\n  }\n  &.bottom .tooltip-arrow {\n    top: 0;\n    left: 50%;\n    margin-left: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-left .tooltip-arrow {\n    top: 0;\n    right: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n  &.bottom-right .tooltip-arrow {\n    top: 0;\n    left: @tooltip-arrow-width;\n    margin-top: -@tooltip-arrow-width;\n    border-width: 0 @tooltip-arrow-width @tooltip-arrow-width;\n    border-bottom-color: @tooltip-arrow-color;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: @tooltip-max-width;\n  padding: 3px 8px;\n  color: @tooltip-color;\n  text-align: center;\n  background-color: @tooltip-bg;\n  border-radius: @border-radius-base;\n}\n\n// Arrows\n.tooltip-arrow {\n  position: absolute;\n  width: 0;\n  height: 0;\n  border-color: transparent;\n  border-style: solid;\n}\n",".reset-text() {\n  font-family: @font-family-base;\n  // We deliberately do NOT reset font-size.\n  font-style: normal;\n  font-weight: 400;\n  line-height: @line-height-base;\n  line-break: auto;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  word-spacing: normal;\n  word-wrap: normal;\n  white-space: normal;\n}\n","//\n// Popovers\n// --------------------------------------------------\n\n\n.popover {\n  position: absolute;\n  top: 0;\n  left: 0;\n  z-index: @zindex-popover;\n  display: none;\n  max-width: @popover-max-width;\n  padding: 1px;\n  // Our parent element can be arbitrary since popovers are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  .reset-text();\n  font-size: @font-size-base;\n  background-color: @popover-bg;\n  background-clip: padding-box;\n  border: 1px solid @popover-fallback-border-color;\n  border: 1px solid @popover-border-color;\n  border-radius: @border-radius-large;\n  .box-shadow(0 5px 10px rgba(0, 0, 0, .2));\n\n  // Offset the popover to account for the popover arrow\n  &.top { margin-top: -@popover-arrow-width; }\n  &.right { margin-left: @popover-arrow-width; }\n  &.bottom { margin-top: @popover-arrow-width; }\n  &.left { margin-left: -@popover-arrow-width; }\n\n  // Arrows\n  // .arrow is outer, .arrow:after is inner\n  > .arrow {\n    border-width: @popover-arrow-outer-width;\n\n    &,\n    &:after {\n      position: absolute;\n      display: block;\n      width: 0;\n      height: 0;\n      border-color: transparent;\n      border-style: solid;\n    }\n\n    &:after {\n      content: \"\";\n      border-width: @popover-arrow-width;\n    }\n  }\n\n  &.top > .arrow {\n    bottom: -@popover-arrow-outer-width;\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-top-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-top-color: @popover-arrow-outer-color;\n    border-bottom-width: 0;\n    &:after {\n      bottom: 1px;\n      margin-left: -@popover-arrow-width;\n      content: \" \";\n      border-top-color: @popover-arrow-color;\n      border-bottom-width: 0;\n    }\n  }\n  &.right > .arrow {\n    top: 50%;\n    left: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-right-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-right-color: @popover-arrow-outer-color;\n    border-left-width: 0;\n    &:after {\n      bottom: -@popover-arrow-width;\n      left: 1px;\n      content: \" \";\n      border-right-color: @popover-arrow-color;\n      border-left-width: 0;\n    }\n  }\n  &.bottom > .arrow {\n    top: -@popover-arrow-outer-width;\n    left: 50%;\n    margin-left: -@popover-arrow-outer-width;\n    border-top-width: 0;\n    border-bottom-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-bottom-color: @popover-arrow-outer-color;\n    &:after {\n      top: 1px;\n      margin-left: -@popover-arrow-width;\n      content: \" \";\n      border-top-width: 0;\n      border-bottom-color: @popover-arrow-color;\n    }\n  }\n\n  &.left > .arrow {\n    top: 50%;\n    right: -@popover-arrow-outer-width;\n    margin-top: -@popover-arrow-outer-width;\n    border-right-width: 0;\n    border-left-color: @popover-arrow-outer-fallback-color; // IE8 fallback\n    border-left-color: @popover-arrow-outer-color;\n    &:after {\n      right: 1px;\n      bottom: -@popover-arrow-width;\n      content: \" \";\n      border-right-width: 0;\n      border-left-color: @popover-arrow-color;\n    }\n  }\n}\n\n.popover-title {\n  padding: 8px 14px;\n  margin: 0; // reset heading margin\n  font-size: @font-size-base;\n  background-color: @popover-title-bg;\n  border-bottom: 1px solid darken(@popover-title-bg, 5%);\n  border-radius: (@border-radius-large - 1) (@border-radius-large - 1) 0 0;\n}\n\n.popover-content {\n  padding: 9px 14px;\n}\n","// stylelint-disable media-feature-name-no-unknown\n\n//\n// Carousel\n// --------------------------------------------------\n\n\n// Wrapper for the slide container and indicators\n.carousel {\n  position: relative;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n\n  > .item {\n    position: relative;\n    display: none;\n    .transition(.6s ease-in-out left);\n\n    // Account for jankitude on images\n    > img,\n    > a > img {\n      &:extend(.img-responsive);\n      line-height: 1;\n    }\n\n    // WebKit CSS3 transforms for supported devices\n    @media all and (transform-3d), (-webkit-transform-3d) {\n      .transition-transform(~\"0.6s ease-in-out\");\n      .backface-visibility(~\"hidden\");\n      .perspective(1000px);\n\n      &.next,\n      &.active.right {\n        .translate3d(100%, 0, 0);\n        left: 0;\n      }\n      &.prev,\n      &.active.left {\n        .translate3d(-100%, 0, 0);\n        left: 0;\n      }\n      &.next.left,\n      &.prev.right,\n      &.active {\n        .translate3d(0, 0, 0);\n        left: 0;\n      }\n    }\n  }\n\n  > .active,\n  > .next,\n  > .prev {\n    display: block;\n  }\n\n  > .active {\n    left: 0;\n  }\n\n  > .next,\n  > .prev {\n    position: absolute;\n    top: 0;\n    width: 100%;\n  }\n\n  > .next {\n    left: 100%;\n  }\n  > .prev {\n    left: -100%;\n  }\n  > .next.left,\n  > .prev.right {\n    left: 0;\n  }\n\n  > .active.left {\n    left: -100%;\n  }\n  > .active.right {\n    left: 100%;\n  }\n\n}\n\n// Left/right controls for nav\n// ---------------------------\n\n.carousel-control {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  left: 0;\n  width: @carousel-control-width;\n  font-size: @carousel-control-font-size;\n  color: @carousel-control-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n  background-color: rgba(0, 0, 0, 0); // Fix IE9 click-thru bug\n  .opacity(@carousel-control-opacity);\n  // We can't have this transition here because WebKit cancels the carousel\n  // animation if you trip this while in the middle of another animation.\n\n  // Set gradients for backgrounds\n  &.left {\n    #gradient > .horizontal(@start-color: rgba(0, 0, 0, .5); @end-color: rgba(0, 0, 0, .0001));\n  }\n  &.right {\n    right: 0;\n    left: auto;\n    #gradient > .horizontal(@start-color: rgba(0, 0, 0, .0001); @end-color: rgba(0, 0, 0, .5));\n  }\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: @carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    .opacity(.9);\n  }\n\n  // Toggles\n  .icon-prev,\n  .icon-next,\n  .glyphicon-chevron-left,\n  .glyphicon-chevron-right {\n    position: absolute;\n    top: 50%;\n    z-index: 5;\n    display: inline-block;\n    margin-top: -10px;\n  }\n  .icon-prev,\n  .glyphicon-chevron-left {\n    left: 50%;\n    margin-left: -10px;\n  }\n  .icon-next,\n  .glyphicon-chevron-right {\n    right: 50%;\n    margin-right: -10px;\n  }\n  .icon-prev,\n  .icon-next {\n    width: 20px;\n    height: 20px;\n    font-family: serif;\n    line-height: 1;\n  }\n\n  .icon-prev {\n    &:before {\n      content: \"\\2039\";// SINGLE LEFT-POINTING ANGLE QUOTATION MARK (U+2039)\n    }\n  }\n  .icon-next {\n    &:before {\n      content: \"\\203a\";// SINGLE RIGHT-POINTING ANGLE QUOTATION MARK (U+203A)\n    }\n  }\n}\n\n// Optional indicator pips\n//\n// Add an unordered list with the following class and add a list item for each\n// slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  bottom: 10px;\n  left: 50%;\n  z-index: 15;\n  width: 60%;\n  padding-left: 0;\n  margin-left: -30%;\n  text-align: center;\n  list-style: none;\n\n  li {\n    display: inline-block;\n    width: 10px;\n    height: 10px;\n    margin: 1px;\n    text-indent: -999px;\n    cursor: pointer;\n    // IE8-9 hack for event handling\n    //\n    // Internet Explorer 8-9 does not support clicks on elements without a set\n    // `background-color`. We cannot use `filter` since that's not viewed as a\n    // background color by the browser. Thus, a hack is needed.\n    // See https://developer.mozilla.org/en-US/docs/Web/Events/click#Internet_Explorer\n    //\n    // For IE8, we set solid black as it doesn't support `rgba()`. For IE9, we\n    // set alpha transparency for the best results possible.\n    background-color: #000 \\9; // IE8\n    background-color: rgba(0, 0, 0, 0); // IE9\n\n    border: 1px solid @carousel-indicator-border-color;\n    border-radius: 10px;\n  }\n\n  .active {\n    width: 12px;\n    height: 12px;\n    margin: 0;\n    background-color: @carousel-indicator-active-bg;\n  }\n}\n\n// Optional captions\n// -----------------------------\n// Hidden by default for smaller viewports\n.carousel-caption {\n  position: absolute;\n  right: 15%;\n  bottom: 20px;\n  left: 15%;\n  z-index: 10;\n  padding-top: 20px;\n  padding-bottom: 20px;\n  color: @carousel-caption-color;\n  text-align: center;\n  text-shadow: @carousel-text-shadow;\n\n  & .btn {\n    text-shadow: none; // No shadow for button elements in carousel-caption\n  }\n}\n\n\n// Scale up controls for tablets and up\n@media screen and (min-width: @screen-sm-min) {\n\n  // Scale up the controls a smidge\n  .carousel-control {\n    .glyphicon-chevron-left,\n    .glyphicon-chevron-right,\n    .icon-prev,\n    .icon-next {\n      width: (@carousel-control-font-size * 1.5);\n      height: (@carousel-control-font-size * 1.5);\n      margin-top: (@carousel-control-font-size / -2);\n      font-size: (@carousel-control-font-size * 1.5);\n    }\n    .glyphicon-chevron-left,\n    .icon-prev {\n      margin-left: (@carousel-control-font-size / -2);\n    }\n    .glyphicon-chevron-right,\n    .icon-next {\n      margin-right: (@carousel-control-font-size / -2);\n    }\n  }\n\n  // Show and left align the captions\n  .carousel-caption {\n    right: 20%;\n    left: 20%;\n    padding-bottom: 30px;\n  }\n\n  // Move up the indicators\n  .carousel-indicators {\n    bottom: 20px;\n  }\n}\n","// Clearfix\n//\n// For modern browsers\n// 1. The space content is one way to avoid an Opera bug when the\n//    contenteditable attribute is included anywhere else in the document.\n//    Otherwise it causes space to appear at the top and bottom of elements\n//    that are clearfixed.\n// 2. The use of `table` rather than `block` is only necessary if using\n//    `:before` to contain the top-margins of child elements.\n//\n// Source: http://nicolasgallagher.com/micro-clearfix-hack/\n\n.clearfix() {\n  &:before,\n  &:after {\n    display: table; // 2\n    content: \" \"; // 1\n  }\n  &:after {\n    clear: both;\n  }\n}\n","// Center-align a block level element\n\n.center-block() {\n  display: block;\n  margin-right: auto;\n  margin-left: auto;\n}\n","// stylelint-disable font-family-name-quotes, font-family-no-missing-generic-family-keyword\n\n// CSS image replacement\n//\n// Heads up! v3 launched with only `.hide-text()`, but per our pattern for\n// mixins being reused as classes with the same name, this doesn't hold up. As\n// of v3.0.1 we have added `.text-hide()` and deprecated `.hide-text()`.\n//\n// Source: https://github.com/h5bp/html5-boilerplate/commit/aa0396eae757\n\n// Deprecated as of v3.0.1 (has been removed in v4)\n.hide-text() {\n  font: ~\"0/0\" a;\n  color: transparent;\n  text-shadow: none;\n  background-color: transparent;\n  border: 0;\n}\n\n// New mixin to use as of v3.0.1\n.text-hide() {\n  .hide-text();\n}\n","// stylelint-disable declaration-no-important, at-rule-no-vendor-prefix\n\n//\n// Responsive: Utility classes\n// --------------------------------------------------\n\n\n// IE10 in Windows (Phone) 8\n//\n// Support for responsive views via media queries is kind of borked in IE10, for\n// Surface/desktop in split view and for Windows Phone 8. This particular fix\n// must be accompanied by a snippet of JavaScript to sniff the user agent and\n// apply some conditional CSS to *only* the Surface/desktop Windows 8. Look at\n// our Getting Started page for more information on this bug.\n//\n// For more information, see the following:\n//\n// Issue: https://github.com/twbs/bootstrap/issues/10497\n// Docs: https://getbootstrap.com/docs/3.4/getting-started/#support-ie10-width\n// Source: https://timkadlec.com/2013/01/windows-phone-8-and-device-width/\n// Source: https://timkadlec.com/2012/10/ie10-snap-mode-and-responsive-design/\n\n@-ms-viewport {\n  width: device-width;\n}\n\n\n// Visibility utilities\n// Note: Deprecated .visible-xs, .visible-sm, .visible-md, and .visible-lg as of v3.2.0\n.visible-xs,\n.visible-sm,\n.visible-md,\n.visible-lg {\n  .responsive-invisibility();\n}\n\n.visible-xs-block,\n.visible-xs-inline,\n.visible-xs-inline-block,\n.visible-sm-block,\n.visible-sm-inline,\n.visible-sm-inline-block,\n.visible-md-block,\n.visible-md-inline,\n.visible-md-inline-block,\n.visible-lg-block,\n.visible-lg-inline,\n.visible-lg-inline-block {\n  display: none !important;\n}\n\n.visible-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-visibility();\n  }\n}\n.visible-xs-block {\n  @media (max-width: @screen-xs-max) {\n    display: block !important;\n  }\n}\n.visible-xs-inline {\n  @media (max-width: @screen-xs-max) {\n    display: inline !important;\n  }\n}\n.visible-xs-inline-block {\n  @media (max-width: @screen-xs-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-visibility();\n  }\n}\n.visible-sm-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: block !important;\n  }\n}\n.visible-sm-inline {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline !important;\n  }\n}\n.visible-sm-inline-block {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-visibility();\n  }\n}\n.visible-md-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: block !important;\n  }\n}\n.visible-md-inline {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline !important;\n  }\n}\n.visible-md-inline-block {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    display: inline-block !important;\n  }\n}\n\n.visible-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-visibility();\n  }\n}\n.visible-lg-block {\n  @media (min-width: @screen-lg-min) {\n    display: block !important;\n  }\n}\n.visible-lg-inline {\n  @media (min-width: @screen-lg-min) {\n    display: inline !important;\n  }\n}\n.visible-lg-inline-block {\n  @media (min-width: @screen-lg-min) {\n    display: inline-block !important;\n  }\n}\n\n.hidden-xs {\n  @media (max-width: @screen-xs-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-sm {\n  @media (min-width: @screen-sm-min) and (max-width: @screen-sm-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-md {\n  @media (min-width: @screen-md-min) and (max-width: @screen-md-max) {\n    .responsive-invisibility();\n  }\n}\n.hidden-lg {\n  @media (min-width: @screen-lg-min) {\n    .responsive-invisibility();\n  }\n}\n\n\n// Print utilities\n//\n// Media queries are placed on the inside to be mixin-friendly.\n\n// Note: Deprecated .visible-print as of v3.2.0\n.visible-print {\n  .responsive-invisibility();\n\n  @media print {\n    .responsive-visibility();\n  }\n}\n.visible-print-block {\n  display: none !important;\n\n  @media print {\n    display: block !important;\n  }\n}\n.visible-print-inline {\n  display: none !important;\n\n  @media print {\n    display: inline !important;\n  }\n}\n.visible-print-inline-block {\n  display: none !important;\n\n  @media print {\n    display: inline-block !important;\n  }\n}\n\n.hidden-print {\n  @media print {\n    .responsive-invisibility();\n  }\n}\n","// stylelint-disable declaration-no-important\n\n.responsive-visibility() {\n  display: block !important;\n  table&  { display: table !important; }\n  tr&     { display: table-row !important; }\n  th&,\n  td&     { display: table-cell !important; }\n}\n\n.responsive-invisibility() {\n  display: none !important;\n}\n"]}
\ No newline at end of file
diff --git a/_static/bootstrap-3.4.1/fonts/glyphicons-halflings-regular.eot b/_static/bootstrap-3.4.1/fonts/glyphicons-halflings-regular.eot
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diff --git a/_static/bootstrap-3.4.1/js/bootstrap.js b/_static/bootstrap-3.4.1/js/bootstrap.js
new file mode 100644
index 000000000..dc089c44e
--- /dev/null
+++ b/_static/bootstrap-3.4.1/js/bootstrap.js
@@ -0,0 +1,2580 @@
+/*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+
+if (typeof (window.$jqTheme || window.jQuery) === 'undefined') {
+  throw new Error('Bootstrap\'s JavaScript requires jQuery')
+}
+
++function ($) {
+  'use strict';
+  var version = $.fn.jquery.split(' ')[0].split('.')
+  if ((version[0] < 2 && version[1] < 9) || (version[0] == 1 && version[1] == 9 && version[2] < 1) || (version[0] > 3)) {
+    throw new Error('Bootstrap\'s JavaScript requires jQuery version 1.9.1 or higher, but lower than version 4')
+  }
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: transition.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#transitions
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // CSS TRANSITION SUPPORT (Shoutout: https://modernizr.com/)
+  // ============================================================
+
+  function transitionEnd() {
+    var el = document.createElement('bootstrap')
+
+    var transEndEventNames = {
+      WebkitTransition : 'webkitTransitionEnd',
+      MozTransition    : 'transitionend',
+      OTransition      : 'oTransitionEnd otransitionend',
+      transition       : 'transitionend'
+    }
+
+    for (var name in transEndEventNames) {
+      if (el.style[name] !== undefined) {
+        return { end: transEndEventNames[name] }
+      }
+    }
+
+    return false // explicit for ie8 (  ._.)
+  }
+
+  // https://blog.alexmaccaw.com/css-transitions
+  $.fn.emulateTransitionEnd = function (duration) {
+    var called = false
+    var $el = this
+    $(this).one('bsTransitionEnd', function () { called = true })
+    var callback = function () { if (!called) $($el).trigger($.support.transition.end) }
+    setTimeout(callback, duration)
+    return this
+  }
+
+  $(function () {
+    $.support.transition = transitionEnd()
+
+    if (!$.support.transition) return
+
+    $.event.special.bsTransitionEnd = {
+      bindType: $.support.transition.end,
+      delegateType: $.support.transition.end,
+      handle: function (e) {
+        if ($(e.target).is(this)) return e.handleObj.handler.apply(this, arguments)
+      }
+    }
+  })
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: alert.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#alerts
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // ALERT CLASS DEFINITION
+  // ======================
+
+  var dismiss = '[data-dismiss="alert"]'
+  var Alert   = function (el) {
+    $(el).on('click', dismiss, this.close)
+  }
+
+  Alert.VERSION = '3.4.1'
+
+  Alert.TRANSITION_DURATION = 150
+
+  Alert.prototype.close = function (e) {
+    var $this    = $(this)
+    var selector = $this.attr('data-target')
+
+    if (!selector) {
+      selector = $this.attr('href')
+      selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7
+    }
+
+    selector    = selector === '#' ? [] : selector
+    var $parent = $(document).find(selector)
+
+    if (e) e.preventDefault()
+
+    if (!$parent.length) {
+      $parent = $this.closest('.alert')
+    }
+
+    $parent.trigger(e = $.Event('close.bs.alert'))
+
+    if (e.isDefaultPrevented()) return
+
+    $parent.removeClass('in')
+
+    function removeElement() {
+      // detach from parent, fire event then clean up data
+      $parent.detach().trigger('closed.bs.alert').remove()
+    }
+
+    $.support.transition && $parent.hasClass('fade') ?
+      $parent
+        .one('bsTransitionEnd', removeElement)
+        .emulateTransitionEnd(Alert.TRANSITION_DURATION) :
+      removeElement()
+  }
+
+
+  // ALERT PLUGIN DEFINITION
+  // =======================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this = $(this)
+      var data  = $this.data('bs.alert')
+
+      if (!data) $this.data('bs.alert', (data = new Alert(this)))
+      if (typeof option == 'string') data[option].call($this)
+    })
+  }
+
+  var old = $.fn.alert
+
+  $.fn.alert             = Plugin
+  $.fn.alert.Constructor = Alert
+
+
+  // ALERT NO CONFLICT
+  // =================
+
+  $.fn.alert.noConflict = function () {
+    $.fn.alert = old
+    return this
+  }
+
+
+  // ALERT DATA-API
+  // ==============
+
+  $(document).on('click.bs.alert.data-api', dismiss, Alert.prototype.close)
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: button.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#buttons
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // BUTTON PUBLIC CLASS DEFINITION
+  // ==============================
+
+  var Button = function (element, options) {
+    this.$element  = $(element)
+    this.options   = $.extend({}, Button.DEFAULTS, options)
+    this.isLoading = false
+  }
+
+  Button.VERSION  = '3.4.1'
+
+  Button.DEFAULTS = {
+    loadingText: 'loading...'
+  }
+
+  Button.prototype.setState = function (state) {
+    var d    = 'disabled'
+    var $el  = this.$element
+    var val  = $el.is('input') ? 'val' : 'html'
+    var data = $el.data()
+
+    state += 'Text'
+
+    if (data.resetText == null) $el.data('resetText', $el[val]())
+
+    // push to event loop to allow forms to submit
+    setTimeout($.proxy(function () {
+      $el[val](data[state] == null ? this.options[state] : data[state])
+
+      if (state == 'loadingText') {
+        this.isLoading = true
+        $el.addClass(d).attr(d, d).prop(d, true)
+      } else if (this.isLoading) {
+        this.isLoading = false
+        $el.removeClass(d).removeAttr(d).prop(d, false)
+      }
+    }, this), 0)
+  }
+
+  Button.prototype.toggle = function () {
+    var changed = true
+    var $parent = this.$element.closest('[data-toggle="buttons"]')
+
+    if ($parent.length) {
+      var $input = this.$element.find('input')
+      if ($input.prop('type') == 'radio') {
+        if ($input.prop('checked')) changed = false
+        $parent.find('.active').removeClass('active')
+        this.$element.addClass('active')
+      } else if ($input.prop('type') == 'checkbox') {
+        if (($input.prop('checked')) !== this.$element.hasClass('active')) changed = false
+        this.$element.toggleClass('active')
+      }
+      $input.prop('checked', this.$element.hasClass('active'))
+      if (changed) $input.trigger('change')
+    } else {
+      this.$element.attr('aria-pressed', !this.$element.hasClass('active'))
+      this.$element.toggleClass('active')
+    }
+  }
+
+
+  // BUTTON PLUGIN DEFINITION
+  // ========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.button')
+      var options = typeof option == 'object' && option
+
+      if (!data) $this.data('bs.button', (data = new Button(this, options)))
+
+      if (option == 'toggle') data.toggle()
+      else if (option) data.setState(option)
+    })
+  }
+
+  var old = $.fn.button
+
+  $.fn.button             = Plugin
+  $.fn.button.Constructor = Button
+
+
+  // BUTTON NO CONFLICT
+  // ==================
+
+  $.fn.button.noConflict = function () {
+    $.fn.button = old
+    return this
+  }
+
+
+  // BUTTON DATA-API
+  // ===============
+
+  $(document)
+    .on('click.bs.button.data-api', '[data-toggle^="button"]', function (e) {
+      var $btn = $(e.target).closest('.btn')
+      Plugin.call($btn, 'toggle')
+      if (!($(e.target).is('input[type="radio"], input[type="checkbox"]'))) {
+        // Prevent double click on radios, and the double selections (so cancellation) on checkboxes
+        e.preventDefault()
+        // The target component still receive the focus
+        if ($btn.is('input,button')) $btn.trigger('focus')
+        else $btn.find('input:visible,button:visible').first().trigger('focus')
+      }
+    })
+    .on('focus.bs.button.data-api blur.bs.button.data-api', '[data-toggle^="button"]', function (e) {
+      $(e.target).closest('.btn').toggleClass('focus', /^focus(in)?$/.test(e.type))
+    })
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: carousel.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#carousel
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // CAROUSEL CLASS DEFINITION
+  // =========================
+
+  var Carousel = function (element, options) {
+    this.$element    = $(element)
+    this.$indicators = this.$element.find('.carousel-indicators')
+    this.options     = options
+    this.paused      = null
+    this.sliding     = null
+    this.interval    = null
+    this.$active     = null
+    this.$items      = null
+
+    this.options.keyboard && this.$element.on('keydown.bs.carousel', $.proxy(this.keydown, this))
+
+    this.options.pause == 'hover' && !('ontouchstart' in document.documentElement) && this.$element
+      .on('mouseenter.bs.carousel', $.proxy(this.pause, this))
+      .on('mouseleave.bs.carousel', $.proxy(this.cycle, this))
+  }
+
+  Carousel.VERSION  = '3.4.1'
+
+  Carousel.TRANSITION_DURATION = 600
+
+  Carousel.DEFAULTS = {
+    interval: 5000,
+    pause: 'hover',
+    wrap: true,
+    keyboard: true
+  }
+
+  Carousel.prototype.keydown = function (e) {
+    if (/input|textarea/i.test(e.target.tagName)) return
+    switch (e.which) {
+      case 37: this.prev(); break
+      case 39: this.next(); break
+      default: return
+    }
+
+    e.preventDefault()
+  }
+
+  Carousel.prototype.cycle = function (e) {
+    e || (this.paused = false)
+
+    this.interval && clearInterval(this.interval)
+
+    this.options.interval
+      && !this.paused
+      && (this.interval = setInterval($.proxy(this.next, this), this.options.interval))
+
+    return this
+  }
+
+  Carousel.prototype.getItemIndex = function (item) {
+    this.$items = item.parent().children('.item')
+    return this.$items.index(item || this.$active)
+  }
+
+  Carousel.prototype.getItemForDirection = function (direction, active) {
+    var activeIndex = this.getItemIndex(active)
+    var willWrap = (direction == 'prev' && activeIndex === 0)
+                || (direction == 'next' && activeIndex == (this.$items.length - 1))
+    if (willWrap && !this.options.wrap) return active
+    var delta = direction == 'prev' ? -1 : 1
+    var itemIndex = (activeIndex + delta) % this.$items.length
+    return this.$items.eq(itemIndex)
+  }
+
+  Carousel.prototype.to = function (pos) {
+    var that        = this
+    var activeIndex = this.getItemIndex(this.$active = this.$element.find('.item.active'))
+
+    if (pos > (this.$items.length - 1) || pos < 0) return
+
+    if (this.sliding)       return this.$element.one('slid.bs.carousel', function () { that.to(pos) }) // yes, "slid"
+    if (activeIndex == pos) return this.pause().cycle()
+
+    return this.slide(pos > activeIndex ? 'next' : 'prev', this.$items.eq(pos))
+  }
+
+  Carousel.prototype.pause = function (e) {
+    e || (this.paused = true)
+
+    if (this.$element.find('.next, .prev').length && $.support.transition) {
+      this.$element.trigger($.support.transition.end)
+      this.cycle(true)
+    }
+
+    this.interval = clearInterval(this.interval)
+
+    return this
+  }
+
+  Carousel.prototype.next = function () {
+    if (this.sliding) return
+    return this.slide('next')
+  }
+
+  Carousel.prototype.prev = function () {
+    if (this.sliding) return
+    return this.slide('prev')
+  }
+
+  Carousel.prototype.slide = function (type, next) {
+    var $active   = this.$element.find('.item.active')
+    var $next     = next || this.getItemForDirection(type, $active)
+    var isCycling = this.interval
+    var direction = type == 'next' ? 'left' : 'right'
+    var that      = this
+
+    if ($next.hasClass('active')) return (this.sliding = false)
+
+    var relatedTarget = $next[0]
+    var slideEvent = $.Event('slide.bs.carousel', {
+      relatedTarget: relatedTarget,
+      direction: direction
+    })
+    this.$element.trigger(slideEvent)
+    if (slideEvent.isDefaultPrevented()) return
+
+    this.sliding = true
+
+    isCycling && this.pause()
+
+    if (this.$indicators.length) {
+      this.$indicators.find('.active').removeClass('active')
+      var $nextIndicator = $(this.$indicators.children()[this.getItemIndex($next)])
+      $nextIndicator && $nextIndicator.addClass('active')
+    }
+
+    var slidEvent = $.Event('slid.bs.carousel', { relatedTarget: relatedTarget, direction: direction }) // yes, "slid"
+    if ($.support.transition && this.$element.hasClass('slide')) {
+      $next.addClass(type)
+      if (typeof $next === 'object' && $next.length) {
+        $next[0].offsetWidth // force reflow
+      }
+      $active.addClass(direction)
+      $next.addClass(direction)
+      $active
+        .one('bsTransitionEnd', function () {
+          $next.removeClass([type, direction].join(' ')).addClass('active')
+          $active.removeClass(['active', direction].join(' '))
+          that.sliding = false
+          setTimeout(function () {
+            that.$element.trigger(slidEvent)
+          }, 0)
+        })
+        .emulateTransitionEnd(Carousel.TRANSITION_DURATION)
+    } else {
+      $active.removeClass('active')
+      $next.addClass('active')
+      this.sliding = false
+      this.$element.trigger(slidEvent)
+    }
+
+    isCycling && this.cycle()
+
+    return this
+  }
+
+
+  // CAROUSEL PLUGIN DEFINITION
+  // ==========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.carousel')
+      var options = $.extend({}, Carousel.DEFAULTS, $this.data(), typeof option == 'object' && option)
+      var action  = typeof option == 'string' ? option : options.slide
+
+      if (!data) $this.data('bs.carousel', (data = new Carousel(this, options)))
+      if (typeof option == 'number') data.to(option)
+      else if (action) data[action]()
+      else if (options.interval) data.pause().cycle()
+    })
+  }
+
+  var old = $.fn.carousel
+
+  $.fn.carousel             = Plugin
+  $.fn.carousel.Constructor = Carousel
+
+
+  // CAROUSEL NO CONFLICT
+  // ====================
+
+  $.fn.carousel.noConflict = function () {
+    $.fn.carousel = old
+    return this
+  }
+
+
+  // CAROUSEL DATA-API
+  // =================
+
+  var clickHandler = function (e) {
+    var $this   = $(this)
+    var href    = $this.attr('href')
+    if (href) {
+      href = href.replace(/.*(?=#[^\s]+$)/, '') // strip for ie7
+    }
+
+    var target  = $this.attr('data-target') || href
+    var $target = $(document).find(target)
+
+    if (!$target.hasClass('carousel')) return
+
+    var options = $.extend({}, $target.data(), $this.data())
+    var slideIndex = $this.attr('data-slide-to')
+    if (slideIndex) options.interval = false
+
+    Plugin.call($target, options)
+
+    if (slideIndex) {
+      $target.data('bs.carousel').to(slideIndex)
+    }
+
+    e.preventDefault()
+  }
+
+  $(document)
+    .on('click.bs.carousel.data-api', '[data-slide]', clickHandler)
+    .on('click.bs.carousel.data-api', '[data-slide-to]', clickHandler)
+
+  $(window).on('load', function () {
+    $('[data-ride="carousel"]').each(function () {
+      var $carousel = $(this)
+      Plugin.call($carousel, $carousel.data())
+    })
+  })
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: collapse.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#collapse
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+/* jshint latedef: false */
+
++function ($) {
+  'use strict';
+
+  // COLLAPSE PUBLIC CLASS DEFINITION
+  // ================================
+
+  var Collapse = function (element, options) {
+    this.$element      = $(element)
+    this.options       = $.extend({}, Collapse.DEFAULTS, options)
+    this.$trigger      = $('[data-toggle="collapse"][href="#' + element.id + '"],' +
+                           '[data-toggle="collapse"][data-target="#' + element.id + '"]')
+    this.transitioning = null
+
+    if (this.options.parent) {
+      this.$parent = this.getParent()
+    } else {
+      this.addAriaAndCollapsedClass(this.$element, this.$trigger)
+    }
+
+    if (this.options.toggle) this.toggle()
+  }
+
+  Collapse.VERSION  = '3.4.1'
+
+  Collapse.TRANSITION_DURATION = 350
+
+  Collapse.DEFAULTS = {
+    toggle: true
+  }
+
+  Collapse.prototype.dimension = function () {
+    var hasWidth = this.$element.hasClass('width')
+    return hasWidth ? 'width' : 'height'
+  }
+
+  Collapse.prototype.show = function () {
+    if (this.transitioning || this.$element.hasClass('in')) return
+
+    var activesData
+    var actives = this.$parent && this.$parent.children('.panel').children('.in, .collapsing')
+
+    if (actives && actives.length) {
+      activesData = actives.data('bs.collapse')
+      if (activesData && activesData.transitioning) return
+    }
+
+    var startEvent = $.Event('show.bs.collapse')
+    this.$element.trigger(startEvent)
+    if (startEvent.isDefaultPrevented()) return
+
+    if (actives && actives.length) {
+      Plugin.call(actives, 'hide')
+      activesData || actives.data('bs.collapse', null)
+    }
+
+    var dimension = this.dimension()
+
+    this.$element
+      .removeClass('collapse')
+      .addClass('collapsing')[dimension](0)
+      .attr('aria-expanded', true)
+
+    this.$trigger
+      .removeClass('collapsed')
+      .attr('aria-expanded', true)
+
+    this.transitioning = 1
+
+    var complete = function () {
+      this.$element
+        .removeClass('collapsing')
+        .addClass('collapse in')[dimension]('')
+      this.transitioning = 0
+      this.$element
+        .trigger('shown.bs.collapse')
+    }
+
+    if (!$.support.transition) return complete.call(this)
+
+    var scrollSize = $.camelCase(['scroll', dimension].join('-'))
+
+    this.$element
+      .one('bsTransitionEnd', $.proxy(complete, this))
+      .emulateTransitionEnd(Collapse.TRANSITION_DURATION)[dimension](this.$element[0][scrollSize])
+  }
+
+  Collapse.prototype.hide = function () {
+    if (this.transitioning || !this.$element.hasClass('in')) return
+
+    var startEvent = $.Event('hide.bs.collapse')
+    this.$element.trigger(startEvent)
+    if (startEvent.isDefaultPrevented()) return
+
+    var dimension = this.dimension()
+
+    this.$element[dimension](this.$element[dimension]())[0].offsetHeight
+
+    this.$element
+      .addClass('collapsing')
+      .removeClass('collapse in')
+      .attr('aria-expanded', false)
+
+    this.$trigger
+      .addClass('collapsed')
+      .attr('aria-expanded', false)
+
+    this.transitioning = 1
+
+    var complete = function () {
+      this.transitioning = 0
+      this.$element
+        .removeClass('collapsing')
+        .addClass('collapse')
+        .trigger('hidden.bs.collapse')
+    }
+
+    if (!$.support.transition) return complete.call(this)
+
+    this.$element
+      [dimension](0)
+      .one('bsTransitionEnd', $.proxy(complete, this))
+      .emulateTransitionEnd(Collapse.TRANSITION_DURATION)
+  }
+
+  Collapse.prototype.toggle = function () {
+    this[this.$element.hasClass('in') ? 'hide' : 'show']()
+  }
+
+  Collapse.prototype.getParent = function () {
+    return $(document).find(this.options.parent)
+      .find('[data-toggle="collapse"][data-parent="' + this.options.parent + '"]')
+      .each($.proxy(function (i, element) {
+        var $element = $(element)
+        this.addAriaAndCollapsedClass(getTargetFromTrigger($element), $element)
+      }, this))
+      .end()
+  }
+
+  Collapse.prototype.addAriaAndCollapsedClass = function ($element, $trigger) {
+    var isOpen = $element.hasClass('in')
+
+    $element.attr('aria-expanded', isOpen)
+    $trigger
+      .toggleClass('collapsed', !isOpen)
+      .attr('aria-expanded', isOpen)
+  }
+
+  function getTargetFromTrigger($trigger) {
+    var href
+    var target = $trigger.attr('data-target')
+      || (href = $trigger.attr('href')) && href.replace(/.*(?=#[^\s]+$)/, '') // strip for ie7
+
+    return $(document).find(target)
+  }
+
+
+  // COLLAPSE PLUGIN DEFINITION
+  // ==========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.collapse')
+      var options = $.extend({}, Collapse.DEFAULTS, $this.data(), typeof option == 'object' && option)
+
+      if (!data && options.toggle && /show|hide/.test(option)) options.toggle = false
+      if (!data) $this.data('bs.collapse', (data = new Collapse(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.collapse
+
+  $.fn.collapse             = Plugin
+  $.fn.collapse.Constructor = Collapse
+
+
+  // COLLAPSE NO CONFLICT
+  // ====================
+
+  $.fn.collapse.noConflict = function () {
+    $.fn.collapse = old
+    return this
+  }
+
+
+  // COLLAPSE DATA-API
+  // =================
+
+  $(document).on('click.bs.collapse.data-api', '[data-toggle="collapse"]', function (e) {
+    var $this   = $(this)
+
+    if (!$this.attr('data-target')) e.preventDefault()
+
+    var $target = getTargetFromTrigger($this)
+    var data    = $target.data('bs.collapse')
+    var option  = data ? 'toggle' : $this.data()
+
+    Plugin.call($target, option)
+  })
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: dropdown.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#dropdowns
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // DROPDOWN CLASS DEFINITION
+  // =========================
+
+  var backdrop = '.dropdown-backdrop'
+  var toggle   = '[data-toggle="dropdown"]'
+  var Dropdown = function (element) {
+    $(element).on('click.bs.dropdown', this.toggle)
+  }
+
+  Dropdown.VERSION = '3.4.1'
+
+  function getParent($this) {
+    var selector = $this.attr('data-target')
+
+    if (!selector) {
+      selector = $this.attr('href')
+      selector = selector && /#[A-Za-z]/.test(selector) && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7
+    }
+
+    var $parent = selector !== '#' ? $(document).find(selector) : null
+
+    return $parent && $parent.length ? $parent : $this.parent()
+  }
+
+  function clearMenus(e) {
+    if (e && e.which === 3) return
+    $(backdrop).remove()
+    $(toggle).each(function () {
+      var $this         = $(this)
+      var $parent       = getParent($this)
+      var relatedTarget = { relatedTarget: this }
+
+      if (!$parent.hasClass('open')) return
+
+      if (e && e.type == 'click' && /input|textarea/i.test(e.target.tagName) && $.contains($parent[0], e.target)) return
+
+      $parent.trigger(e = $.Event('hide.bs.dropdown', relatedTarget))
+
+      if (e.isDefaultPrevented()) return
+
+      $this.attr('aria-expanded', 'false')
+      $parent.removeClass('open').trigger($.Event('hidden.bs.dropdown', relatedTarget))
+    })
+  }
+
+  Dropdown.prototype.toggle = function (e) {
+    var $this = $(this)
+
+    if ($this.is('.disabled, :disabled')) return
+
+    var $parent  = getParent($this)
+    var isActive = $parent.hasClass('open')
+
+    clearMenus()
+
+    if (!isActive) {
+      if ('ontouchstart' in document.documentElement && !$parent.closest('.navbar-nav').length) {
+        // if mobile we use a backdrop because click events don't delegate
+        $(document.createElement('div'))
+          .addClass('dropdown-backdrop')
+          .insertAfter($(this))
+          .on('click', clearMenus)
+      }
+
+      var relatedTarget = { relatedTarget: this }
+      $parent.trigger(e = $.Event('show.bs.dropdown', relatedTarget))
+
+      if (e.isDefaultPrevented()) return
+
+      $this
+        .trigger('focus')
+        .attr('aria-expanded', 'true')
+
+      $parent
+        .toggleClass('open')
+        .trigger($.Event('shown.bs.dropdown', relatedTarget))
+    }
+
+    return false
+  }
+
+  Dropdown.prototype.keydown = function (e) {
+    if (!/(38|40|27|32)/.test(e.which) || /input|textarea/i.test(e.target.tagName)) return
+
+    var $this = $(this)
+
+    e.preventDefault()
+    e.stopPropagation()
+
+    if ($this.is('.disabled, :disabled')) return
+
+    var $parent  = getParent($this)
+    var isActive = $parent.hasClass('open')
+
+    if (!isActive && e.which != 27 || isActive && e.which == 27) {
+      if (e.which == 27) $parent.find(toggle).trigger('focus')
+      return $this.trigger('click')
+    }
+
+    var desc = ' li:not(.disabled):visible a'
+    var $items = $parent.find('.dropdown-menu' + desc)
+
+    if (!$items.length) return
+
+    var index = $items.index(e.target)
+
+    if (e.which == 38 && index > 0)                 index--         // up
+    if (e.which == 40 && index < $items.length - 1) index++         // down
+    if (!~index)                                    index = 0
+
+    $items.eq(index).trigger('focus')
+  }
+
+
+  // DROPDOWN PLUGIN DEFINITION
+  // ==========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this = $(this)
+      var data  = $this.data('bs.dropdown')
+
+      if (!data) $this.data('bs.dropdown', (data = new Dropdown(this)))
+      if (typeof option == 'string') data[option].call($this)
+    })
+  }
+
+  var old = $.fn.dropdown
+
+  $.fn.dropdown             = Plugin
+  $.fn.dropdown.Constructor = Dropdown
+
+
+  // DROPDOWN NO CONFLICT
+  // ====================
+
+  $.fn.dropdown.noConflict = function () {
+    $.fn.dropdown = old
+    return this
+  }
+
+
+  // APPLY TO STANDARD DROPDOWN ELEMENTS
+  // ===================================
+
+  $(document)
+    .on('click.bs.dropdown.data-api', clearMenus)
+    .on('click.bs.dropdown.data-api', '.dropdown form', function (e) { e.stopPropagation() })
+    .on('click.bs.dropdown.data-api', toggle, Dropdown.prototype.toggle)
+    .on('keydown.bs.dropdown.data-api', toggle, Dropdown.prototype.keydown)
+    .on('keydown.bs.dropdown.data-api', '.dropdown-menu', Dropdown.prototype.keydown)
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: modal.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#modals
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // MODAL CLASS DEFINITION
+  // ======================
+
+  var Modal = function (element, options) {
+    this.options = options
+    this.$body = $(document.body)
+    this.$element = $(element)
+    this.$dialog = this.$element.find('.modal-dialog')
+    this.$backdrop = null
+    this.isShown = null
+    this.originalBodyPad = null
+    this.scrollbarWidth = 0
+    this.ignoreBackdropClick = false
+    this.fixedContent = '.navbar-fixed-top, .navbar-fixed-bottom'
+
+    if (this.options.remote) {
+      this.$element
+        .find('.modal-content')
+        .load(this.options.remote, $.proxy(function () {
+          this.$element.trigger('loaded.bs.modal')
+        }, this))
+    }
+  }
+
+  Modal.VERSION = '3.4.1'
+
+  Modal.TRANSITION_DURATION = 300
+  Modal.BACKDROP_TRANSITION_DURATION = 150
+
+  Modal.DEFAULTS = {
+    backdrop: true,
+    keyboard: true,
+    show: true
+  }
+
+  Modal.prototype.toggle = function (_relatedTarget) {
+    return this.isShown ? this.hide() : this.show(_relatedTarget)
+  }
+
+  Modal.prototype.show = function (_relatedTarget) {
+    var that = this
+    var e = $.Event('show.bs.modal', { relatedTarget: _relatedTarget })
+
+    this.$element.trigger(e)
+
+    if (this.isShown || e.isDefaultPrevented()) return
+
+    this.isShown = true
+
+    this.checkScrollbar()
+    this.setScrollbar()
+    this.$body.addClass('modal-open')
+
+    this.escape()
+    this.resize()
+
+    this.$element.on('click.dismiss.bs.modal', '[data-dismiss="modal"]', $.proxy(this.hide, this))
+
+    this.$dialog.on('mousedown.dismiss.bs.modal', function () {
+      that.$element.one('mouseup.dismiss.bs.modal', function (e) {
+        if ($(e.target).is(that.$element)) that.ignoreBackdropClick = true
+      })
+    })
+
+    this.backdrop(function () {
+      var transition = $.support.transition && that.$element.hasClass('fade')
+
+      if (!that.$element.parent().length) {
+        that.$element.appendTo(that.$body) // don't move modals dom position
+      }
+
+      that.$element
+        .show()
+        .scrollTop(0)
+
+      that.adjustDialog()
+
+      if (transition) {
+        that.$element[0].offsetWidth // force reflow
+      }
+
+      that.$element.addClass('in')
+
+      that.enforceFocus()
+
+      var e = $.Event('shown.bs.modal', { relatedTarget: _relatedTarget })
+
+      transition ?
+        that.$dialog // wait for modal to slide in
+          .one('bsTransitionEnd', function () {
+            that.$element.trigger('focus').trigger(e)
+          })
+          .emulateTransitionEnd(Modal.TRANSITION_DURATION) :
+        that.$element.trigger('focus').trigger(e)
+    })
+  }
+
+  Modal.prototype.hide = function (e) {
+    if (e) e.preventDefault()
+
+    e = $.Event('hide.bs.modal')
+
+    this.$element.trigger(e)
+
+    if (!this.isShown || e.isDefaultPrevented()) return
+
+    this.isShown = false
+
+    this.escape()
+    this.resize()
+
+    $(document).off('focusin.bs.modal')
+
+    this.$element
+      .removeClass('in')
+      .off('click.dismiss.bs.modal')
+      .off('mouseup.dismiss.bs.modal')
+
+    this.$dialog.off('mousedown.dismiss.bs.modal')
+
+    $.support.transition && this.$element.hasClass('fade') ?
+      this.$element
+        .one('bsTransitionEnd', $.proxy(this.hideModal, this))
+        .emulateTransitionEnd(Modal.TRANSITION_DURATION) :
+      this.hideModal()
+  }
+
+  Modal.prototype.enforceFocus = function () {
+    $(document)
+      .off('focusin.bs.modal') // guard against infinite focus loop
+      .on('focusin.bs.modal', $.proxy(function (e) {
+        if (document !== e.target &&
+          this.$element[0] !== e.target &&
+          !this.$element.has(e.target).length) {
+          this.$element.trigger('focus')
+        }
+      }, this))
+  }
+
+  Modal.prototype.escape = function () {
+    if (this.isShown && this.options.keyboard) {
+      this.$element.on('keydown.dismiss.bs.modal', $.proxy(function (e) {
+        e.which == 27 && this.hide()
+      }, this))
+    } else if (!this.isShown) {
+      this.$element.off('keydown.dismiss.bs.modal')
+    }
+  }
+
+  Modal.prototype.resize = function () {
+    if (this.isShown) {
+      $(window).on('resize.bs.modal', $.proxy(this.handleUpdate, this))
+    } else {
+      $(window).off('resize.bs.modal')
+    }
+  }
+
+  Modal.prototype.hideModal = function () {
+    var that = this
+    this.$element.hide()
+    this.backdrop(function () {
+      that.$body.removeClass('modal-open')
+      that.resetAdjustments()
+      that.resetScrollbar()
+      that.$element.trigger('hidden.bs.modal')
+    })
+  }
+
+  Modal.prototype.removeBackdrop = function () {
+    this.$backdrop && this.$backdrop.remove()
+    this.$backdrop = null
+  }
+
+  Modal.prototype.backdrop = function (callback) {
+    var that = this
+    var animate = this.$element.hasClass('fade') ? 'fade' : ''
+
+    if (this.isShown && this.options.backdrop) {
+      var doAnimate = $.support.transition && animate
+
+      this.$backdrop = $(document.createElement('div'))
+        .addClass('modal-backdrop ' + animate)
+        .appendTo(this.$body)
+
+      this.$element.on('click.dismiss.bs.modal', $.proxy(function (e) {
+        if (this.ignoreBackdropClick) {
+          this.ignoreBackdropClick = false
+          return
+        }
+        if (e.target !== e.currentTarget) return
+        this.options.backdrop == 'static'
+          ? this.$element[0].focus()
+          : this.hide()
+      }, this))
+
+      if (doAnimate) this.$backdrop[0].offsetWidth // force reflow
+
+      this.$backdrop.addClass('in')
+
+      if (!callback) return
+
+      doAnimate ?
+        this.$backdrop
+          .one('bsTransitionEnd', callback)
+          .emulateTransitionEnd(Modal.BACKDROP_TRANSITION_DURATION) :
+        callback()
+
+    } else if (!this.isShown && this.$backdrop) {
+      this.$backdrop.removeClass('in')
+
+      var callbackRemove = function () {
+        that.removeBackdrop()
+        callback && callback()
+      }
+      $.support.transition && this.$element.hasClass('fade') ?
+        this.$backdrop
+          .one('bsTransitionEnd', callbackRemove)
+          .emulateTransitionEnd(Modal.BACKDROP_TRANSITION_DURATION) :
+        callbackRemove()
+
+    } else if (callback) {
+      callback()
+    }
+  }
+
+  // these following methods are used to handle overflowing modals
+
+  Modal.prototype.handleUpdate = function () {
+    this.adjustDialog()
+  }
+
+  Modal.prototype.adjustDialog = function () {
+    var modalIsOverflowing = this.$element[0].scrollHeight > document.documentElement.clientHeight
+
+    this.$element.css({
+      paddingLeft: !this.bodyIsOverflowing && modalIsOverflowing ? this.scrollbarWidth : '',
+      paddingRight: this.bodyIsOverflowing && !modalIsOverflowing ? this.scrollbarWidth : ''
+    })
+  }
+
+  Modal.prototype.resetAdjustments = function () {
+    this.$element.css({
+      paddingLeft: '',
+      paddingRight: ''
+    })
+  }
+
+  Modal.prototype.checkScrollbar = function () {
+    var fullWindowWidth = window.innerWidth
+    if (!fullWindowWidth) { // workaround for missing window.innerWidth in IE8
+      var documentElementRect = document.documentElement.getBoundingClientRect()
+      fullWindowWidth = documentElementRect.right - Math.abs(documentElementRect.left)
+    }
+    this.bodyIsOverflowing = document.body.clientWidth < fullWindowWidth
+    this.scrollbarWidth = this.measureScrollbar()
+  }
+
+  Modal.prototype.setScrollbar = function () {
+    var bodyPad = parseInt((this.$body.css('padding-right') || 0), 10)
+    this.originalBodyPad = document.body.style.paddingRight || ''
+    var scrollbarWidth = this.scrollbarWidth
+    if (this.bodyIsOverflowing) {
+      this.$body.css('padding-right', bodyPad + scrollbarWidth)
+      $(this.fixedContent).each(function (index, element) {
+        var actualPadding = element.style.paddingRight
+        var calculatedPadding = $(element).css('padding-right')
+        $(element)
+          .data('padding-right', actualPadding)
+          .css('padding-right', parseFloat(calculatedPadding) + scrollbarWidth + 'px')
+      })
+    }
+  }
+
+  Modal.prototype.resetScrollbar = function () {
+    this.$body.css('padding-right', this.originalBodyPad)
+    $(this.fixedContent).each(function (index, element) {
+      var padding = $(element).data('padding-right')
+      $(element).removeData('padding-right')
+      element.style.paddingRight = padding ? padding : ''
+    })
+  }
+
+  Modal.prototype.measureScrollbar = function () { // thx walsh
+    var scrollDiv = document.createElement('div')
+    scrollDiv.className = 'modal-scrollbar-measure'
+    this.$body.append(scrollDiv)
+    var scrollbarWidth = scrollDiv.offsetWidth - scrollDiv.clientWidth
+    this.$body[0].removeChild(scrollDiv)
+    return scrollbarWidth
+  }
+
+
+  // MODAL PLUGIN DEFINITION
+  // =======================
+
+  function Plugin(option, _relatedTarget) {
+    return this.each(function () {
+      var $this = $(this)
+      var data = $this.data('bs.modal')
+      var options = $.extend({}, Modal.DEFAULTS, $this.data(), typeof option == 'object' && option)
+
+      if (!data) $this.data('bs.modal', (data = new Modal(this, options)))
+      if (typeof option == 'string') data[option](_relatedTarget)
+      else if (options.show) data.show(_relatedTarget)
+    })
+  }
+
+  var old = $.fn.modal
+
+  $.fn.modal = Plugin
+  $.fn.modal.Constructor = Modal
+
+
+  // MODAL NO CONFLICT
+  // =================
+
+  $.fn.modal.noConflict = function () {
+    $.fn.modal = old
+    return this
+  }
+
+
+  // MODAL DATA-API
+  // ==============
+
+  $(document).on('click.bs.modal.data-api', '[data-toggle="modal"]', function (e) {
+    var $this = $(this)
+    var href = $this.attr('href')
+    var target = $this.attr('data-target') ||
+      (href && href.replace(/.*(?=#[^\s]+$)/, '')) // strip for ie7
+
+    var $target = $(document).find(target)
+    var option = $target.data('bs.modal') ? 'toggle' : $.extend({ remote: !/#/.test(href) && href }, $target.data(), $this.data())
+
+    if ($this.is('a')) e.preventDefault()
+
+    $target.one('show.bs.modal', function (showEvent) {
+      if (showEvent.isDefaultPrevented()) return // only register focus restorer if modal will actually get shown
+      $target.one('hidden.bs.modal', function () {
+        $this.is(':visible') && $this.trigger('focus')
+      })
+    })
+    Plugin.call($target, option, this)
+  })
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: tooltip.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#tooltip
+ * Inspired by the original jQuery.tipsy by Jason Frame
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
++function ($) {
+  'use strict';
+
+  var DISALLOWED_ATTRIBUTES = ['sanitize', 'whiteList', 'sanitizeFn']
+
+  var uriAttrs = [
+    'background',
+    'cite',
+    'href',
+    'itemtype',
+    'longdesc',
+    'poster',
+    'src',
+    'xlink:href'
+  ]
+
+  var ARIA_ATTRIBUTE_PATTERN = /^aria-[\w-]*$/i
+
+  var DefaultWhitelist = {
+    // Global attributes allowed on any supplied element below.
+    '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],
+    a: ['target', 'href', 'title', 'rel'],
+    area: [],
+    b: [],
+    br: [],
+    col: [],
+    code: [],
+    div: [],
+    em: [],
+    hr: [],
+    h1: [],
+    h2: [],
+    h3: [],
+    h4: [],
+    h5: [],
+    h6: [],
+    i: [],
+    img: ['src', 'alt', 'title', 'width', 'height'],
+    li: [],
+    ol: [],
+    p: [],
+    pre: [],
+    s: [],
+    small: [],
+    span: [],
+    sub: [],
+    sup: [],
+    strong: [],
+    u: [],
+    ul: []
+  }
+
+  /**
+   * A pattern that recognizes a commonly useful subset of URLs that are safe.
+   *
+   * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts
+   */
+  var SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file):|[^&:/?#]*(?:[/?#]|$))/gi
+
+  /**
+   * A pattern that matches safe data URLs. Only matches image, video and audio types.
+   *
+   * Shoutout to Angular 7 https://github.com/angular/angular/blob/7.2.4/packages/core/src/sanitization/url_sanitizer.ts
+   */
+  var DATA_URL_PATTERN = /^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[a-z0-9+/]+=*$/i
+
+  function allowedAttribute(attr, allowedAttributeList) {
+    var attrName = attr.nodeName.toLowerCase()
+
+    if ($.inArray(attrName, allowedAttributeList) !== -1) {
+      if ($.inArray(attrName, uriAttrs) !== -1) {
+        return Boolean(attr.nodeValue.match(SAFE_URL_PATTERN) || attr.nodeValue.match(DATA_URL_PATTERN))
+      }
+
+      return true
+    }
+
+    var regExp = $(allowedAttributeList).filter(function (index, value) {
+      return value instanceof RegExp
+    })
+
+    // Check if a regular expression validates the attribute.
+    for (var i = 0, l = regExp.length; i < l; i++) {
+      if (attrName.match(regExp[i])) {
+        return true
+      }
+    }
+
+    return false
+  }
+
+  function sanitizeHtml(unsafeHtml, whiteList, sanitizeFn) {
+    if (unsafeHtml.length === 0) {
+      return unsafeHtml
+    }
+
+    if (sanitizeFn && typeof sanitizeFn === 'function') {
+      return sanitizeFn(unsafeHtml)
+    }
+
+    // IE 8 and below don't support createHTMLDocument
+    if (!document.implementation || !document.implementation.createHTMLDocument) {
+      return unsafeHtml
+    }
+
+    var createdDocument = document.implementation.createHTMLDocument('sanitization')
+    createdDocument.body.innerHTML = unsafeHtml
+
+    var whitelistKeys = $.map(whiteList, function (el, i) { return i })
+    var elements = $(createdDocument.body).find('*')
+
+    for (var i = 0, len = elements.length; i < len; i++) {
+      var el = elements[i]
+      var elName = el.nodeName.toLowerCase()
+
+      if ($.inArray(elName, whitelistKeys) === -1) {
+        el.parentNode.removeChild(el)
+
+        continue
+      }
+
+      var attributeList = $.map(el.attributes, function (el) { return el })
+      var whitelistedAttributes = [].concat(whiteList['*'] || [], whiteList[elName] || [])
+
+      for (var j = 0, len2 = attributeList.length; j < len2; j++) {
+        if (!allowedAttribute(attributeList[j], whitelistedAttributes)) {
+          el.removeAttribute(attributeList[j].nodeName)
+        }
+      }
+    }
+
+    return createdDocument.body.innerHTML
+  }
+
+  // TOOLTIP PUBLIC CLASS DEFINITION
+  // ===============================
+
+  var Tooltip = function (element, options) {
+    this.type       = null
+    this.options    = null
+    this.enabled    = null
+    this.timeout    = null
+    this.hoverState = null
+    this.$element   = null
+    this.inState    = null
+
+    this.init('tooltip', element, options)
+  }
+
+  Tooltip.VERSION  = '3.4.1'
+
+  Tooltip.TRANSITION_DURATION = 150
+
+  Tooltip.DEFAULTS = {
+    animation: true,
+    placement: 'top',
+    selector: false,
+    template: '<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',
+    trigger: 'hover focus',
+    title: '',
+    delay: 0,
+    html: false,
+    container: false,
+    viewport: {
+      selector: 'body',
+      padding: 0
+    },
+    sanitize : true,
+    sanitizeFn : null,
+    whiteList : DefaultWhitelist
+  }
+
+  Tooltip.prototype.init = function (type, element, options) {
+    this.enabled   = true
+    this.type      = type
+    this.$element  = $(element)
+    this.options   = this.getOptions(options)
+    this.$viewport = this.options.viewport && $(document).find($.isFunction(this.options.viewport) ? this.options.viewport.call(this, this.$element) : (this.options.viewport.selector || this.options.viewport))
+    this.inState   = { click: false, hover: false, focus: false }
+
+    if (this.$element[0] instanceof document.constructor && !this.options.selector) {
+      throw new Error('`selector` option must be specified when initializing ' + this.type + ' on the window.document object!')
+    }
+
+    var triggers = this.options.trigger.split(' ')
+
+    for (var i = triggers.length; i--;) {
+      var trigger = triggers[i]
+
+      if (trigger == 'click') {
+        this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
+      } else if (trigger != 'manual') {
+        var eventIn  = trigger == 'hover' ? 'mouseenter' : 'focusin'
+        var eventOut = trigger == 'hover' ? 'mouseleave' : 'focusout'
+
+        this.$element.on(eventIn  + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
+        this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
+      }
+    }
+
+    this.options.selector ?
+      (this._options = $.extend({}, this.options, { trigger: 'manual', selector: '' })) :
+      this.fixTitle()
+  }
+
+  Tooltip.prototype.getDefaults = function () {
+    return Tooltip.DEFAULTS
+  }
+
+  Tooltip.prototype.getOptions = function (options) {
+    var dataAttributes = this.$element.data()
+
+    for (var dataAttr in dataAttributes) {
+      if (dataAttributes.hasOwnProperty(dataAttr) && $.inArray(dataAttr, DISALLOWED_ATTRIBUTES) !== -1) {
+        delete dataAttributes[dataAttr]
+      }
+    }
+
+    options = $.extend({}, this.getDefaults(), dataAttributes, options)
+
+    if (options.delay && typeof options.delay == 'number') {
+      options.delay = {
+        show: options.delay,
+        hide: options.delay
+      }
+    }
+
+    if (options.sanitize) {
+      options.template = sanitizeHtml(options.template, options.whiteList, options.sanitizeFn)
+    }
+
+    return options
+  }
+
+  Tooltip.prototype.getDelegateOptions = function () {
+    var options  = {}
+    var defaults = this.getDefaults()
+
+    this._options && $.each(this._options, function (key, value) {
+      if (defaults[key] != value) options[key] = value
+    })
+
+    return options
+  }
+
+  Tooltip.prototype.enter = function (obj) {
+    var self = obj instanceof this.constructor ?
+      obj : $(obj.currentTarget).data('bs.' + this.type)
+
+    if (!self) {
+      self = new this.constructor(obj.currentTarget, this.getDelegateOptions())
+      $(obj.currentTarget).data('bs.' + this.type, self)
+    }
+
+    if (obj instanceof $.Event) {
+      self.inState[obj.type == 'focusin' ? 'focus' : 'hover'] = true
+    }
+
+    if (self.tip().hasClass('in') || self.hoverState == 'in') {
+      self.hoverState = 'in'
+      return
+    }
+
+    clearTimeout(self.timeout)
+
+    self.hoverState = 'in'
+
+    if (!self.options.delay || !self.options.delay.show) return self.show()
+
+    self.timeout = setTimeout(function () {
+      if (self.hoverState == 'in') self.show()
+    }, self.options.delay.show)
+  }
+
+  Tooltip.prototype.isInStateTrue = function () {
+    for (var key in this.inState) {
+      if (this.inState[key]) return true
+    }
+
+    return false
+  }
+
+  Tooltip.prototype.leave = function (obj) {
+    var self = obj instanceof this.constructor ?
+      obj : $(obj.currentTarget).data('bs.' + this.type)
+
+    if (!self) {
+      self = new this.constructor(obj.currentTarget, this.getDelegateOptions())
+      $(obj.currentTarget).data('bs.' + this.type, self)
+    }
+
+    if (obj instanceof $.Event) {
+      self.inState[obj.type == 'focusout' ? 'focus' : 'hover'] = false
+    }
+
+    if (self.isInStateTrue()) return
+
+    clearTimeout(self.timeout)
+
+    self.hoverState = 'out'
+
+    if (!self.options.delay || !self.options.delay.hide) return self.hide()
+
+    self.timeout = setTimeout(function () {
+      if (self.hoverState == 'out') self.hide()
+    }, self.options.delay.hide)
+  }
+
+  Tooltip.prototype.show = function () {
+    var e = $.Event('show.bs.' + this.type)
+
+    if (this.hasContent() && this.enabled) {
+      this.$element.trigger(e)
+
+      var inDom = $.contains(this.$element[0].ownerDocument.documentElement, this.$element[0])
+      if (e.isDefaultPrevented() || !inDom) return
+      var that = this
+
+      var $tip = this.tip()
+
+      var tipId = this.getUID(this.type)
+
+      this.setContent()
+      $tip.attr('id', tipId)
+      this.$element.attr('aria-describedby', tipId)
+
+      if (this.options.animation) $tip.addClass('fade')
+
+      var placement = typeof this.options.placement == 'function' ?
+        this.options.placement.call(this, $tip[0], this.$element[0]) :
+        this.options.placement
+
+      var autoToken = /\s?auto?\s?/i
+      var autoPlace = autoToken.test(placement)
+      if (autoPlace) placement = placement.replace(autoToken, '') || 'top'
+
+      $tip
+        .detach()
+        .css({ top: 0, left: 0, display: 'block' })
+        .addClass(placement)
+        .data('bs.' + this.type, this)
+
+      this.options.container ? $tip.appendTo($(document).find(this.options.container)) : $tip.insertAfter(this.$element)
+      this.$element.trigger('inserted.bs.' + this.type)
+
+      var pos          = this.getPosition()
+      var actualWidth  = $tip[0].offsetWidth
+      var actualHeight = $tip[0].offsetHeight
+
+      if (autoPlace) {
+        var orgPlacement = placement
+        var viewportDim = this.getPosition(this.$viewport)
+
+        placement = placement == 'bottom' && pos.bottom + actualHeight > viewportDim.bottom ? 'top'    :
+                    placement == 'top'    && pos.top    - actualHeight < viewportDim.top    ? 'bottom' :
+                    placement == 'right'  && pos.right  + actualWidth  > viewportDim.width  ? 'left'   :
+                    placement == 'left'   && pos.left   - actualWidth  < viewportDim.left   ? 'right'  :
+                    placement
+
+        $tip
+          .removeClass(orgPlacement)
+          .addClass(placement)
+      }
+
+      var calculatedOffset = this.getCalculatedOffset(placement, pos, actualWidth, actualHeight)
+
+      this.applyPlacement(calculatedOffset, placement)
+
+      var complete = function () {
+        var prevHoverState = that.hoverState
+        that.$element.trigger('shown.bs.' + that.type)
+        that.hoverState = null
+
+        if (prevHoverState == 'out') that.leave(that)
+      }
+
+      $.support.transition && this.$tip.hasClass('fade') ?
+        $tip
+          .one('bsTransitionEnd', complete)
+          .emulateTransitionEnd(Tooltip.TRANSITION_DURATION) :
+        complete()
+    }
+  }
+
+  Tooltip.prototype.applyPlacement = function (offset, placement) {
+    var $tip   = this.tip()
+    var width  = $tip[0].offsetWidth
+    var height = $tip[0].offsetHeight
+
+    // manually read margins because getBoundingClientRect includes difference
+    var marginTop = parseInt($tip.css('margin-top'), 10)
+    var marginLeft = parseInt($tip.css('margin-left'), 10)
+
+    // we must check for NaN for ie 8/9
+    if (isNaN(marginTop))  marginTop  = 0
+    if (isNaN(marginLeft)) marginLeft = 0
+
+    offset.top  += marginTop
+    offset.left += marginLeft
+
+    // $.fn.offset doesn't round pixel values
+    // so we use setOffset directly with our own function B-0
+    $.offset.setOffset($tip[0], $.extend({
+      using: function (props) {
+        $tip.css({
+          top: Math.round(props.top),
+          left: Math.round(props.left)
+        })
+      }
+    }, offset), 0)
+
+    $tip.addClass('in')
+
+    // check to see if placing tip in new offset caused the tip to resize itself
+    var actualWidth  = $tip[0].offsetWidth
+    var actualHeight = $tip[0].offsetHeight
+
+    if (placement == 'top' && actualHeight != height) {
+      offset.top = offset.top + height - actualHeight
+    }
+
+    var delta = this.getViewportAdjustedDelta(placement, offset, actualWidth, actualHeight)
+
+    if (delta.left) offset.left += delta.left
+    else offset.top += delta.top
+
+    var isVertical          = /top|bottom/.test(placement)
+    var arrowDelta          = isVertical ? delta.left * 2 - width + actualWidth : delta.top * 2 - height + actualHeight
+    var arrowOffsetPosition = isVertical ? 'offsetWidth' : 'offsetHeight'
+
+    $tip.offset(offset)
+    this.replaceArrow(arrowDelta, $tip[0][arrowOffsetPosition], isVertical)
+  }
+
+  Tooltip.prototype.replaceArrow = function (delta, dimension, isVertical) {
+    this.arrow()
+      .css(isVertical ? 'left' : 'top', 50 * (1 - delta / dimension) + '%')
+      .css(isVertical ? 'top' : 'left', '')
+  }
+
+  Tooltip.prototype.setContent = function () {
+    var $tip  = this.tip()
+    var title = this.getTitle()
+
+    if (this.options.html) {
+      if (this.options.sanitize) {
+        title = sanitizeHtml(title, this.options.whiteList, this.options.sanitizeFn)
+      }
+
+      $tip.find('.tooltip-inner').html(title)
+    } else {
+      $tip.find('.tooltip-inner').text(title)
+    }
+
+    $tip.removeClass('fade in top bottom left right')
+  }
+
+  Tooltip.prototype.hide = function (callback) {
+    var that = this
+    var $tip = $(this.$tip)
+    var e    = $.Event('hide.bs.' + this.type)
+
+    function complete() {
+      if (that.hoverState != 'in') $tip.detach()
+      if (that.$element) { // TODO: Check whether guarding this code with this `if` is really necessary.
+        that.$element
+          .removeAttr('aria-describedby')
+          .trigger('hidden.bs.' + that.type)
+      }
+      callback && callback()
+    }
+
+    this.$element.trigger(e)
+
+    if (e.isDefaultPrevented()) return
+
+    $tip.removeClass('in')
+
+    $.support.transition && $tip.hasClass('fade') ?
+      $tip
+        .one('bsTransitionEnd', complete)
+        .emulateTransitionEnd(Tooltip.TRANSITION_DURATION) :
+      complete()
+
+    this.hoverState = null
+
+    return this
+  }
+
+  Tooltip.prototype.fixTitle = function () {
+    var $e = this.$element
+    if ($e.attr('title') || typeof $e.attr('data-original-title') != 'string') {
+      $e.attr('data-original-title', $e.attr('title') || '').attr('title', '')
+    }
+  }
+
+  Tooltip.prototype.hasContent = function () {
+    return this.getTitle()
+  }
+
+  Tooltip.prototype.getPosition = function ($element) {
+    $element   = $element || this.$element
+
+    var el     = $element[0]
+    var isBody = el.tagName == 'BODY'
+
+    var elRect    = el.getBoundingClientRect()
+    if (elRect.width == null) {
+      // width and height are missing in IE8, so compute them manually; see https://github.com/twbs/bootstrap/issues/14093
+      elRect = $.extend({}, elRect, { width: elRect.right - elRect.left, height: elRect.bottom - elRect.top })
+    }
+    var isSvg = window.SVGElement && el instanceof window.SVGElement
+    // Avoid using $.offset() on SVGs since it gives incorrect results in jQuery 3.
+    // See https://github.com/twbs/bootstrap/issues/20280
+    var elOffset  = isBody ? { top: 0, left: 0 } : (isSvg ? null : $element.offset())
+    var scroll    = { scroll: isBody ? document.documentElement.scrollTop || document.body.scrollTop : $element.scrollTop() }
+    var outerDims = isBody ? { width: $(window).width(), height: $(window).height() } : null
+
+    return $.extend({}, elRect, scroll, outerDims, elOffset)
+  }
+
+  Tooltip.prototype.getCalculatedOffset = function (placement, pos, actualWidth, actualHeight) {
+    return placement == 'bottom' ? { top: pos.top + pos.height,   left: pos.left + pos.width / 2 - actualWidth / 2 } :
+           placement == 'top'    ? { top: pos.top - actualHeight, left: pos.left + pos.width / 2 - actualWidth / 2 } :
+           placement == 'left'   ? { top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left - actualWidth } :
+        /* placement == 'right' */ { top: pos.top + pos.height / 2 - actualHeight / 2, left: pos.left + pos.width }
+
+  }
+
+  Tooltip.prototype.getViewportAdjustedDelta = function (placement, pos, actualWidth, actualHeight) {
+    var delta = { top: 0, left: 0 }
+    if (!this.$viewport) return delta
+
+    var viewportPadding = this.options.viewport && this.options.viewport.padding || 0
+    var viewportDimensions = this.getPosition(this.$viewport)
+
+    if (/right|left/.test(placement)) {
+      var topEdgeOffset    = pos.top - viewportPadding - viewportDimensions.scroll
+      var bottomEdgeOffset = pos.top + viewportPadding - viewportDimensions.scroll + actualHeight
+      if (topEdgeOffset < viewportDimensions.top) { // top overflow
+        delta.top = viewportDimensions.top - topEdgeOffset
+      } else if (bottomEdgeOffset > viewportDimensions.top + viewportDimensions.height) { // bottom overflow
+        delta.top = viewportDimensions.top + viewportDimensions.height - bottomEdgeOffset
+      }
+    } else {
+      var leftEdgeOffset  = pos.left - viewportPadding
+      var rightEdgeOffset = pos.left + viewportPadding + actualWidth
+      if (leftEdgeOffset < viewportDimensions.left) { // left overflow
+        delta.left = viewportDimensions.left - leftEdgeOffset
+      } else if (rightEdgeOffset > viewportDimensions.right) { // right overflow
+        delta.left = viewportDimensions.left + viewportDimensions.width - rightEdgeOffset
+      }
+    }
+
+    return delta
+  }
+
+  Tooltip.prototype.getTitle = function () {
+    var title
+    var $e = this.$element
+    var o  = this.options
+
+    title = $e.attr('data-original-title')
+      || (typeof o.title == 'function' ? o.title.call($e[0]) :  o.title)
+
+    return title
+  }
+
+  Tooltip.prototype.getUID = function (prefix) {
+    do prefix += ~~(Math.random() * 1000000)
+    while (document.getElementById(prefix))
+    return prefix
+  }
+
+  Tooltip.prototype.tip = function () {
+    if (!this.$tip) {
+      this.$tip = $(this.options.template)
+      if (this.$tip.length != 1) {
+        throw new Error(this.type + ' `template` option must consist of exactly 1 top-level element!')
+      }
+    }
+    return this.$tip
+  }
+
+  Tooltip.prototype.arrow = function () {
+    return (this.$arrow = this.$arrow || this.tip().find('.tooltip-arrow'))
+  }
+
+  Tooltip.prototype.enable = function () {
+    this.enabled = true
+  }
+
+  Tooltip.prototype.disable = function () {
+    this.enabled = false
+  }
+
+  Tooltip.prototype.toggleEnabled = function () {
+    this.enabled = !this.enabled
+  }
+
+  Tooltip.prototype.toggle = function (e) {
+    var self = this
+    if (e) {
+      self = $(e.currentTarget).data('bs.' + this.type)
+      if (!self) {
+        self = new this.constructor(e.currentTarget, this.getDelegateOptions())
+        $(e.currentTarget).data('bs.' + this.type, self)
+      }
+    }
+
+    if (e) {
+      self.inState.click = !self.inState.click
+      if (self.isInStateTrue()) self.enter(self)
+      else self.leave(self)
+    } else {
+      self.tip().hasClass('in') ? self.leave(self) : self.enter(self)
+    }
+  }
+
+  Tooltip.prototype.destroy = function () {
+    var that = this
+    clearTimeout(this.timeout)
+    this.hide(function () {
+      that.$element.off('.' + that.type).removeData('bs.' + that.type)
+      if (that.$tip) {
+        that.$tip.detach()
+      }
+      that.$tip = null
+      that.$arrow = null
+      that.$viewport = null
+      that.$element = null
+    })
+  }
+
+  Tooltip.prototype.sanitizeHtml = function (unsafeHtml) {
+    return sanitizeHtml(unsafeHtml, this.options.whiteList, this.options.sanitizeFn)
+  }
+
+  // TOOLTIP PLUGIN DEFINITION
+  // =========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.tooltip')
+      var options = typeof option == 'object' && option
+
+      if (!data && /destroy|hide/.test(option)) return
+      if (!data) $this.data('bs.tooltip', (data = new Tooltip(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.tooltip
+
+  $.fn.tooltip             = Plugin
+  $.fn.tooltip.Constructor = Tooltip
+
+
+  // TOOLTIP NO CONFLICT
+  // ===================
+
+  $.fn.tooltip.noConflict = function () {
+    $.fn.tooltip = old
+    return this
+  }
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: popover.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#popovers
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // POPOVER PUBLIC CLASS DEFINITION
+  // ===============================
+
+  var Popover = function (element, options) {
+    this.init('popover', element, options)
+  }
+
+  if (!$.fn.tooltip) throw new Error('Popover requires tooltip.js')
+
+  Popover.VERSION  = '3.4.1'
+
+  Popover.DEFAULTS = $.extend({}, $.fn.tooltip.Constructor.DEFAULTS, {
+    placement: 'right',
+    trigger: 'click',
+    content: '',
+    template: '<div class="popover" role="tooltip"><div class="arrow"></div><h3 class="popover-title"></h3><div class="popover-content"></div></div>'
+  })
+
+
+  // NOTE: POPOVER EXTENDS tooltip.js
+  // ================================
+
+  Popover.prototype = $.extend({}, $.fn.tooltip.Constructor.prototype)
+
+  Popover.prototype.constructor = Popover
+
+  Popover.prototype.getDefaults = function () {
+    return Popover.DEFAULTS
+  }
+
+  Popover.prototype.setContent = function () {
+    var $tip    = this.tip()
+    var title   = this.getTitle()
+    var content = this.getContent()
+
+    if (this.options.html) {
+      var typeContent = typeof content
+
+      if (this.options.sanitize) {
+        title = this.sanitizeHtml(title)
+
+        if (typeContent === 'string') {
+          content = this.sanitizeHtml(content)
+        }
+      }
+
+      $tip.find('.popover-title').html(title)
+      $tip.find('.popover-content').children().detach().end()[
+        typeContent === 'string' ? 'html' : 'append'
+      ](content)
+    } else {
+      $tip.find('.popover-title').text(title)
+      $tip.find('.popover-content').children().detach().end().text(content)
+    }
+
+    $tip.removeClass('fade top bottom left right in')
+
+    // IE8 doesn't accept hiding via the `:empty` pseudo selector, we have to do
+    // this manually by checking the contents.
+    if (!$tip.find('.popover-title').html()) $tip.find('.popover-title').hide()
+  }
+
+  Popover.prototype.hasContent = function () {
+    return this.getTitle() || this.getContent()
+  }
+
+  Popover.prototype.getContent = function () {
+    var $e = this.$element
+    var o  = this.options
+
+    return $e.attr('data-content')
+      || (typeof o.content == 'function' ?
+        o.content.call($e[0]) :
+        o.content)
+  }
+
+  Popover.prototype.arrow = function () {
+    return (this.$arrow = this.$arrow || this.tip().find('.arrow'))
+  }
+
+
+  // POPOVER PLUGIN DEFINITION
+  // =========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.popover')
+      var options = typeof option == 'object' && option
+
+      if (!data && /destroy|hide/.test(option)) return
+      if (!data) $this.data('bs.popover', (data = new Popover(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.popover
+
+  $.fn.popover             = Plugin
+  $.fn.popover.Constructor = Popover
+
+
+  // POPOVER NO CONFLICT
+  // ===================
+
+  $.fn.popover.noConflict = function () {
+    $.fn.popover = old
+    return this
+  }
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: scrollspy.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#scrollspy
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // SCROLLSPY CLASS DEFINITION
+  // ==========================
+
+  function ScrollSpy(element, options) {
+    this.$body          = $(document.body)
+    this.$scrollElement = $(element).is(document.body) ? $(window) : $(element)
+    this.options        = $.extend({}, ScrollSpy.DEFAULTS, options)
+    this.selector       = (this.options.target || '') + ' .nav li > a'
+    this.offsets        = []
+    this.targets        = []
+    this.activeTarget   = null
+    this.scrollHeight   = 0
+
+    this.$scrollElement.on('scroll.bs.scrollspy', $.proxy(this.process, this))
+    this.refresh()
+    this.process()
+  }
+
+  ScrollSpy.VERSION  = '3.4.1'
+
+  ScrollSpy.DEFAULTS = {
+    offset: 10
+  }
+
+  ScrollSpy.prototype.getScrollHeight = function () {
+    return this.$scrollElement[0].scrollHeight || Math.max(this.$body[0].scrollHeight, document.documentElement.scrollHeight)
+  }
+
+  ScrollSpy.prototype.refresh = function () {
+    var that          = this
+    var offsetMethod  = 'offset'
+    var offsetBase    = 0
+
+    this.offsets      = []
+    this.targets      = []
+    this.scrollHeight = this.getScrollHeight()
+
+    if (!$.isWindow(this.$scrollElement[0])) {
+      offsetMethod = 'position'
+      offsetBase   = this.$scrollElement.scrollTop()
+    }
+
+    this.$body
+      .find(this.selector)
+      .map(function () {
+        var $el   = $(this)
+        var href  = $el.data('target') || $el.attr('href')
+        var $href = /^#./.test(href) && $(href)
+
+        return ($href
+          && $href.length
+          && $href.is(':visible')
+          && [[$href[offsetMethod]().top + offsetBase, href]]) || null
+      })
+      .sort(function (a, b) { return a[0] - b[0] })
+      .each(function () {
+        that.offsets.push(this[0])
+        that.targets.push(this[1])
+      })
+  }
+
+  ScrollSpy.prototype.process = function () {
+    var scrollTop    = this.$scrollElement.scrollTop() + this.options.offset
+    var scrollHeight = this.getScrollHeight()
+    var maxScroll    = this.options.offset + scrollHeight - this.$scrollElement.height()
+    var offsets      = this.offsets
+    var targets      = this.targets
+    var activeTarget = this.activeTarget
+    var i
+
+    if (this.scrollHeight != scrollHeight) {
+      this.refresh()
+    }
+
+    if (scrollTop >= maxScroll) {
+      return activeTarget != (i = targets[targets.length - 1]) && this.activate(i)
+    }
+
+    if (activeTarget && scrollTop < offsets[0]) {
+      this.activeTarget = null
+      return this.clear()
+    }
+
+    for (i = offsets.length; i--;) {
+      activeTarget != targets[i]
+        && scrollTop >= offsets[i]
+        && (offsets[i + 1] === undefined || scrollTop < offsets[i + 1])
+        && this.activate(targets[i])
+    }
+  }
+
+  ScrollSpy.prototype.activate = function (target) {
+    this.activeTarget = target
+
+    this.clear()
+
+    var selector = this.selector +
+      '[data-target="' + target + '"],' +
+      this.selector + '[href="' + target + '"]'
+
+    var active = $(selector)
+      .parents('li')
+      .addClass('active')
+
+    if (active.parent('.dropdown-menu').length) {
+      active = active
+        .closest('li.dropdown')
+        .addClass('active')
+    }
+
+    active.trigger('activate.bs.scrollspy')
+  }
+
+  ScrollSpy.prototype.clear = function () {
+    $(this.selector)
+      .parentsUntil(this.options.target, '.active')
+      .removeClass('active')
+  }
+
+
+  // SCROLLSPY PLUGIN DEFINITION
+  // ===========================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.scrollspy')
+      var options = typeof option == 'object' && option
+
+      if (!data) $this.data('bs.scrollspy', (data = new ScrollSpy(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.scrollspy
+
+  $.fn.scrollspy             = Plugin
+  $.fn.scrollspy.Constructor = ScrollSpy
+
+
+  // SCROLLSPY NO CONFLICT
+  // =====================
+
+  $.fn.scrollspy.noConflict = function () {
+    $.fn.scrollspy = old
+    return this
+  }
+
+
+  // SCROLLSPY DATA-API
+  // ==================
+
+  $(window).on('load.bs.scrollspy.data-api', function () {
+    $('[data-spy="scroll"]').each(function () {
+      var $spy = $(this)
+      Plugin.call($spy, $spy.data())
+    })
+  })
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: tab.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#tabs
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // TAB CLASS DEFINITION
+  // ====================
+
+  var Tab = function (element) {
+    // jscs:disable requireDollarBeforejQueryAssignment
+    this.element = $(element)
+    // jscs:enable requireDollarBeforejQueryAssignment
+  }
+
+  Tab.VERSION = '3.4.1'
+
+  Tab.TRANSITION_DURATION = 150
+
+  Tab.prototype.show = function () {
+    var $this    = this.element
+    var $ul      = $this.closest('ul:not(.dropdown-menu)')
+    var selector = $this.data('target')
+
+    if (!selector) {
+      selector = $this.attr('href')
+      selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7
+    }
+
+    if ($this.parent('li').hasClass('active')) return
+
+    var $previous = $ul.find('.active:last a')
+    var hideEvent = $.Event('hide.bs.tab', {
+      relatedTarget: $this[0]
+    })
+    var showEvent = $.Event('show.bs.tab', {
+      relatedTarget: $previous[0]
+    })
+
+    $previous.trigger(hideEvent)
+    $this.trigger(showEvent)
+
+    if (showEvent.isDefaultPrevented() || hideEvent.isDefaultPrevented()) return
+
+    var $target = $(document).find(selector)
+
+    this.activate($this.closest('li'), $ul)
+    this.activate($target, $target.parent(), function () {
+      $previous.trigger({
+        type: 'hidden.bs.tab',
+        relatedTarget: $this[0]
+      })
+      $this.trigger({
+        type: 'shown.bs.tab',
+        relatedTarget: $previous[0]
+      })
+    })
+  }
+
+  Tab.prototype.activate = function (element, container, callback) {
+    var $active    = container.find('> .active')
+    var transition = callback
+      && $.support.transition
+      && ($active.length && $active.hasClass('fade') || !!container.find('> .fade').length)
+
+    function next() {
+      $active
+        .removeClass('active')
+        .find('> .dropdown-menu > .active')
+        .removeClass('active')
+        .end()
+        .find('[data-toggle="tab"]')
+        .attr('aria-expanded', false)
+
+      element
+        .addClass('active')
+        .find('[data-toggle="tab"]')
+        .attr('aria-expanded', true)
+
+      if (transition) {
+        element[0].offsetWidth // reflow for transition
+        element.addClass('in')
+      } else {
+        element.removeClass('fade')
+      }
+
+      if (element.parent('.dropdown-menu').length) {
+        element
+          .closest('li.dropdown')
+          .addClass('active')
+          .end()
+          .find('[data-toggle="tab"]')
+          .attr('aria-expanded', true)
+      }
+
+      callback && callback()
+    }
+
+    $active.length && transition ?
+      $active
+        .one('bsTransitionEnd', next)
+        .emulateTransitionEnd(Tab.TRANSITION_DURATION) :
+      next()
+
+    $active.removeClass('in')
+  }
+
+
+  // TAB PLUGIN DEFINITION
+  // =====================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this = $(this)
+      var data  = $this.data('bs.tab')
+
+      if (!data) $this.data('bs.tab', (data = new Tab(this)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.tab
+
+  $.fn.tab             = Plugin
+  $.fn.tab.Constructor = Tab
+
+
+  // TAB NO CONFLICT
+  // ===============
+
+  $.fn.tab.noConflict = function () {
+    $.fn.tab = old
+    return this
+  }
+
+
+  // TAB DATA-API
+  // ============
+
+  var clickHandler = function (e) {
+    e.preventDefault()
+    Plugin.call($(this), 'show')
+  }
+
+  $(document)
+    .on('click.bs.tab.data-api', '[data-toggle="tab"]', clickHandler)
+    .on('click.bs.tab.data-api', '[data-toggle="pill"]', clickHandler)
+
+}(window.$jqTheme || window.jQuery);
+
+/* ========================================================================
+ * Bootstrap: affix.js v3.4.1
+ * https://getbootstrap.com/docs/3.4/javascript/#affix
+ * ========================================================================
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ * ======================================================================== */
+
+
++function ($) {
+  'use strict';
+
+  // AFFIX CLASS DEFINITION
+  // ======================
+
+  var Affix = function (element, options) {
+    this.options = $.extend({}, Affix.DEFAULTS, options)
+
+    var target = this.options.target === Affix.DEFAULTS.target ? $(this.options.target) : $(document).find(this.options.target)
+
+    this.$target = target
+      .on('scroll.bs.affix.data-api', $.proxy(this.checkPosition, this))
+      .on('click.bs.affix.data-api',  $.proxy(this.checkPositionWithEventLoop, this))
+
+    this.$element     = $(element)
+    this.affixed      = null
+    this.unpin        = null
+    this.pinnedOffset = null
+
+    this.checkPosition()
+  }
+
+  Affix.VERSION  = '3.4.1'
+
+  Affix.RESET    = 'affix affix-top affix-bottom'
+
+  Affix.DEFAULTS = {
+    offset: 0,
+    target: window
+  }
+
+  Affix.prototype.getState = function (scrollHeight, height, offsetTop, offsetBottom) {
+    var scrollTop    = this.$target.scrollTop()
+    var position     = this.$element.offset()
+    var targetHeight = this.$target.height()
+
+    if (offsetTop != null && this.affixed == 'top') return scrollTop < offsetTop ? 'top' : false
+
+    if (this.affixed == 'bottom') {
+      if (offsetTop != null) return (scrollTop + this.unpin <= position.top) ? false : 'bottom'
+      return (scrollTop + targetHeight <= scrollHeight - offsetBottom) ? false : 'bottom'
+    }
+
+    var initializing   = this.affixed == null
+    var colliderTop    = initializing ? scrollTop : position.top
+    var colliderHeight = initializing ? targetHeight : height
+
+    if (offsetTop != null && scrollTop <= offsetTop) return 'top'
+    if (offsetBottom != null && (colliderTop + colliderHeight >= scrollHeight - offsetBottom)) return 'bottom'
+
+    return false
+  }
+
+  Affix.prototype.getPinnedOffset = function () {
+    if (this.pinnedOffset) return this.pinnedOffset
+    this.$element.removeClass(Affix.RESET).addClass('affix')
+    var scrollTop = this.$target.scrollTop()
+    var position  = this.$element.offset()
+    return (this.pinnedOffset = position.top - scrollTop)
+  }
+
+  Affix.prototype.checkPositionWithEventLoop = function () {
+    setTimeout($.proxy(this.checkPosition, this), 1)
+  }
+
+  Affix.prototype.checkPosition = function () {
+    if (!this.$element.is(':visible')) return
+
+    var height       = this.$element.height()
+    var offset       = this.options.offset
+    var offsetTop    = offset.top
+    var offsetBottom = offset.bottom
+    var scrollHeight = Math.max($(document).height(), $(document.body).height())
+
+    if (typeof offset != 'object')         offsetBottom = offsetTop = offset
+    if (typeof offsetTop == 'function')    offsetTop    = offset.top(this.$element)
+    if (typeof offsetBottom == 'function') offsetBottom = offset.bottom(this.$element)
+
+    var affix = this.getState(scrollHeight, height, offsetTop, offsetBottom)
+
+    if (this.affixed != affix) {
+      if (this.unpin != null) this.$element.css('top', '')
+
+      var affixType = 'affix' + (affix ? '-' + affix : '')
+      var e         = $.Event(affixType + '.bs.affix')
+
+      this.$element.trigger(e)
+
+      if (e.isDefaultPrevented()) return
+
+      this.affixed = affix
+      this.unpin = affix == 'bottom' ? this.getPinnedOffset() : null
+
+      this.$element
+        .removeClass(Affix.RESET)
+        .addClass(affixType)
+        .trigger(affixType.replace('affix', 'affixed') + '.bs.affix')
+    }
+
+    if (affix == 'bottom') {
+      this.$element.offset({
+        top: scrollHeight - height - offsetBottom
+      })
+    }
+  }
+
+
+  // AFFIX PLUGIN DEFINITION
+  // =======================
+
+  function Plugin(option) {
+    return this.each(function () {
+      var $this   = $(this)
+      var data    = $this.data('bs.affix')
+      var options = typeof option == 'object' && option
+
+      if (!data) $this.data('bs.affix', (data = new Affix(this, options)))
+      if (typeof option == 'string') data[option]()
+    })
+  }
+
+  var old = $.fn.affix
+
+  $.fn.affix             = Plugin
+  $.fn.affix.Constructor = Affix
+
+
+  // AFFIX NO CONFLICT
+  // =================
+
+  $.fn.affix.noConflict = function () {
+    $.fn.affix = old
+    return this
+  }
+
+
+  // AFFIX DATA-API
+  // ==============
+
+  $(window).on('load', function () {
+    $('[data-spy="affix"]').each(function () {
+      var $spy = $(this)
+      var data = $spy.data()
+
+      data.offset = data.offset || {}
+
+      if (data.offsetBottom != null) data.offset.bottom = data.offsetBottom
+      if (data.offsetTop    != null) data.offset.top    = data.offsetTop
+
+      Plugin.call($spy, data)
+    })
+  })
+
+}(window.$jqTheme || window.jQuery);
diff --git a/_static/bootstrap-3.4.1/js/bootstrap.min.js b/_static/bootstrap-3.4.1/js/bootstrap.min.js
new file mode 100644
index 000000000..a5df1aca9
--- /dev/null
+++ b/_static/bootstrap-3.4.1/js/bootstrap.min.js
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under the MIT license
+ */
+if("undefined"==typeof (window.$jqTheme || window.jQuery))throw new Error("Bootstrap's JavaScript requires jQuery");!function(t){"use strict";var e=jQuery.fn.jquery.split(" ")[0].split(".");if(e[0]<2&&e[1]<9||1==e[0]&&9==e[1]&&e[2]<1||3<e[0])throw new Error("Bootstrap's JavaScript requires jQuery version 1.9.1 or higher, but lower than version 4")}(),function(n){"use strict";n.fn.emulateTransitionEnd=function(t){var e=!1,i=this;n(this).one("bsTransitionEnd",function(){e=!0});return setTimeout(function(){e||n(i).trigger(n.support.transition.end)},t),this},n(function(){n.support.transition=function o(){var t=document.createElement("bootstrap"),e={WebkitTransition:"webkitTransitionEnd",MozTransition:"transitionend",OTransition:"oTransitionEnd otransitionend",transition:"transitionend"};for(var i in e)if(t.style[i]!==undefined)return{end:e[i]};return!1}(),n.support.transition&&(n.event.special.bsTransitionEnd={bindType:n.support.transition.end,delegateType:n.support.transition.end,handle:function(t){if(n(t.target).is(this))return t.handleObj.handler.apply(this,arguments)}})})}(window.$jqTheme || window.jQuery),function(s){"use strict";var e='[data-dismiss="alert"]',a=function(t){s(t).on("click",e,this.close)};a.VERSION="3.4.1",a.TRANSITION_DURATION=150,a.prototype.close=function(t){var e=s(this),i=e.attr("data-target");i||(i=(i=e.attr("href"))&&i.replace(/.*(?=#[^\s]*$)/,"")),i="#"===i?[]:i;var o=s(document).find(i);function n(){o.detach().trigger("closed.bs.alert").remove()}t&&t.preventDefault(),o.length||(o=e.closest(".alert")),o.trigger(t=s.Event("close.bs.alert")),t.isDefaultPrevented()||(o.removeClass("in"),s.support.transition&&o.hasClass("fade")?o.one("bsTransitionEnd",n).emulateTransitionEnd(a.TRANSITION_DURATION):n())};var t=s.fn.alert;s.fn.alert=function o(i){return this.each(function(){var t=s(this),e=t.data("bs.alert");e||t.data("bs.alert",e=new a(this)),"string"==typeof i&&e[i].call(t)})},s.fn.alert.Constructor=a,s.fn.alert.noConflict=function(){return s.fn.alert=t,this},s(document).on("click.bs.alert.data-api",e,a.prototype.close)}(window.$jqTheme || window.jQuery),function(s){"use strict";var n=function(t,e){this.$element=s(t),this.options=s.extend({},n.DEFAULTS,e),this.isLoading=!1};function i(o){return this.each(function(){var t=s(this),e=t.data("bs.button"),i="object"==typeof o&&o;e||t.data("bs.button",e=new n(this,i)),"toggle"==o?e.toggle():o&&e.setState(o)})}n.VERSION="3.4.1",n.DEFAULTS={loadingText:"loading..."},n.prototype.setState=function(t){var e="disabled",i=this.$element,o=i.is("input")?"val":"html",n=i.data();t+="Text",null==n.resetText&&i.data("resetText",i[o]()),setTimeout(s.proxy(function(){i[o](null==n[t]?this.options[t]:n[t]),"loadingText"==t?(this.isLoading=!0,i.addClass(e).attr(e,e).prop(e,!0)):this.isLoading&&(this.isLoading=!1,i.removeClass(e).removeAttr(e).prop(e,!1))},this),0)},n.prototype.toggle=function(){var t=!0,e=this.$element.closest('[data-toggle="buttons"]');if(e.length){var i=this.$element.find("input");"radio"==i.prop("type")?(i.prop("checked")&&(t=!1),e.find(".active").removeClass("active"),this.$element.addClass("active")):"checkbox"==i.prop("type")&&(i.prop("checked")!==this.$element.hasClass("active")&&(t=!1),this.$element.toggleClass("active")),i.prop("checked",this.$element.hasClass("active")),t&&i.trigger("change")}else this.$element.attr("aria-pressed",!this.$element.hasClass("active")),this.$element.toggleClass("active")};var t=s.fn.button;s.fn.button=i,s.fn.button.Constructor=n,s.fn.button.noConflict=function(){return s.fn.button=t,this},s(document).on("click.bs.button.data-api",'[data-toggle^="button"]',function(t){var e=s(t.target).closest(".btn");i.call(e,"toggle"),s(t.target).is('input[type="radio"], input[type="checkbox"]')||(t.preventDefault(),e.is("input,button")?e.trigger("focus"):e.find("input:visible,button:visible").first().trigger("focus"))}).on("focus.bs.button.data-api blur.bs.button.data-api",'[data-toggle^="button"]',function(t){s(t.target).closest(".btn").toggleClass("focus",/^focus(in)?$/.test(t.type))})}(window.$jqTheme || window.jQuery),function(p){"use strict";var c=function(t,e){this.$element=p(t),this.$indicators=this.$element.find(".carousel-indicators"),this.options=e,this.paused=null,this.sliding=null,this.interval=null,this.$active=null,this.$items=null,this.options.keyboard&&this.$element.on("keydown.bs.carousel",p.proxy(this.keydown,this)),"hover"==this.options.pause&&!("ontouchstart"in document.documentElement)&&this.$element.on("mouseenter.bs.carousel",p.proxy(this.pause,this)).on("mouseleave.bs.carousel",p.proxy(this.cycle,this))};function r(n){return this.each(function(){var t=p(this),e=t.data("bs.carousel"),i=p.extend({},c.DEFAULTS,t.data(),"object"==typeof n&&n),o="string"==typeof n?n:i.slide;e||t.data("bs.carousel",e=new c(this,i)),"number"==typeof n?e.to(n):o?e[o]():i.interval&&e.pause().cycle()})}c.VERSION="3.4.1",c.TRANSITION_DURATION=600,c.DEFAULTS={interval:5e3,pause:"hover",wrap:!0,keyboard:!0},c.prototype.keydown=function(t){if(!/input|textarea/i.test(t.target.tagName)){switch(t.which){case 37:this.prev();break;case 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s=p.extend({},n.data(),e.data()),a=e.attr("data-slide-to");a&&(s.interval=!1),r.call(n,s),a&&n.data("bs.carousel").to(a),t.preventDefault()}};p(document).on("click.bs.carousel.data-api","[data-slide]",e).on("click.bs.carousel.data-api","[data-slide-to]",e),p(window).on("load",function(){p('[data-ride="carousel"]').each(function(){var t=p(this);r.call(t,t.data())})})}(window.$jqTheme || window.jQuery),function(a){"use strict";var r=function(t,e){this.$element=a(t),this.options=a.extend({},r.DEFAULTS,e),this.$trigger=a('[data-toggle="collapse"][href="#'+t.id+'"],[data-toggle="collapse"][data-target="#'+t.id+'"]'),this.transitioning=null,this.options.parent?this.$parent=this.getParent():this.addAriaAndCollapsedClass(this.$element,this.$trigger),this.options.toggle&&this.toggle()};function n(t){var e,i=t.attr("data-target")||(e=t.attr("href"))&&e.replace(/.*(?=#[^\s]+$)/,"");return a(document).find(i)}function l(o){return this.each(function(){var 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n=function(){this.$element.removeClass("collapsing").addClass("collapse in")[o](""),this.transitioning=0,this.$element.trigger("shown.bs.collapse")};if(!a.support.transition)return n.call(this);var s=a.camelCase(["scroll",o].join("-"));this.$element.one("bsTransitionEnd",a.proxy(n,this)).emulateTransitionEnd(r.TRANSITION_DURATION)[o](this.$element[0][s])}}}},r.prototype.hide=function(){if(!this.transitioning&&this.$element.hasClass("in")){var t=a.Event("hide.bs.collapse");if(this.$element.trigger(t),!t.isDefaultPrevented()){var e=this.dimension();this.$element[e](this.$element[e]())[0].offsetHeight,this.$element.addClass("collapsing").removeClass("collapse in").attr("aria-expanded",!1),this.$trigger.addClass("collapsed").attr("aria-expanded",!1),this.transitioning=1;var i=function(){this.transitioning=0,this.$element.removeClass("collapsing").addClass("collapse").trigger("hidden.bs.collapse")};if(!a.support.transition)return i.call(this);this.$element[e](0).one("bsTransitionEnd",a.proxy(i,this)).emulateTransitionEnd(r.TRANSITION_DURATION)}}},r.prototype.toggle=function(){this[this.$element.hasClass("in")?"hide":"show"]()},r.prototype.getParent=function(){return a(document).find(this.options.parent).find('[data-toggle="collapse"][data-parent="'+this.options.parent+'"]').each(a.proxy(function(t,e){var i=a(e);this.addAriaAndCollapsedClass(n(i),i)},this)).end()},r.prototype.addAriaAndCollapsedClass=function(t,e){var i=t.hasClass("in");t.attr("aria-expanded",i),e.toggleClass("collapsed",!i).attr("aria-expanded",i)};var t=a.fn.collapse;a.fn.collapse=l,a.fn.collapse.Constructor=r,a.fn.collapse.noConflict=function(){return a.fn.collapse=t,this},a(document).on("click.bs.collapse.data-api",'[data-toggle="collapse"]',function(t){var e=a(this);e.attr("data-target")||t.preventDefault();var i=n(e),o=i.data("bs.collapse")?"toggle":e.data();l.call(i,o)})}(window.$jqTheme || window.jQuery),function(a){"use strict";var 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t=this;clearTimeout(this.timeout),this.hide(function(){t.$element.off("."+t.type).removeData("bs."+t.type),t.$tip&&t.$tip.detach(),t.$tip=null,t.$arrow=null,t.$viewport=null,t.$element=null})},m.prototype.sanitizeHtml=function(t){return n(t,this.options.whiteList,this.options.sanitizeFn)};var e=g.fn.tooltip;g.fn.tooltip=function i(o){return this.each(function(){var t=g(this),e=t.data("bs.tooltip"),i="object"==typeof o&&o;!e&&/destroy|hide/.test(o)||(e||t.data("bs.tooltip",e=new m(this,i)),"string"==typeof o&&e[o]())})},g.fn.tooltip.Constructor=m,g.fn.tooltip.noConflict=function(){return g.fn.tooltip=e,this}}(window.$jqTheme || window.jQuery),function(n){"use strict";var s=function(t,e){this.init("popover",t,e)};if(!n.fn.tooltip)throw new Error("Popover requires tooltip.js");s.VERSION="3.4.1",s.DEFAULTS=n.extend({},n.fn.tooltip.Constructor.DEFAULTS,{placement:"right",trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="arrow"></div><h3 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e.content?e.content.call(t[0]):e.content)},s.prototype.arrow=function(){return this.$arrow=this.$arrow||this.tip().find(".arrow")};var t=n.fn.popover;n.fn.popover=function e(o){return this.each(function(){var t=n(this),e=t.data("bs.popover"),i="object"==typeof o&&o;!e&&/destroy|hide/.test(o)||(e||t.data("bs.popover",e=new s(this,i)),"string"==typeof o&&e[o]())})},n.fn.popover.Constructor=s,n.fn.popover.noConflict=function(){return n.fn.popover=t,this}}(window.$jqTheme || window.jQuery),function(s){"use strict";function n(t,e){this.$body=s(document.body),this.$scrollElement=s(t).is(document.body)?s(window):s(t),this.options=s.extend({},n.DEFAULTS,e),this.selector=(this.options.target||"")+" .nav li > a",this.offsets=[],this.targets=[],this.activeTarget=null,this.scrollHeight=0,this.$scrollElement.on("scroll.bs.scrollspy",s.proxy(this.process,this)),this.refresh(),this.process()}function e(o){return this.each(function(){var t=s(this),e=t.data("bs.scrollspy"),i="object"==typeof o&&o;e||t.data("bs.scrollspy",e=new n(this,i)),"string"==typeof o&&e[o]()})}n.VERSION="3.4.1",n.DEFAULTS={offset:10},n.prototype.getScrollHeight=function(){return this.$scrollElement[0].scrollHeight||Math.max(this.$body[0].scrollHeight,document.documentElement.scrollHeight)},n.prototype.refresh=function(){var t=this,o="offset",n=0;this.offsets=[],this.targets=[],this.scrollHeight=this.getScrollHeight(),s.isWindow(this.$scrollElement[0])||(o="position",n=this.$scrollElement.scrollTop()),this.$body.find(this.selector).map(function(){var t=s(this),e=t.data("target")||t.attr("href"),i=/^#./.test(e)&&s(e);return i&&i.length&&i.is(":visible")&&[[i[o]().top+n,e]]||null}).sort(function(t,e){return t[0]-e[0]}).each(function(){t.offsets.push(this[0]),t.targets.push(this[1])})},n.prototype.process=function(){var t,e=this.$scrollElement.scrollTop()+this.options.offset,i=this.getScrollHeight(),o=this.options.offset+i-this.$scrollElement.height(),n=this.offsets,s=this.targets,a=this.activeTarget;if(this.scrollHeight!=i&&this.refresh(),o<=e)return a!=(t=s[s.length-1])&&this.activate(t);if(a&&e<n[0])return this.activeTarget=null,this.clear();for(t=n.length;t--;)a!=s[t]&&e>=n[t]&&(n[t+1]===undefined||e<n[t+1])&&this.activate(s[t])},n.prototype.activate=function(t){this.activeTarget=t,this.clear();var e=this.selector+'[data-target="'+t+'"],'+this.selector+'[href="'+t+'"]',i=s(e).parents("li").addClass("active");i.parent(".dropdown-menu").length&&(i=i.closest("li.dropdown").addClass("active")),i.trigger("activate.bs.scrollspy")},n.prototype.clear=function(){s(this.selector).parentsUntil(this.options.target,".active").removeClass("active")};var t=s.fn.scrollspy;s.fn.scrollspy=e,s.fn.scrollspy.Constructor=n,s.fn.scrollspy.noConflict=function(){return s.fn.scrollspy=t,this},s(window).on("load.bs.scrollspy.data-api",function(){s('[data-spy="scroll"]').each(function(){var t=s(this);e.call(t,t.data())})})}(window.$jqTheme || window.jQuery),function(r){"use strict";var a=function(t){this.element=r(t)};function e(i){return this.each(function(){var t=r(this),e=t.data("bs.tab");e||t.data("bs.tab",e=new a(this)),"string"==typeof i&&e[i]()})}a.VERSION="3.4.1",a.TRANSITION_DURATION=150,a.prototype.show=function(){var t=this.element,e=t.closest("ul:not(.dropdown-menu)"),i=t.data("target");if(i||(i=(i=t.attr("href"))&&i.replace(/.*(?=#[^\s]*$)/,"")),!t.parent("li").hasClass("active")){var o=e.find(".active:last a"),n=r.Event("hide.bs.tab",{relatedTarget:t[0]}),s=r.Event("show.bs.tab",{relatedTarget:o[0]});if(o.trigger(n),t.trigger(s),!s.isDefaultPrevented()&&!n.isDefaultPrevented()){var a=r(document).find(i);this.activate(t.closest("li"),e),this.activate(a,a.parent(),function(){o.trigger({type:"hidden.bs.tab",relatedTarget:t[0]}),t.trigger({type:"shown.bs.tab",relatedTarget:o[0]})})}}},a.prototype.activate=function(t,e,i){var o=e.find("> .active"),n=i&&r.support.transition&&(o.length&&o.hasClass("fade")||!!e.find("> .fade").length);function s(){o.removeClass("active").find("> .dropdown-menu > .active").removeClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!1),t.addClass("active").find('[data-toggle="tab"]').attr("aria-expanded",!0),n?(t[0].offsetWidth,t.addClass("in")):t.removeClass("fade"),t.parent(".dropdown-menu").length&&t.closest("li.dropdown").addClass("active").end().find('[data-toggle="tab"]').attr("aria-expanded",!0),i&&i()}o.length&&n?o.one("bsTransitionEnd",s).emulateTransitionEnd(a.TRANSITION_DURATION):s(),o.removeClass("in")};var t=r.fn.tab;r.fn.tab=e,r.fn.tab.Constructor=a,r.fn.tab.noConflict=function(){return r.fn.tab=t,this};var 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n=this.$target.scrollTop(),s=this.$element.offset(),a=this.$target.height();if(null!=i&&"top"==this.affixed)return n<i&&"top";if("bottom"==this.affixed)return null!=i?!(n+this.unpin<=s.top)&&"bottom":!(n+a<=t-o)&&"bottom";var r=null==this.affixed,l=r?n:s.top;return null!=i&&n<=i?"top":null!=o&&t-o<=l+(r?a:e)&&"bottom"},h.prototype.getPinnedOffset=function(){if(this.pinnedOffset)return this.pinnedOffset;this.$element.removeClass(h.RESET).addClass("affix");var t=this.$target.scrollTop(),e=this.$element.offset();return this.pinnedOffset=e.top-t},h.prototype.checkPositionWithEventLoop=function(){setTimeout(l.proxy(this.checkPosition,this),1)},h.prototype.checkPosition=function(){if(this.$element.is(":visible")){var t=this.$element.height(),e=this.options.offset,i=e.top,o=e.bottom,n=Math.max(l(document).height(),l(document.body).height());"object"!=typeof e&&(o=i=e),"function"==typeof i&&(i=e.top(this.$element)),"function"==typeof o&&(o=e.bottom(this.$element));var s=this.getState(n,t,i,o);if(this.affixed!=s){null!=this.unpin&&this.$element.css("top","");var a="affix"+(s?"-"+s:""),r=l.Event(a+".bs.affix");if(this.$element.trigger(r),r.isDefaultPrevented())return;this.affixed=s,this.unpin="bottom"==s?this.getPinnedOffset():null,this.$element.removeClass(h.RESET).addClass(a).trigger(a.replace("affix","affixed")+".bs.affix")}"bottom"==s&&this.$element.offset({top:n-t-o})}};var t=l.fn.affix;l.fn.affix=i,l.fn.affix.Constructor=h,l.fn.affix.noConflict=function(){return l.fn.affix=t,this},l(window).on("load",function(){l('[data-spy="affix"]').each(function(){var t=l(this),e=t.data();e.offset=e.offset||{},null!=e.offsetBottom&&(e.offset.bottom=e.offsetBottom),null!=e.offsetTop&&(e.offset.top=e.offsetTop),i.call(t,e)})})}(window.$jqTheme || window.jQuery);
diff --git a/_static/bootstrap-3.4.1/js/npm.js b/_static/bootstrap-3.4.1/js/npm.js
new file mode 100644
index 000000000..bf6aa8060
--- /dev/null
+++ b/_static/bootstrap-3.4.1/js/npm.js
@@ -0,0 +1,13 @@
+// This file is autogenerated via the `commonjs` Grunt task. You can require() this file in a CommonJS environment.
+require('../../js/transition.js')
+require('../../js/alert.js')
+require('../../js/button.js')
+require('../../js/carousel.js')
+require('../../js/collapse.js')
+require('../../js/dropdown.js')
+require('../../js/modal.js')
+require('../../js/tooltip.js')
+require('../../js/popover.js')
+require('../../js/scrollspy.js')
+require('../../js/tab.js')
+require('../../js/affix.js')
\ No newline at end of file
diff --git a/_static/bootstrap-sphinx.css b/_static/bootstrap-sphinx.css
new file mode 100644
index 000000000..679647869
--- /dev/null
+++ b/_static/bootstrap-sphinx.css
@@ -0,0 +1,223 @@
+/*
+ * bootstrap-sphinx.css
+ * ~~~~~~~~~~~~~~~~~~~~
+ *
+ * Sphinx stylesheet -- Bootstrap theme.
+ */
+
+/*
+ * Imports to aggregate everything together.
+ */
+
+@import url("./basic.css");
+
+
+  
+
+
+
+  
+  
+    @import url("./bootswatch-3.4.1/yeti/bootstrap.min.css");
+  
+
+
+/*
+ * Styles
+ */
+
+.navbar-inverse .brand {
+  color: #FFF;
+}
+
+/*
+ * Reset navbar styles from overrides in:
+ * https://bitbucket.org/birkenfeld/sphinx/commits/78d8ebf76b630ab4073a7328af9d91e8123b8d96
+ */
+.navbar .container {
+  padding-top: 0;
+}
+
+/*
+ * Reset the logo image dimensions. Sites like RTD can override with bad
+ * results on mobile (mega-huge logo...)
+ *
+ * https://github.com/ryan-roemer/sphinx-bootstrap-theme/issues/142
+ */
+.navbar-brand img {
+  width: auto;
+  height: 100%;
+}
+
+.page-top {
+  top: 0px;
+}
+
+
+
+
+
+  body {
+    
+      padding-top: 60px;
+    
+  }
+  .page-top {
+    
+      top: 60px;
+    
+  }
+
+
+
+.navbar-inner {
+  padding-left:  12px !important;
+  padding-right: 12px !important;
+}
+
+
+table {
+  border: 0;
+}
+
+.highlighttable .code pre {
+    font-size: 12px;
+}
+
+.highlighttable .linenos pre {
+    word-break: normal;
+    font-size: 12px;
+}
+
+div.highlight {
+  background: none;
+}
+
+a.headerlink {
+  margin-left: 0.25em;
+}
+
+a.footnote-reference {
+  vertical-align: super;
+  font-size: 75%;
+}
+
+table.footnote td.label {
+  color: inherit;
+  font-size: 100%;
+  display: block;
+  line-height: normal;
+  background: inherit;
+}
+
+table.footnote {
+  width: auto;
+  margin-bottom: 0px;
+}
+
+table.field-list {
+  width: auto;
+}
+
+.footer {
+  width: 100%;
+  border-top: 1px solid #ccc;
+  padding-top: 10px;
+}
+
+.bs-sidenav form, .bs-sidenav #sourcelink {
+  padding: 5px 20px;
+}
+
+
+
+/* The code below is based on the bootstrap website sidebar */
+
+.bs-sidenav.affix {
+  position: static;
+}
+
+/* First level of nav */
+.bs-sidenav {
+  margin-top: 30px;
+  margin-bottom: 30px;
+  padding-top:    10px;
+  padding-bottom: 10px;
+  text-shadow: 0 1px 0 #fff;
+  background-color: #f7f5fa;
+  border-radius: 5px;
+}
+
+/* All levels of nav */
+.bs-sidenav .nav > li > a {
+  display: block;
+  color: #716b7a;
+  padding: 5px 20px;
+}
+.bs-sidenav .nav > li > a:hover,
+.bs-sidenav .nav > li > a:focus {
+  text-decoration: none;
+  background-color: #e5e3e9;
+  border-right: 1px solid #dbd8e0;
+}
+.bs-sidenav .nav > .active > a,
+.bs-sidenav .nav > .active:hover > a,
+.bs-sidenav .nav > .active:focus > a {
+  font-weight: bold;
+  color: #563d7c;
+  background-color: transparent;
+  border-right: 1px solid #563d7c;
+}
+
+.bs-sidenav .nav .nav > li > a {
+  padding-top:    3px;
+  padding-bottom: 3px;
+  padding-left: 30px;
+  font-size: 90%;
+}
+
+.bs-sidenav .nav .nav .nav > li > a {
+  padding-top:    3px;
+  padding-bottom: 3px;
+  padding-left: 40px;
+  font-size: 90%;
+}
+
+.bs-sidenav .nav .nav .nav .nav > li > a {
+  padding-top:    3px;
+  padding-bottom: 3px;
+  padding-left: 50px;
+  font-size: 90%;
+}
+
+/* Show and affix the side nav when space allows it */
+@media screen and (min-width: 992px) {
+  .bs-sidenav .nav > .active > ul {
+    display: block;
+  }
+  /* Widen the fixed sidenav */
+  .bs-sidenav.affix,
+  .bs-sidenav.affix-bottom {
+    width: 213px;
+  }
+  .bs-sidenav.affix {
+    position: fixed; /* Undo the static from mobile first approach */
+  }
+  .bs-sidenav.affix-bottom {
+    position: absolute; /* Undo the static from mobile first approach */
+  }
+  .bs-sidenav.affix-bottom .bs-sidenav,
+  .bs-sidenav.affix .bs-sidenav {
+    margin-top: 0;
+    margin-bottom: 0;
+  }
+}
+@media screen and (min-width: 1200px) {
+  /* Widen the fixed sidenav again */
+  .bs-sidenav.affix-bottom,
+  .bs-sidenav.affix {
+    width: 263px;
+  }
+}
+
+
diff --git a/_static/bootstrap-sphinx.js b/_static/bootstrap-sphinx.js
new file mode 100644
index 000000000..17e81981d
--- /dev/null
+++ b/_static/bootstrap-sphinx.js
@@ -0,0 +1,175 @@
+(function ($) {
+  /**
+   * Patch TOC list.
+   *
+   * Will mutate the underlying span to have a correct ul for nav.
+   *
+   * @param $span: Span containing nested UL"s to mutate.
+   * @param minLevel: Starting level for nested lists. (1: global, 2: local).
+   */
+  var patchToc = function ($ul, minLevel) {
+    var findA,
+      patchTables,
+      $localLi;
+
+    // Find all a "internal" tags, traversing recursively.
+    findA = function ($elem, level) {
+      level = level || 0;
+      var $items = $elem.find("> li > a.internal, > ul, > li > ul");
+
+      // Iterate everything in order.
+      $items.each(function (index, item) {
+        var $item = $(item),
+          tag = item.tagName.toLowerCase(),
+          $childrenLi = $item.children("li"),
+          $parentLi = $($item.parent("li"), $item.parent().parent("li"));
+
+        // Add dropdowns if more children and above minimum level.
+        if (tag === "ul" && level >= minLevel && $childrenLi.length > 0) {
+          $parentLi
+            .addClass("dropdown-submenu")
+            .children("a").first().attr("tabindex", -1);
+
+          $item.addClass("dropdown-menu");
+        }
+
+        findA($item, level + 1);
+      });
+    };
+
+    findA($ul);
+  };
+
+  /**
+   * Patch all tables to remove ``docutils`` class and add Bootstrap base
+   * ``table`` class.
+   */
+  patchTables = function () {
+    $("table.docutils")
+      .removeClass("docutils")
+      .addClass("table")
+      .attr("border", 0);
+  };
+
+  $(window).load(function () {
+    /*
+     * Scroll the window to avoid the topnav bar
+     * https://github.com/twbs/bootstrap/issues/1768
+     */
+    if ($("#navbar.navbar-fixed-top").length > 0) {
+      var navHeight = $("#navbar").height(),
+        shiftWindow = function() { scrollBy(0, -navHeight - 10); };
+
+      if (location.hash) {
+        setTimeout(shiftWindow, 1);
+      }
+
+      window.addEventListener("hashchange", shiftWindow);
+    }
+  });
+
+  $(document).ready(function () {
+    // Add styling, structure to TOC"s.
+    $(".dropdown-menu").each(function () {
+      $(this).find("ul").each(function (index, item){
+        var $item = $(item);
+        $item.addClass("unstyled");
+      });
+    });
+
+    // Global TOC.
+    if ($("ul.globaltoc li").length) {
+      patchToc($("ul.globaltoc"), 1);
+    } else {
+      // Remove Global TOC.
+      $(".globaltoc-container").remove();
+    }
+
+    // Local TOC.
+    $(".bs-sidenav ul").addClass("nav nav-list");
+    $(".bs-sidenav > ul > li > a").addClass("nav-header");
+
+    
+    // back to top
+    setTimeout(function () {
+      var $sideBar = $(".bs-sidenav");
+      var $content = $(".content");
+
+      // Enlarge content if sidebar is larger.
+      if ($sideBar.outerHeight(true) > $content.outerHeight(true)) {
+        $content.css("min-height", $sideBar.outerHeight(true));
+      }
+
+      $sideBar
+        // Add affix.
+        .affix({
+          offset: {
+            top: function () {
+              var offsetTop      = $sideBar.offset().top;
+              var sideBarMargin  = parseInt($sideBar.css("margin-top"), 10);
+              var navOuterHeight = $("#navbar").outerHeight(true);
+
+              return (this.top = offsetTop - navOuterHeight);
+            },
+            bottom: function () {
+              return (this.bottom = $(".footer").outerHeight(true));
+            }
+          }
+        })
+        // Trigger to reset if page content is scrolled to bottom.
+        .trigger("scroll.bs.affix.data-api");
+    }, 0);
+    
+
+    // Local TOC.
+    patchToc($("ul.localtoc"), 2);
+
+    // Mutate sub-lists (for bs-2.3.0).
+    $(".dropdown-menu ul").not(".dropdown-menu").each(function () {
+      var $ul = $(this),
+        $parent = $ul.parent(),
+        tag = $parent[0].tagName.toLowerCase(),
+        $kids = $ul.children().detach();
+
+      // Replace list with items if submenu header.
+      if (tag === "ul") {
+        $ul.replaceWith($kids);
+      } else if (tag === "li") {
+        // Insert into previous list.
+        $parent.after($kids);
+        $ul.remove();
+      }
+    });
+
+    // Add divider in page TOC.
+    $localLi = $("ul.localtoc li");
+    if ($localLi.length > 2) {
+      $localLi.first().after("<li class=\"divider\"></li>");
+    }
+
+    // Patch tables.
+    patchTables();
+
+    // Add Note, Warning styles. (BS v2,3 compatible).
+    $(".admonition").addClass("alert alert-info")
+      .filter(".warning, .caution")
+        .removeClass("alert-info")
+        .addClass("alert-warning").end()
+      .filter(".error, .danger")
+        .removeClass("alert-info")
+        .addClass("alert-danger alert-error").end();
+
+    // Inline code styles to Bootstrap style.
+    $("tt.docutils.literal").not(".xref").each(function (i, e) {
+      // ignore references
+      if (!$(e).parent().hasClass("reference")) {
+        $(e).replaceWith(function () {
+          return $("<code />").html($(this).html());
+        });
+      }});
+
+    // Update sourcelink to remove outerdiv (fixes appearance in navbar).
+    var $srcLink = $(".nav #sourcelink");
+    $srcLink.parent().html($srcLink.html());
+  });
+}(window.$jqTheme || window.jQuery));
\ No newline at end of file
diff --git a/_static/bootswatch-2.3.2/amelia/bootstrap.min.css b/_static/bootswatch-2.3.2/amelia/bootstrap.min.css
new file mode 100644
index 000000000..b8cea3283
--- /dev/null
+++ b/_static/bootswatch-2.3.2/amelia/bootstrap.min.css
@@ -0,0 +1,9 @@
+@import url('//fonts.googleapis.com/css?family=Lobster|Cabin:400,700');/*!
+ * Bootstrap v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
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new file mode 100644
index 000000000..9740c15e5
--- /dev/null
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diff --git a/_static/bootswatch-2.3.2/cosmo/bootstrap.min.css b/_static/bootswatch-2.3.2/cosmo/bootstrap.min.css
new file mode 100644
index 000000000..cae7e4f8d
--- /dev/null
+++ b/_static/bootswatch-2.3.2/cosmo/bootstrap.min.css
@@ -0,0 +1,9 @@
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+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
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diff --git a/_static/bootswatch-2.3.2/cyborg/bootstrap.min.css b/_static/bootswatch-2.3.2/cyborg/bootstrap.min.css
new file mode 100644
index 000000000..894f3aacd
--- /dev/null
+++ b/_static/bootswatch-2.3.2/cyborg/bootstrap.min.css
@@ -0,0 +1,9 @@
+@import url('//fonts.googleapis.com/css?family=Droid+Sans:400,700');/*!
+ * Bootstrap v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
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diff --git a/_static/bootswatch-2.3.2/journal/bootstrap.min.css b/_static/bootswatch-2.3.2/journal/bootstrap.min.css
new file mode 100644
index 000000000..f310a5592
--- /dev/null
+++ b/_static/bootswatch-2.3.2/journal/bootstrap.min.css
@@ -0,0 +1,9 @@
+@import url('//fonts.googleapis.com/css?family=News+Cycle:400,700');/*!
+ * Bootstrap v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Designed and built with all the love in the world @twitter by @mdo and @fat.
+ */.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{width:auto\9;height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img,.google-maps 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diff --git a/_static/bootswatch-2.3.2/readable/bootstrap.min.css b/_static/bootswatch-2.3.2/readable/bootstrap.min.css
new file mode 100644
index 000000000..0efc48fb6
--- /dev/null
+++ b/_static/bootswatch-2.3.2/readable/bootstrap.min.css
@@ -0,0 +1,9 @@
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+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
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diff --git a/_static/bootswatch-2.3.2/simplex/bootstrap.min.css b/_static/bootswatch-2.3.2/simplex/bootstrap.min.css
new file mode 100644
index 000000000..a3e828f3c
--- /dev/null
+++ b/_static/bootswatch-2.3.2/simplex/bootstrap.min.css
@@ -0,0 +1,9 @@
+@import url('//fonts.googleapis.com/css?family=Josefin+Sans:300,400,700');/*!
+ * Bootstrap v2.3.2
+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
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diff --git a/_static/bootswatch-2.3.2/slate/bootstrap.min.css b/_static/bootswatch-2.3.2/slate/bootstrap.min.css
new file mode 100644
index 000000000..40746a2af
--- /dev/null
+++ b/_static/bootswatch-2.3.2/slate/bootstrap.min.css
@@ -0,0 +1,9 @@
+/*!
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+ *
+ * Copyright 2012 Twitter, Inc
+ * Licensed under the Apache License v2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
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new file mode 100644
index 000000000..7b7b4bb4a
--- /dev/null
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new file mode 100644
index 000000000..6d5279cb6
--- /dev/null
+++ b/_static/bootswatch-2.3.2/spruce/bootstrap.min.css
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new file mode 100644
index 000000000..29cb6b617
--- /dev/null
+++ b/_static/bootswatch-2.3.2/united/bootstrap.min.css
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diff --git a/_static/bootswatch-3.4.1/cerulean/bootstrap.min.css b/_static/bootswatch-3.4.1/cerulean/bootstrap.min.css
new file mode 100644
index 000000000..f4f211c5a
--- /dev/null
+++ b/_static/bootswatch-3.4.1/cerulean/bootstrap.min.css
@@ -0,0 +1,11 @@
+/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/cosmo/bootstrap.min.css b/_static/bootswatch-3.4.1/cosmo/bootstrap.min.css
new file mode 100644
index 000000000..86e727438
--- /dev/null
+++ b/_static/bootswatch-3.4.1/cosmo/bootstrap.min.css
@@ -0,0 +1,11 @@
+@import url("https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,400,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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index 000000000..4add2a402
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+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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+ * Licensed under MIT
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+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
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diff --git a/_static/bootswatch-3.4.1/flatly/bootstrap.min.css b/_static/bootswatch-3.4.1/flatly/bootstrap.min.css
new file mode 100644
index 000000000..a209d4e7c
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+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/journal/bootstrap.min.css b/_static/bootswatch-3.4.1/journal/bootstrap.min.css
new file mode 100644
index 000000000..8fec3e8fc
--- /dev/null
+++ b/_static/bootswatch-3.4.1/journal/bootstrap.min.css
@@ -0,0 +1,11 @@
+@import url("https://fonts.googleapis.com/css?family=News+Cycle:400,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
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diff --git a/_static/bootswatch-3.4.1/paper/bootstrap.min.css b/_static/bootswatch-3.4.1/paper/bootstrap.min.css
new file mode 100644
index 000000000..95f0b6c3e
--- /dev/null
+++ b/_static/bootswatch-3.4.1/paper/bootstrap.min.css
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+@import url("https://fonts.googleapis.com/css?family=Roboto:300,400,500,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/readable/bootstrap.min.css b/_static/bootswatch-3.4.1/readable/bootstrap.min.css
new file mode 100644
index 000000000..904414367
--- /dev/null
+++ b/_static/bootswatch-3.4.1/readable/bootstrap.min.css
@@ -0,0 +1,11 @@
+@import url("https://fonts.googleapis.com/css?family=Raleway:400,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/sandstone/bootstrap.min.css b/_static/bootswatch-3.4.1/sandstone/bootstrap.min.css
new file mode 100644
index 000000000..982451092
--- /dev/null
+++ b/_static/bootswatch-3.4.1/sandstone/bootstrap.min.css
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+@import url("https://fonts.googleapis.com/css?family=Roboto:400,500,700");/*!
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+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/simplex/bootstrap.min.css b/_static/bootswatch-3.4.1/simplex/bootstrap.min.css
new file mode 100644
index 000000000..18f4280bf
--- /dev/null
+++ b/_static/bootswatch-3.4.1/simplex/bootstrap.min.css
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+@import url("https://fonts.googleapis.com/css?family=Open+Sans:400,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/slate/bootstrap.min.css b/_static/bootswatch-3.4.1/slate/bootstrap.min.css
new file mode 100644
index 000000000..b058da5a6
--- /dev/null
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@@ -0,0 +1,11 @@
+/*!
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+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/spacelab/bootstrap.min.css b/_static/bootswatch-3.4.1/spacelab/bootstrap.min.css
new file mode 100644
index 000000000..8fb6103f1
--- /dev/null
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+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/superhero/bootstrap.min.css b/_static/bootswatch-3.4.1/superhero/bootstrap.min.css
new file mode 100644
index 000000000..786679b5e
--- /dev/null
+++ b/_static/bootswatch-3.4.1/superhero/bootstrap.min.css
@@ -0,0 +1,11 @@
+@import url("https://fonts.googleapis.com/css?family=Lato:300,400,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/united/bootstrap.min.css b/_static/bootswatch-3.4.1/united/bootstrap.min.css
new file mode 100644
index 000000000..5298a503f
--- /dev/null
+++ b/_static/bootswatch-3.4.1/united/bootstrap.min.css
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+@import url("https://fonts.googleapis.com/css?family=Ubuntu:400,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
+ * Copyright 2011-2019 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/_static/bootswatch-3.4.1/yeti/bootstrap.min.css b/_static/bootswatch-3.4.1/yeti/bootstrap.min.css
new file mode 100644
index 000000000..8031a1772
--- /dev/null
+++ b/_static/bootswatch-3.4.1/yeti/bootstrap.min.css
@@ -0,0 +1,11 @@
+@import url("https://fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,700italic,400,300,700");/*!
+ * bootswatch v3.4.1
+ * Homepage: http://bootswatch.com
+ * Copyright 2012-2019 Thomas Park
+ * Licensed under MIT
+ * Based on Bootstrap
+*//*!
+ * Bootstrap v3.4.1 (https://getbootstrap.com/)
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diff --git a/_static/doctools.js b/_static/doctools.js
index daccd209d..d06a71d75 100644
--- a/_static/doctools.js
+++ b/_static/doctools.js
@@ -2,314 +2,155 @@
  * doctools.js
  * ~~~~~~~~~~~
  *
- * Sphinx JavaScript utilities for all documentation.
+ * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
-  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
-    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
-    "profile", "profileEnd"];
-  window.console = {};
-  for (var i = 0; i < names.length; ++i)
-    window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- */
-jQuery.urldecode = function(x) {
-  return decodeURIComponent(x).replace(/\+/g, ' ');
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-  if (typeof s === 'undefined')
-    s = document.location.search;
-  var parts = s.substr(s.indexOf('?') + 1).split('&');
-  var result = {};
-  for (var i = 0; i < parts.length; i++) {
-    var tmp = parts[i].split('=', 2);
-    var key = jQuery.urldecode(tmp[0]);
-    var value = jQuery.urldecode(tmp[1]);
-    if (key in result)
-      result[key].push(value);
-    else
-      result[key] = [value];
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
   }
-  return result;
 };
 
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-  function highlight(node, addItems) {
-    if (node.nodeType === 3) {
-      var val = node.nodeValue;
-      var pos = val.toLowerCase().indexOf(text);
-      if (pos >= 0 &&
-          !jQuery(node.parentNode).hasClass(className) &&
-          !jQuery(node.parentNode).hasClass("nohighlight")) {
-        var span;
-        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-        if (isInSVG) {
-          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-        } else {
-          span = document.createElement("span");
-          span.className = className;
-        }
-        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling));
-        node.nodeValue = val.substr(0, pos);
-        if (isInSVG) {
-          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-          var bbox = node.parentElement.getBBox();
-          rect.x.baseVal.value = bbox.x;
-          rect.y.baseVal.value = bbox.y;
-          rect.width.baseVal.value = bbox.width;
-          rect.height.baseVal.value = bbox.height;
-          rect.setAttribute('class', className);
-          addItems.push({
-              "parent": node.parentNode,
-              "target": rect});
-        }
-      }
-    }
-    else if (!jQuery(node).is("button, select, textarea")) {
-      jQuery.each(node.childNodes, function() {
-        highlight(this, addItems);
-      });
-    }
-  }
-  var addItems = [];
-  var result = this.each(function() {
-    highlight(this, addItems);
-  });
-  for (var i = 0; i < addItems.length; ++i) {
-    jQuery(addItems[i].parent).before(addItems[i].target);
-  }
-  return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-  jQuery.uaMatch = function(ua) {
-    ua = ua.toLowerCase();
-
-    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-      /(msie) ([\w.]+)/.exec(ua) ||
-      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-      [];
-
-    return {
-      browser: match[ 1 ] || "",
-      version: match[ 2 ] || "0"
-    };
-  };
-  jQuery.browser = {};
-  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
 /**
  * Small JavaScript module for the documentation.
  */
-var Documentation = {
-
-  init : function() {
-    this.fixFirefoxAnchorBug();
-    this.highlightSearchWords();
-    this.initIndexTable();
-    if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
-      this.initOnKeyListeners();
-    }
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
   },
 
   /**
    * i18n support
    */
-  TRANSLATIONS : {},
-  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
-  LOCALE : 'unknown',
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
 
   // gettext and ngettext don't access this so that the functions
   // can safely bound to a different name (_ = Documentation.gettext)
-  gettext : function(string) {
-    var translated = Documentation.TRANSLATIONS[string];
-    if (typeof translated === 'undefined')
-      return string;
-    return (typeof translated === 'string') ? translated : translated[0];
-  },
-
-  ngettext : function(singular, plural, n) {
-    var translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated === 'undefined')
-      return (n == 1) ? singular : plural;
-    return translated[Documentation.PLURALEXPR(n)];
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
   },
 
-  addTranslations : function(catalog) {
-    for (var key in catalog.messages)
-      this.TRANSLATIONS[key] = catalog.messages[key];
-    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
-    this.LOCALE = catalog.locale;
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
   },
 
-  /**
-   * add context elements like header anchor links
-   */
-  addContextElements : function() {
-    $('div[id] > :header:first').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this headline')).
-      appendTo(this);
-    });
-    $('dt[id]').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this definition')).
-      appendTo(this);
-    });
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
   },
 
   /**
-   * workaround a firefox stupidity
-   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   * helper function to focus on search bar
    */
-  fixFirefoxAnchorBug : function() {
-    if (document.location.hash && $.browser.mozilla)
-      window.setTimeout(function() {
-        document.location.href += '';
-      }, 10);
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
   },
 
   /**
-   * highlight the search words provided in the url in the text
+   * Initialise the domain index toggle buttons
    */
-  highlightSearchWords : function() {
-    var params = $.getQueryParameters();
-    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
-    if (terms.length) {
-      var body = $('div.body');
-      if (!body.length) {
-        body = $('body');
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
       }
-      window.setTimeout(function() {
-        $.each(terms, function() {
-          body.highlightText(this.toLowerCase(), 'highlighted');
-        });
-      }, 10);
-      $('<p class="highlight-link"><a href="javascript:Documentation.' +
-        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
-          .appendTo($('#searchbox'));
-    }
-  },
-
-  /**
-   * init the domain index toggle buttons
-   */
-  initIndexTable : function() {
-    var togglers = $('img.toggler').click(function() {
-      var src = $(this).attr('src');
-      var idnum = $(this).attr('id').substr(7);
-      $('tr.cg-' + idnum).toggle();
-      if (src.substr(-9) === 'minus.png')
-        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
-      else
-        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
-    }).css('display', '');
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
-        togglers.click();
-    }
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords : function() {
-    $('#searchbox .highlight-link').fadeOut(300);
-    $('span.highlighted').removeClass('highlighted');
-  },
-
-  /**
-   * make the url absolute
-   */
-  makeURL : function(relativeURL) {
-    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
-  },
+    };
 
-  /**
-   * get the current relative url
-   */
-  getCurrentURL : function() {
-    var path = document.location.pathname;
-    var parts = path.split(/\//);
-    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
-      if (this === '..')
-        parts.pop();
-    });
-    var url = parts.join('/');
-    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
   },
 
-  initOnKeyListeners: function() {
-    $(document).keydown(function(event) {
-      var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box or textarea
-      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
             }
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
             }
+            break;
         }
       }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
     });
-  }
+  },
 };
 
 // quick alias for translations
-_ = Documentation.gettext;
+const _ = Documentation.gettext;
 
-$(document).ready(function() {
-  Documentation.init();
-});
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 4790c4d3c..d39e25a30 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,11 +1,13 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '',
-    LANGUAGE: 'None',
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '4.9.0',
+    LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
     FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '.txt',
-    NAVIGATION_WITH_KEYS: false
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };
\ No newline at end of file
diff --git a/_static/images/neighboorsetLIMA_US.png b/_static/images/neighboorsetLIMA_US.png
new file mode 100644
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diff --git a/_static/js/jquery-fix.js b/_static/js/jquery-fix.js
new file mode 100644
index 000000000..a30ca53dc
--- /dev/null
+++ b/_static/js/jquery-fix.js
@@ -0,0 +1,2 @@
+// No Conflict in later (our) version of jQuery
+window.$jqTheme = jQuery.noConflict(true);
\ No newline at end of file
diff --git a/_static/language_data.js b/_static/language_data.js
index d2b4ee91b..250f5665f 100644
--- a/_static/language_data.js
+++ b/_static/language_data.js
@@ -5,15 +5,16 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
 
-var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version JS is _stemmer.js if file is provided */ 
+/* Non-minified version is copied as a separate JS file, is available */
+
 /**
  * Porter Stemmer
  */
@@ -196,102 +197,3 @@ var Stemmer = function() {
   }
 }
 
-
-
-
-
-var splitChars = (function() {
-    var result = {};
-    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
-         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
-         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
-         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
-         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
-         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
-         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
-         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
-         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
-         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
-    var i, j, start, end;
-    for (i = 0; i < singles.length; i++) {
-        result[singles[i]] = true;
-    }
-    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
-         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
-         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
-         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
-         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
-         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
-         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
-         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
-         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
-         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
-         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
-         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
-         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
-         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
-         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
-         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
-         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
-         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
-         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
-         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
-         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
-         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
-         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
-         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
-         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
-         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
-         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
-         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
-         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
-         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
-         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
-         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
-         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
-         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
-         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
-         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
-         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
-         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
-         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
-         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
-         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
-         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
-         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
-         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
-         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
-         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
-         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
-         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
-         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
-    for (i = 0; i < ranges.length; i++) {
-        start = ranges[i][0];
-        end = ranges[i][1];
-        for (j = start; j <= end; j++) {
-            result[j] = true;
-        }
-    }
-    return result;
-})();
-
-function splitQuery(query) {
-    var result = [];
-    var start = -1;
-    for (var i = 0; i < query.length; i++) {
-        if (splitChars[query.charCodeAt(i)]) {
-            if (start !== -1) {
-                result.push(query.slice(start, i));
-                start = -1;
-            }
-        } else if (start === -1) {
-            start = i;
-        }
-    }
-    if (start !== -1) {
-        result.push(query.slice(start));
-    }
-    return result;
-}
-
-
diff --git a/_static/nbsphinx-broken-thumbnail.svg b/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 000000000..4919ca882
--- /dev/null
+++ b/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
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diff --git a/_static/nbsphinx-code-cells.css b/_static/nbsphinx-code-cells.css
new file mode 100644
index 000000000..a3fb27c30
--- /dev/null
+++ b/_static/nbsphinx-code-cells.css
@@ -0,0 +1,259 @@
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/_static/nbsphinx-gallery.css b/_static/nbsphinx-gallery.css
new file mode 100644
index 000000000..365c27a96
--- /dev/null
+++ b/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/_static/nbsphinx-no-thumbnail.svg b/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 000000000..9dca7588f
--- /dev/null
+++ b/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: #ccc; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: #999; }
+    }
+  </style>
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+</svg>
diff --git a/_static/plot_directive.css b/_static/plot_directive.css
new file mode 100644
index 000000000..d45593c93
--- /dev/null
+++ b/_static/plot_directive.css
@@ -0,0 +1,16 @@
+/*
+ * plot_directive.css
+ * ~~~~~~~~~~~~
+ *
+ * Stylesheet controlling images created using the `plot` directive within
+ * Sphinx.
+ *
+ * :copyright: Copyright 2020-* by the Matplotlib development team.
+ * :license: Matplotlib, see LICENSE for details.
+ *
+ */
+
+img.plot-directive {
+    border: 0;
+    max-width: 100%;
+}
diff --git a/_static/pygments.css b/_static/pygments.css
index dd6621d88..0d49244ed 100644
--- a/_static/pygments.css
+++ b/_static/pygments.css
@@ -1,77 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #8f5902; font-style: italic } /* Comment */
-.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */
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+        flex-direction: column;
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+    .row.equal-height.row:after,
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+    }
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+        flex-direction: column;
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+    .row.equal-height > [class*='col-'] > .thumbnail > .caption > .flex-text {
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+    .row.equal-height > [class*='col-'] > .thumbnail > img {
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diff --git a/_static/references.bib b/_static/references.bib
new file mode 100644
index 000000000..1dc4917ef
--- /dev/null
+++ b/_static/references.bib
@@ -0,0 +1,39 @@
+@article{Anselin1996b,
+author = {Anselin, Luc and Smirnov, Oleg},
+title = {Efficient algorithms for constructing proper higher order spatial lag operators},
+journal = {Journal of Regional Science},
+
+volume = {36},
+number = {1},
+pages = {67-89},
+doi = {10.1111/j.1467-9787.1996.tb01101.x},
+eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x},
+abstract = {ABSTRACT. This paper extends the work of Blommestein and Koper (1992)–BK–on the construction of higher-order spatial lag operators without redundant and circular paths. For the case most relevant in spatial econometrics and spatial statistics, i.e., when contiguity between two observations (locations) is defined in a simple binary fashion, some deficiencies of the BK algorithms are outlined, corrected and an improvement suggested. In addition, three new algorithms are introduced and compared in terms of performance for a number of empirical contiguity structures. Particular attention is paid to a graph theoretic perspective on spatial lag operators and to the most efficient data structures for the storage and manipulation of spatial lags. The new forward iterative algorithm which uses a list form rather than a matrix to store the spatial lag information is shown to be several orders of magnitude faster than the BK solution. This allows the computation of proper higher-order spatial lags “on the fly” for even moderately large data sets such as 3,111 contiguous U. S. counties, which is not practical with the other algorithms.},
+year = {1996}
+}
+
+
+
+
+@Article{pysal2007,
+  author={Rey, Sergio J. and Anselin, Luc},
+  title={{PySAL: A Python Library of Spatial Analytical Methods}},
+  journal={The Review of Regional Studies},
+  year=2007,
+  volume={37},
+  number={1},
+  pages={5-27},
+  keywords={Open Source; Software; Spatial}
+}
+
+
+@Article{Watts1998,
+  author={Watts, D.J. and S.H. Strogatz},
+  year={1998},
+  title={Collective dynamics of 'small-world' networks},
+  journal={Nature},
+  volume={393},
+  pages={440-442},
+  keywords={networks}
+}
+
diff --git a/_static/searchtools.js b/_static/searchtools.js
index d11b33a78..7918c3fab 100644
--- a/_static/searchtools.js
+++ b/_static/searchtools.js
@@ -4,22 +4,24 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
+"use strict";
 
-if (!Scorer) {
-  /**
-   * Simple result scoring code.
-   */
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
   var Scorer = {
     // Implement the following function to further tweak the score for each result
-    // The function takes a result array [filename, title, anchor, descr, score]
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
     // and returns the new score.
     /*
-    score: function(result) {
-      return result[4];
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
     },
     */
 
@@ -28,9 +30,11 @@ if (!Scorer) {
     // or matches in the last dotted part of the object name
     objPartialMatch: 6,
     // Additive scores depending on the priority of the object
-    objPrio: {0:  15,   // used to be importantResults
-              1:  5,   // used to be objectResults
-              2: -5},  // used to be unimportantResults
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
     //  Used when the priority is not in the mapping.
     objPrioDefault: 0,
 
@@ -39,442 +43,503 @@ if (!Scorer) {
     partialTitle: 7,
     // query found in terms
     term: 5,
-    partialTerm: 2
+    partialTerm: 2,
   };
 }
 
-if (!splitQuery) {
-  function splitQuery(query) {
-    return query.split(/\s+/);
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
   }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
 }
 
 /**
  * Search Module
  */
-var Search = {
-
-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  htmlToText : function(htmlString) {
-      var htmlElement = document.createElement('span');
-      htmlElement.innerHTML = htmlString;
-      $(htmlElement).find('.headerlink').remove();
-      docContent = $(htmlElement).find('[role=main]')[0];
-      if(docContent === undefined) {
-          console.warn("Content block not found. Sphinx search tries to obtain it " +
-                       "via '[role=main]'. Could you check your theme or template.");
-          return "";
-      }
-      return docContent.textContent || docContent.innerText;
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
   },
 
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
   },
 
-  loadIndex : function(url) {
-    $.ajax({type: "GET", url: url, data: null,
-            dataType: "script", cache: true,
-            complete: function(jqxhr, textstatus) {
-              if (textstatus != "success") {
-                document.getElementById("searchindexloader").src = url;
-              }
-            }});
-  },
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
 
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
     }
   },
 
-  hasIndex : function() {
-      return this._index !== null;
-  },
+  hasIndex: () => Search._index !== null,
 
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
+  deferQuery: (query) => (Search._queued_query = query),
 
-  stopPulse : function() {
-      this._pulse_status = 0;
-  },
+  stopPulse: () => (Search._pulse_status = -1),
 
-  startPulse : function() {
-    if (this._pulse_status >= 0)
-        return;
-    function pulse() {
-      var i;
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
       Search._pulse_status = (Search._pulse_status + 1) % 4;
-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
     pulse();
   },
 
   /**
    * perform a search for something (or wait until index is loaded)
    */
-  performSearch : function(query) {
+  performSearch: (query) => {
     // create the required interface elements
-    this.out = $('#search-results');
-    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-    this.dots = $('<span></span>').appendTo(this.title);
-    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
-    this.output = $('<ul class="search"/>').appendTo(this.out);
-
-    $('#search-progress').text(_('Preparing search...'));
-    this.startPulse();
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
 
     // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
   },
 
   /**
    * execute search (requires search index to be loaded)
    */
-  query : function(query) {
-    var i;
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = splitQuery(query);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
 
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
-          tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
       // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
-      var toAppend;
+      let word = stemmer.stemWord(queryTermLower);
       // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
       else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
       }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
-    }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+    });
 
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
 
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
 
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
 
     // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
-    }
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
 
     // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
-    }
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
 
     // now sort the results by score (in opposite order of appearance, since the
     // display function below uses pop() to retrieve items) and then
     // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
         // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
       }
+      return leftScore > rightScore ? 1 : -1;
     });
 
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
     // for debugging
     //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
+    // console.info("search results:", Search.lastresults);
 
     // print the results
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        var listItem = $('<li style="display:none"></li>');
-        var requestUrl = "";
-        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
-
-        } else {
-          // normal html builders
-          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
-        }
-        listItem.append($('<a/>').attr('href',
-            requestUrl +
-            highlightstring + item[2]).html(item[1]));
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          $.ajax({url: requestUrl,
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
-                    }
-                    Search.output.append(listItem);
-                    listItem.slideDown(5, function() {
-                      displayNextItem();
-                    });
-                  }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
    * search for object names
    */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var docnames = this._index.docnames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var name in objects[prefix]) {
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        var fullnameLower = fullname.toLowerCase()
-        if (fullnameLower.indexOf(object) > -1) {
-          var score = 0;
-          var parts = fullnameLower.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullnameLower == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var match = objects[prefix][name];
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
-        }
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
       }
-    }
 
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
     return results;
   },
 
   /**
    * search for full-text terms in the index
    */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var docnames = this._index.docnames;
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
 
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
+    const scoreMap = new Map();
+    const fileMap = new Map();
 
     // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
       ];
       // add support for partial matches
       if (word.length > 2) {
-        for (var w in terms) {
-          if (w.match(word) && !terms[word]) {
-            _o.push({files: terms[w], score: Scorer.partialTerm})
-          }
-        }
-        for (var w in titleterms) {
-          if (w.match(word) && !titleterms[word]) {
-              _o.push({files: titleterms[w], score: Scorer.partialTitle})
-          }
-        }
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
       }
 
       // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
+      if (arr.every((record) => record.files === undefined)) return;
+
       // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {};
-          scoreMap[file][word] = o.score;
-        }
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
       });
 
       // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap && fileMap[file].indexOf(word) === -1)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
 
     // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
+    const results = [];
+    for (const [file, wordList] of fileMap) {
       // check if all requirements are matched
-      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
-        searchterms.filter(function(term){return term.length > 2}).length
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
       if (
-        fileMap[file].length != searchterms.length &&
-        fileMap[file].length != filteredTermCount
-      ) continue;
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
 
       // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-            titleterms[excluded[i]] == file ||
-            $u.contains(terms[excluded[i]] || [], file) ||
-            $u.contains(titleterms[excluded[i]] || [], file)) {
-          valid = false;
-          break;
-        }
-      }
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
 
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
-      }
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
     }
     return results;
   },
@@ -482,31 +547,28 @@ var Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
+   * of stemmed words.
    */
-  makeSearchSummary : function(htmlText, keywords, hlwords) {
-    var text = Search.htmlToText(htmlText);
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<div class="context"></div>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
 };
 
-$(document).ready(function() {
-  Search.init();
-});
+_ready(Search.init);
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 000000000..8a96c69a1
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/api.html b/api.html
new file mode 100644
index 000000000..2b27ade6f
--- /dev/null
+++ b/api.html
@@ -0,0 +1,651 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="./">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>libpysal API reference &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="_static/pysal-styles.css?v=38b0ce36" />
+    <script src="_static/documentation_options.js?v=7a99edf7"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>window.MathJax = {"TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": true}}, "tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="icon" href="_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="libpysal.weights.W" href="generated/libpysal.weights.W.html" />
+    <link rel="prev" title="Datasets for use with libpysal" href="notebooks/examples.html" />
+<meta charset='utf-8'>
+<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
+<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
+<meta name="apple-mobile-web-app-capable" content="yes">
+<script type="text/javascript" src="_static/js/jquery-1.12.4.min.js"></script>
+<script type="text/javascript" src="_static/js/jquery-fix.js"></script>
+<script type="text/javascript" src="_static/bootstrap-3.4.1/js/bootstrap.min.js"></script>
+<script type="text/javascript" src="_static/bootstrap-sphinx.js"></script>
+
+  </head><body>
+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
+    <div class="container">
+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </button>
+        <a class="navbar-brand" href="index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="installation.html">Installation</a></li>
+                <li><a href="tutorial.html">Tutorial</a></li>
+                <li><a href="#">API</a></li>
+                <li><a href="references.html">References</a></li>
+            
+            
+              <li class="dropdown globaltoc-container">
+  <a role="button"
+     id="dLabelGlobalToc"
+     data-toggle="dropdown"
+     data-target="#"
+     href="index.html">Site <b class="caret"></b></a>
+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#io">io</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#examples">examples</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#experimental">Experimental</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#graph">graph</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
+  <a role="button"
+     id="dLabelLocalToc"
+     data-toggle="dropdown"
+     data-target="#"
+     href="#">Page <b class="caret"></b></a>
+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">libpysal API reference</a><ul>
+<li><a class="reference internal" href="#spatial-weights">Spatial Weights</a><ul>
+<li><a class="reference internal" href="#distance-weights">Distance Weights</a></li>
+<li><a class="reference internal" href="#contiguity-weights">Contiguity Weights</a></li>
+<li><a class="reference internal" href="#spint-weights">spint Weights</a></li>
+<li><a class="reference internal" href="#weights-tools-to-interface-with-rasters">Weights tools to interface with rasters</a></li>
+<li><a class="reference internal" href="#weights-util-classes-and-functions">Weights Util Classes and Functions</a></li>
+<li><a class="reference internal" href="#weights-user-classes-and-functions">Weights user Classes and Functions</a></li>
+<li><a class="reference internal" href="#set-theoretic-weights">Set Theoretic Weights</a></li>
+<li><a class="reference internal" href="#spatial-lag">Spatial Lag</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#cg-computational-geometry">cg: Computational Geometry</a><ul>
+<li><a class="reference internal" href="#alpha-shapes">alpha_shapes</a></li>
+<li><a class="reference internal" href="#voronoi">voronoi</a></li>
+<li><a class="reference internal" href="#sphere">sphere</a></li>
+<li><a class="reference internal" href="#shapes">shapes</a></li>
+<li><a class="reference internal" href="#standalone">standalone</a></li>
+<li><a class="reference internal" href="#locators">locators</a></li>
+<li><a class="reference internal" href="#kdtree">kdtree</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#io">io</a></li>
+<li><a class="reference internal" href="#examples">examples</a></li>
+<li><a class="reference internal" href="#experimental">Experimental</a></li>
+<li><a class="reference internal" href="#graph">graph</a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
+<form class="navbar-form navbar-right" action="search.html" method="get">
+ <div class="form-group">
+  <input type="text" name="q" class="form-control" placeholder="Search" />
+ </div>
+  <input type="hidden" name="check_keywords" value="yes" />
+  <input type="hidden" name="area" value="default" />
+</form>
+          
+        </div>
+    </div>
+  </div>
+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-api-reference">
+<span id="api-ref"></span><h1>libpysal API reference<a class="headerlink" href="#libpysal-api-reference" title="Link to this heading">¶</a></h1>
+<section id="spatial-weights">
+<h2>Spatial Weights<a class="headerlink" href="#spatial-weights" title="Link to this heading">¶</a></h2>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a>(neighbors[, weights, ...])</p></td>
+<td><p>Spatial weights class.</p></td>
+</tr>
+</tbody>
+</table>
+<section id="distance-weights">
+<h3>Distance Weights<a class="headerlink" href="#distance-weights" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand" title="libpysal.weights.DistanceBand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.DistanceBand</span></code></a>(data, threshold)</p></td>
+<td><p>Spatial weights based on distance band.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Kernel</span></code></a>(data[, bandwidth, ...])</p></td>
+<td><p>Spatial weights based on kernel functions.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN" title="libpysal.weights.KNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.KNN</span></code></a>(data[, k, p, ids, ...])</p></td>
+<td><p>Creates nearest neighbor weights matrix based on k nearest neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.Gabriel.html#libpysal.weights.Gabriel" title="libpysal.weights.Gabriel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Gabriel</span></code></a>(coordinates, **kwargs)</p></td>
+<td><p>Constructs the Gabriel graph of a set of points.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay" title="libpysal.weights.Delaunay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Delaunay</span></code></a>(coordinates, **kwargs)</p></td>
+<td><p>Constructor of the Delaunay graph of a set of input points.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.Relative_Neighborhood.html#libpysal.weights.Relative_Neighborhood" title="libpysal.weights.Relative_Neighborhood"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Relative_Neighborhood</span></code></a>(...)</p></td>
+<td><p>Constructs the Relative Neighborhood graph from a set of points.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="contiguity-weights">
+<h3>Contiguity Weights<a class="headerlink" href="#contiguity-weights" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen" title="libpysal.weights.Queen"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Queen</span></code></a>(polygons, **kw)</p></td>
+<td><p>Construct a weights object from a collection of pysal polygons that share at least one vertex.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook" title="libpysal.weights.Rook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Rook</span></code></a>(polygons, **kw)</p></td>
+<td><p>Construct a weights object from a collection of pysal polygons that share at least one edge.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.Voronoi.html#libpysal.weights.Voronoi" title="libpysal.weights.Voronoi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.Voronoi</span></code></a>(points[, ...])</p></td>
+<td><p>Voronoi weights for a 2-d point set</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a>(neighbors[, weights, ...])</p></td>
+<td><p>Spatial weights class.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="spint-weights">
+<h3>spint Weights<a class="headerlink" href="#spint-weights" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a>(sparse[, id_order, index])</p></td>
+<td><p>Thin <code class="docutils literal notranslate"><span class="pre">W</span></code> class for <code class="docutils literal notranslate"><span class="pre">spreg</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.netW.html#libpysal.weights.netW" title="libpysal.weights.netW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.netW</span></code></a>(link_list[, share, ...])</p></td>
+<td><p>Create a network-contiguity based weight object based on different nodal relationships encoded in a network.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.mat2L.html#libpysal.weights.mat2L" title="libpysal.weights.mat2L"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.mat2L</span></code></a>(edge_matrix)</p></td>
+<td><p>Convert a matrix denoting network connectivity (edges or flows) to a list denoting edges</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.ODW.html#libpysal.weights.ODW" title="libpysal.weights.ODW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.ODW</span></code></a>(Wo, Wd[, transform, ...])</p></td>
+<td><p>Constructs an o*d by o*d origin-destination style spatial weight for o*d flows using standard spatial weights on o origins and d destinations.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.vecW.html#libpysal.weights.vecW" title="libpysal.weights.vecW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.vecW</span></code></a>(origin_x, origin_y, ...)</p></td>
+<td><p>Distance-based spatial weight for vectors that is computed using a 4-dimensional distance between the origin x,y-coordinates and the destination x,y-coordinates</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="weights-tools-to-interface-with-rasters">
+<h3>Weights tools to interface with rasters<a class="headerlink" href="#weights-tools-to-interface-with-rasters" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.da2W.html#libpysal.weights.da2W" title="libpysal.weights.da2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.da2W</span></code></a>(da[, criterion, ...])</p></td>
+<td><p>Create a W object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.da2WSP.html#libpysal.weights.da2WSP" title="libpysal.weights.da2WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.da2WSP</span></code></a>(da[, criterion, ...])</p></td>
+<td><p>Create a WSP object from xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w2da.html#libpysal.weights.w2da" title="libpysal.weights.w2da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w2da</span></code></a>(data, w[, attrs, coords])</p></td>
+<td><p>Creates xarray.DataArray object from passed data aligned with W object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.wsp2da.html#libpysal.weights.wsp2da" title="libpysal.weights.wsp2da"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.wsp2da</span></code></a>(data, wsp[, attrs, ...])</p></td>
+<td><p>Creates xarray.DataArray object from passed data aligned with WSP object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.testDataArray.html#libpysal.weights.testDataArray" title="libpysal.weights.testDataArray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.testDataArray</span></code></a>([shape, ...])</p></td>
+<td><p>Creates 2 or 3 dimensional test xarray.DataArray object</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="weights-util-classes-and-functions">
+<h3>Weights Util Classes and Functions<a class="headerlink" href="#weights-util-classes-and-functions" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.block_weights.html#libpysal.weights.block_weights" title="libpysal.weights.block_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.block_weights</span></code></a>(regimes[, ...])</p></td>
+<td><p>Construct spatial weights for regime neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.lat2W.html#libpysal.weights.lat2W" title="libpysal.weights.lat2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lat2W</span></code></a>([nrows, ncols, rook, ...])</p></td>
+<td><p>Create a W object for a regular lattice.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.comb.html#libpysal.weights.comb" title="libpysal.weights.comb"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.comb</span></code></a>(items[, n])</p></td>
+<td><p>Combinations of size n taken from items</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.order.html#libpysal.weights.order" title="libpysal.weights.order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.order</span></code></a>(w[, kmax])</p></td>
+<td><p>Determine the non-redundant order of contiguity up to a specific order.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.higher_order.html#libpysal.weights.higher_order" title="libpysal.weights.higher_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.higher_order</span></code></a>(w[, k])</p></td>
+<td><p>Contiguity weights object of order k.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.shimbel.html#libpysal.weights.shimbel" title="libpysal.weights.shimbel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.shimbel</span></code></a>(w)</p></td>
+<td><p>Find the Shimbel matrix for first order contiguity matrix.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.remap_ids.html#libpysal.weights.remap_ids" title="libpysal.weights.remap_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.remap_ids</span></code></a>(w, old2new[, ...])</p></td>
+<td><p>Remaps the IDs in a spatial weights object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.full2W.html#libpysal.weights.full2W" title="libpysal.weights.full2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.full2W</span></code></a>(m[, ids])</p></td>
+<td><p>Create a PySAL W object from a full array.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.full.html#libpysal.weights.full" title="libpysal.weights.full"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.full</span></code></a>(w)</p></td>
+<td><p>Generate a full numpy array.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.WSP2W.html#libpysal.weights.WSP2W" title="libpysal.weights.WSP2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP2W</span></code></a>(wsp, **kwargs)</p></td>
+<td><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.get_ids.html#libpysal.weights.get_ids" title="libpysal.weights.get_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.get_ids</span></code></a>(in_shps, idVariable)</p></td>
+<td><p>Gets the IDs from the DBF file that moves with a given shape file or a geopandas.GeoDataFrame.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.get_points_array_from_shapefile.html#libpysal.weights.get_points_array_from_shapefile" title="libpysal.weights.get_points_array_from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.get_points_array_from_shapefile</span></code></a>(...)</p></td>
+<td><p>Gets a data array of x and y coordinates from a given shapefile.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.fill_diagonal.html#libpysal.weights.fill_diagonal" title="libpysal.weights.fill_diagonal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.fill_diagonal</span></code></a>(w[, val, wsp])</p></td>
+<td><p>Returns a new weights object with values inserted along the main diagonal.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="weights-user-classes-and-functions">
+<h3>Weights user Classes and Functions<a class="headerlink" href="#weights-user-classes-and-functions" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.min_threshold_distance.html#libpysal.weights.min_threshold_distance" title="libpysal.weights.min_threshold_distance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.min_threshold_distance</span></code></a>(data)</p></td>
+<td><p>Get the maximum nearest neighbor distance.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.lat2SW.html#libpysal.weights.lat2SW" title="libpysal.weights.lat2SW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lat2SW</span></code></a>([nrows, ncols, ...])</p></td>
+<td><p>Create a sparse W matrix for a regular lattice.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_local_cluster.html#libpysal.weights.w_local_cluster" title="libpysal.weights.w_local_cluster"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_local_cluster</span></code></a>(w)</p></td>
+<td><p>Local clustering coefficients for each unit as a node in a graph.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.higher_order_sp.html#libpysal.weights.higher_order_sp" title="libpysal.weights.higher_order_sp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.higher_order_sp</span></code></a>(w[, k, ...])</p></td>
+<td><p>Contiguity weights for either a sparse W or W for order k.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.hexLat2W.html#libpysal.weights.hexLat2W" title="libpysal.weights.hexLat2W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.hexLat2W</span></code></a>([nrows, ncols])</p></td>
+<td><p>Create a W object for a hexagonal lattice.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.attach_islands.html#libpysal.weights.attach_islands" title="libpysal.weights.attach_islands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.attach_islands</span></code></a>(w, w_knn1, ...)</p></td>
+<td><p>Attach nearest neighbor to islands in spatial weight w.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.nonplanar_neighbors.html#libpysal.weights.nonplanar_neighbors" title="libpysal.weights.nonplanar_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.nonplanar_neighbors</span></code></a>(w, ...)</p></td>
+<td><p>Detect neighbors for non-planar polygon collections</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.fuzzy_contiguity.html#libpysal.weights.fuzzy_contiguity" title="libpysal.weights.fuzzy_contiguity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.fuzzy_contiguity</span></code></a>(gdf[, ...])</p></td>
+<td><p>Fuzzy contiguity spatial weights</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.min_threshold_dist_from_shapefile.html#libpysal.weights.min_threshold_dist_from_shapefile" title="libpysal.weights.min_threshold_dist_from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.min_threshold_dist_from_shapefile</span></code></a>(...)</p></td>
+<td><p>Get the maximum nearest neighbor distance between observations in the shapefile.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.build_lattice_shapefile.html#libpysal.weights.build_lattice_shapefile" title="libpysal.weights.build_lattice_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.build_lattice_shapefile</span></code></a>(...)</p></td>
+<td><p>Build a lattice shapefile with nrows rows and ncols cols.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.spw_from_gal.html#libpysal.weights.spw_from_gal" title="libpysal.weights.spw_from_gal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.spw_from_gal</span></code></a>(galfile)</p></td>
+<td><p>Sparse scipy matrix for w from a gal file.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.neighbor_equality.html#libpysal.weights.neighbor_equality" title="libpysal.weights.neighbor_equality"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.neighbor_equality</span></code></a>(w1, w2)</p></td>
+<td><p>Test if the neighbor sets are equal between two weights objects</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="set-theoretic-weights">
+<h3>Set Theoretic Weights<a class="headerlink" href="#set-theoretic-weights" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_union.html#libpysal.weights.w_union" title="libpysal.weights.w_union"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_union</span></code></a>(w1, w2, **kwargs)</p></td>
+<td><p>Returns a binary weights object, w, that includes all neighbor pairs that exist in either w1 or w2.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.w_intersection.html#libpysal.weights.w_intersection" title="libpysal.weights.w_intersection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_intersection</span></code></a>(w1, w2[, ...])</p></td>
+<td><p>Returns a binary weights object, w, that includes only those neighbor pairs that exist in both w1 and w2.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_difference.html#libpysal.weights.w_difference" title="libpysal.weights.w_difference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_difference</span></code></a>(w1, w2[, ...])</p></td>
+<td><p>Returns a binary weights object, w, that includes only neighbor pairs in w1 that are not in w2.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.w_symmetric_difference.html#libpysal.weights.w_symmetric_difference" title="libpysal.weights.w_symmetric_difference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_symmetric_difference</span></code></a>(w1, w2)</p></td>
+<td><p>Returns a binary weights object, w, that includes only neighbor pairs that are not shared by w1 and w2.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.w_subset.html#libpysal.weights.w_subset" title="libpysal.weights.w_subset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_subset</span></code></a>(w1, ids, **kwargs)</p></td>
+<td><p>Returns a binary weights object, w, that includes only those observations in ids.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.w_clip.html#libpysal.weights.w_clip" title="libpysal.weights.w_clip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.w_clip</span></code></a>(w1, w2[, outSP])</p></td>
+<td><p>Clip a continuous W object (w1) with a different W object (w2) so only cells where w2 has a non-zero value remain with non-zero values in w1.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="spatial-lag">
+<h3>Spatial Lag<a class="headerlink" href="#spatial-lag" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.weights.lag_spatial.html#libpysal.weights.lag_spatial" title="libpysal.weights.lag_spatial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lag_spatial</span></code></a>(w, y)</p></td>
+<td><p>Spatial lag operator.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.weights.lag_categorical.html#libpysal.weights.lag_categorical" title="libpysal.weights.lag_categorical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.lag_categorical</span></code></a>(w, y[, ties])</p></td>
+<td><p>Spatial lag operator for categorical variables.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="cg-computational-geometry">
+<h2>cg: Computational Geometry<a class="headerlink" href="#cg-computational-geometry" title="Link to this heading">¶</a></h2>
+<section id="alpha-shapes">
+<h3>alpha_shapes<a class="headerlink" href="#alpha-shapes" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.alpha_shape.html#libpysal.cg.alpha_shape" title="libpysal.cg.alpha_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape</span></code></a>(xys, alpha)</p></td>
+<td><p>Alpha-shape delineation (Edelsbrunner, Kirkpatrick &amp; Seidel, 1983) from a collection of points</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.alpha_shape_auto.html#libpysal.cg.alpha_shape_auto" title="libpysal.cg.alpha_shape_auto"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape_auto</span></code></a>(xys[, step, ...])</p></td>
+<td><p>Computation of alpha-shape delineation with automated selection of alpha.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="voronoi">
+<h3>voronoi<a class="headerlink" href="#voronoi" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.voronoi_frames.html#libpysal.cg.voronoi_frames" title="libpysal.cg.voronoi_frames"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.voronoi_frames</span></code></a>(points[, radius, ...])</p></td>
+<td><p>Composite helper to return Voronoi regions and generator points as individual dataframes.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="sphere">
+<h3>sphere<a class="headerlink" href="#sphere" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.RADIUS_EARTH_KM.html#libpysal.cg.RADIUS_EARTH_KM" title="libpysal.cg.RADIUS_EARTH_KM"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.RADIUS_EARTH_KM</span></code></a></p></td>
+<td><p>Convert a string or number to a floating point number, if possible.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.RADIUS_EARTH_MILES.html#libpysal.cg.RADIUS_EARTH_MILES" title="libpysal.cg.RADIUS_EARTH_MILES"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.RADIUS_EARTH_MILES</span></code></a></p></td>
+<td><p>Convert a string or number to a floating point number, if possible.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.arcdist.html#libpysal.cg.arcdist" title="libpysal.cg.arcdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.arcdist</span></code></a>(pt0, pt1[, radius])</p></td>
+<td><p>Arc distance between two points on a sphere.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.arcdist2linear.html#libpysal.cg.arcdist2linear" title="libpysal.cg.arcdist2linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.arcdist2linear</span></code></a>(arc_dist[, radius])</p></td>
+<td><p>Convert an arc distance (spherical earth) to a linear distance (R3) in the unit sphere.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.brute_knn.html#libpysal.cg.brute_knn" title="libpysal.cg.brute_knn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.brute_knn</span></code></a>(pts, k[, mode, radius])</p></td>
+<td><p>Computes a brute-force <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.fast_knn.html#libpysal.cg.fast_knn" title="libpysal.cg.fast_knn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.fast_knn</span></code></a>(pts, k[, return_dist, ...])</p></td>
+<td><p>Computes <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors on a sphere.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.fast_threshold.html#libpysal.cg.fast_threshold" title="libpysal.cg.fast_threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.fast_threshold</span></code></a>(pts, dist[, radius])</p></td>
+<td><p>Find all neighbors on a sphere within a threshold distance.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.linear2arcdist.html#libpysal.cg.linear2arcdist" title="libpysal.cg.linear2arcdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.linear2arcdist</span></code></a>(linear_dist[, radius])</p></td>
+<td><p>Convert a linear distance in the unit sphere (R3) to an arc distance based on supplied radius.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.toLngLat.html#libpysal.cg.toLngLat" title="libpysal.cg.toLngLat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.toLngLat</span></code></a>(xyz)</p></td>
+<td><p>Convert a point's x,y,z to latitude and longitude.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.toXYZ.html#libpysal.cg.toXYZ" title="libpysal.cg.toXYZ"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.toXYZ</span></code></a>(pt)</p></td>
+<td><p>Convert a point's latitude and longitude to x,y,z.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.lonlat.html#libpysal.cg.lonlat" title="libpysal.cg.lonlat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.lonlat</span></code></a>(pointslist)</p></td>
+<td><p>Converts point order from lat-lon tuples to lon-lat (x,y) tuples.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.harcdist.html#libpysal.cg.harcdist" title="libpysal.cg.harcdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.harcdist</span></code></a>(p0, p1[, lonx, radius])</p></td>
+<td><p>Alternative the arc distance function, uses the haversine formula.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.geointerpolate.html#libpysal.cg.geointerpolate" title="libpysal.cg.geointerpolate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.geointerpolate</span></code></a>(p0, p1, t[, lonx])</p></td>
+<td><p>Finds a point on a sphere along the great circle distance between two points on a sphere also known as a way point in great circle navigation.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.geogrid.html#libpysal.cg.geogrid" title="libpysal.cg.geogrid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.geogrid</span></code></a>(pup, pdown, k[, lonx])</p></td>
+<td><p>Computes a <span class="math notranslate nohighlight">\(k+1\)</span> by <span class="math notranslate nohighlight">\(k+1\)</span> set of grid points for a bounding box in lat-lon.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="shapes">
+<h3>shapes<a class="headerlink" href="#shapes" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>(loc)</p></td>
+<td><p>Geometric class for point objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>(start_pt, end_pt)</p></td>
+<td><p>Geometric representation of line segment objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Line</span></code></a>(m, b)</p></td>
+<td><p>Geometric representation of line objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a>(origin, second_p)</p></td>
+<td><p>Geometric representation of ray objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain" title="libpysal.cg.Chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Chain</span></code></a>(vertices)</p></td>
+<td><p>Geometric representation of a chain, also known as a polyline.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a>(vertices[, holes])</p></td>
+<td><p>Geometric representation of polygon objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a>(left, lower, right, upper)</p></td>
+<td><p>Geometric representation of rectangle objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.asShape.html#libpysal.cg.asShape" title="libpysal.cg.asShape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.asShape</span></code></a>(obj)</p></td>
+<td><p>Returns a PySAL shape object from <code class="docutils literal notranslate"><span class="pre">obj</span></code>, which must support the <code class="docutils literal notranslate"><span class="pre">__geo_interface__</span></code>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="standalone">
+<h3>standalone<a class="headerlink" href="#standalone" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.bbcommon.html#libpysal.cg.bbcommon" title="libpysal.cg.bbcommon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.bbcommon</span></code></a>(bb, bbother)</p></td>
+<td><p>Old Stars method for bounding box overlap testing.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_bounding_box.html#libpysal.cg.get_bounding_box" title="libpysal.cg.get_bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_bounding_box</span></code></a>(items)</p></td>
+<td><p>Find bounding box for a list of geometries.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_angle_between.html#libpysal.cg.get_angle_between" title="libpysal.cg.get_angle_between"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_angle_between</span></code></a>(ray1, ray2)</p></td>
+<td><p>Returns the angle formed between a pair of rays which share an origin.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.is_collinear.html#libpysal.cg.is_collinear" title="libpysal.cg.is_collinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.is_collinear</span></code></a>(p1, p2, p3)</p></td>
+<td><p>Returns whether a triplet of points is collinear.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect" title="libpysal.cg.get_segments_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segments_intersect</span></code></a>(seg1, seg2)</p></td>
+<td><p>Returns the intersection of two segments if one exists.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_segment_point_intersect.html#libpysal.cg.get_segment_point_intersect" title="libpysal.cg.get_segment_point_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segment_point_intersect</span></code></a>(seg, pt)</p></td>
+<td><p>Returns the intersection of a segment and point.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_polygon_point_intersect.html#libpysal.cg.get_polygon_point_intersect" title="libpysal.cg.get_polygon_point_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_polygon_point_intersect</span></code></a>(poly, pt)</p></td>
+<td><p>Returns the intersection of a polygon and point.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_rectangle_point_intersect.html#libpysal.cg.get_rectangle_point_intersect" title="libpysal.cg.get_rectangle_point_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_rectangle_point_intersect</span></code></a>(...)</p></td>
+<td><p>Returns the intersection of a rectangle and point.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_ray_segment_intersect.html#libpysal.cg.get_ray_segment_intersect" title="libpysal.cg.get_ray_segment_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_ray_segment_intersect</span></code></a>(ray, seg)</p></td>
+<td><p>Returns the intersection of a ray and line segment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_rectangle_rectangle_intersection.html#libpysal.cg.get_rectangle_rectangle_intersection" title="libpysal.cg.get_rectangle_rectangle_intersection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_rectangle_rectangle_intersection</span></code></a>(r0, r1)</p></td>
+<td><p>Returns the intersection between two rectangles.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_polygon_point_dist.html#libpysal.cg.get_polygon_point_dist" title="libpysal.cg.get_polygon_point_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_polygon_point_dist</span></code></a>(poly, pt)</p></td>
+<td><p>Returns the distance between a polygon and point.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_points_dist.html#libpysal.cg.get_points_dist" title="libpysal.cg.get_points_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_points_dist</span></code></a>(pt1, pt2)</p></td>
+<td><p>Returns the distance between a pair of points.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.get_segment_point_dist.html#libpysal.cg.get_segment_point_dist" title="libpysal.cg.get_segment_point_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segment_point_dist</span></code></a>(seg, pt)</p></td>
+<td><p>Returns (1) the distance between a line segment and point and (2) the distance along the segment to the closest location on the segment from the point as a ratio of the length of the segment.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_point_at_angle_and_dist.html#libpysal.cg.get_point_at_angle_and_dist" title="libpysal.cg.get_point_at_angle_and_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_point_at_angle_and_dist</span></code></a>(ray, ...)</p></td>
+<td><p>Returns the point at a distance and angle relative to the origin of a ray.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.convex_hull.html#libpysal.cg.convex_hull" title="libpysal.cg.convex_hull"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.convex_hull</span></code></a>(points)</p></td>
+<td><p>Returns the convex hull of a set of points.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.is_clockwise.html#libpysal.cg.is_clockwise" title="libpysal.cg.is_clockwise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.is_clockwise</span></code></a>(vertices)</p></td>
+<td><p>Returns whether a list of points describing a polygon are clockwise or counterclockwise.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.point_touches_rectangle.html#libpysal.cg.point_touches_rectangle" title="libpysal.cg.point_touches_rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.point_touches_rectangle</span></code></a>(point, rect)</p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> (<code class="docutils literal notranslate"><span class="pre">1</span></code>) if the point is in the rectangle or touches it's boundary, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code> (<code class="docutils literal notranslate"><span class="pre">0</span></code>).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.get_shared_segments.html#libpysal.cg.get_shared_segments" title="libpysal.cg.get_shared_segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_shared_segments</span></code></a>(poly1, poly2)</p></td>
+<td><p>Returns the line segments in common to both polygons.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.distance_matrix.html#libpysal.cg.distance_matrix" title="libpysal.cg.distance_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.distance_matrix</span></code></a>(X[, p, threshold])</p></td>
+<td><p>Calculate a distance matrix.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="locators">
+<h3>locators<a class="headerlink" href="#locators" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid" title="libpysal.cg.Grid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Grid</span></code></a>(bounds, resolution)</p></td>
+<td><p>Representation of a binning data structure.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator" title="libpysal.cg.PointLocator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.PointLocator</span></code></a>(points)</p></td>
+<td><p>An abstract representation of a point indexing data structure.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator" title="libpysal.cg.PolygonLocator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.PolygonLocator</span></code></a>(polygons)</p></td>
+<td><p>An abstract representation of a polygon indexing data structure.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="kdtree">
+<h3>kdtree<a class="headerlink" href="#kdtree" title="Link to this heading">¶</a></h3>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.cg.KDTree.html#libpysal.cg.KDTree" title="libpysal.cg.KDTree"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.KDTree</span></code></a>(data[, leafsize, ...])</p></td>
+<td><p>kd-tree built on top of kd-tree functionality in scipy.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="io">
+<h2>io<a class="headerlink" href="#io" title="Link to this heading">¶</a></h2>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.io.open.html#libpysal.io.open" title="libpysal.io.open"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.io.open</span></code></a></p></td>
+<td><p>alias of <a class="reference internal" href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO" title="libpysal.io.fileio.FileIO"><code class="xref py py-class docutils literal notranslate"><span class="pre">FileIO</span></code></a></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO" title="libpysal.io.fileio.FileIO"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.io.fileio.FileIO</span></code></a>([dataPath, mode, ...])</p></td>
+<td><p>Metaclass for supporting spatial data file read and write.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="examples">
+<h2>examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h2>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.examples.available.html#libpysal.examples.available" title="libpysal.examples.available"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.examples.available</span></code></a>()</p></td>
+<td><p>Return a dataframe with available datasets.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/libpysal.examples.explain.html#libpysal.examples.explain" title="libpysal.examples.explain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.examples.explain</span></code></a>(name)</p></td>
+<td><p>Explain a dataset by name.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.examples.get_path.html#libpysal.examples.get_path" title="libpysal.examples.get_path"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.examples.get_path</span></code></a>(file_name)</p></td>
+<td><p>Get the path for a file by searching installed datasets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="experimental">
+<h2>Experimental<a class="headerlink" href="#experimental" title="Link to this heading">¶</a></h2>
+<p>Experimental modules with unstable API.</p>
+</section>
+<section id="graph">
+<h2>graph<a class="headerlink" href="#graph" title="Link to this heading">¶</a></h2>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.graph.Graph</span></code></a>(adjacency[, transformation])</p></td>
+<td><p>Graph class encoding spatial weights matrices</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+
+
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\ No newline at end of file
diff --git a/generated/libpysal.cg.Chain.html b/generated/libpysal.cg.Chain.html
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
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+<li><a class="reference internal" href="#">libpysal.cg.Chain</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Chain"><code class="docutils literal notranslate"><span class="pre">Chain</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Chain.__init__"><code class="docutils literal notranslate"><span class="pre">Chain.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Chain.arclen"><code class="docutils literal notranslate"><span class="pre">Chain.arclen</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Chain.bounding_box"><code class="docutils literal notranslate"><span class="pre">Chain.bounding_box</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Chain.len"><code class="docutils literal notranslate"><span class="pre">Chain.len</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Chain.parts"><code class="docutils literal notranslate"><span class="pre">Chain.parts</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Chain.segments"><code class="docutils literal notranslate"><span class="pre">Chain.segments</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Chain.vertices"><code class="docutils literal notranslate"><span class="pre">Chain.vertices</span></code></a></li>
+</ul>
+</li>
+</ul>
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+      
+  <section id="libpysal-cg-chain">
+<h1>libpysal.cg.Chain<a class="headerlink" href="#libpysal-cg-chain" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Chain">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Chain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertices</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Chain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Chain" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric representation of a chain, also known as a polyline.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>vertices</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A point list or list of point lists.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><a class="reference internal" href="#libpysal.cg.Chain.vertices" title="libpysal.cg.Chain.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Returns the vertices of the chain in clockwise order.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Chain.len" title="libpysal.cg.Chain.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the geometric length of the chain.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertices</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Chain.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Chain.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.__init__" title="libpysal.cg.Chain.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(vertices)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.arclen" title="libpysal.cg.Chain.arclen"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arclen</span></code></a></p></td>
+<td><p>Returns the geometric length of the chain computed using 'arcdistance' (meters).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Chain.bounding_box" title="libpysal.cg.Chain.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a></p></td>
+<td><p>Returns the bounding box of the chain.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.len" title="libpysal.cg.Chain.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a></p></td>
+<td><p>Returns the geometric length of the chain.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Chain.parts" title="libpysal.cg.Chain.parts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parts</span></code></a></p></td>
+<td><p>Returns the parts (lists of <code class="docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code> objects) of the chain.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Chain.segments" title="libpysal.cg.Chain.segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">segments</span></code></a></p></td>
+<td><p>Returns the segments that compose the chain.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Chain.vertices" title="libpysal.cg.Chain.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a></p></td>
+<td><p>Returns the vertices of the chain in clockwise order.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.arclen">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">arclen</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.Chain.arclen" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the geometric length of the chain
+computed using ‘arcdistance’ (meters).</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.bounding_box">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">bounding_box</span></span><a class="headerlink" href="#libpysal.cg.Chain.bounding_box" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the bounding box of the chain.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._bounding_box</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>The bounding box of the chain.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.len">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">len</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#libpysal.cg.Chain.len" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the geometric length of the chain.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">len</span>
+<span class="go">3.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span>
+<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span>
+<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">11</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">))]</span>
+<span class="gp">... </span>    <span class="p">]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span><span class="o">.</span><span class="n">len</span>
+<span class="go">4.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.parts">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">parts</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></em><a class="headerlink" href="#libpysal.cg.Chain.parts" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the parts (lists of <code class="docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code> objects) of the chain.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span>
+<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span>
+<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
+<span class="gp">... </span>    <span class="p">]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span>
+<span class="go">2</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.segments">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">segments</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></em><a class="headerlink" href="#libpysal.cg.Chain.segments" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the segments that compose the chain.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Chain.vertices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vertices</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></em><a class="headerlink" href="#libpysal.cg.Chain.vertices" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the vertices of the chain in clockwise order.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Chain</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">verts</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">vertices</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">verts</span><span class="p">)</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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diff --git a/generated/libpysal.cg.Grid.html b/generated/libpysal.cg.Grid.html
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+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
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+<li><a class="reference internal" href="#">libpysal.cg.Grid</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Grid"><code class="docutils literal notranslate"><span class="pre">Grid</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Grid.__init__"><code class="docutils literal notranslate"><span class="pre">Grid.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Grid.add"><code class="docutils literal notranslate"><span class="pre">Grid.add()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Grid.bounds"><code class="docutils literal notranslate"><span class="pre">Grid.bounds()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Grid.in_grid"><code class="docutils literal notranslate"><span class="pre">Grid.in_grid()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Grid.nearest"><code class="docutils literal notranslate"><span class="pre">Grid.nearest()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Grid.proximity"><code class="docutils literal notranslate"><span class="pre">Grid.proximity()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Grid.remove"><code class="docutils literal notranslate"><span class="pre">Grid.remove()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-cg-grid">
+<h1>libpysal.cg.Grid<a class="headerlink" href="#libpysal-cg-grid" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Grid">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid" title="Link to this definition">¶</a></dt>
+<dd><p>Representation of a binning data structure.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resolution</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a grid with specified properties.</p>
+<p>__init__(Rectangle, number) -&gt; Grid</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>bounds</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">encompass</span></code></span></dt><dd></dd>
+<dt><strong>resolution</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">diameter</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">each</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#bin" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bin</span></code></a></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>TODO: complete this doctest
+&gt;&gt;&gt; g = Grid(Rectangle(0, 0, 10, 10), 1)</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.__init__" title="libpysal.cg.Grid.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(bounds, resolution)</p></td>
+<td><p>Returns a grid with specified properties.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Grid.add" title="libpysal.cg.Grid.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add</span></code></a>(item, pt)</p></td>
+<td><p>Adds an item to the grid at a specified location.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.bounds" title="libpysal.cg.Grid.bounds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounds</span></code></a>(bounds)</p></td>
+<td><p>Returns a list of items found in the grid within the bounds specified.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Grid.in_grid" title="libpysal.cg.Grid.in_grid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">in_grid</span></code></a>(loc)</p></td>
+<td><p>Returns whether a 2-tuple location _loc_ lies inside the grid bounds.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.nearest" title="libpysal.cg.Grid.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a>(pt)</p></td>
+<td><p>Returns the nearest item to a point.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Grid.proximity" title="libpysal.cg.Grid.proximity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">proximity</span></code></a>(pt, r)</p></td>
+<td><p>Returns a list of items found in the grid within a specified distance of a point.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Grid.remove" title="libpysal.cg.Grid.remove"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code></a>(item, pt)</p></td>
+<td><p>Removes an item from the grid at a specified location.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.add">
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.add" title="Link to this definition">¶</a></dt>
+<dd><p>Adds an item to the grid at a specified location.</p>
+<p>add(x, Point) -&gt; x</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>item</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">insert</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">into</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code></span></dt><dd></dd>
+<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">insert</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">at</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.2</span><span class="p">,</span> <span class="mf">8.7</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.bounds">
+<span class="sig-name descname"><span class="pre">bounds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounds</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.bounds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.bounds" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a list of items found in the grid within the bounds specified.</p>
+<p>bounds(Rectangle) -&gt; x list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>item</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <a class="reference internal" href="#libpysal.cg.Grid.remove" title="libpysal.cg.Grid.remove"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code></span></dt><dd></dd>
+<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">was</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">added</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">at</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)))</span>
+<span class="go">&#39;B&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="go">[&#39;A&#39;]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="go">[&#39;B&#39;]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">bounds</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
+<span class="go">[&#39;A&#39;, &#39;B&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.in_grid">
+<span class="sig-name descname"><span class="pre">in_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.in_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.in_grid" title="Link to this definition">¶</a></dt>
+<dd><p>Returns whether a 2-tuple location _loc_ lies inside the grid bounds.</p>
+<p>Test tag: &lt;tc&gt;#is#Grid.in_grid&lt;/tc&gt;</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.nearest">
+<span class="sig-name descname"><span class="pre">nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.nearest" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the nearest item to a point.</p>
+<p>nearest(Point) -&gt; x</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">search</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">near</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)))</span>
+<span class="go">&#39;B&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">7.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">)))</span>
+<span class="go">&#39;B&#39;</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.proximity">
+<span class="sig-name descname"><span class="pre">proximity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.proximity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.proximity" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a list of items found in the grid
+within a specified distance of a point.</p>
+<p>proximity(Point, number) -&gt; x list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">search</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">around</span></code></span></dt><dd></dd>
+<dt><strong>r</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">distance</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">search</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">around</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">)))</span>
+<span class="go">&#39;B&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
+<span class="go">[&#39;A&#39;]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">6.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">)),</span> <span class="mf">3.0</span><span class="p">)</span>
+<span class="go">[&#39;B&#39;]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">g</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)),</span> <span class="mf">4.0</span><span class="p">))</span>
+<span class="go">[&#39;A&#39;, &#39;B&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Grid.remove">
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#Grid.remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Grid.remove" title="Link to this definition">¶</a></dt>
+<dd><p>Removes an item from the grid at a specified location.</p>
+<p>remove(x, Point) -&gt; x</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>item</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <a class="reference internal" href="#libpysal.cg.Grid.remove" title="libpysal.cg.Grid.remove"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">grid</span></code></span></dt><dd></dd>
+<dt><strong>pt</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">location</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">item</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">was</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">added</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">at</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">g</span> <span class="o">=</span> <span class="n">Grid</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.2</span><span class="p">,</span> <span class="mf">8.7</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">g</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="n">Point</span><span class="p">((</span><span class="mf">4.2</span><span class="p">,</span> <span class="mf">8.7</span><span class="p">)))</span>
+<span class="go">&#39;A&#39;</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.KDTree</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.KDTree"><code class="docutils literal notranslate"><span class="pre">KDTree()</span></code></a></li>
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+      
+  <section id="libpysal-cg-kdtree">
+<h1>libpysal.cg.KDTree<a class="headerlink" href="#libpysal-cg-kdtree" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.KDTree">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">KDTree</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leafsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/kdtree.html#KDTree"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.KDTree" title="Link to this definition">¶</a></dt>
+<dd><p>kd-tree built on top of kd-tree functionality in scipy. If using scipy
+0.12 or greater uses the scipy.spatial.cKDTree, otherwise uses
+scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance. Note
+that Arc distance is only appropriate when points in latitude and
+longitude, and the radius set to meaningful value (see docs below).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>The data points to be indexed. This array is not copied, and so
+modifying this data will result in bogus results. Typically nx2.</p>
+</dd>
+<dt><strong>leafsize</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of points at which the algorithm switches
+over to brute-force. Has to be positive. Optional, default is 10.</p>
+</dd>
+<dt><strong>distance_metric</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Options: “Euclidean” (default) and “Arc”.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Radius of the sphere on which to compute distances. Assumes data in
+latitude and longitude. Ignored if distance_metric=”Euclidean”. Typical
+values: pysal.cg.RADIUS_EARTH_KM (default) pysal.cg.RADIUS_EARTH_MILES</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">libpysal.cg.Line</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Line"><code class="docutils literal notranslate"><span class="pre">Line</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Line.__init__"><code class="docutils literal notranslate"><span class="pre">Line.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Line.x"><code class="docutils literal notranslate"><span class="pre">Line.x()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Line.y"><code class="docutils literal notranslate"><span class="pre">Line.y()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-line">
+<h1>libpysal.cg.Line<a class="headerlink" href="#libpysal-cg-line" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Line">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Line</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Line" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric representation of line objects.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>m</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The slope of the line. <code class="docutils literal notranslate"><span class="pre">m</span></code> is also an attribute.</p>
+</dd>
+<dt><strong>b</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The <span class="math notranslate nohighlight">\(y\)</span>-intercept of the line. <code class="docutils literal notranslate"><span class="pre">b</span></code> is also an attribute.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#ArithmeticError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArithmeticError</span></code></a></dt><dd><p>Raised when infinity is passed in as the slope.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">m</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">b</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Line.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Line.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Line.__init__" title="libpysal.cg.Line.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(m, b)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Line.x" title="libpysal.cg.Line.x"><code class="xref py py-obj docutils literal notranslate"><span class="pre">x</span></code></a>(y)</p></td>
+<td><p>Returns the <span class="math notranslate nohighlight">\(x\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(y\)</span>-value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Line.y" title="libpysal.cg.Line.y"><code class="xref py py-obj docutils literal notranslate"><span class="pre">y</span></code></a>(x)</p></td>
+<td><p>Returns the <span class="math notranslate nohighlight">\(y\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(x\)</span>-value.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Line.x">
+<span class="sig-name descname"><span class="pre">x</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line.x"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Line.x" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the <span class="math notranslate nohighlight">\(x\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(y\)</span>-value.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>y</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The <span class="math notranslate nohighlight">\(y\)</span>-value at which to compute <span class="math notranslate nohighlight">\(x\)</span>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#ArithmeticError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ArithmeticError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">0.</span></code> is passed in as the slope.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">x</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
+<span class="go">0.5</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Line.y">
+<span class="sig-name descname"><span class="pre">y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Line.y"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Line.y" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the <span class="math notranslate nohighlight">\(y\)</span>-value of the line at a particular <span class="math notranslate nohighlight">\(x\)</span>-value.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>x</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>}</span></dt><dd><p>The <span class="math notranslate nohighlight">\(x\)</span>-value at which to compute <span class="math notranslate nohighlight">\(y\)</span>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">Line</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">y</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+<li><a class="reference internal" href="#">libpysal.cg.LineSegment</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment"><code class="docutils literal notranslate"><span class="pre">LineSegment</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.__init__"><code class="docutils literal notranslate"><span class="pre">LineSegment.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.bounding_box"><code class="docutils literal notranslate"><span class="pre">LineSegment.bounding_box</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.get_swap"><code class="docutils literal notranslate"><span class="pre">LineSegment.get_swap()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.intersect"><code class="docutils literal notranslate"><span class="pre">LineSegment.intersect()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.is_ccw"><code class="docutils literal notranslate"><span class="pre">LineSegment.is_ccw()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.is_cw"><code class="docutils literal notranslate"><span class="pre">LineSegment.is_cw()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.len"><code class="docutils literal notranslate"><span class="pre">LineSegment.len</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.line"><code class="docutils literal notranslate"><span class="pre">LineSegment.line</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.p1"><code class="docutils literal notranslate"><span class="pre">LineSegment.p1</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.p2"><code class="docutils literal notranslate"><span class="pre">LineSegment.p2</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.LineSegment.sw_ccw"><code class="docutils literal notranslate"><span class="pre">LineSegment.sw_ccw()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</ul>
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+      
+  <section id="libpysal-cg-linesegment">
+<h1>libpysal.cg.LineSegment<a class="headerlink" href="#libpysal-cg-linesegment" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">LineSegment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_pt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric representation of line segment objects.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>start_pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point where the segment begins.</p>
+</dd>
+<dt><strong>end_pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point where the segment ends.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><a class="reference internal" href="#libpysal.cg.LineSegment.p1" title="libpysal.cg.LineSegment.p1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p1</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p><strong>HELPER METHOD. DO NOT CALL.</strong></p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.LineSegment.p2" title="libpysal.cg.LineSegment.p2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p2</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Point</span></code></a></span></dt><dd><p><strong>HELPER METHOD. DO NOT CALL.</strong></p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.LineSegment.bounding_box" title="libpysal.cg.LineSegment.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>Returns the minimum bounding box of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.LineSegment.len" title="libpysal.cg.LineSegment.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the length of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.LineSegment.line" title="libpysal.cg.LineSegment.line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">line</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Line</span></code></a></span></dt><dd><p>Returns a <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_pt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.__init__" title="libpysal.cg.LineSegment.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(start_pt, end_pt)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.get_swap" title="libpysal.cg.LineSegment.get_swap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_swap</span></code></a>()</p></td>
+<td><p>Returns a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object which has its endpoints swapped.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.intersect" title="libpysal.cg.LineSegment.intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">intersect</span></code></a>(other)</p></td>
+<td><p>Test whether segment intersects with other segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.is_ccw" title="libpysal.cg.LineSegment.is_ccw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_ccw</span></code></a>(pt)</p></td>
+<td><p>Returns whether a point is counterclockwise of the segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.is_cw" title="libpysal.cg.LineSegment.is_cw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_cw</span></code></a>(pt)</p></td>
+<td><p>Returns whether a point is clockwise of the segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.sw_ccw" title="libpysal.cg.LineSegment.sw_ccw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sw_ccw</span></code></a>(pt)</p></td>
+<td><p>Sedgewick test for <code class="docutils literal notranslate"><span class="pre">pt</span></code> being ccw of segment.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.bounding_box" title="libpysal.cg.LineSegment.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a></p></td>
+<td><p>Returns the minimum bounding box of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.len" title="libpysal.cg.LineSegment.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a></p></td>
+<td><p>Returns the length of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.line" title="libpysal.cg.LineSegment.line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">line</span></code></a></p></td>
+<td><p>Returns a <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.p1" title="libpysal.cg.LineSegment.p1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p1</span></code></a></p></td>
+<td><p><a href="#id1"><span class="problematic" id="id2">**</span></a>HELPER METHOD.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.LineSegment.p2" title="libpysal.cg.LineSegment.p2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">p2</span></code></a></p></td>
+<td><p><a href="#id3"><span class="problematic" id="id4">**</span></a>HELPER METHOD.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.bounding_box">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">bounding_box</span></span><a class="headerlink" href="#libpysal.cg.LineSegment.bounding_box" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the minimum bounding box of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._bounding_box</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>The bounding box of the line segment.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+<span class="go">5.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+<span class="go">6.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.get_swap">
+<span class="sig-name descname"><span class="pre">get_swap</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.get_swap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment.get_swap" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object which has its endpoints swapped.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>line_seg</strong><span class="classifier"><a class="reference internal" href="#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object which has its endpoints swapped.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">get_swap</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">5.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">6.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">swap</span><span class="o">.</span><span class="n">p2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.intersect">
+<span class="sig-name descname"><span class="pre">intersect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment.intersect" title="Link to this definition">¶</a></dt>
+<dd><p>Test whether segment intersects with other segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or
+not (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Handles endpoints of segments being on other segment.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>other</strong><span class="classifier"><a class="reference internal" href="#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>Another line segment to check against.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls1</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">intersect</span><span class="p">(</span><span class="n">ls1</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">intersect</span><span class="p">(</span><span class="n">ls2</span><span class="p">)</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">intersect</span><span class="p">(</span><span class="n">ls2</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.is_ccw">
+<span class="sig-name descname"><span class="pre">is_ccw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.is_ccw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment.is_ccw" title="Link to this definition">¶</a></dt>
+<dd><p>Returns whether a point is counterclockwise of the
+segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Exclusive.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point lying ccw or cw of a segment.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_ccw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_ccw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)))</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.is_cw">
+<span class="sig-name descname"><span class="pre">is_cw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.is_cw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment.is_cw" title="Link to this definition">¶</a></dt>
+<dd><p>Returns whether a point is clockwise of the
+segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Exclusive.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point lying ccw or cw of a segment.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_cw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">is_cw</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.len">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">len</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.LineSegment.len" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the length of a <code class="docutils literal notranslate"><span class="pre">LineSegment</span></code> object.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span><span class="o">.</span><span class="n">len</span>
+<span class="go">3.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.line">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">line</span></span><a class="headerlink" href="#libpysal.cg.LineSegment.line" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._line</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Line</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">Line</span></code> object of the line on which the segment lies.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">line</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">m</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">b</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.p1">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">p1</span></span><a class="headerlink" href="#libpysal.cg.LineSegment.p1" title="Link to this definition">¶</a></dt>
+<dd><p><strong>HELPER METHOD. DO NOT CALL.</strong>
+Returns the <code class="docutils literal notranslate"><span class="pre">p1</span></code> attribute of the line segment.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._p1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">_p1</span></code> attribute.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">_get_p1</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">==</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.p2">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">p2</span></span><a class="headerlink" href="#libpysal.cg.LineSegment.p2" title="Link to this definition">¶</a></dt>
+<dd><p><strong>HELPER METHOD. DO NOT CALL.</strong>
+Returns the <code class="docutils literal notranslate"><span class="pre">p2</span></code> attribute of the line segment.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._p2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The <code class="docutils literal notranslate"><span class="pre">_p2</span></code> attribute.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ls</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">ls</span><span class="o">.</span><span class="n">_get_p2</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">==</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.LineSegment.sw_ccw">
+<span class="sig-name descname"><span class="pre">sw_ccw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#LineSegment.sw_ccw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.LineSegment.sw_ccw" title="Link to this definition">¶</a></dt>
+<dd><p>Sedgewick test for <code class="docutils literal notranslate"><span class="pre">pt</span></code> being ccw of segment.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>is_ccw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">1</span></code> if turn from <code class="docutils literal notranslate"><span class="pre">self.p1</span></code> to <code class="docutils literal notranslate"><span class="pre">self.p2</span></code> to <code class="docutils literal notranslate"><span class="pre">pt</span></code> is ccw.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> if turn from <code class="docutils literal notranslate"><span class="pre">self.p1</span></code> to <code class="docutils literal notranslate"><span class="pre">self.p2</span></code> to <code class="docutils literal notranslate"><span class="pre">pt</span></code> is cw.
+<code class="docutils literal notranslate"><span class="pre">-1</span></code> if the points are collinear and <code class="docutils literal notranslate"><span class="pre">self.p1</span></code> is in the middle.
+<code class="docutils literal notranslate"><span class="pre">1</span></code> if the points are collinear and <code class="docutils literal notranslate"><span class="pre">self.p2</span></code> is in the middle.
+<code class="docutils literal notranslate"><span class="pre">0</span></code> if the points are collinear and <code class="docutils literal notranslate"><span class="pre">pt</span></code> is in the middle.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
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+<li><a class="reference internal" href="#">libpysal.cg.Point</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Point"><code class="docutils literal notranslate"><span class="pre">Point</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Point.__init__"><code class="docutils literal notranslate"><span class="pre">Point.__init__()</span></code></a></li>
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+      
+  <section id="libpysal-cg-point">
+<h1>libpysal.cg.Point<a class="headerlink" href="#libpysal-cg-point" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Point">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Point" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric class for point objects.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>loc</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The point’s location (number <span class="math notranslate nohighlight">\(x\)</span>-tuple, <span class="math notranslate nohighlight">\(x\)</span> &gt; 1).</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Point.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Point.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Point.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Point.__init__" title="libpysal.cg.Point.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(loc)</p></td>
+<td><p></p></td>
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+<li><a class="reference internal" href="#">libpysal.cg.PointLocator</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator"><code class="docutils literal notranslate"><span class="pre">PointLocator</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator.__init__"><code class="docutils literal notranslate"><span class="pre">PointLocator.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator.nearest"><code class="docutils literal notranslate"><span class="pre">PointLocator.nearest()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator.overlapping"><code class="docutils literal notranslate"><span class="pre">PointLocator.overlapping()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator.polygon"><code class="docutils literal notranslate"><span class="pre">PointLocator.polygon()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator.proximity"><code class="docutils literal notranslate"><span class="pre">PointLocator.proximity()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PointLocator.region"><code class="docutils literal notranslate"><span class="pre">PointLocator.region()</span></code></a></li>
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+      
+  <section id="libpysal-cg-pointlocator">
+<h1>libpysal.cg.PointLocator<a class="headerlink" href="#libpysal-cg-pointlocator" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">PointLocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">points</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PointLocator" title="Link to this definition">¶</a></dt>
+<dd><p>An abstract representation of a point indexing data structure.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">points</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PointLocator.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a point locator object.</p>
+<p>__init__(Point list) -&gt; PointLocator</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>points</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">index</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.__init__" title="libpysal.cg.PointLocator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(points)</p></td>
+<td><p>Returns a point locator object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.nearest" title="libpysal.cg.PointLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a>(query_point)</p></td>
+<td><p>Returns the nearest point indexed to a query point.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.overlapping" title="libpysal.cg.PointLocator.overlapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">overlapping</span></code></a>(region_rect)</p></td>
+<td><p>Returns the indexed points located inside a rectangular query region.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.polygon" title="libpysal.cg.PointLocator.polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code></a>(polygon)</p></td>
+<td><p>Returns the indexed points located inside a polygon</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.proximity" title="libpysal.cg.PointLocator.proximity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">proximity</span></code></a>(origin, r)</p></td>
+<td><p>Returns the indexed points located within some distance of an origin point.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PointLocator.region" title="libpysal.cg.PointLocator.region"><code class="xref py py-obj docutils literal notranslate"><span class="pre">region</span></code></a>(region_rect)</p></td>
+<td><p>Returns the indexed points located inside a rectangular query region.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator.nearest">
+<span class="sig-name descname"><span class="pre">nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PointLocator.nearest" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the nearest point indexed to a query point.</p>
+<p>nearest(Point) -&gt; Point</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>query_point</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <a class="reference internal" href="#libpysal.cg.PointLocator.nearest" title="libpysal.cg.PointLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="go">&#39;(0.0, 0.0)&#39;</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator.overlapping">
+<span class="sig-name descname"><span class="pre">overlapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">region_rect</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#libpysal.cg.PointLocator.overlapping" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the indexed points located inside a rectangular query region.</p>
+<p>region(Rectangle) -&gt; Point list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>region_rect</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">rectangular</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#range" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+<span class="go">3</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator.polygon">
+<span class="sig-name descname"><span class="pre">polygon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygon</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.polygon"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PointLocator.polygon" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the indexed points located inside a polygon</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator.proximity">
+<span class="sig-name descname"><span class="pre">proximity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">origin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.proximity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PointLocator.proximity" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the indexed points located within some distance of an origin point.</p>
+<p>proximity(Point, number) -&gt; Point list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>origin</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">near</span></code></span></dt><dd></dd>
+<dt><strong>r</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">distance</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">origin</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PointLocator.region">
+<span class="sig-name descname"><span class="pre">region</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">region_rect</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PointLocator.region"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PointLocator.region" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the indexed points located inside a rectangular query region.</p>
+<p>region(Rectangle) -&gt; Point list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>region_rect</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">rectangular</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#range" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mf">5.4</span><span class="p">,</span> <span class="mf">1.4</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PointLocator</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+<span class="go">3</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.Polygon</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Polygon"><code class="docutils literal notranslate"><span class="pre">Polygon</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.__init__"><code class="docutils literal notranslate"><span class="pre">Polygon.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.area"><code class="docutils literal notranslate"><span class="pre">Polygon.area</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.bbox"><code class="docutils literal notranslate"><span class="pre">Polygon.bbox</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.bounding_box"><code class="docutils literal notranslate"><span class="pre">Polygon.bounding_box</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.build_quad_tree_structure"><code class="docutils literal notranslate"><span class="pre">Polygon.build_quad_tree_structure()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.centroid"><code class="docutils literal notranslate"><span class="pre">Polygon.centroid</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.contains_point"><code class="docutils literal notranslate"><span class="pre">Polygon.contains_point()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.holes"><code class="docutils literal notranslate"><span class="pre">Polygon.holes</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.len"><code class="docutils literal notranslate"><span class="pre">Polygon.len</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.parts"><code class="docutils literal notranslate"><span class="pre">Polygon.parts</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.perimeter"><code class="docutils literal notranslate"><span class="pre">Polygon.perimeter</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Polygon.vertices"><code class="docutils literal notranslate"><span class="pre">Polygon.vertices</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
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+      
+  <section id="libpysal-cg-polygon">
+<h1>libpysal.cg.Polygon<a class="headerlink" href="#libpysal-cg-polygon" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Polygon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">holes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Polygon" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric representation of polygon objects.
+Returns a polygon created from the objects specified.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>vertices</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of vertices or a list of lists of vertices.</p>
+</dd>
+<dt><strong>holes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of sub-polygons to be considered as holes.
+Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.vertices" title="libpysal.cg.Polygon.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Returns the vertices of the polygon in clockwise order.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.len" title="libpysal.cg.Polygon.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Returns the number of vertices in the polygon.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.perimeter" title="libpysal.cg.Polygon.perimeter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">perimeter</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the perimeter of the polygon.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.bounding_box" title="libpysal.cg.Polygon.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>Returns the bounding box of the polygon.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.bbox" title="libpysal.cg.Polygon.bbox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bbox</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Returns the bounding box of the polygon as a list.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.area" title="libpysal.cg.Polygon.area"><code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Returns the area of the polygon.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.cg.Polygon.centroid" title="libpysal.cg.Polygon.centroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">centroid</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>Returns the centroid of the polygon.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">holes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Polygon.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.__init__" title="libpysal.cg.Polygon.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(vertices[, holes])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.build_quad_tree_structure" title="libpysal.cg.Polygon.build_quad_tree_structure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_quad_tree_structure</span></code></a>()</p></td>
+<td><p>Build the quad tree structure for this polygon.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.contains_point" title="libpysal.cg.Polygon.contains_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">contains_point</span></code></a>(point)</p></td>
+<td><p>Test if a polygon contains a point.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.area" title="libpysal.cg.Polygon.area"><code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code></a></p></td>
+<td><p>Returns the area of the polygon.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.bbox" title="libpysal.cg.Polygon.bbox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bbox</span></code></a></p></td>
+<td><p>Returns the bounding box of the polygon as a list.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.bounding_box" title="libpysal.cg.Polygon.bounding_box"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bounding_box</span></code></a></p></td>
+<td><p>Returns the bounding box of the polygon.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.centroid" title="libpysal.cg.Polygon.centroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">centroid</span></code></a></p></td>
+<td><p>Returns the centroid of the polygon.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.holes" title="libpysal.cg.Polygon.holes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">holes</span></code></a></p></td>
+<td><p>Returns the holes of the polygon in clockwise order.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.len" title="libpysal.cg.Polygon.len"><code class="xref py py-obj docutils literal notranslate"><span class="pre">len</span></code></a></p></td>
+<td><p>Returns the number of vertices in the polygon.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.parts" title="libpysal.cg.Polygon.parts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parts</span></code></a></p></td>
+<td><p>Returns the parts of the polygon in clockwise order.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.perimeter" title="libpysal.cg.Polygon.perimeter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">perimeter</span></code></a></p></td>
+<td><p>Returns the perimeter of the polygon.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Polygon.vertices" title="libpysal.cg.Polygon.vertices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vertices</span></code></a></p></td>
+<td><p>Returns the vertices of the polygon in clockwise order.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.area">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">area</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.area" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the area of the polygon.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">area</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))],</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">area</span>
+<span class="go">99.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.bbox">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">bbox</span></span><a class="headerlink" href="#libpysal.cg.Polygon.bbox" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the bounding box of the polygon as a list.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._bbox</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>The bounding box of the polygon as a list.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.bounding_box</span></code></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.bounding_box">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">bounding_box</span></span><a class="headerlink" href="#libpysal.cg.Polygon.bounding_box" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the bounding box of the polygon.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self._bounding_box</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>The bounding box of the polygon.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">left</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">lower</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">right</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">bounding_box</span><span class="o">.</span><span class="n">upper</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.build_quad_tree_structure">
+<span class="sig-name descname"><span class="pre">build_quad_tree_structure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon.build_quad_tree_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Polygon.build_quad_tree_structure" title="Link to this definition">¶</a></dt>
+<dd><p>Build the quad tree structure for this polygon. Once
+the structure is built, speed for testing if a point is
+inside the ring will be increased significantly.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.centroid">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">centroid</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.centroid" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the centroid of the polygon.</p>
+<p class="rubric">Notes</p>
+<p>The centroid returned by this method is the geometric
+centroid and respects multipart polygons with holes.
+Also known as the ‘center of gravity’ or ‘center of mass’.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))],</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">centroid</span>
+<span class="go">(5.0353535353535355, 5.0353535353535355)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.contains_point">
+<span class="sig-name descname"><span class="pre">contains_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Polygon.contains_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Polygon.contains_point" title="Link to this definition">¶</a></dt>
+<dd><p>Test if a polygon contains a point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>point</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to test for containment.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>contains</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if the polygon contains <code class="docutils literal notranslate"><span class="pre">point</span></code> otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Points falling exactly on polygon edges may yield unpredictable results.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">))]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="go">0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mf">2.9</span><span class="p">))</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
+<span class="go">0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+<span class="go">0</span>
+</pre></div>
+</div>
+<p>Handles holes.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">))],</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">))</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.holes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">holes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.holes" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the holes of the polygon in clockwise order.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))],</span>
+<span class="gp">... </span>    <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">holes</span><span class="p">)</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.len">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">len</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.len" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the number of vertices in the polygon.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">len</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.parts">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">parts</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.parts" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the parts of the polygon in clockwise order.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="p">[</span>
+<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))],</span>
+<span class="gp">... </span>        <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
+<span class="gp">... </span>    <span class="p">]</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">parts</span><span class="p">)</span>
+<span class="go">2</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.perimeter">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">perimeter</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.perimeter" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the perimeter of the polygon.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p</span><span class="o">.</span><span class="n">perimeter</span>
+<span class="go">4.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Polygon.vertices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vertices</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></em><a class="headerlink" href="#libpysal.cg.Polygon.vertices" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the vertices of the polygon in clockwise order.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">p1</span><span class="o">.</span><span class="n">vertices</span><span class="p">)</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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\ No newline at end of file
diff --git a/generated/libpysal.cg.PolygonLocator.html b/generated/libpysal.cg.PolygonLocator.html
new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
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+<li><a class="reference internal" href="#">libpysal.cg.PolygonLocator</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator"><code class="docutils literal notranslate"><span class="pre">PolygonLocator</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.__init__"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.contains_point"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.contains_point()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.inside"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.inside()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.nearest()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.overlapping"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.overlapping()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.proximity"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.proximity()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.PolygonLocator.region"><code class="docutils literal notranslate"><span class="pre">PolygonLocator.region()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
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+      
+  <section id="libpysal-cg-polygonlocator">
+<h1>libpysal.cg.PolygonLocator<a class="headerlink" href="#libpysal-cg-polygonlocator" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">PolygonLocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygons</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator" title="Link to this definition">¶</a></dt>
+<dd><p>An abstract representation of a polygon indexing data structure.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygons</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a polygon locator object.</p>
+<p>__init__(Polygon list) -&gt; PolygonLocator</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>polygons</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">index</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">pl</span><span class="p">,</span> <span class="n">PolygonLocator</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.__init__" title="libpysal.cg.PolygonLocator.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polygons)</p></td>
+<td><p>Returns a polygon locator object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.contains_point" title="libpysal.cg.PolygonLocator.contains_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">contains_point</span></code></a>(point)</p></td>
+<td><p>Returns polygons that contain point</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.inside" title="libpysal.cg.PolygonLocator.inside"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inside</span></code></a>(query_rectangle)</p></td>
+<td><p>Returns polygons that are inside query_rectangle</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a>(query_point[, rule])</p></td>
+<td><p>Returns the nearest polygon indexed to a query point based on various rules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.overlapping" title="libpysal.cg.PolygonLocator.overlapping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">overlapping</span></code></a>(query_rectangle)</p></td>
+<td><p>Returns list of polygons that overlap query_rectangle</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.proximity" title="libpysal.cg.PolygonLocator.proximity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">proximity</span></code></a>(origin, r[, rule])</p></td>
+<td><p>Returns the indexed polygons located within some distance of an origin point based on various rules.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.PolygonLocator.region" title="libpysal.cg.PolygonLocator.region"><code class="xref py py-obj docutils literal notranslate"><span class="pre">region</span></code></a>(region_rect)</p></td>
+<td><p>Returns the indexed polygons located inside a rectangular query region.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.contains_point">
+<span class="sig-name descname"><span class="pre">contains_point</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.contains_point"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.contains_point" title="Link to this definition">¶</a></dt>
+<dd><p>Returns polygons that contain point</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>point: point (x,y)</strong></dt><dd></dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">containing</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+<span class="go">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+<span class="go">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)))</span>
+<span class="go">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+<span class="go">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)))</span>
+<span class="go">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span><span class="o">.</span><span class="n">centroid</span>
+<span class="go">(3.3333333333333335, 1.3333333333333333)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span><span class="o">.</span><span class="n">contains_point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+<span class="go">(3.3333333333333335, 1.3333333333333333)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.inside">
+<span class="sig-name descname"><span class="pre">inside</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query_rectangle</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.inside"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.inside" title="Link to this definition">¶</a></dt>
+<dd><p>Returns polygons that are inside query_rectangle</p>
+<p class="rubric">Notes</p>
+<p>inside means the intersection of the query rectangle and a
+polygon is not empty and is equal to the area of the polygon</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p3</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">inside</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">3</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.nearest">
+<span class="sig-name descname"><span class="pre">nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vertex'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.nearest" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the nearest polygon indexed to a query point based on
+various rules.</p>
+<p>nearest(Polygon) -&gt; Polygon</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>query_point</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">a</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code></span></dt><dd></dd>
+<dt><strong>rule</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">representative</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> <a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> query.</span></dt><dd><p>vertex – measures distance between vertices and query_point
+centroid – measures distance between centroid and
+query_point
+edge   – measures the distance between edges and query_point</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">try</span><span class="p">:</span> <span class="n">n</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">nearest</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
+<span class="gp">... </span><span class="k">except</span> <span class="ne">NotImplementedError</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;future test: str(min(n.vertices())) == (0.0, 1.0)&quot;</span><span class="p">)</span>
+<span class="go">future test: str(min(n.vertices())) == (0.0, 1.0)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.overlapping">
+<span class="sig-name descname"><span class="pre">overlapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query_rectangle</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.overlapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.overlapping" title="Link to this definition">¶</a></dt>
+<dd><p>Returns list of polygons that overlap query_rectangle</p>
+<p class="rubric">Notes</p>
+<p>overlapping means the intersection of the query rectangle and a
+polygon is not empty and is no larger than the area of the polygon</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p3</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span> <span class="n">qr</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">qr</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">res</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">overlapping</span><span class="p">(</span><span class="n">qr</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.proximity">
+<span class="sig-name descname"><span class="pre">proximity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">origin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vertex'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.proximity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.proximity" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the indexed polygons located within some distance of an
+origin point based on various rules.</p>
+<p>proximity(Polygon, number) -&gt; Polygon list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>origin</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">near</span></code></span></dt><dd></dd>
+<dt><strong>r</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">distance</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">from</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">origin</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code></span></dt><dd></dd>
+<dt><strong>rule</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">representative</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">point</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygon</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> <a class="reference internal" href="#libpysal.cg.PolygonLocator.nearest" title="libpysal.cg.PolygonLocator.nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nearest</span></code></a> query.</span></dt><dd><p>vertex – measures distance between vertices and query_point
+centroid – measures distance between centroid and
+query_point
+edge   – measures the distance between edges and query_point</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">try</span><span class="p">:</span>
+<span class="gp">... </span>    <span class="nb">len</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">proximity</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
+<span class="gp">... </span><span class="k">except</span> <span class="ne">NotImplementedError</span><span class="p">:</span>
+<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;future test: len(pl.proximity(Point((0, 0)), 2)) == 2&quot;</span><span class="p">)</span>
+<span class="go">future test: len(pl.proximity(Point((0, 0)), 2)) == 2</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.PolygonLocator.region">
+<span class="sig-name descname"><span class="pre">region</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">region_rect</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/locators.html#PolygonLocator.region"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.PolygonLocator.region" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the indexed polygons located inside a rectangular query region.</p>
+<p>region(Rectangle) -&gt; Polygon list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>region_rect</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">rectangular</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#range" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">find</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">indexed</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">polygons</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">9</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pl</span> <span class="o">=</span> <span class="n">PolygonLocator</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">region</span><span class="p">(</span><span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="go">2</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/generated/libpysal.cg.PolygonLocator.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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index 000000000..b460104ea
--- /dev/null
+++ b/generated/libpysal.cg.RADIUS_EARTH_KM.html
@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
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+     id="dLabelLocalToc"
+     data-toggle="dropdown"
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+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">libpysal.cg.RADIUS_EARTH_KM</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.RADIUS_EARTH_KM"><code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
+<form class="navbar-form navbar-right" action="../search.html" method="get">
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+  <input type="hidden" name="area" value="default" />
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+          
+        </div>
+    </div>
+  </div>
+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-cg-radius-earth-km">
+<h1>libpysal.cg.RADIUS_EARTH_KM<a class="headerlink" href="#libpysal-cg-radius-earth-km" title="Link to this heading">¶</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="libpysal.cg.RADIUS_EARTH_KM">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">RADIUS_EARTH_KM</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">6371.0</span></em><a class="headerlink" href="#libpysal.cg.RADIUS_EARTH_KM" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a string or number to a floating point number, if possible.</p>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
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+  <a href="../_sources/generated/libpysal.cg.RADIUS_EARTH_KM.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/generated/libpysal.cg.RADIUS_EARTH_MILES.html b/generated/libpysal.cg.RADIUS_EARTH_MILES.html
new file mode 100644
index 000000000..95ca10359
--- /dev/null
+++ b/generated/libpysal.cg.RADIUS_EARTH_MILES.html
@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>libpysal.cg.RADIUS_EARTH_MILES &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../genindex.html" />
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+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </button>
+        <a class="navbar-brand" href="../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+
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+          <ul class="nav navbar-nav">
+            
+                <li><a href="../installation.html">Installation</a></li>
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+            
+            
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+     id="dLabelGlobalToc"
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+  <ul class="dropdown-menu globaltoc"
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+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#io">io</a></li>
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+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
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+     data-toggle="dropdown"
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+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">libpysal.cg.RADIUS_EARTH_MILES</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.RADIUS_EARTH_MILES"><code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
+<form class="navbar-form navbar-right" action="../search.html" method="get">
+ <div class="form-group">
+  <input type="text" name="q" class="form-control" placeholder="Search" />
+ </div>
+  <input type="hidden" name="check_keywords" value="yes" />
+  <input type="hidden" name="area" value="default" />
+</form>
+          
+        </div>
+    </div>
+  </div>
+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-cg-radius-earth-miles">
+<h1>libpysal.cg.RADIUS_EARTH_MILES<a class="headerlink" href="#libpysal-cg-radius-earth-miles" title="Link to this heading">¶</a></h1>
+<dl class="py data">
+<dt class="sig sig-object py" id="libpysal.cg.RADIUS_EARTH_MILES">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">RADIUS_EARTH_MILES</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">3958.755865744055</span></em><a class="headerlink" href="#libpysal.cg.RADIUS_EARTH_MILES" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a string or number to a floating point number, if possible.</p>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/generated/libpysal.cg.RADIUS_EARTH_MILES.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/generated/libpysal.cg.Ray.html b/generated/libpysal.cg.Ray.html
new file mode 100644
index 000000000..3c7258733
--- /dev/null
+++ b/generated/libpysal.cg.Ray.html
@@ -0,0 +1,214 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
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+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
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+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../genindex.html" />
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+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
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+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </button>
+        <a class="navbar-brand" href="../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="../installation.html">Installation</a></li>
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+            
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+     id="dLabelGlobalToc"
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+     href="../index.html">Site <b class="caret"></b></a>
+  <ul class="dropdown-menu globaltoc"
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+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#io">io</a></li>
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+</ul>
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+              
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+<li><a class="reference internal" href="#">libpysal.cg.Ray</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Ray"><code class="docutils literal notranslate"><span class="pre">Ray</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Ray.__init__"><code class="docutils literal notranslate"><span class="pre">Ray.__init__()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
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+      
+  <section id="libpysal-cg-ray">
+<h1>libpysal.cg.Ray<a class="headerlink" href="#libpysal-cg-ray" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Ray">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Ray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">origin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">second_p</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Ray"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Ray" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric representation of ray objects.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>origin</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point where the ray originates.</p>
+</dd>
+<dt><strong>second_p</strong></dt><dd><p>The second point specifying the ray (not <code class="docutils literal notranslate"><span class="pre">origin</span></code>.)</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">o</span><span class="p">)</span>
+<span class="go">&#39;(0.0, 0.0)&#39;</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">p</span><span class="p">)</span>
+<span class="go">&#39;(1.0, 0.0)&#39;</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>o</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The origin (point where ray originates). See <code class="docutils literal notranslate"><span class="pre">origin</span></code>.</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The second point on the ray (not the point where the
+ray originates). See <code class="docutils literal notranslate"><span class="pre">second_p</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Ray.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">origin</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">second_p</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Ray.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Ray.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Ray.__init__" title="libpysal.cg.Ray.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(origin, second_p)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.Rectangle</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle"><code class="docutils literal notranslate"><span class="pre">Rectangle</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle.__init__"><code class="docutils literal notranslate"><span class="pre">Rectangle.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle.area"><code class="docutils literal notranslate"><span class="pre">Rectangle.area</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle.height"><code class="docutils literal notranslate"><span class="pre">Rectangle.height</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle.set_centroid"><code class="docutils literal notranslate"><span class="pre">Rectangle.set_centroid()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle.set_scale"><code class="docutils literal notranslate"><span class="pre">Rectangle.set_scale()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.cg.Rectangle.width"><code class="docutils literal notranslate"><span class="pre">Rectangle.width</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-rectangle">
+<h1>libpysal.cg.Rectangle<a class="headerlink" href="#libpysal-cg-rectangle" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">Rectangle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">left</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Rectangle" title="Link to this definition">¶</a></dt>
+<dd><p>Geometric representation of rectangle objects.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">17</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">left</span> <span class="c1">#minx</span>
+<span class="go">-4.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">lower</span> <span class="c1">#miny</span>
+<span class="go">3.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">right</span> <span class="c1">#maxx</span>
+<span class="go">10.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">upper</span> <span class="c1">#maxy</span>
+<span class="go">17.0</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>left</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minimum x-value of the rectangle.</p>
+</dd>
+<dt><strong>lower</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minimum y-value of the rectangle.</p>
+</dd>
+<dt><strong>right</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Maximum x-value of the rectangle.</p>
+</dd>
+<dt><strong>upper</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Maximum y-value of the rectangle.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">left</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">right</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Rectangle.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.__init__" title="libpysal.cg.Rectangle.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(left, lower, right, upper)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.set_centroid" title="libpysal.cg.Rectangle.set_centroid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_centroid</span></code></a>(new_center)</p></td>
+<td><p>Moves the rectangle center to a new specified point.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.set_scale" title="libpysal.cg.Rectangle.set_scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_scale</span></code></a>(scale)</p></td>
+<td><p>Rescales the rectangle around its center.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.area" title="libpysal.cg.Rectangle.area"><code class="xref py py-obj docutils literal notranslate"><span class="pre">area</span></code></a></p></td>
+<td><p>Returns the area of the Rectangle.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.height" title="libpysal.cg.Rectangle.height"><code class="xref py py-obj docutils literal notranslate"><span class="pre">height</span></code></a></p></td>
+<td><p>Returns the height of the Rectangle.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.cg.Rectangle.width" title="libpysal.cg.Rectangle.width"><code class="xref py py-obj docutils literal notranslate"><span class="pre">width</span></code></a></p></td>
+<td><p>Returns the width of the Rectangle.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle.area">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">area</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.Rectangle.area" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the area of the Rectangle.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">area</span>
+<span class="go">16.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle.height">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">height</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.Rectangle.height" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the height of the Rectangle.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">height</span>
+<span class="go">4.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle.set_centroid">
+<span class="sig-name descname"><span class="pre">set_centroid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_center</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle.set_centroid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Rectangle.set_centroid" title="Link to this definition">¶</a></dt>
+<dd><p>Moves the rectangle center to a new specified point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>new_center</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The new location of the centroid of the polygon.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">set_centroid</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">left</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">right</span>
+<span class="go">6.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">lower</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">upper</span>
+<span class="go">6.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle.set_scale">
+<span class="sig-name descname"><span class="pre">set_scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#Rectangle.set_scale"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.Rectangle.set_scale" title="Link to this definition">¶</a></dt>
+<dd><p>Rescales the rectangle around its center.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>scale</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The ratio of the new scale to the old
+scale (e.g. 1.0 is current size).</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">set_scale</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">left</span>
+<span class="go">-2.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">right</span>
+<span class="go">6.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">lower</span>
+<span class="go">-2.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">upper</span>
+<span class="go">6.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.cg.Rectangle.width">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">width</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#libpysal.cg.Rectangle.width" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the width of the Rectangle.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r</span><span class="o">.</span><span class="n">width</span>
+<span class="go">4.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
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diff --git a/generated/libpysal.cg.alpha_shape.html b/generated/libpysal.cg.alpha_shape.html
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+<li><a class="reference internal" href="#">libpysal.cg.alpha_shape</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.alpha_shape"><code class="docutils literal notranslate"><span class="pre">alpha_shape()</span></code></a></li>
+</ul>
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+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-cg-alpha-shape">
+<h1>libpysal.cg.alpha_shape<a class="headerlink" href="#libpysal-cg-alpha-shape" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.alpha_shape">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">alpha_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/alpha_shapes.html#alpha_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.alpha_shape" title="Link to this definition">¶</a></dt>
+<dd><p>Alpha-shape delineation (Edelsbrunner, Kirkpatrick &amp; Seidel, 1983)
+from a collection of points</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>xys</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a></span></dt><dd><p>(N, 2) array with one point per row and coordinates structured as X and Y</p>
+</dd>
+<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Alpha value to delineate the alpha-shape</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>shapes</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GeoSeries</span></code></span></dt><dd><p>Polygon(s) resulting from the alpha shape algorithm. The GeoSeries
+object remains so even if only a single polygon is returned. There is
+no CRS included in the object. Note that the returned shape(s) may
+have holes, as per the definition of the shape in Edselbrunner et al. (1983)</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<dl class="simple">
+<dt>Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</dt><dd><p>a set of points in the plane. IEEE Transactions on information theory,
+29(4), 551-559.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.1</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">alpha_shape</span><span class="p">(</span><span class="n">pts</span><span class="p">,</span> <span class="n">alpha</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span>
+<span class="go">0    POLYGON ((0.00000 1.00000, 3.00000 5.00000, 6....</span>
+<span class="go">dtype: geometry</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span><span class="o">.</span><span class="n">centroid</span>
+<span class="go">0    POINT (4.69048 3.45238)</span>
+<span class="go">dtype: geometry</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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diff --git a/generated/libpysal.cg.alpha_shape_auto.html b/generated/libpysal.cg.alpha_shape_auto.html
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+<li><a class="reference internal" href="#libpysal.cg.alpha_shape_auto"><code class="docutils literal notranslate"><span class="pre">alpha_shape_auto()</span></code></a></li>
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+  <section id="libpysal-cg-alpha-shape-auto">
+<h1>libpysal.cg.alpha_shape_auto<a class="headerlink" href="#libpysal-cg-alpha-shape-auto" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.alpha_shape_auto">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">alpha_shape_auto</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_circles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/alpha_shapes.html#alpha_shape_auto"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.alpha_shape_auto" title="Link to this definition">¶</a></dt>
+<dd><p>Computation of alpha-shape delineation with automated selection of alpha.</p>
+<p>This method uses the algorithm proposed by  Edelsbrunner, Kirkpatrick &amp;
+Seidel (1983) to return the tightest polygon that contains all points in
+<cite>xys</cite>. The algorithm ranks every point based on its radius and iterates
+over each point, checking whether the maximum alpha that would keep the
+point and all the other ones in the set with smaller radii results in a
+single polygon. If that is the case, it moves to the next point;
+otherwise, it retains the previous alpha value and returns the polygon
+as <cite>shapely</cite> geometry. Note that this geometry may have holes.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>xys</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a></span></dt><dd><p>Nx2 array with one point per row and coordinates structured as X and Y</p>
+</dd>
+<dt><strong>step</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>[Optional. Default=1] Number of points in <cite>xys</cite> to jump ahead after
+checking whether the largest possible alpha that includes the point and
+all the other ones with smaller radii</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Boolean</span></code></span></dt><dd><p>[Optional. Default=False] If True, it prints alpha values
+being tried at every step.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>poly</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shapely.Polygon</span></code></span></dt><dd><p>Tightest alpha-shape polygon containing all points in <cite>xys</cite></p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<dl class="simple">
+<dt>Edelsbrunner, H., Kirkpatrick, D., &amp; Seidel, R. (1983). On the shape of</dt><dd><p>a set of points in the plane. IEEE Transactions on information theory,
+29(4), 551-559.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">3</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">alpha_shape_auto</span><span class="p">(</span><span class="n">pts</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span><span class="o">.</span><span class="n">bounds</span>
+<span class="go">(0.0, 1.0, 9.0, 7.0)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="n">poly</span><span class="o">.</span><span class="n">centroid</span><span class="o">.</span><span class="n">y</span>
+<span class="go">(4.690476190476191, 3.4523809523809526)</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+  <section id="libpysal-cg-arcdist">
+<h1>libpysal.cg.arcdist<a class="headerlink" href="#libpysal-cg-arcdist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.arcdist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">arcdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#arcdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.arcdist" title="Link to this definition">¶</a></dt>
+<dd><p>Arc distance between two points on a sphere.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt0</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (longitude,latitude).</p>
+</dd>
+<dt><strong>pt1</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (longitude,latitude).</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The arc distance between <code class="docutils literal notranslate"><span class="pre">pt0</span></code> and <code class="docutils literal notranslate"><span class="pre">pt1</span></code> using supplied <code class="docutils literal notranslate"><span class="pre">radius</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt0</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">180</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">==</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">RADIUS_EARTH_MILES</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">libpysal.cg.arcdist2linear</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.arcdist2linear"><code class="docutils literal notranslate"><span class="pre">arcdist2linear()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
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+      
+  <section id="libpysal-cg-arcdist2linear">
+<h1>libpysal.cg.arcdist2linear<a class="headerlink" href="#libpysal-cg-arcdist2linear" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.arcdist2linear">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">arcdist2linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arc_dist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#arcdist2linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.arcdist2linear" title="Link to this definition">¶</a></dt>
+<dd><p>Convert an arc distance (spherical earth)
+to a linear distance (R3) in the unit sphere.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>arc_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The arc distance to convert.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>linear_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The linear distance conversion of <code class="docutils literal notranslate"><span class="pre">arc_dist</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt0</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">180</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">==</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">RADIUS_EARTH_MILES</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">arcdist2linear</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
+<span class="go">2.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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+    </p>
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+        &copy; Copyright 2018-, pysal developers.<br/>
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diff --git a/generated/libpysal.cg.asShape.html b/generated/libpysal.cg.asShape.html
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+<li><a class="reference internal" href="#">libpysal.cg.asShape</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.asShape"><code class="docutils literal notranslate"><span class="pre">asShape()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-asshape">
+<h1>libpysal.cg.asShape<a class="headerlink" href="#libpysal-cg-asshape" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.asShape">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">asShape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/shapes.html#asShape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.asShape" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a PySAL shape object from <code class="docutils literal notranslate"><span class="pre">obj</span></code>, which
+must support the <code class="docutils literal notranslate"><span class="pre">__geo_interface__</span></code>.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>obj</strong><span class="classifier">{libpysal.cg.{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LineSegment</span></code></a>, <a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line</span></code></a>, <a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ray</span></code></a>, <a class="reference internal" href="libpysal.cg.Chain.html#libpysal.cg.Chain" title="libpysal.cg.Chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Chain</span></code></a>, <a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Polygon</span></code></a>}</span></dt><dd><p>A geometric representation of an object.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>obj</strong><span class="classifier">{libpysal.cg.{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LineSegment</span></code></a>, <a class="reference internal" href="libpysal.cg.Line.html#libpysal.cg.Line" title="libpysal.cg.Line"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Line</span></code></a>, <a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Ray</span></code></a>, <a class="reference internal" href="libpysal.cg.Chain.html#libpysal.cg.Chain" title="libpysal.cg.Chain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Chain</span></code></a>, <a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Polygon</span></code></a>}</span></dt><dd><p>A new geometric representation of the object.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#TypeError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">obj</span></code> is not a supported shape.</p>
+</dd>
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#NotImplementedError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">geo_type</span></code> is not a supported type.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.bbcommon</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.bbcommon"><code class="docutils literal notranslate"><span class="pre">bbcommon()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-bbcommon">
+<h1>libpysal.cg.bbcommon<a class="headerlink" href="#libpysal-cg-bbcommon" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.bbcommon">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">bbcommon</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bbother</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#bbcommon"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.bbcommon" title="Link to this definition">¶</a></dt>
+<dd><p>Old Stars method for bounding box overlap testing.
+Also defined in <code class="docutils literal notranslate"><span class="pre">pysal.weights._cont_binning</span></code>.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>bb</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A bounding box.</p>
+</dd>
+<dt><strong>bbother</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>The bounding box to test against.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>chflag</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">bb</span></code> overlaps <code class="docutils literal notranslate"><span class="pre">bbother</span></code>, otherwise <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">b0</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bbcommon</span><span class="p">(</span><span class="n">b0</span><span class="p">,</span> <span class="n">b1</span><span class="p">)</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+<li><a class="reference internal" href="#libpysal.cg.brute_knn"><code class="docutils literal notranslate"><span class="pre">brute_knn()</span></code></a></li>
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+  <section id="libpysal-cg-brute-knn">
+<h1>libpysal.cg.brute_knn<a class="headerlink" href="#libpysal-cg-brute-knn" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.brute_knn">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">brute_knn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'arc'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#brute_knn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.brute_knn" title="Link to this definition">¶</a></dt>
+<dd><p>Computes a brute-force <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of <span class="math notranslate nohighlight">\(x,y\)</span> pairs.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of points to query.</p>
+</dd>
+<dt><strong>mode</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The mode of distance. Valid modes are <code class="docutils literal notranslate"><span class="pre">'arc'</span></code>
+and <code class="docutils literal notranslate"><span class="pre">'xyz'</span></code>. Default is <code class="docutils literal notranslate"><span class="pre">'arc'</span></code>.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor ID lookup.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.convex_hull</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.convex_hull"><code class="docutils literal notranslate"><span class="pre">convex_hull()</span></code></a></li>
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+  <section id="libpysal-cg-convex-hull">
+<h1>libpysal.cg.convex_hull<a class="headerlink" href="#libpysal-cg-convex-hull" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.convex_hull">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">convex_hull</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">points</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#convex_hull"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.convex_hull" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the convex hull of a set of points.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of points for computing the convex hull.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>stack</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of points representing the convex hull.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">convex_hull</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="go">[(0.0, 0.0), (4.0, 0.0), (4.0, 4.0)]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#libpysal.cg.distance_matrix"><code class="docutils literal notranslate"><span class="pre">distance_matrix()</span></code></a></li>
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+  <section id="libpysal-cg-distance-matrix">
+<h1>libpysal.cg.distance_matrix<a class="headerlink" href="#libpysal-cg-distance-matrix" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.distance_matrix">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">distance_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50000000.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#distance_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.distance_matrix" title="Link to this definition">¶</a></dt>
+<dd><p>Calculate a distance matrix.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>X</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd><p>An <span class="math notranslate nohighlight">\(n \\times k\)</span> array where <span class="math notranslate nohighlight">\(n\)</span> is the number
+of observations and <span class="math notranslate nohighlight">\(k\)</span> is the number of dimensions
+(2 for <span class="math notranslate nohighlight">\(x,y\)</span>).</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski <cite>p</cite>-norm distance metric parameter where
+<span class="math notranslate nohighlight">\(1&lt;=\mathtt{p}&lt;=\infty\)</span>. <code class="docutils literal notranslate"><span class="pre">2</span></code> is Euclidean distance and
+<code class="docutils literal notranslate"><span class="pre">1</span></code> is Manhattan distance. Default is <code class="docutils literal notranslate"><span class="pre">2.0</span></code>.</p>
+</dd>
+<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>If <span class="math notranslate nohighlight">\((\mathtt{n}**2)*32 &gt; \mathtt{threshold}\)</span> use
+<code class="docutils literal notranslate"><span class="pre">scipy.spatial.distance_matrix</span></code> instead of working in RAM,
+this is roughly the amount of RAM (in bytes) that will be used.
+Must be positive. Default is <code class="docutils literal notranslate"><span class="pre">5e7</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>D</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd><p>An n by <span class="math notranslate nohighlight">\(m\)</span> <span class="math notranslate nohighlight">\(p\)</span>-norm distance matrix.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#TypeError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeError</span></code></a></dt><dd><p>Raised when an invalid dimensional array is passed in.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Needs optimization/integration with other weights in PySAL.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">indices</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">distance_matrix</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+<span class="go">array([[0.        , 1.        , 2.        , 1.        , 1.41421356,</span>
+<span class="go">        2.23606798, 2.        , 2.23606798, 2.82842712],</span>
+<span class="go">       [1.        , 0.        , 1.        , 1.41421356, 1.        ,</span>
+<span class="go">        1.41421356, 2.23606798, 2.        , 2.23606798],</span>
+<span class="go">       [2.        , 1.        , 0.        , 2.23606798, 1.41421356,</span>
+<span class="go">        1.        , 2.82842712, 2.23606798, 2.        ],</span>
+<span class="go">       [1.        , 1.41421356, 2.23606798, 0.        , 1.        ,</span>
+<span class="go">        2.        , 1.        , 1.41421356, 2.23606798],</span>
+<span class="go">       [1.41421356, 1.        , 1.41421356, 1.        , 0.        ,</span>
+<span class="go">        1.        , 1.41421356, 1.        , 1.41421356],</span>
+<span class="go">       [2.23606798, 1.41421356, 1.        , 2.        , 1.        ,</span>
+<span class="go">        0.        , 2.23606798, 1.41421356, 1.        ],</span>
+<span class="go">       [2.        , 2.23606798, 2.82842712, 1.        , 1.41421356,</span>
+<span class="go">        2.23606798, 0.        , 1.        , 2.        ],</span>
+<span class="go">       [2.23606798, 2.        , 2.23606798, 1.41421356, 1.        ,</span>
+<span class="go">        1.41421356, 1.        , 0.        , 1.        ],</span>
+<span class="go">       [2.82842712, 2.23606798, 2.        , 2.23606798, 1.41421356,</span>
+<span class="go">        1.        , 2.        , 1.        , 0.        ]])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+<li><a class="reference internal" href="#">libpysal.cg.fast_knn</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.fast_knn"><code class="docutils literal notranslate"><span class="pre">fast_knn()</span></code></a></li>
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+  <section id="libpysal-cg-fast-knn">
+<h1>libpysal.cg.fast_knn<a class="headerlink" href="#libpysal-cg-fast-knn" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.fast_knn">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">fast_knn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#fast_knn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.fast_knn" title="Link to this definition">¶</a></dt>
+<dd><p>Computes <span class="math notranslate nohighlight">\(k\)</span> nearest neighbors on a sphere.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of <span class="math notranslate nohighlight">\(x,y\)</span> pairs.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of points to query.</p>
+</dd>
+<dt><strong>return_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Return distances in the <code class="docutils literal notranslate"><span class="pre">wd</span></code> container object (<code class="docutils literal notranslate"><span class="pre">True</span></code>).
+Default is <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>wn</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor ID lookup.</p>
+</dd>
+<dt><strong>wd</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor distance lookup (optional).</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">libpysal.cg.fast_threshold</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.fast_threshold"><code class="docutils literal notranslate"><span class="pre">fast_threshold()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-fast-threshold">
+<h1>libpysal.cg.fast_threshold<a class="headerlink" href="#libpysal-cg-fast-threshold" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.fast_threshold">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">fast_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#fast_threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.fast_threshold" title="Link to this definition">¶</a></dt>
+<dd><p>Find all neighbors on a sphere within a threshold distance.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pointslist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of lat-lon tuples. This <strong>must</strong> be a list, even for one point.</p>
+</dd>
+<dt><strong>dist: float</strong></dt><dd><p>The threshold distance.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>wd</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>A neighbor distance lookup where the key is the ID
+of a point and the value is a list of IDs for other
+points within <code class="docutils literal notranslate"><span class="pre">dist</span></code> of the key point,</p>
+</dd>
+</dl>
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+<li><a class="reference internal" href="#">libpysal.cg.geogrid</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.geogrid"><code class="docutils literal notranslate"><span class="pre">geogrid()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+      
+  <section id="libpysal-cg-geogrid">
+<h1>libpysal.cg.geogrid<a class="headerlink" href="#libpysal-cg-geogrid" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.geogrid">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">geogrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pup</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdown</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lonx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#geogrid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.geogrid" title="Link to this definition">¶</a></dt>
+<dd><p>Computes a <span class="math notranslate nohighlight">\(k+1\)</span> by <span class="math notranslate nohighlight">\(k+1\)</span> set of grid
+points for a bounding box in lat-lon. Uses <code class="docutils literal notranslate"><span class="pre">geointerpolate</span></code>.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pup</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The lat-lon or lon-lat for the upper left corner of the bounding box.</p>
+</dd>
+<dt><strong>pdown</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The lat-lon or lon-lat for The lower right corner of The bounding box.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>The number of grid cells (grid points will be one more).</p>
+</dd>
+<dt><strong>lonx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>The method to assess the order of the coordinates.
+<code class="docutils literal notranslate"><span class="pre">True</span></code> for (lon,lat); <code class="docutils literal notranslate"><span class="pre">False</span></code> for (lat,lon).
+Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>grid</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of tuples with (lat-lon) or (lon-lat) for grid points,
+row by row, starting with the top row and moving to the bottom;
+coordinate tuples are returned in same order as input.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pup</span> <span class="o">=</span> <span class="p">(</span><span class="mf">42.023768</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.946389</span><span class="p">)</span>       <span class="c1"># Arlington Heights, IL</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pdown</span> <span class="o">=</span> <span class="p">(</span><span class="mf">41.644415</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.524102</span><span class="p">)</span>     <span class="c1"># Hammond, IN</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">geogrid</span><span class="p">(</span><span class="n">pup</span><span class="p">,</span><span class="n">pdown</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="go">[(42.023768, -87.946389),</span>
+<span class="go"> (42.02393997819538, -87.80562679358316),</span>
+<span class="go"> (42.02393997819538, -87.66486420641684),</span>
+<span class="go"> (42.023768, -87.524102),</span>
+<span class="go"> (41.897317, -87.94638900000001),</span>
+<span class="go"> (41.8974888973743, -87.80562679296166),</span>
+<span class="go"> (41.8974888973743, -87.66486420703835),</span>
+<span class="go"> (41.897317, -87.524102),</span>
+<span class="go"> (41.770866000000005, -87.94638900000001),</span>
+<span class="go"> (41.77103781320412, -87.80562679234043),</span>
+<span class="go"> (41.77103781320412, -87.66486420765956),</span>
+<span class="go"> (41.770866000000005, -87.524102),</span>
+<span class="go"> (41.644415, -87.946389),</span>
+<span class="go"> (41.64458672568646, -87.80562679171955),</span>
+<span class="go"> (41.64458672568646, -87.66486420828045),</span>
+<span class="go"> (41.644415, -87.524102)]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+      
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+<li><a class="reference internal" href="#libpysal.cg.geointerpolate"><code class="docutils literal notranslate"><span class="pre">geointerpolate()</span></code></a></li>
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+  <section id="libpysal-cg-geointerpolate">
+<h1>libpysal.cg.geointerpolate<a class="headerlink" href="#libpysal-cg-geointerpolate" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.geointerpolate">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">geointerpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lonx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#geointerpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.geointerpolate" title="Link to this definition">¶</a></dt>
+<dd><p>Finds a point on a sphere along the great circle distance between
+two points on a sphere also known as a way point in great circle navigation.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>p0</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The first point decimal degrees.</p>
+</dd>
+<dt><strong>p1</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The second point decimal degrees.</p>
+</dd>
+<dt><strong>t</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The proportion along great circle distance between <code class="docutils literal notranslate"><span class="pre">p0</span></code>
+and <code class="docutils literal notranslate"><span class="pre">p1</span></code> (e.g., <span class="math notranslate nohighlight">\(\mathtt{t}=0.5\)</span> would find the mid-point).</p>
+</dd>
+<dt><strong>lonx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>The method to assess the order of the coordinates.
+<code class="docutils literal notranslate"><span class="pre">True</span></code> for (lon,lat); <code class="docutils literal notranslate"><span class="pre">False</span></code> for (lat,lon).
+Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>newpx, newpy</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The new point in decimal degrees of (lon-lat) by
+default or (lat-lon) if <code class="docutils literal notranslate"><span class="pre">lonx</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p0</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.893517</span><span class="p">,</span> <span class="mf">41.981417</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.519295</span><span class="p">,</span> <span class="mf">41.657498</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">geointerpolate</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>             <span class="c1"># using lon-lat</span>
+<span class="go">(-87.85592403438788, 41.949079912574796)</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p3</span> <span class="o">=</span> <span class="p">(</span><span class="mf">41.981417</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.893517</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p4</span> <span class="o">=</span> <span class="p">(</span><span class="mf">41.657498</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.519295</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">geointerpolate</span><span class="p">(</span><span class="n">p3</span><span class="p">,</span> <span class="n">p4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="n">lonx</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="c1"># using lat-lon</span>
+<span class="go">(41.949079912574796, -87.85592403438788)</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.get_angle_between</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_angle_between"><code class="docutils literal notranslate"><span class="pre">get_angle_between()</span></code></a></li>
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+  <section id="libpysal-cg-get-angle-between">
+<h1>libpysal.cg.get_angle_between<a class="headerlink" href="#libpysal-cg-get-angle-between" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_angle_between">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_angle_between</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ray1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ray2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_angle_between"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_angle_between" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the angle formed between a pair of rays which share an origin.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ray1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>A ray forming the beginning of the angle measured.</p>
+</dd>
+<dt><strong>ray2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>A ray forming the end of the angle measured.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>angle</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The angle between <code class="docutils literal notranslate"><span class="pre">ray1</span></code> and <code class="docutils literal notranslate"><span class="pre">ray2</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#ValueError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ValueError</span></code></a></dt><dd><p>Raised when rays do not have the same origin.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">get_angle_between</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))),</span>
+<span class="gp">... </span>    <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.get_bounding_box</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_bounding_box"><code class="docutils literal notranslate"><span class="pre">get_bounding_box()</span></code></a></li>
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+      
+  <section id="libpysal-cg-get-bounding-box">
+<h1>libpysal.cg.get_bounding_box<a class="headerlink" href="#libpysal-cg-get-bounding-box" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_bounding_box">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_bounding_box</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_bounding_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_bounding_box" title="Link to this definition">¶</a></dt>
+<dd><p>Find bounding box for a list of geometries.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>items</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>PySAL shapes.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">rect</span></code> = <a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></dt><dd><p>The bounding box for a list of geometries.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span> <span class="o">=</span> <span class="n">get_bounding_box</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">left</span>
+<span class="go">-1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">lower</span>
+<span class="go">5.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">right</span>
+<span class="go">11.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bb</span><span class="o">.</span><span class="n">upper</span>
+<span class="go">12.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
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+<li><a class="reference internal" href="#libpysal.cg.get_point_at_angle_and_dist"><code class="docutils literal notranslate"><span class="pre">get_point_at_angle_and_dist()</span></code></a></li>
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+  <section id="libpysal-cg-get-point-at-angle-and-dist">
+<h1>libpysal.cg.get_point_at_angle_and_dist<a class="headerlink" href="#libpysal-cg-get-point-at-angle-and-dist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_point_at_angle_and_dist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_point_at_angle_and_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dist</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_point_at_angle_and_dist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_point_at_angle_and_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the point at a distance and angle relative to the origin of a ray.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ray</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>The ray to which <code class="docutils literal notranslate"><span class="pre">angle</span></code> and <code class="docutils literal notranslate"><span class="pre">dist</span></code> are relative.</p>
+</dd>
+<dt><strong>angle</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The angle relative to <code class="docutils literal notranslate"><span class="pre">ray</span></code> at which <code class="docutils literal notranslate"><span class="pre">point</span></code> is located.</p>
+</dd>
+<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance from the origin of <code class="docutils literal notranslate"><span class="pre">ray</span></code> at which <code class="docutils literal notranslate"><span class="pre">point</span></code> is located.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>point</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The point at <code class="docutils literal notranslate"><span class="pre">dist</span></code> and <code class="docutils literal notranslate"><span class="pre">angle</span></code> relative to the origin of <code class="docutils literal notranslate"><span class="pre">ray</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ray</span> <span class="o">=</span> <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">get_point_at_angle_and_dist</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">pt</span><span class="p">,</span> <span class="n">Point</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">8</span><span class="p">)</span>
+<span class="go">-1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">pt</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">8</span><span class="p">)</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.get_points_dist</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_points_dist"><code class="docutils literal notranslate"><span class="pre">get_points_dist()</span></code></a></li>
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+      
+  <section id="libpysal-cg-get-points-dist">
+<h1>libpysal.cg.get_points_dist<a class="headerlink" href="#libpysal-cg-get-points-dist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_points_dist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_points_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_points_dist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_points_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the distance between a pair of points.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
+</dd>
+<dt><strong>pt2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>The other point.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance between <code class="docutils literal notranslate"><span class="pre">pt1</span></code> and <code class="docutils literal notranslate"><span class="pre">pt2</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">get_points_dist</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">)))</span>
+<span class="go">4.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">get_points_dist</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
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diff --git a/generated/libpysal.cg.get_polygon_point_dist.html b/generated/libpysal.cg.get_polygon_point_dist.html
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+<li><a class="reference internal" href="#">libpysal.cg.get_polygon_point_dist</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_polygon_point_dist"><code class="docutils literal notranslate"><span class="pre">get_polygon_point_dist()</span></code></a></li>
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+      
+  <section id="libpysal-cg-get-polygon-point-dist">
+<h1>libpysal.cg.get_polygon_point_dist<a class="headerlink" href="#libpysal-cg-get-polygon-point-dist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_polygon_point_dist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_polygon_point_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poly</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_polygon_point_dist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_polygon_point_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the distance between a polygon and point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>poly</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A polygon to compute distance from.</p>
+</dd>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>a point to compute distance from</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance between <code class="docutils literal notranslate"><span class="pre">poly</span></code> and <code class="docutils literal notranslate"><span class="pre">point</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_polygon_point_dist</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_polygon_point_dist</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
+<span class="go">0.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.get_polygon_point_intersect</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_polygon_point_intersect"><code class="docutils literal notranslate"><span class="pre">get_polygon_point_intersect()</span></code></a></li>
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+      
+  <section id="libpysal-cg-get-polygon-point-intersect">
+<h1>libpysal.cg.get_polygon_point_intersect<a class="headerlink" href="#libpysal-cg-get-polygon-point-intersect" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_polygon_point_intersect">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_polygon_point_intersect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poly</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_polygon_point_intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_polygon_point_intersect" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the intersection of a polygon and point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>poly</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A polygon to check for an intersection.</p>
+</dd>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to check <code class="docutils literal notranslate"><span class="pre">poly</span></code> for an intersection.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>ret</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersection of a <code class="docutils literal notranslate"><span class="pre">poly</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code> if one exists, otherwise <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="go">&#39;(0.5, 0.5)&#39;</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_polygon_point_intersect</span><span class="p">(</span><span class="n">poly</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
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new file mode 100644
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+<li><a class="reference internal" href="#libpysal.cg.get_ray_segment_intersect"><code class="docutils literal notranslate"><span class="pre">get_ray_segment_intersect()</span></code></a></li>
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+  <section id="libpysal-cg-get-ray-segment-intersect">
+<h1>libpysal.cg.get_ray_segment_intersect<a class="headerlink" href="#libpysal-cg-get-ray-segment-intersect" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_ray_segment_intersect">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_ray_segment_intersect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_ray_segment_intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_ray_segment_intersect" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the intersection of a ray and line segment.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ray</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Ray.html#libpysal.cg.Ray" title="libpysal.cg.Ray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Ray</span></code></a></span></dt><dd><p>A ray to check for an intersection.</p>
+</dd>
+<dt><strong>seg</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A segment to check for an intersection against <code class="docutils literal notranslate"><span class="pre">ray</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>intersection</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersecting point or line between <code class="docutils literal notranslate"><span class="pre">ray</span></code> and
+<code class="docutils literal notranslate"><span class="pre">seg</span></code> if an intersection exists or <code class="docutils literal notranslate"><span class="pre">None</span></code> if
+<code class="docutils literal notranslate"><span class="pre">ray</span></code> and <code class="docutils literal notranslate"><span class="pre">seg</span></code> do not intersect.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect" title="libpysal.cg.get_segments_intersect"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.get_segments_intersect</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ray</span> <span class="o">=</span> <span class="n">Ray</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_ray_segment_intersect</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">seg</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">Point</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="go">&#39;(0.0, 10.0)&#39;</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_ray_segment_intersect</span><span class="p">(</span><span class="n">ray</span><span class="p">,</span> <span class="n">seg2</span><span class="p">)</span>
+</pre></div>
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+<li><a class="reference internal" href="#">libpysal.cg.get_rectangle_point_intersect</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_rectangle_point_intersect"><code class="docutils literal notranslate"><span class="pre">get_rectangle_point_intersect()</span></code></a></li>
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+  <section id="libpysal-cg-get-rectangle-point-intersect">
+<h1>libpysal.cg.get_rectangle_point_intersect<a class="headerlink" href="#libpysal-cg-get-rectangle-point-intersect" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_rectangle_point_intersect">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_rectangle_point_intersect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rect</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_rectangle_point_intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_rectangle_point_intersect" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the intersection of a rectangle and point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>rect</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle to check for an intersection.</p>
+</dd>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to check <code class="docutils literal notranslate"><span class="pre">rect</span></code> for an intersection.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>pt</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersection of a <code class="docutils literal notranslate"><span class="pre">rect</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code> if one exists, otherwise <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rect</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">rect</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="go">&#39;(1.0, 1.0)&#39;</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_rectangle_point_intersect</span><span class="p">(</span><span class="n">rect</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
+</pre></div>
+</div>
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new file mode 100644
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+<li><a class="reference internal" href="#libpysal.cg.get_rectangle_rectangle_intersection"><code class="docutils literal notranslate"><span class="pre">get_rectangle_rectangle_intersection()</span></code></a></li>
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+  <section id="libpysal-cg-get-rectangle-rectangle-intersection">
+<h1>libpysal.cg.get_rectangle_rectangle_intersection<a class="headerlink" href="#libpysal-cg-get-rectangle-rectangle-intersection" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_rectangle_rectangle_intersection">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_rectangle_rectangle_intersection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">r0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkOverlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_rectangle_rectangle_intersection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_rectangle_rectangle_intersection" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the intersection between two rectangles.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>r0</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle to check for an intersection.</p>
+</dd>
+<dt><strong>r1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle to check for an intersection against <code class="docutils literal notranslate"><span class="pre">r0</span></code>.</p>
+</dd>
+<dt><strong>checkOverlap</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Call <code class="docutils literal notranslate"><span class="pre">bbcommon(r0,</span> <span class="pre">r1)</span></code> prior to complex geometry
+checking. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>. Prior to setting as
+<code class="docutils literal notranslate"><span class="pre">False</span></code> see the Notes section.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>intersection</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>, <a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersecting point, line, or rectangle between
+<code class="docutils literal notranslate"><span class="pre">r0</span></code> and <code class="docutils literal notranslate"><span class="pre">r1</span></code> if an intersection exists or <code class="docutils literal notranslate"><span class="pre">None</span></code>
+if <code class="docutils literal notranslate"><span class="pre">r0</span></code> and <code class="docutils literal notranslate"><span class="pre">r1</span></code> do not intersect.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The algorithm assumes the rectangles overlap. The keyword
+<code class="docutils literal notranslate"><span class="pre">checkOverlap=False</span></code> should be used with extreme caution.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r0</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">9</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r1</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">7</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span> <span class="o">=</span> <span class="n">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span><span class="n">r1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span><span class="p">[:]</span>
+<span class="go">[4.0, 4.0, 6.0, 7.0]</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r0</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r1</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span> <span class="o">=</span> <span class="n">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span><span class="n">r1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span><span class="p">[:]</span>
+<span class="go">[2.0, 1.0, 4.0, 3.0]</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">r0</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">r1</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span> <span class="o">=</span> <span class="n">get_rectangle_rectangle_intersection</span><span class="p">(</span><span class="n">r0</span><span class="p">,</span><span class="n">r1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ri</span><span class="p">[:]</span> <span class="o">==</span> <span class="n">r1</span><span class="p">[:]</span>
+<span class="go">True</span>
+</pre></div>
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+<h1>libpysal.cg.get_segment_point_dist<a class="headerlink" href="#libpysal-cg-get-segment-point-dist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_segment_point_dist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_segment_point_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_segment_point_dist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_segment_point_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Returns (1) the distance between a line segment and point
+and (2) the distance along the segment to the closest location on the
+segment from the point as a ratio of the length of the segment.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>seg</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A line segment to compute distance from.</p>
+</dd>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to compute distance from.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance between <code class="docutils literal notranslate"><span class="pre">seg</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code>.</p>
+</dd>
+<dt><strong>ratio</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance along <code class="docutils literal notranslate"><span class="pre">seg</span></code> to the closest location on
+<code class="docutils literal notranslate"><span class="pre">seg</span></code> from <code class="docutils literal notranslate"><span class="pre">pt</span></code> as a ratio of the length of <code class="docutils literal notranslate"><span class="pre">seg</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
+<span class="go">(5.0, 0.5)</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_segment_point_dist</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
+<span class="go">(0.0, 0.0)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
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+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
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\ No newline at end of file
diff --git a/generated/libpysal.cg.get_segment_point_intersect.html b/generated/libpysal.cg.get_segment_point_intersect.html
new file mode 100644
index 000000000..b06bb11de
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@@ -0,0 +1,195 @@
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+<li><a class="reference internal" href="#">libpysal.cg.get_segment_point_intersect</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_segment_point_intersect"><code class="docutils literal notranslate"><span class="pre">get_segment_point_intersect()</span></code></a></li>
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+      
+  <section id="libpysal-cg-get-segment-point-intersect">
+<h1>libpysal.cg.get_segment_point_intersect<a class="headerlink" href="#libpysal-cg-get-segment-point-intersect" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_segment_point_intersect">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_segment_point_intersect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_segment_point_intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_segment_point_intersect" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the intersection of a segment and point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>seg</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A segment to check for an intersection.</p>
+</dd>
+<dt><strong>pt</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point to check <code class="docutils literal notranslate"><span class="pre">seg</span></code> for an intersection.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>pt</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersection of a <code class="docutils literal notranslate"><span class="pre">seg</span></code> and <code class="docutils literal notranslate"><span class="pre">pt</span></code> if one exists, otherwise <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="go">&#39;(0.0, 5.0)&#39;</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt2</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_segment_point_intersect</span><span class="p">(</span><span class="n">seg</span><span class="p">,</span> <span class="n">pt2</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+      
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+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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\ No newline at end of file
diff --git a/generated/libpysal.cg.get_segments_intersect.html b/generated/libpysal.cg.get_segments_intersect.html
new file mode 100644
index 000000000..e3a2c6a5b
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@@ -0,0 +1,201 @@
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+<li><a class="reference internal" href="#">libpysal.cg.get_segments_intersect</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.get_segments_intersect"><code class="docutils literal notranslate"><span class="pre">get_segments_intersect()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-get-segments-intersect">
+<h1>libpysal.cg.get_segments_intersect<a class="headerlink" href="#libpysal-cg-get-segments-intersect" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_segments_intersect">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_segments_intersect</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seg1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seg2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_segments_intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_segments_intersect" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the intersection of two segments if one exists.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>seg1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>A segment to check for an intersection.</p>
+</dd>
+<dt><strong>seg2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a></span></dt><dd><p>The segment to check against <code class="docutils literal notranslate"><span class="pre">seg1</span></code> for an intersection.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>intersection</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference internal" href="libpysal.cg.LineSegment.html#libpysal.cg.LineSegment" title="libpysal.cg.LineSegment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.LineSegment</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>}</span></dt><dd><p>The intersecting point or line between <code class="docutils literal notranslate"><span class="pre">seg1</span></code> and
+<code class="docutils literal notranslate"><span class="pre">seg2</span></code> if an intersection exists or <code class="docutils literal notranslate"><span class="pre">None</span></code> if
+<code class="docutils literal notranslate"><span class="pre">seg1</span></code> and <code class="docutils literal notranslate"><span class="pre">seg2</span></code> do not intersect.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg1</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">seg2</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">seg1</span><span class="p">,</span> <span class="n">seg2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">Point</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+<span class="go">&#39;(0.0, 5.0)&#39;</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">seg3</span> <span class="o">=</span> <span class="n">LineSegment</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">101</span><span class="p">)))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">i</span> <span class="o">=</span> <span class="n">get_segments_intersect</span><span class="p">(</span><span class="n">seg2</span><span class="p">,</span> <span class="n">seg3</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
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diff --git a/generated/libpysal.cg.get_shared_segments.html b/generated/libpysal.cg.get_shared_segments.html
new file mode 100644
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+<li><a class="reference internal" href="#libpysal.cg.get_shared_segments"><code class="docutils literal notranslate"><span class="pre">get_shared_segments()</span></code></a></li>
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+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-cg-get-shared-segments">
+<h1>libpysal.cg.get_shared_segments<a class="headerlink" href="#libpysal-cg-get-shared-segments" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.get_shared_segments">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">get_shared_segments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poly1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poly2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bool_ret</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#get_shared_segments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.get_shared_segments" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the line segments in common to both polygons.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>poly1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A Polygon.</p>
+</dd>
+<dt><strong>poly2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Polygon.html#libpysal.cg.Polygon" title="libpysal.cg.Polygon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Polygon</span></code></a></span></dt><dd><p>A Polygon.</p>
+</dd>
+<dt><strong>bool_ret</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Return only a <code class="docutils literal notranslate"><span class="pre">bool</span></code>. Default is <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>common</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>The shared line segments between <code class="docutils literal notranslate"><span class="pre">poly1</span></code> and <code class="docutils literal notranslate"><span class="pre">poly2</span></code>.</p>
+</dd>
+<dt><strong>_ret_bool</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Whether <code class="docutils literal notranslate"><span class="pre">poly1</span></code> and <code class="docutils literal notranslate"><span class="pre">poly2</span></code> share a
+segment (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or not (<code class="docutils literal notranslate"><span class="pre">False</span></code>).</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.cg.shapes</span> <span class="kn">import</span> <span class="n">Polygon</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)))</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">+</span><span class="mi">1</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">x</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">poly2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">Point</span><span class="p">,</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)))</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">get_shared_segments</span><span class="p">(</span><span class="n">poly1</span><span class="p">,</span> <span class="n">poly2</span><span class="p">,</span> <span class="n">bool_ret</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">libpysal.cg.harcdist</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.harcdist"><code class="docutils literal notranslate"><span class="pre">harcdist()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-harcdist">
+<h1>libpysal.cg.harcdist<a class="headerlink" href="#libpysal-cg-harcdist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.harcdist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">harcdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lonx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#harcdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.harcdist" title="Link to this definition">¶</a></dt>
+<dd><p>Alternative the arc distance function, uses the haversine formula.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>p0</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The first point decimal degrees.</p>
+</dd>
+<dt><strong>p1</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The second point decimal degrees.</p>
+</dd>
+<dt><strong>lonx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>The method to assess the order of the coordinates.
+<code class="docutils literal notranslate"><span class="pre">True</span></code> for (lon,lat); <code class="docutils literal notranslate"><span class="pre">False</span></code> for (lat,lon).
+Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option. Set to <code class="docutils literal notranslate"><span class="pre">None</span></code> for radians.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>harc_dist</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">harc_dist</span></code></span></dt><dd><p>The distance in units specified, km, miles or radians.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Uses the <code class="docutils literal notranslate"><span class="pre">radangle</span></code> function to compute radian angle.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">p0</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.893517</span><span class="p">,</span> <span class="mf">41.981417</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mf">87.519295</span><span class="p">,</span> <span class="mf">41.657498</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">harcdist</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">)</span>
+<span class="go">47.52873002976876</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">harcdist</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="go">0.007460167953189258</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
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+<li><a class="reference internal" href="#">libpysal.cg.is_clockwise</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.is_clockwise"><code class="docutils literal notranslate"><span class="pre">is_clockwise()</span></code></a></li>
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+      
+  <section id="libpysal-cg-is-clockwise">
+<h1>libpysal.cg.is_clockwise<a class="headerlink" href="#libpysal-cg-is-clockwise" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.is_clockwise">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">is_clockwise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#is_clockwise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.is_clockwise" title="Link to this definition">¶</a></dt>
+<dd><p>Returns whether a list of points describing
+a polygon are clockwise or counterclockwise.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>vertices</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of points that form a single ring.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>clockwise</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if <code class="docutils literal notranslate"><span class="pre">vertices</span></code> are clockwise, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.ccw</span></code></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_clockwise</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">))])</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_clockwise</span><span class="p">([</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">))])</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">v</span> <span class="o">=</span> <span class="p">[</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57798</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.583412</span><span class="p">,</span> <span class="mf">35.174141999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58417999999999</span><span class="p">,</span> <span class="mf">35.174143000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58377999999999</span><span class="p">,</span> <span class="mf">35.175542999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58287999999999</span><span class="p">,</span> <span class="mf">35.180543</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58263099999999</span><span class="p">,</span> <span class="mf">35.181455</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58257999999999</span><span class="p">,</span> <span class="mf">35.181643000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58198299999999</span><span class="p">,</span> <span class="mf">35.184615000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58148</span><span class="p">,</span> <span class="mf">35.187242999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58127999999999</span><span class="p">,</span> <span class="mf">35.188243</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58138</span><span class="p">,</span> <span class="mf">35.188243</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58108</span><span class="p">,</span> <span class="mf">35.189442999999997</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58104</span><span class="p">,</span> <span class="mf">35.189644000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.58028</span><span class="p">,</span> <span class="mf">35.193442999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.580029</span><span class="p">,</span> <span class="mf">35.194541000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57974399999999</span><span class="p">,</span> <span class="mf">35.195785999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.579475</span><span class="p">,</span> <span class="mf">35.196961999999999</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57922699999999</span><span class="p">,</span> <span class="mf">35.198042999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.578397</span><span class="p">,</span> <span class="mf">35.201665999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57827999999999</span><span class="p">,</span> <span class="mf">35.201642999999997</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57737999999999</span><span class="p">,</span> <span class="mf">35.201642999999997</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57697999999999</span><span class="p">,</span> <span class="mf">35.201543000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56436599999999</span><span class="p">,</span> <span class="mf">35.200311999999997</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56058</span><span class="p">,</span> <span class="mf">35.199942999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.197342999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.195842999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.194342999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.193142999999999</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.191873999999999</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.191742999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56048</span><span class="p">,</span> <span class="mf">35.190242999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.188642999999999</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.187242999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.186842999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.186552999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.185842999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.184443000000002</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.182943000000002</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.181342999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.180433000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.179943000000002</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.178542999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.177790999999999</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.177143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.175643999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.174444000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56037999999999</span><span class="p">,</span> <span class="mf">35.174043999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.560526</span><span class="p">,</span> <span class="mf">35.174043999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56478</span><span class="p">,</span> <span class="mf">35.174043999999995</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56627999999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.566541</span><span class="p">,</span> <span class="mf">35.174144999999996</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.569023</span><span class="p">,</span> <span class="mf">35.174157000000001</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56917199999999</span><span class="p">,</span> <span class="mf">35.174157999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.56938</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57061499999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57097999999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57679999999999</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">),</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="o">-</span><span class="mf">106.57798</span><span class="p">,</span> <span class="mf">35.174143999999998</span><span class="p">)</span>
+<span class="gp">... </span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">is_clockwise</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+
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+  <section id="libpysal-cg-is-collinear">
+<h1>libpysal.cg.is_collinear<a class="headerlink" href="#libpysal-cg-is-collinear" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.is_collinear">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">is_collinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#is_collinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.is_collinear" title="Link to this definition">¶</a></dt>
+<dd><p>Returns whether a triplet of points is collinear.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>p1</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
+</dd>
+<dt><strong>p2</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
+</dd>
+<dt><strong>p3</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a></span></dt><dd><p>A point.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>collinear</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if <code class="docutils literal notranslate"><span class="pre">{p1,</span> <span class="pre">p2,</span> <span class="pre">p3}</span></code> are collinear, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_collinear</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">is_collinear</span><span class="p">(</span><span class="n">Point</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.linear2arcdist</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.linear2arcdist"><code class="docutils literal notranslate"><span class="pre">linear2arcdist()</span></code></a></li>
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+      
+  <section id="libpysal-cg-linear2arcdist">
+<h1>libpysal.cg.linear2arcdist<a class="headerlink" href="#libpysal-cg-linear2arcdist" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.linear2arcdist">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">linear2arcdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear_dist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6371.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#linear2arcdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.linear2arcdist" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a linear distance in the unit sphere
+(R3) to an arc distance based on supplied radius.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>linear_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The linear distance to convert.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The radius of a sphere. Default is Earth’s radius in
+kilometers, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_KM</span></code> (<code class="docutils literal notranslate"><span class="pre">6371.0</span></code>). Earth’s
+radius in miles, <code class="docutils literal notranslate"><span class="pre">RADIUS_EARTH_MILES</span></code> (<code class="docutils literal notranslate"><span class="pre">3958.76</span></code>)
+is also an option.
+Source: <a class="reference external" href="http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html">http://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html</a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>arc_dist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The arc distance conversion of <code class="docutils literal notranslate"><span class="pre">linear_dist</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#ValueError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ValueError</span></code></a></dt><dd><p>Raised when <code class="docutils literal notranslate"><span class="pre">linear_dist</span></code> exceeds the diameter of the unit sphere.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">pt0</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pt1</span> <span class="o">=</span> <span class="p">(</span><span class="mi">180</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">arcdist</span><span class="p">(</span><span class="n">pt0</span><span class="p">,</span> <span class="n">pt1</span><span class="p">,</span> <span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">==</span> <span class="n">linear2arcdist</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">libpysal.cg.lonlat</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.lonlat"><code class="docutils literal notranslate"><span class="pre">lonlat()</span></code></a></li>
+</ul>
+</li>
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+      
+  <section id="libpysal-cg-lonlat">
+<h1>libpysal.cg.lonlat<a class="headerlink" href="#libpysal-cg-lonlat" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.lonlat">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">lonlat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pointslist</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#lonlat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.lonlat" title="Link to this definition">¶</a></dt>
+<dd><p>Converts point order from lat-lon tuples to lon-lat (x,y) tuples.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pointslist</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list of lat-lon tuples. This <strong>must</strong> be a list, even for one point.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>newpts</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list with tuples of points in lon-lat order.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[</span>
+<span class="gp">... </span>    <span class="p">(</span><span class="mf">41.981417</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.893517</span><span class="p">),</span> <span class="p">(</span><span class="mf">41.980396</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.776787</span><span class="p">),</span> <span class="p">(</span><span class="mf">41.980906</span><span class="p">,</span> <span class="o">-</span><span class="mf">87.696450</span><span class="p">)</span>
+<span class="gp">... </span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">newpoints</span> <span class="o">=</span> <span class="n">lonlat</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">newpoints</span>
+<span class="go">[(-87.893517, 41.981417), (-87.776787, 41.980396), (-87.69645, 41.980906)]</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#libpysal.cg.point_touches_rectangle"><code class="docutils literal notranslate"><span class="pre">point_touches_rectangle()</span></code></a></li>
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+  <section id="libpysal-cg-point-touches-rectangle">
+<h1>libpysal.cg.point_touches_rectangle<a class="headerlink" href="#libpysal-cg-point-touches-rectangle" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.point_touches_rectangle">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">point_touches_rectangle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rect</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/standalone.html#point_touches_rectangle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.point_touches_rectangle" title="Link to this definition">¶</a></dt>
+<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> (<code class="docutils literal notranslate"><span class="pre">1</span></code>) if the point is in the rectangle
+or touches it’s boundary, otherwise <code class="docutils literal notranslate"><span class="pre">False</span></code> (<code class="docutils literal notranslate"><span class="pre">0</span></code>).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>point</strong><span class="classifier">{<a class="reference internal" href="libpysal.cg.Point.html#libpysal.cg.Point" title="libpysal.cg.Point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Point</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a>}</span></dt><dd><p>A point or point coordinates.</p>
+</dd>
+<dt><strong>rect</strong><span class="classifier"><a class="reference internal" href="libpysal.cg.Rectangle.html#libpysal.cg.Rectangle" title="libpysal.cg.Rectangle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.cg.Rectangle</span></code></a></span></dt><dd><p>A rectangle.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>chflag</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">1</span></code> if <code class="docutils literal notranslate"><span class="pre">point</span></code> is in (or touches
+boundary of) <code class="docutils literal notranslate"><span class="pre">rect</span></code>, otherwise <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rect</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="n">Point</span><span class="p">((</span><span class="mi">11</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">point_touches_rectangle</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">rect</span><span class="p">)</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">point_touches_rectangle</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">rect</span><span class="p">)</span>
+<span class="go">1</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">point_touches_rectangle</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">rect</span><span class="p">)</span>
+<span class="go">0</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.cg.toLngLat</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.toLngLat"><code class="docutils literal notranslate"><span class="pre">toLngLat()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+      
+  <section id="libpysal-cg-tolnglat">
+<h1>libpysal.cg.toLngLat<a class="headerlink" href="#libpysal-cg-tolnglat" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.toLngLat">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">toLngLat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xyz</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#toLngLat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.toLngLat" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a point’s x,y,z to latitude and longitude.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>xyz</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (x,y,z).</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>phi, theta</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point in form (phi, theta) [y,x].</p>
+</dd>
+</dl>
+</dd>
+</dl>
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+
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diff --git a/generated/libpysal.cg.toXYZ.html b/generated/libpysal.cg.toXYZ.html
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+<li><a class="reference internal" href="#">libpysal.cg.toXYZ</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.toXYZ"><code class="docutils literal notranslate"><span class="pre">toXYZ()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-toxyz">
+<h1>libpysal.cg.toXYZ<a class="headerlink" href="#libpysal-cg-toxyz" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.toXYZ">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">toXYZ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pt</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/sphere.html#toXYZ"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.toXYZ" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a point’s latitude and longitude to x,y,z.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>pt</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point assumed to be in form (lng,lat).</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>x, y, z</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>A point in form (x, y, z).</p>
+</dd>
+</dl>
+</dd>
+</dl>
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+
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diff --git a/generated/libpysal.cg.voronoi_frames.html b/generated/libpysal.cg.voronoi_frames.html
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+<li><a class="reference internal" href="#">libpysal.cg.voronoi_frames</a><ul>
+<li><a class="reference internal" href="#libpysal.cg.voronoi_frames"><code class="docutils literal notranslate"><span class="pre">voronoi_frames()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-cg-voronoi-frames">
+<h1>libpysal.cg.voronoi_frames<a class="headerlink" href="#libpysal-cg-voronoi-frames" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.cg.voronoi_frames">
+<span class="sig-prename descclassname"><span class="pre">libpysal.cg.</span></span><span class="sig-name descname"><span class="pre">voronoi_frames</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">points</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'extent'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/cg/voronoi.html#voronoi_frames"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.cg.voronoi_frames" title="Link to this definition">¶</a></dt>
+<dd><p>Composite helper to return Voronoi regions and
+generator points as individual dataframes.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a></span></dt><dd><p>The originator points.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>The distance to ‘points at infinity’ used in building voronoi cells.
+Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+</dd>
+<dt><strong>clip</strong><span class="classifier">{<a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">shapely.geometry.Polygon</span></code>}</span></dt><dd><p>An overloaded option about how to clip the voronoi cells.
+Default is <code class="docutils literal notranslate"><span class="pre">'extent'</span></code>. Options are as follows.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">'none'</span></code>/<code class="docutils literal notranslate"><span class="pre">None</span></code> – No clip is applied. Voronoi cells may be arbitrarily larger that the source map. Note that this may lead to cells that are many orders of magnitude larger in extent than the original map. Not recommended.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'bbox'</span></code>/<code class="docutils literal notranslate"><span class="pre">'extent'</span></code>/<code class="docutils literal notranslate"><span class="pre">'bounding</span> <span class="pre">box'</span></code> – Clip the voronoi cells to the bounding box of the input points.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'chull</span></code>/<code class="docutils literal notranslate"><span class="pre">'convex</span> <span class="pre">hull'</span></code> – Clip the voronoi cells to the convex hull of the input points.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'ashape'</span></code>/<code class="docutils literal notranslate"><span class="pre">'ahull'</span></code> – Clip the voronoi cells to the tightest hull that contains all points (e.g. the smallest alphashape, using <code class="docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape_auto</span></code>).</p></li>
+<li><p>Polygon – Clip to an arbitrary Polygon.</p></li>
+</ul>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>reg_vtx</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>Two <code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> (or <code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> if <code class="docutils literal notranslate"><span class="pre">geopandas</span></code>
+is unavailable) objects–<code class="docutils literal notranslate"><span class="pre">(region_df,</span> <span class="pre">points_df)</span></code>–of finite
+Voronoi polygons and the originator points as geometries.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">geopandas</span></code> is not available the return types will be
+<code class="docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code> objects, each with a geometry column populated
+with PySAL shapes. If <code class="docutils literal notranslate"><span class="pre">geopandas</span></code> is available, return types are
+<code class="docutils literal notranslate"><span class="pre">pandas.GeoDataFrame</span></code> objects with a geometry column populated
+with shapely geometry types.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">10.2</span><span class="p">,</span> <span class="mf">5.1</span><span class="p">),</span> <span class="p">(</span><span class="mf">4.7</span><span class="p">,</span> <span class="mf">2.2</span><span class="p">),</span> <span class="p">(</span><span class="mf">5.3</span><span class="p">,</span> <span class="mf">5.7</span><span class="p">),</span> <span class="p">(</span><span class="mf">2.7</span><span class="p">,</span> <span class="mf">5.3</span><span class="p">)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regions_df</span><span class="p">,</span> <span class="n">points_df</span> <span class="o">=</span> <span class="n">voronoi_frames</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regions_df</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">(4, 1)</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">regions_df</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">points_df</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
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diff --git a/generated/libpysal.examples.available.html b/generated/libpysal.examples.available.html
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+<li><a class="reference internal" href="#">libpysal.examples.available</a><ul>
+<li><a class="reference internal" href="#libpysal.examples.available"><code class="docutils literal notranslate"><span class="pre">available()</span></code></a></li>
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+  <section id="libpysal-examples-available">
+<h1>libpysal.examples.available<a class="headerlink" href="#libpysal-examples-available" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.examples.available">
+<span class="sig-prename descclassname"><span class="pre">libpysal.examples.</span></span><span class="sig-name descname"><span class="pre">available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.2)"><span class="pre">DataFrame</span></a></span></span><a class="reference internal" href="../_modules/libpysal/examples.html#available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.examples.available" title="Link to this definition">¶</a></dt>
+<dd><p>Return a dataframe with available datasets.</p>
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+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.examples.explain">
+<span class="sig-prename descclassname"><span class="pre">libpysal.examples.</span></span><span class="sig-name descname"><span class="pre">explain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><a class="reference internal" href="../_modules/libpysal/examples.html#explain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.examples.explain" title="Link to this definition">¶</a></dt>
+<dd><p>Explain a dataset by name.</p>
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+<li><a class="reference internal" href="#libpysal.examples.get_path"><code class="docutils literal notranslate"><span class="pre">get_path()</span></code></a></li>
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+  <section id="libpysal-examples-get-path">
+<h1>libpysal.examples.get_path<a class="headerlink" href="#libpysal-examples-get-path" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.examples.get_path">
+<span class="sig-prename descclassname"><span class="pre">libpysal.examples.</span></span><span class="sig-name descname"><span class="pre">get_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><a class="reference internal" href="../_modules/libpysal/examples.html#get_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.examples.get_path" title="Link to this definition">¶</a></dt>
+<dd><p>Get the path for a file by searching installed datasets.</p>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
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+<li><a class="reference internal" href="#">libpysal.graph.Graph</a><ul>
+<li><a class="reference internal" href="#libpysal.graph.Graph"><code class="docutils literal notranslate"><span class="pre">Graph</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.graph.Graph.__init__"><code class="docutils literal notranslate"><span class="pre">Graph.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.adjacency"><code class="docutils literal notranslate"><span class="pre">Graph.adjacency</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.asymmetry"><code class="docutils literal notranslate"><span class="pre">Graph.asymmetry()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_block_contiguity"><code class="docutils literal notranslate"><span class="pre">Graph.build_block_contiguity()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_contiguity"><code class="docutils literal notranslate"><span class="pre">Graph.build_contiguity()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_distance_band"><code class="docutils literal notranslate"><span class="pre">Graph.build_distance_band()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_fuzzy_contiguity"><code class="docutils literal notranslate"><span class="pre">Graph.build_fuzzy_contiguity()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_kernel"><code class="docutils literal notranslate"><span class="pre">Graph.build_kernel()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_knn"><code class="docutils literal notranslate"><span class="pre">Graph.build_knn()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.build_triangulation"><code class="docutils literal notranslate"><span class="pre">Graph.build_triangulation()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.cardinalities"><code class="docutils literal notranslate"><span class="pre">Graph.cardinalities</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.component_labels"><code class="docutils literal notranslate"><span class="pre">Graph.component_labels</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.copy"><code class="docutils literal notranslate"><span class="pre">Graph.copy()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.explore"><code class="docutils literal notranslate"><span class="pre">Graph.explore()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.from_W"><code class="docutils literal notranslate"><span class="pre">Graph.from_W()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.from_adjacency"><code class="docutils literal notranslate"><span class="pre">Graph.from_adjacency()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.from_arrays"><code class="docutils literal notranslate"><span class="pre">Graph.from_arrays()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.from_dicts"><code class="docutils literal notranslate"><span class="pre">Graph.from_dicts()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.from_sparse"><code class="docutils literal notranslate"><span class="pre">Graph.from_sparse()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.from_weights_dict"><code class="docutils literal notranslate"><span class="pre">Graph.from_weights_dict()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.higher_order"><code class="docutils literal notranslate"><span class="pre">Graph.higher_order()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.isolates"><code class="docutils literal notranslate"><span class="pre">Graph.isolates</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.lag"><code class="docutils literal notranslate"><span class="pre">Graph.lag()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.n"><code class="docutils literal notranslate"><span class="pre">Graph.n</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.n_components"><code class="docutils literal notranslate"><span class="pre">Graph.n_components</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.n_edges"><code class="docutils literal notranslate"><span class="pre">Graph.n_edges</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.n_nodes"><code class="docutils literal notranslate"><span class="pre">Graph.n_nodes</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.neighbors"><code class="docutils literal notranslate"><span class="pre">Graph.neighbors</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.nonzero"><code class="docutils literal notranslate"><span class="pre">Graph.nonzero</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.pct_nonzero"><code class="docutils literal notranslate"><span class="pre">Graph.pct_nonzero</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.plot"><code class="docutils literal notranslate"><span class="pre">Graph.plot()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.sparse"><code class="docutils literal notranslate"><span class="pre">Graph.sparse</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.to_W"><code class="docutils literal notranslate"><span class="pre">Graph.to_W()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.to_networkx"><code class="docutils literal notranslate"><span class="pre">Graph.to_networkx()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.to_parquet"><code class="docutils literal notranslate"><span class="pre">Graph.to_parquet()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.transform"><code class="docutils literal notranslate"><span class="pre">Graph.transform()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.unique_ids"><code class="docutils literal notranslate"><span class="pre">Graph.unique_ids</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.graph.Graph.weights"><code class="docutils literal notranslate"><span class="pre">Graph.weights</span></code></a></li>
+</ul>
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+  <section id="libpysal-graph-graph">
+<h1>libpysal.graph.Graph<a class="headerlink" href="#libpysal-graph-graph" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.graph.Graph">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.graph.</span></span><span class="sig-name descname"><span class="pre">Graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjacency</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'O'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph" title="Link to this definition">¶</a></dt>
+<dd><p>Graph class encoding spatial weights matrices</p>
+<p>The <a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">Graph</span></code></a> is currently experimental
+and its API is incomplete and unstable.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjacency</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'O'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Weights base class based on adjacency list</p>
+<p>It is recommenced to use one of the <code class="docutils literal notranslate"><span class="pre">from_*</span></code> or <code class="docutils literal notranslate"><span class="pre">build_*</span></code> constructors
+rather than invoking <code class="docutils literal notranslate"><span class="pre">__init__</span></code> directly.</p>
+<p>Each observation needs to be present in the focal,
+at least as a self-loop with a weight 0.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>adjacency</strong><span class="classifier"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></span></dt><dd><p>A MultiIndexed pandas.Series with <code class="docutils literal notranslate"><span class="pre">&quot;focal&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;neigbor&quot;</span></code> levels
+encoding adjacency, and values encoding weights. By convention,
+isolates are encoded as self-loops with a weight 0.</p>
+</dd>
+<dt><strong>transformation</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code> “O”</span></dt><dd><p>weights transformation used to produce the table.</p>
+<ul class="simple">
+<li><p><strong>O</strong> – Original</p></li>
+<li><p><strong>B</strong> – Binary</p></li>
+<li><p><strong>R</strong> – Row-standardization (global sum <span class="math notranslate nohighlight">\(=n\)</span>)</p></li>
+<li><p><strong>D</strong> – Double-standardization (global sum <span class="math notranslate nohighlight">\(=1\)</span>)</p></li>
+<li><p><strong>V</strong> – Variance stabilizing</p></li>
+</ul>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.__init__" title="libpysal.graph.Graph.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(adjacency[, transformation])</p></td>
+<td><p>Weights base class based on adjacency list</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.asymmetry" title="libpysal.graph.Graph.asymmetry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code></a>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_block_contiguity" title="libpysal.graph.Graph.build_block_contiguity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_block_contiguity</span></code></a>(regimes)</p></td>
+<td><p>Generate Graph from block contiguity (regime neighbors)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_contiguity" title="libpysal.graph.Graph.build_contiguity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_contiguity</span></code></a>(geometry[, rook, ...])</p></td>
+<td><p>Generate Graph from geometry based on contiguity</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_distance_band" title="libpysal.graph.Graph.build_distance_band"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_distance_band</span></code></a>(data, threshold[, ...])</p></td>
+<td><p>Generate Graph from geometry based on a distance band</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_fuzzy_contiguity" title="libpysal.graph.Graph.build_fuzzy_contiguity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_fuzzy_contiguity</span></code></a>(geometry[, ...])</p></td>
+<td><p>Generate Graph from fuzzy contiguity</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_kernel" title="libpysal.graph.Graph.build_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_kernel</span></code></a>(data[, kernel, k, bandwidth, ...])</p></td>
+<td><p>Generate Graph from geometry data based on a kernel function</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_knn" title="libpysal.graph.Graph.build_knn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_knn</span></code></a>(data, k[, metric, p, coincident])</p></td>
+<td><p>Generate Graph from geometry data based on k-nearest neighbors search</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.build_triangulation" title="libpysal.graph.Graph.build_triangulation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_triangulation</span></code></a>(data[, method, ...])</p></td>
+<td><p>Generate Graph from geometry based on triangulation</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.copy" title="libpysal.graph.Graph.copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code></a>([deep])</p></td>
+<td><p>Make a copy of this Graph's adjacency table and transformation</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">difference</span></code>(right)</p></td>
+<td><p>Provide the set difference between the graph on the left and the graph on the right.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">equals</span></code>(right)</p></td>
+<td><p>Check that two graphs are identical.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.explore" title="libpysal.graph.Graph.explore"><code class="xref py py-obj docutils literal notranslate"><span class="pre">explore</span></code></a>(gdf[, focal, nodes, color, ...])</p></td>
+<td><p>Plot graph as an interactive Folium Map</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.from_W" title="libpysal.graph.Graph.from_W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_W</span></code></a>(w)</p></td>
+<td><p>Create an experimental Graph from libpysal.weights.W object</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.from_adjacency" title="libpysal.graph.Graph.from_adjacency"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjacency</span></code></a>(adjacency[, focal_col, ...])</p></td>
+<td><p>Create a Graph from a pandas DataFrame formatted as an adjacency list</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.from_arrays" title="libpysal.graph.Graph.from_arrays"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_arrays</span></code></a>(focal_ids, neighbor_ids, weight)</p></td>
+<td><p>Generate Graph from arrays of indices and weights of the same length</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.from_dicts" title="libpysal.graph.Graph.from_dicts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dicts</span></code></a>(neighbors[, weights])</p></td>
+<td><p>Generate Graph from dictionaries of neighbors and weights</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.from_sparse" title="libpysal.graph.Graph.from_sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code></a>(sparse[, ids])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">Graph</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.from_weights_dict" title="libpysal.graph.Graph.from_weights_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_weights_dict</span></code></a>(weights_dict)</p></td>
+<td><p>Generate Graph from a dict of dicts</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.higher_order" title="libpysal.graph.Graph.higher_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">higher_order</span></code></a>([k, shortest_path, diagonal, ...])</p></td>
+<td><p>Contiguity weights object of order <span class="math notranslate nohighlight">\(k\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">intersection</span></code>(right)</p></td>
+<td><p>Returns a binary Graph, that includes only those neighbor pairs that exist in both left and right.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">intersects</span></code>(right)</p></td>
+<td><p>Returns True if left and right share at least one link, irrespective of weights value.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">isomorphic</span></code>(right)</p></td>
+<td><p>Check that two graphs are isomorphic.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">issubgraph</span></code>(right)</p></td>
+<td><p>Return True if every link in the left Graph also occurs in the right Graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.lag" title="libpysal.graph.Graph.lag"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lag</span></code></a>(y)</p></td>
+<td><p>Spatial lag operator</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.plot" title="libpysal.graph.Graph.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>(gdf[, focal, nodes, color, edge_kws, ...])</p></td>
+<td><p>Plot edges and nodes of the Graph</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetric_difference</span></code>(right)</p></td>
+<td><p>Filter out links that are in both left and right Graph objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.to_W" title="libpysal.graph.Graph.to_W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_W</span></code></a>()</p></td>
+<td><p>Convert Graph to a libpysal.weights.W object</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.to_networkx" title="libpysal.graph.Graph.to_networkx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code></a>()</p></td>
+<td><p>Convert Graph to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.to_parquet" title="libpysal.graph.Graph.to_parquet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_parquet</span></code></a>(path, **kwargs)</p></td>
+<td><p>Save Graph to a Apache Parquet</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.transform" title="libpysal.graph.Graph.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(transformation)</p></td>
+<td><p>Transformation of weights</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">union</span></code>(right)</p></td>
+<td><p>Provide the union of two Graph objects, collecing all links that are in either graph.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.adjacency" title="libpysal.graph.Graph.adjacency"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adjacency</span></code></a></p></td>
+<td><p>Return a copy of the adjacency list</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.cardinalities" title="libpysal.graph.Graph.cardinalities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></a></p></td>
+<td><p>Number of neighbors for each observation</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.component_labels" title="libpysal.graph.Graph.component_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></a></p></td>
+<td><p>Get component labels per observation</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.isolates" title="libpysal.graph.Graph.isolates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isolates</span></code></a></p></td>
+<td><p>Index of observations with no neighbors</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.n" title="libpysal.graph.Graph.n"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></a></p></td>
+<td><p>Number of observations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.n_components" title="libpysal.graph.Graph.n_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></a></p></td>
+<td><p>Get a number of connected components</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.n_edges" title="libpysal.graph.Graph.n_edges"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_edges</span></code></a></p></td>
+<td><p>Number of observations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.n_nodes" title="libpysal.graph.Graph.n_nodes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_nodes</span></code></a></p></td>
+<td><p>Number of observations.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.neighbors" title="libpysal.graph.Graph.neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbors</span></code></a></p></td>
+<td><p>Get neighbors dictionary</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.nonzero" title="libpysal.graph.Graph.nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></a></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.pct_nonzero" title="libpysal.graph.Graph.pct_nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></a></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.sparse" title="libpysal.graph.Graph.sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></a></p></td>
+<td><p>Return a scipy.sparse array (COO)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.graph.Graph.unique_ids" title="libpysal.graph.Graph.unique_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unique_ids</span></code></a></p></td>
+<td><p>Unique IDs used in the Graph</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.graph.Graph.weights" title="libpysal.graph.Graph.weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">weights</span></code></a></p></td>
+<td><p>Get weights dictionary</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.adjacency">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">adjacency</span></span><a class="headerlink" href="#libpysal.graph.Graph.adjacency" title="Link to this definition">¶</a></dt>
+<dd><p>Return a copy of the adjacency list</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></dt><dd><p>Underlying adjacency list</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.asymmetry">
+<span class="sig-name descname"><span class="pre">asymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">intrinsic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.asymmetry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.asymmetry" title="Link to this definition">¶</a></dt>
+<dd><p>Asymmetry check.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>intrinsic</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>. Intrinsic symmetry is defined as:</p>
+<div class="math notranslate nohighlight">
+\[w_{i,j} == w_{j,i}\]</div>
+<p>If <code class="docutils literal notranslate"><span class="pre">intrinsic</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> symmetry is defined as:</p>
+<div class="math notranslate nohighlight">
+\[i \in N_j \ \&amp; \ j \in N_i\]</div>
+<p>where <span class="math notranslate nohighlight">\(N_j\)</span> is the set of neighbors for <span class="math notranslate nohighlight">\(j\)</span>,
+e.g., <code class="docutils literal notranslate"><span class="pre">True</span></code> requires equality of the weight to consider
+two links equal, <code class="docutils literal notranslate"><span class="pre">False</span></code> requires only a presence of a link
+with a non-zero weight.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></dt><dd><p>A <code class="docutils literal notranslate"><span class="pre">Series</span></code> of <code class="docutils literal notranslate"><span class="pre">(i,j)</span></code> pairs of asymmetries sorted
+ascending by the focal observation (index value),
+where <code class="docutils literal notranslate"><span class="pre">i</span></code> is the focal and <code class="docutils literal notranslate"><span class="pre">j</span></code> is the neighbor.
+An empty <code class="docutils literal notranslate"><span class="pre">Series</span></code> is returned if no asymmetries are found.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_block_contiguity">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_block_contiguity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">regimes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_block_contiguity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_block_contiguity" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from block contiguity (regime neighbors)</p>
+<p>Block contiguity structures are relevant when defining neighbor relations
+based on membership in a regime. For example, all counties belonging to
+the same state could be defined as neighbors, in an analysis of all
+counties in the US.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>regimes</strong><span class="classifier">list-like</span></dt><dd><p>list-like of regimes. If pandas.Series, its index is used to encode Graph.
+Otherwise a default RangeIndex is used.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding block contiguity</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_contiguity">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_contiguity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">by_perimeter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_contiguity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_contiguity" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from geometry based on contiguity</p>
+<p>Contiguity builder assumes that all geometries are forming a coverage, i.e.
+a non-overlapping mesh and neighbouring geometries share only points or
+segments of their exterior boundaries. In practice, <code class="docutils literal notranslate"><span class="pre">build_contiguity</span></code> is
+capable of creating a Graph of partially overlapping geometries when
+<code class="docutils literal notranslate"><span class="pre">strict=False,</span> <span class="pre">by_perimeter=False</span></code>, but that would not strictly follow the
+definition of queen or rook contiguity.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>geometry</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">shapely.Geometry</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">objects</span></code></span></dt><dd><p>Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case the
+resulting Graph is indexed by the original index. If an array of
+shapely.Geometry objects is passed, Graph will assume a RangeIndex.</p>
+</dd>
+<dt><strong>rook</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Contiguity method. If True, two geometries are considered neighbours if
+they share at least one edge. If False, two geometries are considered
+neighbours if they share at least one vertex. By default True</p>
+</dd>
+<dt><strong>by_perimeter</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>If True, <code class="docutils literal notranslate"><span class="pre">weight</span></code> represents the length of the shared boundary between
+adjacent units, by default False. For row-standardized version of perimeter
+weights, use
+<code class="docutils literal notranslate"><span class="pre">Graph.build_contiguity(gdf,</span> <span class="pre">by_perimeter=True).transform(&quot;r&quot;)</span></code>.</p>
+</dd>
+<dt><strong>strict</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Use the strict topological method. If False, the contiguity is determined
+based on shared coordinates or coordinate sequences representing edges.
+This assumes geometry coverage that is topologically correct. This method
+is faster but can miss some relations. If True, the contiguity is
+determined based on geometric relations that do not require precise
+topology. This method is slower but will result in correct contiguity
+even if the topology of geometries is not optimal. By default False.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding contiguity weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_distance_band">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_distance_band</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_distance_band"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_distance_band" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from geometry based on a distance band</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoSeries.html#geopandas.GeoSeries" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoSeries</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>geometries containing locations to compute the
+delaunay triangulation. If a geopandas object with Point
+geometry is provided, the .geometry attribute is used. If a numpy.ndarray
+with shapely geometry is used, then the coordinates are extracted and used.
+If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y
+coordinates.</p>
+</dd>
+<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance band</p>
+</dd>
+<dt><strong>binary</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>If True <span class="math notranslate nohighlight">\(w_{ij}=1\)</span> if <span class="math notranslate nohighlight">\(d_{i,j}&lt;=threshold\)</span>, otherwise
+<span class="math notranslate nohighlight">\(w_{i,j}=0\)</span>.
+If False <span class="math notranslate nohighlight">\(wij=dij^{alpha}\)</span>, by default True.</p>
+</dd>
+<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>distance decay parameter for weight (default -1.0)
+if alpha is positive the weights will not decline with
+distance. Ignored if <code class="docutils literal notranslate"><span class="pre">binary=True</span></code> or <code class="docutils literal notranslate"><span class="pre">kernel</span></code> is not None.</p>
+</dd>
+<dt><strong>kernel</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>kernel function to use in order to weight the output graph. See
+<a class="reference internal" href="#libpysal.graph.Graph.build_kernel" title="libpysal.graph.Graph.build_kernel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.build_kernel()</span></code></a> for details. Ignored if <code class="docutils literal notranslate"><span class="pre">binary=True</span></code>.</p>
+</dd>
+<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a> (default: <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>)</span></dt><dd><p>distance to use in the kernel computation. Should be on the same scale as
+the input coordinates. Ignored if <code class="docutils literal notranslate"><span class="pre">binary=True</span></code> or <code class="docutils literal notranslate"><span class="pre">kernel=None</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding distance band weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_fuzzy_contiguity">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_fuzzy_contiguity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'intersects'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_fuzzy_contiguity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_fuzzy_contiguity" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from fuzzy contiguity</p>
+<p>Fuzzy contiguity relaxes the notion of contiguity neighbors
+for the case of geometry collections that violate the condition
+of planar enforcement. It handles three types of conditions present
+in such collections that would result in missing links when using
+the regular contiguity methods.</p>
+<p>The first are edges for nearby polygons that should be shared, but are
+digitized separately for the individual polygons and the resulting edges
+do not coincide, but instead the edges intersect. This case can also be
+covered by <code class="docutils literal notranslate"><span class="pre">build_contiguty</span></code> with the <code class="docutils literal notranslate"><span class="pre">strict=False</span></code> parameter.</p>
+<p>The second case is similar to the first, only the resultant edges do not
+intersect but are “close”. The optional buffering of geometry then closes the
+gaps between the polygons and a resulting intersection is encoded as a link.</p>
+<p>The final case arises when one polygon is “inside” a second polygon but is not
+encoded to represent a hole in the containing polygon.</p>
+<p>It is also possible to create a contiguity based on a custom spatial predicate.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>geoms</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">shapely.Geometry</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">objects</span></code></span></dt><dd><p>Could be geopandas.GeoSeries or geopandas.GeoDataFrame, in which case the
+resulting Graph is indexed by the original index. If an array of
+shapely.Geometry objects is passed, Graph will assume a RangeIndex.</p>
+</dd>
+<dt><strong>tolerance</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>The percentage of the length of the minimum side of the bounding rectangle
+for the <code class="docutils literal notranslate"><span class="pre">geoms</span></code> to use in determining the buffering distance. Either
+<code class="docutils literal notranslate"><span class="pre">tolerance</span></code> or <code class="docutils literal notranslate"><span class="pre">buffer</span></code> may be specified but not both.
+By default None.</p>
+</dd>
+<dt><strong>buffer</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Exact buffering distance in the units of <code class="docutils literal notranslate"><span class="pre">geoms.crs</span></code>. Either
+<code class="docutils literal notranslate"><span class="pre">tolerance</span></code> or <code class="docutils literal notranslate"><span class="pre">buffer</span></code> may be specified but not both.
+By default None.</p>
+</dd>
+<dt><strong>predicate</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>The predicate to use for determination of neighbors. Default is
+‘intersects’. If None is passed, neighbours are determined based
+on the intersection of bounding boxes. See the documentation of
+<code class="docutils literal notranslate"><span class="pre">geopandas.GeoSeries.sindex.query</span></code> for allowed predicates.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding fuzzy contiguity</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_kernel">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coincident</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_kernel" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from geometry data based on a kernel function</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoSeries.html#geopandas.GeoSeries" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoSeries</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>geometries over which to compute a kernel. If a geopandas object with Point
+geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray
+with shapely geoemtry is used, then the coordinates are extracted and used.
+If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y
+coordinates. If metric=”precomputed”, data is assumed to contain a
+precomputed distance metric.</p>
+</dd>
+<dt><strong>kernel</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#callable" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">callable()</span></code></a> (default: ‘gaussian’)</span></dt><dd><p>kernel function to apply over the distance matrix computed by <cite>metric</cite>.
+The following kernels are supported:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;triangular&quot;</span></code>:</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;parabolic&quot;</span></code>:</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;gaussian&quot;</span></code>:</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;bisquare&quot;</span></code>:</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;cosine&quot;</span></code>:</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'boxcar'</span></code>/discrete: all distances less than <cite>bandwidth</cite> are 1, and all
+other distances are 0</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;identity&quot;</span></code>/None : do nothing, weight similarity based on raw distance</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">callable</span></code> : a user-defined function that takes the distance vector and
+the bandwidth and returns the kernel: kernel(distances, bandwidth)</p></li>
+</ul>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> (default: <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>)</span></dt><dd><p>number of nearest neighbors used to truncate the kernel. This is assumed
+to be constant across samples. If None, no truncation is conduted.</p>
+</dd>
+<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a> (default: <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>)</span></dt><dd><p>distance to use in the kernel computation. Should be on the same scale as
+the input coordinates.</p>
+</dd>
+<dt><strong>metric</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#callable" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">callable()</span></code></a> (default: ‘euclidean’)</span></dt><dd><p>distance function to apply over the input coordinates. Supported options
+depend on whether or not scikit-learn is installed. If so, then any
+distance function supported by scikit-learn is supported here. Otherwise,
+only euclidean, minkowski, and manhattan/cityblock distances are admitted.</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> (default: 2)</span></dt><dd><p>parameter for minkowski metric, ignored if metric != “minkowski”.</p>
+</dd>
+<dt><strong>coincident: str, optional (default “raise”)</strong></dt><dd><p>Method for handling coincident points when <code class="docutils literal notranslate"><span class="pre">k</span></code> is not None. Options are
+<code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (raising an exception when coincident points are present),
+<code class="docutils literal notranslate"><span class="pre">'jitter'</span></code> (randomly displace coincident points to produce uniqueness), &amp;
+<code class="docutils literal notranslate"><span class="pre">'clique'</span></code> (induce fully-connected sub cliques for coincident points).</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding kernel weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_knn">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_knn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coincident</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_knn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_knn" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from geometry data based on k-nearest neighbors search</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoSeries.html#geopandas.GeoSeries" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoSeries</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>geometries over which to compute a kernel. If a geopandas object with Point
+geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray
+with shapely geoemtry is used, then the coordinates are extracted and used.
+If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y
+coordinates.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors.</p>
+</dd>
+<dt><strong>metric</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#callable" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">callable()</span></code></a> (default: ‘euclidean’)</span></dt><dd><p>distance function to apply over the input coordinates. Supported options
+depend on whether or not scikit-learn is installed. If so, then any
+distance function supported by scikit-learn is supported here. Otherwise,
+only euclidean, minkowski, and manhattan/cityblock distances are admitted.</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> (default: 2)</span></dt><dd><p>parameter for minkowski metric, ignored if metric != “minkowski”.</p>
+</dd>
+<dt><strong>coincident: str, optional (default “raise”)</strong></dt><dd><p>Method for handling coincident points. Options include
+<code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (raising an exception when coincident points are present),
+<code class="docutils literal notranslate"><span class="pre">'jitter'</span></code> (randomly displace coincident points to produce uniqueness), &amp;
+<code class="docutils literal notranslate"><span class="pre">'clique'</span></code> (induce fully-connected sub cliques for coincident points).</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding KNN weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.build_triangulation">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build_triangulation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'delaunay'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">inf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'boxcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'extent'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coincident</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.build_triangulation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.build_triangulation" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from geometry based on triangulation</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoSeries.html#geopandas.GeoSeries" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoSeries</span></code></a>, <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>geometries containing locations to compute the
+delaunay triangulation. If a geopandas object with Point
+geoemtry is provided, the .geometry attribute is used. If a numpy.ndarray
+with shapely geoemtry is used, then the coordinates are extracted and used.
+If a numpy.ndarray of a shape (2,n) is used, it is assumed to contain x, y
+coordinates.</p>
+</dd>
+<dt><strong>method</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, (<code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code> “delaunay”)</span></dt><dd><p>method of extracting the weights from triangulation. Supports:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;delaunay&quot;</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;gabriel&quot;</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;relative_neighborhood&quot;</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">&quot;voronoi&quot;</span></code></p></li>
+</ul>
+</dd>
+<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>distance to use in the kernel computation. Should be on the same scale as
+the input coordinates, by default numpy.inf</p>
+</dd>
+<dt><strong>kernel</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>kernel function to use in order to weight the output graph. See
+<a class="reference internal" href="#libpysal.graph.Graph.build_kernel" title="libpysal.graph.Graph.build_kernel"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.build_kernel()</span></code></a> for details. By default “boxcar”</p>
+</dd>
+<dt><strong>clip</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> (default: ‘bbox’)</span></dt><dd><p>Clipping method when <code class="docutils literal notranslate"><span class="pre">method=&quot;voronoi&quot;</span></code>. Ignored otherwise.
+Default is <code class="docutils literal notranslate"><span class="pre">'extent'</span></code>. Options are as follows.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">'none'</span></code>/<code class="docutils literal notranslate"><span class="pre">None</span></code>: No clip is applied. Voronoi cells may be
+arbitrarily larger that the source map. Note that this may lead to
+cells that are many orders of magnitude larger in extent than the
+original map. Not recommended.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'bbox'</span></code>/<code class="docutils literal notranslate"><span class="pre">'extent'</span></code>/<code class="docutils literal notranslate"><span class="pre">'bounding</span> <span class="pre">box'</span></code>: Clip the voronoi cells to
+the bounding box of the input points.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'chull</span></code>/<code class="docutils literal notranslate"><span class="pre">'convex</span> <span class="pre">hull'</span></code>: Clip the voronoi cells to the convex hull
+of the input points.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'ashape'</span></code>/<code class="docutils literal notranslate"><span class="pre">'ahull'</span></code>: Clip the voronoi cells to the tightest hull
+that contains all points (e.g. the smallest alphashape, using
+<a class="reference internal" href="libpysal.cg.alpha_shape_auto.html#libpysal.cg.alpha_shape_auto" title="libpysal.cg.alpha_shape_auto"><code class="xref py py-func docutils literal notranslate"><span class="pre">libpysal.cg.alpha_shape_auto()</span></code></a>).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">shapely.Polygon</span></code>: Clip to an arbitrary Polygon.</p></li>
+</ul>
+</dd>
+<dt><strong>rook</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Contiguity method when <code class="docutils literal notranslate"><span class="pre">method=&quot;voronoi&quot;</span></code>. Ignored otherwise.
+If True, two geometries are considered neighbours if they
+share at least one edge. If False, two geometries are considered neighbours
+if they share at least one vertex. By default True</p>
+</dd>
+<dt><strong>coincident: str, optional (default “raise”)</strong></dt><dd><p>Method for handling coincident points. Options include
+<code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (raising an exception when coincident points are present),
+<code class="docutils literal notranslate"><span class="pre">'jitter'</span></code> (randomly displace coincident points to produce uniqueness), &amp;
+<code class="docutils literal notranslate"><span class="pre">'clique'</span></code> (induce fully-connected sub cliques for coincident points).</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph encoding triangulation weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.cardinalities">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">cardinalities</span></span><a class="headerlink" href="#libpysal.graph.Graph.cardinalities" title="Link to this definition">¶</a></dt>
+<dd><p>Number of neighbors for each observation</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></dt><dd><p>Series with a number of neighbors per each observation</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.component_labels">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">component_labels</span></span><a class="headerlink" href="#libpysal.graph.Graph.component_labels" title="Link to this definition">¶</a></dt>
+<dd><p>Get component labels per observation</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.array</span></code></a></dt><dd><p>Array of component labels</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.copy" title="Link to this definition">¶</a></dt>
+<dd><p>Make a copy of this Graph’s adjacency table and transformation</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>deep</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Make a deep copy of the adjacency table, by default True</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph as a copy of the original</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.explore">
+<span class="sig-name descname"><span class="pre">explore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gdf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.explore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.explore" title="Link to this definition">¶</a></dt>
+<dd><p>Plot graph as an interactive Folium Map</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>gdf</strong><span class="classifier"><a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>geodataframe used to instantiate to Graph</p>
+</dd>
+<dt><strong>focal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>subset of focal observations to plot in the map, by default None.
+If none, all relationships are plotted</p>
+</dd>
+<dt><strong>nodes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>whether to display observations as nodes in the map, by default True</p>
+</dd>
+<dt><strong>color</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>color applied to nodes and edges, by default “black”</p>
+</dd>
+<dt><strong>edge_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>additional keyword arguments passed to geopandas explore function
+when plotting edges, by default None</p>
+</dd>
+<dt><strong>node_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>additional keyword arguments passed to geopandas explore function
+when plotting nodes, by default None</p>
+</dd>
+<dt><strong>focal_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>additional keyword arguments passed to geopandas explore function
+when plotting focal observations, by default None. Only applicable when
+passing a subset of nodes with the <cite>focal</cite> argument</p>
+</dd>
+<dt><strong>m</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Folilum.Map</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>folium map objecto to plot on top of, by default None</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">folium.Map</span></code></dt><dd><p>folium map</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.from_W">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.from_W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.from_W" title="Link to this definition">¶</a></dt>
+<dd><p>Create an experimental Graph from libpysal.weights.W object</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd></dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph from W</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.from_adjacency">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_adjacency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjacency</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'focal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbor_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'neighbor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.from_adjacency"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.from_adjacency" title="Link to this definition">¶</a></dt>
+<dd><p>Create a Graph from a pandas DataFrame formatted as an adjacency list</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>adjacency</strong><span class="classifier"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></span></dt><dd><p>a dataframe formatted as an ajacency list. Should have columns
+“focal”, “neighbor”, and “weight”, or columns that can be mapped
+to these (e.g. origin, destination, cost)</p>
+</dd>
+<dt><strong>focal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>name of column holding focal/origin index, by default ‘focal’</p>
+</dd>
+<dt><strong>neighbor</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>name of column holding neighbor/destination index, by default ‘neighbor’</p>
+</dd>
+<dt><strong>weight</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>name of column holding weight values, by default ‘weight’</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.from_arrays">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_arrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">focal_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbor_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.from_arrays"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.from_arrays" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from arrays of indices and weights of the same length</p>
+<p>The arrays needs to be sorted in a way ensuring that focal_ids.unique() is
+equal to the index of original observations from which the Graph is being built</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>focal_index</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a></span></dt><dd><p>focal indices</p>
+</dd>
+<dt><strong>neighbor_index</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a></span></dt><dd><p>neighbor indices</p>
+</dd>
+<dt><strong>weight</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a></span></dt><dd><p>weights</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph based on arrays</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.from_dicts">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dicts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">neighbors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.from_dicts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.from_dicts" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from dictionaries of neighbors and weights</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>dictionary of neighbors with the <code class="docutils literal notranslate"><span class="pre">{focal:</span> <span class="pre">[neighbor1,</span> <span class="pre">neighbor2]}</span></code>
+structure</p>
+</dd>
+<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>dictionary of neighbors with the <code class="docutils literal notranslate"><span class="pre">{focal:</span> <span class="pre">[weight1,</span> <span class="pre">weight2]}</span></code>
+structure. If None, assumes binary weights.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph based on dictionaries</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.from_sparse">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sparse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.from_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.from_sparse" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">Graph</span></code> object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.11.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>sparse representation of a graph</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>list-like of ids for geometries that is mappable to
+positions from sparse. If None, the positions are used as labels.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph based on sparse</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.from_weights_dict">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_weights_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.from_weights_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.from_weights_dict" title="Link to this definition">¶</a></dt>
+<dd><p>Generate Graph from a dict of dicts</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>weights_dict</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionaries</span></code></span></dt><dd><p>weights dictionary with the <code class="docutils literal notranslate"><span class="pre">{focal:</span> <span class="pre">{neighbor:</span> <span class="pre">weight}}</span></code> structure.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>libpysal.graph.Graph based on weights dictionary of dictionaries</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.higher_order">
+<span class="sig-name descname"><span class="pre">higher_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shortest_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.higher_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.higher_order" title="Link to this definition">¶</a></dt>
+<dd><p>Contiguity weights object of order <span class="math notranslate nohighlight">\(k\)</span>.</p>
+<p>Proper higher order neighbors are returned such that <span class="math notranslate nohighlight">\(i\)</span> and <span class="math notranslate nohighlight">\(j\)</span>
+are <span class="math notranslate nohighlight">\(k\)</span>-order neighbors if the shortest path from <span class="math notranslate nohighlight">\(i-j\)</span> is of
+length <span class="math notranslate nohighlight">\(k\)</span>.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Order of contiguity. By default 2.</p>
+</dd>
+<dt><strong>shortest_path</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>If True, <span class="math notranslate nohighlight">\(i,j\)</span> and <span class="math notranslate nohighlight">\(k\)</span>-order neighbors if the shortest
+path for <span class="math notranslate nohighlight">\(i,j\)</span> is <span class="math notranslate nohighlight">\(k\)</span>. If False, <span class="math notranslate nohighlight">\(i,j\)</span> are
+<cite>k</cite>-order neighbors if there is a path from <span class="math notranslate nohighlight">\(i,j\)</span> of length
+<span class="math notranslate nohighlight">\(k\)</span>. By default True.</p>
+</dd>
+<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>If True, keep <span class="math notranslate nohighlight">\(k\)</span>-order (<span class="math notranslate nohighlight">\(i,j\)</span>) joins when <span class="math notranslate nohighlight">\(i==j\)</span>.
+If False, remove <span class="math notranslate nohighlight">\(k\)</span>-order (<span class="math notranslate nohighlight">\(i,j\)</span>) joins when
+<span class="math notranslate nohighlight">\(i==j\)</span>. By default False.</p>
+</dd>
+<dt><strong>lower_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>If True, include lower order contiguities. If False return only weights of
+order <span class="math notranslate nohighlight">\(k\)</span>. By default False.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>higher order weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.isolates">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">isolates</span></span><a class="headerlink" href="#libpysal.graph.Graph.isolates" title="Link to this definition">¶</a></dt>
+<dd><p>Index of observations with no neighbors</p>
+<p>Isolates are encoded as a self-loop with
+the weight == 0 in the adjacency table.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.Index</span></code></a></dt><dd><p>Index with a subset of observations that do not have any neighbor</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.lag">
+<span class="sig-name descname"><span class="pre">lag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.lag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.lag" title="Link to this definition">¶</a></dt>
+<dd><p>Spatial lag operator</p>
+<p>If weights are row standardized, returns the mean of each
+observation’s neighbors; if not, returns the weighted sum
+of each observation’s neighbors.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>y</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a></span></dt><dd><p>array-like (N,) shape where N is equal to number of observations in self.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></dt><dd><p>array of numeric values for the spatial lag</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.n">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n</span></span><a class="headerlink" href="#libpysal.graph.Graph.n" title="Link to this definition">¶</a></dt>
+<dd><p>Number of observations.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.n_components">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_components</span></span><a class="headerlink" href="#libpysal.graph.Graph.n_components" title="Link to this definition">¶</a></dt>
+<dd><p>Get a number of connected components</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></dt><dd><p>number of components</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.n_edges">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_edges</span></span><a class="headerlink" href="#libpysal.graph.Graph.n_edges" title="Link to this definition">¶</a></dt>
+<dd><p>Number of observations.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.n_nodes">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_nodes</span></span><a class="headerlink" href="#libpysal.graph.Graph.n_nodes" title="Link to this definition">¶</a></dt>
+<dd><p>Number of observations.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.neighbors">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">neighbors</span></span><a class="headerlink" href="#libpysal.graph.Graph.neighbors" title="Link to this definition">¶</a></dt>
+<dd><p>Get neighbors dictionary</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></dt><dd><p>dict of tuples representing neighbors</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>It is recommended to work directly with <a class="reference internal" href="#libpysal.graph.Graph.adjacency" title="libpysal.graph.Graph.adjacency"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.adjacency()</span></code></a> rather than
+using the <a class="reference internal" href="#libpysal.graph.Graph.neighbors" title="libpysal.graph.Graph.neighbors"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.neighbors()</span></code></a>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.nonzero">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nonzero</span></span><a class="headerlink" href="#libpysal.graph.Graph.nonzero" title="Link to this definition">¶</a></dt>
+<dd><p>Number of nonzero weights.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.pct_nonzero">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pct_nonzero</span></span><a class="headerlink" href="#libpysal.graph.Graph.pct_nonzero" title="Link to this definition">¶</a></dt>
+<dd><p>Percentage of nonzero weights.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gdf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_extent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.plot" title="Link to this definition">¶</a></dt>
+<dd><p>Plot edges and nodes of the Graph</p>
+<p>Creates a <code class="docutils literal notranslate"><span class="pre">maptlotlib</span></code> plot based on the topology stored in the
+Graph and spatial location defined in <code class="docutils literal notranslate"><span class="pre">gdf</span></code>.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>gdf</strong><span class="classifier"><a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>Geometries indexed using the same index as Graph. Geometry types other than
+points are converted to centroids encoding start and end point of Graph
+edges.</p>
+</dd>
+<dt><strong>focal</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hashable</span></code> | <a class="reference external" href="https://numpy.org/doc/stable/glossary.html#term-array_like" title="(in NumPy v1.26)"><span>array_like</span></a>[<code class="xref py py-obj docutils literal notranslate"><span class="pre">hashable</span></code>] | <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>ID or an array-like of IDs of focal geometries whose weights shall be
+plotted. If None, all weights from all focal geometries are plotted.
+By default None</p>
+</dd>
+<dt><strong>nodes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Plot nodes as points, by default True</p>
+</dd>
+<dt><strong>color</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>The color of all objects, by default “k”</p>
+</dd>
+<dt><strong>edge_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">LineCollection</span></code>,
+which provides fine-grained control over the aesthetics
+of the edges in the plot. By default None</p>
+</dd>
+<dt><strong>node_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">ax.scatter</span></code>,
+which provides fine-grained control over the aesthetics
+of the nodes in the plot. By default None</p>
+</dd>
+<dt><strong>focal_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">ax.scatter</span></code>,
+which provides fine-grained control over the aesthetics
+of the focal nodes in the plot on top of generic <code class="docutils literal notranslate"><span class="pre">node_kws</span></code>.
+Values of <code class="docutils literal notranslate"><span class="pre">node_kws</span></code> are updated from <code class="docutils literal notranslate"><span class="pre">focal_kws</span></code>.
+Ignored if <code class="docutils literal notranslate"><span class="pre">focal=None</span></code>. By default None</p>
+</dd>
+<dt><strong>ax</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.axes.Axes</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>Axis on which to plot the weights. If None, a new figure and axis are
+created. By default None</p>
+</dd>
+<dt><strong>figsize</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>figsize used to create a new axis. By default None</p>
+</dd>
+<dt><strong>limit_extent</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">optional</span></code></span></dt><dd><p>limit the extent of the axis to the extent of the plotted graph, by default
+False</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.axes.Axes</span></code></a></dt><dd><p>Axis with the resulting plot</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If you’d like to overlay the actual geometries from the
+<code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code>, create an axis by plotting the <code class="docutils literal notranslate"><span class="pre">GeoDataFrame</span></code>
+and plot the Graph on top.</p>
+<blockquote>
+<div><p>ax = gdf.plot()
+gdf_graph.plot(gdf, ax=ax)</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.sparse">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sparse</span></span><a class="headerlink" href="#libpysal.graph.Graph.sparse" title="Link to this definition">¶</a></dt>
+<dd><p>Return a scipy.sparse array (COO)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.COO</span></code></dt><dd><p>sparse representation of the adjacency</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.to_W">
+<span class="sig-name descname"><span class="pre">to_W</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.to_W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.to_W" title="Link to this definition">¶</a></dt>
+<dd><p>Convert Graph to a libpysal.weights.W object</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></dt><dd><p>representation of graph as a weights.W object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.to_networkx">
+<span class="sig-name descname"><span class="pre">to_networkx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.to_networkx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.to_networkx" title="Link to this definition">¶</a></dt>
+<dd><p>Convert Graph to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p>
+<p>If Graph is symmetric, returns <code class="docutils literal notranslate"><span class="pre">nx.Graph</span></code>, otherwise returns a <code class="docutils literal notranslate"><span class="pre">nx.DiGraph</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://networkx.org/documentation/stable/reference/classes/graph.html#networkx.Graph" title="(in NetworkX v3.2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.Graph</span></code></a> | <a class="reference external" href="https://networkx.org/documentation/stable/reference/classes/digraph.html#networkx.DiGraph" title="(in NetworkX v3.2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.DiGraph</span></code></a></dt><dd><p>Representation of libpysal Graph as networkx graph</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.to_parquet">
+<span class="sig-name descname"><span class="pre">to_parquet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.to_parquet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.to_parquet" title="Link to this definition">¶</a></dt>
+<dd><p>Save Graph to a Apache Parquet</p>
+<p>Graph is serialized to the Apache Parquet using the underlying adjacency
+object stored as a Parquet table and custom metadata containing transformation.</p>
+<p>Requires pyarrow package.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>path</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> | <code class="xref py py-obj docutils literal notranslate"><span class="pre">pyarrow.NativeFile</span></code></span></dt><dd><p>path or any stream supported by pyarrow</p>
+</dd>
+<dt><strong>**kwargs</strong></dt><dd><p>additional keyword arguments passed to pyarrow.parquet.write_table</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_parquet</span></code></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transformation</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/graph/base.html#Graph.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.graph.Graph.transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transformation of weights</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>transformation</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Transformation method. The following are
+valid transformations.</p>
+<ul class="simple">
+<li><p><strong>B</strong> – Binary</p></li>
+<li><p><strong>R</strong> – Row-standardization (global sum <span class="math notranslate nohighlight">\(=n\)</span>)</p></li>
+<li><p><strong>D</strong> – Double-standardization (global sum <span class="math notranslate nohighlight">\(=1\)</span>)</p></li>
+<li><p><strong>V</strong> – Variance stabilizing</p></li>
+</ul>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.graph.Graph" title="libpysal.graph.Graph"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Graph</span></code></a></dt><dd><p>transformed weights</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#ValueError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ValueError</span></code></a></dt><dd><p>Value error for unsupported transformation</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.unique_ids">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">unique_ids</span></span><a class="headerlink" href="#libpysal.graph.Graph.unique_ids" title="Link to this definition">¶</a></dt>
+<dd><p>Unique IDs used in the Graph</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.graph.Graph.weights">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">weights</span></span><a class="headerlink" href="#libpysal.graph.Graph.weights" title="Link to this definition">¶</a></dt>
+<dd><p>Get weights dictionary</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></dt><dd><p>dict of tuples representing weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>It is recommended to work directly with <a class="reference internal" href="#libpysal.graph.Graph.adjacency" title="libpysal.graph.Graph.adjacency"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.adjacency()</span></code></a> rather than
+using the <a class="reference internal" href="#libpysal.graph.Graph.weights" title="libpysal.graph.Graph.weights"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.weights()</span></code></a>.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/generated/libpysal.graph.Graph.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
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+</html>
\ No newline at end of file
diff --git a/generated/libpysal.io.fileio.FileIO.html b/generated/libpysal.io.fileio.FileIO.html
new file mode 100644
index 000000000..820abe92a
--- /dev/null
+++ b/generated/libpysal.io.fileio.FileIO.html
@@ -0,0 +1,398 @@
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+
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+    <title>libpysal.io.fileio.FileIO &#8212; libpysal v4.9.0 Manual</title>
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+        <a class="navbar-brand" href="../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+            
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+                <li><a href="../references.html">References</a></li>
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+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#io">io</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../api.html#experimental">Experimental</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#graph">graph</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
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+     id="dLabelLocalToc"
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+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">libpysal.io.fileio.FileIO</a><ul>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO"><code class="docutils literal notranslate"><span class="pre">FileIO</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.__init__"><code class="docutils literal notranslate"><span class="pre">FileIO.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.by_row"><code class="docutils literal notranslate"><span class="pre">FileIO.by_row</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.cast"><code class="docutils literal notranslate"><span class="pre">FileIO.cast()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.check"><code class="docutils literal notranslate"><span class="pre">FileIO.check()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.close"><code class="docutils literal notranslate"><span class="pre">FileIO.close()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.flush"><code class="docutils literal notranslate"><span class="pre">FileIO.flush()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.get"><code class="docutils literal notranslate"><span class="pre">FileIO.get()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.getType"><code class="docutils literal notranslate"><span class="pre">FileIO.getType()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.ids"><code class="docutils literal notranslate"><span class="pre">FileIO.ids</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.open"><code class="docutils literal notranslate"><span class="pre">FileIO.open()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.rIds"><code class="docutils literal notranslate"><span class="pre">FileIO.rIds</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.read"><code class="docutils literal notranslate"><span class="pre">FileIO.read()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.seek"><code class="docutils literal notranslate"><span class="pre">FileIO.seek()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.tell"><code class="docutils literal notranslate"><span class="pre">FileIO.tell()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.truncate"><code class="docutils literal notranslate"><span class="pre">FileIO.truncate()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.io.fileio.FileIO.write"><code class="docutils literal notranslate"><span class="pre">FileIO.write()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
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+ <div class="form-group">
+  <input type="text" name="q" class="form-control" placeholder="Search" />
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-io-fileio-fileio">
+<h1>libpysal.io.fileio.FileIO<a class="headerlink" href="#libpysal-io-fileio-fileio" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.io.fileio.</span></span><span class="sig-name descname"><span class="pre">FileIO</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataPath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataFormat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO" title="Link to this definition">¶</a></dt>
+<dd><p>Metaclass for supporting spatial data file read and write.</p>
+<p>How this works:</p>
+<p><code class="docutils literal notranslate"><span class="pre">FileIO.open(\*args)</span> <span class="pre">==</span> <span class="pre">FileIO(\*args)</span></code></p>
+<p>When creating a new instance of <cite>FileIO</cite> the <code class="docutils literal notranslate"><span class="pre">.__new__</span></code> method intercepts.
+<code class="docutils literal notranslate"><span class="pre">.__new__</span></code> parses the filename to determine the <code class="docutils literal notranslate"><span class="pre">fileType</span></code>. Next,
+<code class="docutils literal notranslate"><span class="pre">.__registry</span></code> and checked for that type. Each type supports one or more modes
+(<code class="docutils literal notranslate"><span class="pre">['r',</span> <span class="pre">'w',</span> <span class="pre">'a',</span> <span class="pre">etc.]</span></code>). If we support the type and mode, an instance of the
+appropriate handler is created and returned. All handlers must inherit from this
+class, and by doing so are automatically added to the <code class="docutils literal notranslate"><span class="pre">.__registry</span></code> and are
+forced to conform to the prescribed API. The metaclass takes care of the
+registration by parsing the class definition. It doesn’t make much sense to
+treat weights in the same way as shapefiles and dbfs, so…</p>
+<ul class="simple">
+<li><p>… for now we’ll just return an instance of <cite>W</cite> on <code class="docutils literal notranslate"><span class="pre">mode='r'</span></code>.</p></li>
+<li><p>… on <code class="docutils literal notranslate"><span class="pre">mode='w'</span></code>, <code class="docutils literal notranslate"><span class="pre">.write</span></code> will expect an instance of <cite>W</cite>.</p></li>
+</ul>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataPath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataFormat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.__init__" title="libpysal.io.fileio.FileIO.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([dataPath, mode, dataFormat])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.cast" title="libpysal.io.fileio.FileIO.cast"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cast</span></code></a>(key, typ)</p></td>
+<td><p>Cast <code class="docutils literal notranslate"><span class="pre">key</span></code> as <code class="docutils literal notranslate"><span class="pre">typ</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.check" title="libpysal.io.fileio.FileIO.check"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check</span></code></a>()</p></td>
+<td><p>Prints the contents of the registry.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.close" title="libpysal.io.fileio.FileIO.close"><code class="xref py py-obj docutils literal notranslate"><span class="pre">close</span></code></a>()</p></td>
+<td><p>Subclasses should clean themselves up and then call this method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.flush" title="libpysal.io.fileio.FileIO.flush"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flush</span></code></a>()</p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><p></p></dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.get" title="libpysal.io.fileio.FileIO.get"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code></a>(n)</p></td>
+<td><p>Seeks the file to <code class="docutils literal notranslate"><span class="pre">n</span></code> and returns <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.getType" title="libpysal.io.fileio.FileIO.getType"><code class="xref py py-obj docutils literal notranslate"><span class="pre">getType</span></code></a>(dataPath, mode[, dataFormat])</p></td>
+<td><p>Parse the <code class="docutils literal notranslate"><span class="pre">dataPath</span></code> and return the data type.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.open" title="libpysal.io.fileio.FileIO.open"><code class="xref py py-obj docutils literal notranslate"><span class="pre">open</span></code></a>(*args, **kwargs)</p></td>
+<td><p>Alias for <code class="docutils literal notranslate"><span class="pre">FileIO()</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.read" title="libpysal.io.fileio.FileIO.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a>([n])</p></td>
+<td><p>Read at most <code class="docutils literal notranslate"><span class="pre">n</span></code> objects, less if read hits EOF.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.seek" title="libpysal.io.fileio.FileIO.seek"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seek</span></code></a>(n)</p></td>
+<td><p>Seek the <cite>FileObj</cite> to the beginning of the <code class="docutils literal notranslate"><span class="pre">n</span></code>'th record.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.tell" title="libpysal.io.fileio.FileIO.tell"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tell</span></code></a>()</p></td>
+<td><p>Return ID (or offset) of next object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.truncate" title="libpysal.io.fileio.FileIO.truncate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncate</span></code></a>([size])</p></td>
+<td><p>Should be implemented by subclasses and redefine this doc string.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.write" title="libpysal.io.fileio.FileIO.write"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write</span></code></a>(obj)</p></td>
+<td><p>Must be implemented by subclasses that support 'w' subclasses Should increment <code class="docutils literal notranslate"><span class="pre">.pos</span></code>.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.by_row" title="libpysal.io.fileio.FileIO.by_row"><code class="xref py py-obj docutils literal notranslate"><span class="pre">by_row</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.ids" title="libpysal.io.fileio.FileIO.ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ids</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.io.fileio.FileIO.rIds" title="libpysal.io.fileio.FileIO.rIds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rIds</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.by_row">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">by_row</span></span><a class="headerlink" href="#libpysal.io.fileio.FileIO.by_row" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.cast">
+<span class="sig-name descname"><span class="pre">cast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">typ</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.cast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.cast" title="Link to this definition">¶</a></dt>
+<dd><p>Cast <code class="docutils literal notranslate"><span class="pre">key</span></code> as <code class="docutils literal notranslate"><span class="pre">typ</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#TypeError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TypeError</span></code></a></dt><dd><p>Raised when a cast object in not callable.</p>
+</dd>
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#KeyError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KeyError</span></code></a></dt><dd><p>Raised when a key is not present.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.check">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.check" title="Link to this definition">¶</a></dt>
+<dd><p>Prints the contents of the registry.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.close">
+<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.close" title="Link to this definition">¶</a></dt>
+<dd><p>Subclasses should clean themselves up and then call this method.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.flush">
+<span class="sig-name descname"><span class="pre">flush</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.flush"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.flush" title="Link to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#NotImplementedError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.get">
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a></span></span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.get" title="Link to this definition">¶</a></dt>
+<dd><p>Seeks the file to <code class="docutils literal notranslate"><span class="pre">n</span></code> and returns <code class="docutils literal notranslate"><span class="pre">n</span></code>. If <code class="docutils literal notranslate"><span class="pre">.ids</span></code> is set
+<code class="docutils literal notranslate"><span class="pre">n</span></code> should be an id, else, <code class="docutils literal notranslate"><span class="pre">n</span></code> should be an offset.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.getType">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">getType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataPath</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataFormat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.getType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.getType" title="Link to this definition">¶</a></dt>
+<dd><p>Parse the <code class="docutils literal notranslate"><span class="pre">dataPath</span></code> and return the data type.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.ids">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ids</span></span><a class="headerlink" href="#libpysal.io.fileio.FileIO.ids" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.open">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">open</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.open"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.open" title="Link to this definition">¶</a></dt>
+<dd><p>Alias for <code class="docutils literal notranslate"><span class="pre">FileIO()</span></code>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.rIds">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">rIds</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#libpysal.io.fileio.FileIO.rIds" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.read">
+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.read" title="Link to this definition">¶</a></dt>
+<dd><p>Read at most <code class="docutils literal notranslate"><span class="pre">n</span></code> objects, less if read hits EOF.
+If size is negative or omitted read all objects until EOF.
+Returns <code class="docutils literal notranslate"><span class="pre">None</span></code> if EOF is reached before any objects.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#StopIteration" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StopIteration</span></code></a></dt><dd><p>Raised at the EOF.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.seek">
+<span class="sig-name descname"><span class="pre">seek</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.seek"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.seek" title="Link to this definition">¶</a></dt>
+<dd><p>Seek the <cite>FileObj</cite> to the beginning of the <code class="docutils literal notranslate"><span class="pre">n</span></code>’th record.
+If IDs are set, seeks to the beginning of the record at ID, <code class="docutils literal notranslate"><span class="pre">n</span></code>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.tell">
+<span class="sig-name descname"><span class="pre">tell</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.tell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.tell" title="Link to this definition">¶</a></dt>
+<dd><p>Return ID (or offset) of next object.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.truncate">
+<span class="sig-name descname"><span class="pre">truncate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.truncate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.truncate" title="Link to this definition">¶</a></dt>
+<dd><p>Should be implemented by subclasses and redefine this doc string.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#NotImplementedError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.io.fileio.FileIO.write">
+<span class="sig-name descname"><span class="pre">write</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/io/fileio.html#FileIO.write"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.io.fileio.FileIO.write" title="Link to this definition">¶</a></dt>
+<dd><p>Must be implemented by subclasses that support ‘w’ subclasses
+Should increment <code class="docutils literal notranslate"><span class="pre">.pos</span></code>. Subclasses should also check if <code class="docutils literal notranslate"><span class="pre">obj</span></code>
+is an instance of type(list) and redefine this doc string.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Raises<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/exceptions.html#NotImplementedError" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">NotImplementedError</span></code></a></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/generated/libpysal.io.fileio.FileIO.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
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+  </body>
+</html>
\ No newline at end of file
diff --git a/generated/libpysal.io.open.html b/generated/libpysal.io.open.html
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--- /dev/null
+++ b/generated/libpysal.io.open.html
@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>libpysal.io.open &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../genindex.html" />
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+    <link rel="next" title="libpysal.io.fileio.FileIO" href="libpysal.io.fileio.FileIO.html" />
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+
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+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
+    <div class="container">
+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
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+        </button>
+        <a class="navbar-brand" href="../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="../installation.html">Installation</a></li>
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+                <li><a href="../references.html">References</a></li>
+            
+            
+              <li class="dropdown globaltoc-container">
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+     id="dLabelGlobalToc"
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+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-development-version">Installing development version</a></li>
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+<dd><p>alias of <a class="reference internal" href="libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO" title="libpysal.io.fileio.FileIO"><code class="xref py py-class docutils literal notranslate"><span class="pre">FileIO</span></code></a></p>
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+<li><a class="reference internal" href="#">libpysal.weights.Delaunay</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Delaunay"><code class="docutils literal notranslate"><span class="pre">Delaunay</span></code></a><ul>
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+<li><a class="reference internal" href="#libpysal.weights.Delaunay.from_dataframe"><code class="docutils literal notranslate"><span class="pre">Delaunay.from_dataframe()</span></code></a></li>
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+  <section id="libpysal-weights-delaunay">
+<h1>libpysal.weights.Delaunay<a class="headerlink" href="#libpysal-weights-delaunay" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.Delaunay">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Delaunay</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Delaunay"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Delaunay" title="Link to this definition">¶</a></dt>
+<dd><p>Constructor of the Delaunay graph of a set of input points.
+Relies on scipy.spatial.Delaunay and numba to quickly construct
+a graph from the input set of points. Will be slower without numba,
+and will warn if this is missing.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>coordinates</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code>, (N,2)</span></dt><dd><p>numpy array of coordinates containing locations to compute the
+delaunay triangulation</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">argument</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>keyword arguments passed directly to weights.W</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The Delaunay triangulation can result in quite a few non-local links among
+spatial coordinates. For a more useful graph, consider the weights.Voronoi
+constructor or the Gabriel graph.</p>
+<p>The weights.Voronoi class builds a voronoi diagram among the points, clips the
+Voronoi cells, and then constructs an adjacency graph among the clipped cells.
+This graph among the clipped Voronoi cells generally represents the structure
+of local adjacencies better than the “raw” Delaunay graph.</p>
+<p>The weights.gabriel.Gabriel graph constructs a Delaunay graph, but only
+includes the “short” links in the Delaunay graph.</p>
+<p>However, if the unresricted Delaunay triangulation is needed, this class
+will compute it much more quickly than Voronoi(coordinates, clip=None).</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Delaunay.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Delaunay.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Delaunay.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Delaunay.__init__" title="libpysal.weights.Delaunay.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(coordinates, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Delaunay.from_dataframe" title="libpysal.weights.Delaunay.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, ids, use_index])</p></td>
+<td><p>Construct a Delaunay triangulation from a geopandas GeoDataFrame.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code>(*args, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Delaunay.from_dataframe">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Delaunay.from_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Delaunay.from_dataframe" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a Delaunay triangulation from a geopandas GeoDataFrame.
+Not that the input geometries in the dataframe must be Points.
+Polygons or lines must be converted to points (e.g. using
+df.geometry.centroid).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>df</strong><span class="classifier"><a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>GeoDataFrame containing points to construct the Delaunay
+Triangulation.</p>
+</dd>
+<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
+geometries. Defaults to active geometry column.</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>a list-like of ids to use to index the spatial weights object or
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used if <cite>use_index=True</cite> or
+a positional index is used if <cite>use_index=False</cite>.
+Order of the resulting W is not respected from this list.</p>
+</dd>
+<dt><strong>use_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>use index of <cite>df</cite> as <cite>ids</cite> to index the spatial weights object.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>Keyword arguments that are passed downwards to the weights.W
+constructor.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+<li><a class="reference internal" href="#">libpysal.weights.DistanceBand</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.DistanceBand"><code class="docutils literal notranslate"><span class="pre">DistanceBand</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.DistanceBand.__init__"><code class="docutils literal notranslate"><span class="pre">DistanceBand.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.DistanceBand.from_array"><code class="docutils literal notranslate"><span class="pre">DistanceBand.from_array()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.DistanceBand.from_dataframe"><code class="docutils literal notranslate"><span class="pre">DistanceBand.from_dataframe()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.DistanceBand.from_shapefile"><code class="docutils literal notranslate"><span class="pre">DistanceBand.from_shapefile()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-distanceband">
+<h1>libpysal.weights.DistanceBand<a class="headerlink" href="#libpysal-weights-distanceband" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.DistanceBand">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">DistanceBand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_sp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.DistanceBand" title="Link to this definition">¶</a></dt>
+<dd><p>Spatial weights based on distance band.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,k) or KDTree where KDtree.data is array (n,k)
+n observations on k characteristics used to measure
+distances between the n objects</p>
+</dd>
+<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance band</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>DEPRECATED: use <cite>distance_metric</cite>
+Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance</p>
+</dd>
+<dt><strong>binary</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0
+If false wij=dij^{alpha}</p>
+</dd>
+<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance decay parameter for weight (default -1.0)
+if alpha is positive the weights will not decline with
+distance. If binary is True, alpha is ignored</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>values to use for keys of the neighbors and weights dicts</p>
+</dd>
+<dt><strong>build_sp</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>DEPRECATED
+True to build sparse distance matrix and false to build dense
+distance matrix; significant speed gains may be obtained
+dending on the sparsity of the of distance_matrix and
+threshold that is applied</p>
+</dd>
+<dt><strong>silent</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>By default libpysal will print a warning if the
+dataset contains any disconnected observations or
+islands. To silence this warning set this
+parameter to True.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span><span class="o">=</span><span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wcheck</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">W</span><span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">]})</span>
+</pre></div>
+</div>
+<p>WARNING: there is one disconnected observation (no neighbors)
+Island id:  [2]
+&gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=11.2)</p>
+<p>WARNING: there is one disconnected observation (no neighbors)
+Island id:  [2]
+&gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)
+True
+&gt;&gt;&gt; w=libpysal.weights.DistanceBand(points,threshold=14.2)
+&gt;&gt;&gt; wcheck = libpysal.weights.W({0: [1, 3], 1: [0, 3, 4], 2: [4], 3: [1, 0], 4: [5, 2, 1], 5: [4]})
+&gt;&gt;&gt; libpysal.weights.util.neighbor_equality(w, wcheck)
+True</p>
+<p>inverse distance weights</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">DistanceBand</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mf">11.2</span><span class="p">,</span><span class="n">binary</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>WARNING: there is one disconnected observation (no neighbors)
+Island id:  [2]
+&gt;&gt;&gt; w.weights[0]
+[0.1, 0.08944271909999159]
+&gt;&gt;&gt; w.neighbors[0].tolist()
+[1, 3]</p>
+<p>gravity weights</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">DistanceBand</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mf">11.2</span><span class="p">,</span><span class="n">binary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">alpha</span><span class="o">=-</span><span class="mf">2.</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>WARNING: there is one disconnected observation (no neighbors)
+Island id:  [2]
+&gt;&gt;&gt; w.weights[0]
+[0.01, 0.007999999999999998]</p>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>of neighbor weights keyed by observation id</p>
+</dd>
+<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>of neighbors keyed by observation id</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.DistanceBand.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_sp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.__init__" title="Link to this definition">¶</a></dt>
+<dd><p>Casting to floats is a work around for a bug in scipy.spatial.
+See detail in pysal issue #126.</p>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.__init__" title="libpysal.weights.DistanceBand.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(data, threshold[, p, alpha, ...])</p></td>
+<td><p>Casting to floats is a work around for a bug in scipy.spatial.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.from_array" title="libpysal.weights.DistanceBand.from_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_array</span></code></a>(array, threshold, **kwargs)</p></td>
+<td><p>Construct a DistanceBand weights from an array.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.from_dataframe" title="libpysal.weights.DistanceBand.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df, threshold[, geom_col, ...])</p></td>
+<td><p>Make DistanceBand weights from a dataframe.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.DistanceBand.from_shapefile" title="libpysal.weights.DistanceBand.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath, threshold[, idVariable])</p></td>
+<td><p>Distance-band based weights from shapefile</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.DistanceBand.from_array">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.from_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.from_array" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a DistanceBand weights from an array. Supports all the same options
+as <a class="reference internal" href="#libpysal.weights.DistanceBand" title="libpysal.weights.DistanceBand"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.DistanceBand</span></code></a></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.DistanceBand.from_dataframe">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.from_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.from_dataframe" title="Link to this definition">¶</a></dt>
+<dd><p>Make DistanceBand weights from a dataframe.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.dataframe</span></code></span></dt><dd><p>a dataframe with a geometry column that can be used to
+construct a W object</p>
+</dd>
+<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
+geometries. Defaults to active geometry column.</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>a list-like of ids to use to index the spatial weights object or
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used if <cite>use_index=True</cite> or
+a positional index is used if <cite>use_index=False</cite>.
+Order of the resulting W is not respected from this list.</p>
+</dd>
+<dt><strong>use_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>use index of <cite>df</cite> as <cite>ids</cite> to index the spatial weights object.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.DistanceBand.from_shapefile">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#DistanceBand.from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.DistanceBand.from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Distance-band based weights from shapefile</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of column in shapefile’s DBF to use for ids</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.Kernel.html#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Kernel</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Weights</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Object</span></code></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
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+      
+  </div>
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+  <a href="../_sources/generated/libpysal.weights.DistanceBand.rst.txt"
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+<li><a class="reference internal" href="#">libpysal.weights.Gabriel</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Gabriel"><code class="docutils literal notranslate"><span class="pre">Gabriel</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Gabriel.__init__"><code class="docutils literal notranslate"><span class="pre">Gabriel.__init__()</span></code></a></li>
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+      
+  <section id="libpysal-weights-gabriel">
+<h1>libpysal.weights.Gabriel<a class="headerlink" href="#libpysal-weights-gabriel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.Gabriel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Gabriel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Gabriel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Gabriel" title="Link to this definition">¶</a></dt>
+<dd><p>Constructs the Gabriel graph of a set of points. This graph is a subset of
+the Delaunay triangulation where only “short” links are retained. This
+function is also accelerated using numba, and implemented on top of the
+scipy.spatial.Delaunay class.</p>
+<p>For a link (i,j) connecting node i to j in the Delaunay triangulation
+to be retained in the Gabriel graph, it must pass a point set exclusion test:</p>
+<ol class="arabic simple">
+<li><p>Construct the circle C_ij containing link (i,j) as its diameter</p></li>
+<li><p>If any other node k is contained within C_ij, then remove link (i,j)
+from the graph.</p></li>
+<li><p>Once all links are evaluated, the remaining graph is the Gabriel graph.</p></li>
+</ol>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>coordinates</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code>, (N,2)</span></dt><dd><p>numpy array of coordinates containing locations to compute the
+delaunay triangulation</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">argument</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>keyword arguments passed directly to weights.W</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Gabriel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Gabriel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Gabriel.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Gabriel.__init__" title="libpysal.weights.Gabriel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(coordinates, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code>(df[, geom_col, ids, use_index])</p></td>
+<td><p>Construct a Delaunay triangulation from a geopandas GeoDataFrame.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code>(*args, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.weights.KNN</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.KNN"><code class="docutils literal notranslate"><span class="pre">KNN</span></code></a><ul>
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+<li><a class="reference internal" href="#libpysal.weights.KNN.from_shapefile"><code class="docutils literal notranslate"><span class="pre">KNN.from_shapefile()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.KNN.reweight"><code class="docutils literal notranslate"><span class="pre">KNN.reweight()</span></code></a></li>
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+      
+  <section id="libpysal-weights-knn">
+<h1>libpysal.weights.KNN<a class="headerlink" href="#libpysal-weights-knn" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.KNN">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">KNN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.KNN" title="Link to this definition">¶</a></dt>
+<dd><p>Creates nearest neighbor weights matrix based on k nearest
+neighbors.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>kdtree</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a></span></dt><dd><p>PySAL KDTree or ArcKDTree where KDtree.data is array (n,k)
+n observations on k characteristics used to measure
+distances between the n objects</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance
+Ignored if the KDTree is an ArcKDTree</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>identifiers to attach to each observation</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance
+Weights object with binary weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Ties between neighbors of equal distance are arbitrarily broken.</p>
+<p>Further, if many points occupy the same spatial location (i.e. observations are
+coincident), then you may need to increase k for those observations to
+acquire neighbors at different spatial locations. For example, if five
+points are coincident, then their four nearest neighbors will all
+occupy the same spatial location; only the fifth nearest neighbor will
+result in those coincident points becoming connected to the graph as a
+whole.</p>
+<p>Solutions to this problem include jittering the points (by adding
+a small random value to each observation’s location) or by adding
+higher-k neighbors only to the coincident points, using the
+weights.w_sets.w_union() function.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kd</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">cg</span><span class="o">.</span><span class="n">KDTree</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">points</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">KNN</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">{1: 1.0, 3: 1.0}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">{0: 1.0, 3: 1.0}</span>
+</pre></div>
+</div>
+<p>now with 1 rather than 0 offset</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">KNN</span><span class="p">(</span><span class="n">kd</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">{2: 1.0, 4: 1.0}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+<span class="go">{1: 1.0, 4: 1.0}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="mi">0</span> <span class="ow">in</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.KNN.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.KNN.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.__init__" title="libpysal.weights.KNN.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(data[, k, p, ids, radius, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.from_array" title="libpysal.weights.KNN.from_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_array</span></code></a>(array, *args, **kwargs)</p></td>
+<td><p>Creates nearest neighbor weights matrix based on k nearest neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.KNN.from_dataframe" title="libpysal.weights.KNN.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, ids, use_index])</p></td>
+<td><p>Make KNN weights from a dataframe.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.from_shapefile" title="libpysal.weights.KNN.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath, *args, **kwargs)</p></td>
+<td><p>Nearest neighbor weights from a shapefile.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.KNN.reweight" title="libpysal.weights.KNN.reweight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reweight</span></code></a>([k, p, new_data, new_ids, inplace])</p></td>
+<td><p>Redo K-Nearest Neighbor weights construction using given parameters</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.KNN.from_array">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.from_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.KNN.from_array" title="Link to this definition">¶</a></dt>
+<dd><p>Creates nearest neighbor weights matrix based on k nearest
+neighbors.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>array</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np.ndarray</span></code></a></span></dt><dd><p>(n, k) array representing n observations on
+k characteristics used to measure distances
+between the n objects</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">see</span></code> <a class="reference internal" href="libpysal.weights.Rook.html#libpysal.weights.Rook" title="libpysal.weights.Rook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rook</span></code></a></span></dt><dd></dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance
+Weights object with binary weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Ties between neighbors of equal distance are arbitrarily broken.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">KNN</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">{1: 1.0, 3: 1.0}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">{0: 1.0, 3: 1.0}</span>
+</pre></div>
+</div>
+<p>now with 1 rather than 0 offset</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_array</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">{2: 1.0, 4: 1.0}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnn2</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+<span class="go">{1: 1.0, 4: 1.0}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="mi">0</span> <span class="ow">in</span> <span class="n">wnn2</span><span class="o">.</span><span class="n">neighbors</span>
+<span class="go">False</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.KNN.from_dataframe">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.from_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.KNN.from_dataframe" title="Link to this definition">¶</a></dt>
+<dd><p>Make KNN weights from a dataframe.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.dataframe</span></code></span></dt><dd><p>a dataframe with a geometry column that can be used to
+construct a W object</p>
+</dd>
+<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
+geometries. Defaults to active geometry column.</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>a list-like of ids to use to index the spatial weights object or
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used if <cite>use_index=True</cite> or
+a positional index is used if <cite>use_index=False</cite>.
+Order of the resulting W is not respected from this list.</p>
+</dd>
+<dt><strong>use_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>use index of <cite>df</cite> as <cite>ids</cite> to index the spatial weights object.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.KNN.from_shapefile">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.KNN.from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Nearest neighbor weights from a shapefile.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile containing attribute data.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>identifiers to attach to each observation</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>If supplied arc_distances will be calculated
+based on the given radius. p will be ignored.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.KNN" title="libpysal.weights.KNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KNN</span></code></a></span></dt><dd><p>instance; Weights object with binary weights.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Ties between neighbors of equal distance are arbitrarily broken.</p>
+<p class="rubric">Examples</p>
+<p>Polygon shapefile
+&gt;&gt;&gt; import libpysal
+&gt;&gt;&gt; from libpysal.weights import KNN
+&gt;&gt;&gt; wc=KNN.from_shapefile(libpysal.examples.get_path(“columbus.shp”))
+&gt;&gt;&gt; “%.4f”%wc.pct_nonzero
+‘4.0816’
+&gt;&gt;&gt; set([2,1]) == set(wc.neighbors[0])
+True
+&gt;&gt;&gt; wc3=KNN.from_shapefile(libpysal.examples.get_path(“columbus.shp”),k=3)
+&gt;&gt;&gt; set(wc3.neighbors[0]) == set([2,1,3])
+True
+&gt;&gt;&gt; set(wc3.neighbors[2]) == set([4,3,0])
+True</p>
+<p>Point shapefile</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;juvenile.shp&quot;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">1.1904761904761905</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">=</span><span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;juvenile.shp&quot;</span><span class="p">),</span><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w1</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;0.595&#39;</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.KNN.reweight">
+<span class="sig-name descname"><span class="pre">reweight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#KNN.reweight"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.KNN.reweight" title="Link to this definition">¶</a></dt>
+<dd><p>Redo K-Nearest Neighbor weights construction using given parameters</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>new_data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np.ndarray</span></code></a></span></dt><dd><p>an array containing additional data to use in the KNN
+weight</p>
+</dd>
+<dt><strong>new_ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list aligned with new_data that provides the ids for
+each new observation</p>
+</dd>
+<dt><strong>inplace</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>a flag denoting whether to modify the KNN object
+in place or to return a new KNN object</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of nearest neighbors</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance
+Ignored if the KDTree is an ArcKDTree</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">A</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/copy.html#module-copy" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">using</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">new</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">parameterization</span></code>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">or</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">if</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code></dt><dd></dd>
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">is</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">reweighted</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">in</span></code> place.</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
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+  <a href="../_sources/generated/libpysal.weights.KNN.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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+<li><a class="reference internal" href="#">libpysal.weights.Kernel</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Kernel"><code class="docutils literal notranslate"><span class="pre">Kernel</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Kernel.__init__"><code class="docutils literal notranslate"><span class="pre">Kernel.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Kernel.from_array"><code class="docutils literal notranslate"><span class="pre">Kernel.from_array()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Kernel.from_dataframe"><code class="docutils literal notranslate"><span class="pre">Kernel.from_dataframe()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Kernel.from_shapefile"><code class="docutils literal notranslate"><span class="pre">Kernel.from_shapefile()</span></code></a></li>
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+      
+  <section id="libpysal-weights-kernel">
+<h1>libpysal.weights.Kernel<a class="headerlink" href="#libpysal-weights-kernel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.Kernel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'triangular'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0000001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Kernel" title="Link to this definition">¶</a></dt>
+<dd><p>Spatial weights based on kernel functions.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,k) or KDTree where KDtree.data is array (n,k)
+n observations on k characteristics used to measure
+distances between the n objects</p>
+</dd>
+<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>or array-like (optional)
+the bandwidth <span class="math notranslate nohighlight">\(h_i\)</span> for the kernel.</p>
+</dd>
+<dt><strong>fixed</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binary</span></code></span></dt><dd><p>If true then <span class="math notranslate nohighlight">\(h_i=h \forall i\)</span>. If false then
+bandwidth is adaptive across observations.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>the number of nearest neighbors to use for determining
+bandwidth. For fixed bandwidth, <span class="math notranslate nohighlight">\(h_i=max(dknn) \forall i\)</span>
+where <span class="math notranslate nohighlight">\(dknn\)</span> is a vector of k-nearest neighbor
+distances (the distance to the kth nearest neighbor for each
+observation).  For adaptive bandwidths, <span class="math notranslate nohighlight">\(h_i=dknn_i\)</span></p>
+</dd>
+<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true, set diagonal weights = 1.0, if false (default),
+diagonals weights are set to value according to kernel
+function.</p>
+</dd>
+<dt><strong>function</strong><span class="classifier">{‘triangular’,’uniform’,’quadratic’,’quartic’,’gaussian’}</span></dt><dd><p>kernel function defined as follows with</p>
+<div class="math notranslate nohighlight">
+\[z_{i,j} = d_{i,j}/h_i\]</div>
+<p>triangular</p>
+<div class="math notranslate nohighlight">
+\[K(z) = (1 - |z|) \ if |z| \le 1\]</div>
+<p>uniform</p>
+<div class="math notranslate nohighlight">
+\[K(z) = 1/2 \ if |z| \le 1\]</div>
+<p>quadratic</p>
+<div class="math notranslate nohighlight">
+\[K(z) = (3/4)(1-z^2) \ if |z| \le 1\]</div>
+<p>quartic</p>
+<div class="math notranslate nohighlight">
+\[K(z) = (15/16)(1-z^2)^2 \ if |z| \le 1\]</div>
+<p>gaussian</p>
+<div class="math notranslate nohighlight">
+\[K(z) = (2\pi)^{(-1/2)} exp(-z^2 / 2)\]</div>
+</dd>
+<dt><strong>eps</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>adjustment to ensure knn distance range is closed on the
+knnth observations</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Kernel</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span><span class="o">=</span><span class="p">[(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">40</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 0.500000049999995, 0.4409830615267465]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0, 1, 3]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw</span><span class="o">.</span><span class="n">bandwidth</span>
+<span class="go">array([[20.000002],</span>
+<span class="go">       [20.000002],</span>
+<span class="go">       [20.000002],</span>
+<span class="go">       [20.000002],</span>
+<span class="go">       [20.000002],</span>
+<span class="go">       [20.000002]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">bandwidth</span><span class="o">=</span><span class="mf">15.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">{0: 1.0, 1: 0.33333333333333337, 3: 0.2546440075000701}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0, 1, 3]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kw15</span><span class="o">.</span><span class="n">bandwidth</span>
+<span class="go">array([[15.],</span>
+<span class="go">       [15.],</span>
+<span class="go">       [15.],</span>
+<span class="go">       [15.],</span>
+<span class="go">       [15.],</span>
+<span class="go">       [15.]])</span>
+</pre></div>
+</div>
+<p>Adaptive bandwidths user specified</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">bw</span><span class="o">=</span><span class="p">[</span><span class="mf">25.0</span><span class="p">,</span><span class="mf">15.0</span><span class="p">,</span><span class="mf">25.0</span><span class="p">,</span><span class="mf">16.0</span><span class="p">,</span><span class="mf">14.5</span><span class="p">,</span><span class="mf">25.0</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bw</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 0.6, 0.552786404500042, 0.10557280900008403]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0, 1, 3, 4]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwa</span><span class="o">.</span><span class="n">bandwidth</span>
+<span class="go">array([[25. ],</span>
+<span class="go">       [15. ],</span>
+<span class="go">       [25. ],</span>
+<span class="go">       [16. ],</span>
+<span class="go">       [14.5],</span>
+<span class="go">       [25. ]])</span>
+</pre></div>
+</div>
+<p>Endogenous adaptive bandwidths</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">fixed</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 0.10557289844279438, 9.99999900663795e-08]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0, 1, 3]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kwea</span><span class="o">.</span><span class="n">bandwidth</span>
+<span class="go">array([[11.18034101],</span>
+<span class="go">       [11.18034101],</span>
+<span class="go">       [20.000002  ],</span>
+<span class="go">       [11.18034101],</span>
+<span class="go">       [14.14213704],</span>
+<span class="go">       [18.02775818]])</span>
+</pre></div>
+</div>
+<p>Endogenous adaptive bandwidths with Gaussian kernel</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">kweag</span><span class="o">=</span><span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">fixed</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">function</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kweag</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0.3989422804014327, 0.2674190291577696, 0.2419707487162134]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kweag</span><span class="o">.</span><span class="n">bandwidth</span>
+<span class="go">array([[11.18034101],</span>
+<span class="go">       [11.18034101],</span>
+<span class="go">       [20.000002  ],</span>
+<span class="go">       [11.18034101],</span>
+<span class="go">       [14.14213704],</span>
+<span class="go">       [18.02775818]])</span>
+</pre></div>
+</div>
+<p>Diagonals to 1.0</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">kq</span> <span class="o">=</span> <span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">function</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kq</span><span class="o">.</span><span class="n">weights</span>
+<span class="go">{0: [0.3989422804014327, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 0.3989422804014327, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 0.3989422804014327, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 0.3989422804014327, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 0.3989422804014327, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 0.3989422804014327]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kqd</span> <span class="o">=</span> <span class="n">Kernel</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">kqd</span><span class="o">.</span><span class="n">weights</span>
+<span class="go">{0: [1.0, 0.35206533556593145, 0.3412334260702758], 1: [0.35206533556593145, 1.0, 0.2419707487162134, 0.3412334260702758, 0.31069657591175387], 2: [0.2419707487162134, 1.0, 0.31069657591175387], 3: [0.3412334260702758, 0.3412334260702758, 1.0, 0.3011374490937829, 0.26575287272131043], 4: [0.31069657591175387, 0.31069657591175387, 0.3011374490937829, 1.0, 0.35206533556593145], 5: [0.26575287272131043, 0.35206533556593145, 1.0]}</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Dictionary keyed by id with a list of weights for each neighbor</p>
+</dd>
+<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>of lists of neighbors keyed by observation id</p>
+</dd>
+<dt><strong>bandwidth</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>array of bandwidths</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Kernel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'triangular'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0000001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Kernel.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.__init__" title="libpysal.weights.Kernel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(data[, bandwidth, fixed, k, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.from_array" title="libpysal.weights.Kernel.from_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_array</span></code></a>(array, **kwargs)</p></td>
+<td><p>Construct a Kernel weights from an array.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.from_dataframe" title="libpysal.weights.Kernel.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, ids, use_index])</p></td>
+<td><p>Make Kernel weights from a dataframe.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Kernel.from_shapefile" title="libpysal.weights.Kernel.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath[, idVariable])</p></td>
+<td><p>Kernel based weights from shapefile</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Kernel.from_array">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.from_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Kernel.from_array" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a Kernel weights from an array. Supports all the same options
+as <a class="reference internal" href="#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.Kernel</span></code></a></p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Kernel.from_dataframe">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.from_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Kernel.from_dataframe" title="Link to this definition">¶</a></dt>
+<dd><p>Make Kernel weights from a dataframe.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.dataframe</span></code></span></dt><dd><p>a dataframe with a geometry column that can be used to
+construct a W object</p>
+</dd>
+<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
+geometries. Defaults to active geometry column.</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>a list-like of ids to use to index the spatial weights object or
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used if <cite>use_index=True</cite> or
+a positional index is used if <cite>use_index=False</cite>.
+Order of the resulting W is not respected from this list.</p>
+</dd>
+<dt><strong>use_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>use index of <cite>df</cite> as <cite>ids</cite> to index the spatial weights object.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Kernel.from_shapefile">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/distance.html#Kernel.from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Kernel.from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Kernel based weights from shapefile</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of column in shapefile’s DBF to use for ids</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.Kernel" title="libpysal.weights.Kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Kernel</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Weights</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">Object</span></code></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
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diff --git a/generated/libpysal.weights.ODW.html b/generated/libpysal.weights.ODW.html
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.ODW</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.ODW"><code class="docutils literal notranslate"><span class="pre">ODW()</span></code></a></li>
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+      
+  <section id="libpysal-weights-odw">
+<h1>libpysal.weights.ODW<a class="headerlink" href="#libpysal-weights-odw" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.ODW">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">ODW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Wo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Wd</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'r'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#ODW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.ODW" title="Link to this definition">¶</a></dt>
+<dd><p>Constructs an o*d by o*d origin-destination style spatial weight for o*d
+flows using standard spatial weights on o origins and d destinations. Input
+spatial weights must be binary or able to be sutiably transformed to binary.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>Wo</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">origin</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">locations</span></code></span></dt><dd><p>o x o spatial weight object amongst o origins</p>
+</dd>
+<dt><strong>Wd</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">destination</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">locations</span></code></span></dt><dd><p>d x d spatial weight object amongst d destinations</p>
+</dd>
+<dt><strong>transform</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformation</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">standardization</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">final</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">OD</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> weight; <code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code></span></dt><dd><p>is ‘r’ for row standardized</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>W</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">contiguity</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">assocations</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">between</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">flows</span></code></span></dt><dd><p>o*d x o*d spatial weight object amongst o*d flows between o
+origins and d destinations</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">O</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">D</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">ODW</span><span class="p">(</span><span class="n">O</span><span class="p">,</span><span class="n">D</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0.25, 0.25, 0.25, 0.25]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[5, 6, 9, 10]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">array([0.  , 0.  , 0.  , 0.  , 0.  , 0.25, 0.25, 0.  , 0.  , 0.25, 0.25,</span>
+<span class="go">       0.  , 0.  , 0.  , 0.  , 0.  ])</span>
+</pre></div>
+</div>
+</dd></dl>
+
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diff --git a/generated/libpysal.weights.Queen.html b/generated/libpysal.weights.Queen.html
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+<li><a class="reference internal" href="#">libpysal.weights.Queen</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Queen"><code class="docutils literal notranslate"><span class="pre">Queen</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Queen.__init__"><code class="docutils literal notranslate"><span class="pre">Queen.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Queen.from_dataframe"><code class="docutils literal notranslate"><span class="pre">Queen.from_dataframe()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Queen.from_iterable"><code class="docutils literal notranslate"><span class="pre">Queen.from_iterable()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Queen.from_shapefile"><code class="docutils literal notranslate"><span class="pre">Queen.from_shapefile()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Queen.from_xarray"><code class="docutils literal notranslate"><span class="pre">Queen.from_xarray()</span></code></a></li>
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+  <section id="libpysal-weights-queen">
+<h1>libpysal.weights.Queen<a class="headerlink" href="#libpysal-weights-queen" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.Queen">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Queen</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygons</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Queen" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a collection of pysal polygons that share at least one vertex.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>polygons</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a collection of PySAL shapes to build weights from</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of names to use to build the weights</p>
+</dd>
+<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Queen.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygons</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Queen.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.__init__" title="libpysal.weights.Queen.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polygons, **kw)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_dataframe" title="libpysal.weights.Queen.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, idVariable, ...])</p></td>
+<td><p>Construct a weights object from a (geo)pandas dataframe with a geometry column.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_iterable" title="libpysal.weights.Queen.from_iterable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_iterable</span></code></a>(iterable[, sparse])</p></td>
+<td><p>Construct a weights object from a collection of arbitrary polygons.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_shapefile" title="libpysal.weights.Queen.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath[, idVariable, full])</p></td>
+<td><p>Queen contiguity weights from a polygon shapefile.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Queen.from_xarray" title="libpysal.weights.Queen.from_xarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_xarray</span></code></a>(da[, z_value, coords_labels, k, ...])</p></td>
+<td><p>Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Queen.from_dataframe">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Queen.from_dataframe" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a (geo)pandas dataframe with a geometry
+column. This will cast the polygons to PySAL polygons, then build the W
+using ids from the dataframe.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame</span></code></span></dt><dd><p>a :class: <cite>pandas.DataFrame</cite> containing geometries to use
+for spatial weights</p>
+</dd>
+<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
+geometries. Defaults to active geometry column.</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>DEPRECATED - use <cite>ids</cite> instead.
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>a list-like of ids to use to index the spatial weights object or
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used if <cite>use_index=True</cite> or
+a positional index is used if <cite>use_index=False</cite>.
+Order of the resulting W is not respected from this list.</p>
+</dd>
+<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>DEPRECATED - argument is deprecated and will be removed.
+An ordered list of ids to use to index the spatial weights
+object. If used, the resulting weights object will iterate
+over results in the order of the names provided in this
+argument.</p>
+</dd>
+<dt><strong>use_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>use index of <cite>df</cite> as <cite>ids</cite> to index the spatial weights object.
+Defaults to False but in future will default to True.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+<dt><a class="reference internal" href="#libpysal.weights.Queen" title="libpysal.weights.contiguity.Queen"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Queen</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Queen.from_iterable">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_iterable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_iterable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Queen.from_iterable" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a collection of arbitrary polygons. This
+will cast the polygons to PySAL polygons, then build the W.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>iterable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/glossary.html#term-iterable" title="(in Python v3.12)"><span>iterable</span></a></span></dt><dd><p>a collection of of shapes to be cast to PySAL shapes. Must
+support iteration. Contents may either be a shapely or PySAL shape.</p>
+</dd>
+<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for  <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+<dt><strong>See Also</strong></dt><dd></dd>
+<dt><strong>———</strong></dt><dd></dd>
+<dt><strong>:class:`libpysal.weights.weights.W`</strong></dt><dd></dd>
+<dt><strong>:class:`libpysal.weights.contiguiyt.Queen`</strong></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Queen.from_shapefile">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Queen.from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Queen contiguity weights from a polygon shapefile.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of polygon shapefile including suffix.</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of a column in the shapefile’s DBF to use for ids.</p>
+</dd>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return WSP instance
+If False return W instance</p>
+</dd>
+<dt><strong>Returns</strong></dt><dd></dd>
+<dt><strong>——-</strong></dt><dd></dd>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+<dt><a class="reference internal" href="#libpysal.weights.Queen" title="libpysal.weights.contiguity.Queen"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Queen</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Queen</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="o">=</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">wq</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;9.829&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="o">=</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span><span class="s2">&quot;POLYID&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">wq</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;9.829&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="o">=</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pct_sp</span> <span class="o">=</span> <span class="n">wq</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">*</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">wq</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">pct_sp</span>
+<span class="go">&#39;0.098&#39;</span>
+</pre></div>
+</div>
+<p>Notes</p>
+<p>Queen contiguity defines as neighbors any pair of polygons that share at
+least one vertex in their polygon definitions.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Queen.from_xarray">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_xarray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">da</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_nodata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Queen.from_xarray"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Queen.from_xarray" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a xarray.DataArray with an additional
+attribute index containing coordinate values of the raster
+in the form of Pandas.Index/MultiIndex.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
+</dd>
+<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
+</dd>
+<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
+belong to default dimensions, which are (band/time, y/lat, x/lon)
+e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
+Default is {} empty dictionary.</p>
+</dd>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>type of weight object. Default is True. For libpysal.weights.W, sparse = False</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
+Default is 1.</p>
+</dd>
+<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
+selecting higher_order neighbors, Default is False</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
+all available cores are used. Default is 1.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments passed when sparse = False</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>w</strong><span class="classifier">libpysal.weights.W/libpysal.weights.WSP</span></dt><dd><p>instance of spatial weights class W or WSP with an index attribute</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+<dt><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.weights.WSP"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.WSP</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<ol class="arabic simple">
+<li><p>Lower order contiguities are also selected.</p></li>
+<li><p>Returned object contains <cite>index</cite> attribute that includes a
+<cite>Pandas.MultiIndex</cite> object from the DataArray.</p></li>
+</ol>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/generated/libpysal.weights.Queen.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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new file mode 100644
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+<li><a class="reference internal" href="#">libpysal.weights.Relative_Neighborhood</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Relative_Neighborhood"><code class="docutils literal notranslate"><span class="pre">Relative_Neighborhood</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Relative_Neighborhood.__init__"><code class="docutils literal notranslate"><span class="pre">Relative_Neighborhood.__init__()</span></code></a></li>
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+  <section id="libpysal-weights-relative-neighborhood">
+<h1>libpysal.weights.Relative_Neighborhood<a class="headerlink" href="#libpysal-weights-relative-neighborhood" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.Relative_Neighborhood">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Relative_Neighborhood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Relative_Neighborhood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Relative_Neighborhood" title="Link to this definition">¶</a></dt>
+<dd><p>Constructs the Relative Neighborhood graph from a set of points.
+This graph is a subset of the Delaunay triangulation, where only
+“relative neighbors” are retained. Further, it is a superset of
+the Minimum Spanning Tree, with additional “relative neighbors”
+introduced.</p>
+<p>A relative neighbor pair of points i,j must be closer than the 
+maximum distance between i (or j) and each other point k. 
+This means that the points are at least as close to one another 
+as they are to any other point.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>coordinates</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">points</span></code>, (N,2)</span></dt><dd><p>numpy array of coordinates containing locations to compute the
+delaunay triangulation</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">argument</span></code> <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>keyword arguments passed directly to weights.W</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Relative_Neighborhood.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/gabriel.html#Relative_Neighborhood.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Relative_Neighborhood.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Relative_Neighborhood.__init__" title="libpysal.weights.Relative_Neighborhood.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(coordinates[, binary])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code>(df[, geom_col, ids, use_index])</p></td>
+<td><p>Construct a Delaunay triangulation from a geopandas GeoDataFrame.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code>(*args, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">libpysal.weights.Rook</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Rook"><code class="docutils literal notranslate"><span class="pre">Rook</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.Rook.__init__"><code class="docutils literal notranslate"><span class="pre">Rook.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Rook.from_dataframe"><code class="docutils literal notranslate"><span class="pre">Rook.from_dataframe()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Rook.from_iterable"><code class="docutils literal notranslate"><span class="pre">Rook.from_iterable()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Rook.from_shapefile"><code class="docutils literal notranslate"><span class="pre">Rook.from_shapefile()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.Rook.from_xarray"><code class="docutils literal notranslate"><span class="pre">Rook.from_xarray()</span></code></a></li>
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+      
+  <section id="libpysal-weights-rook">
+<h1>libpysal.weights.Rook<a class="headerlink" href="#libpysal-weights-rook" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.Rook">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Rook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygons</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Rook" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a collection of pysal polygons that share at least one edge.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>polygons</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a collection of PySAL shapes to build weights from</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of names to use to build the weights</p>
+</dd>
+<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Rook.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">polygons</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Rook.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.__init__" title="libpysal.weights.Rook.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(polygons, **kw)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_dataframe" title="libpysal.weights.Rook.from_dataframe"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_dataframe</span></code></a>(df[, geom_col, idVariable, ...])</p></td>
+<td><p>Construct a weights object from a (geo)pandas dataframe with a geometry column.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_iterable" title="libpysal.weights.Rook.from_iterable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_iterable</span></code></a>(iterable[, sparse])</p></td>
+<td><p>Construct a weights object from a collection of arbitrary polygons.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_shapefile" title="libpysal.weights.Rook.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(filepath[, idVariable, full])</p></td>
+<td><p>Rook contiguity weights from a polygon shapefile.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.Rook.from_xarray" title="libpysal.weights.Rook.from_xarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_xarray</span></code></a>(da[, z_value, coords_labels, k, ...])</p></td>
+<td><p>Construct a weights object from a xarray.DataArray with an additional attribute index containing coordinate values of the raster in the form of Pandas.Index/MultiIndex.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Rook.from_dataframe">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dataframe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geom_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_dataframe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Rook.from_dataframe" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a (geo)pandas dataframe with a geometry
+column. This will cast the polygons to PySAL polygons, then build the W
+using ids from the dataframe.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>df</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame</span></code></span></dt><dd><p>a :class: <cite>pandas.DataFrame</cite> containing geometries to use
+for spatial weights</p>
+</dd>
+<dt><strong>geom_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>the name of the column in <cite>df</cite> that contains the
+geometries. Defaults to active geometry column.</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>DEPRECATED - use <cite>ids</cite> instead.
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier">list-like, <a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>a list-like of ids to use to index the spatial weights object or
+the name of the column to use as IDs. If nothing is
+provided, the dataframe index is used if <cite>use_index=True</cite> or
+a positional index is used if <cite>use_index=False</cite>.
+Order of the resulting W is not respected from this list.</p>
+</dd>
+<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>DEPRECATED - argument is deprecated and will be removed.
+An ordered list of ids to use to index the spatial weights
+object. If used, the resulting weights object will iterate
+over results in the order of the names provided in this
+argument.</p>
+</dd>
+<dt><strong>use_index</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>use index of <cite>df</cite> as <cite>ids</cite> to index the spatial weights object.
+Defaults to False but in future will default to True.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+<dt><a class="reference internal" href="#libpysal.weights.Rook" title="libpysal.weights.contiguity.Rook"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Rook</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Rook.from_iterable">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_iterable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_iterable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Rook.from_iterable" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a collection of arbitrary polygons. This
+will cast the polygons to PySAL polygons, then build the W.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>iterable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/glossary.html#term-iterable" title="(in Python v3.12)"><span>iterable</span></a></span></dt><dd><p>a collection of of shapes to be cast to PySAL shapes. Must
+support iteration. Can be either Shapely or PySAL shapes.</p>
+</dd>
+<dt><strong>**kw</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for  <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+<dt><strong>See Also</strong></dt><dd></dd>
+<dt><strong>——–</strong></dt><dd></dd>
+<dt><strong>:class:`libpysal.weights.weights.W`</strong></dt><dd></dd>
+<dt><strong>:class:`libpysal.weights.contiguity.Rook`</strong></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Rook.from_shapefile">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filepath</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Rook.from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Rook contiguity weights from a polygon shapefile.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of polygon shapefile including suffix.</p>
+</dd>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return WSP instance
+If False return W instance</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+<dt><a class="reference internal" href="#libpysal.weights.Rook" title="libpysal.weights.contiguity.Rook"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.contiguity.Rook</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Rook contiguity defines as neighbors any pair of polygons that share a
+common edge in their polygon definitions.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Rook</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wr</span><span class="o">=</span><span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="s2">&quot;POLYID&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">wr</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;8.330&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wr</span><span class="o">=</span><span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pct_sp</span> <span class="o">=</span> <span class="n">wr</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">*</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">wr</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">pct_sp</span>
+<span class="go">&#39;0.083&#39;</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.Rook.from_xarray">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_xarray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">da</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_nodata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Rook.from_xarray"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Rook.from_xarray" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a weights object from a xarray.DataArray with an additional
+attribute index containing coordinate values of the raster
+in the form of Pandas.Index/MultiIndex.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
+</dd>
+<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
+</dd>
+<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
+belong to default dimensions, which are (band/time, y/lat, x/lon)
+e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
+Default is {} empty dictionary.</p>
+</dd>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>type of weight object. Default is True. For libpysal.weights.W, sparse = False</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
+Default is 1.</p>
+</dd>
+<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
+selecting higher_order neighbors, Default is False</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
+all available cores are used. Default is 1.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments passed when sparse = False</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>w</strong><span class="classifier">libpysal.weights.W/libpysal.weights.WSP</span></dt><dd><p>instance of spatial weights class W or WSP with an index attribute</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+<dt><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.weights.WSP"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.WSP</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<ol class="arabic simple">
+<li><p>Lower order contiguities are also selected.</p></li>
+<li><p>Returned object contains <cite>index</cite> attribute that includes a
+<cite>Pandas.MultiIndex</cite> object from the DataArray.</p></li>
+</ol>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
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+<li><a class="reference internal" href="#libpysal.weights.Voronoi"><code class="docutils literal notranslate"><span class="pre">Voronoi()</span></code></a></li>
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+      
+  <section id="libpysal-weights-voronoi">
+<h1>libpysal.weights.Voronoi<a class="headerlink" href="#libpysal-weights-voronoi" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.Voronoi">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">Voronoi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">points</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rook'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ahull'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/contiguity.html#Voronoi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.Voronoi" title="Link to this definition">¶</a></dt>
+<dd><p>Voronoi weights for a 2-d point set</p>
+<p>Points are Voronoi neighbors if their polygons share an edge or vertex.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,2)
+coordinates for point locations</p>
+</dd>
+<dt><strong>kwargs</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">pass</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">to</span></code> <a class="reference internal" href="libpysal.weights.Rook.html#libpysal.weights.Rook" title="libpysal.weights.Rook"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Rook</span></code></a>, <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">underlying</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">contiguity</span></code> class.</span></dt><dd></dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Voronoi</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">points</span><span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span><span class="o">*</span><span class="mi">10</span> <span class="o">+</span> <span class="mi">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">Voronoi</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span>
+<span class="go">{0: [2, 3, 4], 1: [2], 2: [0, 1, 4], 3: [0, 4], 4: [0, 2, 3]}</span>
+</pre></div>
+</div>
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+<li><a class="reference internal" href="#">libpysal.weights.W</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.W"><code class="docutils literal notranslate"><span class="pre">W</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.W.__init__"><code class="docutils literal notranslate"><span class="pre">W.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.asymmetries"><code class="docutils literal notranslate"><span class="pre">W.asymmetries</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.asymmetry"><code class="docutils literal notranslate"><span class="pre">W.asymmetry()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.cardinalities"><code class="docutils literal notranslate"><span class="pre">W.cardinalities</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.component_labels"><code class="docutils literal notranslate"><span class="pre">W.component_labels</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.diagW2"><code class="docutils literal notranslate"><span class="pre">W.diagW2</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.diagWtW"><code class="docutils literal notranslate"><span class="pre">W.diagWtW</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.diagWtW_WW"><code class="docutils literal notranslate"><span class="pre">W.diagWtW_WW</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.from_WSP"><code class="docutils literal notranslate"><span class="pre">W.from_WSP()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.from_adjlist"><code class="docutils literal notranslate"><span class="pre">W.from_adjlist()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.from_file"><code class="docutils literal notranslate"><span class="pre">W.from_file()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.from_networkx"><code class="docutils literal notranslate"><span class="pre">W.from_networkx()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.from_shapefile"><code class="docutils literal notranslate"><span class="pre">W.from_shapefile()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.from_sparse"><code class="docutils literal notranslate"><span class="pre">W.from_sparse()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.full"><code class="docutils literal notranslate"><span class="pre">W.full()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.get_transform"><code class="docutils literal notranslate"><span class="pre">W.get_transform()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.histogram"><code class="docutils literal notranslate"><span class="pre">W.histogram</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.id2i"><code class="docutils literal notranslate"><span class="pre">W.id2i</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.id_order"><code class="docutils literal notranslate"><span class="pre">W.id_order</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.id_order_set"><code class="docutils literal notranslate"><span class="pre">W.id_order_set</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.islands"><code class="docutils literal notranslate"><span class="pre">W.islands</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.max_neighbors"><code class="docutils literal notranslate"><span class="pre">W.max_neighbors</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.mean_neighbors"><code class="docutils literal notranslate"><span class="pre">W.mean_neighbors</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.min_neighbors"><code class="docutils literal notranslate"><span class="pre">W.min_neighbors</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.n"><code class="docutils literal notranslate"><span class="pre">W.n</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.n_components"><code class="docutils literal notranslate"><span class="pre">W.n_components</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.neighbor_offsets"><code class="docutils literal notranslate"><span class="pre">W.neighbor_offsets</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.nonzero"><code class="docutils literal notranslate"><span class="pre">W.nonzero</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.pct_nonzero"><code class="docutils literal notranslate"><span class="pre">W.pct_nonzero</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.plot"><code class="docutils literal notranslate"><span class="pre">W.plot()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.remap_ids"><code class="docutils literal notranslate"><span class="pre">W.remap_ids()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.s0"><code class="docutils literal notranslate"><span class="pre">W.s0</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.s1"><code class="docutils literal notranslate"><span class="pre">W.s1</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.s2"><code class="docutils literal notranslate"><span class="pre">W.s2</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.s2array"><code class="docutils literal notranslate"><span class="pre">W.s2array</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.sd"><code class="docutils literal notranslate"><span class="pre">W.sd</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.set_shapefile"><code class="docutils literal notranslate"><span class="pre">W.set_shapefile()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.set_transform"><code class="docutils literal notranslate"><span class="pre">W.set_transform()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.sparse"><code class="docutils literal notranslate"><span class="pre">W.sparse</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.symmetrize"><code class="docutils literal notranslate"><span class="pre">W.symmetrize()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.to_WSP"><code class="docutils literal notranslate"><span class="pre">W.to_WSP()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.to_adjlist"><code class="docutils literal notranslate"><span class="pre">W.to_adjlist()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.to_file"><code class="docutils literal notranslate"><span class="pre">W.to_file()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.to_networkx"><code class="docutils literal notranslate"><span class="pre">W.to_networkx()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.to_sparse"><code class="docutils literal notranslate"><span class="pre">W.to_sparse()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.transform"><code class="docutils literal notranslate"><span class="pre">W.transform</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.trcW2"><code class="docutils literal notranslate"><span class="pre">W.trcW2</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.trcWtW"><code class="docutils literal notranslate"><span class="pre">W.trcWtW</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.W.trcWtW_WW"><code class="docutils literal notranslate"><span class="pre">W.trcWtW_WW</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
+<form class="navbar-form navbar-right" action="../search.html" method="get">
+ <div class="form-group">
+  <input type="text" name="q" class="form-control" placeholder="Search" />
+ </div>
+  <input type="hidden" name="check_keywords" value="yes" />
+  <input type="hidden" name="area" value="default" />
+</form>
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-w">
+<h1>libpysal.weights.W<a class="headerlink" href="#libpysal-weights-w" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.W">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">neighbors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W" title="Link to this definition">¶</a></dt>
+<dd><p>Spatial weights class. Class attributes are described by their
+docstrings. to view, use the <code class="docutils literal notranslate"><span class="pre">help</span></code> function.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>neighbors</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Key is region ID, value is a list of neighbor IDS.
+For example, <code class="docutils literal notranslate"><span class="pre">{'a':['b'],'b':['a','c'],'c':['b']}</span></code>.</p>
+</dd>
+<dt><strong>weights</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Key is region ID, value is a list of edge weights.
+If not supplied all edge weights are assumed to have a weight of 1.
+For example, <code class="docutils literal notranslate"><span class="pre">{'a':[0.5],'b':[0.5,1.5],'c':[1.5]}</span></code>.</p>
+</dd>
+<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>An ordered list of ids, defines the order of observations when
+iterating over <code class="docutils literal notranslate"><span class="pre">W</span></code> if not set, lexicographical ordering is used
+to iterate and the <code class="docutils literal notranslate"><span class="pre">id_order_set</span></code> property will return <code class="docutils literal notranslate"><span class="pre">False</span></code>.
+This can be set after creation by setting the <code class="docutils literal notranslate"><span class="pre">id_order</span></code> property.</p>
+</dd>
+<dt><strong>silence_warnings</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>By default <code class="docutils literal notranslate"><span class="pre">libpysal</span></code> will print a warning if the dataset contains
+any disconnected components or islands. To silence this warning set this
+parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Values to use for keys of the neighbors and weights <code class="docutils literal notranslate"><span class="pre">dict</span></code> objects.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;29.630&#39;</span>
+</pre></div>
+</div>
+<p>Read from external <a class="reference external" href="https://geodacenter.github.io/workbook/4a_contig_weights/lab4a.html#gal-weights-file">.gal file</a>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;stl.gal&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
+<span class="go">78</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;6.542&#39;</span>
+</pre></div>
+</div>
+<p>Set weights implicitly.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="go">29.63</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">trcW2</span>
+<span class="go">39600.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">trcWtW</span>
+<span class="go">39600.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">trcW2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="go">2530.722</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">round</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">trcWtW</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="go">2533.667</span>
+</pre></div>
+</div>
+<p>Cardinality Histogram:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">histogram</span>
+<span class="go">[(2, 4), (3, 392), (4, 9604)]</span>
+</pre></div>
+</div>
+<p>Disconnected observations (islands):</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">({</span><span class="mi">1</span><span class="p">:[</span><span class="mi">0</span><span class="p">],</span><span class="mi">0</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="mi">2</span><span class="p">:[],</span> <span class="mi">3</span><span class="p">:[]})</span>
+</pre></div>
+</div>
+<p>UserWarning: The weights matrix is not fully connected:
+There are 3 disconnected components.
+There are 2 islands with ids: 2, 3.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.asymmetries" title="libpysal.weights.W.asymmetries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></a></dt><dd><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.cardinalities" title="libpysal.weights.W.cardinalities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></a></dt><dd><p>Number of neighbors for each observation.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.component_labels" title="libpysal.weights.W.component_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></a></dt><dd><p>Store the graph component in which each observation falls.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.diagW2" title="libpysal.weights.W.diagW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></a></dt><dd><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.diagWtW" title="libpysal.weights.W.diagWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></a></dt><dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.diagWtW_WW" title="libpysal.weights.W.diagWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></a></dt><dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.histogram" title="libpysal.weights.W.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a></dt><dd><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.id2i" title="libpysal.weights.W.id2i"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></a></dt><dd><p>Dictionary where the key is an ID and the value is that ID’s index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.id_order" title="libpysal.weights.W.id_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></a></dt><dd><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.id_order_set" title="libpysal.weights.W.id_order_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></a></dt><dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.islands" title="libpysal.weights.W.islands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></a></dt><dd><p>List of ids without any neighbors.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.max_neighbors" title="libpysal.weights.W.max_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></a></dt><dd><p>Largest number of neighbors.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.mean_neighbors" title="libpysal.weights.W.mean_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></a></dt><dd><p>Average number of neighbors.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.min_neighbors" title="libpysal.weights.W.min_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></a></dt><dd><p>Minimum number of neighbors.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.n" title="libpysal.weights.W.n"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></a></dt><dd><p>Number of units.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.n_components" title="libpysal.weights.W.n_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></a></dt><dd><p>Store whether the adjacency matrix is fully connected.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.neighbor_offsets" title="libpysal.weights.W.neighbor_offsets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></a></dt><dd><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.nonzero" title="libpysal.weights.W.nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></a></dt><dd><p>Number of nonzero weights.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.pct_nonzero" title="libpysal.weights.W.pct_nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></a></dt><dd><p>Percentage of nonzero weights.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.s0" title="libpysal.weights.W.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.s1" title="libpysal.weights.W.s1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.s2" title="libpysal.weights.W.s2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></a></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.s2array" title="libpysal.weights.W.s2array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></a></dt><dd><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.sd" title="libpysal.weights.W.sd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></a></dt><dd><p>Standard deviation of number of neighbors.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.sparse" title="libpysal.weights.W.sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></a></dt><dd><p>Sparse matrix object.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.trcW2" title="libpysal.weights.W.trcW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></a></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.trcWtW" title="libpysal.weights.W.trcWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></a></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.trcWtW_WW" title="libpysal.weights.W.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.W.transform" title="libpysal.weights.W.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a></dt><dd><p>Getter for transform property.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">neighbors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.__init__" title="libpysal.weights.W.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(neighbors[, weights, id_order, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.asymmetry" title="libpysal.weights.W.asymmetry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetry</span></code></a>([intrinsic])</p></td>
+<td><p>Asymmetry check.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.from_WSP" title="libpysal.weights.W.from_WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_WSP</span></code></a>(WSP[, silence_warnings])</p></td>
+<td><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.from_adjlist" title="libpysal.weights.W.from_adjlist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_adjlist</span></code></a>(adjlist[, focal_col, ...])</p></td>
+<td><p>Return an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.from_file" title="libpysal.weights.W.from_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_file</span></code></a>([path, format])</p></td>
+<td><p>Read a weights file into a W object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.from_networkx" title="libpysal.weights.W.from_networkx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_networkx</span></code></a>(graph[, weight_col])</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.from_shapefile" title="libpysal.weights.W.from_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_shapefile</span></code></a>(*args, **kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.from_sparse" title="libpysal.weights.W.from_sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_sparse</span></code></a>(sparse)</p></td>
+<td><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.full" title="libpysal.weights.W.full"><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code></a>()</p></td>
+<td><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.get_transform" title="libpysal.weights.W.get_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transform</span></code></a>()</p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.plot" title="libpysal.weights.W.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>(gdf[, indexed_on, ax, color, node_kws, ...])</p></td>
+<td><p>Plot spatial weights objects.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.remap_ids" title="libpysal.weights.W.remap_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remap_ids</span></code></a>(new_ids)</p></td>
+<td><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.set_shapefile" title="libpysal.weights.W.set_shapefile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_shapefile</span></code></a>(shapefile[, idVariable, full])</p></td>
+<td><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.set_transform" title="libpysal.weights.W.set_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code></a>([value])</p></td>
+<td><p>Transformations of weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.symmetrize" title="libpysal.weights.W.symmetrize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">symmetrize</span></code></a>([inplace])</p></td>
+<td><p>Construct a symmetric KNN weight.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.to_WSP" title="libpysal.weights.W.to_WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_WSP</span></code></a>()</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.to_adjlist" title="libpysal.weights.W.to_adjlist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_adjlist</span></code></a>([remove_symmetric, drop_islands, ...])</p></td>
+<td><p>Compute an adjacency list representation of a weights object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.to_file" title="libpysal.weights.W.to_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_file</span></code></a>([path, format])</p></td>
+<td><p>Write a weights to a file.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.to_networkx" title="libpysal.weights.W.to_networkx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_networkx</span></code></a>()</p></td>
+<td><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.to_sparse" title="libpysal.weights.W.to_sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_sparse</span></code></a>([fmt])</p></td>
+<td><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.asymmetries" title="libpysal.weights.W.asymmetries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asymmetries</span></code></a></p></td>
+<td><p>List of id pairs with asymmetric weights sorted in ascending <em>index location</em> order.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.cardinalities" title="libpysal.weights.W.cardinalities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cardinalities</span></code></a></p></td>
+<td><p>Number of neighbors for each observation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.component_labels" title="libpysal.weights.W.component_labels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">component_labels</span></code></a></p></td>
+<td><p>Store the graph component in which each observation falls.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.diagW2" title="libpysal.weights.W.diagW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></a></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.diagWtW" title="libpysal.weights.W.diagWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></a></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.diagWtW_WW" title="libpysal.weights.W.diagWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></a></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.histogram" title="libpysal.weights.W.histogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">histogram</span></code></a></p></td>
+<td><p>Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.id2i" title="libpysal.weights.W.id2i"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id2i</span></code></a></p></td>
+<td><p>Dictionary where the key is an ID and the value is that ID's index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.id_order" title="libpysal.weights.W.id_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></a></p></td>
+<td><p>Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.id_order_set" title="libpysal.weights.W.id_order_set"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order_set</span></code></a></p></td>
+<td><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.islands" title="libpysal.weights.W.islands"><code class="xref py py-obj docutils literal notranslate"><span class="pre">islands</span></code></a></p></td>
+<td><p>List of ids without any neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.max_neighbors" title="libpysal.weights.W.max_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_neighbors</span></code></a></p></td>
+<td><p>Largest number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.mean_neighbors" title="libpysal.weights.W.mean_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_neighbors</span></code></a></p></td>
+<td><p>Average number of neighbors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.min_neighbors" title="libpysal.weights.W.min_neighbors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min_neighbors</span></code></a></p></td>
+<td><p>Minimum number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.n" title="libpysal.weights.W.n"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n</span></code></a></p></td>
+<td><p>Number of units.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.n_components" title="libpysal.weights.W.n_components"><code class="xref py py-obj docutils literal notranslate"><span class="pre">n_components</span></code></a></p></td>
+<td><p>Store whether the adjacency matrix is fully connected.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.neighbor_offsets" title="libpysal.weights.W.neighbor_offsets"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neighbor_offsets</span></code></a></p></td>
+<td><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the offsets of the id's neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.nonzero" title="libpysal.weights.W.nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></a></p></td>
+<td><p>Number of nonzero weights.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.pct_nonzero" title="libpysal.weights.W.pct_nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pct_nonzero</span></code></a></p></td>
+<td><p>Percentage of nonzero weights.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.s0" title="libpysal.weights.W.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.s1" title="libpysal.weights.W.s1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s1</span></code></a></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.s2" title="libpysal.weights.W.s2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></a></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.s2array" title="libpysal.weights.W.s2array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2array</span></code></a></p></td>
+<td><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.sd" title="libpysal.weights.W.sd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sd</span></code></a></p></td>
+<td><p>Standard deviation of number of neighbors.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.sparse" title="libpysal.weights.W.sparse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse</span></code></a></p></td>
+<td><p>Sparse matrix object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.transform" title="libpysal.weights.W.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a></p></td>
+<td><p>Getter for transform property.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.trcW2" title="libpysal.weights.W.trcW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></a></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.W.trcWtW" title="libpysal.weights.W.trcWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></a></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.W.trcWtW_WW" title="libpysal.weights.W.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.asymmetries">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">asymmetries</span></span><a class="headerlink" href="#libpysal.weights.W.asymmetries" title="Link to this definition">¶</a></dt>
+<dd><p>List of id pairs with asymmetric weights
+sorted in ascending <em>index location</em> order.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.asymmetry">
+<span class="sig-name descname"><span class="pre">asymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">intrinsic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.asymmetry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.asymmetry" title="Link to this definition">¶</a></dt>
+<dd><p>Asymmetry check.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>intrinsic</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>. Intrinsic symmetry is defined as:</p>
+<div class="math notranslate nohighlight">
+\[w_{i,j} == w_{j,i}\]</div>
+<p>If <code class="docutils literal notranslate"><span class="pre">intrinsic</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> symmetry is defined as:</p>
+<div class="math notranslate nohighlight">
+\[i \in N_j \ \&amp; \ j \in N_i\]</div>
+<p>where <span class="math notranslate nohighlight">\(N_j\)</span> is the set of neighbors for <span class="math notranslate nohighlight">\(j\)</span>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>asymmetries</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Empty if no asymmetries are found. If there are asymmetries,
+then a <code class="docutils literal notranslate"><span class="pre">list</span></code> of <code class="docutils literal notranslate"><span class="pre">(i,j)</span></code> tuples is returned sorted in
+ascending <em>index location</em> order.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
+<span class="go">[(0, 1), (0, 3), (1, 0), (1, 2), (1, 4), (2, 1), (2, 5), (3, 0), (3, 4), (3, 6), (4, 1), (4, 3), (4, 5), (4, 7), (5, 2), (5, 4), (5, 8), (6, 3), (6, 7), (7, 4), (7, 6), (7, 8), (8, 5), (8, 7)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">(</span><span class="n">intrinsic</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="mi">1</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="mi">2</span><span class="p">:[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">:[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span> <span class="mi">3</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span><span class="n">weights</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">asymmetry</span><span class="p">()</span>
+<span class="go">[(0, 1), (1, 0)]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.cardinalities">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">cardinalities</span></span><a class="headerlink" href="#libpysal.weights.W.cardinalities" title="Link to this definition">¶</a></dt>
+<dd><p>Number of neighbors for each observation.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.component_labels">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">component_labels</span></span><a class="headerlink" href="#libpysal.weights.W.component_labels" title="Link to this definition">¶</a></dt>
+<dd><p>Store the graph component in which each observation falls.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.diagW2">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diagW2</span></span><a class="headerlink" href="#libpysal.weights.W.diagW2" title="Link to this definition">¶</a></dt>
+<dd><p>Diagonal of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.trcW2" title="libpysal.weights.W.trcW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcW2</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.diagWtW">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diagWtW</span></span><a class="headerlink" href="#libpysal.weights.W.diagWtW" title="Link to this definition">¶</a></dt>
+<dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.trcWtW" title="libpysal.weights.W.trcWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.diagWtW_WW">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diagWtW_WW</span></span><a class="headerlink" href="#libpysal.weights.W.diagWtW_WW" title="Link to this definition">¶</a></dt>
+<dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.from_WSP">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_WSP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">WSP</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_WSP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.from_WSP" title="Link to this definition">¶</a></dt>
+<dd><p>Create a pysal W from a pysal WSP object (thin weights matrix).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>wsp</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></span></dt><dd><p>PySAL sparse weights object</p>
+</dd>
+<dt><strong>silence_warnings</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>By default <code class="docutils literal notranslate"><span class="pre">libpysal</span></code> will print a warning if the dataset contains
+any disconnected components or islands. To silence this warning set this
+parameter to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">W</span>
+</pre></div>
+</div>
+<p>Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells
+(rook contiguity), then construct a PySAL sparse weights object (wsp).</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sp</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sp</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
+<span class="go">matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)</span>
+</pre></div>
+</div>
+<p>Create a standard PySAL W from this sparse weights object.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="o">.</span><span class="n">from_WSP</span><span class="p">(</span><span class="n">wsp</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<span class="go">[0 1 0 0 0 1 0 0 0 0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.from_adjlist">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_adjlist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">adjlist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'focal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbor_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'neighbor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_adjlist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.from_adjlist" title="Link to this definition">¶</a></dt>
+<dd><p>Return an adjacency list representation of a weights object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>adjlist</strong><span class="classifier"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.2)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></span></dt><dd><p>Adjacency list with a minimum of two columns.</p>
+</dd>
+<dt><strong>focal_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column with the “source” node ids.</p>
+</dd>
+<dt><strong>neighbor_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column with the “destination” node ids.</p>
+</dd>
+<dt><strong>weight_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column with the weight information. If not provided and
+the dataframe has no column named “weight” then all weights
+are assumed to be 1.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.from_file">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.from_file" title="Link to this definition">¶</a></dt>
+<dd><p>Read a weights file into a W object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>path</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>location to save the file</p>
+</dd>
+<dt><strong>format</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>string denoting the format to write the weights to.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.from_networkx">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_networkx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_networkx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.from_networkx" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>graph</strong><span class="classifier"><a class="reference external" href="https://networkx.org/documentation/stable/reference/classes/graph.html#networkx.Graph" title="(in NetworkX v3.2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.Graph</span></code></a></span></dt><dd><p>The graph to convert to a <code class="docutils literal notranslate"><span class="pre">W</span></code>.</p>
+</dd>
+<dt><strong>weight_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>If the graph is labeled, this should be the name of the field
+to use as the weight for the <code class="docutils literal notranslate"><span class="pre">W</span></code>.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>A <code class="docutils literal notranslate"><span class="pre">W</span></code> object containing the same graph as the <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.from_shapefile">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.from_shapefile" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.from_sparse">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sparse</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.from_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.from_sparse" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array to a PySAL <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.11.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd></dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>A <code class="docutils literal notranslate"><span class="pre">W</span></code> object containing the same graph as the <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> graph.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>When the sparse array has a zero in its data attribute, and
+the corresponding row and column values are equal, the value
+for the pysal weight will be 0 for the “loop”.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.full">
+<span class="sig-name descname"><span class="pre">full</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.full"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.full" title="Link to this definition">¶</a></dt>
+<dd><p>Generate a full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code>.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>(fullw, keys)</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>The first element being the full <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> and second
+element keys being the ids associated with each row in the array.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">full</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="s1">&#39;first&#39;</span><span class="p">,</span><span class="s1">&#39;third&#39;</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">full</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span>
+<span class="go">array([[0., 1., 0.],</span>
+<span class="go">       [1., 0., 1.],</span>
+<span class="go">       [0., 1., 0.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span>
+<span class="go">[&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.get_transform">
+<span class="sig-name descname"><span class="pre">get_transform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.get_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.get_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Getter for transform property.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>transformation</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Valid transformation value. See the <code class="docutils literal notranslate"><span class="pre">transform</span></code>
+parameters in <code class="docutils literal notranslate"><span class="pre">set_transform()</span></code> for a detailed description.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.set_transform" title="libpysal.weights.W.set_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span>
+<span class="go">&#39;O&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0.5, 0.5]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;b&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.histogram">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">histogram</span></span><a class="headerlink" href="#libpysal.weights.W.histogram" title="Link to this definition">¶</a></dt>
+<dd><p>Cardinality histogram as a dictionary where key is the id and
+value is the number of neighbors for that unit.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.id2i">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">id2i</span></span><a class="headerlink" href="#libpysal.weights.W.id2i" title="Link to this definition">¶</a></dt>
+<dd><p>Dictionary where the key is an ID and the value is that ID’s
+index in <code class="docutils literal notranslate"><span class="pre">W.id_order</span></code>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.id_order">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">id_order</span></span><a class="headerlink" href="#libpysal.weights.W.id_order" title="Link to this definition">¶</a></dt>
+<dd><p>Returns the ids for the observations in the order in which they
+would be encountered if iterating over the weights.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.id_order_set">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">id_order_set</span></span><a class="headerlink" href="#libpysal.weights.W.id_order_set" title="Link to this definition">¶</a></dt>
+<dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if user has set <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">False</span></code> if not.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order_set</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.islands">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">islands</span></span><a class="headerlink" href="#libpysal.weights.W.islands" title="Link to this definition">¶</a></dt>
+<dd><p>List of ids without any neighbors.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.max_neighbors">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">max_neighbors</span></span><a class="headerlink" href="#libpysal.weights.W.max_neighbors" title="Link to this definition">¶</a></dt>
+<dd><p>Largest number of neighbors.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.mean_neighbors">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">mean_neighbors</span></span><a class="headerlink" href="#libpysal.weights.W.mean_neighbors" title="Link to this definition">¶</a></dt>
+<dd><p>Average number of neighbors.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.min_neighbors">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">min_neighbors</span></span><a class="headerlink" href="#libpysal.weights.W.min_neighbors" title="Link to this definition">¶</a></dt>
+<dd><p>Minimum number of neighbors.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.n">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n</span></span><a class="headerlink" href="#libpysal.weights.W.n" title="Link to this definition">¶</a></dt>
+<dd><p>Number of units.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.n_components">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_components</span></span><a class="headerlink" href="#libpysal.weights.W.n_components" title="Link to this definition">¶</a></dt>
+<dd><p>Store whether the adjacency matrix is fully connected.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.neighbor_offsets">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">neighbor_offsets</span></span><a class="headerlink" href="#libpysal.weights.W.neighbor_offsets" title="Link to this definition">¶</a></dt>
+<dd><p>Given the current <code class="docutils literal notranslate"><span class="pre">id_order</span></code>, <code class="docutils literal notranslate"><span class="pre">neighbor_offsets[id]</span></code> is the
+offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>neighbor_list</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>Offsets of the id’s neighbors in <code class="docutils literal notranslate"><span class="pre">id_order</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">],</span> <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span><span class="p">{</span><span class="s1">&#39;c&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">],</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span><span class="n">weights</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbor_offsets</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
+<span class="go">[2, 0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbor_offsets</span><span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">]</span>
+<span class="go">[2, 1]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.nonzero">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">nonzero</span></span><a class="headerlink" href="#libpysal.weights.W.nonzero" title="Link to this definition">¶</a></dt>
+<dd><p>Number of nonzero weights.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.pct_nonzero">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">pct_nonzero</span></span><a class="headerlink" href="#libpysal.weights.W.pct_nonzero" title="Link to this definition">¶</a></dt>
+<dd><p>Percentage of nonzero weights.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gdf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indexed_on</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'k'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_kws</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.plot" title="Link to this definition">¶</a></dt>
+<dd><p>Plot spatial weights objects. <strong>Requires</strong> <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code>, and
+implicitly requires a <code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> as input.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>gdf</strong><span class="classifier"><a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>The original shapes whose topological relations are modelled in <code class="docutils literal notranslate"><span class="pre">W</span></code>.</p>
+</dd>
+<dt><strong>indexed_on</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Column of <code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> that the weights object uses
+as an index. Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>, so the index of the
+<code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code> is used.</p>
+</dd>
+<dt><strong>ax</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.axes.Axes</span></code></a></span></dt><dd><p>Axis on which to plot the weights. Default is <code class="docutils literal notranslate"><span class="pre">None</span></code>, so
+plots on the current figure.</p>
+</dd>
+<dt><strong>color</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> color string, will color both nodes and edges
+the same by default.</p>
+</dd>
+<dt><strong>node_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">pyplot.scatter</span></code>,
+which provides fine-grained control over the aesthetics
+of the nodes in the plot.</p>
+</dd>
+<dt><strong>edge_kws</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#dict" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">dict</span></code></a></span></dt><dd><p>Keyword arguments dictionary to send to <code class="docutils literal notranslate"><span class="pre">pyplot.plot</span></code>,
+which provides fine-grained control over the aesthetics
+of the edges in the plot.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>f</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></span></dt><dd><p>Figure on which the plot is made.</p>
+</dd>
+<dt><strong>ax</strong><span class="classifier"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matplotlib.axes.Axes</span></code></a></span></dt><dd><p>Axis on which the plot is made.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If you’d like to overlay the actual shapes from the
+<code class="docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code>, call <code class="docutils literal notranslate"><span class="pre">gdf.plot(ax=ax)</span></code> after this.
+To plot underneath, adjust the z-order of the plot as follows:
+<code class="docutils literal notranslate"><span class="pre">gdf.plot(ax=ax,zorder=0)</span></code>.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">Queen</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span> <span class="k">as</span> <span class="nn">lp</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">gdf</span> <span class="o">=</span> <span class="n">geopandas</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">lp</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">gdf</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">tmp</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;firebrickred&#39;</span><span class="p">,</span> <span class="n">node_kws</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">marker</span><span class="o">=</span><span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.remap_ids">
+<span class="sig-name descname"><span class="pre">remap_ids</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.remap_ids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.remap_ids" title="Link to this definition">¶</a></dt>
+<dd><p>In place modification throughout <code class="docutils literal notranslate"><span class="pre">W</span></code> of id values from
+<code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> to <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> in all.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>new_ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a></span></dt><dd><p>Aligned list of new ids to be inserted. Note that first
+element of <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> will replace first element of
+<code class="docutils literal notranslate"><span class="pre">w.id_order</span></code>, second element of <code class="docutils literal notranslate"><span class="pre">new_ids</span></code> replaces second
+element of <code class="docutils literal notranslate"><span class="pre">w.id_order</span></code> and so on.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
+<span class="go">[0, 1, 2, 3, 4, 5, 6, 7, 8]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[3, 1]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">new_ids</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;id</span><span class="si">%i</span><span class="s1">&#39;</span><span class="o">%</span><span class="nb">id</span> <span class="k">for</span> <span class="nb">id</span> <span class="ow">in</span> <span class="n">w</span><span class="o">.</span><span class="n">id_order</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">_</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">remap_ids</span><span class="p">(</span><span class="n">new_ids</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
+<span class="go">[&#39;id0&#39;, &#39;id1&#39;, &#39;id2&#39;, &#39;id3&#39;, &#39;id4&#39;, &#39;id5&#39;, &#39;id6&#39;, &#39;id7&#39;, &#39;id8&#39;]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span>
+<span class="go">[&#39;id3&#39;, &#39;id1&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.s0">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">s0</span></span><a class="headerlink" href="#libpysal.weights.W.s0" title="Link to this definition">¶</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as</p>
+<div class="math notranslate nohighlight">
+\[s0=\sum_i \sum_j w_{i,j}\]</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.s1">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">s1</span></span><a class="headerlink" href="#libpysal.weights.W.s1" title="Link to this definition">¶</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">s1</span></code> is defined as</p>
+<div class="math notranslate nohighlight">
+\[s1=1/2 \sum_i \sum_j \Big(w_{i,j} + w_{j,i}\Big)^2\]</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.s2">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">s2</span></span><a class="headerlink" href="#libpysal.weights.W.s2" title="Link to this definition">¶</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">s2</span></code> is defined as</p>
+<div class="math notranslate nohighlight">
+\[s2=\sum_j \Big(\sum_i w_{i,j} + \sum_i w_{j,i}\Big)^2\]</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.s2array">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">s2array</span></span><a class="headerlink" href="#libpysal.weights.W.s2array" title="Link to this definition">¶</a></dt>
+<dd><p>Individual elements comprising <code class="docutils literal notranslate"><span class="pre">s2</span></code>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.s2" title="libpysal.weights.W.s2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s2</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.sd">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sd</span></span><a class="headerlink" href="#libpysal.weights.W.sd" title="Link to this definition">¶</a></dt>
+<dd><p>Standard deviation of number of neighbors.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.set_shapefile">
+<span class="sig-name descname"><span class="pre">set_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shapefile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.set_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.set_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Adding metadata for writing headers of <code class="docutils literal notranslate"><span class="pre">.gal</span></code> and <code class="docutils literal notranslate"><span class="pre">.gwt</span></code> files.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The shapefile name used to construct weights.</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>The name of the attribute in the shapefile to associate
+with ids in the weights.</p>
+</dd>
+<dt><strong>full</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Write out the entire path for a shapefile (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or
+only the base of the shapefile without extension (<code class="docutils literal notranslate"><span class="pre">False</span></code>).
+Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.set_transform">
+<span class="sig-name descname"><span class="pre">set_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'B'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.set_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.set_transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transformations of weights.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>transform</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>This parameter is not case sensitive. The following are
+valid transformations.</p>
+<ul class="simple">
+<li><p><strong>B</strong> – Binary</p></li>
+<li><p><strong>R</strong> – Row-standardization (global sum <span class="math notranslate nohighlight">\(=n\)</span>)</p></li>
+<li><p><strong>D</strong> – Double-standardization (global sum <span class="math notranslate nohighlight">\(=1\)</span>)</p></li>
+<li><p><strong>V</strong> – Variance stabilizing</p></li>
+<li><p><strong>O</strong> – Restore original transformation (from instantiation)</p></li>
+</ul>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Transformations are applied only to the value of the weights at
+instantiation. Chaining of transformations cannot be done on a <code class="docutils literal notranslate"><span class="pre">W</span></code>
+instance.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span>
+<span class="go">&#39;O&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0.5, 0.5]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;b&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.sparse">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sparse</span></span><a class="headerlink" href="#libpysal.weights.W.sparse" title="Link to this definition">¶</a></dt>
+<dd><p>Sparse matrix object. For any matrix manipulations required for w,
+<code class="docutils literal notranslate"><span class="pre">w.sparse</span></code> should be used. This is based on <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.symmetrize">
+<span class="sig-name descname"><span class="pre">symmetrize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.symmetrize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.symmetrize" title="Link to this definition">¶</a></dt>
+<dd><p>Construct a symmetric KNN weight. This ensures that the neighbors
+of each focal observation consider the focal observation itself as
+a neighbor. This returns a generic <code class="docutils literal notranslate"><span class="pre">W</span></code> object, since the object is no
+longer guaranteed to have <code class="docutils literal notranslate"><span class="pre">k</span></code> neighbors for each observation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.to_WSP">
+<span class="sig-name descname"><span class="pre">to_WSP</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_WSP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.to_WSP" title="Link to this definition">¶</a></dt>
+<dd><p>Generate a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>implicit</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a></span></dt><dd><p>Thin <code class="docutils literal notranslate"><span class="pre">W</span></code> class</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">WSP</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="s1">&#39;first&#39;</span><span class="p">,</span><span class="s1">&#39;third&#39;</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span><span class="n">weights</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">=</span><span class="n">w</span><span class="o">.</span><span class="n">to_WSP</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">isinstance</span><span class="p">(</span><span class="n">wsp</span><span class="p">,</span> <span class="n">WSP</span><span class="p">)</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
+<span class="go">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">s0</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.to_adjlist">
+<span class="sig-name descname"><span class="pre">to_adjlist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">remove_symmetric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_islands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">focal_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'focal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">neighbor_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'neighbor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort_joins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_adjlist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.to_adjlist" title="Link to this definition">¶</a></dt>
+<dd><p>Compute an adjacency list representation of a weights object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>remove_symmetric</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Whether or not to remove symmetric entries. If the <code class="docutils literal notranslate"><span class="pre">W</span></code>
+is symmetric, a standard directed adjacency list will contain
+both the forward and backward links by default because adjacency
+lists are a directed graph representation. If this is <code class="docutils literal notranslate"><span class="pre">True</span></code>,
+a <code class="docutils literal notranslate"><span class="pre">W</span></code> created from this adjacency list <strong>MAY NOT BE THE SAME</strong>
+as the original <code class="docutils literal notranslate"><span class="pre">W</span></code>. If you would like to consider (1,2) and
+(2,1) as distinct links, leave this as <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
+</dd>
+<dt><strong>drop_islands</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Whether or not to preserve islands as entries in the adjacency
+list. By default, observations with no neighbors do not appear
+in the adjacency list. If islands are kept, they are coded as
+self-neighbors with zero weight.</p>
+</dd>
+<dt><strong>focal_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column in which to store “source” node ids.</p>
+</dd>
+<dt><strong>neighbor_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column in which to store “destination” node ids.</p>
+</dd>
+<dt><strong>weight_col</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Name of the column in which to store weight information.</p>
+</dd>
+<dt><strong>sort_joins</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Whether or not to lexicographically sort the adjacency
+list by (focal_col, neighbor_col). Default is False.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.to_file">
+<span class="sig-name descname"><span class="pre">to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.to_file" title="Link to this definition">¶</a></dt>
+<dd><p>Write a weights to a file. The format is guessed automatically
+from the path, but can be overridden with the format argument.</p>
+<p>See libpysal.io.FileIO for more information.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>path</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>location to save the file</p>
+</dd>
+<dt><strong>format</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>string denoting the format to write the weights to.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.to_networkx">
+<span class="sig-name descname"><span class="pre">to_networkx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_networkx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.to_networkx" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a weights object to a <code class="docutils literal notranslate"><span class="pre">networkx</span></code> graph.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">A</span></code> <code class="docutils literal notranslate"><span class="pre">networkx</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">graph</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">representation</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">the</span></code> <code class="docutils literal notranslate"><span class="pre">W</span></code> object.</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.W.to_sparse">
+<span class="sig-name descname"><span class="pre">to_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fmt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'coo'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#W.to_sparse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.W.to_sparse" title="Link to this definition">¶</a></dt>
+<dd><p>Generate a <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> array object from a pysal W.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>fmt</strong><span class="classifier">{‘bsr’, ‘coo’, ‘csc’, ‘csr’}</span></dt><dd><p>scipy.sparse format</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.11.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></dt><dd><p>A scipy.sparse array with a format of fmt.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The keys of the w.neighbors are encoded
+to determine row,col in the sparse array.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#libpysal.weights.W.transform" title="Link to this definition">¶</a></dt>
+<dd><p>Getter for transform property.</p>
+<dl class="field-list">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>transformation</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></span></dt><dd><p>Valid transformation value. See the <code class="docutils literal notranslate"><span class="pre">transform</span></code>
+parameters in <code class="docutils literal notranslate"><span class="pre">set_transform()</span></code> for a detailed description.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.set_transform" title="libpysal.weights.W.set_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_transform</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">=</span><span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span>
+<span class="go">&#39;O&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;r&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[0.5, 0.5]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span><span class="o">=</span><span class="s1">&#39;b&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.trcW2">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">trcW2</span></span><a class="headerlink" href="#libpysal.weights.W.trcW2" title="Link to this definition">¶</a></dt>
+<dd><p>Trace of <span class="math notranslate nohighlight">\(WW\)</span>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.diagW2" title="libpysal.weights.W.diagW2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagW2</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.trcWtW">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">trcWtW</span></span><a class="headerlink" href="#libpysal.weights.W.trcWtW" title="Link to this definition">¶</a></dt>
+<dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W\)</span>.</p>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="#libpysal.weights.W.diagWtW" title="libpysal.weights.W.diagWtW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW</span></code></a></dt><dd></dd>
+</dl>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.W.trcWtW_WW">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">trcWtW_WW</span></span><a class="headerlink" href="#libpysal.weights.W.trcWtW_WW" title="Link to this definition">¶</a></dt>
+<dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd></dl>
+
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">libpysal.weights.WSP</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.WSP"><code class="docutils literal notranslate"><span class="pre">WSP</span></code></a><ul>
+<li><a class="reference internal" href="#libpysal.weights.WSP.__init__"><code class="docutils literal notranslate"><span class="pre">WSP.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.WSP.diagWtW_WW"><code class="docutils literal notranslate"><span class="pre">WSP.diagWtW_WW</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.WSP.from_W"><code class="docutils literal notranslate"><span class="pre">WSP.from_W()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.WSP.id_order"><code class="docutils literal notranslate"><span class="pre">WSP.id_order</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.WSP.s0"><code class="docutils literal notranslate"><span class="pre">WSP.s0</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.WSP.to_W"><code class="docutils literal notranslate"><span class="pre">WSP.to_W()</span></code></a></li>
+<li><a class="reference internal" href="#libpysal.weights.WSP.trcWtW_WW"><code class="docutils literal notranslate"><span class="pre">WSP.trcWtW_WW</span></code></a></li>
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+      
+  <section id="libpysal-weights-wsp">
+<h1>libpysal.weights.WSP<a class="headerlink" href="#libpysal-weights-wsp" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="libpysal.weights.WSP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">WSP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sparse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.WSP" title="Link to this definition">¶</a></dt>
+<dd><p>Thin <code class="docutils literal notranslate"><span class="pre">W</span></code> class for <code class="docutils literal notranslate"><span class="pre">spreg</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>sparse</strong><span class="classifier">scipy.sparse.{matrix-type}</span></dt><dd><p>NxN object from <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code></p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>From GAL information</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">scipy.sparse</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">WSP</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">rows</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">cols</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span>  <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">sparse</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">csr_matrix</span><span class="p">((</span><span class="n">weights</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">)),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sparse</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">s0</span>
+<span class="go">4.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">trcWtW_WW</span>
+<span class="go">6.395</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
+<span class="go">4</span>
+</pre></div>
+</div>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>n</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>description</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.WSP.s0" title="libpysal.weights.WSP.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as:</p>
+</dd>
+<dt><a class="reference internal" href="#libpysal.weights.WSP.trcWtW_WW" title="libpysal.weights.WSP.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sparse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.WSP.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.__init__" title="libpysal.weights.WSP.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(sparse[, id_order, index])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.WSP.from_W" title="libpysal.weights.WSP.from_W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_W</span></code></a>(W)</p></td>
+<td><p>Constructs a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object from the <code class="docutils literal notranslate"><span class="pre">W</span></code>'s sparse matrix.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.to_W" title="libpysal.weights.WSP.to_W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_W</span></code></a>([silence_warnings])</p></td>
+<td><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.diagWtW_WW" title="libpysal.weights.WSP.diagWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">diagWtW_WW</span></code></a></p></td>
+<td><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.WSP.id_order" title="libpysal.weights.WSP.id_order"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_order</span></code></a></p></td>
+<td><p>An ordered list of ids, assumed to match the ordering in <code class="docutils literal notranslate"><span class="pre">sparse</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#libpysal.weights.WSP.s0" title="libpysal.weights.WSP.s0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">s0</span></code></a></p></td>
+<td><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as:</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#libpysal.weights.WSP.trcWtW_WW" title="libpysal.weights.WSP.trcWtW_WW"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trcWtW_WW</span></code></a></p></td>
+<td><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.diagWtW_WW">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">diagWtW_WW</span></span><a class="headerlink" href="#libpysal.weights.WSP.diagWtW_WW" title="Link to this definition">¶</a></dt>
+<dd><p>Diagonal of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.from_W">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">W</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP.from_W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.WSP.from_W" title="Link to this definition">¶</a></dt>
+<dd><p>Constructs a <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object from the <code class="docutils literal notranslate"><span class="pre">W</span></code>’s sparse matrix.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>W</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>A PySAL weights object with a sparse form and ids.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">A</span></code> <code class="docutils literal notranslate"><span class="pre">WSP</span></code> instance.</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.id_order">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">id_order</span></span><a class="headerlink" href="#libpysal.weights.WSP.id_order" title="Link to this definition">¶</a></dt>
+<dd><p>An ordered list of ids, assumed to match the ordering in <code class="docutils literal notranslate"><span class="pre">sparse</span></code>.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.s0">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">s0</span></span><a class="headerlink" href="#libpysal.weights.WSP.s0" title="Link to this definition">¶</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">s0</span></code> is defined as:</p>
+<div class="math notranslate nohighlight">
+\[s0=\sum_i \sum_j w_{i,j}\]</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.to_W">
+<span class="sig-name descname"><span class="pre">to_W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">silence_warnings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/weights.html#WSP.to_W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.WSP.to_W" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>self</strong><span class="classifier"><a class="reference internal" href="#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></span></dt><dd><p>PySAL sparse weights object.</p>
+</dd>
+<dt><strong>silence_warnings</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Switch to <code class="docutils literal notranslate"><span class="pre">True</span></code> to turn off print statements for every
+observation with islands. Default is <code class="docutils literal notranslate"><span class="pre">False</span></code>, which does
+not silence warnings.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2SW</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">WSP2W</span>
+</pre></div>
+</div>
+<p>Build a 10x10 <code class="docutils literal notranslate"><span class="pre">scipy.sparse</span></code> matrix for a rectangular 2x5
+region of cells (rook contiguity), then construct a <code class="docutils literal notranslate"><span class="pre">libpysal</span></code>
+sparse weights object (<code class="docutils literal notranslate"><span class="pre">self</span></code>).</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sp</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sp</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
+<span class="go">[[0 1 0 0 0 1 0 0 0 0]]</span>
+</pre></div>
+</div>
+<p>Convert this sparse weights object to a standard PySAL weights object.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<span class="go">[0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="libpysal.weights.WSP.trcWtW_WW">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">trcWtW_WW</span></span><a class="headerlink" href="#libpysal.weights.WSP.trcWtW_WW" title="Link to this definition">¶</a></dt>
+<dd><p>Trace of <span class="math notranslate nohighlight">\(W^{'}W + WW\)</span>.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
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+  <a href="../_sources/generated/libpysal.weights.WSP.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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diff --git a/generated/libpysal.weights.WSP2W.html b/generated/libpysal.weights.WSP2W.html
new file mode 100644
index 000000000..08441bcd0
--- /dev/null
+++ b/generated/libpysal.weights.WSP2W.html
@@ -0,0 +1,205 @@
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+<li><a class="reference internal" href="#">libpysal.weights.WSP2W</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.WSP2W"><code class="docutils literal notranslate"><span class="pre">WSP2W()</span></code></a></li>
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+      
+  <section id="libpysal-weights-wsp2w">
+<h1>libpysal.weights.WSP2W<a class="headerlink" href="#libpysal-weights-wsp2w" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.WSP2W">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">WSP2W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wsp</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#WSP2W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.WSP2W" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a pysal WSP object (thin weights matrix) to a pysal W object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>wsp</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WSP</span></code></a></span></dt><dd><p>PySAL sparse weights object</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">WSP</span><span class="p">,</span> <span class="n">WSP2W</span>
+</pre></div>
+</div>
+<p>Build a 10x10 scipy.sparse matrix for a rectangular 2x5 region of cells
+(rook contiguity), then construct a PySAL sparse weights object (wsp).</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">sp</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">WSP</span><span class="p">(</span><span class="n">sp</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
+<span class="go">matrix([[0, 1, 0, 0, 0, 1, 0, 0, 0, 0]], dtype=int8)</span>
+</pre></div>
+</div>
+<p>Convert this sparse weights object to a standard PySAL weights object.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">WSP2W</span><span class="p">(</span><span class="n">wsp</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<span class="go">[0. 1. 0. 0. 0. 1. 0. 0. 0. 0.]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
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+      
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\ No newline at end of file
diff --git a/generated/libpysal.weights.attach_islands.html b/generated/libpysal.weights.attach_islands.html
new file mode 100644
index 000000000..6bec4937e
--- /dev/null
+++ b/generated/libpysal.weights.attach_islands.html
@@ -0,0 +1,199 @@
+<!DOCTYPE html>
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+<li><a class="reference internal" href="#">libpysal.weights.attach_islands</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.attach_islands"><code class="docutils literal notranslate"><span class="pre">attach_islands()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-attach-islands">
+<h1>libpysal.weights.attach_islands<a class="headerlink" href="#libpysal-weights-attach-islands" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.attach_islands">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">attach_islands</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_knn1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#attach_islands"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.attach_islands" title="Link to this definition">¶</a></dt>
+<dd><p>Attach nearest neighbor to islands in spatial weight w.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>pysal spatial weight object (unstandardized).</p>
+</dd>
+<dt><strong>w_knn1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>Nearest neighbor pysal spatial weight object (k=1).</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt>: <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></dt><dd><p>pysal spatial weight object w without islands.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">Rook</span><span class="p">,</span> <span class="n">KNN</span><span class="p">,</span> <span class="n">attach_islands</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;10740.shp&#39;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[163]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_knn1</span> <span class="o">=</span> <span class="n">KNN</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;10740.shp&#39;</span><span class="p">),</span><span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_attach</span> <span class="o">=</span> <span class="n">attach_islands</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">w_knn1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_attach</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_attach</span><span class="p">[</span><span class="n">w</span><span class="o">.</span><span class="n">islands</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+<span class="go">{166: 1.0}</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.block_weights</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.block_weights"><code class="docutils literal notranslate"><span class="pre">block_weights()</span></code></a></li>
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+  <section id="libpysal-weights-block-weights">
+<h1>libpysal.weights.block_weights<a class="headerlink" href="#libpysal-weights-block-weights" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.block_weights">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">block_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">regimes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#block_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.block_weights" title="Link to this definition">¶</a></dt>
+<dd><p>Construct spatial weights for regime neighbors.</p>
+<p>Block contiguity structures are relevant when defining neighbor relations
+based on membership in a regime. For example, all counties belonging to
+the same state could be defined as neighbors, in an analysis of all
+counties in the US.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>regimes</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>ids of which regime an observation belongs to</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a>, <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Ordered sequence of IDs for the observations</p>
+</dd>
+<dt><strong>sparse</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return WSP instance
+If False return W instance</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>W</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">weights</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">instance</span></code></span></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">block_weights</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">25</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="mi">21</span><span class="p">,</span><span class="mi">25</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span>
+<span class="go">array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2.,</span>
+<span class="go">       2., 2., 2., 1., 3., 3., 3., 3.])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">block_weights</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[1, 2, 3, 4, 5, 6, 7, 8, 9, 20]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">regimes</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;n&#39;</span><span class="p">,</span><span class="s1">&#39;n&#39;</span><span class="p">,</span><span class="s1">&#39;s&#39;</span><span class="p">,</span><span class="s1">&#39;s&#39;</span><span class="p">,</span><span class="s1">&#39;e&#39;</span><span class="p">,</span><span class="s1">&#39;e&#39;</span><span class="p">,</span><span class="s1">&#39;w&#39;</span><span class="p">,</span><span class="s1">&#39;w&#39;</span><span class="p">,</span><span class="s1">&#39;e&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">block_weights</span><span class="p">(</span><span class="n">regimes</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span> <span class="o">==</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="mi">3</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">4</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">5</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="mi">6</span><span class="p">:</span> <span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="mi">7</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="mi">8</span><span class="p">:</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]}</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+      
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diff --git a/generated/libpysal.weights.build_lattice_shapefile.html b/generated/libpysal.weights.build_lattice_shapefile.html
new file mode 100644
index 000000000..a54e801ff
--- /dev/null
+++ b/generated/libpysal.weights.build_lattice_shapefile.html
@@ -0,0 +1,184 @@
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+    <title>libpysal.weights.build_lattice_shapefile &#8212; libpysal v4.9.0 Manual</title>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.build_lattice_shapefile</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.build_lattice_shapefile"><code class="docutils literal notranslate"><span class="pre">build_lattice_shapefile()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-build-lattice-shapefile">
+<h1>libpysal.weights.build_lattice_shapefile<a class="headerlink" href="#libpysal-weights-build-lattice-shapefile" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.build_lattice_shapefile">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">build_lattice_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nrows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outFileName</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/user.html#build_lattice_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.build_lattice_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Build a lattice shapefile with nrows rows and ncols cols.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of rows</p>
+</dd>
+<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cols</p>
+</dd>
+<dt><strong>outFileName</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><a class="reference external" href="https://docs.python.org/3.12/library/constants.html#None" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">None</span></code></a></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
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+
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\ No newline at end of file
diff --git a/generated/libpysal.weights.comb.html b/generated/libpysal.weights.comb.html
new file mode 100644
index 000000000..919f57f5b
--- /dev/null
+++ b/generated/libpysal.weights.comb.html
@@ -0,0 +1,196 @@
+<!DOCTYPE html>
+
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-development-version">Installing development version</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
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+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
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+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
+  <a role="button"
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+<li><a class="reference internal" href="#">libpysal.weights.comb</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.comb"><code class="docutils literal notranslate"><span class="pre">comb()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
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+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-comb">
+<h1>libpysal.weights.comb<a class="headerlink" href="#libpysal-weights-comb" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.comb">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">comb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#comb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.comb" title="Link to this definition">¶</a></dt>
+<dd><p>Combinations of size n taken from items</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>items</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>items to be drawn from</p>
+</dd>
+<dt><strong>n</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">integer</span></code></span></dt><dd><p>size of combinations to take from items</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>implicit</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/glossary.html#term-generator" title="(in Python v3.12)"><span>generator</span></a></span></dt><dd><p>combinations of size n taken from items</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="go">[0, 1]</span>
+<span class="go">[0, 2]</span>
+<span class="go">[0, 3]</span>
+<span class="go">[1, 2]</span>
+<span class="go">[1, 3]</span>
+<span class="go">[2, 3]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+      
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+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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diff --git a/generated/libpysal.weights.da2W.html b/generated/libpysal.weights.da2W.html
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index 000000000..be1378c60
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+++ b/generated/libpysal.weights.da2W.html
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+</ul>
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+<li><a class="reference internal" href="#">libpysal.weights.da2W</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.da2W"><code class="docutils literal notranslate"><span class="pre">da2W()</span></code></a></li>
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+      
+  <section id="libpysal-weights-da2w">
+<h1>libpysal.weights.da2W<a class="headerlink" href="#libpysal-weights-da2w" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.da2W">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">da2W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">da</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'queen'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_nodata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#da2W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.da2W" title="Link to this definition">¶</a></dt>
+<dd><p>Create a W object from xarray.DataArray with an additional
+attribute index containing coordinate values of the raster
+in the form of Pandas.Index/MultiIndex.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
+</dd>
+<dt><strong>criterion</strong><span class="classifier">{“rook”, “queen”}</span></dt><dd><p>Type of contiguity. Default is queen.</p>
+</dd>
+<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
+</dd>
+<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
+belong to default dimensions, which are (band/time, y/lat, x/lon)
+e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
+Default is {} empty dictionary.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
+Default is 1.</p>
+</dd>
+<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
+selecting higher_order neighbors, Default is False</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
+all available cores are used. Default is 1.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>Optional arguments for <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>instance of spatial weights class W with an index attribute</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.weights.W"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.W</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<ol class="arabic simple">
+<li><p>Lower order contiguities are also selected.</p></li>
+<li><p>Returned object contains <cite>index</cite> attribute that includes a
+<cite>Pandas.MultiIndex</cite> object from the DataArray.</p></li>
+</ol>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.raster</span> <span class="kn">import</span> <span class="n">da2W</span><span class="p">,</span> <span class="n">testDataArray</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+<span class="go">        {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">dims</span>
+<span class="go">(&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">(3, 4, 4)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">coords</span>
+<span class="go">Coordinates:</span>
+<span class="go">    * layer      (layer) int64 1 2 3</span>
+<span class="go">    * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
+<span class="go">    * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">attrs</span>
+<span class="go">{&#39;nodatavals&#39;: (-32768.0,)}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{</span>
+<span class="go">    &quot;z_label&quot;: &quot;layer&quot;,</span>
+<span class="go">    &quot;y_label&quot;: &quot;latitude&quot;,</span>
+<span class="go">    &quot;x_label&quot;: &quot;longitude&quot;</span>
+<span class="go">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coords_labels</span><span class="o">=</span><span class="n">coords_labels</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;30.000&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="p">[(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">90.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">)]</span> <span class="o">==</span> <span class="p">{(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">90.0</span><span class="p">,</span> <span class="mf">60.0</span><span class="p">):</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mf">30.0</span><span class="p">,</span> <span class="mf">180.0</span><span class="p">):</span> <span class="mi">1</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<span class="go">MultiIndex([(2, 90.0,  60.0),</span>
+<span class="go">            (2, 90.0, 180.0)],</span>
+<span class="go">           names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
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+  <a href="../_sources/generated/libpysal.weights.da2W.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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+</html>
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diff --git a/generated/libpysal.weights.da2WSP.html b/generated/libpysal.weights.da2WSP.html
new file mode 100644
index 000000000..a49db3ef4
--- /dev/null
+++ b/generated/libpysal.weights.da2WSP.html
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+<li><a class="reference internal" href="#libpysal.weights.da2WSP"><code class="docutils literal notranslate"><span class="pre">da2WSP()</span></code></a></li>
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+  <section id="libpysal-weights-da2wsp">
+<h1>libpysal.weights.da2WSP<a class="headerlink" href="#libpysal-weights-da2wsp" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.da2WSP">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">da2WSP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">da</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'queen'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">z_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_nodata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#da2WSP"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.da2WSP" title="Link to this definition">¶</a></dt>
+<dd><p>Create a WSP object from xarray.DataArray with an additional
+attribute index containing coordinate values of the raster
+in the form of Pandas.Index/MultiIndex.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>Input 2D or 3D DataArray with shape=(z, y, x)</p>
+</dd>
+<dt><strong>criterion</strong><span class="classifier">{“rook”, “queen”}</span></dt><dd><p>Type of contiguity. Default is queen.</p>
+</dd>
+<dt><strong>z_value</strong><span class="classifier">int/string/float</span></dt><dd><p>Select the z_value of 3D DataArray with multiple layers.</p>
+</dd>
+<dt><strong>coords_labels</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Pass dimension labels for coordinates and layers if they do not
+belong to default dimensions, which are (band/time, y/lat, x/lon)
+e.g. coords_labels = {“y_label”: “latitude”, “x_label”: “longitude”, “z_label”: “year”}
+Default is {} empty dictionary.</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity, this will select all neighbors upto kth order.
+Default is 1.</p>
+</dd>
+<dt><strong>include_nodata</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, missing values will be assumed as non-missing when
+selecting higher_order neighbors, Default is False</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Number of cores to be used in the sparse weight construction. If -1,
+all available cores are used. Default is 1.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>wsp</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a></span></dt><dd><p>instance of spatial weights class WSP with an index attribute</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.weights.WSP"><code class="xref py py-class docutils literal notranslate"><span class="pre">libpysal.weights.weights.WSP</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<ol class="arabic simple">
+<li><p>Lower order contiguities are also selected.</p></li>
+<li><p>Returned object contains <cite>index</cite> attribute that includes a
+<cite>Pandas.MultiIndex</cite> object from the DataArray.</p></li>
+</ol>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.raster</span> <span class="kn">import</span> <span class="n">da2WSP</span><span class="p">,</span> <span class="n">testDataArray</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+<span class="go">        {&#39;band&#39;: &#39;layer&#39;, &#39;x&#39;: &#39;longitude&#39;, &#39;y&#39;: &#39;latitude&#39;})</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">dims</span>
+<span class="go">(&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">(3, 4, 4)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">coords</span>
+<span class="go">Coordinates:</span>
+<span class="go">    * layer      (layer) int64 1 2 3</span>
+<span class="go">    * latitude   (latitude) float64 90.0 30.0 -30.0 -90.0</span>
+<span class="go">    * longitude  (longitude) float64 -180.0 -60.0 60.0 180.0</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">attrs</span>
+<span class="go">{&#39;nodatavals&#39;: (-32768.0,)}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{</span>
+<span class="go">    &quot;z_label&quot;: &quot;layer&quot;,</span>
+<span class="go">    &quot;y_label&quot;: &quot;latitude&quot;,</span>
+<span class="go">    &quot;x_label&quot;: &quot;longitude&quot;</span>
+<span class="go">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">coords_labels</span><span class="o">=</span><span class="n">coords_labels</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">n</span>
+<span class="go">10</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">pct_sp</span> <span class="o">=</span> <span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span> <span class="o">*</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">wsp</span><span class="o">.</span><span class="n">n</span><span class="o">**</span><span class="mi">2</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">pct_sp</span>
+<span class="go">&#39;0.300&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">sparse</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
+<span class="go">[[0 0 1 0 0 1 1 1 0 0]]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span><span class="o">.</span><span class="n">index</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<span class="go">MultiIndex([(2, 90.0,  60.0),</span>
+<span class="go">            (2, 90.0, 180.0)],</span>
+<span class="go">           names=[&#39;layer&#39;, &#39;latitude&#39;, &#39;longitude&#39;])</span>
+</pre></div>
+</div>
+</dd></dl>
+
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diff --git a/generated/libpysal.weights.fill_diagonal.html b/generated/libpysal.weights.fill_diagonal.html
new file mode 100644
index 000000000..90b46ba6d
--- /dev/null
+++ b/generated/libpysal.weights.fill_diagonal.html
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+<li><a class="reference internal" href="#">libpysal.weights.fill_diagonal</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.fill_diagonal"><code class="docutils literal notranslate"><span class="pre">fill_diagonal()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-fill-diagonal">
+<h1>libpysal.weights.fill_diagonal<a class="headerlink" href="#libpysal-weights-fill-diagonal" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.fill_diagonal">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">fill_diagonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wsp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#fill_diagonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.fill_diagonal" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a new weights object with values inserted along the main diagonal.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object</p>
+</dd>
+<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a>, <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>Defines the value(s) to which the weights matrix diagonal should
+be set. If a constant is passed then each element along the
+diagonal will get this value (default is 1.0). An array of length
+w.n can be passed to set explicit values to each element along
+the diagonal (assumed to be in the same order as w.id_order).</p>
+</dd>
+<dt><strong>wsp</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True return a thin weights object of the type WSP, if False
+return the standard W object.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<p>Build a basic rook weights matrix, which has zeros on the diagonal, then
+insert ones along the diagonal.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">id_type</span><span class="o">=</span><span class="s1">&#39;string&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_const</span> <span class="o">=</span> <span class="n">insert_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="s1">&#39;id5&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id1&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_const</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="s1">&#39;id5&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id0&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id1&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<p>Insert different values along the main diagonal.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">diag</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">125</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_var</span> <span class="o">=</span> <span class="n">insert_diagonal</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">diag</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_var</span><span class="p">[</span><span class="s1">&#39;id0&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="s1">&#39;id5&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;id0&#39;</span><span class="p">:</span> <span class="mf">100.0</span><span class="p">,</span> <span class="s1">&#39;id1&#39;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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diff --git a/generated/libpysal.weights.full.html b/generated/libpysal.weights.full.html
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.full</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.full"><code class="docutils literal notranslate"><span class="pre">full()</span></code></a></li>
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-full">
+<h1>libpysal.weights.full<a class="headerlink" href="#libpysal-weights-full" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.full">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">full</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#full"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.full" title="Link to this definition">¶</a></dt>
+<dd><p>Generate a full numpy array.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>(fullw, keys)</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>first element being the full numpy array and second element
+keys being the ids associated with each row in the array.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">W</span><span class="p">,</span> <span class="n">full</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="s1">&#39;first&#39;</span><span class="p">,</span><span class="s1">&#39;third&#39;</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="s1">&#39;second&#39;</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">weights</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;first&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;second&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],</span><span class="s1">&#39;third&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">]}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">neighbors</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="p">,</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">full</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span>
+<span class="go">array([[0., 1., 0.],</span>
+<span class="go">       [1., 0., 1.],</span>
+<span class="go">       [0., 1., 0.]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span>
+<span class="go">[&#39;first&#39;, &#39;second&#39;, &#39;third&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+<li><a class="reference internal" href="#">libpysal.weights.full2W</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.full2W"><code class="docutils literal notranslate"><span class="pre">full2W()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-full2w">
+<h1>libpysal.weights.full2W<a class="headerlink" href="#libpysal-weights-full2w" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.full2W">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">full2W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#full2W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.full2W" title="Link to this definition">¶</a></dt>
+<dd><p>Create a PySAL W object from a full array.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>m</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>nxn array with the full weights matrix</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>User ids assumed to be aligned with m</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL weights object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">full2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+</pre></div>
+</div>
+<p>Create an array of zeros</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>For loop to fill it with random numbers</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">)):</span>
+<span class="gp">... </span>    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])):</span>
+<span class="gp">... </span>        <span class="k">if</span> <span class="n">i</span><span class="o">!=</span><span class="n">j</span><span class="p">:</span>
+<span class="gp">... </span>            <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Create W object</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">full2W</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">a</span>
+<span class="go">array([[ True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True]])</span>
+</pre></div>
+</div>
+<p>Create list of user ids</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;myID0&#39;</span><span class="p">,</span> <span class="s1">&#39;myID1&#39;</span><span class="p">,</span> <span class="s1">&#39;myID2&#39;</span><span class="p">,</span> <span class="s1">&#39;myID3&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">full2W</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">ids</span><span class="o">=</span><span class="n">ids</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">a</span>
+<span class="go">array([[ True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True]])</span>
+</pre></div>
+</div>
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+<li><a class="reference internal" href="#libpysal.weights.fuzzy_contiguity"><code class="docutils literal notranslate"><span class="pre">fuzzy_contiguity()</span></code></a></li>
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+  <section id="libpysal-weights-fuzzy-contiguity">
+<h1>libpysal.weights.fuzzy_contiguity<a class="headerlink" href="#libpysal-weights-fuzzy-contiguity" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.fuzzy_contiguity">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">fuzzy_contiguity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gdf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.005</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'intersects'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#fuzzy_contiguity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.fuzzy_contiguity" title="Link to this definition">¶</a></dt>
+<dd><p>Fuzzy contiguity spatial weights</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>gdf:   GeoDataFrame</strong></dt><dd></dd>
+<dt><strong>tolerance: float</strong></dt><dd><p>The percentage of the length of the minimum side of the bounding rectangle for the GeoDataFrame to use in determining the buffering distance.</p>
+</dd>
+<dt><strong>buffering: boolean</strong></dt><dd><p>If False (default) joins will only be detected for features that intersect (touch, contain, within).
+If True then features will be buffered and intersections will be based on buffered features.</p>
+</dd>
+<dt><strong>drop: boolean</strong></dt><dd><p>If True (default), the buffered features are removed from the GeoDataFrame. If False, buffered features are added to the GeoDataFrame.</p>
+</dd>
+<dt><strong>buffer</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Specify exact buffering distance. Ignores <cite>tolerance</cite>.</p>
+</dd>
+<dt><strong>predicate</strong><span class="classifier">{‘intersects’, ‘within’, ‘contains’, ‘overlaps’, ‘crosses’, ‘touches’}</span></dt><dd><p>The predicate to use for determination of neighbors. Default is ‘intersects’. If None is passed, neighbours are determined based on
+the intersection of bounding boxes.</p>
+</dd>
+<dt><strong>**kwargs: keyword arguments</strong></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt>w:  <code class="xref py py-obj docutils literal notranslate"><span class="pre">PySAL</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></dt><dd><p>Spatial weights based on fuzzy contiguity. Weights are binary.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This relaxes the notion of contiguity neighbors for the case of feature
+collections that violate the condition of planar enforcement. It handles
+three types of conditions present in such collections that would result in
+islands when using the regular PySAL contiguity methods. The first are
+edges for nearby polygons that should be shared, but are digitized
+separately for the individual polygons and the resulting edges do not
+coincide, but instead the edges intersect. The second case is similar to
+the first, only the resultant edges do not intersect but are “close”. The
+final case arises when one polygon is “inside” a second polygon but is not
+encoded to represent a hole in the containing polygon.</p>
+<p>Detection of the second case will require setting buffering=True and exploring different values for tolerance.</p>
+<p>The buffering check assumes the geometry coordinates are projected.</p>
+<p class="rubric">References</p>
+<p>Planar Enforcement: <a class="reference external" href="http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6">http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</a></p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">fuzzy_contiguity</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">rs</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;map_RS_BR.shp&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">rs_df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">rs</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">len</span><span class="p">(</span><span class="n">wq</span><span class="o">.</span><span class="n">islands</span><span class="p">)</span>
+<span class="go">29</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wq</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">{}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="nb">dict</span><span class="p">({</span><span class="mi">239</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">59</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">152</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">23</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">107</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<p>Example needing to use buffering</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p0</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p1</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">10</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">2</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">p2</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">12</span><span class="p">,</span><span class="mf">2.001</span><span class="p">),</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mf">2.001</span><span class="p">),</span> <span class="p">(</span><span class="mi">13</span><span class="p">,</span><span class="mi">10</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">gs</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">([</span><span class="n">p0</span><span class="p">,</span><span class="n">p1</span><span class="p">,</span><span class="n">p2</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">gdf</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">(</span><span class="n">geometry</span><span class="o">=</span><span class="n">gs</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">gdf</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[2]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wfb</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">gdf</span><span class="p">,</span> <span class="n">buffering</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wfb</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wfb</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+<span class="go">{1: 1.0}</span>
+</pre></div>
+</div>
+<p>Example with a custom index</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rs_df_ix</span> <span class="o">=</span> <span class="n">rs_df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;NM_MUNICIP&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf_ix</span> <span class="o">=</span> <span class="n">fuzzy_contiguity</span><span class="p">(</span><span class="n">rs_df</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wf_ix</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="s2">&quot;TAVARES&quot;</span><span class="p">]</span>
+<span class="go">[&#39;SÃO JOSÉ DO NORTE&#39;, &#39;MOSTARDAS&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
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+<li><a class="reference internal" href="#">libpysal.weights.get_ids</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.get_ids"><code class="docutils literal notranslate"><span class="pre">get_ids()</span></code></a></li>
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+  <section id="libpysal-weights-get-ids">
+<h1>libpysal.weights.get_ids<a class="headerlink" href="#libpysal-weights-get-ids" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.get_ids">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">get_ids</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_shps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idVariable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#get_ids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.get_ids" title="Link to this definition">¶</a></dt>
+<dd><p>Gets the IDs from the DBF file that moves with a given shape file or
+a geopandas.GeoDataFrame.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>in_shps</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a> or <a class="reference external" href="https://geopandas.org/en/latest/docs/reference/api/geopandas.GeoDataFrame.html#geopandas.GeoDataFrame" title="(in GeoPandas v0.14.0.dev+15)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">geopandas.GeoDataFrame</span></code></a></span></dt><dd><p>The input geographic data. Either
+(1) a path to a shapefile including suffix (str); or
+(2) a geopandas.GeoDataFrame.</p>
+</dd>
+<dt><strong>idVariable</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of a column in the shapefile’s DBF or the
+geopandas.GeoDataFrame to use for ids.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>a list of IDs</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">get_ids</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span> <span class="o">=</span> <span class="n">get_ids</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">),</span> <span class="s2">&quot;POLYID&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
+<span class="go">[1, 2, 3, 4, 5]</span>
+</pre></div>
+</div>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">get_ids</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">gdf</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span> <span class="o">=</span> <span class="n">gdf</span><span class="p">[</span><span class="s2">&quot;POLYID&quot;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">polyids</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
+<span class="go">0    1</span>
+<span class="go">1    2</span>
+<span class="go">2    3</span>
+<span class="go">3    4</span>
+<span class="go">4    5</span>
+<span class="go">Name: POLYID, dtype: int64</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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diff --git a/generated/libpysal.weights.get_points_array_from_shapefile.html b/generated/libpysal.weights.get_points_array_from_shapefile.html
new file mode 100644
index 000000000..b395b69c5
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@@ -0,0 +1,204 @@
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+<li><a class="reference internal" href="#">libpysal.weights.get_points_array_from_shapefile</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.get_points_array_from_shapefile"><code class="docutils literal notranslate"><span class="pre">get_points_array_from_shapefile()</span></code></a></li>
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+      
+  <section id="libpysal-weights-get-points-array-from-shapefile">
+<h1>libpysal.weights.get_points_array_from_shapefile<a class="headerlink" href="#libpysal-weights-get-points-array-from-shapefile" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.get_points_array_from_shapefile">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">get_points_array_from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shapefile</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#get_points_array_from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.get_points_array_from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Gets a data array of x and y coordinates from a given shapefile.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of a shape file including suffix</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>points</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n, 2)
+a data array of x and y coordinates</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If the given shape file includes polygons,
+this function returns x and y coordinates of the polygons’ centroids</p>
+<p class="rubric">Examples</p>
+<p>Point shapefile</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">get_points_array_from_shapefile</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;juvenile.shp&#39;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+<span class="go">array([[94., 93.],</span>
+<span class="go">       [80., 95.],</span>
+<span class="go">       [79., 90.]])</span>
+</pre></div>
+</div>
+<p>Polygon shapefile</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span> <span class="o">=</span> <span class="n">get_points_array_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;columbus.shp&#39;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">xy</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+<span class="go">array([[ 8.82721847, 14.36907602],</span>
+<span class="go">       [ 8.33265837, 14.03162401],</span>
+<span class="go">       [ 9.01226541, 13.81971908]])</span>
+</pre></div>
+</div>
+</dd></dl>
+
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diff --git a/generated/libpysal.weights.hexLat2W.html b/generated/libpysal.weights.hexLat2W.html
new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.hexLat2W</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.hexLat2W"><code class="docutils literal notranslate"><span class="pre">hexLat2W()</span></code></a></li>
+</ul>
+</li>
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+      
+  <section id="libpysal-weights-hexlat2w">
+<h1>libpysal.weights.hexLat2W<a class="headerlink" href="#libpysal-weights-hexlat2w" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.hexLat2W">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">hexLat2W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nrows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#hexLat2W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.hexLat2W" title="Link to this definition">¶</a></dt>
+<dd><p>Create a W object for a hexagonal lattice.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of rows</p>
+</dd>
+<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of columns</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</p>
+<p>Construction is based on shifting every other column of a regular lattice
+down 1/2 of a cell.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">hexLat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">[0, 6, 2]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">21</span><span class="p">]</span>
+<span class="go">[16, 20, 22]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wh</span> <span class="o">=</span> <span class="n">hexLat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wh</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="go">[0, 6, 2, 5, 7]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wh</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">21</span><span class="p">]</span>
+<span class="go">[16, 20, 22]</span>
+</pre></div>
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+<li><a class="reference internal" href="#libpysal.weights.higher_order"><code class="docutils literal notranslate"><span class="pre">higher_order()</span></code></a></li>
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+  <section id="libpysal-weights-higher-order">
+<h1>libpysal.weights.higher_order<a class="headerlink" href="#libpysal-weights-higher-order" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.higher_order">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">higher_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#higher_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.higher_order" title="Link to this definition">¶</a></dt>
+<dd><p>Contiguity weights object of order k.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>order of contiguity</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>implicit</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Proper higher order neighbors are returned such that i and j are k-order
+neighbors iff the shortest path from i-j is of length k.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">higher_order</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w10</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w10_2</span> <span class="o">=</span> <span class="n">higher_order</span><span class="p">(</span><span class="n">w10</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w10_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">20</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_2</span> <span class="o">=</span> <span class="n">higher_order</span><span class="p">(</span><span class="n">w5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+</pre></div>
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+<li><a class="reference internal" href="#libpysal.weights.higher_order_sp"><code class="docutils literal notranslate"><span class="pre">higher_order_sp()</span></code></a></li>
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+  <section id="libpysal-weights-higher-order-sp">
+<h1>libpysal.weights.higher_order_sp<a class="headerlink" href="#libpysal-weights-higher-order-sp" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.higher_order_sp">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">higher_order_sp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shortest_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#higher_order_sp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.higher_order_sp" title="Link to this definition">¶</a></dt>
+<dd><p>Contiguity weights for either a sparse W or W for order k.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>sparse_matrix, spatial weights object or
+scipy.sparse.csr.csr_instance</p>
+</dd>
+<dt><strong>k</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>Order of contiguity</p>
+</dd>
+<dt><strong>shortest_path</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True: i,j and k-order neighbors if the
+shortest path for i,j is k.
+False: i,j are k-order neighbors if there
+is a path from i,j of length k.</p>
+</dd>
+<dt><strong>diagonal</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True:  keep k-order (i,j) joins when i==j
+False: remove k-order (i,j) joins when i==j</p>
+</dd>
+<dt><strong>lower_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True: include lower order contiguities
+False: return only weights of order k</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>wk</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>WSP, type matches type of w argument</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">higher_order_sp</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25</span><span class="o">.</span><span class="n">n</span>
+<span class="go">25</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">diagonal</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span>  <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">15</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">shortest_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span> <span class="o">=</span> <span class="n">higher_order_sp</span><span class="p">(</span><span class="n">w25</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">lower_order</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w25_3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">5</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+</pre></div>
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+<li><a class="reference internal" href="#">libpysal.weights.lag_categorical</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.lag_categorical"><code class="docutils literal notranslate"><span class="pre">lag_categorical()</span></code></a></li>
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+      
+  <section id="libpysal-weights-lag-categorical">
+<h1>libpysal.weights.lag_categorical<a class="headerlink" href="#libpysal-weights-lag-categorical" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.lag_categorical">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">lag_categorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ties</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tryself'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spatial_lag.html#lag_categorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.lag_categorical" title="Link to this definition">¶</a></dt>
+<dd><p>Spatial lag operator for categorical variables.</p>
+<p>Constructs the most common categories of neighboring observations, weighted
+by their weight strength.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>PySAL spatial weightsobject</p>
+</dd>
+<dt><strong>y</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/glossary.html#term-iterable" title="(in Python v3.12)"><span>iterable</span></a></span></dt><dd><p>iterable collection of categories (either int or
+string) with dimensionality conforming to w (see examples)</p>
+</dd>
+<dt><strong>ties</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>string describing the method to use when resolving
+ties. By default, the option is “tryself”,
+and the category of the focal observation
+is included with its neighbors to try
+and break a tie. If this does not resolve the tie,
+a winner is chosen randomly. To just use random choice to
+break ties, pass “random” instead.</p>
+</dd>
+<dt><strong>Returns</strong></dt><dd></dd>
+<dt><strong>——-</strong></dt><dd></dd>
+<dt><strong>an (n x k) column vector containing the most common neighboring observation</strong></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This works on any array where the number of unique elements along the column
+axis is less than the number of elements in the array, for any dtype.
+That means the routine should work on any dtype that np.unique() can
+compare.</p>
+<p class="rubric">Examples</p>
+<p>Set up a 9x9 weights matrix describing a 3x3 regular lattice. Lag one list of
+categorical variables with no ties.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y_l</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">y_l</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]))</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<p>Explicitly reshape y into a (9x1) array and calculate lag again</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">yvect</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yvect_l</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span><span class="n">yvect</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">check</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span> <span class="p">[</span> <span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span>  <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">]]</span> <span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">yvect_l</span><span class="p">,</span> <span class="n">check</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+<p>compute the lag of a 9x2 matrix of categories</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y2</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ym</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span><span class="n">y2</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ym_lag</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_categorical</span><span class="p">(</span><span class="n">w</span><span class="p">,</span><span class="n">ym</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">check</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">check</span><span class="p">,</span> <span class="n">ym_lag</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+<li><a class="reference internal" href="#">libpysal.weights.lag_spatial</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.lag_spatial"><code class="docutils literal notranslate"><span class="pre">lag_spatial()</span></code></a></li>
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+  <section id="libpysal-weights-lag-spatial">
+<h1>libpysal.weights.lag_spatial<a class="headerlink" href="#libpysal-weights-lag-spatial" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.lag_spatial">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">lag_spatial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spatial_lag.html#lag_spatial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.lag_spatial" title="Link to this definition">¶</a></dt>
+<dd><p>Spatial lag operator.</p>
+<p>If w is row standardized, returns the average of each observation’s neighbors;
+if not, returns the weighted sum of each observation’s neighbors.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>libpysal spatial weightsobject</p>
+</dd>
+<dt><strong>y</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>numpy array with dimensionality conforming to w (see examples)</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>wy</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>array of numeric values for the spatial lag</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Setup a 9x9 binary spatial weights matrix and vector of data; compute the
+spatial lag of the vector.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
+<span class="go">array([ 4.,  6.,  6., 10., 16., 14., 10., 18., 12.])</span>
+</pre></div>
+</div>
+<p>Row standardize the weights matrix and recompute the spatial lag</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
+<span class="go">array([2.        , 2.        , 3.        , 3.33333333, 4.        ,</span>
+<span class="go">       4.66666667, 5.        , 6.        , 6.        ])</span>
+</pre></div>
+</div>
+<p>Explicitly define data vector as 9x1 and recompute the spatial lag</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
+<span class="go">array([[2.        ],</span>
+<span class="go">       [2.        ],</span>
+<span class="go">       [3.        ],</span>
+<span class="go">       [3.33333333],</span>
+<span class="go">       [4.        ],</span>
+<span class="go">       [4.66666667],</span>
+<span class="go">       [5.        ],</span>
+<span class="go">       [6.        ],</span>
+<span class="go">       [6.        ]])</span>
+</pre></div>
+</div>
+<p>Take the spatial lag of a 9x2 data matrix</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">yr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yr</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">y</span><span class="p">,</span> <span class="n">yr</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">lag_spatial</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">yl</span>
+<span class="go">array([[2.        , 6.        ],</span>
+<span class="go">       [2.        , 6.        ],</span>
+<span class="go">       [3.        , 5.        ],</span>
+<span class="go">       [3.33333333, 4.66666667],</span>
+<span class="go">       [4.        , 4.        ],</span>
+<span class="go">       [4.66666667, 3.33333333],</span>
+<span class="go">       [5.        , 3.        ],</span>
+<span class="go">       [6.        , 2.        ],</span>
+<span class="go">       [6.        , 2.        ]])</span>
+</pre></div>
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+<li><a class="reference internal" href="#">libpysal.weights.lat2SW</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.lat2SW"><code class="docutils literal notranslate"><span class="pre">lat2SW()</span></code></a></li>
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+      
+  <section id="libpysal-weights-lat2sw">
+<h1>libpysal.weights.lat2SW<a class="headerlink" href="#libpysal-weights-lat2sw" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.lat2SW">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">lat2SW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nrows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rook'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_st</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#lat2SW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.lat2SW" title="Link to this definition">¶</a></dt>
+<dd><p>Create a sparse W matrix for a regular lattice.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of rows</p>
+</dd>
+<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of columns</p>
+</dd>
+<dt><strong>rook</strong><span class="classifier">{“rook”, “queen”, “bishop”}</span></dt><dd><p>type of contiguity. Default is rook.</p>
+</dd>
+<dt><strong>row_st</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, the created sparse W object is row-standardized so
+every row sums up to one. Defaults to False.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.dia_matrix.html#scipy.sparse.dia_matrix" title="(in SciPy v1.11.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.dia_matrix</span></code></a></span></dt><dd><p>instance of a scipy sparse matrix</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.
+This method directly creates the W matrix using the strucuture of the contiguity type.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2SW</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9r</span> <span class="o">=</span> <span class="n">lat2SW</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="n">row_st</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9r</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="mf">1.</span><span class="o">/</span><span class="mi">3</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">libpysal.weights.lat2W</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.lat2W"><code class="docutils literal notranslate"><span class="pre">lat2W()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+      
+  <section id="libpysal-weights-lat2w">
+<h1>libpysal.weights.lat2W<a class="headerlink" href="#libpysal-weights-lat2w" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.lat2W">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">lat2W</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nrows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rook</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'int'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#lat2W"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.lat2W" title="Link to this definition">¶</a></dt>
+<dd><p>Create a W object for a regular lattice.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>nrows</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of rows</p>
+</dd>
+<dt><strong>ncols</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>number of columns</p>
+</dd>
+<dt><strong>rook</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>type of contiguity. Default is rook. For queen, rook =False</p>
+</dd>
+<dt><strong>id_type</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>string defining the type of IDs to use in the final W object;
+options are ‘int’ (0, 1, 2 …; default), ‘float’ (0.0,
+1.0, 2.0, …) and ‘string’ (‘id0’, ‘id1’, ‘id2’, …)</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Observations are row ordered: first k observations are in row 0, next k in row 1, and so on.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="s2">&quot;</span><span class="si">%.3f</span><span class="s2">&quot;</span><span class="o">%</span><span class="n">w9</span><span class="o">.</span><span class="n">pct_nonzero</span>
+<span class="go">&#39;29.630&#39;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w9</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}</span>
+<span class="go">True</span>
+</pre></div>
+</div>
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+
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diff --git a/generated/libpysal.weights.mat2L.html b/generated/libpysal.weights.mat2L.html
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+<li><a class="reference internal" href="#libpysal.weights.mat2L"><code class="docutils literal notranslate"><span class="pre">mat2L()</span></code></a></li>
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+      
+  <section id="libpysal-weights-mat2l">
+<h1>libpysal.weights.mat2L<a class="headerlink" href="#libpysal-weights-mat2l" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.mat2L">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">mat2L</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">edge_matrix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#mat2L"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.mat2L" title="Link to this definition">¶</a></dt>
+<dd><p>Convert a matrix denoting network connectivity (edges or flows) to a list
+denoting edges</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>edge_matrix</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>where rows denote network edge origins, columns denote
+network edge destinations, and non-zero entries denote the
+existence of an edge between a given origin and destination</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>edge_list</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>of tuples where each tuple is of the form (o,d) where o is an
+origin id and d is a destination id</p>
+</dd>
+</dl>
+</dd>
+</dl>
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diff --git a/generated/libpysal.weights.min_threshold_dist_from_shapefile.html b/generated/libpysal.weights.min_threshold_dist_from_shapefile.html
new file mode 100644
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@@ -0,0 +1,205 @@
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+<li><a class="reference internal" href="#">libpysal.weights.min_threshold_dist_from_shapefile</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.min_threshold_dist_from_shapefile"><code class="docutils literal notranslate"><span class="pre">min_threshold_dist_from_shapefile()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
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+      
+  <section id="libpysal-weights-min-threshold-dist-from-shapefile">
+<h1>libpysal.weights.min_threshold_dist_from_shapefile<a class="headerlink" href="#libpysal-weights-min-threshold-dist-from-shapefile" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.min_threshold_dist_from_shapefile">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">min_threshold_dist_from_shapefile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shapefile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/user.html#min_threshold_dist_from_shapefile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.min_threshold_dist_from_shapefile" title="Link to this definition">¶</a></dt>
+<dd><p>Get the maximum nearest neighbor distance between observations in the
+shapefile.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shapefile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>shapefile name with shp suffix.</p>
+</dd>
+<dt><strong>radius</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>If supplied arc_distances will be calculated
+based on the given radius. p will be ignored.</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>d</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Maximum nearest neighbor distance between the n
+observations.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Supports polygon or point shapefiles. For polygon shapefiles, distance is
+based on polygon centroids. Distances are defined using coordinates in
+shapefile which are assumed to be projected and not geographical
+coordinates.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">md</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">min_threshold_dist_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;columbus.shp&quot;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">md</span>
+<span class="go">0.6188641580768541</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">min_threshold_dist_from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;stl_hom.shp&quot;</span><span class="p">),</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">cg</span><span class="o">.</span><span class="n">sphere</span><span class="o">.</span><span class="n">RADIUS_EARTH_MILES</span><span class="p">)</span>
+<span class="go">31.846942936393717</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+      
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+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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+</html>
\ No newline at end of file
diff --git a/generated/libpysal.weights.min_threshold_distance.html b/generated/libpysal.weights.min_threshold_distance.html
new file mode 100644
index 000000000..c9c74db5e
--- /dev/null
+++ b/generated/libpysal.weights.min_threshold_distance.html
@@ -0,0 +1,198 @@
+<!DOCTYPE html>
+
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
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+<li><a class="reference internal" href="#">libpysal.weights.min_threshold_distance</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.min_threshold_distance"><code class="docutils literal notranslate"><span class="pre">min_threshold_distance()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-min-threshold-distance">
+<h1>libpysal.weights.min_threshold_distance<a class="headerlink" href="#libpysal-weights-min-threshold-distance" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.min_threshold_distance">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">min_threshold_distance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#min_threshold_distance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.min_threshold_distance" title="Link to this definition">¶</a></dt>
+<dd><p>Get the maximum nearest neighbor distance.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(n,k) or KDTree where KDtree.data is array (n,k)
+n observations on k attributes</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>nnd</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>maximum nearest neighbor distance between the n observations</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">min_threshold_distance</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">indices</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">min_threshold_distance</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="go">1.0</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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+      
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+    </p>
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+</html>
\ No newline at end of file
diff --git a/generated/libpysal.weights.neighbor_equality.html b/generated/libpysal.weights.neighbor_equality.html
new file mode 100644
index 000000000..61dfe4c23
--- /dev/null
+++ b/generated/libpysal.weights.neighbor_equality.html
@@ -0,0 +1,209 @@
+<!DOCTYPE html>
+
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+<li><a class="reference internal" href="#">libpysal.weights.neighbor_equality</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.neighbor_equality"><code class="docutils literal notranslate"><span class="pre">neighbor_equality()</span></code></a></li>
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+  <section id="libpysal-weights-neighbor-equality">
+<h1>libpysal.weights.neighbor_equality<a class="headerlink" href="#libpysal-weights-neighbor-equality" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.neighbor_equality">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">neighbor_equality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#neighbor_equality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.neighbor_equality" title="Link to this definition">¶</a></dt>
+<dd><p>Test if the neighbor sets are equal between two weights objects</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
+</dd>
+<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>instance of spatial weights class W</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">Boolean</span></code></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Only set membership is evaluated, no check of the weight values is carried out.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights.util</span> <span class="kn">import</span> <span class="n">neighbor_equality</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w3</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w3</span><span class="p">)</span>
+<span class="go">False</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n4</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n4</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n4</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w4</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">n4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w4</span><span class="p">)</span>
+<span class="go">False</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n5</span> <span class="o">=</span> <span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[3, 1]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">n5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span> <span class="o">=</span> <span class="n">W</span><span class="p">(</span><span class="n">n5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">neighbor_equality</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w5</span><span class="p">)</span>
+<span class="go">True</span>
+</pre></div>
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+<li><a class="reference internal" href="#">libpysal.weights.netW</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.netW"><code class="docutils literal notranslate"><span class="pre">netW()</span></code></a></li>
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+      
+  <section id="libpysal-weights-netw">
+<h1>libpysal.weights.netW<a class="headerlink" href="#libpysal-weights-netw" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.netW">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">netW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">link_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">share</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'A'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'r'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#netW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.netW" title="Link to this definition">¶</a></dt>
+<dd><p>Create a network-contiguity based weight object based on different nodal
+relationships encoded in a network.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>link_list</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>of tuples where each tuple is of the form (o,d) where o is an
+origin id and d is a destination id</p>
+</dd>
+<dt><strong>share</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>denoting how to define the nodal relationship used to determine neighboring edges; defualt is ‘A’ for any shared nodes between two network edges; options include: O a shared origin node; D a shared destination node; OD; a shared origin or a shared destination node; C a shared node that is the destination of the first edge and the origin of the second edge - i.e., a directed chain is formed moving from edge one to edge two.</p>
+</dd>
+<dt><strong>transform</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transformation</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">standardization</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">of</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">final</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">OD</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">spatial</span></code> weight; <code class="xref py py-obj docutils literal notranslate"><span class="pre">default</span></code></span></dt><dd><p>is ‘r’ for row standardized</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>W</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nodal</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">contiguity</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">for</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">networkd</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">edges</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">flows</span></code></span></dt><dd><p>W Object representing the binary adjacency of the network edges
+given a definition of nodal relationshilibpysal.weights.spintW.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">links</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;b&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;c&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;d&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;a&#39;</span><span class="p">)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">O</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">netW</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s1">&#39;O&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">O</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">)]</span>
+<span class="go">[(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">netW</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s1">&#39;OD&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">OD</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">)]</span>
+<span class="go">[(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;)]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">any_common</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">netW</span><span class="p">(</span><span class="n">links</span><span class="p">,</span> <span class="n">share</span><span class="o">=</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">any_common</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">)]</span>
+<span class="go">[(&#39;a&#39;, &#39;c&#39;), (&#39;a&#39;, &#39;d&#39;), (&#39;c&#39;, &#39;b&#39;), (&#39;c&#39;, &#39;a&#39;)]</span>
+</pre></div>
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+<li><a class="reference internal" href="#">libpysal.weights.nonplanar_neighbors</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.nonplanar_neighbors"><code class="docutils literal notranslate"><span class="pre">nonplanar_neighbors()</span></code></a></li>
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+  <section id="libpysal-weights-nonplanar-neighbors">
+<h1>libpysal.weights.nonplanar_neighbors<a class="headerlink" href="#libpysal-weights-nonplanar-neighbors" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.nonplanar_neighbors">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">nonplanar_neighbors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geodataframe</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#nonplanar_neighbors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.nonplanar_neighbors" title="Link to this definition">¶</a></dt>
+<dd><p>Detect neighbors for non-planar polygon collections</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>w:   pysal W</strong></dt><dd><p>A spatial weights object with reported islands</p>
+</dd>
+<dt><strong>geodataframe: GeoDataframe</strong></dt><dd><p>The polygon dataframe from which w was constructed.</p>
+</dd>
+<dt><strong>tolerance: float</strong></dt><dd><p>The percentage of the minimum horizontal or vertical extent (minextent) of
+the dataframe to use in defining  a buffering distance to allow for fuzzy
+contiguity detection. The buffering distance is equal to tolerance*minextent.</p>
+</dd>
+<dt><strong>**kwargs:  keyword arguments</strong></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt>w: <code class="xref py py-obj docutils literal notranslate"><span class="pre">pysal</span></code> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></dt><dd><p>Spatial weights object that encodes fuzzy neighbors.
+This will have an attribute <cite>non_planar_joins</cite> to indicate what new joins were detected.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This relaxes the notion of contiguity neighbors for the case of shapefiles
+that violate the condition of planar enforcement. It handles three types
+of conditions present in such files that would result in islands when using
+the regular PySAL contiguity methods. The first are edges for nearby
+polygons that should be shared, but are digitized separately for the
+individual polygons and the resulting edges do not coincide, but instead
+the edges intersect. The second case is similar to the first, only the
+resultant edges do not intersect but are “close”. The final case arises
+when one polygon is “inside” a second polygon but is not encoded to
+represent a hole in the containing polygon.</p>
+<p>The buffering check assumes the geometry coordinates are projected.</p>
+<p class="rubric">References</p>
+<p>Planar Enforcement: <a class="reference external" href="http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6">http://ibis.geog.ubc.ca/courses/klink/gis.notes/ncgia/u12.html#SEC12.6</a></p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;map_RS_BR.shp&#39;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">Queen</span><span class="o">.</span><span class="n">from_dataframe</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">nonplanar_neighbors</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">df</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span><span class="o">.</span><span class="n">islands</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[23, 59, 152, 239]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wnp</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">23</span><span class="p">]</span>
+<span class="go">[0, 45, 59, 107, 152, 185, 246]</span>
+</pre></div>
+</div>
+<p>Also see <cite>nonplanarweights.ipynb</cite></p>
+<dl class="field-list">
+<dt class="field-odd">Attributes<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>non_planar_joins</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Stores the new joins detected. Key is the id of the focal unit, value is a list of neighbor ids.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+<ul class="current">
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
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+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">libpysal.weights.order</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.order"><code class="docutils literal notranslate"><span class="pre">order()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
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+            
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+    </div>
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+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-order">
+<h1>libpysal.weights.order<a class="headerlink" href="#libpysal-weights-order" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.order">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.order" title="Link to this definition">¶</a></dt>
+<dd><p>Determine the non-redundant order of contiguity up to a specific
+order.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+<dt><strong>kmax</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#int" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">int</span></code></a></span></dt><dd><p>maximum order of contiguity</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>info</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>observation id is the key, value is a list of contiguity
+orders with a negative 1 in the ith position</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Implements the algorithm in <span id="id1">[<a class="reference internal" href="../references.html#id2" title="Luc Anselin and Oleg Smirnov. Efficient algorithms for constructing proper higher order spatial lag operators. Journal of Regional Science, 36(1):67-89, 1996. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x, doi:10.1111/j.1467-9787.1996.tb01101.x.">AS96</a>]</span>.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">order</span><span class="p">,</span> <span class="n">Rook</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">Rook</span><span class="o">.</span><span class="n">from_shapefile</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s1">&#39;10740.shp&#39;</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>WARNING: there is one disconnected observation (no neighbors)
+Island id:  [163]
+&gt;&gt;&gt; w3 = order(w, kmax = 3)
+&gt;&gt;&gt; w3[1][0:5]
+[1, -1, 1, 2, 1]</p>
+</dd></dl>
+
+</section>
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diff --git a/generated/libpysal.weights.remap_ids.html b/generated/libpysal.weights.remap_ids.html
new file mode 100644
index 000000000..b10d06b86
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@@ -0,0 +1,205 @@
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+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.remap_ids</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.remap_ids"><code class="docutils literal notranslate"><span class="pre">remap_ids()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+<div class="container">
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+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-remap-ids">
+<h1>libpysal.weights.remap_ids<a class="headerlink" href="#libpysal-weights-remap-ids" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.remap_ids">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">remap_ids</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old2new</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#remap_ids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.remap_ids" title="Link to this definition">¶</a></dt>
+<dd><p>Remaps the IDs in a spatial weights object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object</p>
+</dd>
+<dt><strong>old2new</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dictionary</span></code></span></dt><dd><p>Dictionary where the keys are the IDs in w (i.e. “old IDs”) and
+the values are the IDs to replace them (i.e. “new IDs”)</p>
+</dd>
+<dt><strong>id_order</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>An ordered list of new IDs, which defines the order of observations when
+iterating over W. If not set then the id_order in w will be
+used.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>implicit</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>Spatial weights object with new IDs</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">id_order</span>
+<span class="go">[0, 1, 2, 3, 4, 5]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">[2, 1]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">old_to_new</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span><span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span><span class="s1">&#39;f&#39;</span><span class="p">}</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_new</span> <span class="o">=</span> <span class="n">remap_ids</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">old_to_new</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_new</span><span class="o">.</span><span class="n">id_order</span>
+<span class="go">[&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_new</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">]</span>
+<span class="go">[&#39;c&#39;, &#39;b&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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diff --git a/generated/libpysal.weights.shimbel.html b/generated/libpysal.weights.shimbel.html
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+<li><a class="reference internal" href="#">libpysal.weights.shimbel</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.shimbel"><code class="docutils literal notranslate"><span class="pre">shimbel()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-shimbel">
+<h1>libpysal.weights.shimbel<a class="headerlink" href="#libpysal-weights-shimbel" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.shimbel">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">shimbel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#shimbel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.shimbel" title="Link to this definition">¶</a></dt>
+<dd><p>Find the Shimbel matrix for first order contiguity matrix.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>info</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>list of lists; one list for each observation which stores
+the shortest order between it and each of the the other observations.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">shimbel</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_shimbel</span> <span class="o">=</span> <span class="n">shimbel</span><span class="p">(</span><span class="n">w5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_shimbel</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">24</span><span class="p">]</span>
+<span class="go">8</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w5_shimbel</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">:</span><span class="mi">4</span><span class="p">]</span>
+<span class="go">[-1, 1, 2, 3]</span>
+</pre></div>
+</div>
+</dd></dl>
+
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diff --git a/generated/libpysal.weights.spw_from_gal.html b/generated/libpysal.weights.spw_from_gal.html
new file mode 100644
index 000000000..699e64cd2
--- /dev/null
+++ b/generated/libpysal.weights.spw_from_gal.html
@@ -0,0 +1,190 @@
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+<li><a class="reference internal" href="#">libpysal.weights.spw_from_gal</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.spw_from_gal"><code class="docutils literal notranslate"><span class="pre">spw_from_gal()</span></code></a></li>
+</ul>
+</li>
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+      
+  <section id="libpysal-weights-spw-from-gal">
+<h1>libpysal.weights.spw_from_gal<a class="headerlink" href="#libpysal-weights-spw-from-gal" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.spw_from_gal">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">spw_from_gal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">galfile</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/user.html#spw_from_gal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.spw_from_gal" title="Link to this definition">¶</a></dt>
+<dd><p>Sparse scipy matrix for w from a gal file.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>galfile</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>name of gal file including suffix</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>spw</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse_matrix</span></code></span></dt><dd><p>scipy sparse matrix in CSR format</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>identifiers for rows/cols of spw</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">spw</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">spw_from_gal</span><span class="p">(</span><span class="n">libpysal</span><span class="o">.</span><span class="n">examples</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s2">&quot;sids2.gal&quot;</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">spw</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">nnz</span>
+<span class="go">462</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
+    </div>
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+     rel="nofollow">Source</a>
+</div>
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+    </p>
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+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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diff --git a/generated/libpysal.weights.testDataArray.html b/generated/libpysal.weights.testDataArray.html
new file mode 100644
index 000000000..af3138226
--- /dev/null
+++ b/generated/libpysal.weights.testDataArray.html
@@ -0,0 +1,191 @@
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+
+<html lang="en" data-content_root="../">
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+<li><a class="reference internal" href="#">libpysal.weights.testDataArray</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.testDataArray"><code class="docutils literal notranslate"><span class="pre">testDataArray()</span></code></a></li>
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+      
+  <section id="libpysal-weights-testdataarray">
+<h1>libpysal.weights.testDataArray<a class="headerlink" href="#libpysal-weights-testdataarray" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.testDataArray">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">testDataArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(3,</span> <span class="pre">4,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">missing_vals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#testDataArray"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.testDataArray" title="Link to this definition">¶</a></dt>
+<dd><p>Creates 2 or 3 dimensional test xarray.DataArray object</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#tuple" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">tuple</span></code></a></span></dt><dd><p>Tuple containing shape of the DataArray aligned with
+following dimension = (lat, lon) or (layer, lat, lon)
+Default shape = (3, 4, 4)</p>
+</dd>
+<dt><strong>time</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Type of layer, if True then layer=time else layer=band
+Default is False.</p>
+</dd>
+<dt><strong>rand</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True, creates a DataArray filled with unique and random data.
+Default is false (generates seeded random data)</p>
+</dd>
+<dt><strong>missing_vals</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>Create a DataArray filled with missing values. Default is True.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>instance of xarray.DataArray</p>
+</dd>
+</dl>
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diff --git a/generated/libpysal.weights.vecW.html b/generated/libpysal.weights.vecW.html
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+<li><a class="reference internal" href="#">libpysal.weights.vecW</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.vecW"><code class="docutils literal notranslate"><span class="pre">vecW()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-vecw">
+<h1>libpysal.weights.vecW<a class="headerlink" href="#libpysal-weights-vecw" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.vecW">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">vecW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">origin_x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">origin_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_sp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/spintW.html#vecW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.vecW" title="Link to this definition">¶</a></dt>
+<dd><p>Distance-based spatial weight for vectors that is computed using a
+4-dimensional distance between the origin x,y-coordinates and the
+destination x,y-coordinates</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>origin_x</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector origin x-coordinates</p>
+</dd>
+<dt><strong>origin_y</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector origin y-coordinates</p>
+</dd>
+<dt><strong>dest_x</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector destination x-coordinates</p>
+</dd>
+<dt><strong>dest_y</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>of vector destination y-coordinates</p>
+</dd>
+<dt><strong>threshold</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance band</p>
+</dd>
+<dt><strong>p</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>Minkowski p-norm distance metric parameter:
+1&lt;=p&lt;=infinity
+2: Euclidean distance
+1: Manhattan distance</p>
+</dd>
+<dt><strong>binary</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If true w_{ij}=1 if d_{i,j}&lt;=threshold, otherwise w_{i,j}=0
+If false wij=dij^{alpha}</p>
+</dd>
+<dt><strong>alpha</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/functions.html#float" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">float</span></code></a></span></dt><dd><p>distance decay parameter for weight (default -1.0)
+if alpha is positive the weights will not decline with
+distance. If binary is True, alpha is ignored</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>values to use for keys of the neighbors and weights dicts</p>
+</dd>
+<dt><strong>build_sp</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>True to build sparse distance matrix and false to build dense
+distance matrix; significant speed gains may be obtained
+dending on the sparsity of the of distance_matrix and
+threshold that is applied</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>W</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand" title="libpysal.weights.DistanceBand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistanceBand</span></code></a> <a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a> <a class="reference external" href="https://docs.python.org/3.12/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">object</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">that</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">uses</span></code> 4-dimenional <code class="xref py py-obj docutils literal notranslate"><span class="pre">distances</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">between</span></code></span></dt><dd><p>vectors origin and destination coordinates.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">x2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">9</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">y2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">W1</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">vecW</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mi">999</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">W1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="go">[1, 2]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">W2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">vecW</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">x1</span><span class="p">,</span> <span class="n">y2</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">8.5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">W2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="go">[1, 2]</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">libpysal.weights.w2da</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w2da"><code class="docutils literal notranslate"><span class="pre">w2da()</span></code></a></li>
+</ul>
+</li>
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+      
+  <section id="libpysal-weights-w2da">
+<h1>libpysal.weights.w2da<a class="headerlink" href="#libpysal-weights-w2da" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w2da">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w2da</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#w2da"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w2da" title="Link to this definition">¶</a></dt>
+<dd><p>Creates xarray.DataArray object from passed data aligned with W object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier">array/list/pd.Series</span></dt><dd><p>1d array-like data with dimensionality conforming to w</p>
+</dd>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code></a></span></dt><dd><p>Spatial weights object aligned with passed data</p>
+</dd>
+<dt><strong>attrs</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dictionary</span></code></span></dt><dd><p>Attributes stored in dict related to DataArray, e.g. da.attrs
+Default is {} empty dictionary.</p>
+</dd>
+<dt><strong>coords</strong><span class="classifier">Dictionary/xarray.core.coordinates.DataArrayCoordinates</span></dt><dd><p>Coordinates corresponding to DataArray, e.g. da.coords</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>instance of xarray.DataArray</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.raster</span> <span class="kn">import</span> <span class="n">da2W</span><span class="p">,</span> <span class="n">testDataArray</span><span class="p">,</span> <span class="n">w2da</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">(3, 4, 4)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">da2W</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da1</span> <span class="o">=</span> <span class="n">w2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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diff --git a/generated/libpysal.weights.w_clip.html b/generated/libpysal.weights.w_clip.html
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+<li><a class="reference internal" href="#">libpysal.weights.w_clip</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w_clip"><code class="docutils literal notranslate"><span class="pre">w_clip()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-w-clip">
+<h1>libpysal.weights.w_clip<a class="headerlink" href="#libpysal-weights-w-clip" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_clip">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_clip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outSP</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_clip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_clip" title="Link to this definition">¶</a></dt>
+<dd><p>Clip a continuous W object (w1) with a different W object (w2) so only cells where
+w2 has a non-zero value remain with non-zero values in w1.</p>
+<p>Checks on w1 and w2 are performed to make sure they conform to the
+appropriate format and, if not, they are converted.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>W, scipy.sparse.csr.csr_matrix
+Potentially continuous weights matrix to be clipped. The clipped
+matrix wc will have at most the same elements as w1.</p>
+</dd>
+<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>W, scipy.sparse.csr.csr_matrix
+Weights matrix to use as shell to clip w1. Automatically
+converted to binary format. Only non-zero elements in w2 will be
+kept non-zero in wc. NOTE: assumed to be of the same shape as w1</p>
+</dd>
+<dt><strong>outSP</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If True (default) return sparse version of the clipped W, if
+False, return W object of the clipped matrix</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>wc</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>W, scipy.sparse.csr.csr_matrix
+Clipped W object (sparse if outSP=Ture). It inherits <code class="docutils literal notranslate"><span class="pre">id_order</span></code> from w1.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span>
+</pre></div>
+</div>
+<p>First create a W object from a lattice using queen contiguity and
+row-standardize it (note that these weights will stay when we clip the
+object, but they will not neccesarily represent a row-standardization
+anymore):</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s1">&#39;R&#39;</span>
+</pre></div>
+</div>
+<p>We will clip that geography assuming observations 0, 2, 3 and 4 belong to
+one group and 1, 5 belong to another group and we don’t want both groups
+to interact with each other in our weights (i.e. w_ij = 0 if i and j in
+different groups). For that, we use the following method:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">libpysal</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">block_weights</span><span class="p">([</span><span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r2&#39;</span><span class="p">,</span> <span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r1&#39;</span><span class="p">,</span> <span class="s1">&#39;r2&#39;</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>To illustrate that w2 will only be considered as binary even when the
+object passed is not, we can row-standardize it</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="s1">&#39;R&#39;</span>
+</pre></div>
+</div>
+<p>The clipped object <code class="docutils literal notranslate"><span class="pre">wc</span></code> will contain only the spatial queen
+relationships that occur within one group (‘r1’ or ‘r2’) but will have
+gotten rid of those that happen across groups</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wcs</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">w_clip</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">outSP</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>This will create a sparse object (recommended when n is large).</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wcs</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
+<span class="go">array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="go">        0.        ]])</span>
+</pre></div>
+</div>
+<p>If we wanted an original W object, we can control that with the argument
+<code class="docutils literal notranslate"><span class="pre">outSP</span></code>:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wc</span> <span class="o">=</span> <span class="n">libpysal</span><span class="o">.</span><span class="n">weights</span><span class="o">.</span><span class="n">w_clip</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">outSP</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wc</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">array([[0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.2       , 0.        , 0.        , 0.2       , 0.2       ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.2       , 0.        , 0.2       , 0.        , 0.2       ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.        , 0.        , 0.33333333, 0.33333333, 0.        ,</span>
+<span class="go">        0.        ],</span>
+<span class="go">       [0.        , 0.        , 0.        , 0.        , 0.        ,</span>
+<span class="go">        0.        ]])</span>
+</pre></div>
+</div>
+<p>You can check they are actually the same:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">wcs</span><span class="o">.</span><span class="n">sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span> <span class="o">==</span> <span class="n">wc</span><span class="o">.</span><span class="n">full</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">array([[ True,  True,  True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True,  True,  True],</span>
+<span class="go">       [ True,  True,  True,  True,  True,  True]])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">libpysal.weights.w_difference</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w_difference"><code class="docutils literal notranslate"><span class="pre">w_difference()</span></code></a></li>
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+      
+  <section id="libpysal-weights-w-difference">
+<h1>libpysal.weights.w_difference<a class="headerlink" href="#libpysal-weights-w-difference" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_difference">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_difference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'w1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constrained</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_difference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_difference" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a binary weights object, w, that includes only neighbor pairs
+in w1 that are not in w2. The w_shape and constrained parameters
+determine which pairs in w1 that are not in w2 are returned.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w_shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Defines the shape of the returned weights matrix. ‘w1’ returns a
+matrix with the same IDs as w1; ‘all’ returns a matrix with all
+the unique IDs from w1 and w2; and ‘min’ returns a matrix with
+the IDs occurring in w1 and not in w2.</p>
+</dd>
+<dt><strong>constrained</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If False then the full set of neighbor pairs in w1 that are
+not in w2 are returned. If True then those pairs that would
+not be possible if w_shape=’min’ are dropped. Ignored if
+w_shape is set to ‘min’.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>ID comparisons are performed using ==, therefore the integer ID 2 is
+equivalent to the float ID 2.0.</p>
+<p class="rubric">Examples</p>
+<p>Construct rook (w2) and queen (w1) weights matrices for two 4x4 regions
+(16 areas). A queen matrix has all the joins a rook matrix does plus joins
+between areas that share a corner. The new matrix formed by the difference
+of rook from queen contains only join at corners (typically called a
+bishop matrix). Note that the difference of queen from rook would result
+in a weights matrix with no joins.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_difference</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">False</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[10, 11, 14]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[10]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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diff --git a/generated/libpysal.weights.w_intersection.html b/generated/libpysal.weights.w_intersection.html
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+<li><a class="reference internal" href="#">libpysal.weights.w_intersection</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w_intersection"><code class="docutils literal notranslate"><span class="pre">w_intersection()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-w-intersection">
+<h1>libpysal.weights.w_intersection<a class="headerlink" href="#libpysal-weights-w-intersection" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_intersection">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_intersection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'w1'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_intersection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_intersection" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a binary weights object, w, that includes only
+those neighbor pairs that exist in both w1 and w2.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w_shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Defines the shape of the returned weights matrix. ‘w1’ returns a
+matrix with the same IDs as w1; ‘all’ returns a matrix with all
+the unique IDs from w1 and w2; and ‘min’ returns a matrix with
+only the IDs occurring in both w1 and w2.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>ID comparisons are performed using ==, therefore the integer ID 2 is
+equivalent to the float ID 2.0.</p>
+<p class="rubric">Examples</p>
+<p>Construct rook weights matrices for two regions, one is 4x4 (16 areas)
+and the other is 6x4 (24 areas). An intersection of these two weights
+matrices results in the new weights matrix matching the smaller one.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_intersection</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_intersection</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14, 19]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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diff --git a/generated/libpysal.weights.w_local_cluster.html b/generated/libpysal.weights.w_local_cluster.html
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+</ul>
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+<li class="toctree-l2 current"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
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+<li><a class="reference internal" href="#">libpysal.weights.w_local_cluster</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w_local_cluster"><code class="docutils literal notranslate"><span class="pre">w_local_cluster()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-w-local-cluster">
+<h1>libpysal.weights.w_local_cluster<a class="headerlink" href="#libpysal-weights-w-local-cluster" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_local_cluster">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_local_cluster</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/util.html#w_local_cluster"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_local_cluster" title="Link to this definition">¶</a></dt>
+<dd><p>Local clustering coefficients for each unit as a node in a graph.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>spatial weights object</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>c</strong><span class="classifier"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">array</span></code></a></span></dt><dd><p>(w.n,1)
+local clustering coefficients</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The local clustering coefficient <span class="math notranslate nohighlight">\(c_i\)</span> quantifies how close the
+neighbors of observation <span class="math notranslate nohighlight">\(i\)</span> are to being a clique:</p>
+<div class="math notranslate nohighlight">
+\[c_i = | \{w_{j,k}\} |/ (k_i(k_i - 1)): j,k \in N_i\]</div>
+<p>where <span class="math notranslate nohighlight">\(N_i\)</span> is the set of neighbors to <span class="math notranslate nohighlight">\(i\)</span>, <span class="math notranslate nohighlight">\(k_i =
+|N_i|\)</span> and <span class="math notranslate nohighlight">\(\{w_{j,k}\}\)</span> is the set of non-zero elements of the
+weights between pairs in <span class="math notranslate nohighlight">\(N_i\)</span> <span id="id1">[<a class="reference internal" href="../references.html#id4" title="D.J. Watts and S.H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393:440-442, 1998.">WS98</a>]</span>.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_local_cluster</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span> <span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w_local_cluster</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+<span class="go">array([[1.        ],</span>
+<span class="go">       [0.6       ],</span>
+<span class="go">       [1.        ],</span>
+<span class="go">       [0.6       ],</span>
+<span class="go">       [0.42857143],</span>
+<span class="go">       [0.6       ],</span>
+<span class="go">       [1.        ],</span>
+<span class="go">       [0.6       ],</span>
+<span class="go">       [1.        ]])</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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diff --git a/generated/libpysal.weights.w_subset.html b/generated/libpysal.weights.w_subset.html
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index 000000000..eee861b5e
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+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../api.html">API</a><ul class="current">
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+<li><a class="reference internal" href="#">libpysal.weights.w_subset</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w_subset"><code class="docutils literal notranslate"><span class="pre">w_subset()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-weights-w-subset">
+<h1>libpysal.weights.w_subset<a class="headerlink" href="#libpysal-weights-w-subset" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_subset">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_subset" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a binary weights object, w, that includes only those
+observations in ids.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>ids</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#list" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">list</span></code></a></span></dt><dd><p>A list containing the IDs to be include in the returned weights
+object.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Construct a rook weights matrix for a 6x4 region (24 areas). By default
+PySAL assigns integer IDs to the areas in a region. By passing in a list
+of integers from 0 to 15, the first 16 areas are extracted from the
+previous weights matrix, and only those joins relevant to the new region
+are retained.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_subset</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">ids</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_subset</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">ids</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14, 19]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
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+      
+        <br/>
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+  <a href="../_sources/generated/libpysal.weights.w_subset.rst.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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diff --git a/generated/libpysal.weights.w_symmetric_difference.html b/generated/libpysal.weights.w_symmetric_difference.html
new file mode 100644
index 000000000..01f916064
--- /dev/null
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+<li><a class="reference internal" href="#">libpysal.weights.w_symmetric_difference</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.w_symmetric_difference"><code class="docutils literal notranslate"><span class="pre">w_symmetric_difference()</span></code></a></li>
+</ul>
+</li>
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+  <section id="libpysal-weights-w-symmetric-difference">
+<h1>libpysal.weights.w_symmetric_difference<a class="headerlink" href="#libpysal-weights-w-symmetric-difference" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_symmetric_difference">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_symmetric_difference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constrained</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_symmetric_difference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_symmetric_difference" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a binary weights object, w, that includes only neighbor pairs
+that are not shared by w1 and w2. The w_shape and constrained parameters
+determine which pairs that are not shared by w1 and w2 are returned.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w_shape</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#str" title="(in Python v3.12)"><code class="docutils literal notranslate"><span class="pre">str</span></code></a></span></dt><dd><p>Defines the shape of the returned weights matrix. ‘all’ returns a
+matrix with all the unique IDs from w1 and w2; and ‘min’ returns
+a matrix with the IDs not shared by w1 and w2.</p>
+</dd>
+<dt><strong>constrained</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/stdtypes.html#bltin-boolean-values" title="(in Python v3.12)"><span class="xref std std-ref">bool</span></a></span></dt><dd><p>If False then the full set of neighbor pairs that are not
+shared by w1 and w2 are returned. If True then those pairs
+that would not be possible if w_shape=’min’ are dropped.
+Ignored if w_shape is set to ‘min’.</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>ID comparisons are performed using ==, therefore the integer ID 2 is
+equivalent to the float ID 2.0.</p>
+<p class="rubric">Examples</p>
+<p>Construct queen weights matrix for a 4x4 (16 areas) region (w1) and a rook
+matrix for a 6x4 (24 areas) region (w2). The symmetric difference of these
+two matrices (with w_shape set to ‘all’ and constrained set to False)
+contains the corner joins in the overlap area, all the joins in the
+non-overlap area.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_symmetric_difference</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="n">rook</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_symmetric_difference</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">,</span> <span class="n">constrained</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">False</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[10, 11, 14]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14, 19]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">set</span><span class="p">(</span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">])</span> <span class="o">==</span> <span class="nb">set</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">19</span><span class="p">])</span>
+<span class="go">True</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
+
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diff --git a/generated/libpysal.weights.w_union.html b/generated/libpysal.weights.w_union.html
new file mode 100644
index 000000000..a3ebea625
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+<li><a class="reference internal" href="#libpysal.weights.w_union"><code class="docutils literal notranslate"><span class="pre">w_union()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-w-union">
+<h1>libpysal.weights.w_union<a class="headerlink" href="#libpysal-weights-w-union" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.w_union">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">w_union</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">w2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/set_operations.html#w_union"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.w_union" title="Link to this definition">¶</a></dt>
+<dd><p>Returns a binary weights object, w, that includes all neighbor pairs that
+exist in either w1 or w2.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>w1</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>w2</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+<dt><strong>**kwargs</strong><span class="classifier"><a class="reference external" href="https://docs.python.org/3.12/library/keyword.html#module-keyword" title="(in Python v3.12)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">keyword</span></code></a> <code class="xref py py-obj docutils literal notranslate"><span class="pre">arguments</span></code></span></dt><dd><p>optional arguments for <code class="xref py py-class docutils literal notranslate"><span class="pre">pysal.weights.W</span></code></p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>w</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.W.html#libpysal.weights.W" title="libpysal.weights.W"><code class="xref py py-obj docutils literal notranslate"><span class="pre">W</span></code></a></span></dt><dd><p>object</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>ID comparisons are performed using ==, therefore the integer ID 2 is
+equivalent to the float ID 2.0. Returns a matrix with all the unique IDs
+from w1 and w2.</p>
+<p class="rubric">Examples</p>
+<p>Construct rook weights matrices for two regions, one is 4x4 (16 areas)
+and the other is 6x4 (24 areas). A union of these two weights matrices
+results in the new weights matrix matching the larger one.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.weights</span> <span class="kn">import</span> <span class="n">lat2W</span><span class="p">,</span> <span class="n">w_union</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span> <span class="o">=</span> <span class="n">lat2W</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span> <span class="o">=</span> <span class="n">w_union</span><span class="p">(</span><span class="n">w1</span><span class="p">,</span> <span class="n">w2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="go">True</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w1</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w2</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[11, 14, 19]</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">w</span><span class="o">.</span><span class="n">neighbors</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span>
+<span class="go">[19, 11, 14]</span>
+</pre></div>
+</div>
+</dd></dl>
+
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+<li><a class="reference internal" href="#">libpysal.weights.wsp2da</a><ul>
+<li><a class="reference internal" href="#libpysal.weights.wsp2da"><code class="docutils literal notranslate"><span class="pre">wsp2da()</span></code></a></li>
+</ul>
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+      
+  <section id="libpysal-weights-wsp2da">
+<h1>libpysal.weights.wsp2da<a class="headerlink" href="#libpysal-weights-wsp2da" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="libpysal.weights.wsp2da">
+<span class="sig-prename descclassname"><span class="pre">libpysal.weights.</span></span><span class="sig-name descname"><span class="pre">wsp2da</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wsp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/libpysal/weights/raster.html#wsp2da"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#libpysal.weights.wsp2da" title="Link to this definition">¶</a></dt>
+<dd><p>Creates xarray.DataArray object from passed data aligned with WSP object.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>data</strong><span class="classifier">array/list/pd.Series</span></dt><dd><p>1d array-like data with dimensionality conforming to wsp</p>
+</dd>
+<dt><strong>wsp</strong><span class="classifier"><a class="reference internal" href="libpysal.weights.WSP.html#libpysal.weights.WSP" title="libpysal.weights.WSP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">libpysal.weights.WSP</span></code></a></span></dt><dd><p>Sparse weights object aligned with passed data</p>
+</dd>
+<dt><strong>attrs</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dictionary</span></code></span></dt><dd><p>Attributes stored in dict related to DataArray, e.g. da.attrs
+Default is {} empty dictionary.</p>
+</dd>
+<dt><strong>coords</strong><span class="classifier">Dictionary/xarray.core.coordinates.DataArrayCoordinates</span></dt><dd><p>coordinates corresponding to DataArray, e.g. da.coords</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl>
+<dt><strong>da</strong><span class="classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">xarray.DataArray</span></code></span></dt><dd><p>instance of xarray.DataArray</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">libpysal.raster</span> <span class="kn">import</span> <span class="n">da2WSP</span><span class="p">,</span> <span class="n">testDataArray</span><span class="p">,</span> <span class="n">wsp2da</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span> <span class="o">=</span> <span class="n">testDataArray</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">shape</span>
+<span class="go">(3, 4, 4)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">wsp</span> <span class="o">=</span> <span class="n">da2WSP</span><span class="p">(</span><span class="n">da</span><span class="p">,</span> <span class="n">z_value</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">wsp</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">da1</span> <span class="o">=</span> <span class="n">w2da</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">wsp</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+</section>
+
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+  <a role="button"
+     id="dLabelLocalToc"
+     data-toggle="dropdown"
+     data-target="#"
+     href="#">Page <b class="caret"></b></a>
+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"></ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
+<form class="navbar-form navbar-right" action="search.html" method="get">
+ <div class="form-group">
+  <input type="text" name="q" class="form-control" placeholder="Search" />
+ </div>
+  <input type="hidden" name="check_keywords" value="yes" />
+  <input type="hidden" name="area" value="default" />
+</form>
+          
+        </div>
+    </div>
+  </div>
+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
 
 <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#Q"><strong>Q</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ | <a href="#X"><strong>X</strong></a>
+ | <a href="#Y"><strong>Y</strong></a>
  
 </div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.__init__">__init__() (libpysal.cg.Chain method)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.__init__">(libpysal.cg.Grid method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line.__init__">(libpysal.cg.Line method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.__init__">(libpysal.cg.LineSegment method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Point.html#libpysal.cg.Point.__init__">(libpysal.cg.Point method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.__init__">(libpysal.cg.PointLocator method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.__init__">(libpysal.cg.Polygon method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.__init__">(libpysal.cg.PolygonLocator method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Ray.html#libpysal.cg.Ray.__init__">(libpysal.cg.Ray method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.__init__">(libpysal.cg.Rectangle method)</a>
+</li>
+        <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.__init__">(libpysal.graph.Graph method)</a>
+</li>
+        <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.__init__">(libpysal.io.fileio.FileIO method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay.__init__">(libpysal.weights.Delaunay method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.__init__">(libpysal.weights.DistanceBand method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Gabriel.html#libpysal.weights.Gabriel.__init__">(libpysal.weights.Gabriel method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.__init__">(libpysal.weights.Kernel method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.__init__">(libpysal.weights.KNN method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.__init__">(libpysal.weights.Queen method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Relative_Neighborhood.html#libpysal.weights.Relative_Neighborhood.__init__">(libpysal.weights.Relative_Neighborhood method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.__init__">(libpysal.weights.Rook method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.__init__">(libpysal.weights.W method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.__init__">(libpysal.weights.WSP method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
 
-          </div>
-          
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-<h1 class="logo"><a href="index.html">No Errors Test Project</a></h1>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.add">add() (libpysal.cg.Grid method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.adjacency">adjacency (libpysal.graph.Graph property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.alpha_shape.html#libpysal.cg.alpha_shape">alpha_shape() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.alpha_shape_auto.html#libpysal.cg.alpha_shape_auto">alpha_shape_auto() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.arcdist.html#libpysal.cg.arcdist">arcdist() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.arcdist2linear.html#libpysal.cg.arcdist2linear">arcdist2linear() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.arclen">arclen (libpysal.cg.Chain property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.area">area (libpysal.cg.Polygon property)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.area">(libpysal.cg.Rectangle property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.asShape.html#libpysal.cg.asShape">asShape() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.asymmetries">asymmetries (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.asymmetry">asymmetry() (libpysal.graph.Graph method)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.asymmetry">(libpysal.weights.W method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.weights.attach_islands.html#libpysal.weights.attach_islands">attach_islands() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.examples.available.html#libpysal.examples.available">available() (in module libpysal.examples)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.bbcommon.html#libpysal.cg.bbcommon">bbcommon() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.bbox">bbox (libpysal.cg.Polygon property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.block_weights.html#libpysal.weights.block_weights">block_weights() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.bounding_box">bounding_box (libpysal.cg.Chain property)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.bounding_box">(libpysal.cg.LineSegment property)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.bounding_box">(libpysal.cg.Polygon property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.bounds">bounds() (libpysal.cg.Grid method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.brute_knn.html#libpysal.cg.brute_knn">brute_knn() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_block_contiguity">build_block_contiguity() (libpysal.graph.Graph class method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_contiguity">build_contiguity() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_distance_band">build_distance_band() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_fuzzy_contiguity">build_fuzzy_contiguity() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_kernel">build_kernel() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_knn">build_knn() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.build_lattice_shapefile.html#libpysal.weights.build_lattice_shapefile">build_lattice_shapefile() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.build_quad_tree_structure">build_quad_tree_structure() (libpysal.cg.Polygon method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.build_triangulation">build_triangulation() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.by_row">by_row (libpysal.io.fileio.FileIO property)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.cardinalities">cardinalities (libpysal.graph.Graph property)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.cardinalities">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.cast">cast() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.centroid">centroid (libpysal.cg.Polygon property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain">Chain (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.check">check() (libpysal.io.fileio.FileIO class method)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.close">close() (libpysal.io.fileio.FileIO method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.comb.html#libpysal.weights.comb">comb() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.component_labels">component_labels (libpysal.graph.Graph property)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.component_labels">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.contains_point">contains_point() (libpysal.cg.Polygon method)</a>
 
-<h3>Navigation</h3>
+      <ul>
+        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.contains_point">(libpysal.cg.PolygonLocator method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.convex_hull.html#libpysal.cg.convex_hull">convex_hull() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.copy">copy() (libpysal.graph.Graph method)</a>
+</li>
+  </ul></td>
+</tr></table>
 
-<div class="relations">
-<h3>Related Topics</h3>
-<ul>
-  <li><a href="index.html">Documentation overview</a><ul>
-  </ul></li>
-</ul>
-</div>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" />
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>$('#searchbox').show(0);</script>
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.da2W.html#libpysal.weights.da2W">da2W() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.da2WSP.html#libpysal.weights.da2WSP">da2WSP() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay">Delaunay (class in libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.diagW2">diagW2 (libpysal.weights.W property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.diagWtW">diagWtW (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.diagWtW_WW">diagWtW_WW (libpysal.weights.W property)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.diagWtW_WW">(libpysal.weights.WSP property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.distance_matrix.html#libpysal.cg.distance_matrix">distance_matrix() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand">DistanceBand (class in libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.examples.explain.html#libpysal.examples.explain">explain() (in module libpysal.examples)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.explore">explore() (libpysal.graph.Graph method)</a>
+</li>
+  </ul></td>
+</tr></table>
 
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.fast_knn.html#libpysal.cg.fast_knn">fast_knn() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.fast_threshold.html#libpysal.cg.fast_threshold">fast_threshold() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO">FileIO (class in libpysal.io.fileio)</a>
+</li>
+      <li><a href="generated/libpysal.weights.fill_diagonal.html#libpysal.weights.fill_diagonal">fill_diagonal() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.flush">flush() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_adjacency">from_adjacency() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_adjlist">from_adjlist() (libpysal.weights.W class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_array">from_array() (libpysal.weights.DistanceBand class method)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_array">(libpysal.weights.Kernel class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_array">(libpysal.weights.KNN class method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_arrays">from_arrays() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.Delaunay.html#libpysal.weights.Delaunay.from_dataframe">from_dataframe() (libpysal.weights.Delaunay class method)</a>
 
+      <ul>
+        <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_dataframe">(libpysal.weights.DistanceBand class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_dataframe">(libpysal.weights.Kernel class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_dataframe">(libpysal.weights.KNN class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_dataframe">(libpysal.weights.Queen class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_dataframe">(libpysal.weights.Rook class method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_dicts">from_dicts() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_file">from_file() (libpysal.weights.W class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_iterable">from_iterable() (libpysal.weights.Queen class method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_iterable">(libpysal.weights.Rook class method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_networkx">from_networkx() (libpysal.weights.W class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.DistanceBand.html#libpysal.weights.DistanceBand.from_shapefile">from_shapefile() (libpysal.weights.DistanceBand class method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel.from_shapefile">(libpysal.weights.Kernel class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.from_shapefile">(libpysal.weights.KNN class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_shapefile">(libpysal.weights.Queen class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_shapefile">(libpysal.weights.Rook class method)</a>
+</li>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_shapefile">(libpysal.weights.W class method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_sparse">from_sparse() (libpysal.graph.Graph class method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_sparse">(libpysal.weights.W class method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_W">from_W() (libpysal.graph.Graph class method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.from_W">(libpysal.weights.WSP class method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.from_weights_dict">from_weights_dict() (libpysal.graph.Graph class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.from_WSP">from_WSP() (libpysal.weights.W class method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen.from_xarray">from_xarray() (libpysal.weights.Queen class method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook.from_xarray">(libpysal.weights.Rook class method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.weights.full.html#libpysal.weights.full">full() (in module libpysal.weights)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.full">(libpysal.weights.W method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.weights.full2W.html#libpysal.weights.full2W">full2W() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.fuzzy_contiguity.html#libpysal.weights.fuzzy_contiguity">fuzzy_contiguity() (in module libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.Gabriel.html#libpysal.weights.Gabriel">Gabriel (class in libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.geogrid.html#libpysal.cg.geogrid">geogrid() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.geointerpolate.html#libpysal.cg.geointerpolate">geointerpolate() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.get">get() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_angle_between.html#libpysal.cg.get_angle_between">get_angle_between() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_bounding_box.html#libpysal.cg.get_bounding_box">get_bounding_box() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.weights.get_ids.html#libpysal.weights.get_ids">get_ids() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.examples.get_path.html#libpysal.examples.get_path">get_path() (in module libpysal.examples)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_point_at_angle_and_dist.html#libpysal.cg.get_point_at_angle_and_dist">get_point_at_angle_and_dist() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.weights.get_points_array_from_shapefile.html#libpysal.weights.get_points_array_from_shapefile">get_points_array_from_shapefile() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_points_dist.html#libpysal.cg.get_points_dist">get_points_dist() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_polygon_point_dist.html#libpysal.cg.get_polygon_point_dist">get_polygon_point_dist() (in module libpysal.cg)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.get_polygon_point_intersect.html#libpysal.cg.get_polygon_point_intersect">get_polygon_point_intersect() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_ray_segment_intersect.html#libpysal.cg.get_ray_segment_intersect">get_ray_segment_intersect() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_rectangle_point_intersect.html#libpysal.cg.get_rectangle_point_intersect">get_rectangle_point_intersect() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_rectangle_rectangle_intersection.html#libpysal.cg.get_rectangle_rectangle_intersection">get_rectangle_rectangle_intersection() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_segment_point_dist.html#libpysal.cg.get_segment_point_dist">get_segment_point_dist() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_segment_point_intersect.html#libpysal.cg.get_segment_point_intersect">get_segment_point_intersect() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_segments_intersect.html#libpysal.cg.get_segments_intersect">get_segments_intersect() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.get_shared_segments.html#libpysal.cg.get_shared_segments">get_shared_segments() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.get_swap">get_swap() (libpysal.cg.LineSegment method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.get_transform">get_transform() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.getType">getType() (libpysal.io.fileio.FileIO static method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph">Graph (class in libpysal.graph)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid">Grid (class in libpysal.cg)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.harcdist.html#libpysal.cg.harcdist">harcdist() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.height">height (libpysal.cg.Rectangle property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.hexLat2W.html#libpysal.weights.hexLat2W">hexLat2W() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.higher_order.html#libpysal.weights.higher_order">higher_order() (in module libpysal.weights)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.higher_order">(libpysal.graph.Graph method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.higher_order_sp.html#libpysal.weights.higher_order_sp">higher_order_sp() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.histogram">histogram (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.holes">holes (libpysal.cg.Polygon property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.id2i">id2i (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.id_order">id_order (libpysal.weights.W property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.id_order">(libpysal.weights.WSP property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.id_order_set">id_order_set (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.ids">ids (libpysal.io.fileio.FileIO property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.in_grid">in_grid() (libpysal.cg.Grid method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.inside">inside() (libpysal.cg.PolygonLocator method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.intersect">intersect() (libpysal.cg.LineSegment method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_ccw">is_ccw() (libpysal.cg.LineSegment method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.is_clockwise.html#libpysal.cg.is_clockwise">is_clockwise() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.is_collinear.html#libpysal.cg.is_collinear">is_collinear() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.is_cw">is_cw() (libpysal.cg.LineSegment method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.islands">islands (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.isolates">isolates (libpysal.graph.Graph property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.KDTree.html#libpysal.cg.KDTree">KDTree() (in module libpysal.cg)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.Kernel.html#libpysal.weights.Kernel">Kernel (class in libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN">KNN (class in libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.lag">lag() (libpysal.graph.Graph method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.lag_categorical.html#libpysal.weights.lag_categorical">lag_categorical() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.lag_spatial.html#libpysal.weights.lag_spatial">lag_spatial() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.lat2SW.html#libpysal.weights.lat2SW">lat2SW() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.lat2W.html#libpysal.weights.lat2W">lat2W() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.len">len (libpysal.cg.Chain property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.len">(libpysal.cg.LineSegment property)</a>
+</li>
+        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.len">(libpysal.cg.Polygon property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line">Line (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.line">line (libpysal.cg.LineSegment property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.linear2arcdist.html#libpysal.cg.linear2arcdist">linear2arcdist() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment">LineSegment (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.lonlat.html#libpysal.cg.lonlat">lonlat() (in module libpysal.cg)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.mat2L.html#libpysal.weights.mat2L">mat2L() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.max_neighbors">max_neighbors (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.mean_neighbors">mean_neighbors (libpysal.weights.W property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.min_neighbors">min_neighbors (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.min_threshold_dist_from_shapefile.html#libpysal.weights.min_threshold_dist_from_shapefile">min_threshold_dist_from_shapefile() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.min_threshold_distance.html#libpysal.weights.min_threshold_distance">min_threshold_distance() (in module libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.n">n (libpysal.graph.Graph property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.n">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.n_components">n_components (libpysal.graph.Graph property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.n_components">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.n_edges">n_edges (libpysal.graph.Graph property)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.n_nodes">n_nodes (libpysal.graph.Graph property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.nearest">nearest() (libpysal.cg.Grid method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.nearest">(libpysal.cg.PointLocator method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.nearest">(libpysal.cg.PolygonLocator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.neighbor_equality.html#libpysal.weights.neighbor_equality">neighbor_equality() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.neighbor_offsets">neighbor_offsets (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.neighbors">neighbors (libpysal.graph.Graph property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.netW.html#libpysal.weights.netW">netW() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.nonplanar_neighbors.html#libpysal.weights.nonplanar_neighbors">nonplanar_neighbors() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.nonzero">nonzero (libpysal.graph.Graph property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.nonzero">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.ODW.html#libpysal.weights.ODW">ODW() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.io.open.html#libpysal.io.open">open (in module libpysal.io)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.open">open() (libpysal.io.fileio.FileIO class method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.order.html#libpysal.weights.order">order() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.overlapping">overlapping() (libpysal.cg.PointLocator method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.overlapping">(libpysal.cg.PolygonLocator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.p1">p1 (libpysal.cg.LineSegment property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.p2">p2 (libpysal.cg.LineSegment property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.parts">parts (libpysal.cg.Chain property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.parts">(libpysal.cg.Polygon property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.pct_nonzero">pct_nonzero (libpysal.graph.Graph property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.pct_nonzero">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.perimeter">perimeter (libpysal.cg.Polygon property)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.plot">plot() (libpysal.graph.Graph method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.plot">(libpysal.weights.W method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Point.html#libpysal.cg.Point">Point (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.point_touches_rectangle.html#libpysal.cg.point_touches_rectangle">point_touches_rectangle() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator">PointLocator (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon">Polygon (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.polygon">polygon() (libpysal.cg.PointLocator method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator">PolygonLocator (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.proximity">proximity() (libpysal.cg.Grid method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.proximity">(libpysal.cg.PointLocator method)</a>
+</li>
+        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.proximity">(libpysal.cg.PolygonLocator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Q">Q</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.Queen.html#libpysal.weights.Queen">Queen (class in libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.RADIUS_EARTH_KM.html#libpysal.cg.RADIUS_EARTH_KM">RADIUS_EARTH_KM (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.RADIUS_EARTH_MILES.html#libpysal.cg.RADIUS_EARTH_MILES">RADIUS_EARTH_MILES (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Ray.html#libpysal.cg.Ray">Ray (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.read">read() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle">Rectangle (class in libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.PointLocator.html#libpysal.cg.PointLocator.region">region() (libpysal.cg.PointLocator method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.PolygonLocator.html#libpysal.cg.PolygonLocator.region">(libpysal.cg.PolygonLocator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.Relative_Neighborhood.html#libpysal.weights.Relative_Neighborhood">Relative_Neighborhood (class in libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.remap_ids.html#libpysal.weights.remap_ids">remap_ids() (in module libpysal.weights)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.remap_ids">(libpysal.weights.W method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.cg.Grid.html#libpysal.cg.Grid.remove">remove() (libpysal.cg.Grid method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.KNN.html#libpysal.weights.KNN.reweight">reweight() (libpysal.weights.KNN method)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.rIds">rIds (libpysal.io.fileio.FileIO property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.Rook.html#libpysal.weights.Rook">Rook (class in libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s0">s0 (libpysal.weights.W property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.s0">(libpysal.weights.WSP property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s1">s1 (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s2">s2 (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.s2array">s2array (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.sd">sd (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.seek">seek() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.segments">segments (libpysal.cg.Chain property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_centroid">set_centroid() (libpysal.cg.Rectangle method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.set_scale">set_scale() (libpysal.cg.Rectangle method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.set_shapefile">set_shapefile() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.set_transform">set_transform() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.shimbel.html#libpysal.weights.shimbel">shimbel() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.sparse">sparse (libpysal.graph.Graph property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.sparse">(libpysal.weights.W property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.weights.spw_from_gal.html#libpysal.weights.spw_from_gal">spw_from_gal() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.LineSegment.html#libpysal.cg.LineSegment.sw_ccw">sw_ccw() (libpysal.cg.LineSegment method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.symmetrize">symmetrize() (libpysal.weights.W method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.tell">tell() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.testDataArray.html#libpysal.weights.testDataArray">testDataArray() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_adjlist">to_adjlist() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_file">to_file() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.to_networkx">to_networkx() (libpysal.graph.Graph method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_networkx">(libpysal.weights.W method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.to_parquet">to_parquet() (libpysal.graph.Graph method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_sparse">to_sparse() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.to_W">to_W() (libpysal.graph.Graph method)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.to_W">(libpysal.weights.WSP method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.to_WSP">to_WSP() (libpysal.weights.W method)</a>
+</li>
+      <li><a href="generated/libpysal.cg.toLngLat.html#libpysal.cg.toLngLat">toLngLat() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.cg.toXYZ.html#libpysal.cg.toXYZ">toXYZ() (in module libpysal.cg)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.transform">transform (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.transform">transform() (libpysal.graph.Graph method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.trcW2">trcW2 (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.trcWtW">trcWtW (libpysal.weights.W property)</a>
+</li>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W.trcWtW_WW">trcWtW_WW (libpysal.weights.W property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP.trcWtW_WW">(libpysal.weights.WSP property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.truncate">truncate() (libpysal.io.fileio.FileIO method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.unique_ids">unique_ids (libpysal.graph.Graph property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.vecW.html#libpysal.weights.vecW">vecW() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Chain.html#libpysal.cg.Chain.vertices">vertices (libpysal.cg.Chain property)</a>
+
+      <ul>
+        <li><a href="generated/libpysal.cg.Polygon.html#libpysal.cg.Polygon.vertices">(libpysal.cg.Polygon property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.Voronoi.html#libpysal.weights.Voronoi">Voronoi() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.cg.voronoi_frames.html#libpysal.cg.voronoi_frames">voronoi_frames() (in module libpysal.cg)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.W.html#libpysal.weights.W">W (class in libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w2da.html#libpysal.weights.w2da">w2da() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w_clip.html#libpysal.weights.w_clip">w_clip() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w_difference.html#libpysal.weights.w_difference">w_difference() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w_intersection.html#libpysal.weights.w_intersection">w_intersection() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w_local_cluster.html#libpysal.weights.w_local_cluster">w_local_cluster() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w_subset.html#libpysal.weights.w_subset">w_subset() (in module libpysal.weights)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.weights.w_symmetric_difference.html#libpysal.weights.w_symmetric_difference">w_symmetric_difference() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.w_union.html#libpysal.weights.w_union">w_union() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.graph.Graph.html#libpysal.graph.Graph.weights">weights (libpysal.graph.Graph property)</a>
+</li>
+      <li><a href="generated/libpysal.cg.Rectangle.html#libpysal.cg.Rectangle.width">width (libpysal.cg.Rectangle property)</a>
+</li>
+      <li><a href="generated/libpysal.io.fileio.FileIO.html#libpysal.io.fileio.FileIO.write">write() (libpysal.io.fileio.FileIO method)</a>
+</li>
+      <li><a href="generated/libpysal.weights.WSP.html#libpysal.weights.WSP">WSP (class in libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.wsp2da.html#libpysal.weights.wsp2da">wsp2da() (in module libpysal.weights)</a>
+</li>
+      <li><a href="generated/libpysal.weights.WSP2W.html#libpysal.weights.WSP2W">WSP2W() (in module libpysal.weights)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="X">X</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line.x">x() (libpysal.cg.Line method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Y">Y</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/libpysal.cg.Line.html#libpysal.cg.Line.y">y() (libpysal.cg.Line method)</a>
+</li>
+  </ul></td>
+</tr></table>
 
 
 
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+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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diff --git a/index.html b/index.html
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-
 <!DOCTYPE html>
 
-<html xmlns="http://www.w3.org/1999/xhtml">
+<html lang="en" data-content_root="./">
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-    <title>Welcome to No Errors Test Project’s documentation! &#8212; No Errors Test Project  documentation</title>
-    <link rel="stylesheet" href="_static/alabaster.css" type="text/css" />
-    <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>libpysal: Python Spatial Analysis Library Core &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="_static/pysal-styles.css?v=38b0ce36" />
+    <script src="_static/documentation_options.js?v=7a99edf7"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <link rel="icon" href="_static/pysal_favicon.ico"/>
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+<meta charset='utf-8'>
+<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
+<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
+<meta name="apple-mobile-web-app-capable" content="yes">
+<script type="text/javascript" src="_static/js/jquery-1.12.4.min.js"></script>
+<script type="text/javascript" src="_static/js/jquery-fix.js"></script>
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+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
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+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+          <span class="icon-bar"></span>
+        </button>
+        <a class="navbar-brand" href="#">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
 
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+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
             
-  <div class="section" id="welcome-to-no-errors-test-project-s-documentation">
-<h1>Welcome to No Errors Test Project’s documentation!<a class="headerlink" href="#welcome-to-no-errors-test-project-s-documentation" title="Permalink to this headline">¶</a></h1>
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-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
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-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
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+<li class="toctree-l2"><a class="reference internal" href="api.html#io">io</a></li>
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+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">libpysal: Python Spatial Analysis Library Core</a><ul>
+<li><a class="reference internal" href="#introduction">Introduction</a></li>
+<li><a class="reference internal" href="#development">Development</a></li>
+<li><a class="reference internal" href="#getting-involved">Getting Involved</a></li>
+<li><a class="reference internal" href="#bug-reports">Bug reports</a></li>
+<li><a class="reference internal" href="#citing-libpysal">Citing libpysal</a></li>
+<li><a class="reference internal" href="#license-information">License information</a></li>
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="libpysal-python-spatial-analysis-library-core">
+<h1>libpysal: Python Spatial Analysis Library Core<a class="headerlink" href="#libpysal-python-spatial-analysis-library-core" title="Link to this heading">¶</a></h1>
+<a class="reference external image-reference" href="https://github.com/pysal/libpysal/actions?query=workflow%3A.github%2Fworkflows%2Funittests.yml"><img alt="https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg" src="https://github.com/pysal/libpysal/workflows/.github/workflows/unittests.yml/badge.svg" /></a>
+<a class="reference external image-reference" href="https://gitter.im/pysal/pysal"><img alt="https://badges.gitter.im/pysal/pysal.svg" src="https://badges.gitter.im/pysal/pysal.svg" /></a>
+<a class="reference external image-reference" href="https://badge.fury.io/py/libpysal"><img alt="https://badge.fury.io/py/libpysal.svg" src="https://badge.fury.io/py/libpysal.svg" /></a>
+<div class="container-fluid">
+  <div class="row equal-height">
+    <div class="col-sm-1 col-xs-hidden">
+    </div>
+    <div class="col-sm-10 col-xs-12">
+        <a href="http://nbviewer.jupyter.org/github/pysal/libpysal/blob/main/notebooks/weights.ipynb" class="thumbnail">
+            <img src="_static/images/npweights.png" class="img-responsive center-block">
+            <div class="caption text-center">
+            <h6>Weights for nonplanar enforced geometries</h6>
+            </div>
+        </a>
+    </div>
+    <div class="col-sm-1 col-xs-hidden">
+    </div>
+  </div>
+</div><section id="introduction">
+<h2>Introduction<a class="headerlink" href="#introduction" title="Link to this heading">¶</a></h2>
+<p><strong>libpysal</strong> offers four modules that form the building blocks in many upstream packages in the <a class="reference external" href="https://pysal.org">PySAL family</a>:</p>
+<ul class="simple">
+<li><p>Spatial Weights: libpysal.weights</p></li>
+<li><p>Input-and output: libpysal.io</p></li>
+<li><p>Computational geometry: libpysal.cg</p></li>
+<li><p>Built-in example datasets libpysal.examples</p></li>
 </ul>
+<p>Examples demonstrating some of <strong>libpysal</strong> functionality are available in the <a class="reference external" href="tutorial.html">tutorial</a>.</p>
+<p>Details are available in the <a class="reference external" href="api.html">libpysal api</a>.</p>
+<p>For background information see <span id="id1">[<a class="reference internal" href="references.html#id3" title="Sergio J. Rey and Luc Anselin. PySAL: A Python Library of Spatial Analytical Methods. The Review of Regional Studies, 37(1):5-27, 2007.">RA07</a>]</span>.</p>
+</section>
+<section id="development">
+<h2>Development<a class="headerlink" href="#development" title="Link to this heading">¶</a></h2>
+<p>libpysal development is hosted on <a class="reference external" href="https://github.com/pysal/libpysal">github</a>.</p>
+<p>Discussions of development occurs on the
+<a class="reference external" href="http://groups.google.com/group/pysal-dev">developer list</a>
+as well as <a class="reference external" href="https://gitter.im/pysal/pysal?">gitter</a>.</p>
+</section>
+<section id="getting-involved">
+<h2>Getting Involved<a class="headerlink" href="#getting-involved" title="Link to this heading">¶</a></h2>
+<p>If you are interested in contributing to PySAL please see our
+<a class="reference external" href="https://github.com/pysal/pysal/wiki">development guidelines</a>.</p>
+</section>
+<section id="bug-reports">
+<h2>Bug reports<a class="headerlink" href="#bug-reports" title="Link to this heading">¶</a></h2>
+<p>To search for or report bugs, please see libpysal’s <a class="reference external" href="http://github.com/pysal/libpysal/issues">issues</a>.</p>
+</section>
+<section id="citing-libpysal">
+<h2>Citing libpysal<a class="headerlink" href="#citing-libpysal" title="Link to this heading">¶</a></h2>
+<p>If you use PySAL in a scientific publication, we would appreciate citations to the following paper:</p>
+<blockquote>
+<div><p><a class="reference external" href="http://journal.srsa.org/ojs/index.php/RRS/article/view/134/85">PySAL: A Python Library of Spatial Analytical Methods</a>, <em>Rey, S.J. and L. Anselin</em>, Review of Regional Studies 37, 5-27 2007.</p>
+<p>Bibtex entry:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@Article</span><span class="p">{</span><span class="n">pysal2007</span><span class="p">,</span>
+  <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Rey</span><span class="p">,</span> <span class="n">Sergio</span> <span class="n">J</span><span class="o">.</span> <span class="ow">and</span> <span class="n">Anselin</span><span class="p">,</span> <span class="n">Luc</span><span class="p">},</span>
+  <span class="n">title</span><span class="o">=</span><span class="p">{{</span><span class="n">PySAL</span><span class="p">:</span> <span class="n">A</span> <span class="n">Python</span> <span class="n">Library</span> <span class="n">of</span> <span class="n">Spatial</span> <span class="n">Analytical</span> <span class="n">Methods</span><span class="p">}},</span>
+  <span class="n">journal</span><span class="o">=</span><span class="p">{</span><span class="n">The</span> <span class="n">Review</span> <span class="n">of</span> <span class="n">Regional</span> <span class="n">Studies</span><span class="p">},</span>
+  <span class="n">year</span><span class="o">=</span><span class="mi">2007</span><span class="p">,</span>
+  <span class="n">volume</span><span class="o">=</span><span class="p">{</span><span class="mi">37</span><span class="p">},</span>
+  <span class="n">number</span><span class="o">=</span><span class="p">{</span><span class="mi">1</span><span class="p">},</span>
+  <span class="n">pages</span><span class="o">=</span><span class="p">{</span><span class="mi">5</span><span class="o">-</span><span class="mi">27</span><span class="p">},</span>
+  <span class="n">keywords</span><span class="o">=</span><span class="p">{</span><span class="n">Open</span> <span class="n">Source</span><span class="p">;</span> <span class="n">Software</span><span class="p">;</span> <span class="n">Spatial</span><span class="p">}</span>
+<span class="p">}</span>
+</pre></div>
 </div>
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-      <input type="submit" value="Go" />
-    </form>
-    </div>
+</div></blockquote>
+</section>
+<section id="license-information">
+<h2>License information<a class="headerlink" href="#license-information" title="Link to this heading">¶</a></h2>
+<p>See the file “LICENSE.txt” for information on the history of this
+software, terms &amp; conditions for usage, and a DISCLAIMER OF ALL
+WARRANTIES.</p>
+</section>
+</section>
+<section id="libpysal">
+<h1>libpysal<a class="headerlink" href="#libpysal" title="Link to this heading">¶</a></h1>
+<p>Core components of the Python Spatial Analysis Library (<a class="reference external" href="https://github.com/pysal/pysal">PySAL</a>)</p>
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+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
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+          <ul class="nav navbar-nav">
+            
+                <li><a href="#">Installation</a></li>
+                <li><a href="tutorial.html">Tutorial</a></li>
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+                <li><a href="references.html">References</a></li>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#installing-released-version">Installing released version</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="tutorial.html">Tutorial</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#io">io</a></li>
+<li class="toctree-l2"><a class="reference internal" href="api.html#examples">examples</a></li>
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+              
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+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">Installation</a><ul>
+<li><a class="reference internal" href="#installing-released-version">Installing released version</a><ul>
+<li><a class="reference internal" href="#conda">conda</a></li>
+<li><a class="reference internal" href="#pypi">pypi</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#installing-development-version">Installing development version</a></li>
+</ul>
+</li>
+</ul>
+</ul>
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+<form class="navbar-form navbar-right" action="search.html" method="get">
+ <div class="form-group">
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+  <input type="hidden" name="area" value="default" />
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+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <section id="installation">
+<h1>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h1>
+<p>libpysal supports python &gt;= <a class="reference external" href="https://docs.python.org/3.8/">3.8</a> only. Please make sure that you are
+operating in a python 3 environment.</p>
+<section id="installing-released-version">
+<h2>Installing released version<a class="headerlink" href="#installing-released-version" title="Link to this heading">¶</a></h2>
+<section id="conda">
+<h3>conda<a class="headerlink" href="#conda" title="Link to this heading">¶</a></h3>
+<p>libpysal is available through conda:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">libpysal</span>
+</pre></div>
+</div>
+</section>
+<section id="pypi">
+<h3>pypi<a class="headerlink" href="#pypi" title="Link to this heading">¶</a></h3>
+<p>libpysal is available on the <a class="reference external" href="https://pypi.org/project/libpysal/">Python Package Index</a>. Therefore, you can either
+install directly with <cite>pip</cite> from the command line:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">U</span> <span class="n">libpysal</span>
+</pre></div>
+</div>
+<p>or download the source distribution (.tar.gz) and decompress it to your selected
+destination. Open a command shell and navigate to the decompressed folder.
+Type:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="o">.</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="installing-development-version">
+<h2>Installing development version<a class="headerlink" href="#installing-development-version" title="Link to this heading">¶</a></h2>
+<p>Potentially, you might want to use the newest features in the development
+version of libpysal on github - <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a> while have not been incorporated
+in the Pypi released version. You can achieve that by installing <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a>
+by running the following from a command shell:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">pysal</span><span class="o">/</span><span class="n">libpysal</span><span class="o">.</span><span class="n">git</span>
+</pre></div>
+</div>
+<p>You can  also <a class="reference external" href="https://help.github.com/articles/fork-a-repo/">fork</a> the <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a> repo and create a local clone of
+your fork. By making changes
+to your local clone and submitting a pull request to <a class="reference external" href="https://github.com/pysal/libpysal">pysal/libpysal</a>, you can
+contribute to libpysal development.</p>
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+              
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+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">Raster awareness API</a><ul>
+<li><a class="reference internal" href="#Loading-Data">Loading Data</a><ul>
+<li><a class="reference internal" href="#Using-xarray-example-dataset">Using xarray example dataset</a></li>
+<li><a class="reference internal" href="#Using-local-NetCDF-dataset">Using local <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> dataset</a></li>
+<li><a class="reference internal" href="#Using-local-GeoTIFF-dataset">Using local <code class="docutils literal notranslate"><span class="pre">GeoTIFF</span></code> dataset</a></li>
+<li><a class="reference internal" href="#higher_order-neighbors"><code class="docutils literal notranslate"><span class="pre">higher_order</span></code> neighbors</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Additional-resources">Additional resources</a></li>
+</ul>
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+<p>This page was generated from <a class="reference external" href="https://github.com/pysal/libpysal/blob/master/notebooks/Raster_awareness_API.ipynb">notebooks/Raster_awareness_API.ipynb</a>.
+Interactive online version:
+<span class="raw-html"><a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath=notebooks/Raster_awareness_API.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+</div>
+<section id="Raster-awareness-API">
+<h1>Raster awareness API<a class="headerlink" href="#Raster-awareness-API" title="Link to this heading">¶</a></h1>
+<p>This notebook will give an overview of newly developed raster interface. We’ll cover basic usage of the functionality offered by the interface which mainly involves: 1. converting <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> object to the PySAL’s weights object (<code class="docutils literal notranslate"><span class="pre">libpysal.weights.W</span></code>/<code class="docutils literal notranslate"><span class="pre">WSP</span></code>). 2. going back to the <code class="docutils literal notranslate"><span class="pre">xarray.DataArray</span></code> from weights object.</p>
+<p>using different datasets: - with missing values. - with multiple layers. - with non conventional dimension names.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+
+from libpysal.weights import Rook, Queen, raster
+import matplotlib.pyplot as plt
+from splot import libpysal as splot
+import numpy as np
+import xarray as xr
+import pandas as pd
+from esda import Moran_Local
+</pre></div>
+</div>
+</div>
+<section id="Loading-Data">
+<h2>Loading Data<a class="headerlink" href="#Loading-Data" title="Link to this heading">¶</a></h2>
+<p><em>The interface only accepts ``xarray.DataArray``</em>, this can be easily obtained from raster data format using <code class="docutils literal notranslate"><span class="pre">xarray</span></code>’s I/O functionality which can read from a variety of data formats some of them are listed below: - <a class="reference external" href="https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html">GDAL Raster Formats</a> via <code class="docutils literal notranslate"><span class="pre">open_rasterio</span></code> method. - <a class="reference external" href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> via <code class="docutils literal notranslate"><span class="pre">open_dataset</span></code> method.</p>
+<p>In this notebook we’ll work with <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> and <code class="docutils literal notranslate"><span class="pre">GeoTIFF</span></code> data.</p>
+<section id="Using-xarray-example-dataset">
+<h3>Using xarray example dataset<a class="headerlink" href="#Using-xarray-example-dataset" title="Link to this heading">¶</a></h3>
+<p>First lets load up a <code class="docutils literal notranslate"><span class="pre">netCDF</span></code> dataset offered by xarray.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ds = xr.tutorial.open_dataset(&quot;air_temperature.nc&quot;)  # -&gt; returns a xarray.Dataset object
+da = ds[&quot;air&quot;]  # we&#39;ll use the &quot;air&quot; data variable for further analysis
+print(da)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.DataArray &#39;air&#39; (time: 2920, lat: 25, lon: 53)&gt;
+[3869000 values with dtype=float32]
+Coordinates:
+  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
+  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
+  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
+Attributes:
+    long_name:     4xDaily Air temperature at sigma level 995
+    units:         degK
+    precision:     2
+    GRIB_id:       11
+    GRIB_name:     TMP
+    var_desc:      Air temperature
+    dataset:       NMC Reanalysis
+    level_desc:    Surface
+    statistic:     Individual Obs
+    parent_stat:   Other
+    actual_range:  [185.16 322.1 ]
+</pre></div></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">xarray</span></code>’s data structures like <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> and <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> provides <code class="docutils literal notranslate"><span class="pre">pandas</span></code> like functionality for multidimensional-array or ndarray.</p>
+<p>In our case we’ll mainly deal with <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, we can see above that the <code class="docutils literal notranslate"><span class="pre">da</span></code> holds the data for air temperature, it has 2 dims coordinate dimensions <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>, and it’s layered on <code class="docutils literal notranslate"><span class="pre">time</span></code> dimension so in total 3 dims (<code class="docutils literal notranslate"><span class="pre">time</span></code>, <code class="docutils literal notranslate"><span class="pre">lat</span></code>, <code class="docutils literal notranslate"><span class="pre">lon</span></code>).</p>
+<p>We’ll now group <code class="docutils literal notranslate"><span class="pre">da</span></code> by month and take average over the <code class="docutils literal notranslate"><span class="pre">time</span></code> dimension</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>da = da.groupby(&#39;time.month&#39;).mean()
+print(da.coords)  # as a result time dim is replaced by month
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Coordinates:
+  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
+  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
+  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># let&#39;s plot over month, each facet will represent the mean air temperature in a given month.
+da.plot(col=&quot;month&quot;, col_wrap=4,)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.plot.facetgrid.FacetGrid at 0x7f68f71b8100&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Raster_awareness_API_6_1.png" src="../_images/notebooks_Raster_awareness_API_6_1.png" />
+</div>
+</div>
+<p>We can use <code class="docutils literal notranslate"><span class="pre">from_xarray</span></code> method from the contiguity classes like <code class="docutils literal notranslate"><span class="pre">Rook</span></code> and <code class="docutils literal notranslate"><span class="pre">Queen</span></code>, and also from <code class="docutils literal notranslate"><span class="pre">KNN</span></code>.</p>
+<p>This uses a util function in <code class="docutils literal notranslate"><span class="pre">raster.py</span></code> file called <code class="docutils literal notranslate"><span class="pre">da2W</span></code>, which can also be called directly to build <code class="docutils literal notranslate"><span class="pre">W</span></code> object, similarly <code class="docutils literal notranslate"><span class="pre">da2WSP</span></code> for building <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p>
+<p><strong>Weight builders (``from_xarray``, ``da2W``, ``da2WSP``) can recognise dimensions belonging to this list ``[band, time, lat, y, lon, x]``, if any of the dimension in the ``DataArray`` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension’s name) to the weight builder.</strong></p>
+<p>e.g. <code class="docutils literal notranslate"><span class="pre">dims</span></code> dictionary:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">da</span><span class="o">.</span><span class="n">dims</span>                  <span class="c1"># none of the dimension belong to the default dimension list</span>
+<span class="go">(&#39;year&#39;, &#39;height&#39;, &#39;width&#39;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">coords_labels</span> <span class="o">=</span> <span class="p">{</span>                 <span class="c1"># dimension values should be properly aligned with the following keys</span>
+<span class="go">        &quot;z_label&quot;: &quot;year&quot;,</span>
+<span class="go">        &quot;y_label&quot;: &quot;height&quot;,</span>
+<span class="go">        &quot;x_label&quot;: &quot;width&quot;</span>
+<span class="go">    }</span>
+</pre></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>coords_labels = {}
+coords_labels[&quot;z_label&quot;] = &quot;month&quot;  # since month does not belong to the default list we need to pass it using a dictionary
+w_queen = Queen.from_xarray(
+    da, z_value=12, coords_labels=coords_labels, sparse=False)  # We&#39;ll use data from 12th layer (in our case layer=month)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/data/GSoC/libpysal/libpysal/weights/raster.py:119: UserWarning: You are trying to build a full W object from xarray.DataArray (raster) object. This computation can be very slow and not scale well. It is recommended, if possible, to instead build WSP object, which is more efficient and faster. You can do this by using da2WSP method.
+  warn(
+</pre></div></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">index</span></code> is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to <code class="docutils literal notranslate"><span class="pre">pandas.Series</span></code> created from the passed <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, this series can be easily obtained using <code class="docutils literal notranslate"><span class="pre">DataArray.to_series()</span></code> method.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_queen.index[:5]  # indices are aligned to the ids of the weight object
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+MultiIndex([(12, 75.0, 200.0),
+            (12, 75.0, 202.5),
+            (12, 75.0, 205.0),
+            (12, 75.0, 207.5),
+            (12, 75.0, 210.0)],
+           names=[&#39;month&#39;, &#39;lat&#39;, &#39;lon&#39;])
+</pre></div></div>
+</div>
+<p>We can then obtain raster data by converting the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> to <code class="docutils literal notranslate"><span class="pre">Series</span></code> and then using indices from <code class="docutils literal notranslate"><span class="pre">index</span></code> attribute to get non-missing values by subsetting the <code class="docutils literal notranslate"><span class="pre">Series</span></code>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>data = da.to_series()[w_queen.index]
+</pre></div>
+</div>
+</div>
+<p>We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let’s compute a local Moran statistic for the extracted data.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Quickly computing and loading a LISA
+np.random.seed(12345)
+lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)
+</pre></div>
+</div>
+</div>
+<p>After getting our calculated results it’s time to store them back to the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, we can use <code class="docutils literal notranslate"><span class="pre">w2da</span></code> function directly to convert the <code class="docutils literal notranslate"><span class="pre">W</span></code> object back to <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>.</p>
+<p><em>Your use case might differ but the steps for using the interface will be similar to this example.</em></p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Converting obtained data back to DataArray
+moran_da = raster.w2da(lisa.p_sim, w_queen)  # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data
+print(moran_da)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.DataArray (month: 1, lat: 25, lon: 53)&gt;
+array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],
+        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],
+        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],
+        ...,
+        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],
+        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],
+        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])
+Coordinates:
+  * month    (month) int64 12
+  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
+  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>moran_da.plot()
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.collections.QuadMesh at 0x7f68f4044940&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Raster_awareness_API_17_1.png" src="../_images/notebooks_Raster_awareness_API_17_1.png" />
+</div>
+</div>
+</section>
+<section id="Using-local-NetCDF-dataset">
+<h3>Using local <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> dataset<a class="headerlink" href="#Using-local-NetCDF-dataset" title="Link to this heading">¶</a></h3>
+<p>In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder.</p>
+<p>In this small example we’ll build <code class="docutils literal notranslate"><span class="pre">KNN</span></code> distance weight object using a local <code class="docutils literal notranslate"><span class="pre">NetCDF</span></code> dataset with different dimensions names which doesn’t belong to the default list of dimensions.</p>
+<p>We’ll also see how to speed up the reverse journey (from weights object to <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>) by passing prebuilt <code class="docutils literal notranslate"><span class="pre">coords</span></code> and <code class="docutils literal notranslate"><span class="pre">attrs</span></code> to <code class="docutils literal notranslate"><span class="pre">w2da</span></code> method.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Lets load a netCDF Surface dataset
+ds = xr.open_dataset(&#39;ECMWF_ERA-40_subset.nc&#39;)    # After loading netCDF dataset we obtained a xarray.Dataset object
+print(ds)                                         # This Dataset object containes several data variables
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.Dataset&gt;
+Dimensions:    (latitude: 73, longitude: 144, time: 62)
+Coordinates:
+  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5
+  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0
+  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00
+Data variables:
+    tcw        (time, latitude, longitude) float32 ...
+    tcwv       (time, latitude, longitude) float32 ...
+    lsp        (time, latitude, longitude) float32 ...
+    cp         (time, latitude, longitude) float32 ...
+    msl        (time, latitude, longitude) float32 ...
+    blh        (time, latitude, longitude) float32 ...
+    tcc        (time, latitude, longitude) float32 ...
+    p10u       (time, latitude, longitude) float32 ...
+    p10v       (time, latitude, longitude) float32 ...
+    p2t        (time, latitude, longitude) float32 ...
+    p2d        (time, latitude, longitude) float32 ...
+    e          (time, latitude, longitude) float32 ...
+    lcc        (time, latitude, longitude) float32 ...
+    mcc        (time, latitude, longitude) float32 ...
+    hcc        (time, latitude, longitude) float32 ...
+    tco3       (time, latitude, longitude) float32 ...
+    tp         (time, latitude, longitude) float32 ...
+Attributes:
+    Conventions:  CF-1.0
+    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92
+</pre></div></div>
+</div>
+<p>Out of 17 data variables we’ll use <code class="docutils literal notranslate"><span class="pre">p2t</span></code> for our analysis. This will give us our desired <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> object <code class="docutils literal notranslate"><span class="pre">da</span></code>, we will further group <code class="docutils literal notranslate"><span class="pre">da</span></code> by day, taking average over the <code class="docutils literal notranslate"><span class="pre">time</span></code> dimension.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>da = ds[&quot;p2t&quot;]  # this will give us the required DataArray with p2t (2 metre temperature) data variable
+da = da.groupby(&#39;time.day&#39;).mean()
+print(da.dims)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(&#39;day&#39;, &#39;latitude&#39;, &#39;longitude&#39;)
+</pre></div></div>
+</div>
+<p><strong>We can see that the none of dimensions of ``da`` matches with the default dimensions (``[band, time, lat, y, lon, x]``)</strong></p>
+<p>This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>coords_labels = {}
+coords_labels[&quot;y_label&quot;] = &quot;latitude&quot;
+coords_labels[&quot;x_label&quot;] = &quot;longitude&quot;
+coords_labels[&quot;z_label&quot;] = &quot;day&quot;
+w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>data = da.to_series()[w_rook.index]  # we derived the data from DataArray similar to our last example
+</pre></div>
+</div>
+</div>
+<p>In the last example we only passed the <code class="docutils literal notranslate"><span class="pre">data</span></code> values and weight object to <code class="docutils literal notranslate"><span class="pre">w2da</span></code> method, which then created the necessary <code class="docutils literal notranslate"><span class="pre">coords</span></code> to build our required <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>. This process can be speed up by passing <code class="docutils literal notranslate"><span class="pre">coords</span></code> from the existing <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> <code class="docutils literal notranslate"><span class="pre">da</span></code> which we used earlier.</p>
+<p>Along with <code class="docutils literal notranslate"><span class="pre">coords</span></code> we can also pass <code class="docutils literal notranslate"><span class="pre">attrs</span></code> of the same <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> this will help <code class="docutils literal notranslate"><span class="pre">w2da</span></code> to retain all the properties of original <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>.</p>
+<p>Let’s compare the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> returned by <code class="docutils literal notranslate"><span class="pre">w2da</span></code> and original <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>. For this we’ll ship the derived data straight to <code class="docutils literal notranslate"><span class="pre">w2da</span></code> without any statistical analysis.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)
+xr.DataArray.equals(da[12:13], da1)  # method to compare 2 DataArray, if true then w2da was successfull
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+True
+</pre></div></div>
+</div>
+</section>
+<section id="Using-local-GeoTIFF-dataset">
+<h3>Using local <code class="docutils literal notranslate"><span class="pre">GeoTIFF</span></code> dataset<a class="headerlink" href="#Using-local-GeoTIFF-dataset" title="Link to this heading">¶</a></h3>
+<p>Up until now we’ve only played with <code class="docutils literal notranslate"><span class="pre">netCDF</span></code> datasets but in this example we’ll use a <code class="docutils literal notranslate"><span class="pre">raster.tif</span></code> file to see how interface interacts with it. We’ll also see how these methods handle missing data.</p>
+<p>Unlike earlier we’ll use weight builder methods from <code class="docutils literal notranslate"><span class="pre">raster.py</span></code>, which we can call directly. Just a reminder that <code class="docutils literal notranslate"><span class="pre">from_xarray</span></code> uses methods from <code class="docutils literal notranslate"><span class="pre">raster.py</span></code> and therefore only difference exists in the API.</p>
+<p>To access GDAL Raster Formats <code class="docutils literal notranslate"><span class="pre">xarray</span></code> offers <code class="docutils literal notranslate"><span class="pre">open_rasterio</span></code> method which uses <code class="docutils literal notranslate"><span class="pre">rasterio</span></code> as backend. It loads metadata, coordinate values from the raster file and assign them to the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Loading raster data with missing values
+da = xr.open_rasterio(&#39;/data/Downloads/lux_ppp_2019.tif&#39;)
+print(da)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.DataArray (band: 1, y: 880, x: 940)&gt;
+[827200 values with dtype=float32]
+Coordinates:
+  * band     (band) int64 1
+  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45
+  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527
+Attributes:
+    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...
+    crs:            +init=epsg:4326
+    res:            (0.0008333333297872345, 0.0008333333295454553)
+    is_tiled:       0
+    nodatavals:     (-99999.0,)
+    scales:         (1.0,)
+    offsets:        (0.0,)
+    AREA_OR_POINT:  Area
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>da.where(da.values&gt;da.attrs[&quot;nodatavals&quot;][0]).plot() # we can see that the DataArray contains missing values.
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.collections.QuadMesh at 0x7f68ef4c5ca0&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Raster_awareness_API_29_1.png" src="../_images/notebooks_Raster_awareness_API_29_1.png" />
+</div>
+</div>
+<p>We’ll look at how weight builders handle missing values. Firstly we’ll slice the <code class="docutils literal notranslate"><span class="pre">DataArray</span></code> to reduce overall size for easier visualization.</p>
+<p>This time we’ll create <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object using <code class="docutils literal notranslate"><span class="pre">da2WSP</span></code> method inside <code class="docutils literal notranslate"><span class="pre">raster.py</span></code>. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Slicing the dataarray
+da_s = da[:, 330:340, 129:139]
+w_queen = raster.da2WSP(da_s)  # default contiguity is queen
+w_rook = raster.da2WSP(da_s, &quot;rook&quot;)
+</pre></div>
+</div>
+</div>
+<p>After plotting both contiguities and sliced <code class="docutils literal notranslate"><span class="pre">DataArray</span></code>, we can see that the missing values are ignored by the <code class="docutils literal notranslate"><span class="pre">da2WSP</span></code> method and only indices of non missing values are stored in <code class="docutils literal notranslate"><span class="pre">index</span></code> attribute of <code class="docutils literal notranslate"><span class="pre">WSP</span></code> object.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect=&#39;equal&#39;))
+da_s.where(da_s.values&gt;da_s.attrs[&quot;nodatavals&quot;][0]).plot(ax=ax[0])
+ax[0].set_title(&quot;Sliced raster&quot;)
+splot.plot_spatial_weights(w_rook, data=da_s, ax=ax[1])
+ax[1].set_title(&quot;Rook contiguity&quot;)
+splot.plot_spatial_weights(w_queen, data=da_s, ax=ax[2])
+ax[2].set_title(&quot;Queen contiguity&quot;)
+plt.show()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Raster_awareness_API_33_0.png" src="../_images/notebooks_Raster_awareness_API_33_0.png" />
+</div>
+</div>
+</section>
+<section id="higher_order-neighbors">
+<h3><code class="docutils literal notranslate"><span class="pre">higher_order</span></code> neighbors<a class="headerlink" href="#higher_order-neighbors" title="Link to this heading">¶</a></h3>
+<p>In some cases <code class="docutils literal notranslate"><span class="pre">Rook</span></code> and <code class="docutils literal notranslate"><span class="pre">Queen</span></code> contiguities don’t provide sufficient neighbors when performing spatial analysis on a raster data, this is because <code class="docutils literal notranslate"><span class="pre">Rook</span></code> contiguity provides max 4 neighbors and <code class="docutils literal notranslate"><span class="pre">Queen</span></code> provides max 8.</p>
+<p>Therefore we’ve added <code class="docutils literal notranslate"><span class="pre">higher_order</span></code> functionality inside the builder method. We can now pass <code class="docutils literal notranslate"><span class="pre">k</span></code> value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> argument. Now lets take a look at this functionality.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span># Building a test DataArray
+da_s = raster.testDataArray((1,5,10), rand=True)
+</pre></div>
+</div>
+</div>
+<p>Below we can see that builder selected all the neighbors of order less than equal to 2, with <code class="docutils literal notranslate"><span class="pre">rook</span></code> contiguity</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook2 = raster.da2WSP(da_s, &quot;rook&quot;, k=2, n_jobs=-1)
+splot.plot_spatial_weights(w_rook2, data=da_s)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/opt/anaconda/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
+  self._set_arrayXarray(i, j, x)
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(&lt;Figure size 720x720 with 1 Axes&gt;, &lt;AxesSubplot:&gt;)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Raster_awareness_API_37_2.png" src="../_images/notebooks_Raster_awareness_API_37_2.png" />
+</div>
+</div>
+<p>Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use <code class="docutils literal notranslate"><span class="pre">include_nas</span></code> argument to do the same.</p>
+<p>We can also look in both the examples we used <code class="docutils literal notranslate"><span class="pre">n_jobs</span></code> parameter, and assigned -1 which equats to all the cores present in the computer for multithreading</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook2 = raster.da2WSP(da_s, &quot;rook&quot;, k=2, n_jobs=-1, include_nodata=True)
+splot.plot_spatial_weights(w_rook2, data=da_s)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(&lt;Figure size 720x720 with 1 Axes&gt;, &lt;AxesSubplot:&gt;)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Raster_awareness_API_39_1.png" src="../_images/notebooks_Raster_awareness_API_39_1.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Additional-resources">
+<h2>Additional resources<a class="headerlink" href="#Additional-resources" title="Link to this heading">¶</a></h2>
+<ol class="arabic simple">
+<li><p><a class="reference external" href="http://xarray.pydata.org/en/stable/io.html">Reading and writing files using Xarray</a></p></li>
+<li><p><a class="reference external" href="http://xarray.pydata.org/en/stable/data-structures.html">Xarray Data Structures</a></p></li>
+<li><p>Dataset links:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://www.unidata.ucar.edu/software/netcdf/examples/files.html">ECMWF_ERA-40_subset.nc</a></p></li>
+<li><p><a class="reference external" href="https://data.humdata.org/dataset/worldpop-population-counts-for-luxembourg">lux_ppp_2019.tif</a></p></li>
+</ul>
+</li>
+</ol>
+<div class="nbinput nblast docutils container">
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+  <a href="../_sources/notebooks/Raster_awareness_API.ipynb.txt"
+     rel="nofollow">Source</a>
+</div>
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+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
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diff --git a/notebooks/Raster_awareness_API.ipynb b/notebooks/Raster_awareness_API.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Raster awareness API\n",
+    "\n",
+    "This notebook will give an overview of newly developed raster interface. We'll cover \n",
+    "basic usage of the functionality offered by the interface which mainly involves:\n",
+    "1. converting `xarray.DataArray` object to the PySAL's weights object (`libpysal.weights.W`/`WSP`).\n",
+    "2. going back to the `xarray.DataArray` from weights object.\n",
+    "\n",
+    "using different datasets:\n",
+    "- with missing values.\n",
+    "- with multiple layers.\n",
+    "- with non conventional dimension names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from libpysal.weights import Rook, Queen, raster\n",
+    "import matplotlib.pyplot as plt\n",
+    "from splot import libpysal as splot\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "from esda import Moran_Local"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Data\n",
+    "\n",
+    "*The interface only accepts `xarray.DataArray`*, this can be easily obtained from raster data\n",
+    "format using `xarray`'s I/O functionality which can read from a variety of data formats some of them are listed below: \n",
+    "- [GDAL Raster Formats](https://svn.osgeo.org/gdal/tags/gdal_1_2_5/frmts/formats_list.html) via `open_rasterio` method.\n",
+    "- [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) via `open_dataset` method.\n",
+    "\n",
+    "In this notebook we'll work with `NetCDF` and `GeoTIFF` data. \n",
+    "\n",
+    "### Using xarray example dataset\n",
+    "First lets load up a `netCDF` dataset offered by xarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+      "[3869000 values with dtype=float32]\n",
+      "Coordinates:\n",
+      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+      "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+      "Attributes:\n",
+      "    long_name:     4xDaily Air temperature at sigma level 995\n",
+      "    units:         degK\n",
+      "    precision:     2\n",
+      "    GRIB_id:       11\n",
+      "    GRIB_name:     TMP\n",
+      "    var_desc:      Air temperature\n",
+      "    dataset:       NMC Reanalysis\n",
+      "    level_desc:    Surface\n",
+      "    statistic:     Individual Obs\n",
+      "    parent_stat:   Other\n",
+      "    actual_range:  [185.16 322.1 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = xr.tutorial.open_dataset(\"air_temperature.nc\")  # -> returns a xarray.Dataset object\n",
+    "da = ds[\"air\"]  # we'll use the \"air\" data variable for further analysis\n",
+    "print(da)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`xarray`'s data structures like `Dataset` and `DataArray` provides `pandas` like functionality for multidimensional-array or ndarray. \n",
+    "\n",
+    "In our case we'll mainly deal with `DataArray`, we can see above that the `da` holds the data for air temperature, it has 2 dims coordinate dimensions `x` and `y`, and it's layered on `time` dimension so in total 3 dims (`time`, `lat`, `lon`).\n",
+    "\n",
+    "We'll now group `da` by month and take average over the `time` dimension\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coordinates:\n",
+      "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+      "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+      "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "da = da.groupby('time.month').mean()\n",
+    "print(da.coords)  # as a result time dim is replaced by month "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.plot.facetgrid.FacetGrid at 0x7f68f71b8100>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 936x648 with 13 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's plot over month, each facet will represent the mean air temperature in a given month.\n",
+    "da.plot(col=\"month\", col_wrap=4,) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `from_xarray` method from the contiguity classes like `Rook` and `Queen`, and also from `KNN`.\n",
+    "\n",
+    "This uses a util function in `raster.py` file called `da2W`, which can also be called directly to build `W` object, similarly `da2WSP` for building `WSP` object.\n",
+    "\n",
+    "**Weight builders (`from_xarray`, `da2W`, `da2WSP`) can recognise dimensions belonging to this list `[band, time, lat, y, lon, x]`, if any of the dimension in the `DataArray` does not belong to the mentioned list then we need to pass a dictionary (specifying that dimension's name) to the weight builder.** \n",
+    "\n",
+    "e.g. `dims` dictionary:\n",
+    "```python\n",
+    ">>> da.dims                  # none of the dimension belong to the default dimension list\n",
+    "('year', 'height', 'width')\n",
+    ">>> coords_labels = {                 # dimension values should be properly aligned with the following keys\n",
+    "        \"z_label\": \"year\",\n",
+    "        \"y_label\": \"height\",\n",
+    "        \"x_label\": \"width\"\n",
+    "    }\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/data/GSoC/libpysal/libpysal/weights/raster.py:119: UserWarning: You are trying to build a full W object from xarray.DataArray (raster) object. This computation can be very slow and not scale well. It is recommended, if possible, to instead build WSP object, which is more efficient and faster. You can do this by using da2WSP method.\n",
+      "  warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "coords_labels = {}\n",
+    "coords_labels[\"z_label\"] = \"month\"  # since month does not belong to the default list we need to pass it using a dictionary\n",
+    "w_queen = Queen.from_xarray(\n",
+    "    da, z_value=12, coords_labels=coords_labels, sparse=False)  # We'll use data from 12th layer (in our case layer=month)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`index` is a newly added attribute to the weights object, this holds the multi-indices of the non-missing values belonging to `pandas.Series` created from the passed `DataArray`, this series can be easily obtained using `DataArray.to_series()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex([(12, 75.0, 200.0),\n",
+       "            (12, 75.0, 202.5),\n",
+       "            (12, 75.0, 205.0),\n",
+       "            (12, 75.0, 207.5),\n",
+       "            (12, 75.0, 210.0)],\n",
+       "           names=['month', 'lat', 'lon'])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.index[:5]  # indices are aligned to the ids of the weight object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then obtain raster data by converting the `DataArray` to `Series` and then using indices from `index` attribute to get non-missing values by subsetting the `Series`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = da.to_series()[w_queen.index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now have the required data for further analysis (we can now use methods such as ESDA/spatial regression), for this example let's compute a local Moran statistic for the extracted data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quickly computing and loading a LISA\n",
+    "np.random.seed(12345)\n",
+    "lisa = Moran_Local(np.array(data, dtype=np.float64), w_queen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After getting our calculated results it's time to store them back to the `DataArray`, we can use `w2da` function directly to convert the `W` object back to `DataArray`. \n",
+    "\n",
+    "*Your use case might differ but the steps for using the interface will be similar to this example.* "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (month: 1, lat: 25, lon: 53)>\n",
+      "array([[[0.018, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
+      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
+      "        [0.003, 0.001, 0.001, ..., 0.001, 0.001, 0.001],\n",
+      "        ...,\n",
+      "        [0.002, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
+      "        [0.001, 0.001, 0.001, ..., 0.001, 0.001, 0.003],\n",
+      "        [0.002, 0.001, 0.001, ..., 0.001, 0.002, 0.006]]])\n",
+      "Coordinates:\n",
+      "  * month    (month) int64 12\n",
+      "  * lat      (lat) float64 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+      "  * lon      (lon) float64 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Converting obtained data back to DataArray\n",
+    "moran_da = raster.w2da(lisa.p_sim, w_queen)  # w2da accepts list/1d array/pd.Series and a weight object aligned to passed data\n",
+    "print(moran_da)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f68f4044940>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "moran_da.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using local `NetCDF` dataset\n",
+    "\n",
+    "In the earlier example we used an example dataset from xarray for building weights object. Additonally, we had to pass the custom layer name to the builder. \n",
+    "\n",
+    "In this small example we'll build `KNN` distance weight object using a local `NetCDF` dataset with different dimensions names which doesn't belong to the default list of dimensions.\n",
+    "\n",
+    "We'll also see how to speed up the reverse journey (from weights object to `DataArray`) by passing prebuilt `coords` and `attrs` to `w2da` method. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:    (latitude: 73, longitude: 144, time: 62)\n",
+      "Coordinates:\n",
+      "  * longitude  (longitude) float32 0.0 2.5 5.0 7.5 ... 350.0 352.5 355.0 357.5\n",
+      "  * latitude   (latitude) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
+      "  * time       (time) datetime64[ns] 2002-07-01T12:00:00 ... 2002-07-31T18:00:00\n",
+      "Data variables:\n",
+      "    tcw        (time, latitude, longitude) float32 ...\n",
+      "    tcwv       (time, latitude, longitude) float32 ...\n",
+      "    lsp        (time, latitude, longitude) float32 ...\n",
+      "    cp         (time, latitude, longitude) float32 ...\n",
+      "    msl        (time, latitude, longitude) float32 ...\n",
+      "    blh        (time, latitude, longitude) float32 ...\n",
+      "    tcc        (time, latitude, longitude) float32 ...\n",
+      "    p10u       (time, latitude, longitude) float32 ...\n",
+      "    p10v       (time, latitude, longitude) float32 ...\n",
+      "    p2t        (time, latitude, longitude) float32 ...\n",
+      "    p2d        (time, latitude, longitude) float32 ...\n",
+      "    e          (time, latitude, longitude) float32 ...\n",
+      "    lcc        (time, latitude, longitude) float32 ...\n",
+      "    mcc        (time, latitude, longitude) float32 ...\n",
+      "    hcc        (time, latitude, longitude) float32 ...\n",
+      "    tco3       (time, latitude, longitude) float32 ...\n",
+      "    tp         (time, latitude, longitude) float32 ...\n",
+      "Attributes:\n",
+      "    Conventions:  CF-1.0\n",
+      "    history:      2004-09-15 17:04:29 GMT by mars2netcdf-0.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Lets load a netCDF Surface dataset\n",
+    "ds = xr.open_dataset('ECMWF_ERA-40_subset.nc')    # After loading netCDF dataset we obtained a xarray.Dataset object\n",
+    "print(ds)                                         # This Dataset object containes several data variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Out of 17 data variables we'll use `p2t` for our analysis. This will give us our desired `DataArray` object `da`, we will further group `da` by day, taking average over the `time` dimension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('day', 'latitude', 'longitude')\n"
+     ]
+    }
+   ],
+   "source": [
+    "da = ds[\"p2t\"]  # this will give us the required DataArray with p2t (2 metre temperature) data variable\n",
+    "da = da.groupby('time.day').mean()\n",
+    "print(da.dims)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**We can see that the none of dimensions of `da` matches with the default dimensions (`[band, time, lat, y, lon, x]`)**\n",
+    "\n",
+    "This means we have to create a dictionary mentioning the dimensions and ship it to weight builder, similar to our last example. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coords_labels = {}\n",
+    "coords_labels[\"y_label\"] = \"latitude\"\n",
+    "coords_labels[\"x_label\"] = \"longitude\"\n",
+    "coords_labels[\"z_label\"] = \"day\"\n",
+    "w_rook = Rook.from_xarray(da, z_value=13, coords_labels=coords_labels, sparse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = da.to_series()[w_rook.index]  # we derived the data from DataArray similar to our last example "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the last example we only passed the `data` values and weight object to `w2da` method, which then created the necessary `coords` to build our required `DataArray`. This process can be speed up by passing `coords` from the existing `DataArray` `da` which we used earlier.\n",
+    "\n",
+    "Along with `coords` we can also pass `attrs` of the same `DataArray` this will help `w2da` to retain all the properties of original `DataArray`.\n",
+    "\n",
+    "Let's compare the `DataArray` returned by `w2da` and original `DataArray`. For this we'll ship the derived data straight to `w2da` without any statistical analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da1 = raster.wsp2da(data, w_rook, attrs=da.attrs, coords=da[12:13].coords)\n",
+    "xr.DataArray.equals(da[12:13], da1)  # method to compare 2 DataArray, if true then w2da was successfull"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using local `GeoTIFF` dataset\n",
+    "\n",
+    "Up until now we've only played with `netCDF` datasets but in this example we'll use a `raster.tif` file to see how interface interacts with it. We'll also see how these methods handle missing data. \n",
+    "\n",
+    "Unlike earlier we'll use weight builder methods from `raster.py`, which we can call directly. Just a reminder that `from_xarray` uses methods from `raster.py` and therefore only difference exists in the API. \n",
+    "\n",
+    "To access GDAL Raster Formats `xarray` offers `open_rasterio` method which uses `rasterio` as backend. It loads metadata, coordinate values from the raster file and assign them to the `DataArray`.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (band: 1, y: 880, x: 940)>\n",
+      "[827200 values with dtype=float32]\n",
+      "Coordinates:\n",
+      "  * band     (band) int64 1\n",
+      "  * y        (y) float64 50.18 50.18 50.18 50.18 ... 49.45 49.45 49.45 49.45\n",
+      "  * x        (x) float64 5.745 5.746 5.747 5.747 ... 6.525 6.526 6.527 6.527\n",
+      "Attributes:\n",
+      "    transform:      (0.0008333333297872345, 0.0, 5.744583325, 0.0, -0.0008333...\n",
+      "    crs:            +init=epsg:4326\n",
+      "    res:            (0.0008333333297872345, 0.0008333333295454553)\n",
+      "    is_tiled:       0\n",
+      "    nodatavals:     (-99999.0,)\n",
+      "    scales:         (1.0,)\n",
+      "    offsets:        (0.0,)\n",
+      "    AREA_OR_POINT:  Area\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Loading raster data with missing values\n",
+    "da = xr.open_rasterio('/data/Downloads/lux_ppp_2019.tif')\n",
+    "print(da)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f68ef4c5ca0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ItFnyp5U9nWvfhjy9xgnXmk4cpl6DOAbfrFp9Vem0KD3zEouGC7kcLYN+tEZzG2HAmAdytqmK4FQ87hCL4KIXHHPPXu4E0bCp4/TjjhMUGdlmE4xAkmBb7cnqkzmUsfQSB4GjVim6UA9dRL4AbOFcq56qPigip4F3Aw8AXwD+mqreKtj3EeBtQAS8Q1Xfssix7gcZTW8vTSrY4z57Pe4Iz33u50mRGvEZ4v5SrSKNOlKvQ6/nTzFihTDmuGUsvcQicafSFr9RVb9KVR/0z98IfEhVnwd8yD8fgIhEwP8NvAz4CuDbReQrDmCse0KWqCy51MOYQrEx/J+tILo97I1bJDduzqfgqUSJuWO+pf8i8gUR+biIfExEHvevnRaRx0Tks/7/qXHHOIyQyyuAd/nH7wK+pWCbrwX+UFU/p6od4Of9fiWmgEQRplbHNBqupVwYApoUGvHhI1OrHwyVMVSQ9H+22STZ3nF89hnYLaNQeuclFgXr+4pO+psBMzvAIRb9i1XggyLyhIi8zr92QVUvA/j/5wv2uwd4Mnj+lH9tCCLyOhF5XEQev3bt2hyHPh5H2Uik/G6pVpFqtf/GNMlXH8fOOh8dlsZMQY4h1U6fZUxH+XMqcXvDsVyiqf72gWkc4AyLZrm8SFUvich54DER+fSU+xVNaYXZPFV9O/B2gAcffPBAeW0TG1ccIjRJXEs5mN3L9XF2xzhJXI/SsF3dIZTqm2oVqcS+h2kydUejMo5eYlGYsQXd2TSM4vF2b7sGD+kcYAX+jX9/wAH2tnQkFmrQVfWS/39VRN6HC6VcEZGLfnAXgasFuz4F3Bc8vxe4tMix7hUTjfoiKImTsJ9WdDmYivuK2G4wVy6Shz7i2NrrZrRHiUBtbzFjKFFiBswQTrkehFFGYa8OcIaFradFZFlEVtPHwMPAJ4BfBl7jN3sN8EsFu/828DwRebaIVIFX+f2OJB6z7x3t/U3J7jhySGPaYrLwy4C87ryQarB72mdGgcwVZKlVbLeH9rrjw0Fj7uVD5pVHdkVV4vZDynKZV+l/6AADAw4wwBgHOMMiLckF4NdF5HeB/wH8F1X9APAW4CER+SzwkH+OiNwtIr8KoKo94G8BjwKfAt6jqp9c4Fjngj0v6WfheB+g8dckwbZbWel91px6zmNIK1cH2EIhayigPobdjdJ9w79pkBr20riX2C/mxXLZpwOcYWEhF1X9HPDCgtdvAC8peP0S8PLg+a8Cv7qo8d0WSHnZqeeaGrRx8eNAjxz68e59U/8WGDYaqZk+Tis+XT2UcgAlDgmqQm9+laIXgPeJCDi7/HOq+gER+W3gPSLyPcCXgLFeSFn6Pyc8ZF6ZGdN5UO3S/dWagXhx2FFoKPyRGvNKjHZ7EBlotw+Mxz2k95KPiaeCYeF4ppDYHTmB5Y49zfFKlJgn5lVYNKsDPAqlQd8DhpbqaRw4MLDOcM3hZKlh9MdMmSeAY58YARHEGNRapF6DxEK3h7bbcxjADENNkkEjHoRMilYNUx1z2m1LQ17igHEUK0VLgz4DCmOuQTx50BOOhj3UvSJjd/i+m7WaM5CnT0KlgtZiEMF0E+gl2EvPZGOZW0hiWg94FEslSZxq5Azx90L530NASX0sMQqlQT9uGGPAjC/qyZJ4k5KfI9+SLF5slpeQagXOnCJZqbu0tiX7H126HuxTgf1ozAxc0JwkfGc4zlCoaNwEuaCQS34Sf8i8sjTqJYCZeegHgtKgB8j/eMMfbhojn8kgWcU06pilBvbWOrbbS98o2tj9D/ttpuyNRt29dfokRJHTOVmqYlodpNUFa9GlmitdA5DUsw2YI6MQhIqOXG/P/P2eqqXd3nMYj6x9ty9cgkdbP8tD0bcdTh1BidsGM5b1Lxx3vEEfR10bem/GH7YmiYtjq04fOkjVEFOGS73mPHJV9NoNtNN1yc7LV1C1qBikEiNLDRd+2dmFXm/6zkbMMSwzL+yFFhncN3cIl0wN8xpFyeH0M37MvtfJ9Pp9H668aigHkPLhS5QA5z/1ygYXRwMHxUFWq6jX885CJ9N2IBID1mI3tjJj4op7Yq+DYKDXw7bavqCgiXZnM+b9Ux2NePVEhK31xqx0UmbMLInpD3Z+DoCHq98x9l6UYZcSKcqQy1FD3hsMWCVziTur5EIAU1oYtdhOB3wIwA3VMWe02Qw2c8dOdnYHzzvLGJlRJiBk9PhK0qkrYqcI/2SFRgX9UKeuVJ3xs8tCavSLlcLPLu/dFzKdgMeSd8903hK3L8oY+lFEyiDJ4sjzjZnuiwOeG8e+aZC5tnemEvcLj2aJO3t+fCoFMPW4xrWgS6mNVS8B0G47/n2OBpmFVabpYDTr55if3Iq6Sk3YNwzhlDj+0NKgH01khveASusHZGC9QTWV2CVOF9nByBtyAOIYY3xjiSRx7d6mPb83rjNPWCOOnXrmEkVgJrS0m+X+5Hu35vfPJ4UJWEW93HY+jo5Usu2yRPes4ypxLHDUkqJHK6J/kAhFoPZIq9vXuYF8IdJCjXkKH8qx3R622cI2m+6v1T6Y848cljrhrXYb/CQxrTZL7kD9v/R5/r288FcyyAZK9eTzxx2in/rJUaLIyfvGlew7VerEHH+ozlecax64cz30Q/amUlqhq/b0pfozdLWfBzKPsz+oBZ9wTKjEh3EQdQZ9XLhjkZz6HLOlMPmaeuuRARUkrdw10o+7H0UFzRJzhpCULJfDR+Y9HZb+R8DESHnPBzqGNOySesBJ0o9XL3Is4+LeqZpizxYmWbMwSF5eYB5Ij5cLv2R5heB8EkWY5SX3eKmBttuOxdTrDYRsbgvGUIl9o4yhHzJCES0Xr/UfyCzx43lh3kaJfhhnUmxbkwSsIpW4MI4803lnvY5piplyx3WGfEFGMp90LUKQRNZu1xl1ESfDUHeFX/R6rl6g2epP1CWOLUotl0NGPq6pSQI9i9RqEEWIVcQsODG5KMxCP0zDG2klae4Yeznv1PATz9iq1DCJuciQ0CyTkVpst+e873abpDMo+ZtOUFKtQrUC3bKj0rGHkhVnHxXcUQY9g1/aA24Z32z135pnGPuolo3PMx497fmKkpSTMI7mOO5c055n1MpgzP79FczgF0UTb9S7PbC2v/IrcaxRslwOCSNZByErIs+QmBXTdqYvarNWtNmMHe4njS39M/Uaplp1zIxxTJIpxjgVUsM867GCQp+Z9t3PJDXu8x/1XcmzYxKLdns8XP2OvY+jxJGH+qToNH8HhTvTQ18gBmLQk0IK0x5rHsnKIIyhQUhpbMy8iGmy1zHM6GWHicWZRMPmUbG6F+RCXhJFaK/Lw5VXoVbLCtJjijLkcsA4UD5wvoBlXsebM/YcNw853PMY2xjDOrJydR7GeI9JXKlWs8lw1CTjqIsWqdSyuHuJ44uS5XInYJLB2K9kbVF1Y9r+bZKuyl6M4V5j4GOOl9IQM972PD3w3HnmKQvs7nMEvW5hviUVUEuVG0scX6iWBn3hmKtHvsCkpsSufDyjt02bqEy3y4x6UL2YFsPMo6FF6InP8R5kOYY4+Oq12wxo6Exxvql6t2ZsnnSnPSaD/XHEV5ROYug4vZlgbGWR0bHFUaMtLvybJiKRiPyOiPyKf/5CEflNEfm4iPxnEVkbsd/3i8gnROSTIvK3pznXZ5743BQDMkhccX+TSssX9WNMy++LjPkU+zqWThfb7WXGxbV466K97vA+YRJ22uTkAsM91vc7ddIDrT0V4aRho8zTH3VN4YQ0bdK76Fj+M5taWXLOE2GJownV6f4OCgfhOnw/8Kng+TuAN6rqnwDeB/xAfgcR+UrgbwBfi+uE/U0i8rx9jUKGGzlnr4/DvOPi+eOmw0gnl2mTekF15aRjFxq1g8AYI5sZxlFGdprJR3MNJ45w7uKh6NvmfswShwtFsNZM9XdQWOiZRORe4C/jjHiKFwAf8Y8fA/6Xgl3/GPBbqrqrqj3g14C/sq/B5IzaVH0+YWAiyFrCzZNOGI5vlm1n5WbPEj6adfuiffcTrvL321SrmHqtL3pVhBGTwth9ph3DtPTTSfBjKwW7jh90yr+DwqKnjrcCfx/XxjjFJ4Bv9o9fCdxXsN8ngG8QkTMisgS8fMR2iMjrRORxEXm8y+RE1ICy3ixL76DCcS8dgYoH0zdEczvmqPPAdMYn7/nPyh/fL5ffH0OTxIWkkoRUd33qcaSaL1NslxntvRj/MqxyZ8MnRaf5mwYF4enTIvKYiHzW/z816RgLM+gi8k3AVVV9IvfWa4HXi8gTwCowJHqhqp8C/hnOg/8A8LtAIf9LVd+uqg+q6oN//E//sYU0Fkj1w4+s4NIkYzSKaiimX1wUGrV88vCwjJan/YW5gmn3A0ZPAmmBVbWKVKuukXe9NmTY9/15585dNr04hpivi54PT78R+JCqPg/4kH8+FotkubwI+GYReTlQB9ZE5GdU9dXAwwAi8nxcSGYIqvqTwE/67f4J8NS0Jw5/OPtZ5ooRL28rSMR0Xt9BIxWN8mMrZH6MiteDa3JRrbrNkgTtdA5m4pqWcbLHxHR6jRJBETXSdVpKIME14R53/rEnmhBaCt4Pv4ulcT8emBdtMQhP/xjwd/3LrwBe7B+/C/gw8IPjjrMwg66qPwT8EICIvBh4g6q+WkTOq+pVETHAjwA/UbR/sN39wF8Fvn4v48j/cKY28HkjIsLCZVH3ULSTGibTaKDWIr2ezw8UGJpQGCvdt9MF36QBq54OuOBimFwl6MjNpqEmjtgPcEqSgHZ7fkLub+P44r66My+0FZ53EvbI+S8bTd/+UMDaqQ36WRF5PHj+dlV9e/D8rbjw9Grw2gVVvQygqpdF5PykkxwGD/3bReT1/vEvAu8EEJG7gXeo6sv9e/9RRM4AXeD1qnprHidPf0QTDXvKYfZe3IGEHfZwjrR1nSaJM0SxM87aag97j7njp4qLqB1YFTpju0BtdB0jU9sfhEtAxyZjs8ySZ8gKrwKZg3wxVl5oa6z08DhPPNWcCRhU00xEpVG/zaHA9B76dVV9sOiNMDztnd8940AMuqp+GLdcQFXfBrytYJtLuORn+vzPL3JMUxl2tYN9JfeCKdgeWZXnXioa0/Lybm826mOwXWZcA3ExCY99GDH0lJKo3Zm99DQnoKncbTIYDx+grqbGuBJDUKAV8vkzjz3/WQbsJwBpNFz4xip2a8vduwmff9lU+vbGnDjmheFp4IqIXPTe+UXg6qQDHbtK0VlwIDSyKYxQWi6+3/NMPfmMCe30GyGbw0sCB+PbS97Cdjr9sE6we5oklTTEBKi1mBNrrntTkjgp5bZjS6ln2BTeh3Dyq1aQeg09exo2t8FaZGkJ2drqTwT+ekYhbbwClEJetxPm8BMZE57+58BrgLf4/7806Vh3rEFfqDGflYOdD0HsYf+pEcSvnUzAoFCXbbcGt52XlEDBOEbF+PvDKfCKZzi22r5mjEQu+SnGe+TLS9h7zmEu34BqBV1qINdvuq5DPl8ixoWyTLUKkXGxdg1UIH0ISJMEmi1Mkrjfd2Kx29sDE3V6r4dWYaGXH0VItdqX3Q0Kp0ojfxQxPSVxj3gL8B4R+R7gSzia91gca4NeZLQfs+9dvGc+L+M3T2XDFKGhm2bb/Yacxhw7wx5olyFTZ6QXrxaI3LXGMebsaeyZNXon6qgIveWIykqVaLeHJAmcP42sbyOqaKuVJYpts+kMff7YuXHZze2hcQ6svsTnLAYuzbWxM42Ga4ohgrTaYGSgjV0pw3tEMedFbC48fQN4ySz7H1uDPspoL8yYz1PZLyzTXwQ0J1p1mCgw5vnJZijkkca9Pd2SPNUyKIpSq5iG286eWqNzZglbM6iAGugtxYhV1MTEGyCry7DbhHsuQLMDzRZs72Sfa/7zHXyeZOfNxpGGriRsIJ1bdfR6btIAzPlzsLWNhtz7gkKpTEog9x0pY/EHCAWdnuVyIDi2Bn2kJ16Q2Nqv4UyTcKZRd13gA8Gs0TuZwZDHqPDDNGPbiye/n2ue9p5Ns13g6WbyCo2Ge8tXiiLp/Rzc1SwteU+2SrK51eeWDwzB/+CiCHP1JtXYIK0OGIOtVRBrkS9c8glRxXa7iDFIu4Nd3xz4HIcm66JVRm4V0f8uDE9UEKwuvCefPHVpxErE/R/QhCmrVI8ASoN+uMj/CPYQf04fhzFO1IKqM0bdLtqeLEOQxmkzNkQ4nimLVaa6hhytbt+riDH0vbQRhDNm1idZ3T2yzWZm5AopiClPvtPBrK4g589CpwOtNrrbzCbLzMD2ehAZ7PZ2/7oCj9806pjlJfdekmBv3ESvXsN645mxYSIDnr8PfhXtPebsuOOue8T9GZiAxlA11U9a+fcHVnzz1g4qMR8cseLxY2fQZw2pSFzJPLtpDZ37gfc96/SHmGxvz/TDcz9kkzFLpuI/71VzBEYalLkjSaBWwyw1oBI7g97tIdb6xGKgE6M2S1y6oSpCgt3YQnab2D/+HJJ6TLzdQT7/NDRbWXs3bbUhMtlniIibSMVgVpa99+7oh3Znd2A1lKcyjppcsgl8mpXGJIxbSRV8xgPfx9xKZhTzpuS2HzBKg36EkBnLyFVadnt9jfIRP9BpmipMhSyOPYYalx4v/SGnhv8oasrkVi/abqOdjgtfnDyB3diExE8sAcMm9ZJDBgpR5Kh/9RrmD74Ez72P5sUlarVnET91Hd3YwtRW3fGMYDe3nQG3CstLIOIaNasdMuQHcR+GzjVlZWyGvEdexP4Zs0p6uPIqPtj9+alO9ciJ17oJN4og8udJLNrtop1udu/KZGwBZissOhAcO4P+mH2vizNOEYZIl93OK2TyD3/eBmEazzkwlAdqmPaC1LNMEkQFaTScNsyNmy7Jlzal8BNTqpWjSeJCHt6wkyTIyRP07jsLIsRXN5ELDXbvqmHO3YNYpXa9jVglurKBdLrY7R23MkhjztOESdIK0igaTlTudVVT5OkX0VKnGZuY7P7kq1zH1hLEFV7a+C4ebf77kYd/KPo2l69IKaxGMi5+eh6pxJkD8VD0baVRL0DZJHrBCAs0xiL9kXmDaVvt/UnY7iUxOeUP+8BCJdMiP+4gbJRCE7CbW4W7h6El58WnUgOJ0940gt64Sdxu03vevbTvO0VnxYBA42mXm4h+/wtgnWSBrK6QpOcqaso84TNZqOhakcHNjHNx0jirPK3V3KolijIKo/GPs+bT4WehNgtHjevGFSZW0+NowtC9y9colOGcApQslwPADEZVe110jLwsOPncwkRe+n695nbvdEb+SPc7zpAxcSgIJskBg+QeOL76NBNUwWQ7IJSVgKiPqVerRLd24dQSSIXGlS4qUP3s5f4EDOhW8cSxL+THOcLz3gvS3MnIsF6qy9Ppem66QZYbSL0OXZcTMPRDL2EyOPW2H2397NBxH668auB6pqkxGJA9gNJTz0GOmId+rFLnU/UUTZFqX09hhNKYb5G+dpEuyMjns8Ivuffk/c8D/v6kJe6mXnP/l5bcX8oDz3mJY483AmFuIu2Paq/fRG7cwux0iFpK7RNfJPqNT5Bcveb6e47qoToPpNcy6pomXeek78KkVYN3ILK/Zgt712nsfReQkyfAi7AVeeJF8fPUmI9dhRaMO7+9GBmcGO5kTKuFfoBG//h56NN6TtMwR9I4as8WGw6f2NRW2x8mldidzzxZqN63vwMOvzbmXqUUREQwJ0/A6jJqDFy5Bj5hNjbZN+OElte0SQ2ZfPESq1frWVz8UFcqkzDhu7TnY1hFugmd88tE9XOYMycwT13B7u66wqggPBKGVAaaaO+HqZMmav1xSk8dQMqk6KGjyNudByc79GbTL/+4opSpDh3QI/eLwNPGiCtp9yJWheMKxaeWGujZk0i7C1euods7k69rgjGfhvWReqdSiT0LIwmYPvsvCEvHWaRtUzi+Cfz78F7ueQIuqpyNIhdyuXSNWscpa7Kz66pLve5Maljy5x0IZ+3XKQjyORJFpVGHA/W+p8GxMujP/9PPgd9hmEOcw0Jof+GXvRJ7TvSM0q+zFBcF+4zV8c4284JTqXG0Ful1+/HR3LmyexfHcGLNGfNegqyuoDtNQh7+yOvII5zoQoMcbD9wv8QV/Ig0HJ1AFbOyjG02nSrinOAUE30islrp67akFapTTMap4ZSay6fQbs8+6YzIL9Drohtd91lv76RvDGwz8TstBjGDRICx++Q/m9x3Mv2u3/GJ0iO2UDxWBh0GudoDBu4g4tCpUfdsg6z7zxQUSlOtgtqpNLSLzpmdbxJjI+Qbpz/yUT/sKMJu72BUHaMkkJWdyhiMey+/QgqNWd5YqUW6LsRjN7fn/hmmevIhBgzeNKEK8ZotkyqEx322+ftAv5+tY6MMx7+ndU6y70Xq7MSVrGhdfIOUVD9+4Ds4QO3Uws/vjjXqJQ998RiZJDuomKs3sJOYDIW77oNnPsmQp+Eb9WXy6QRgO0P15v3EZKsNakk2Noa3ITV6c5ooR+U01KI7TffbSVcU8/wsRzJN9nPIMWOcMo7d11r3OYw4diJe4cQzTR6IYaMvRnyf3KjP2+90xmoLFX2/shqOIFE6bUHTLHi4+h1Z8ncct/4wcNRYLsfKoH/mic/xZ+TZgy8eFkMkf85xCUnN6ZDnMSNDYtRYBnpqjpWcnXweU4nduHyce97Iio9qNaTimjjrToLr6jyn+Pk8EGrHBM26Zx5jQTWoWi/rG7y254kiOI9adYlUErfyCipCZznW0EQRRTxy4rVor+cqd4H3X/nX040rh0zCIw0P+Wbej5x4LQAf2PipPR137rjdDLqI/C3gZ+fV03PhCPnSaSVckhyp4pw+r52pwjFhwu2olP2P9BTnDG23XRx9aYno1ImsInTfrfH2OymEUgfp82rVhW6yxhczHG/E5D+XblajzmcNmvT2vjLMEsDpeBMXckq7Qd11loe/9h8SbezCzi66tQ3GOP0dMWivh3Y6PLrz09khi7SYMnJA2/ZrRkoUYhoP/S7gt0XkfwI/BTyqetQKXh2e/6efw2OPv5uHzCv7gk1h0+QUI348c9MzDyF9BUJg2KMdwzIZGBMHnA8YhwM4b0qJlGrVeXvVqiuqCSe0RVXnTjVA3ygkijJvVKoVaHfQnd3px1E0lqFwx/6GWnTssE1f4Rj2iijCnDoJSw3sUhUSJTmxBKeXkc4psGDaXbi5ju46eeSHq9/Rd1byv4eBfIqvJoYhLrzEFVBbWFAVYkB+eA44aiGXidOdqv4I8DzgJ4H/FfisiPwTEfmyaU4gIpGI/I6I/Ip//kIR+U0R+biI/GcRWRux398RkU+KyCdE5D+ISH3ai3rMvjeLA/uDZdWcA+fwHryp1zCN+thy6b0i866TBET63ks6cYyiDGac9mC7UeGQoEgqFbva56D7Mdy4spD7MhbBtUmj7pg2vR7a67kEntWsyGkmhOynsIhmH/crZQ5JrQpLS30aYVi4NircNurzPyikv5E9jCH7nklfE0niCubMaVheQisx0fUtoi89g9lpE12+idluI50e3LjlPstGHXPxgvvtpQ4YjGdJ+b+BoiurWX7lkROv5ZFT31u4+8OVV833u6y40v9p/g4IU8XQVVVF5BngGaAHnAJ+QUQeU9W/P2H37wc+BaSG+x24Jqi/JiKvBX4A+NFwBxG5B/g/gK9Q1aaIvAd4FfDvprusfoxYmy1MJe6HCPL0vIyNspgflhtH4paKI8YwvNOY5Xp+3/QH5T2Uflx7ivMUIP3CR+fPudPtNp3eSvDjWbgRSg1vFCFx7EMYXms+jrMEoXZ7mHrNSS5Ms+oJWCST9FSmRVr8RBSh65v9PqPjdMwPe4U1DSaMMd9aT6oVtwptttD1DXdfUtXLm+tus0qMWVlGW20XZ7/7LrRRdfr53R72ytXh7lOzDLkSuzGsrvCyB/4O9BLe/9SPAy6xmq1055pUn9+h5oFpYuj/B67j9HWcMf4BVe2KiAE+C4w06CJyL/CXgR8D/q5/+QXAR/zjx4BHyRn0YGwNEekCS8ClaS5oCGr7krhDbymadFwThUVjwT9eTRLXzMFadNczfcYt60cdx/+Ykms3soIWc+ok2mpBq42kTIt5X08RF35lue+dt9qIMaCaKQKK77uZLtXDhszptQx5oOFkmfPQpw25DXQisiZrH5exRCYlllNDPymOP6KeYiGTap6bPsP3RYzFNluI16LvH1L6n4E/ZnKrl90n+9Rl93q14j5X8cwbktkmWv850O25HEvK1Y8ML13+69g03LqA3+BRC7lM46GfBf6qqn4xfFFVrYh804R934oz+KvBa58Avhn4JVwX6/vyO6nq0yLy/8Z1um4CH1TVDxadQEReB7wO4P7775/icsITzRZ7TZeWAwbtCDAu0h+VNpue5ub45QPbTBuH9cYmayKxmSA7zcVL9xZ4z3ZzG+NbwwEuD2Ktj68P9tlMwxwDBVGdHIV1wmeVeZ1SGR2KSPnmAUMpTQqqLVB6TM+bXmPR8zHIqnsjN5lljaPn/b3LrwrHVRCnhjqQYujL8Nr+89x3rl+16idcTzHOs6T25KGrHVyVikHCsGuAx+x7EZlTGOSIGfRpYuhvyhvz4L1PjdrPG/urqvpE7q3XAq8XkSdwhn7IPRaRU8ArgGcDdwPLIvLqEWN4u6o+qKoPnjt3Lnt9roUOqTGv1ZyhGBjAcAjkoCFGMCsrmLNnkBNrLiZZrWbGfey4wtcDQ2Pbrcy7CoWw5uEdpvHWoVh9yo2m74Xq7i5Sq7pYurX9pJv4MBODnqDtuhWEbbaGf8xFIZAC3rf4Yp6xse9xrxXFz/Ne+yTPPFcBLMYgcewldfdojEbF84swYeLLb5OFmoL/RRWs6iUnwvez71YqtrbX3EJwXsDlq8JJXsz8C6DuIHGuFwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PGXgM+r6jW/3S8Cfxb4mVkGMM2HN1XLupTRAO6HFXZuz5fe54xGZqD2o7U+DXzS1d5az9q8jVw+5yl3BGMMMSpcMCtyRiQrmAFkeQl6CXZrK7unUqu5ylkfJ0fVTaSV2C2rr10f1JIZOt2E0EmBUU+bOGu4+poiHJKeD3CTUiUe1CufdJzcMbNJLY4R0w/n2GZz8FrDxHHaG3WSkuKsKPLSJz3PvxfQbkfWK4ST3hzyC07bxrpGKUZ4tDX/Yidw4ZbbMeSyJ6jqDwE/BCAiL8YlQl8tIudV9aqPwf8I8BMFu38J+DoRWcKFXF4CPL6IcaZGf6JhVx8nHGMsstiqh6nXyPpcwmzL5BmKidQqttVG7PpAs+Nx+2WVrItGmOBMDV+1iqyuQK3mqIhxjGnUXUItvVe9nvNGV1ey51jFNlsDk1XR5zHUbDn0+rONhg2T0znJfbYTEqwSRc6jTxLMvRfdW09dHo6nTzJUYb0BznFIVyEpm2NohSFpqGcKEbdwHEVGdtRKc7+hnSwklQuPpY9rNRdK6vb2nMgfPqX2V9SLZlgfsQYXh8HS/3YR+QzwaVyi850AInK3iPwqgKp+FPgF4H8CH/fjfPsiBzXVUqyI5pWjUIXLRe10nGFKqVKzeGpRhKnEjlKZhk5GhQBwBto2W4NsnlHnK1r+zzLRTDsRpNdRrzmOtvc8iWPs+oZjNbTaTr1xe8eVoy81snZ1ycYmybUbbtuNTefFh0ybos+j8HK1/yNPP4s8bXEUjbFodVNADZVaDVpt7IklzLkzZGJo4bYTvNmQhmebTRc26nUHrzH8rMLPborPUOIKpl4jWl7CNBqklN2MJRVQX+fGTy+4l2rV1Rc0Gm7iC9oTDn23wrDcNJTcYJKzzRba7fHSxnctTMM99dIn/U08jkhdRP6HiPyup2v/A//6aRF5TEQ+6/+fGnecAyn9V9UPAx/2j98GvK1gm0vAy4PnbwbefBDjS/GYfe+gpx5m/mcxeGr7ib1ZGTSp95SGASDrYJN5keMwIpFV+N4Eb7FQ3IzUw8p5vSO82EzXxsNUYmy7ne1nW+3MoGiTfvwat8LRfGn6KE9yiqX/kCc7KrSSn7SGJvDgaTqsbo9obRX5wiVnSML48SxGUcNOQlNmsiexmdKJdWXZMYci4+mEtwYE1/ISwPtG/ljBOLXbyxK803yHpg7FhCu1Xj8pupBqW2CO8fE28BdVdVtEKsCvi8j7gb8KfEhV3yIibwTeCPzgqIMchod+pJF56uFydtKXqMgLmUbjZIS3kSWLfJIv7eAzdLyQ+pb+5eL3aWHQUDHNuHGMYAeIEUyj7roVNRoFsfjCixn4SxNiAz+8tDuPZz1ItdL33Nrt0YZ3Fszizc7g9YbjsrfWHRMnZFAEoZmFhLim+AwyB6Hddlr43Z5bFXkWUMhWGZz8+ucYWsVMg1EOhNrs+5xf1Y491l4+f7/fIkTDmNI7n8ZDV4dt/7Ti/xRHDnmXf/1dwLeMO05p0AuQGfUZf9AzIV1Khi3msrdksgFIE2j5tnhBrDRrNuyLPoZ++MHyWuLKwLI73GbIaFRiZHWF6PTJyZPFOBQYTu10nEduC5gSgVGRKOq3wEvfO2jkz5lYUHXx/jBpmUoY1GuD93i/Y/aT+SSjmDkIqUywMdlEmc8rpKyTodDOfscZnGNhGDPxTEV+2AumZ7mcFZHHg7/XDQ3fVdV/DLgKPOZDzxdU9TKA/39+3HCOldriPPFY8u79h18mwR/HJdD6y0LTaGRLYGfURi+DR45Hnba6qcT9RGNwLQMGp1bDrK6Atdid3azBQxiPzwSYOt0sQSm1qlvCN1tZcdZedUfyrJV0UssfL1N59F2XTKOejXfmStZpE8+TwhBico3CNRtr9nhtFZadNIDeuOUKYKYJoY2D2umah2erRkiuXutPBEVJ4txx5tKcfJFG/DDOE0CmP+V1VX1w3AaqmgBfJSIngfeJyFfOOp7SoI/BUEx9HAqoV4U9Qf37plp1Rgmc/kXYgafqmg/YGzdHn29KY5AVQYVGKWfItNN1lZjViout5qr7BrZNElBBt7ZgKzhWevxZvbEiyhr+fkm/YGXg/CS+M5BvG5dew5QFKenKSJMEqVb6Mfr8Pcrft1FQ20/sBYZSbT8hrq02khr93d1hg7oX5EMjk6AjmDzjdikK8wXHmyvmGb+/jaGq6yLyYeAR4IqIXFTVyyJyEee9j0QZcpmAMPwyVso08LZNteqYBEtLWbx5IDSRxhC7PSSO0Y1NV/683AAj6PYO9sbNfrhh1i95PrYejG/osf+B2u1tetdvuiTuhKX2gChSYPgzNkLI8JiEEZNMur9pNDDVqmP8BAlTej03AUHGu58K6XmMuCRhpYJUK/1jj0uIjrwEze7bUCFNeq86HfTmOvaZa3vTxBnFcNpLbDn/+c8S/tnrOWcd1yjMGqYKQnTzVloEZgm5jB+myDnvmSMiDVwtzqeBX8ZJr+D//9K445Qe+h5Q2OotSKKKWtRaFwoQ48u2B3tTqlUkIuNVpx56ymke+MFPm+FPkd9uzP5Fyc8hYzOG8YGEBSMBt31GDzd/HrXqCkNSymNikXoNu7nl2BFiMm1tQ9152VMyitIKU8d/7vYnhLx3PglFq4oR1+VyAwGrY9Lx85/ZIgzo7egRzzBxh6J1U3H1Zx7LdAnPKXEReJeIRDhH+z2q+isi8pvAe0Tke3D1OWNDBqVBnwJh6CXkM+c1PVKkPSqdN59Ar+BHPBQyGQ5xqFXnOUZRJkg1SjVyJIKEZr8RQX8M0+w/FgPjNfR/NHvQfskZdjGSNUPg5Aqouli0tS6EsbriJstuDza3+k2vJ5wjpZJKMAGMLTqaZswTtgmbM089WUyLKWL82TEHvg9HrMxxHgjIAEBA+e3yWPLu+Z9vTrdQVX8P+FMFr9/AFVZOhdKgT4nH7Hvdkm2a2HWQhJpmu4GXcgJJttvDiMk0ZEwU+cTbFF576qX4vpSEkgBTGowBxo3a8eGCwGDM3JYuMDTZDzL9v7qCNmrOb1lbRnbbyPIy1Kuw20Q3t4ZL48ch/HyCyWNu/VHHnDev9JgvOttTO78pvgMOUT+Ml98v3E7GVJAeBKRfyTrT9zX9vqd5Cz/Bf7Dzc4sb6xGbE0uDPgOGmC+LhjeQmUe5B3lTF0MUxBioVoYZL1MiTejR7SJeXqDwhxYyYiaNy28farsASKPuNMbBdShqO9leAFaW0JUGKoLZ2Mk6GPVlV2f8haVhEzFIFIx/0YYsW9nJ7GOegKHcxTSGMfjcUqXOAc2ifQ8qp3k0Dn4spuLO7xQcB4kGY3Vh/HgXwj0PT8VMLJcDQWnQZ8RMzJd5IAxphBVzo5Jk+ZeSBG0WFAnlfvTjfmSaJNBselZIddDLmzDmkeGAXGhJ/LGJYxdiadRdAUyz5ZQWk8Rpv/QSpNVBdnZg1YVhbGtvk1Q4loGVUZDgzucC5t0Qe6gadz9x7SI6IjAU/hqVU8nyO2Z4bPuBmCzpPMQmGgXN9TGYFPpLVzz+PGGf0oXhCIpzlSyXPWDuEpwzwvgiFVOJMwbIUIER9BN9BayErNhkgnHOtEW6PbetZ3NMzWKZAbbVxm7vOM0W36TAnDnlVBZrNbAJdDrYS5ezZtEsNYjOnx1hyPaHgcrJZA/e/0Ejl6QdQsBmGVtduiAWy746XvkxjavAljTXNOdJd/y4pvw7IJQGfY+YaNRlQrn3XqoE02VrGmuu1QYohNN4Pf1DSf9HPc1+4Q/K6uBKIaCGDV3zlAnATOIglDvodNHdJlhF1zewz1zFPuNpuEniKjOTxHWT3y+KJj7NcfJnwZiqxYljGHG8aauHTb2WTfiDb/c/84VNTiPGZwOe/qIQ6uEvhKJYeNIp/w4IZchlEQgTfEVxyHFhkzwKPGu6vo1Xuz178iqfENzHD8wZiP5XaF8VhUPX6ah+0usiu7uukEiMK0tPufPXbrhwTNbIYAovfdZr3sv1pCEG6WuUzyXhKiGLKP96enj17fqG8y0D38O9OBR+v2ziTicHX7w2Nlm+SBR9pnu9vllPfcQWbaVBnwMkMyiSxQgnqgNCFibJysa7vX5XoKGTBGyM/LKz6AczC/87uIbsrTHe1ICnl280Ma8f70A8N3Gdi1IdlCChaLd3nFaNbyxR1PosGPjg/3mNteBea5IgtdiX/1sIFCb3jBGslCJDWjiBjHo8A8SIi1OLuF6z2zvOoB808oVoIVXR3+dUMnehydHSoB8vZIqGjbrzWJqtvmEZ8wPOBLFSnjk4PjUU7zuOCpnjGYfbz3QtlTj7caYSuYXGIh9PXqQHFni36X3NEEUILjQj1Qqm0XByvNGICWaBdMSiMWvYEGUWSLEnHWqGh3RSgeH2gNPy0meEWoVOx60Qd3bBWiefsBf21F7Hkb/+9LeXqnPKgqmKKbRkuRwrPGbf67yAMK4dRdgJ+s6I1+BIeyimEJNVtiE63FUo5HnvdXlb9CNKvf84diyWbteFc45gEjCTX0jpmEtL0Gy5H3clRpLESSpME36ZBvn7Ne7+h9uGn3N/8NOdK4+C/dLCNqmaTH9nauxnYlML+ErptG/rmLh/4flmzbGMOnYUYc6dARNBuw0ry7CxBc0mL61/p9sujnl0+13jj7UfHLGfSGnQ94mUcqettmv/lteR9l/YgVZ3Rd62j49mlW0MqvU5A1EJduhvN0speWaMisIEo7ysKSmSc8eIH37GTe50+7HcSoycO4NYdYZmewfd3R1Oas467jxzRHM8+7AIBnd/M0G0cYZu2sl4xGtpxXARJXXRyMKKKa1w1HWOMOQDfXb3mMfJVmpWYWfbfQeWGsjZ03zgs/8vAB6ufge02+4/LMRrL2PoxwyzlhOH7JgBPnueb+7/Dyg2Bl1l0g5GIxNleYwL2aQhAtsb3iesNk2SrGI1a0QdriL2mEAc98PvL+eD8I8fr0Rk4RZttRER7GoNExmk20VOn0Sv3UB7vUwfp4hvPhFTGFqpxM64eEpnYUGOv65sos7yBIHoW8B7H+v9FsTTR03UC8EcYvC0Z1hZ5JwKTZK+IqmIE2sLuiDBYgz4EI6YQS9pi4eIkdTHPE0w6OySdfdJDVQYssljFtpcSNvLeZcpv1dqNczKslOPrLjwjMSVwbj2qPPPEE5IYeq10VS99P74zjdiDPaLT2HWd+DaTeytdexTvh2c75I00Ahiv0YvxyyxrTba62Z8/SGD75khphJjTqxlnZ8kdkqPEkWY1VXM2kq/MUlR45DcPc0ULuMKplEf/VksAntgkqS1DGmbvpkm1fB3MRDKUsyZ08hd5zB338XLHvg7M49rT5iWsljSFu8czFx5OkvcfI/JpoEwj/qKPTFIksDurms/5yeTST/KVE53wDuehn0jhmRnd+z1ZAwbn3xEDMmXns6qBfNe8L7iteH5Z2APFR4rjpFTdfe0lzhVzijCrm+4lUSe9z4qsRtOKr0uJAGL56h46QVGfxZef2GDDfH9BCLjjPnpU+jairuX1roitAOAUIZcShRgIL4Oe/Jm5wXn7Rnv+Q6eO+Wu22bTvzRdMZPaIHxQ5K3vJbmbDdgMTBqoazYhldhps2TVngpEfe9ulkTduG3ph8Ukdo1JMJKtCvqH0EwrBmtdmKBeg+1d7MYWUq9l3Y7SyWkqzZO0YQWwJ4XLUQjj3ZMmxRETSNZiMQsniUvgFjkCBcn//Dmy+1ytunHFMbKyjJ5YRrabWQvA9z851IN+YbjjDLrX930ceFpVv0lEXgj8BLACfAH4TlXdzO3zAiAMTj8HeJOqvnXR4z0sPBR92+Ql7Lz50wWwkzjF+aTgBAzoUI9jPez1mlKjpv1QgxhBGnWk4pPI3oDa9Y0htcN0+0F64IgxFpx7IPThVwa2VcwQEiMuBxHHoAo7TdKG0uonSXd+15Vp6pDGgr4PGTUwExAr7gEwrngui5cDGIPt7BbnfQpyAv3jJK5QK46zXgSyvOQmxChC1redtw4HaszduA/2dJNwEB769wOfAtb883cAb1DVXxOR1wI/APxouIOq/gHwVZBNCE8D7zuAsR44pi1R7vfYTAYN6iw/5uAHGGKv1aZjDU4B3W9uoaHCQ3jVxdRT32miURuzuoK99zzSTTAn1rBPXXJ8/3zCt1qBbg+bccf74834ztUKrilGrz+RhNcQRW7yIDBy+etLrOu/Wq1CpzNclDODB7xopNTagfOHnnIUjXYA/MRvm013f1O20agVUsG+WY/dSow0Gq5/ba0Ky3XYbcPWNtr2SVARPnDz387r0qfHnWTQReRe4C8DPwb8Xf/yC4CP+MePAY+SM+g5vAT4I1X94qLGeViYRW8i9ZCccQljw7N57VkvU697sW+M8rrzRn8Uk2UvksAjh9KvvNVuz8WmV5aRVg/pdJ0xHeKFe4+PimPDFF0buGpUYwqrdNMVgm6P15RxjCCFSsUZ82Yrqw7uh6L2QbGcJ4Lvl5hU4jhy9y8yLgntVS7DgqdwX/fQSREMvJ6uakZVuuYretPJVFzhniSJW+HUa9Du8IFb75j/9U+DI6i2uGgP/a3A3wdWg9c+AXwzrjfeK4H7JhzjVcB/GPWmiLwOeB3A/fffv4+hHiz2Kh6kVrOKzr2IRqXGY6w0Qd6rnsSbDuPik+LPfvuMyeHj37bV3rcnmiXQ4hiz1MBubaPXbyJnTkG7g712vR9nt0Hc2SrQGtQkyaPXG+2M5b31gnFJrZYVPw3smiZ2p/0sZ7lHgdE0q6uOl58/36QwXhZiczRBoigLZQ3RKqddgaW0zOA+h9+H8Bql0XCdqXwlKOAmZiO8/wv/YuItWDjuFIMuIt8EXFXVJ0TkxcFbrwV+XETehGuAOrIRpIhUccb/h0Zto6pvB94O8OCDDx6x2zsaA3HbnEczatssTisCVrOElZ2yl2bf8MwwEWRGoaCPanDcMIk2LnmWxWWjCERc4wyrWTu4iV2eRp0fF/8XI85wtT3V8sQq9uQy5qYz9K45R7irIsY6jn0WhvENwYNuQgMTwV4nHWOQRsN5lo06stQg2m2S3Npg5GcyTQJ5DNQq8d13DcTo0+MO1jiMPQhpnYIkiZNXmECPLOy7O3B6GQhjZTUOiXXfcd8AXMS4frv1mushay3v/9JbJ132geFOKv1/EfDNIvJyoA6sicjPqOqrgYcBROT5uJDMKLwM+J+qemWB4zxwPFx5Vd9L9dSr1DssQubhLTXAeI2QRuS8xlRXY14a0HmPK+dJjcNYOd6golKtIrFnPGzv9D38fdIK0wlOrSIk/dZ7iY+vV4bDKtmk6BUshzz07PnepV/Fe7Xa6UDTwIbjAMhSo9+ZaRRyseupOgj5e5neb3vtuj9U4BGHBhWg0x1f05Af1oSOQePez/rkWoXEYtZWvL69xZxcc4ZdxBnvp3586jEdBu6YkIuq/hDes/Ye+htU9dUicl5Vr4qIAX4Ex3gZhW9nTLjldsUHuz/Pyy6+HpaXXCz76nVvlCH01EJvVpMEvfc8KkJ0c8tVQHqa20IrBPfgEY46TlrdKUaySlOHfTQ+8AhZEVKteH0Ti+w2Ma22mzT9/RraJx1iSK2EoVj5XuGahm/56w5WUyH9c0o4Q1wZnniGN8xWHwOdrvx7qTMhxvgm5P1iq0JjnL1HPwaenwDVjrxPmRCdVVfZ2+k6JtJd56DTRc6fdkb8xga02lCrHnljftBFQ9PgMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7p8vAQ8B/49DGOPCoedOkazW0dhQAewz1xgl/ZoyIcwfPYVZW3E6JZ0Cb2qRtMZJS/8JceSB7byXOWSQZg0vBJ65VGLM0lLGebbbO86It9qZ0Fi+ucMQFS89337v34hCpKwvZiDbMHXTh4H7m2tblztmKiU8cHwJjG6aVO+BpvtXK0hUc4ZdzERvXYxglpfcITsdsDoY+gsZQun4fUhMqlV41t1oFJEsVegtV1AD9Wd24MQqbG4ffWOe4k406Kr6YeDD/vHbgCGyqKpeAl4ePN8FzhzE+A4D0k2In76B3tpAg0RZyPpw3nc/RGC3tmB7x5fde42TnDEYJ3u7t4HmGhpk0gP+/VHnCEIs2RI8H8PfQ+l4flwAZmXZJT4rMbZWwbS7mOVl2NmBkyfg2g3Y3R0cb1i8Mu8JcFxycCAhPX2IY+y5guR1amQligZ7reZCaSEtUIz0heXSiXaaexMZxIdHBoTdwlVOUCgV5o20GtO8exmNBNNTaleaSKeHbVR49PdvD2NeVoqWyPD+T/1TXvbA30F7rhHyxCU0/QSeev5yahjyCda+Nzos+p9uMxXCxJlapOaMBbu7SFR1LwcNOUb1NS0MCe3XmKciT1aRM6ewaw1svUK03oRbG44aeOYUGhvkxCpSrWA3tpjLZLKPcc9y78dum6spkLjiG4I3M8PcLwoqLopKq0yHVlbTjFGMWylCdr6+6FhOAz51CqoVFz5Ui3QTKls91AimZ9GKQSPh0Sf+weRzHyHIEZOYLg36ISKlXaXynpMQNma2QRI19XozjewwnpxKlfrlcJpYm4X/7SovG+5xJUZWV92k4sM+Yen9QPelcMIBspXDiHOkx8r48ZOMWurp7jad6FWzi1Yilzhe33CND6ox9vQq0mogvR60jEsAHmQj4RSzrASm+Ez8A3fv/cSqSf/9lPVTGOveC4LPI1ylmXqtWIMmuBZNgLZfRZw7Azstqt0Eu1xDI+GDv/WmvY3pMDHHGLqI3Af8NHAXYIG3q+rbROQ0rmr+AVxl/V9T1VujjlMa9COAUOYz46eP8GZTudoixoNazRgEZmUZGk4Aim4PvPCT1GoutmwtkulqjOaZZy3gvFDWkM5IOrY4duqIPo4tvmjGxbL7q4Z8B580/i1+rLrTHNxmlFEXl8yTWg3WVtxriXWJtZVl7P3nnffX6mJubLpuNt0e6GAoamRF5zwxz4R1ELowayuOPdNquy5CXiulb7wT8nmCcOU21sCLGfqsTCUe6FaVTioDoZ0CpDo2UvWrut1dpGng3Gke/e037+0+HBHMMeTSA/6eqv5PEVkFnhCRx4D/FfiQqr5FRN4IvBH4wVEHKQ36EUNeX/0h88rMy5JaLWtX5xoo94aqPTVJHHNmRzAirsQc5zGnFLXMex7gXY8x6oEAlJjYGexAO8W9bhAx6OoydrlGdH0Trt/A1Gv+B58MaKdn19RoILVqv3oyMgh9kauRFa3qqj6104FLV9x9uec80knQmvPWWxdXqHYturaMXL6e6aKn5++PZW9Vt2MRsEDc8xHFOLMeM2DmSK0Gy0tO12R9w92PdhvtjGANheyUyCAZ82c4JNOPe/fj/kPftRnorOnKjSjiA9ffPtt1H2XMyaCr6mXgsn+8JSKfAu4BXgG82G/2LlwusjTotytCzfSX3ff9EEeuKKXZct7Y1WsDXlNmNNttbNhBSa37sZM+TQ20dZQyn7gc9yPNqg5PnYTVZfdis4W9ct0Vm3R7SLuNiWNsGpKZZLySBKpV7PmTSLPrjlGruiKStMR7cxu036UnDD2JMb7JBX6ycwa7e7KOxoJYi9zcHEzapR5zmBhdAByjo+GkBapVF9/2tL9xDZ5HHCwLbeGT1Npuu36izZZbCbkDjj1mKl2QxbRxnnf2nl9JhZ59OuHtC16g69DK9BeEGTz0syLyePD87b4ocviYIg8Afwr4KHDBG3tU9bKInB93ktKg3yZ42T3fB0sNqMQu0RfHQM/FtptNsuV1yioZ1YEoxIBWSdV1gGkNM2fC5bdaxV6/gWxuuUMGMrHas8V0t3AVAANeJlGEbu9gGnXsUo3kKx/AtBJMpwdfvIR2+5PCgJRrkriORd2e8+RVkWu3XJjJuIIiNdA9USeqncfsnkI+9+RQCGIotjyqyCk0/GOYPdnDNJ8Rxy4Ukt7jNGEZ+4KeNOQ17rj+nokRZGU5C7PoTtOFW4Iw0tgJIihS0iRBejKYsxjIyfSvaej+7AGHkrM4CExv0K+r6oOTNhKRFeA/An9bVTfT7860KA36bQLd2s4EimS3ib12o6/1DYHRnD3xlYUB4niQBgdZ0+oBbe+EIcGlUZ69ySiZzntOJ4XMC2w23fkuX8GcOYVtnMZ0esi1W47XHK4a8nkFcR3fpVbF3n2WpF6ht1IhavaItzvYiqFys4l56gqqtr9imHRvRhnzKe7pgDY7uHxF2+nUDEx2OXnYdN9RBtlN1upoqz4pjVoXAksleLs9mEHWYSCEMmkSyMY6O9QqtNtIFPHSlde4WLq//ts6/KLMtfRfRCo4Y/6zqvqL/uUrInLRe+cXgavjjlEa9NsE2unA1namFmhOn8LeWkeiCjZPeyz44Q3odgTGN0ueJUBa/g4DIkmDhTgppqiilMEmDUXnRaW/bl3fJK5UsMt1xA7z68NYb0pblEqMveccWolIlmJ6DYPpOgpc9dou5tLVrCHH0MSUl9wdR62cssgpXR2Z5SVnaH0iOg35mCVH/bS7u74FXezCS+JWGdpqYzud/pgC3nyq6pik0gFeXz0r3hGBDsV67rkx7gmB8wBBQjuOXTivKGQXFlWJIqrY7R2yAqPbGPPkoYtzxX8S+JSq/n+Ct34ZeA3wFv//l8YdpzTotwnUKrTamOUlJ7vabGJOnoAkIapWPZ+92TeigYct9ZoTwYoiZzS2d9B2e8BDy+Lj9ZpLtp064QzExmbWGCLPepjG0y2sHg1ZO6k4lrqyeNncwvQSJ2QVxyg4hkStBu32gMeuSYJubiOfadH9My+gst4m3jaoEaRn2X7uGo21GvHvfQ6sRVMRKD+GNOHnKnSnFDibgOx6IDPiEsf9LjsAIphGwyVprbokc7XqtNTVDhrzcJIJWEepBEAaO0/L6UcmXefJtAnH5QvNiOOxekTpd6Hf6LxyPMIwOjeay4uA7wI+LiIf86/9MM6Qv0dEvgf4Ek6hdiRKg36bQIw4A16vIWur6PWb2Os3svf73lzQ6UaMYzOsLEO9hi7VkK2mUxz0S+D0xynVCub0KddAoBJjl5z3JHHkmkQ8+bR7HoXnimYz8OmYcrHYLJxge9j1DaTdcTHwlJUSrBpSgxAKcGm3R/Wjf4CcPcPOV17AVoT69Q5Rx1L50nU0jUOK9ENI3iMXI6PVKsNxTmkQTSV21E118XO7u4t2OpiVFYhcMQ4wNJlqbgwDK5LwngUeb7iPNnNyyAGbaF/MmlFIJ5deF9Fo8vbp2HyoTJOEDzZ/bvI+Rxzz8tBV9ddxTn8RXjLtcUqDfrsgirC31l1RRmKR1RW02x1kkgwp3SXQtk5tz7c9y+uBh2wRqhXsSgOztYvZ2nEiSXEEqyuYixecjKlnV9DpOo8sLCiZNsZasF2/HF7Qnd1BLzVJCjv7aEK2KgHQK1eRLz+P1gy9RkRSNS4k0ai70Ae4iaLbRRJLsrkFRJmRj06fIrl5ayhs0Kc4RpmBDO9fPpwlvV42IZlGw3W5980viibA1HBHa65tgHa7br8kcffa3+OQ9pkfX2G4bdQKaV4oSr6PC1MFIZ8Pdn9+gQM7IJTiXCVmwSMnXuuWdJ6Drru76Oa2+x/EVweaNYxCQGFMGRPm7Bl0a9tzlztIs4VJEqdUuLGZ6V7LbhMqriEvcezCH80m0kuyUM9w44hBDIVrCl4PZQamKkfPGw9rqP9/P445e5qNP3s/OxcM3aW7qG5bqus9TM8S39qFRJH1TeJqBXv3edoXlqj9+u87rn5K4UzHlGsmEo5LjGRjlWrVSRwnLlxEZDAry2i7janVsNs7wwyg0Iv2nHqp+fBYow7rG24F4pPDo2Lg8zDaY3Xsp9k/5a17YbBxwmvHwph73El66CWmxEvr3+mMto+jpuqAGdKwSOQ00ENmRBYLzjYdrasSSqbK6oorSqnErjfj9g72xs3B7VNj2247DXavXW5WlmG5AatVpNVGW22kavzYBo1CKEVQaLwDDMgWTEJO7Am1mXiYvbVOZfdeGjeF5hlB1GDjCmu/fxN55gbJ+jrmxJrzrp+8TD05T++rn4/pWidPvN3CbLewV6/149KjhlGtuvth1YW1dnZdJWSjge56eVwK6JwD9yhI9p5wXrp95prTyk+NY57FNOM9GrqGoUlh77OCGHGTvG/xJ7Wa0ycqCK/lC+dud5QGvcQQbLeHWNfkImxWMWD8Uk8wUGYc13ghTZ65J6mRTCAxYAS9cdMZ6FAnPP8jz5KPZMeRyCe20iYE9RrmrnOutH5nN2N36OYWttnCVFwhTCYBXAkMdsqHNnbQMI9iY4RGPCwuOnsGvbXuPFx/f+KdHtsXY9RAbSNh+ZNXYXsHVleQZtMZn1oNzp52HruF3koF6SmmHaEnltELJ4ieuk7v0jO5YQyW3lOvOXmFtHlGFGXx7TBROES/FN/sIc11pIlrYzCnT7quPbtNF25ptd3KycviTuVJ55KoUvPVmmGFcXCcTPdnVk9dDCkXfogKeYyNuQu5HK2YS2nQjwJSY23EFefkvPO+jjYusdeow+7uxLZhWWx5IPaa9M/l6Y5u8+m+mI473oIbt5Azp9DlumvxttOG02uocT9uqVSIul3srXX3POd5mxNrIILd2s4MjbbbWRNioL8aCYS7ZKmBnjuFtLrYlQZac63sOLuGabaRdo/uXWtc/ro6GsOZTyY0nt6h9Zyz7F64yOknrmMuXkCrFXonG5h2D+lZoq0W0S0LsWHzy0+y8qVdoisboEp87gxJunpJKzaXfRf6bg977YYz7OnqKjchj4wlQ9YpKa34lKUl5+nvNtGb6/39fQJ7VOhqHLLK2l7P5VIoDoEN5EJmmTRC9ko63nRFmV9JHTOU8rklijEugZXR1gSb6npPeUyg8Mc5iy55qCUjSw1Hr/MeunQTos0d6CXOEBvfVq/dzjxWs7oCS0uws+Mol0nitFuSBLO26ihvG5sudLG6gn3mapY7MCc9fTJJoJdg7z3vFBUbNf7o70UsNTrsfO4EGivRziqVbVADZz+eoJGw/mURG88+wdmPd1i60qV9zxrSU6pXtog2m6T879a9J6hdc/d29Q83MRu70O3CUgO9ftNNQN2eC4vVa+69ZstVbHa6QzIHefnY9D5mieuh5GigOd/zHrn2Rc0GmoLP4j3nEpfiJ/d8EnUgCT1rHL2owjR8L67NdrzbCaVBL5HiITOWUjqEmeKcOc9oZEOKoEgkfX+kB2iMM2aAvXodbtx0Rj7lrCfWhR6iyCkePn3ZhRFWV0jOrqJyEulZTKeHjQ3S7iK3tmB1GanX0GoFbVSQpfuQ3bZbCSwvoY0q0nUGXTo9tp6zStyyLC9tsbNb41u/8Te5WN0A4P/62IupfrrBxnMi7n33F1h+6izSTeicbmA66QQmaGxoX1zFRoJWhOqtDlqJaJ+tY2NB7AqNp3cwX3yGzYe+HBSqWwn13/i0k0hoNp2GzIBevIsjZ2Gk3D12D1P54sH3ZbmB1L065s4O5sJZdHPbr8T20aIvN5nnJ3IxnsoZmb7Y25RFVJn+OQxPNuHKMrH9VdcxQtngosTBIBB/Mo062usFdLuctx7wmx19TxiQW42DAhARdGPLhRV8rFSbLaTddomwTtcZJl/ABECnC70e0UYTFUGrfsnfsyRrDfTUErYSoZHTXhF18e/IGPTkMrZeIWlE9JYibCTUbnWprfewNcOFf1qltxrz4Ytfzz968zt4unuab/2K3+E/fe7PknShd985xFo0NpiepXrVUQd3nnuKXmON3nJE3LJ0ViOe+ZolVr8EzfMgvoD1XHeJeO1Z1NZ71H7rD5wn7sWxTKNB0nEa5K5i0sW/Zdk3mthpBvz2fjgim1jDiVStmwh2my4PUK0iDQbCN3v9HmT/Rxhpta7YylRi7CTJ4v5O/XBKBNrrYuq1QuXGVKv9g83jw2zJoFo2uCjhMKt3PoBxJdzemJvVVadauLyEtNpYL686RG0MfvSaJJjlJUwcOzpiwHPXhKwoJu/Fa+KW8hJFvgy84/TTE4ucOuk8d3Dl/L5a0zZqxNe3YWcX1lbonl0mvrGLbO3AypKbCBpVok4PW12mstXj0ovq2GpE+64eX/bcy3z29++BquXsR+FNb/5e6rcSSJT7mi1MJ0GsJdpoYpdrVG420WqMrceIwuYDFXbugd6KsPbcW/Q+c4rmOUEFNIbuKohVol//OLERWFrCrNUyMTBV61rfpSsbI26VUq0i3S4sg4lMMUsmjS+rdVW8K8voxmZf+rjTdQnc9P6OMrShamPBOSDUMC/4zvjJXJOEZGd3eP8xkg7p/rKyDM1+jgNRl5gWyUJuj7Z+tvg4xwFHy56XBv0wsC9jDhO9J4icXka77Tyn3WZxU+ngWGmi1PqiHvdWnls+2ltMGTeJT+RlWh1e7tcuVdHYgAXT8bFca10Yp9NFLGijAmYZub7uJoQogrsvULnZpHeiTnUTbAXkjzcxopz/shucauzywq99mvf8+tdx/6OwfV+FqBsTtZXapqXz7GUa17tEOz20Yog221z9UzHVTXjW+3dJahG1z25xcbnnQjw9b1S3tr0sry+p397JYtwhG8k19WiAqpNIsOoMfSVGGg1swIIwK8tIo+7yCJ0OUq07o9dqDzCNCkNe4zxsd8Pdv3SCgaxxiFh1dQhpgVZYfDZldW8+iZo2rdCdpmtc3um6vq7VCnrZ60dFER/Y+KnJx7+NUYZcSjiEP5L9aG8UeOuZtGynQ/L0M9n7k6RVwx6TU7eoK9JlSY3LUsMZZd9JSBLF3PICY+sbroGzD2HET113zTjiCCoV17TBWri5DmuryEqN2oaSfNsN/tLFP6JtY1iDX3v3n2btFW3+xJ/8Ajd+7QGWr/SobCV012I6q4blp1pIojz1l5Z51i/fQiPhWf/qUy5EtL1DlOYFdsDeuIlUq9id3eyabbPlL9MxkAYaZvesE/5Kxb887NbW0L1xN0ihWnGqmeubzsimoatAu2VWvvlAsw6vcy5rq644KTLopSue6STFIZzwu1ZgvIfO5bdTq0hKQ40MbGzBidVjp3k+EkomlndUsHCDLiIR8DjwtKp+k4i8EPgJYAXXI+87VXWzYL+TwDuAr8Tduteq6m8uerwHAZOqzMWx01VJBY5yvN1C9kn4w0u1TaJoJENmZmbEOMbCqO1zY848/GYL2h1kZRkDzqC1O66ytNly70fGacisLJGs1NFqRNzpoidX4Mln4L67SFbrYBXTVewvnOHX7RnUQP2WZa1m+f1fej5xC+o15ZE3/f/46M0H+Nyn7ufLfr7D1R9os3FlldXPQOfMErVPfgmWllwlbLOVUSFJEleUlBrzoBgn1GE3J0/461bsZvC5ZbdjWJEya9W2s+tCTAFSBku+peBEemLATsm6WamrZSCK0M0t2NmdrvFFwWtFBV4SRZjlJRdGiyPodLDPXMVubrukaqPB+//wn48e83HE0bLnB+Khfz/wKWDNP38H8AZV/TUReS3wA8CPFuz3NuADqvqtIlIFlg5grAtF2gzaNOqukXFQMg8M09tSFHHFU+562OYsQCZ/65sHHxhSdoNaR10U43RURNzzOHbL9HNnnBAYOOOw28REAm2DLtUgUXjW3Whs0NjQOVnB9KC2YTEJNE8btu+JSCpQvwVJFUxPefe//0b++d/8Sb7mubd45uVCVw2v+dh307p2kvXn17hw6QT2S087lcpn3QvtjjNM2zuuWMnEmXbKgFFLhby2dxznPElcOKaZ9JPKvklIpr0yQkp2IIa+tORlGWxfmSkZ0Sik8Ha7UJf6lQRq0a3u+DzLKKSsl3SCMAa7szt8L+pVt+pK3DWnnv/7r/3E9Oc6JrijQi4ici/wl4EfA/6uf/kFwEf848eAR8kZdBFZA74B1yAVVe0A89E3PQQ8ZF6Z/YilEiOnTzoe99XrLoYacI1D2leKkQY5x13PjES1OiDHmumdz4LcMjw858A24evpMjyUIkgcP106HR+f9zTK9Q1nxOo1p0q4vQNXrmPWVrAbm07DxFrEGCqNOhWRrIpST67Q/NozxE0lagtJDTSCrXsN/8/X/QzPq9ykQsRdkeUNTz3Ea573W/zaqefz8Y8/i927znPys+c4+Xs34epN7MZW/5rS1m5FZfJiMl68vXAKrcZET10n8vFzKhXPv++AtSS3Nkg99OGOP/3VmPR6mTRw2C1qYrgrLBwrSnaPqCAeiTDUIooYk3WLCqUbSBL0qcsQKFhqr8ejOz89/bmOEe40lstbgb8PrAavfQL4ZpxQ+yuB+wr2ew5wDXinD9E8AXy/qu7kNxSR1wGvA7j//vvnOfZ9I5/87HOW1XGrz5/1Hp1Fr9/wnl2BAQ/DGvnY+hDHGAg7/UyKf48ory9s+DAKYfl4ECpQ60MJnW6WLE2vLDOavR7WOmaE9rpOxMq6Rg/OuFpsy3HMswrNTodzH9p1RUzAzp+44JQVVfkXP/wd/NN/9m/4jn/wN/kLf/ujdGzEf7/5XC5tr3Hm2be4eWKFnc06p3bbTmvFCFgnHyxLDZcMLbp2ta64Z3ML0+thL55zRs0Y103qhG8pZwza7mST61jDrBbbamOqlX6ycka9eX/TpzvfhGOFTJhs9ZgePxUQC9QtAT7Yuf0lcPeMO0ltUUS+Cbiqqk+IyIuDt14L/LiIvAnXjaPI846Brwa+T1U/KiJvA95IQWjGN1p9O8CDDz54xG6vR1iCb5Xk0hXMzZrTEomMM2KehVLY2MC9URxTDxF4yaHHlhmocEgZGyJgRYTiWREBd3362OtQOXkwlkzHfIBF4/tapi3tPA/bxYLNIDtHrUtCrS1Dt4u9fjOLaUcdS1IRUPjhN7yOmloev34flSjh6vYKGzdXiK9UOPVHwvlf+FT2O5Q4dgnEVjvz0KniWSHD12tbbbftzXXXBMSrJNor1wZXSH6CGrzpwyE1MYLd3B4MaeyhgGjPfPVwTOlzzTXk9oqTcLyUEvcLV1h0tEzOIj30FwHfLCIvB+rAmoj8jKq+GngYQESejwvJ5PEU8JSqftQ//wWcQb89kS2j+4JZttlEOh0XU853miky3PmY6CjveeBcvuFCNRoM56Q/Yt8+jEqMpJNOWgyjmvGiJ2LKkExYdDLQdSivsmjViXwlyYADZFZXnWBVJYZuNxMCEwvVWx2ny9LuOiqkKp//7/fQfrabKJ7zLqWyvoF55ubgJFGJnYE+dYLuxRNEOx3MjU2nzyL97k/ZGAMvuHf95sC4NfGFXEmCGB9ii9NeokFrv8jpz0i95nMdXtMGt6qZemU04p7nE6xDyFWuhuMyqytor+cSnTiqJapOP6jEMPZQvLtILKweV1V/SFXvVdUHgFcB/01VXy0i5wHEWZAfwTFe8vs+AzwpIi/wL70E+P1FjXVReMy+l8fse/sv6KBORtrAYGzbsLxxD1+bQvAoNT7GGw+pVjGNuvsBB80ptNNxHYKiCO10sSE3elrkr6EgH1A4bs+LVusacGTeZrXi+ewVolR90BhY33AhgSTBnj1JvNUlvraFuXQduXbL/W01uf8DO1z81Qr3vi+mcyL28gFeDTCNyXe6roF0JSapRSTLTqvFLC/5JGkQP07vZy6/kf6h1vd3VWRt1RUM5YS61KtqaqeL7jQz2lumR75fpBzzSbUK+Umg0XDGO4qyyT1DZNCdJtpq89KV1+x/jMcIojrV30HhMHjo3y4ir/ePfxF4J4CI3A28Q1Vf7t/7PuBnPcPlc8B3H/hI54QBox5gIMY+wjhnZeUwKNA0xX6Ak3g9fxatREjPIjduoe1O39PsdMnobkXHmUbdb2Kh0+Dx0vFlKpBFoRvf7IEocnowqtg0Du/vQXTmFGogavsiJWvR0ycRVSclUDGc+OQtUKV7doVkpU58swLNVtY4Qq3FRBFYS/13Po+qhZVlJ2mQNncOCn5cGIohg5giOuVpjcYglYq7zl6Y6AzCTz1Lsr7eP0xKP91DyGVm5L432nQGO9WUT8dgt7b6jKnE7j20cxxxJ8XQQ6jqh4EP+8dvw1ES89tcAl4ePP8Y8OBBjO+oIi1icRQyQeJecfMLGIrPSuqFN+qOXpYKJDXq6MbWwL6pxnZGgfQl/ES+4tRM2cqswPMeei/gzw8h5H+r9dRAR5HLGCBx7Pt2NrB3nUUSxRqnmKgXztA91cBW3Ll6S4Zot4K5tU31yZtoo4qeOQFnT8Lla6CKObHmujZ96enMW9Zmyz0u6DU6aXJzErgN1xxjTFK6UH8+neDyCoj5e7kX5MNi+TCgimO3RBWyDzvj4idZgjvTVC8BlFouJTweir6t0LsOk1MSRa7RwYlV7FIVc30DNrYwtarzsiOTVWGS6pundLK01Ht7J0vUpTFxzXmcrrhmCalVHVUtVUxMl4riVbMmxGQz5b6MgpmTZM3FpMP903EM9OpcbvS95Gavv6KoxI4e+JnPoyIYVVdNurNLpXsSu1zD1iNEI2w1wmxsOerklosLs9SARt11bfKt9WRjM6Mwamdwsilsp1aQ71CrWeXoSLZKWHhVFOfWoBo1jhHPSR8bE899DgPnHJWPyTGTst3S2oF0LDku/kgJiTsVd1BStEQBHq68ajAJKK4PY7isB2fQzMk1l5Ts9pCmwJIzcMkpnwxs9TCtjjPqqmBvOW9btc8aST2xoDx90Ovz3nKzhbbbTmuk14N2G6lWiU6sOQ32sGAoj5RTHTvq3qjwTbod+IbINl0hOCMhyw2MGGcUez1ILMm1G4E36ZQNs+NbBXUTlF3fdEleazFPtvrJoWoFvecCWonQSkRvqYKoEjV7ROu7JCeXMM0ushP7Rhq+2US+SUX+OgqQNZIAHPe/eBUyabWTJYqTxMkTRzVn2NOcxzhu+uCARp8kNxGB6yaVOefVKo/u/DQPV15Fyp0fKjC606GULejuVLy08V0ubFKruSSJT/6liUqp1xzTIbGZcp/d2sacPU1yaoVkuUL1mU3YbRJ5bWmNnK63rtRdwm/pLqdo+KXLfU+cAqOUS8w6z8uFF8QnQsWIi6l63rEzVAkhzXEgbKA2kIsdPo87eGBgrHWeNo7+lXo6mRGMY9c8IunHnFOWSV/VsT1wLu10XFLX00MBR4P8/FNZV6DaxfMkJxqY3Q567QayXKN9fpmaKrK5FYQ9NPNQJ4VZspZ0Xis+YwqNC0FNgp9AtdkapBTOipDuOoqBBP3vIWSrvEfOvq6kKU5C6aHfWUgNOZGj0rmYcOIZFh1XRn7fXdhqjHnSOrqYSEYj09UltGIwncR5k7UKctk1L3ZxbgPnT7sk3IZr9hxKto40RuGPvKA4KexFme3i9ULAJy3z9LhpCmE8T942Wxiz5Mrnod9gOIpcC7ZWe3T5exCP73usvh9mWviSn7R8VapWIjf5gTP6iVJ/ch195trgymJEsdT48bhCo2xCCCpC94SBugIYW/0Zfp7p9efPnb6eN+ZZuMu6RG6t6kXVEh459b13jtjWXjAney4iPwWktTtf6V87DbwbeACne/XXVPXWuOOUBn2BeOnyXwcjmLXVrA+m1mLssvPkpJ04A6nq+NNrq64lmwhaiSESpNUl/qNLLumXxi/PnUHSkEqzhTQ7zphYHWAiTBUuGGVsAmMikTPmsuYLfns97Ppm1plnVlmBlIoITrBK0rJ5qyQbm4MhlnBCSZsYJwmm6il2vZ6jWAZ89rSRdbafGDC487S6yKVrbvtOB9PuYutVzMXz8NTlAapmFv7KF/0Ued5i0HbbtaoDt7LRCXzwaVBklNPXizCJ0jrqdSNIqi+Uhuf8SuiRs68D4APX3z7j4I8/xM4t5vLvgH8JhBoKbwQ+pKpvEZE3+uc/OO4gpUFfAB459b3I6gr6J5+HGnHl69UIW1vBRkLnRIxGIKk3bKC6mbD0+XWX9DMGabYdU8KHZ8AZKanE8MxVZ1zrNScsdekZshZwMJx0TJGvPIUgnEJ2jvw2muDi6M0WZnkp855n4k3nmRvtdhYyCUMned2T0KiqlwgQUezubiYMNXiJSqqYlB2r10XiCnZr28nWpjH/SoxevuoLeiw64sdZ1B8UyJXbO6aI03Ch//qc2CmmWh2gFKrVgRXMQE4mfZx+rj4WP9BsI/iOpFILg6f1fVyTxH0nofTW81DmVlikqh8RkQdyL78CeLF//C4cU7A06AeFP/8t/5zazQ7meffTvtAg3nG9M21FMD2ledZ1y7GREHUUjSBqq2trBq7BwvZu31iRcpOD5gedDirGJf8aDcebpp9YHGnMYXDJHXh+abJrrAiYD8GkBT1Ta6UH5xnUeUkTca4v5ZDMb54nnYp++XCVK87pDLyfniN8nj7OcgSZ4ctJ3w5VtRYb44yTjSvWUp+07u+zGBaI+4zi4ok0nZSjCJMWMyXWhU4qFeh0smYdtEd/P4YSnp5CaU6fzKpvS/QhzFQ0dFZEHg+ev93LlozDBVW9DKCql9OizHEoDfoe8dCf+zHaZ6okFcky3bZmsJUIW4mo3uoQbbXonlmieq2FWd+i/qUadqUOPYu5ehOWl2Bnl96zLhB/8YrjQ6fMD1/koV4eQILiHzGuvZfd3cWcPtXnjG9s+krIMSGXEUvyIQM9YmmeJg1NNeWSR8PGOHe+TNgp530DmWZI/vWi1zJPOMcI6p9K+hrxIySIw2sbZKEM6s2MDE2lFMulJeTkCaTZxG5ueYmEGVQtCzRUhhgs4b2bwHBJE8HaanuKatetSMCNLY5d2G5AcMv2pQhIJyvP6U/bBYJLYPsQWYkcpjfo11V14XU1pUGfES/9mn+AdBOkGhPvJtjVmO6yAXEUpqidYGOhtxzTXVmhdqPlGhWvLsGTz2QsAlUXdiCxxJ+75DyqM6eQ7V1XDBTH6LpbvqtXT0zpjVJ3ol5Sr6NrK0i749q4RZHrR+mRhVKCmHQekxJ+mWEMQzTVikueJYmjGBbvmO075AnntERSOmLGW88dJ0ReeGwg1ODbxWXPo2hA4Cvtbj+gN54zoBPvBfSN4/UbWSeglNqXT8iOg6n0f36jKjCnDdukKxhtt/vX26i7783SkhOAC5uoaNoU3DqBspVl9PRakNgVR5tc33bGfHtI6LQELHrVckVELnrv/CJwddIOpUGfEo+88EfpnF9GT9ep3Gpj6xHxVodop0fDCLZqqFzfQbZb6LIrWU+WfNik03MsirTaMV+YEhn0vovYpQqSnCSpx3ROVqhuniHe6mCevOIaL7faLoa8upL9yKTZzhg02ulmzJOsn+TqCvQS14S40ylsvBA2ych3bR82oNadJxOZMv33AyOeGdN2u8+G6R+0/zAhM/qzcJwzzncUYU6ddLK16xve2/S6876Nne1tQlxzSVFAeyMmoVGrk9x1mXNnXMXt5av9z5TpjW8YIpFqFTl7Gr1xy/Uy3d3tHyccz6QkKOFn5btY7e5iTp7IGn2H26RKmuKbSHNyDa1Vsr6vu/cuUdlJqNSrRDe3Jl/TnYg5xtBH4JeB1wBv8f9/adIOpUGfAg9/3T+EpSrVS1vY5RrRxi7RFzcGquaiyJA8/34iEccDt5bIU7+sj2n2OdXBt8AbMXP1Jr3nXYSKIdrtUQWaF2rUIyGWu5yMmgW6CfqZz7uwgxHXxzEbhIFeGv9xjBe6PVeQ5MW2ChEkCY2nrw0bAbLHYmzWvs2sLLuq1FR7RdV5sJBpmw+cJ4+CApdpILHrzSmnT7o4sbXI8pJbAXkPk0oMnS7m7Bk3rijCXrtBX054RHxkVOioEmNWV9yk0eog6X3t7S0Bqt2eC5e12q4NXq+H8aG1MFYuRjCNFdTarMdpdq5cmKofUvFFWM2WKxQbusTBnIRcvQGdDqZew959nsYzLaRnIZI7r63cDJgXy0VE/gMuAXpWRJ4C3owz5O8Rke8BvoTrHzEWpUGfArYaIYkizTbR1RvuxdjHG5PEeYetdhY6yQps8lrehE/7lDyJIpL7zmE6FluLsI2YVBa2fbJCdznCVgyV7YTGp5+BtVVXhFSvOcU+6PfmjGP0xk2013Peo7XorfXiBgoBpBI741ytui9pp5st08OmxZmnmhb5VGJYXnIt5nw4w968NTAhFF3/fpB6l3RwBvXGLXd9Rtw1+ASevXbDhbIadWcwjWBOrGLXNwZiyYXjy3vF6njyyc11p4We57lPHvRw+Eptxh4Kw2MDlEsvp6DW9pUi8yi4t6nI10BfVILkt090S4SLvbfb7hytNubWJuapJvaBizz6m2+afG13LHRuIRdV/fYRb71kluOUBn0K/NeP/J889KJ/7LjFcQS9hPdf/r8Htnnpymv6SajUWPhQCEVhjtQwxjGcPIGtRmw+u0FSFeK2svJkm7VPXIftXddy7f4TdNYi6mvLSGKd8TaCrCxjr99057EW4sh1QlrfxO7uDnt0IyBR5BpugPufJO74UcX3vOwNJBrNqqdN+iQanY4rCNradoYnP3nkGTa51/IqjOG+A5NIsL9UK/2EpL+X2mpjmxuD17vrEnxy8bxbrTRbLn8RxuwLim2gry0TMmUmTk4FyVjHva9mCex0VeOSwgUyAUEIbGhlNc3kmEoMpBTOAjpq+rrEBjmxhrTaLuRz7QZmaYlHf/vNk89zJ0M5csyf0qBPicf++4+Mff/R7Xfx0vp3Dnh8eZGn1HgNFMyows11Ks0WZz6vdJ53F9F2F9PqoLHB3nuW9tk6SVVY+4MNF1Jp1NFajLS60EsyI2jXNzDtjtP3brUxYTIsM4gaGIv+l9G22kTNFnr+DNLtuRVCHGcTwlCBT+yMkt30IZ9UGRFc4+N2GxnVLNkdcPB/eosKvN3Ue5UlX1marj58SCvtWpQ15WgOHxdwlNAbN7MJdigclKdzTkPNLLiGAepkOIF5TzjrjBSwWEaeY78rmyLaaNHqo9t1xrzTQeLKHdnweU9YbAx9ZpQGfd6YJUGmrk8l1QrS6dD7srvZfFYN06tl3cQlUarblsp2Qvd0g8q1bdfgYWfXMWU8xMetOXUC2dh0xUy7uy4kYqTvGaZFPElC2LJcjLhxbO24Sabnmm+YRt2FT0zcVwBcXnLD39l1HnrPKSGmfUDTsUi16mL4KeUuR/3Lx+bDsQwlSKMIOXPKMYQuPUNaYWuWl6BaBVWSK9f6+/vVRtpARHd2YWe3f9+LYvuh51q0upiAbCXhWUB2py9qplYhZdYkCeqPOZU0w7QYtU8u4Z2uPsLuSUC2sny0+e+nP+cdjjupBd0dg0dOvDZbek1M7KUeUxRhzp91iUsj9O4+Q/NCndqmpb1mSGruR7b8TEJls0u02cbc2HDnWWo4Slmj4TxlgN0mLLt2YayuIM1WsKTHJ1F7jqGRVmq22lCJXTeaJMHeuIk0Gk7Pe9cZP+vFoaSx7DxvI45W2Ww5Wpxv8pwZjQTY3XWStCvL6JXrQEBPDD39AspkSEM09VomuIUIbG55DroPYzWbTu53cztjyUij0b/VYcel0HhOKoYiMM456uXQRB0yYDyt1KwsQ6WCAV9G71cqSXCOcQVIBRNe0dj6FzoivJXb11RiNyFXXELZ7uz2q3SXl9znuWt5uPod2TEfS949epwlypDLsUQUDSr/wVhGR0a5a7awF8/RPd1wLBZvyyo7SmdNMF3YPR/TPhHRuFnBPmsF01WWPnkZWVtzxrgWI7tt8OwLrbmE6gA3OoVV91evOVbIUsNVp3oD7RKNHR/O8FWC9Ri11nm8ccz2n7yArRiijqV+pUn0R13XkzMwImrVhW12mpgTTpvG3lrPmBeZjrtal5RL9wkNlVo36Z1Yg6UGemIFVUWaHYwRV8bedfRJ9fdUuz2X0FXNKJrpscd+LsFnEz43nvapO7suf0LkQ1YyZEDFl+Ubv4qw5086xkqn46UFgolgQvHWwJjz4ZzIUTLN0pJrML617a7bTDbqAyqU1notfTd2u73j9HpqNaerz2CYrUQBVPtyG0cEpUGfB3KNI0iLZ4qWvrkfqNlpUbu+TnLvWXbuqrJ0pUt31Vm56rYStZS1T91Eepbu+VUnE9BqO156p4O5/x60UXUUs+1dp5veqMOFc+jTlxEiFwJYXeknMUXoPes8veUK1Ru7mKeuoru2z11vNNx27TbJc+6GROHmFlQrrPzW592XuObi5drpZJ6p7XgOtJG+9kuj7lhAafEQYE6fQleXkW7P6dCoIiRZTDy7Xd2uWy2cPUFvtYpGgvSW4N6TmHaCdC1mqwmXrvhenrbP4x6F/Ocyxqii6qiP993lOrzvtrHPXHXj9eO0aVtA3/HHXr/h+nO2wtBWmvSNsolL4srIRGXROLP9Uj2XUL0z3WVcRWmQJNVmwKIJ5IbDFY3xx31p47vKEMw4lB768cMHNn7KJUQLMBBmCPnCSeLib1vbznB1E9Y+32TnnjqtU4Z4F5audJ0BF4Gb68SXrjhKXtVzsM+ccoe9etN14en1XIFIJSJZqSNnVoiubGBPr9A6t4QaiHcTomYXFCrrbYwvGjF3nYdaDa1XUFXsUpXmxSVnJIwQn6r57VYx7YRop+NWBimzx4gzAn6pLlHkpAt6vcGKVTHYGzddl6FuN/PU1SrYPi1PvPofSYLGxhnwxCI9S7JU6cd9PctmUqf7QW8zyj6bUduKEWR5CV1bJlmuYbqJu89hX80hg+s1b5pNFyNPVxyRgR7esNsshBOydtKxZ9+XnBdvqlGWC9FO13nRxiBnT7sJdXvX00U7U7NghhLT4mmMs1Iy72SUBv144tHWzwK+tVyuYGXgR5r+eJKE5NaGez2OMU9ewT73HhpX2jSuCdF2l+jzl9z+atHE9hkSVW/Qdnbh1Ak4ecJ9seo1136tFhM/fcN5tnedRHqWymaH+NoWnXtOsnPvEkuXW3RP1th+4B5EXfLVxkJ3WZyYWFdZutJj6XefxN59FvPUVVfAE8fY0yvYRgWqMaYWY1q+0fSNW06SoNP1cgW4661GmLUV6HRdEljE67Z3svuTYqCU/+wZUEu00SRZq2N22kgnwXzxGVcpu7LsNj19Em6uDykGZseMon58PS3VB8eWCSmBaUjMT0B2cws2t4iqVZcPaLackU7CXQZbtDlj6FUYu37F1kuvMeWE02+xd+Y0qGKfvpwlqtOxyqmTaKMGkcCTz7iagrSsv1qFWhW9ej1TUUwnolFVuUXPU4Odxtfp9SAKvH6i0ksfBaUfwjoiWLhBF5EIeBx4WlW/SUReCPwEsIITbf9OVd0s2O8LwBbuV9A7CGGbeSBNIj0UfdvIGORAoi7pL3ujT30Ref59SDtBnrzsKht3djOvUKoVxBgXMz13huT0smtDt77lCooqEeaZm3DhFL17z7L5nAaNq13i3R67F+vUlivEO13WPnaT3vkTxDtd6k+u0zuzwvrzG9hYOPnZFre+vI5FaJ+KqD5wAdPqohfOOAmDSOicbtBdieg1hPqNGlHbYjoJ0VIN/ewX/YTjjVtknKd79qSjGDY7sLHlNGAClUUJvFkxLmlMzZfqx6afF1CF0ycde2Zzy5X2+3vUD2v0BieGtDPSuTNOZ/7yVTc5JokLfXj2SRZzT41jWpq/1IBuF7nrPFy74cSvut2seGqgAYX/P16+2OURJIrcpLy8hDlzGvXiamZt1U1WImijgq1XkOfd7ya0Jy+h3R7J+vpwWCbBxcEhGFtBiKnIg4+i/vcsigYNVRlLH4HZq4MXjYPw0L8f+BSw5p+/A3iDqv6aiLwW+AHgR0fs+42qev0Axjh3pIb94cqr+nHOIM4+8IP34luIQT7xR87bBdRrnPeFpHxxi7WItURfeCZjtWAMdrmGMSdp3bXMzl0xaoTNZ1cxHWcY2yeqrH4JkJNoJPSWYjqna6jgBMUacOMr66BQbapLyl6sI1on6iimbbEVQWNBrLL6+RbRTtvx4bd3nAcOWaNhZ/h66M4u0uu5MnS8sfFt9KRa6TeFaNShEpOcaKDGEK/vul6gsUE6PZKVOqYau/ZxcQ2Wa0jPOr77TjMLZZhKPGi4/L21T1/OlBohNeDDxjf97F5a/85+orVRR1NGkZdyyCOdRPbStu1ld/3vTnStY9CzJ0lOLCGJJanH2KrBVA3Rxi7Uaq6hh1XHlAmZL/jVhfE8+k6XIXmDIJRjUh0XyJQ8M/qpj/OXnvkYKHdWUlRE7gX+MvBjwN/1L78A+Ih//BjwKKMN+m2PD3Z/3nV86XRdp3mffEqb/vaNuzMupGHkMFmXGifrk2DViit3b7edwfGiVNHNJajXMBeWidvQqyvSATVOjx1Rtu6rAlWWr/TYvhijMag/VdRxNElJlF7dUNvoUf/cDVjf7OuB3HcXWnE0QvP0Nde5KJigxEgmx2q9YUgNoDTqaNvHeHu27xW2287Qnl7DNmpOZqHXw9Yqjqq5tQ3WEq8sw4lVWvedwMbumkzXUm21vSa5Ojpes8Vwu7bhMFiIInpeGkY7CLz/mX819NpL//SbqX7+MnrmFETiJB1SNlKKXKLdNluut+kUhkb9CkWqFRf2yTUSH6n9U6KPOyyG/lbg7wOrwWufAL4Zpxz2SuC+Efsq8EERUeDfjBKDF5HXAa8DuP/+++cz6jkjbN31yOm/MfhmmijLo6ByMfWWXnbub6I9ZwQHwjrVCjTqxNtdqnVDdRPinYTOyZhew6A9SGouVr59T4z0yCrdkhpoBNsXI6pbytrnm671Xc8pAKZiYHLpKnj9GJvnZafeok/cZRWT3Z6jVDa7fUMjpt+JyXPapd3DWJBegsYRsrnteNypAFKni0ZC7fIWrXvWnOfaTkjOnSTq9tDNrYztMq3Q11HmWT/6xD/gkVPfi/7RFwBvZEdoz6d5Gk0SkrSAahzScEyv2w855XIZs4il3bG4Uwy6iKQNT58QkRcHb70W+HEReRNOHnKUG/AiVb3ku3Q8JiKfVtWP5Dfyhv7tAA8++ODRursF+MDNfzv2/Ycrr8oef7Dzc4XbzFKW/Zf+wj+hdqNDXaF1toqNDb01yJrbCsRN96cCpz7bJt7oOAbOyrILpYgAXgWwuz14giK6XSV2qn3bO35VURlMWEpf3jcV1aLTRW/eQk6dxJ5Y8j+UFSf6tbIEWzsukfr5pzCNBnWge3YZ07Gu+cfmFllzkGNkiD5w6x1ZPmZcRylgsHBpSrjm0J6qWJGS1TIT9M4x6MCLgG8WkZcDdWBNRH5GVV8NPAwgIs/HhWSGoKqX/P+rIvI+4Gvph2qOLfYSfx2H//prPwzAw1//j4h3LVHTsnQFqptdOmsV2qciWqcMtQ1l6UqX2tOb0O2hN26RPPc+R/Db2u4b7pz3O9TLMjKO8x7HmOVlp1HuQyEDvUO7PaAFz7kPuglcuebCUiKoMZhWh97ZZTizRLTVwegSEpksoWqu3CD28XQSiw067Exj/GYxekcBM01S+XqHIkmA7POKMLXYqy1qlocoMQWU/urxiGBhBl1Vfwj4IQDvob9BVV8tIue9kTbAj+AYLwMQkWXAqOqWf/ww8A8XNdY7AR/8zcE0xUN/7seobLsippN/2KHxyUvuy9npuj6lqkSXrrsqyWSEEQfX9QYcBU8MnD6JXa2jxhBtNeGe80i75xKSYaLSa8dgLb0zS1S2luBkhd65VbprVSSpIarOA+/00Gs3kErFlfqrYnd2nTEHUl2XiRSywKAd5VBLHkXMqZGce2+oU/GvgR6kgZFPk56pxo/Uauju7sD7JabAHeShj8K3i8jr/eNfBN4JICJ3A+9Q1ZcDF4D3ieMLx8DPqeoHDmGsxxaP/bqTBF77gw2SlRr27EnM5Wv9xJiqM55hYw7fDWiglRnemFcqWUNiaUcuVNJL0JSJ4ql+JD4ZJ45NYXqW+MYu3WefR43QXYuxcb9Pa9RMsHevEq/WiDaamDhGU4VHVbQSIUnIWgHE8FhSHK66nfFY8u6BkNxIpJXKkUGimpukw88wDdH4+HlK3cwaYI/rEVsiwNEr/Rc9YjPMfvDggw/q448/PnnDEhkeOfFa1yjDa6MM6beH1Yt55DXKY9/RZ2U5042n2XLSrNs7fdVFyDjemiSOO336JPR6JOdP0brYcMtZgcbTO5CoY7vEsWvW0em4uL7/MZmTay55u7Xlh6wj8w/HCQ+ZEQ1sRoWT8qGYnLRuum907gyI8P6nfnyOoz1aEJEn9lvbciI+p19/8q9Mte2jN/7tvs83DW6vQGKJ+UPVaaanZfgT4qd5oauU5SJZc4wqdq3hhZ/EqyK2ho/jG0xrt4f6pgrsNF0D7p4iidJ4ahtzfQPT7qKn11zVpK/yFNOPC9ut7b48r5g7wpgDPGbfW/yG2kHvOv88fS2HjMIo0lfxLDEeqeDdpL8DQln6f4cjE2ZKcu3NCppgQL8sfvBF42iKjXpW0dm9a414vYnsmCHRrfS4AwnVes0rRgoYoXatibm24aonb65nx0hb6WWywOCSqUds6XtQGGXUHzKvLA6ZjBCMy/TaE+MapZw7s4DRHkMcsQhH6aHf4dBULRD6nlzABBEjmEqMaTQwjYZv3hC7pFq10t8PvA5IhHQT4hu7yM1NdGt7rGaLGGfAtZPK8ArStSSNCiw3nFJjp4Pd2XUefS4clDdQZWWjw2P2vaM9+ACpzo2pVt1nXK+5yl1VXvbA3zmAkd7GSNs+TvN3QCg99BIORUtwIyAV17ChUceeO4HZbEK74+iI4JUfq85zP7lG87lnqV/edgJdm1tDTSb6p+sXwjjtER8Xt5bGF9fRazewrfawBxRMNvOmeB5HpEZ9ZLwdvEyDb+3X7SGdjstrBNK8JUbgiHnopUEvARR4z2myrBK7UEi7jXQTl+A8c8KVl+82EVXsXachUVSE3pKhd6JO5coNVx2aM+YjE6w+1m4ueemeTtd5/CljI9BcuZ0oh0cFj9n3Dhj1gedqnbRDpjNkEGYrYLszoUeOs18a9DscaQLRyf4OQ7s92Nx0HPOnrjhdmJ1mpu0ip06ilYj2+TrdlYilyy2kk6C7zcIydTFS6FmnevLa9uGZQPmwpM/NB/kQzDQhmRJjcCfK55a4PZCpQ1a/AwhobGrR7R1sGiJJRbgqcb8DUatH1PJ0xF5CtL7jGlingl4BVW4UAyUVwnrk9N/IpG0LW8iVKHGUcMScjdKglwD6HnpYiZgPjQwIcSW+ZdzODhIZzEqNeLPl2tl5YzygwMh06oWp1k3msZfGvMQRhXL0vp+lQS8xgKIY9zhD/LIL/xuAq9a09HW1fTI0FX+alRueP+eokFCJEocGvTMbXJS4DbDXROP7r/zr7PFL/9Sb3QPVvixwQWPsgxxfiRKLxFFLih6r0n8RuQZ8cU6HOwschW5J5TgGUY5jEEdlHHB0xjLNOJ6lquf2cxIR+YA/1zS4rqqP7Od80+BYGfR5QkQePwp9TMtxlOO4HcYBR2csR2Uch4GyUrREiRIljglKg16iRIkSxwSlQR+Nwh6mh4ByHIMoxzGIozIOODpjOSrjOHCUMfQSJUqUOCYoPfQSJUqUOCYoDXqJEiVKHBPc0QZdRL4gIh8XkY+JyFDvOhE5ISL/WUR+V0Q+KSLffYhjOSUi7xOR3xOR/yEiX7mgcZwUkV8QkU+LyKdE5Otz74uI/LiI/KEfy1cf0ji+XER+U0TaIvKGRYxhynF8p78PvycivyEiLzykcbzCj+FjIvK4iPy5wxhHsN3XiEgiIt+6iHFMMxYRebGIbPh78jERedOixnJkoKp37B/wBeDsmPd/GPhn/vE54CZQPaSx/HPgzf7xlwMfWtA43gV8r39cBU7m3n858H5AgK8DPnpI4zgPfA3wY8AbFvgdmTSOPwuc8o9fdoj3Y4V+TuxPAp8+jHH41yPgvwG/CnzrIX42LwZ+ZVHnP4p/d7SHPgUUWBURwf1gbgK98bssDF8BfAhAVT8NPCAiF+Z5AhFZA74B+El/no6qruc2ewXw0+rwW8BJEbl40ONQ1auq+ttAd57n3sM4fkNVb/mnvwXce0jj2FZvxYBl3Hf3wMfh8X3AfwSuznsMexjLHYU73aAr8EEReUJEXlfw/r8E/hhwCfg48P2qC1PjmTSW3wX+KoCIfC3wLOZvPJ4DXAPeKSK/IyLvEJHl3Db3AE8Gz5/yrx30OA4Cs47je3Crl0MZh4j8FRH5NPBfgNcexjhE5B7grwCL7o4x7Wfz9T5k+n4R+eMLHtOh40436C9S1a/GLZVfLyLfkHv/pcDHgLuBrwL+pfcMDmMsbwFOicjHcB7Q7zD/1UIMfDXwr1X1TwE7wBtz2xS0G5q7NzjNOA4CU49DRL4RZ9B/8LDGoarvU9UvB74F+EeHNI63Aj+oqotWrZpmLP8Tp9nyQuD/Av7Tgsd06LijDbqqXvL/rwLvA742t8l3A7/owwt/CHweF78+8LGo6qaqfreqfhXw13Ex/c/PeRhPAU+p6kf981/A/Wjy29wXPL8Xt4I56HEcBKYah4j8SeAdwCtU9cZhjSOFqn4E+DIRmVY4ap7jeBD4eRH5AvCtwL8SkW+Z8zimGov/zWz7x78KVBZwT44U7liDLiLLIrKaPgYeBj6R2+xLwEv8NheAFwCfO4yx+Ix+2rX3e4GPqOrmPMehqs8AT4rIC/xLLwF+P7fZLwN/3bNdvg7YUNXLhzCOhWOacYjI/cAvAt+lqp85xHE81+d68MyjKjDXyWWacajqs1X1AVV9AGdk/3dV/U/zHMe0YxGRu4J78rU4e7eICffI4E7WQ78AvM9/3jHwc6r6ARH5mwCq+hO4Zeu/E5GP40INP6iqi5AHnWYsfwz4aRFJcF/c71nAOMCFc37WTx6fA747N45fxTFd/hDYxa1iDnwcInIX8DiwBlgR+dvAV8x7kps0DuBNwBmcJwrQ08Uo/U0ax/+Cm2i7QBP4tiBJepDjOEhMGsu3Av+biPRw9+RVC7onRwZl6X+JEiVKHBPcsSGXEiVKlDhuKA16iRIlShwTlAa9RIkSJY4JSoNeokSJEscEpUEvUaJEiWOC0qCXKFGixDFBadBLlChR4pigNOgljiW8HvfviUjdV+J+UhakIV+ixFFBWVhU4thCRP4xUAcaON2Pf3rIQypRYqEoDXqJYwtfEv7bQAv4swegAFiixKGiDLmUOM44jWtMsorz1EuUONYoPfQSxxYi8svAzwPPBi6q6t865CGVKLFQ3MlqiyWOMUTkr+OUD39ORCLgN0TkL6rqfzvssZUosSiUHnqJEiVKHBOUMfQSJUqUOCYoDXqJEiVKHBOUBr1EiRIljglKg16iRIkSxwSlQS9RokSJY4LSoJcoUaLEMUFp0EuUKFHimOD/D8x5G2eVRAGfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da.where(da.values>da.attrs[\"nodatavals\"][0]).plot() # we can see that the DataArray contains missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll look at how weight builders handle missing values. Firstly we'll slice the `DataArray` to reduce overall size for easier visualization.\n",
+    "\n",
+    "This time we'll create `WSP` object using `da2WSP` method inside `raster.py`. Since our DataArray is single banded and all of its dimensions belong to the default list, we only have to ship the DataArray and the type of contiguity we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Slicing the dataarray\n",
+    "da_s = da[:, 330:340, 129:139]\n",
+    "w_queen = raster.da2WSP(da_s)  # default contiguity is queen\n",
+    "w_rook = raster.da2WSP(da_s, \"rook\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After plotting both contiguities and sliced `DataArray`, we can see that the missing values are ignored by the `da2WSP` method and only indices of non missing values are stored in `index` attribute of `WSP` object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4lqpeHvwR50aShZT+EzgfGEhyuuzvgX9U1UMi8iXgK6o6M9VeSU5jqJVk4ZB/J2nQ+5Kc31+tqk0iMgP4D5JTadpIPrh/i6rWiUglyWeZziT5nNR6YKCqulEI6fs3i+Q04vRqbiNJFvCYTrID+X8k71KWk8wbe4rkcxZK8oH5W1V1TcqwPg+cCziqOjT1/Nh3SD4HNpRk9bl7VfXn2d7bMAzDMAzDMIyOxYxrBilj8iVV/VLq98nAK3xgLkeTNEYfzjb1I03nB0C9qt6T8foEksbnwyLyLDBXVee3sy+dblwN43hFRC4lWfChDHgwNRU9ff1Eknevp5EMPv9J2rr/BXyF5M2NGuDLmqxqahiGYRiGYZQYmyrsQXd+XsEwuhMiUgbcTbJC6ySS060mZTTbB9zGBxmb7rajUq9PV9UzSBpfv5VXDcMwDMMwjA4mXOodOJ5JTU99UD8IU35WRIaQjCTIfF7hq6mfn+PY5xXGkXxeQUg+J/TJlPZfSFbz7Ssi9cD1uUZmDeN4pvqiPrp3X5tnu2WrWuar6qXtNPkwUKuqMQAReYrk8ydr3AaqugvYJSKXZdk+DPQWkVaSVaq3+T8KY+jQoTpu3LhS74ZhdFuWLVu2R1WHebc0OhLr6wyj48nV35lxzUBVFwGL2lk/Lu3nbSSLMLm/fyTHNj8jOX0x83WH5POz/zvLuqxahtHd2LOvjcXzvR8ZLh/x/kQRWZr20lxVnZv2+yiS1atd6vGZCaeqW0XkJySfbW8C/uw+a274Y9y4cSxdutS7oWEYBSEim0u9D4b1dYbRGeTq78y4GoZRYpQ2dfw03KOq09tZL1nFfZCK8rgCOJlkMbLfiMg1qvqYn+0NwzAMwzCMjsWecTUMo6Qo4KCeiw/q+SBqCj4opOaHS4CNqrpbVVtJTum3rDrDMAzDMIwugo24ApHyPhrtNTBw3QkTRwSqV7MvZxHjgikPB5IT3+Fo1sG04jmt/6hA9Rri7waqB1AhvkYj86KsA+5ZLVvVUvDzVw6BHOMSYHwqC3kryeJKV/vctg44J5XB3ARcTDJb2DAMwzAMw+gCmHEFor0GMmNKIJnpx7DgjW8HqnfyY/8eqB7AyOEHAtfsCFrbyjpE9+1LfxCo3sJNpwaqBzAt0ujdKE/6h3oHrlk24r2Cnr9SlLYAYrlUNSEiXyNZrbsMmKeqq0Xk5tT6+0TkRJKGtD/giMg3gEmqujiVq7wcSADvAHOzvY9hGIZhGIbR+ZR0qrCIXCoi60WkVkTuzLJeROTnqfWrRGSan21F5OupdatF5D8641gMwygMBVpxPBdfWqovqeoEVT1FVe9KvXafqt6X+nmHqo5W1f6qOjD186HUuu+q6kRVPUNVv6CqLR11zIZhGIZhGEZ+lGzENS1zcTbJZ9OWiMiLqromrdkcYHxqmQHcC8xob1sRuYhkkZUPqWqLm6FqGEbXxeczrIZhGIZhGEYPpZQjrkczF1U1DriZi+lcATyqSd4CBorICI9tbwF+6I6WpHIbDcPooijQpuq5GIZhGIZhGD2XUhrXbJmLmZVycrVpb9sJwEdEZLGIvCYiZwe614ZhBI7jYzEMwzAMwzB6LqU0rn4yF3O1aW/bMDAIOAf4R+BpEfmb9iJyo4gsFZGlra1H/O+1YRiBoihtPhaje+A4wdyGCEqno3RdnZ6mFzRd/Xi7+udnlI6ufm509e9CV9cLmq5+vF3l8ytlVWE/mYu52kTa2bYeeE5VFXhbRBxgKLA7XVhV55KqGtq/7yi7KjaMUqHQZt/AghGRS4Gfkayk/KCq/jBjvaTWfxxoBL6kqsu9thWRrwNfI1ll+Q+q+k9B7G8oFGLBggVF68yePTuAvflbgt6/nqYXNF39eLv659edsL4uWLr6d6Gr6wVNVz/ervL5lXLE9WjmoohESGYuvpjR5kXgi6nqwucAB1V1u8e2LwAfBRCRCSRN7p4OPxrDMApCEVp9LMbfklaobg4wCbhKRCZlNEsvcncjySJ37W6bUeTudOAnHX80hmEY2bG+zjAMKOGIq5/MReAlknfOaknePftye9umpOcB80TkXSAOXJsafTUMowuigGPf0EI5WqgOQETcQnXp1dmPFrkD3hIRt8jduHa2tSJ3hmF0JayvMwyjpFOFUdWXSJrT9NfuS/tZga/63Tb1ehy4Jtg9NQyjI2mzEdVCyVaoboaPNrmK3LnbukXu7gKagW+p6pIA99swDCMfrK8zDKO0xtUwDEMx41oEnVHk7mySRe4qM2eviMiNJKfkMXbs2Dx22zAMIy+srzMMo6TPuBqGYQDgqHguRlaKKXLX3rZHi9yp6tskE4mGZr65qs5V1emqOn3YsGFFHYhhGEY7WF9nGIYZV8MwSos74uq1GFnpdkXuYrEYixYtIhaLdcbb5U3Q+9fT9IKmqx9vV//8jiOsr+tkuvp3oavrBU1XP97O+vxsqrBhGCVFEVq1rNS7cVzS3YrcxWIx5s+fTyKRYO3atVRXV1NZWdnRb+uboPevp+kFTVc/3q7++R1PWF/XucRiMf64YAGJyIDAvgu/e+tdaGkMUG81NDcEp7d4DTQe6LL/H79bsgEObA/ueJe9B/vqA9P7w/+sgP1bO/zzM+NqGEZJsWdci6M7Fbmrq6sjkUjQMvESWPcydXV1Xeriwd2/xnO/BJE+PN8cJlIbydr2/rrFR3+Ox6NZ28TjVSTOHwcioNp19eJHqHjzYfv/KFSvi35+xxvW13UedXV1JCIDaD7nWpoTLTzfHCrqu9AUn4BOOw0QmlubA9Zr4vnmsiL1TkXPPC15QfI/D3W5/4/X9veheeIlEOlDc6Kp6L6pqfVU9IzTQKA5HoTeRPSsiXBkb4f3dWZcDcMoMUKb2lMLRrLoydq1a2kcfirh2kVdrgiKu39E+tD/jfvbvas8e/ZHjv6cK7TdHXU5dP5NXV4vHA7b/0cRel3x8zNKx/HS1zUnWkCEmZGtzKgakbWt13dhyb4y3tgdovz1+zh8zpdBhAsi9ZxdNbIgvTf3lvH2HiH8+r0cPud6kBAXRbYwrWpUQXp/2R1m+T4IL7qHw+d/heYZ1zAwWp9VqxT8eWc5R/qOpO+iezh8/s1AiOrIJs6oyn7OeB3vS9vL2XioDRb+nMMXfBURYU5kI5OqTipI78Wt5WxtS+AsvIfDndDX2dWiYRglRQGHkOdidH8qKyuprq4mHA53ualaEPz+9TS9oOnqx9vVPz+jdHT1c+Po/pWFmBXdxpt6Mlsa8/87vGRfGW/uK+eGU1qZc/GFhMtCXBDdwRtaybam/Pfrzb1lLNtfzg2nJJhz8SzCZcL50R28rqewszl/vb/sDrPqYJibTkkwZ/ZFhEPCOdHdvKZV7G3JXy9o/ryznNiREDePb2PO7NmEQ8JZ0X0s1Akcas1f76Xt5WxtCnHLeIc51dWEBaZED/Iyp3I4kb/ei1vL2R0PcdN4Tep1wvlsV4OGYZQcK85kuFRWVhKJRLrchZxL0PvX0/SCpqsfb1f//IzS0dXPDXf/ZlSN4NOj4jy7NZKXeXVN6/XjmukT/kDv3KrhfHJknKfro3mZV9e0XjeumYo0vZlVw7n8xDhPbonmZV5d03r9uGaiaXoXVg2jenicx+qiJTWvrmn9yrgWwqEP9u+S8YO5YFichzdF8zKvrmm9flwLoTS96vEDOX9wK/M2RfMyr65p/fJJx+p19PlsxtUwjJKimizO5LUYhmEYhtH5jKlw8jKvmaY1k3F9nLzMa6ZpzaSqn5OXec00rZlM7O+U1LxmmtZMpg508jKvmaY1k7MGt+VlXjNNa2diz7gCokqopYAx8k5GneDPjvotQwLXPGHEgcA1O4L+0WYufvX2QDWjZdWB6gEMjR4JXLNXqCPO9/cK2ipZnMnuoRmGYRhGVyXdvH56VJwxFU7Wdl6m1SXdvF45upmRvbO38zKtLlX9HC4naV6vGtPM8Ox1hDxNq8vE/g6QNK/XjG1mSK/cbYPEy7S6TB2Y3L+HN0X50rhm+pdnb+dlWl3OGtwGwLxNUa4b10zfHJ9NKU0r2IirYRglJ1mcyWsxDMMwDKN0eI28+jWtLl4jr35Nq4vXyKtf0+rS2SOvfk2ri9fIq1/T6uI18lpq0wpmXA3DKDFBFmcSkUtFZL2I1IrInVnWTxSRN0WkRUS+lfb6qSKyIm05JCLfCOwgDcMwDKMbkMu8PvNOfV6m1SWXeX1i6Za8TKtLLvP68Fub8zKtLp1lXn++qDYv0+qSy7z++OUNeZlWl1zm9fvz15XctIJNFTYMowvQpsUXXxKRMuBuYDZQDywRkRdVdU1as33AbcAn07dV1fXA1DSdrcDzRe+UYRiGYXQz0s3r2dsOsn5nA08ur8/btLqkm9cZOxtYvuUAL6zalrdpdUmfNnze7sO8/v5e/rh2Z96m1SV92vD5+xsZM6gif5F2+PmiWt6u25+3aXVJnzZ8weFmHn6rjtU7GvI2rS7p04YvPBLnnr/G2LS3seSmFcy4GoZRYhQJ6hnXDwO1qhoDEJGngCuAo8ZVVXcBu0TksnZ0LgbeV9XNQeyU8bfMnj273fX31y0+JjsuG47jEOqgv6BB719P0wuarn68Xf3zM0pHVz83it2/s7cd5I7fvkt5WYiHrzmLQRWRovTO2rKf259bRbQ8qdc/WrjebGDqxr189ZmVVETKePQL0+kTad/2eOlNqd3D159ZyS/+fkpg5tU1rfOuPouIh2v12r9Jq7fzxUeXMbRvhF9dcxZhj/PGS+/5ldv4/CNLGN6/Fw9ePa0oPZdiz2frJQ3DKCkKtGrYc/HBKGBL2u/1qdfy5XPAkwVsZ3QiXf0iP+j962l6QdPVj7erf35G6ejK58b6nQ2Ul4VQhfoDBQSzZrBu52EiZSESDmw7WPy83PU7DxMNl5FoU3YeLCDoNYMLqoZy+0Xj+fozK9myv7FovXxMqx/WbG+gb68wR1oS7G+MF623esch+vUK09Cc4FBTMEU9iz2fu+63wTCMHoEitKn3AgwVkaVpy40ZUtnmG2s++yIiEeDvgN8UdjSGF46TvRJlqXQ6StfVMT3Ty0fP6D509XOjWN1n3qnnyeX1zLv6TO76xCS+/fs11Gw7WLDeE0u38MKqbTx8zTS+N2cid/z2XdbtbChY7+G3NvPHtTt59AvTuHP2BG5/vobY7sMF67nMrBwciHl1TeuDnzuTSDhU9P/Hj1/ewOodDTx+7Vlcf944bnpqBbsPF27Wvz9/HZv2NvLYtWdxzdljueHJ5ew7UrwZLvY4SzpVWEQuBX4GlAEPquoPM9ZLav3HgUbgS6q6vL1tReR7wA3A7pTM/1bVlzr+aAzDKBSfxZf2qOr0dtbXA2PSfh8NbMtzV+YAy1V1Z57bGT4JhUIsWLCg3TbxeNSzjdcUt0IJev9MrzC60/GW8nw2SkdXPzeK2b/06sFD+kYZ0jfK9y+fxJ0vrGo3KieXXnr14IEVvZhW0YvvzpnIN59d0W5UTi699OrB/aIRzj15CHfMnsDXf/NOu1E5fv8/LqgaCsAtTy4rKConvXpwNDV9uZj/j/TqwZFwmMtOHwHAdY8uaTcqJ5deevXgSDjMp6aMBODaRxe3G5XTGedzyUZc0wqpzAEmAVeJyKSMZnOA8anlRuBen9v+p6pOTS1mWg2jC6NKUHE4S4DxInJyauT0c8CLee7OVdg0YcMwDMPISq7Im8kjB7QblZOLXJE308YMajcqJxe5Im/OPXlIu1E5+XJB1dCCqg3nG3njRa7Im8tOH9FuVE4uckXefGrKyHajcjqLUk4VPlpIRVXjgFtIJZ0rgEc1yVvAQBEZ4XNbwzCOCwTHx+KFqiaArwHzgbXA06q6WkRuFpGbAUTkRBGpB24Hvi0i9SLSP7WugmQ9guc66EANwzAM47jFK6fVK+c1E6+cVq+c10y8clq9cl7zJd+onM4yrS5eOa+ZeOW0euW8dgalNK5+CqnkauO17ddEZJWIzBORQcHtsmEYQaMENuKKqr6kqhNU9RRVvSv12n2qel/q5x2qOlpV+6vqwNTPh1LrGlV1iKoW/pCOYRiGYXRDvEyri1/z6mVaXfyaVy/T6lIq89rZptXFr3n1Mq0upTavpTSufgqp5GrT3rb3AqeQzGTcDvy/rG8ucqNb5CWeKL4ymGEYhaEIrVrmuRiGYRiG0fn4Na0uXubVr2l18TKvfk2rS2eb11KZVhcv8+rXtLqU0ryW0rj6KaSSq03ObVV1p6q2qaoDPEByWvHfoKpzVXW6qk6PhIMNEjYMIz/aCHkuhmEYhmF0LvmaVpdc5jVf0+qSy7zma1pdOsu8ltq0uuQyr/maVpdSmddSXg36KaTyIvBFSXIOcFBVt7e3beoZWJdPAe929IEYhlE4Cjga8lyMnkEsFiMejxOLxUq9K1kJev96ot6iRYu69P51ZT2j+9DVz41YLEZza4I3dofyNq0u6eZ1ce12mlvbeHuP5G1aXdLN65LabTS3trF8H3mbVpd087q8dmvR/x/p5nVFbT2/WraTDQecQExrLBajqbWNjYfa8jatLunm9d3aOppaHbYeTuRtWl3Szeua2s2dcj6X7GrQTyEV4CUgBtSSHD29tb1tU9v8h4jUiMgq4CLgf3XWMRmGUQhCm4/F6P7EYjHmz59PIpFg/vz5Xe6CLuj966l6NTU1XXr/uqqe0X2IxWL8cf4CEgmnS54bsViMF97dgSNllL8+l511he/fmAqHc2Uji5pH4UiI8Otz2VGE3rg+DjNkMwubRyf1Ft3PtiL0qvo5TJfNLGgcFch3dWJ/h6myhfnNY9kVGoQsuoe6TcX3Jc9tBiUEC+9mUxF6Uwc6TGIbf2galxw4WHhPUXpnDW7jVN3B744E8/l5IaqZj5X2PAb0GannnHZj4Lrzl34vUL1xj/4oUD0ATQRvCE4YcSBwzY6gfzSAuSEZRMuCny8xNHokcM1eoeD384GzH13mkbOaldFnDNDbnj7Ps90dp/+pIH2jc5g+fbouXbrUV9tcOW+LFi2ipqaGxnO/RMWbDzN58mRmzZqVtW1H5l567t+sr4Eq4XCYSCSStW366/F49tD2eDxOIpGAcAQS8Z6jJ9KzPj8RKhb9sujzWUSsD+wCBNXXrXxvM80zbySy6kXOHN6rS/V1T769kbre46C8N7Q2Ey4LFfVdaIon0FAZIAHptaGhULB6Ekr2Ta1NhMvKitNrbUuazC6r56BISq+RcFlxfV1SD0i0BNLXQe7+roCBdcMwjOBQFSu+ZAAwduxY1q5dC5E+hMNhxo4dW+pdOoaj+6dK/zfup7q6msrKyqxtZ8/+yNGfc10cuiNyh86/yfS6uV5XPJ+N0uH2Jc2JZuKnX4pEd5V6l46ydF8ZOwdMoO/rczl8zpdBhPMi2zm3anjW9l7fhTf3lvH2HiH8+r0cPuc6EGFmZCszqkb8TVs/en/ZHWb5PggvuofD510PIlwQqefsqpEF6S3cFWb1fiW06B4On3cjSIiLIluYVpUZdOJPb8HOctYfcJBFv+Tw+TeDCBdH6phaNbogvZe2l7PxUBss/DmHP3IriDA7sokPVWXvT7z0XtxWztaGBM7Ce5J6hKiObOKMAvWeqy9nd1sriYX3crgT+jp7cMwwjJITVByOcXxTWVlJdXU14XC4XdNQKoLeP9MzPaNncvTcKCujOrqFFTqGdYdK/3duaaoQ0w2ntDLn4gsJl4X4aHQri/UkNh3Jf//cQkw3nJJgzsWzCJeFmBXdxpt6sq+c10zcQkw3nZJgzuyLCJeFuCC6gze00lfOayYLd4VZ31DGTePbmDP7YsJlwvnRHbyupxRUsGnBznLePxLi5vFtzJk9m3BIOCe6m9e0ylfOayZuIaZbxjvMqa4mHBI+HN3DqzqBA9kHP9vlxW3l7G4JcdN4Pap3VnQfC3WCr5zXTJ6rL+dQIsQN40nqdUJfV/pviWEYPRoFHMRzMXoGlZWVRCKRLnuRH/T+mZ7pGT0T99yYWjWaa8Y2M39npKTm1TWt16UKMbn7d3bVSK4c3cwL2yJ5mdfM6sGu3oyqEb5yXjPJrB7s6p1bNdxXzmsmrmm9/uQWIqEP9GZWDS+o2rBrWt1CTK7ehVXDfOW8ZpJZPdjVu6hqKJecEOeRzdG8zKtrWt1CTK7eJeMH+8p5zcQ1rV8ce6xeR/d1ZlwNwygxYiOuhmEYRo9lSC9Kal4zTWsmI3uTl3n1irzxynnNxCvyxivnNZNM05pJvlE5maY1E6+c10y8Im9OH+DkZV4zTWsmXjmvmWSa1s7ErgYNwygpyTgc8VwMwzAMo7tSKvPqZVpd/JpXvzmtfs2r35xWv+bVy7S6+DWvXqbVxa959ZvT6te8eplWF7/mtZSmFaw40wcEXV1ZhOrp3wtUMvxPwf93JfZHA9dsdYIvtLN/Z7/ANVuGHQ5cs0+vAh468CAkwVf+PmtQXeCaxdBm99AMwzCMHo5rXh+riwJxJvZ3OvT9/JpWF9e8Pl0f5ZMj44zrc+z++TWtLunm9dOj4oypOFbPr2l1STevV45uZmTvY9f7Na0uVf0cLidpXq8a08zwjEtmv6bVJfn/mTSv14xtZkivY9f7Na0upw9I6j2yOcq1JzUzMKMwsF/T6jJ1YFLv4U1RvjSumf7lx64vtWkFG3E1DKPEKEJCyzwXwzAMw+judNbI63Mrt+ZlWl1yjbw+sXRLXqbVJdfI68Nvbc7LtLrkGnm9/6+xvEyrS66R11+89n5eptUl18jrj1/ekJdpdck18nrX/HV5mVaXXCOv3/nDmpKbVrARV8MwSowqtNlUYMMwDMMAjh15nVK7hwuqhgaq/9zKrTyxtD5v0+qSPvJ61pb9rNt5mBdWbcvbtLqkm9eztx1kWd0B/rh2Z96m1SXdvM7Y2cBr7+3mtdo9eZtWl/SR1/N2H+YPa3ayePO+vE2rS/rI6/n7G3lqWT2rdzTkbVpd0kdeZx5sZt5bm9i4tzFv0+qSPvJ6weFmfvFajF0NLSU3rWDG1TCMLoA9w9qz8Aogv79u8THZcdlwHIdQB/0FDXr/TM/0Snk+G6Wj2HPj3H2N/MOzKwECM6+uaX3gqjMZVBFpt63X/s3Y2cDtz60iUhbi0S+eRf9ocXpnbzvIP/32XXqHy/j1tdPpE2nfpnjpnbVlP//r2VX06RXmsS+cRbQIvdnA1I17ufU3KxkQDfPrL55NxMO1eulNqd3DDU++w5CKCI988SzCHn2Al94Z63bx5ceXcWK/Xjz4+WlF601avZ0vPLqM0QOizL3qTM8+qjP6OuslDcMoKYrgaMhzMYx0uvpFftD7Z3qmZ/Q8Thpcwc8+PYWfvvoer9fuKVovH9Pqh+VbDhAtD+EAtbuPFK23rO4AvcNlJFSJ7Sleb8nm/fSJlNHa5rBpfwFBrxksrTtA315hmhMO9fsbi9ZbvGkf/aNhjsQTbD9YQHBspt7mfQzsXc7B5lZ2NxRfc2Xxpv0MqihnX2OcvY3B1HAptq+zntIwjJLThnguRvfAcYIpNuLqmJ7pdSc9o/sQ1P/pmIHRQMyra1rv/+wUBlVEit6/J5Zu4YVV25h39TT+44oz+Lc/rmP5lv0F6z381mb+uHYnj3xhGnddPolv/34NNdsOFqx3/19jvFa7h0evmcZ350zkjt++y7qdDQXr/eK191m8eR+PfeEs7pw9gdufryG2u/Ainz9+eQOrdzTw2BfP4vaPjufrz6xkSxFm+K7569i4t5FHvzCNr37kFG59ekVRZvg7f1jDroYWfn3NNK4/bxw3PbWC3YeLN9fFnnc2VdgwjJLixuEYPYNQKMSCBQvabROPRz3buFPw/Oj5IR+9oPfP9EzPa0qpcfwR5Lnhjrx+9allrFyZf7Xh9OrBQ/pGi96/9OrBAyt6MbCiFz+64gy++eyKrNWGvfTSqwf3i0aYPDLC9y+fxJ0vrMpabdhLL716cEU0wrQxEb47ZyLffHZF1mrDXnrp1YOjkTDnnjyEO2ZP4Ou/eSdrtWEvvfTqwZFw+Og08FueXJa12rCXXnr14Eg4zCUTTwDghseXZK027KWXXj04HA5z2ekjALju0SVZqw176aVTbF9nI66GYZQYqypsGIZhGO1x0uCKgqoN5xt540WuyJuJw/v5ynnNJFfkzeSRA3zlvGaSK/Jm2phBvnJeM8kVeXPuyUN85bxmkivy5oKqob5yXjPJFXlzycQTfOW8ZpIr8uay00f4ynntaMy4GoZRUtyqwl6LYRiGYfRk8o3K6SzT6pIrKicXXjmtuaJycuGV05orKicXXjmtuaJycuGV05orKicXXjmtuaJycuGV05orKqczMeNqGEbJseJMhmEYhuGNX/Pa2abVxa959TKtLn7Nq5dpdfFrXr1Mq4tf8+plWl38mlcv0+ri17x6mVaXUptXuxo0DKOkJKsKey9+EJFLRWS9iNSKyJ1Z1k8UkTdFpEVEvpWxbqCIPCMi60RkrYicG9AhGoZhGEZgeJnXUplWFy/z6te0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TauLl3n1a1pdSmlezbgahlFyHMRz8UJEyoC7gTnAJOAqEZmU0WwfcBvwkywSPwP+pKoTgSnA2mKOyTAMwzA6ilzmtdSm1SWXec3XtLrkMq/5mlaXXOY1X9Pqksu85mtaXXKZ13xNq0su85qvaXUplXktqXH1MToiIvLz1PpVIjItj22/JSIqIsEkNhuG0SEokHDKPBcffBioVdWYqsaBp4Arjnkv1V2qugQ4ppsVkf7ABcBDqXZxVT1Q/NEZ+RKLxYjH48RiscD0Fi1aFKhe0PtneqZn9DyCODfSzetrtbt58u2N/HV3KBDTGovFaG5t4+09krdpdUk3r2/W7qS5tY3l+8jbtLqkm9fFtdtpam1j9X7N27S6pJvXJbXbaGptY/0BJ2/T6pJuXpfXbqWptY2Nh9ryNq0u6eZ1RW09Ta0OWxsSeZtWl3Tzuqq2jqZWh91HWvM2rS7p5vXd2rpO6etKZlx9jo7MAcanlhuBe/1sKyJjgNlAXQcfhmEYxeJjmrDPqcKjgC1pv9enXvNDJbAb+JWIvCMiD4pIn/wOxCiWWCzG/PnzSSQSzJ8/v+g/gK5eTU1NoHpB75/pmZ7Rs4jFYvxxwQISbU7R58aQXnCh1PJW03DqoidT/vpcdtYVf+7+dvUuHAkRfn0uO4rQG9kbzpcYrzeNTOotup9tReiNqXA4VzayqGkkSojQovuo31S43rg+DjNkMwubR6OEkEX3UFeEXlU/h+mymQXNY1FCsPBuNhWhN7G/w1TZwvymscn4wIX3FKV3+gCHD0k9f2walxw4WHhvUXpTBzpMYht/ODyyU/o6UdUOE2/3jZPPj31PVatTv/8zgKr+e1qb+4FFqvpk6vf1wCxgXHvbisgzwP8FfgtMV9V2E5sH9Bmp50y8IcjDAwm+Cur7/xR87G5if47wqSIYOLrwwOhc7N/ZL3DNAcMKD47ORZ9eedQc98nQ3kcC1zxrUPD3dP5t8ovLVHV6vtsNmniCfnTe33u2e+78ezcD6d/luao61/1FRD4DVKvqV1K/fwH4sKp+PVNLRL4HHFbVn6R+nw68BZyvqotF5GfAIVX913yPp7MRkUtJTnMuAx5U1R9mrJfU+o8DjcCXVHW5z22/BfwYGObVj06fPl2XLl3qa59z5bwtWrSImpoaGmd9DVQJh8NEItkD6NJfj8ezf+/i8TiJRCLZHwepF45AIt5z9s/0itMToWLRL5k8eTKzZs3K2tZPtqGIFNTHdhe6W1+3csNGmi+4BRIthENS1LnWFE+goTJAgtVz2gL5LjTF29BQKLl/rc2Ey0IBfLcciEQD0WtqbUuaTAlm/47VayJcVlaknoMCSKhr6yVaAunrIHd/F7wT8k+20ZEZPtqMam9bEfk7YKuqrpR2zKOI3EhyFJdoZEBhR2AYRiD4HFHd43HRVg+MSft9NLDN5y7UA/Wqujj1+zPA3zyC0NVIm30ym+QxLBGRF1V1TVqz9JkrM0jOXJnhtW0pZq6MHTuWtWvXgir937if6upqKisrs7adPfsjR3/OdXHojngdOv+mLq2XSCQIh8Nddv9Mr3i9cDjM2LFjs7YzvOmufV1zaxOIMC2ym4uqsj/Z5nWuLdlXxhu7Q5S/fh+Hz/kSiDA9sosLq4YVpPfm3jLe3iOEX7+XwzO+VPR34S+7wyzfB+FF99Daqx8t0z/LeZHtnFs1vCA9SPtuzbwFRJgZ2cqMqhEF6S3YWc76Aw6y6JccPu8mEOGCSD1nV40sSO+l7eVsPNQGC3/O4Zm3goS4KLKFaVXZJ4B56b24rZytDQmchfdw+CO3gggXR+qYWjW6IL3n6svZ3dZKYuG9R/VmRzbxoars/ZOX3tNbIhxqa6Fl4X0c7oS+rpTPuGa7Us0c/s3VJuvrIlIB/AvwHa83V9W5qjpdVadHwhWeO2sYRsegENRU4SXAeBE5WUQiwOeAF33tg+oOYIuInJp66WJgTTubdBU8n+tN/f6oJnkLGCgiI3xs+5/AP/G3/XKHUVlZSXV1taeJ6256kydP7tL7Z3pdQ6+H0z37urIy5kTrWKWjWX0w/0vyJalCTDec0sqciy8kXFZGdbSeFTrGV85rJm4hphtOSTDn4llFn7tuIaabTkkwZ/ZFTB13Ah+NbmWxnuQr5zUXH3x+IWZFt/Gmnuwr5zUTtxDTzePbmDN7NuGyEBdEd/CGVvrKec3ELcR0y3iHOdXVhMuE86M7eF1P8ZXzmolbiOmm8ZrUCwnnRHfzmlb5ynnNxC3EdMN4jup9OLqHV3WCr5zXTJ7eEqHFgevGS1KvE/q6UhpXP6Mjudrkev0U4GRgpYhsSr2+XERODHTPDcMIlCCMq6omgK8B80lWBH5aVVeLyM0icjOAiJwoIvXA7cC3RaQ+VZgJ4OvA4yKyCpgK/CD4Iw0cP8/15jNzZRQcO3Ml6B32orKykkgkEtgfvuNBb9asWV16/0yv6+j1YLptX/ehqrFce1IzL++K5GVeXdN6faoQk6s3tWq0r5zXTDKrBxd77mZWD3b7urOrRvrKefXC3b8ZVSN85bxmklk92NU7t2q4r5zXTDKrB7t6M6uG+8p5zSSzerCrd2HVMF85r5lkVg929S6qGuor5zUT17R+fkz8GL2O7utKaVz9jI68CHwxVV34HOCgqm7Pta2q1qjqCao6TlXHkeycpqVGUwzD6IIoQkJDnosvLdWXVHWCqp6iqnelXrtPVe9L/bxDVUeran9VHZj6+VBq3YrULIwPqeonVXV/hx10cJR05oqI3CgiS0Vk6e7duz131jAMo0C6dV83MEJe5jXTtGbilfOaSaGRN7nwirzxynnNF6+c10y8Im+8cl4z8Yq88cp5zcQr8sYr5zUTr8gbr5zXTDJNa2dSMuPqZ3QEeAmIAbXAA8Ct7W3byYdgGEYQaGBThXsiJZ25kv7IxbBh2Z+nMgzDCIBu39f5Na9eptXFr3ntbNPqUirz6jen1a959ZvT6te8+s1p9Wte/ea0+jWvpTStUOIcVx+jI6qqX02tn6yqS9vbNov+OK/qcIZhlJYAn3HtidjMFcMwegI9oq/zMq9+TauLl3ktlWl16Wzz6te0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq+lNq1Q2qrCXQcFnGAlRQMW7CDCg5oDj8TZvyP46JpBJzYErhkpawtc84SK4CN2OsK0HW4LPgapGMyYFoaqJkTEnX1SBsxzZ66k1t9HcubKx0nOXGkEvtzetiU4DMMwjHbpSX2da14f2RwF4rjhIc+8U5+XaXVxzetjdcfqPbF0S6Cm9eG3NudlWl1c8/p0fZRPjiw+UjDdvH561Ad6v3jt/bxMq0u6eb1y9Adu88cvb8jLtLpU9XO4nKR5vWrMB3p3zV+Xl2l1mdjfAZLm9ZqxH+h95w9r8jKtLqcPSOo9sjnKtSd9oHfni++W3LSCGVcD2HTTtwLVO+mh/whUz+jeKDaiWgyq+hLJC7b01+5L+1mBr/rdNkubccXvpWEYRnH0pL4u3bxOXr+L/Y1xnlxen7dpdUk3r1Nq91B/oIkXVm0L1LT+ce3OvE2rS7p5PWvLfqaNGVTU/qSb17O3HWTRe3tYvHlf3qbVJd28ztjZwO9qtrN6R0PeptUl3byet/swTy6vZ+PexrxNq0u6eT1/fyMP/M8mdjW05G1aXdLN68yDzfzstVoa420lN61gxtUwjC5Am8/iS0b3wCuA/P66xcdkx2XDcRxCqb+gx7ueH7rT8Zpe+3pG96HYc2PmwWauf2I5ZSHh0S+cxaCKSFF65+9v5Kan3qG8LMSvv3gW/aPF6QH86s1N/GndLuZ9fhp9IsXZihk7G7jjt+/y3TkTmTZmUNGf39nbDvKPL7xL/2iYx754NhEP1+qld9aW/Xzj2VUMrijn0S9OJ+zxnW1PbzYwdeNebv3NSk7o24t510wrWm9K7R6+8uQ7jBoQ5cGrzvTsU7z0zli3iy89tpSTh/ThniunFKXnUmxfZ72kYRglRa04k1EAQV/km57pdSc9o3vwRmwPvcJCSGD5lgNF6/3l/b1Ey8sAWFF/qGi9h9/aHJhpBZg4vB8/uuIM/u2P61i+pfjC/ove20P/aJiWhMP6XcU/cvbK+t0MrCjnSLyN2t1HitZbuGE3gyvKOdjcSt3exqL1Xl6/i2F9Iuw7EmfrwQKCYzP1NuxieP8oOxua2dlQ/DRuKL6vs57SMIySoyqei9E9cJxgnv93dbq6XtB09eM1veL0jO5Dsf+nz7xTz5PL63nwqjO577Nn8svXY7yyflfBek8s3cILq7Yx7+oz+eVnpvDTV9/j9drC65e604MfvGpqIKbVJSjz+ovX3mfx5n08es1ZfP/ySXz792uo2XawYL0fv7yB1TsaePSaaXx3zkTu+O27rNtZuBm+a/46Nu5t5OFrpnHn7Anc/nwNsd2F10n5zh/WsKuhhYc/fya3f3Q8X39mJVv2F26G73zxXRrjbcy7aipf/cgp3Pr0CrYHYIaL/V7YVGHDMEqMjaj2JEKhEAsWLGi3TTwe9WzjTiHr6npB09WP1/SK0zO6D8WcG+nVg4f0TRZTvOfKqdzw+BJqampSzyD610uvHjywohcDK+AXfz+FW55cxsqV8dQzkv710qsH9/OYblwIrnn95rMr+OTIOOP65Ld/6dWDo5Ewk0cO4PuXT+LOF1bx6VFxxlTkp5dePTgSDjNtzCC+O2ci33x2BVeObmZk7+zHkUsvvXpwJBzm3JOHcMfsCXz9N+9w1Zhmhueon5lLL716cDgc5oKqoQDc8uQyrhnbzJBe+emlVw8Oh6dwycQTALjh8SVce1IzA3P8l3dGX2cjroZhlBwbcTUMwzCM3JE3IwZEfeW8ZpIr8mbMoApfOa+Z5Bt5UygTh/crKConV+TN5JEDfOW8ZpIr8mbamEG+cl4zyRV5c+7JQ3zlvGaSK/LmgqqhvnJeM8kVeXPJxBN85bx2NGZcDcMoKZbjahiGYRjeOa1eOa+ZeOW0euW8ZtJZptUl35xXr5xWr5zXTLxyWr1yXjPxymn1ynnNxCun1SvnNROvnFavnNfOwIyrYRilRaFNxXMxDMMwjO6Kl2l18WtevUyri1/z2tmm1cWvefUyrS5+zauXaXXxa169TKuLX/PqZVpd/JpXL9PqUmrzasbVMIySothUYcMwDKPn4te0uniZV7+m1cXLvJbKtLp4mVe/ptXFy7z6Na0uXubVr2l18TKvfk2ri5d59WtaXUppXs24GoZRYrynCdtUYcMwDKM7kq9pdcllXvM1rS65zGupTatLLvOar2l1yWVe8zWtLrnMa76m1SWXec3XtLrkMq/5mlaXUplXM66GYZQcVe/F6BnEYjHi8TixWKxH6AVNVz9e0zOMJLFYjObWBG/sDuVtWl3SzeurtXtobm3j7T2St2l1STevr9Xuprm1jeX7KLlpdUk3r2/W7qSptY31B5y8TatLunldXLudptY2Nh5qy9u0uqSb1yW122hqddjakMjbtLqkm9fltVtpanXYfaQ1b9Pqkm5eV9TW09TqcKixJW/T6pJuXlfV1nVKX2fG1TCMkmNThQ1IXsjNnz+fRCLB/Pnzi/4D2NX1gqarH6/pda3zxSgdsViM3/9lKY6UUf76XHbWFX5uDIzARbKBt5uH4UiI8Otz2VGE3pBecKHU8lbz8KTeovvZVoRe0IzsDedLjNebR6CEkEX3ULep8P0bU+FwrmxkUfMolBAsvJtNReiN6+MwQzazsHl0svjkwnuK0qvq5zBdNrOgeQwKJBbeW5TexP4OU2UL85vHokDLwvuK0jt9gMOHpJ4/HhnZKX2dqA1lMKBipJ5z6g2BakoHfK61/zv4rCyA2iu/HajeSQ/9R6B6AINOLDzkOReRsrbANUf0PRS4ZkdMkz21f+Eh5rn4f1OfXqaq0/PdrnfVSK36qff3790r/k9B+kbnMH36dF26dKmvtrly3hYtWkRNTQ2Ns74GqoTDYSKR7P1e+uvxePZ5SvF4nEQiAeEIJOLB6YlQseiXTJ48mVmzZmVt25G5nMf959fT9AI6X0TE+sAuQFB93cr1G2m+8ObkuRaSos61pngCDZUBAomWAPTa0FAIECIrf8uZw3t1qb7uV8t2sqtsMET7QWsz4bJQccfb2pY0rSIB6TkogIQ6QK+JcFlZl9JrTCg4DrTFA/vbmKu/6wID/4Zh9HTaHBtRNWDs2LGsXbsWVOn/xv1UV1dTWVmZte3s2R85+nOuixt3xOvQ+TcFrhcOhxk7dqzfQ+sUjqfPr6fpdcXzxSgd7ne1ubUZEKaU7+WSqsFZ23qda2/uLePtPUL49Xs5fM6XQYRpkd1cVDW0IL2/7A6zfB+EF93D4fOuI356NRLdnecRdhwLdpZzpO9I+i66h8Pn3QQinBfZzrlVw7O29zrel7aXs/FQGyz8OYdn3gIizIxsZUbViIL0XtxWztaGBM7Ce47qXRCp5+yqkQXpPVdfzu62VhIL7+XwzFtBQlwU2cK0qlEF6T29JcKhthZaFt7H4Y/cCiJcHKljatXogvSeqIvQx2mi5dW5ndLX2VRhwzBKjk0VNgAqKyuprq4mHA63axq6i17QdPXjNb2udb4YpePouVFWxmW961jNSFYcyP+S3C3EdMMpCeZcPItwWRlzonWs0tG+cl4zcQsx3XRKgjmzLyJcVkZ1tJ4VOsZXzmtH4xZiunl8G3NmzyZcFuKj0a0s1pN85bxm4hZiumW8w5zqasJlIWZFt/Gmnuwr5zUTtxDTTeP1qN4F0R28oZW+cl4zcQsx3TCelJ5wfnQHr+spvnJeM3ELMV03XpJ6IeGc6G5e0ypfOa+ZPFEXQYDrJpR1Wl9X0rNQRC4VkfUiUisid2ZZLyLy89T6VSIyzWtbEfm/qbYrROTPIpL9FodhGF0Cxdu0mnHtOVRWVhKJRAL7w9fV9YKmqx+v6RlGEvfcOKNqLF8+qZnXdkfyMq+Z1YNdvQ9VjfWV85pJZvVgV29q1WhfOa8dTWb1YHf/zq4a6SvnNZPM6sGu3oyqEb5yXjPJrB7s6p1bNdxXzmsmmdWDXb2ZVcN95bxmklk92NW7sGqYr5zXTFzTetXY5PThzurrSnYGikgZcDcwB5gEXCUikzKazQHGp5YbgXt9bPtjVf2Qqk4Ffg98p4MPxTCMIlEfi2EYhmF0R/pHyMu8ekXeeOW8ZuIVeeOV89rReEXeeOW8ZuIVeeOV85qJV+SNV85rJl6RN145r5l4Rd545bxmkmlaO5NSjrh+GKhV1ZiqxoGngCsy2lwBPKpJ3gIGisiI9rZV1fTqOH2wa17D6NpocFOFfczimCgib4pIi4h8K2PdJhGpSc3W8Fd5wzAMwzACwK959ZvT6te8+s1pLZV59ZvT6te8+s1p9Wte/ea0+jWvfnNa/ZpXvzmtfs1rKU0rlNa4jgK2pP1en3rNT5t2txWRu0RkC/B5bMTVMLo86ojn4oXPWRz7gNuAn+SQuUhVp1rlTsMwDKOz8TKvfk2ri5d59WtaXTrbvPo1rS5e5tWvaXXxMq9+TauLl3n1a1pdvMyrX9Pq4mVeS21aobTGNduVaOboaK427W6rqv+iqmOAx4GvZX1zkRtFZKmILI0nGn3usmEYHYGq9+IDz1kcqrpLVZcArYEfhGEYhmEUSS7z+sTSLXmZVpdc5vXhtzbnZVpdOsu8/uK19/MyrS65zOuPX96Ql2l1yWVe75q/Li/T6pLLvH7nD2vyMq0uuczrnS++m5dpdcllXm9/blXJTSuUNg6nHhiT9vtoYJvPNhEf2wI8AfwB+G7mClWdC8wFGNBruIb2B5u/6QwbGKgeQGtD8Dmu5f3iVD39/UA1JVweqB5AeSj4zNXyDshx3dtcEbjmKf33Bq65u6Vv4JqFohBU8aVsMzFm5LkrfxYRBe5P9RGGYRiG0am45vVXm6NMWr2dg00JXli1LW/T6uKa10c2R5m8fhdb9jfxx7U78zatLq55fawuypTaPVyQI3qnUH7x2vss3rwvb9Pq4prXp+ujnLVlP6+s383qHQ15m1aXdPN69raDvFiznY17G/M2rS7p5nXGzgaeWLqFXQ0teZtWl6p+DpeTNK/n7T7M3Dc30Rhvy9u0ukzs7wBJ83r+/kb+89VaQiIlN61QWuO6BBgvIicDW4HPAVdntHkR+JqIPEXyAvSgqm4Xkd25thWR8ar6Xmr7vwPWdfyhGIZRMAr4M65DM549nZthLv3M4miP81V1m4icACwQkXWq+noe2xs+8Qogv79u8THZcdlwHIdQ6i9yV9cLmq5+vKZXnJ7RfSj23JjZ0Mx1jy+jLBTisS+eRf9o+wMYnnoHm7n+iWVEwiEev/Zs+kTatwFeeufvb+Trz6wECMy8uqZ13tVnEfFwrV77N2NnA994dhUDK8p57IvTCXt8x7z0zt52kH984V2G9inn4S8Ur3fWlv38wzMrGTEgyryrp3n2Ae3pzQambtzLLU+v4KTBvbnvs2cWrTeldg/XP7GcCSf04ZefOROYXLCeS7F9Xcl6SVVNkJzGOx9YCzytqqtF5GYRuTnV7CUgBtQCDwC3trdtapsfisi7IrIK+BjwD511TIZhFIbPqcJ7VHV62pI5IupnFkc7+6DbUv/uAp4nOfXY6KIEfZHf1fWCpqsfr+kZBryyfjfRcBkhgb+8X/zsq/lrd1IRKQPgrY37itYbM6iCX/z9FH766nu8XrunaL18TKsfflezncEV5TTF21i19WDRei/WbGdon3IaWtpYu6OhaL0XVm1neL9e7G9sZcPuI0Xr/bZmOyP7R9nZEGfT3uIfg3xh1TbGDOpN/YFmtuwP5rHKYvu6Uo64oqovkTSn6a/dl/azAl/1u23q9U8HvJuGYXQ0wdT+9jOLIysi0gcIqWpD6uePAf8nkL0yjiGokSVXx/RMrzvpGd2HYv9Pn1i6hRdWbeOhq8+kpU255b9XAHDZ6SMK0nv4rc38ce1OfvX5aRxucbj16aTexaeeUPA+AowaEOUXfz+l6JFX17Q++LkziYSL/279+OUNrN7RwMPXTCO2t4k7fvsu350zkWljBhWkd9f8dWzc28i8z09j/a4jfPv3a/j+5ZOYPHJAQXrf+cMadjW0MO/qM1m1vYE7fvsuP7riDCYO71eQ3p0vvktjvI0HrprKki0Huf35Gn76qclUDivs0bDbn1tFSIQHPncmf43t4+vPrOQXfz+FMYOKeySu2P/XkhpXwzAM8Fc12AtVTYiIOxOjDJjnzuJIrb9PRE4ElgL9AUdEvkGyAvFQ4HkRgWS/+ISq/qnonTL+hlAoxIIFC9ptE49HPdu4U/C6g54futPxml5uPaP7UMy5kV49eGBFLwDu/exUrv/1EtasWcPUgU5eeunVg/tFI/SLwj1XTuWGx5dQU1PD6QPy00tn9uzZR0deb3lyGStXxlPPSPonvXpwNDV9uZjPL716cCQcZuLwfvzoijP45rMr+OTIOOP65He86dWDI+Ewk0cO4PuXT+LOF1bx6VFxxlTkp5dePTgcDjNtzCC+O2ci33x2BVeObmZk7/yON716cDg8hXNPHsIdsyfw9d+8w1VjmhkezU8vvXpwKDT56M2IW55cxjVjmxnSKz+9dIrt6+z2nmEYpSXAHFdVfUlVJ6jqKap6V+q1+9yZHKq6Q1VHq2p/VR2Y+vlQqhLxlNRyurutYRiGYXQmuSJvhveL+sp5zSRX5M2IAVFfOa9+GTOooqBqw/lG3niRK/Jm4vB+vnJeM8kVeTN55ABfOa+Z5Iq8mTZmkK+c10xyRd6ce/IQXzmvmeSKvLmgaqivnNeOxoyrYRilR30shmEYhtGN8cpp9cp5zcQrp9Ur5zVf8o3K6SzT6uKV85qJV06rV85rJl45rV45r5l45bR65bxm4pXT6pXz2hmYcTUMowsgPhbDMAzD6J54mVYXv+bVy7S6lMq8drZpdfFrXr1Mq4tf8+plWl38mlcv0+ri17x6mVaXUptXM66GYZQeG3E1DMMweih+TauLl3n1a1pdOtu8lsq0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TatLKc2rGVfDMEqPGVfDMAyjB5KvaXXJZV7zNa0unWVeS21aXXKZ13xNq0su85qvaXXJZV7zNa0uucxrvqbVpVTm1YyrYRilRUEd8VyMnkEsFiMejxOLxXqM3qJFi7r0/ple19Ezug+xWIzm1jbe3iN5m1aXdPP68nv7aG5tY/k+8jatLunm9dXaPUWfu+nm9bXa3fxq2U7WH3ACMa2xWIym1jY2HmrL27S6pJvXN2t30tTqsLUhkbdpdUk3r4trt9PU6rD7SGveptUl3bwuqd1GU6vDocaWvE2rS7p5XV67lcaE0tzclLdpdUk3rytq6zulrzPjahhG6bERV4Pkhcj8+fNJJBLMnz+/6D+Ax4teTU1Nl94/0+saekb3IRaL8fu/LMeREOHX57KjrvBzo38ELpYNLGsZikOI8KL72VaE3sAIXCQbeLt5GAnHKfrcHdILLpRa3moezq7wUGTRPdRtKv679dwmByUEC+9mUxF6I3vD+RLj9ZYRKOAsvKcovTEVDufKRha1jEKBxMJ7i9Ib18dhhmxmYctoFGhZeF9RelX9HKbLZha0jAXHoeWVuUX9/07s7/Ah3cL8IyM6pa8TVbsiHNBruJ434upANZ1hAwPVA1h3c45gpyIo71fYXZb2SDSVB6457ISDgWtGwm2Ba4oE/306pf/ewDU7gkdnzFumqtPz3a7XuNF64rf/wbNd3Q3/VJC+0TlMnz5dly5d6qttrpy3RYsWUVNTQ+Osr4Eq4XCYSCSStW366/F49n4sHo+TSCQgHIFEvOvqifSs4+1peiJULPolkydPZtasWVnb+sk2FBHrA7sAQfV1K9e/T/OFt0JbK2GhqHOtKd6GhkKAQKKFcEiK15NQsm8KQq+1LWkyO0KvtYVwWbF6TvL+uISgtZlwWahb6zUmFBwHQmXQ2kS4rKxgPceBZiel1xYPpK+D3P1dARMJDMMwgqUD/L5xHDJ27FjWrl0LqvR/436qq6uprKzM2nb27I8c/TnXxaE74nXo/JtMz/RKqhcOhxk7dmzWdkbPw+3rmuNNiAhnRA9SXTUwa1uvc+0vu8Ms3wfhRfdw+LwvAyGmlO/lkqrBBekt3BVm9X4ltOgeDp/3FRBhWmQ3F1UNLUhvwc5y1h9wkEW/5PB5N4II0yO7uLBqWEF6L20vZ+OhNlj4cw7PvBkkxIzIdmZWDS9I78Vt5WxtSOAsvIfDM28BEc6LbOfcAvWeqy9nd1sriYX3HtWbGdnKjKoRBek9vSXCobYWWhbed1Tvgkg9Z1eNLEjviboIfZwmWl6dy6GZt4KEuCiyhWlVo/LWcxx4bEsEbWmk8dUHOqWvs6nChmGUFj/ThM3Y9ggqKyuprq4mHA63axpMz/R6op7RfUg/Ny7vXcd6OZFl+8ry1nELMd10SoI5sy8iXBbmst51rGGkr5zXTBbuCrO+oYybxrcxZ/bFhMvKmBOtY5WOLqhgk1uI6ebxbcyZPZtwWRnV0XpW6BhfOa+ZuIWYbhnvMKe6mnBZGbOj9SzVk3i/IX89txDTTeM1pRfio9GtLNaTfOW8ZuIWYrphPEf1ZkW38aae7CvnNRO3ENN14+Wo3gXRHbyhlb5yXjNxCzFdN6Esef6VCedHd/C6nuIr5zUd17T2DsG1E8Kd1teZcTUMo8QIOD4Wo0dQWVlJJBIJ7A+f6Zled9Izug/uuTGp6iSuG9fMG/vK8zKvmdWDXb0zqsbypXHNvO6R85qJa1qvP7mFSOgDvQ9VjS2o2nBm9WBXb2rVaF85r5lkVg929aZVjeKqMc38bkckL/OaWT3Y1Tu7aqSvnNdMMqsHu3ozqkb4ynnNJLN6sKt3btVwXzmvmWRWD3b1ZlYN95Xzmk66af3MmGP1OrqvM+NqGEbpsRFXwzAMo4fSN0xe5tUr8qZ/OXmZ10zTmkm+UTlekTdeOa+ZeEXeDI+Sl3n1irzxynnNxCvyxivnNROvyBuvnNdMvCJvvHJe08lmWjsTM66GYZQeM66GYRhGD8avefWb0+rXvHqZVhe/5tVvTqtf8+o3p9WvefWb0+rXvPrNafVrXv3mtPo1r35zWv2Y11KbVjDjahhGqVFAxXsxDMMwjG6Ml3n1a1pdvMyrX9Pq4mVe/ZpWFy/z6te0uniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TatLe+bVcZySm1Yw42oYRhdA1HsxDMMwjO5OLvP68Fub8zKtLrnM6/1/jeVlWl1ymddfvPZ+XqbVJZd5/fHLG/IyrS65zOtd89flZVpdcpnX7/xhTV6m1SWXeb3zxXfzMq0uuczr7c+tysu0umQzr47jcNuzNSU3reDDuIrI10RkUEe8uYhcKiLrRaRWRO7Msl5E5Oep9atEZJrXtiLyYxFZl2r/vIgM7Ih9NwwjQGyqcMFYP2oYRk+gJ/V16eb1+ZXbePitzfxx7c68TatLunn9w+rt3P/XGK/V7snbtLqkm9dX1u/iF6+9z+LN+/I2rS7p5vX12j38+OUNrN7RkLdpdUk3r4s37eOu+evYuLcxb9Pqkm5el2/Zz3f+sIZdDS15m1aXdPNas+0gd774Lo3xtrxNq0u6eV23s4Hbn1tFSCRv0+qSbl5rdzVw27M19I+GS25awV+O64nAEhFZDswD5qtq0ZeRIlIG3A3MBupT7/Giqq5JazYHGJ9aZgD3AjM8tl0A/LOqJkTkR8A/A3e0uzNlZeiAvsUe0rHH1xz8f+5p/xW85rp/7B+4ZijSFrjmgcMVgWtOGrEjcE2nA6a0HmqNBq6ZcLrWZAsbUS2MLtWP5oFXAPn9dYuPyY7LhuM4hFJ/4U3P9LqTnvG39NS+7sIjcb7w66WEQ8ITXzqbPpH2L9u99C443My1v15Gr3CIJ689m2iRejMPNnPd48voEynjiS99mIiHa/XSO39/Izc8+Q79o2Ge+NLZhD2+E1565+0+zK2/WcnginIe/eL0ovVm7GzgH55ZyfB+vXj4mrM8v7NeemdvO8i3nq9h9MDePHDVmUXrnbVlP7c9s5JThvbh3s+eCUwuWG82MOX9Pdz6m5WcNrwfP/jE6cDpRe0fFN/XeW6pqt8m2Qk8BHwJeE9EfiAipxT8rkk+DNSqakxV48BTwBUZba4AHtUkbwEDRWREe9uq6p9VNZHa/i1gdJH7aRhGR2PPuBZKj+1Hg77INz3T60563ZAe2de9WLOdivIQZSHhz2t3Fa333Ipt9EsN2b7y3u6i9Z5ZsZWBFeUkVPnL+3uK1ntqWT1DKiLEEw7/E9tXtN6Ty+s5oW8vjsTbWFZ3oGi9J5ZuYcSAKAebE6zYerBovceXbmH0wN7sORJn9Y6GovUeW7KFkwdXsO1gM+t2FqfnOA5PLN/KKUMq2Lyvkdjuw0XvHxTf1/naOjXCuiO1JIBBwDMi8h9FvPcoYEva7/Wp1/y08bMtwHXAH4vYR8MwOho/04RtRDYXx10/6jhOoDqmZ3rdSc/ISY/r69zpwfM+P40Hr5rGY0vqeH7ltoL13OnBv7rqTOZedSYPvbmZP6zeXrCeOz34V1dP494rz+SXr8d4ZX3h5tqdHvyra87kF5+Zwk9ffY/Xaws3w+704Aevnsp/fmoy//7n9SzeVLgZdqcHP/i5qfzoijP4tz+uY/mW/QXrudOD7//sFL5/+SS+/fs11Gwr3Ay704Pv/exUvjtnInf89t2Czav7TGv/aJi7r5zKHbMncPvzNYGY12K/F55ThUXkNuBaYA/wIPCPqtoqIiHgPeCfCnzvbEMomZenudp4bisi/0LSZD+e9c1FbgRuBIiWBz9d1jCMPDBjWihdph8dO3as174CybutCxYsaLdNPB71bONOwTM90+tOekZOelRfl149uF80AsADV03j2kcXs37dOs4anP2RrFx66dWDK6IRKoD7PzeV6x5dwpo1a5g6MLuZyKWXXj04GgkzIhLmniuncsPjS6ipqeH0AfnppVcPjoTDjBkU5hd/P4VbnlzGypVxJvbPTy+9enAkHKZyWF9++qnJfP037/CJE+Oc0i8/vfTqweFwmInD+/GjK87gm8+u4JMj44zrk59eevXgcHgKk0cO4PuXT+LOF1bx6VFxxlTkp5dePTgUmsy0MYP47pyJfPPZFVw5upmRvbPKZdVLj7y5aGCcUOh0zj15CHfMnsDXf/MOV41pZniOJ9g6o6/zM+I6FPj/VLVaVX+jqq0AquoAlxfx3vXAmLTfRwOZt5JytWl3WxG5NrVvn8/1PK6qzlXV6ao6PRLuU/BBGIZRPFZVuGC6TD86bNiwgg/CMAzDgx7T1+WKvBncJ+Ir5zWTXJE3w/pGfeW8ZpIr8mbEgKivnNdMckXejBlU4SvnNZNckTeVw/r6ynnNJFfkzcTh/XzlvGaSK/Jm8sgBvnJeM8kVeTNtzCBfOa/ptJfTeu7JQzxzXjsDP8+4fkdVN+dYt7aI914CjBeRk0UkAnwOeDGjzYvAF1OV4s4BDqrq9va2FZFLST5Y/3eq2ljE/hmG0VkENFXYR9XJiSLypoi0iMi3sqwvE5F3ROT3hR9Mp2L9qGEYPYEe0dd55bR65bxm4pXT6pXzmolXTqtXzmsmXjmtXjmvmXjltHrlvGbildPqlfOaiVdOq1fOayZeOa1eOa/ptGdaXdrLee0sSlYNIPUw/NeA+cBa4GlVXS0iN4vIzalmLwExoBZ4ALi1vW1T2/wS6AcsEJEVInJfZx2TYRj5IwrieC+eOh9UjpwDTAKuEpFJGc32AbcBP8kh8w8k+5TjAutHDcPoCfSEvs7LtLr4Na9eptXFr3n1Mq0ufs2rl2l18WtevUyri1/z6mVaXfyaVy/T6uLXvHqZVhc/5tWPaXUptXktIA0qOFT1JZIdTfpr96X9rMBX/W6ber0q4N00DKOjCaZq8NHKkQAi4laOPBqXoKq7gF0iclnmxiIyGrgMuAu4PYgd6gysHzUMoyfQnfs6v6bVxTWv8zYlHzbMfObVr2l1cc3rw5uiQPxvnnn1a1pdXPP6yOakXuYzr35Nq4trXh+rS+plPvPq17S6uOb1yS1RPsHfPvPq17S6uOb16fpo1mde/ZpWl3Tzmu2ZV7+m1SXdvGY+85qPaXWp6udwOUnz2t4zrx2B1V83DKP0BDNV2G/lyFz8F8lic1be0zAMw+gU8jWtLrlGXvM1rS65Rl7zNa0uuUZe8zWtLrlGXvM1rS65Rl7zNa0uuUZe8zWtLrlGXvM1rS7ZRl61ANPqUqqRVzOuhmGUHJ/FmYaKyNK05cZMmSzSviyviFwO7FLVZcUdiVEssViMeDxOLBYzPdMzPaPbEovFaG5tY/k+8jatLunmdf57B2hqbWP1fs3btLqkm9eX39tHU2sb6w84eZtWl3Tz+mrtHppa29h4qC1v0+qSbl5fq91NU6vD1oZE3qbVJd28/rV2J02tDruPtOZtWl3SzeubKb1DjS15m1aXdPO6uHY7jQmlubkpb9Pqkm5eF2/YRpOjaEtj3qbVJd28Lq/d2il9nRlXwzBKj78R1z1uVcfUMjdDxU/VyVycD/ydiGwiGU7/URF5rLCDMQolFosxf/58EokE8+fPL/oPoOmZXnfSM7oPsViM37+xHIcQ4UX3s62u8HOjbxguYT0rmgehhAgtuo/6TYXr9S+Hj8oGljUPRQkhi+6hrgi9gRG4SDbwdvMJKCFYeDebitAb0gsulFreahmOAs7Ce4rSGx6FC+R93mgeiQKJhfcWpTeyN5wvMV5P6bUsvK8ovTEVDufKRha1jAbHoeWVuUX1JeP6OJytm1nUOgYch8ZXHihKr6qfwzTdzILGEzulr5Mclb97FAMqRuq5468PVvQ4CRNf94/BZ9iGyrPnixVDWTj4z3PSiB2BazrBPKt5DOFQ8MeecIK/Z/X7C365TFWn57tddNQYHXur9yOl73379nb1RSQMbAAuBraSrCR5dVoRjvS23wMOq+rfFGkSkVnAt1S1mLivHsf06dN16dKlvtrmynlbtGgRNTU1NM76GqgSDoeJRCJZ26a/Ho9nv1scj8dJJBIQjkAibnqmVxo9ESoW/ZLJkycza9asrG39ZBuKSEF9rBEsQfV1K9e+R/NFX4O2BGHRos61pngbKiEQgbY4YaE4vda2pMkUgUQL4ZB0MT0neT9bQsHrtbYQLgtSr5lwWagovcaEJn1FqKxoPceBZqcj9NqgrTWQvg5y93clLc5kGIYB+I67aVdCNSEibuXIMmCeW3Uytf4+ETkRWAr0BxwR+QYwSVUPFb8HRrGMHTuWtWvXgir937if6upqKisrs7adPfsjR3/OdXHojngdOv8m0zO9kuqFw2HGjh2btZ3R83D7uuZ4EwKc1ruBj1cNyNrW61xbuCvM6v1KaNE9HD73ekSEM6IHqa4aWJDegp3lrD/gIIt+yeHzbgBCTCnfyyVVgwvSe2l7ORsPtcHCn3N45s0gwrTIbi6qGlqQ3ovbytnakMBZeM9RvemRXVxYlT1f10vvufpydre1klh4b0ovxIzIdmZWDS9I7+ktEQ61tdCy8D4Oz7wFRDgvsp1zC9R7oi5CH6eJllfnciilNzOylRlVI/LWcwsxaUsjja8+cFTvgkg9Z1eNLEjvkbpeDIk30PDqQ53S19lUYcMwSo7PZ1w9UdWXVHWCqp6iqnelXrvPrTypqjtUdbSq9lfVgamfD2VoLLLR1tJQWVlJdXU14XC4XdNgeqbXE/WM7kP6uXF5RR3vy3De2uudy5qJW4jppvFtzJl9cVKvdx3r5URfOa+ZuIWYbh7fxpzZswmXhbmsdx1rGOkr5zUTtxDTLeMd5lRXEy4rY060jlU62lfOayZuIaabxutRvepoPSt0jK+c10zcQkw3jOeo3uxoPUv1JF85r5m4hZiuGy8pvRAfjW5lsZ7kK+c1E7cQ03UTypLnS1mIWdFtvKkn+8p5TSe9evC1E8JH9S6I7uANrfTMec2m90hdLwaWO1wzIdJpfZ0ZV8MwDKPLUFlZSSQSCewPn+mZXnfSM7oP7rkxqWoc149rZsn+8rzMa2b14A/0TvKV85pJZvVgV++MqrG+cl4zyawe7Op9qGqsr5zXTDKrB7t6U6tG+8p5zSSzerCrN61qlK+c10wyqwe7emdXjfSV85pJZvVgV29G1QhfOa/pZIu8cfXOrRrumfOaTc81rZ8a1XqMXkf3dWZcDcMoPcHE4RiGYRjGcUdFmLzMq1fkTa6onFx4Rd7kisrJhVfkTa6onFx4Rd7kisrJhVfkTa6onFx4Rd7kisrJhVfkTa6onGz4yWnNFpXTnl6mae1MzLgahlFafEwT9jtV2DAMwzCOR/yaV785rX7Nq9+cVr/m1W9Oq1/z6jen1a959ZvT6te8+s1p9Wte/ea0+jGvfkyrix/zWmrTCmZcDcPoCjg+FsMwDMPoxniZV7+m1cXLvPo1rS5e5tWvaXXxMq9+TauLl3n1a1pdvMyrX9Pq4mVe/ZpWl/bMq+M4vk2rS3vm1XGckptWMONqGEaJEWzE1TAMwzAgt3m9/6+xvEyrSy7z+ovX3s/LtLrkMq8/fnlDXqbVJZd5vWv+urxMq0su8/qdP6zJy7S65DKvd774bl6m1SWXeb39uVV5mVaXbObVcRxue7YmL9Pqks28Oo7DLU+vLLlpBYvDMQyjK2DG1DAMwzCAD8zrQ5uiPLWsnoNNcV6r3ZO3aXVxzeu8TVGeX7mN+gNNLN68L2/T6uKa14c3RZm0ejtrtjewekdD3qbVxTWvj2yOMnn9Lt7atI+NexvzNq0urnl9rC7KlNo9vLx+F7saWvI2rS6ueX1yS5QzN+3j+VXbaIy35W1aXVzz+nR9lLO27OexJVsIieRtWl3SzetZ9ft54M06+kfDXDSwML1083r29oP87LUYI/tHOW/InoL0gsSMq2EYpcVGVHscXgHk99ctPiY7LhuO4xBKXTGYnul1Jz2j+1DsuXFhY5zPP7qUcEj47y+dTTTS/mW7p96RONc8uoRoOMRTX55BxMO1euldcLiZLz66jL69wjz55bMJe5zDXnozDzbz5ceXMbB3OY9dO71ovfP3N/KVJ99hWJ8Ij37hLM/vmJfeebsPc8vTKxg5IMpDV08rWm/GzgZue2YlJw+u4P6rpgGTi9I7q34///jb1Zw6rC8/+MTpwOlF6Z25eR/feK6GD40cwHc/fhpwWlF6UHxfZ8YVQBVpbQtWsjz//Cxv0eCv7st7Bz/k35YI/o+v40jgmmt3Zg+DLoZThnXA3agOeL5zf0vv4EWLwYyrkSdBX+Sbnul1Jz2je/Dfy+vp16uMhKO8ULODz501uii9x5duYUDvcuIJhz+s3sGnpowsSu/ht+oY2jfCkZYE89fu5LLTRxSlN++tTZzYrxcHmlt57b09XHzqCUXpPfA/mxg1IMq+I3H+GtvHBVVDi9Kb++YmThrcm50NcZbUHWDGuMHF6b2xkVOG9mHbwWaWb9nPtDGDCtZyHIcH3qzj1GF9qTvQRM22g0weOaBIvc1MGt6P93YfZt3OBiYO71ewnkuxfZ0ZV8MwSo5Y8aUeQ1AjS66O6Zled9Izug/F/p/e/9cYr9XuYd5VZxJ34IYn3wEo2Lz+4rX3Wbx5H7+6ehqNrQ43PLkcoGDz+uOXN7B6RwMPXX0mB5sT3PTUCoCCzetd89excW8jc6+ayt4jCW59OqlXqHn9zh/WsKuhhbmfncL2hjhff2YlQMHm9c4X36Ux3sY9n5lC3f5mbn++hn/+2KkFm9fbn1tFSIS7PzOFDbuPcMdv3+W7cyYWZF7dZ1r7R8N8/7LTWL2jgW//fg3fv3xSQebVfaZ1ZP8o/3rpqazYepA7fvsuP7rijKLNq424GoZx/GMjrj2GUCjEggUL2m0Tj0c927hT8EzP9LqTntF9KObcSK8eXBGNUAE8cNWZfPGRxbz33gbOGZJ9lmAuvfTqwdFImGgEHrhqGtc+upj169Zx1uD89NKrB0fCYYb1DXP/56Zy3aNLWLNmDVMHZr8bnUsvvXpwJBxmxIAw91w5lRseX0JNTQ2nD8hPL716cDgcZsygML/4+ync8uQyVq6MM7F/fnrp1YPD4SlUDuvLTz81ma//5h0+cWKcU/rlp5dePTgUmszE4f340RVn8M1nV/DJkXHG9fGvlx55c9HAOKHQ6UweOYDvXz6JO19YxadHxRlTkZ+eWz34vCF7CIVOY9qYQXx3zkS++ewKrhzdzMgck/Y6o68r6e09EblURNaLSK2I3JllvYjIz1PrV4nINK9tReQzIrJaRBwRmd5Zx2IYRoGoz8UwDMMwujm5Im8GVkR85bxmkivyZnCfiK+c10xyRd4M6xv1lfOaSa7ImxEDor5yXjPJFXkzZlCFr5zXTHJF3lQO6+sr5zWTXJE3E4f385Xzmk57Oa2TRw7wzHnNppcr8mbamEGeOa+dQcmMq4iUAXcDc4BJwFUiMimj2RxgfGq5EbjXx7bvAv8f8HpHH4NhGMFgcTiGYRhGT8crp9Ur5zUTr5xWr5zXTLxyWr1yXjPxymn1ynnNxCun1SvnNROvnFavnNdMvHJavXJe02nPtLq0l/OaTc8rp7W9nNfOopQjrh8GalU1pqpx4Cngiow2VwCPapK3gIEiMqK9bVV1raqu77zDMAyjaGzE1TAMw+jBeJlWF7/m1cu0uvg1r16m1cWvefUyrS5+zauXaXXxa169TKuLX/PqZVpd/JhXP6bVxY959WNaXUptXktpXEcBW9J+r0+95qeNn20NwzhOsBFXwzAMo6fi17S6eJlXv6bVxcu8+jWtLl7m1a9pdfEyr35Nq4uXefVrWl28zKtf0+rSnnnNx7S6tGde8zGtLqU0r6U0rtnyTTIvT3O18bNt+28ucqOILBWRpfFEYz6bGoYRJEoy8sdrMQzDMIxuRr6m1SWXec3XtLrkMq/5mlaXXOY1X9Pqksu85mtaXXKZ13xNq0su85qvaXXJZl61ANPqks28agGm1aVU5rWUxrUeGJP2+2hgm882frZtF1Wdq6rTVXV6JFyRz6aGYQSI+FyMnkEsFiMejxOLxUzP9EzP6LbEYjGaWttYvV/zNq0u6eb1pfcO0tTaxvoDTt6m1SXdvM5/7wBNrW1sPNSWt2l1STevL7+3j6ZWh60NibxNq0u6eX21dg9NrQ67j7TmbVpd0s3ra7W7aWp1ONTYkrdpdUk3r3+t3UljQmlubsrbtLqkm9c3NuykyVG0pTFv0+qSbl7f3LCdJkcpjzfkbVpd0s3rktptndLXldK4LgHGi8jJIhIBPge8mNHmReCLqerC5wAHVXW7z20NwzhesGdcDZIXcvPnzyeRSDB//vyi/wCanul1Jz2j+xCLxfj9GytQQoQW3Uf9psLPjYowzGYdNS0DUELIonuoK0KvbxguYT0rWgahCCy8m01F6PUvh4/KBpa1DElOsFp4T1F6AyNwkWzg7cakXmLhvUXpDekFF0otbzWfiAItC+8rSm94FC6Q93mjeSQ4Di2vzC3quz+yN5yrMf7aOgoch8ZXHihKb0yFwwzdyOvx0eC00fDKQ0XpjevjcLZuZuGR4Z3S14lq6a4IReTjwH8BZcA8Vb1LRG4GUNX7RESAXwKXAo3Al1V1aa5tU69/CvgFMAw4AKxQ1er29mNA7xF63slfDvTYtNx/aXH/osH/X73/r9HANdsSwd8PkVDwx14WDn7+6SnD9gSuGZbg93N/S44QriJ4Y/aPl6lq3hFUFcPH6Pirbvdst+pntxekb3QO06dP16VLl/pqmyvnbdGiRdTU1NA462ugSjgcJhKJZG2b/no8nv3uczweJ5FIQDgCiXjX1RPpWcfb0/REqFj0SyZPnsysWbOytvWTbSgi1gd2AYLq61auXk/zxbdBW4KwaFHnWlNrG0oo2Ze0tQanp22BfBeaWp3k/WeRpF5IgvluRSog0VK03gf7FwpErzGhyYdHQ2XQ2kK4rHA9x4FmJ12vmXBZKAC9NgiFg9NrawOnNZC+DnL3d2FfW3cQqvoS8FLGa/el/azAV/1um3r9eeD5YPfUMIwOxUZUDWDs2LGsXbsWVOn/xv1UV1dTWVmZte3s2R85+nOui0N3xOvQ+Td1ab1EIkE4HO6y+2d6xeuFw2HGjh2btZ3R83D7uuZ4E6LKhIojfKKqf9a2Xufan3eWs+GAgyz6JYfPvQFBOa33YT5eNaAgvZe2l7PxUBss/DmHA/guvLitnK0NCZyF95AIRWk+78tMKd/LJVWDC9KDtO/WzJtBhGmR3VxUNbQgvae3RDjU1kLLwvs4nNKbHtnFhVXDCtJ7oi5CH6eJllfnpvYvxIzIdmZWDc9bzy3EpC2NNL76AIdm3gIinBfZzrkF6j1S14sh8QYaXn3oqN7MyFZmVI0oSG/e5l6c0HqIA6/O65S+rpRThQ3DMJLYVGEDqKyspLq62tPEdTe9yZMnd+n9M72uoWd0H9LPjb/rs4U6OYG/7sl/LOnPO8uJHQlx8/g25syendSr2EJMhvvKec3ELcR0y3iHOQGcu24hppvGK3Oqq5ly6slc1ruONYz0lfOai6OfX1kZc6J1rNLRvnJeM3ELMV03XpLHW1ZGdbSeFTrGV85rJm4hpusmlB3dv9nRepbqSb5yXtNJrx587YRwSi/ER6NbWawneea8ZtNzCzFdMyFyVG9WdBtv6smeOa/Z9OZt7sXwXg5XTejVaX2dGVfDMEqLgjjeix9E5FIRWS8itSJyZ5b1E0XkTRFpEZFvpb0eFZG3RWSliKwWkX8L7gCNfKisrCQSiQT2h+940Js1a1aX3j/T6zp6RvfBPTcmVo3j+nHNrDwQzsu8uqbVLcSUrnfduGaWeuS8ZpJZPbjYczezerDb151RNdZXzqsX7v59qGqsr5zXTDKrB7t6U6tG+8p5zSSzerCrN61qlK+c13SyRd64emdXjfTMec2ml1k92NWbUTXCM+c1m55rWj8x8li9ju7rzLgahlFygshxFZEy4G5gDjAJuEpEJmU02wfcBvwk4/UW4KOqOgWYClyaKghnGIZhGB1KNFUd2K95zTStmVSkqgP7Na+FRt7kwivyxivnNV+8cl4z8Yq88cp5zcQr8sYr5zUdPzmt7eW8ZtPzirxpL+c1m16mae1MzLgahlF6gpkq/GGgVlVjqhoHngKuOOZtVHep6hKgNeN1VdXDqV/LU4tNUDYMwzA6Bb/m1cu0uvg1r51tWl1KZV795rT6Na9+c1r9mFc/ptXFj3n1Y1pd/JjXUptWMONqGEYXIIgRV2AUsCXt9/rUa/72QaRMRFYAu4AFqro4j0MwDMMwjKLwMq9+TauLl3ktlWl16Wzz6te0uniZV7+m1aU98+o4jm/T6tKeeXUcx7dpdWnPvCYcp+SmFcy4GoZRavyMtiaN61ARWZq23JihJDnU/e2GapuqTgVGAx8WkTPyPBLDMAzDKIpc5vXni2rzMq0uuczrj1/eEKhpvWv+urxMq0tnmdc7X3w3L9Pqksu83v7cqrxMq0s28+o4Drc9W5OXaXXJZl4dx+GWp1fmZVpdspnXhONw01PvlNy0QonjcAzDMATfxZf2eGQY1gNj0n4fDWzLd39U9YCILCKZH/1uvtsbhmEYRjG45vWhTVEeXbyZA02tvF23P2/T6uKa13mbojy1rJ4t+xtZvaMhUNO6cW9j3qbVxTWvD2+KMmn1di47PXs0i19c8/rI5iiT1+9iwfpdNMbb8jatLq55fawuypTaPbywahshkbxNq4trXp/cEmXKxj08vnQr/aNhLhpYmJ5rXp+uj3Lm5n088OZmRvaPct6QPQXppZvXM+v388vXY1QN7ct02VeQXpCYcTUMo/QE8zTpEmC8iJwMbAU+B1ztZ0MRGQa0pkxrb+AS4EeB7JXxN3gFkN9ft/iY7LhsOI5DKHUFcrzr+aE7Ha/pta9ndB+KPTc+Gk9w5by3KQ8JT183g4iHa/XSu6g5zud+tYRoeRlPX/dhwh7nnJ9z964/rWPjvkbu+9xUTz0vLjjczE1PrQDgstNHFP35zTzYzJceW8rw/lEe/vw0z++Yl975+xu5/onljBnUm4euPguYXJTeObsauPU3KzllaAU/+MQU4PSi9M7efpBvPFfDpOH9+O7HTwNOK0rvzPr9/NNvV3PmqAH888dOBU4tSg+K7+uslzQMo+SIqufihaomgK8B84G1wNOqulpEbhaRmwFE5EQRqQduB74tIvUi0h8YAbwqIqtIGuAFqvr7DjpcIwCCvsg3PdPrTnpG9+Ch/9nEwGiYSDjEU8u2eG/gwf1/3cTQPhFCAs+8k/eEpL/hrvnBmVaAYX2j3P+5qTz05mb+sHp70Xo/e62Wk4f04VBzK6++V9joYzr/+WotE07ow94jcV6vLU7PcRz+67UYpw3vx/aDLSzeVNxopuM4/Oy1GB8aOYCN+xpZvmV/UXoJx+GXr8c4c9QA1u86TM22g0XpuRTb19mIawoty/Z4XBFIwHqQLOcVMKf8nybe/07vQDWdhvJA9QC0LPgCr20dcJ2wrvnEwDUrR+4OXLNPeWHTUToE/1WDvaVUXwJeynjtvrSfd5CcQpzJKuDMYPbCaI+gRpZcna6uFzRd/XhNrzg9o/tQ7P/pzxfV8nbdfh66ehptwHWPLQPgizNOKkjvxy9vYPWOBh68+kyaEw7XP/4OAJ87K9ufRG/c6cH3XPmhQEyri2te00deC+HOF9+lMd7GL/9+MruPJLj16aTexaeeUJDe7c+tIiTCzz89ha0Hm/n6MysBuKBqaN5a7jOt/aNhvn/ZaWza28jtz9fwzx87lRnjBhekd8vTKxnZP8q/XnoqG3Yf4Y7fvst350xk2phBeeu5z7RWDe3LHZeMZ/WOBr79+zV8//JJTB45IG+9zH0t5nthxtXgvc98O1C9cXN/HKie0f3xWTXY6AaEQiEWLFjQbpt4POrZxp1C1tX1gqarH6/pFadndB+KOTfSqwdHI8lL9XnXnMXnH3qT92MxZg5N5KWXXj04Eg4TCcNDnz+TL/xqMe+9t4FzhrTlpZdePTgSDt5KuOb1ukeXsGbNGqYOzD5wk2v/0qsHh8NTGDEgzD1XTuWGx5dQU1PD6QPy00uvHhwKTWbMoAp+8fdTuOXJZaxcGWdif/966ZE3Fw2MEwqdTuWwvvz0U5P5+m/e4RMnxjmlX356bvXg84bsIRQ6jYnD+/GjK87gm8+u4JMj44zrk5+eWz14uuwjFDqVySMH8P3LJ3HnC6v49Kg4Yyry+/zSKbavs9t7hmGUnmByXA3DMAzjuCZX5E2fSNhXzmsmuSJv+kcjvnJeM8k38qZQhvWNFlRtOFfkzYgBUV85r5nkirwZM6jCV85rOu3ltFYO6+uZ85pNL1fkzcTh/TxzXrPp5Yq8mTxygGfOa2dgxtUwjJIjjvdiGIZhGN0Zr5xWr5zXTLxyWr1yXjPpLNPqkm9UjldOq1fOayZeOa1eOa/ptGdaXdrLec2m55XT2l7OazY9r5zW9nJeOwszroZhlBZNThX2WgzDMAyju+JlWl38mlcv0+ri17x2tml18WtevUyri1/z6mVaXfyYVz+m1cWPefVjWl38mFc/ptWl1ObVjKthGKXHpgoXjIhcKiLrRaRWRO7Msl5E5Oep9atEZJrXtiIyWEQWiMh7qX/zr+5gGIYRIN25r/NrWl28zKtf0+riZV5LZVpdvMyrX9Pq4mVe/ZpWl/bMaz6m1aU985qPaXVpz7zmY1pdSmlezbgahlFSBBtxLRQRKQPuBuYAk4CrRGRSRrM5wPjUciNwr49t7wReUdXxwCup3w3DMEpCd+7r8jWtLrnMa76m1SWXeS21aXXJZV7zNa0uucxrvqbVJZt51QJMq0s286oFmFaXbOZVCzCtLqUyryU1rt357plhGHmg6r0Y2fgwUKuqMVWNA08BV2S0uQJ4VJO8BQwUkREe214BPJL6+RHgkx18HEeJxWLE43FisViP0Auarn68pmcUSLfs65pa29hwwMnbtLqkm9ffvXeIptY2Nh5qy9u0uqSb15feO0hTq8PWhkTJTatLunl9+b19NLU6HGpsydu0uqSb11dr99CYUJqbm/I2rS7p5vXVDbtpchRtaczbtLqkm9e/bNhJk6OUxxvyNq0u6eb1jZRen9ZDeZtWl3Tzurh2e6f0dSU7Dbvz3TPDMPLDRlwLZhSQnkpfn3rNT5v2th2uqtsBUv8WFnyXJ7FYjPnz55NIJJg/f37RfwC7ul7QdPXjNb2udb4cZ3S7vu6l195CEWTRPdRtKvzciIbhYtaxpnkAisDCu9lUhF5FGC5hHTVHKlDAWXhPUXpB078cPiobWNY4EAVaFt5X1P4NjMBFsoG3m4eD49DyytyivqtDesFHtJa34yeC49D4ygNF6Q2Pwkx9n/9pHQlOGw2vPFSU3sjecK7G+GvrKHDaOPDKvKL0xlQ4zNCNLGo8oVP6ulLmuB69AwYgIu4dsDVpbY7ePQPeEhH37tm4dra9ApiV2v4RYBFwR0cfjGEYBaIg2SPkDG8ky2uZNj9XGz/btv/mIjeSvKnI2LFj89k0K3V1dSQSCRDh0Pk38XxzmEhtJGvb++sWH/05Ho9mbROPV5E4f1zwevEjJBIJ6urqqKys9HdwnUCHfX7hiOkVoyfSJc+X44xu19fp4b2QaOHwuTcUfa41tY6HcAgSzRwO5NythFACjuzF6YLn7p76GNENr9B8wc0c/sitPN8sRR1vY2I8hBwIlXGoSD3HgWbnlID1KpPxCqEyDs0MQu9koA1CYQ7NvIXnm0NF6o2DsjZwWju8ryulcc12B2yGjza57p652x5z90xEOuXumWEYRWAjqoVSD4xJ+300sM1nm0g72+4UkRGpPnQEsCvbm6vqXGAuwPTp04v+Xxw7dixr164FVfq/cT/V1dU5//jNnv2Roz/nCjxPH/EKh8OB6wVxARskHfX5HTr/JtMrUq8rni/HGdbXkf1ce2l7ORsPtcHCn+P0sL6uubUZEKaU7+WSqsFZ23od7xN1Efo4TbS8OpdDM28GEaZFdnNR1dC89dxCTNrSSOOrD6T0QkyP7OTCqmEF6T1S14sh8QYaXn3oqN6MyHZmVg0vSG/e5l6c0HqIA6/O49DMW0CE8yLbObcAvYQD8zb1YkTiAHtffbhT+rpSzlgv+d0zEVkqIkvjbY35bGoYRsDYVOGCWQKMF5GTRSQCfA54MaPNi8AXUzUDzgEOpm7utbfti8C1qZ+vBX7b0QcCUFlZSXV1teeFV756kydP7pJ6QdNRn5/pdQ29Ho71dVl4aXs59U0hbhnvMKcn9nVlZVzWu47VjPSV85rJE3XJUcbrJpQd1ZsTrWOVjvaV85pOevXgayeEj+pVR7ewQsd45rxm03MLMV0zIXJUb3a0nqV6kmfOazY9txDTVRN6pfRCfDS6lcV6kmfOayauaR1T4XDlhN6d1teVcsS1y9w9G9B7hF0WG0apUKz4UoGoakJEvgbMB8qAeaq6WkRuTq2/D3gJ+DhQCzQCX25v25T0D4GnReR6oA74TGcdU2VlJZHaSGB/+CorKwP9Ixq0XtB0xOdnel1Hr6difd3f4prWr6QKMfXUvu6MqrGMjTfzq81RIM7UgY6v7V3TenWqEJOr96GU3iMpvdMHeOtli7xx9aZWjWZMSzOP1SX1Jvb3p5dZPdjVm1Y1ilHNzTy5JconiHNKP396mdWDXb2zq0YyqqmZp+ujfHJknHF9vPXSTeucE4/V6+hzppTG9egdMGAryTtgV2e0eRH4WuoZ1hmk7p6JyO52tnXvnv2QTrx7ZhhG4diIauGo6kskL9jSX7sv7WcFvup329Tre4GLg91TwzCMwrG+7gMyTWtPp38EvnySf/OaaVozcasN+zGvfnJa3WrDfsyrn5xWt9qwH/PqJ6fVrTbsx7xmM62dSclOd1VNAO4dsLXA0+7dM/cOGslOJkby7tkDwK3tbZva5ofAbBF5D5id+t0wjC6KkKw54LUYhmEYRk/HTGt2XPP6WkbOayZeptUlV85rOn5Mq0u2nNdsen5zWrPlvGbT85vTmi3nNZNSm1Yo7Yir3T0zDMNyWg3DMAzDB2Za28dr5NWvaXVpb+TVcRzfptWlvZFXx3F8m1aX9kZeE47j27S6tDfyGk84JTetUNriTIZhGIAVZzIMwzCM9vjxyxvMtPog18jr7c+tAvybVpdsI6+O43DbszV5mVaXbCOvjuNwy9Mr8zKtLtlGXhOOw01PvZOXaXXJNvIaTzjc8OTykptWKPGIq2EYBmBxOIZhGIaRgx+/vIHVOw6ZafVJ+sjrpNXbeWX9bkQkb9Pqkj7yevraHfz23Z30j4a5aGBheukjr5M37OLJ5VsZ0T/K+UP2FKSXPvI6eeMe5r25maqhfZku+wrSSx95nbJxH3f/NcaUUQOY3La/IL0gMeNqGEbJsRHVnsXs2bPbXX9/3eJjsuOy4TgOodQVnJeeHzpSL2g6+/Mzvc7VM7oPQZwbP355Pat3NPDg1dMIB3CO9KS+bmZDM198dCkjBkR5+JrpwOSi9M7b38iXn1jOuMEV/OATU4DTi9I7Z+9hbnxqBROG9eV7Hz8NOK0ovQ/vauCrv1nJh0YM4J8/dipwalF607ce5BvP13D22EHc/tHxwPii9KD488V6ScMwSosCjnovhpFG0BdKXV0vaLr68ZqeYbgjrcGZVuhZ5+6PFmxgwgn9ONSc4A+rtxel5TgO//7ye5wxoj+7D8d5ZX3WtM389Ba8x9RRA6g/2MTrtYWNtrokHIcfvbyBD48ZxPt7DrN4U2GjrS7xhMN/LHyPc8YNYvWOQyzfEsxoa7Hni424GoZRcqxqcM8hqLvzPXWEKujPz/S6lp7RfSj2/9SdHjz3c1MDM63HE8V+frc/twoR4WefPoPdR+Lc8t8rALjs9BEF7cttz9bQPxrm+5edxs6GOLc+ndS7+NQTCtK75emVjOgf5TuXnsqWA838w7MrAbigamjeeu4zrVVD+3LHJePZtLeR25+v4Z8/diozxg3OW899pnXKqAF8Y9YpbNh9hDt++y7fnTORaWMG5a2XTrH/r2ZcARQk3hawaBvaOxKw5vFBZE/wp1V8cND/Px2DxoP/47JpZ/6dmBcTR+4IXLMoAqoqLCKXAj8jGTL/oKr+MGP9ROBXwDTgX1T1J6nXxwCPAicCDjBXVX8WyE4ZxxAKhViwYEG7beLxqGebIKa4HY8E/fmZXtfSM7oPxZwb6dWDI+GeealezOeXXj04FJrM8H5R7v3sVK7/9RLWrFmTM+c1m1565M1FA+OEQqczYkCUe66cyg2PL6GmpiZnzmsuPbd68PlD9hAKncZJgyv42aen8NWnlrFyZe6c11x6bvXg6bKPUOhUKof15aefmszXf/MOnzgxd85rNr30yJvJbfsJhcYzcXg/fnTFGXzz2RXt5rx2Rl/XM78NncSfVvyfUu+CYRwXBPGMq4iUAXeTzG+uB5aIyIuquiat2T7gNuCTGZsngG+q6nIR6QcsE5EFGdsahmEYRodikTfFkSvyZni/aLtROdloL6d1xIBozqic9vRyRd6cNLgiZ1ROe3q5Im8qh/XNGZWTi/ZyWicO75czKqczsa+EYRilRX0u3nwYqFXVmKrGgaeAK455K9VdqroEaM14fbuqLk/93ACsBUYVflCGYRiGkR9mWovDK6c1V1RONtozrS7ZonLa0/PKac0WldOenldOa7aonFy0Z1pdskXldDb2tTAMo6QIIKqeiw9GAVvSfq+nAPMpIuOAM4HF+W5rGIZhGIVgprU4vEyrix/z6se0uvgxr35Mq4sf8+rHtLr4Ma9+TKtLqc2rfTUMwyg9jo8FhorI0rTlxgwVyaKc1yRkEekLPAt8Q1UP5XUMhmEYhlEAZlqLw69pdWnPvOZjWl3aM6/5mFaX9sxrPqbVpT3zmo9pdSmlebWvh2EYpUVBHPVcgD2qOj1tmZuhVA+MSft9NLDN726ISDlJ0/q4qj5X7GEZhmEYhhdmWosjX9Pqks28agGm1SWbedUCTKtLNvOqBZhWl2zmVQswrS6lMq/2FTEMo8Rosqqw1+LNEmC8iJwsIhHgc8CLfjYUEQEeAtaq6k8LPhSjaGKxGPF4nFgsVupdOS4J+vMzva6lZ3QfYrEYTa1tbDzUZqa1AGKxGI0Jpbm5KW/T6pJuXv+8fh9NjqItjXmbVpd08/rKhj00OUp5vCFv0+qSbl5f3bCbJkfp03oob9Pqkm5e/7JhJ02OMjBxIG/T6pJuXt+s3dkpfZ19TQzDKDmi3osXqpoAvgbMJ1lc6WlVXS0iN4vIzQAicqKI1AO3A98WkXoR6Q+cD3wB+KiIrEgtH++gwzVyEIvFmD9/PolEgvnz59vFfp4E/fmZXtfSM7oPsViM37+5CkVg4d1s2mTnRj7EYjGe2dkfHIeWV+YW9d3qH4GLdAPvJIaB49D4ygNF6Q2MwIW6gaWtw8FxaHjloaL0hvSCj2gtb7eOAKeNA6/MK0pveBRm6vv8T+soaGtj7ysPF6U3sjfM0BivNw7tlL7O4nAMwyg9AeW4qupLwEsZr92X9vMOklOIM/kr2Z+RNTqRuro6EokExI+QSCSoq6ujsrKy1Lt13HD08xPh0Pk38XxzmEht9jzx++s+qD0Wj0eztonHq0icPw7CEdMrRk/EzmfjGOrq6tADW0Gh6ZQL7NzIkz8dPhFohFCIQxd8leebKfi76jjQ7IwHcSBUxqGP3MrzzdLF9E4B2iAUDkivMqlXFubQzCD0ToayBDiJDu/rzLgahlFaNNm/G8bYsWNZu3YtRPoQDocZO3ZsqXfpuOLo56dK/zfup7q6OufFw+zZHzn6c67AeHfE8ND5N5lekXp2PhvpuN/V5kQzzgnj2VsxrtS7dNzwRF2EaLQNeeMRDp1/EyLCGdGDVFcNzNq+ve+qW4hJWxppfPUBDs28GRCmlO/lkqrBBek9UteLIfEGGl59KKknwrTIbi6qGlqQ3rzNvTih9RAHXp2X0gsxPbKTC6uG5a3nFmIakTjA3lcfPqo3I7KdmVXDC9J7cFMvRif2sevVRzulr7OpwoZhlB5HvRej21NZWUl1dTXhcLhd02BkJ+jPz/S6lp7RfUg/Nz7Zt57dZYN5eWd5qXery+MWYrpuQtnRz+/y3nWslxNZtq8sL6306sHXTkh+R8NlZVzWu47VjPTMec2m5xZiumZC5KjenGgdq3S0Z85rNj23ENNVE3od1auObmGFjvHMec0kvXrwlRN6H9WbHa1nqZ7kmfOaTe/BTb2o6uPw6Ql9Oq2vM+NqGEbJCSjH1egGVFZWEolE7CK/QIL+/Eyva+kZ3Qf33JhQVclXTm6h9nDIzGs7ZFYPdj+/SVUncd24Zt7YV+7bvGaLvHH1zqga65nzmk0vs3qwq/ehqrGeOa/Z9DKrB7t6U6tGe+a8ZpIt8sbVm1Y1yjPnNZuea1ovGX6sXkf3dSUxriIyWEQWiMh7qX8H5Wh3qYisF5FaEbnTa3sRGSIir4rIYRH5ZWcdj2EYRRJMVWHDMAzDOO4IhzDz2g5ekTd9w/g2r35yWtvLec2m5xV5017OazY9r8ib9nJeM/GT09pezms2vUzT2pmUasT1TuAVVR0PvJL6/RhEpAy4G5gDTAKuEpFJHts3A/8KfKtjd98wjMBQwPGxGIZhGEY3xcxrdvzmtPoxr35Mq4sf8+rHtLr4Ma9+TKuLH/Pqx7S6+DGvpTatUDrjegXwSOrnR4BPZmnzYaBWVWOqGgeeSm2Xc3tVPaKqfyVpYA3DOA4QvKcJ21RhwzAMo7tj5vVY/JpWl/bMq+M4vk2rS3vm1XEc36bVpT3zmnAc36bVpT3zGk84vk2rS3vmNZ5wSm5aoXTGdbiqbgdI/XtCljajgC1pv9enXvO7fbuIyI0islRElsbbGvPd3DCMILGpwoZhGIZh5jXF7c+tAvybVpds5tVxHG57tiYv0+qSzbw6jsMtT6/My7S6ZDOvCcfhpqfeycu0umQzr/GEww1PLs/LtLpkM6/xhMN1TywruWmFDozDEZGXgROzrPoXvxJZXgvs6lVV5wJzAQZER9hVsWGUCgXa7CtoGIZhGPCBeX1wYy/ufv19vnrBKaXepU7l9udWISJ5m1YX17zO2xTl2XfqebV2L/2jYS4aWJiea15/tTnKae9u43fv7mRE/yjnD9lTkJ5rXh/ZHGXS2h389ztbqRral+myryA917w+Vhfl9HW7eGRJHVNGDWBy2/6C9Fzz+uSWKGe8v4f73tjIueMGM6HlQEF6QdJhxlVVL8m1TkR2isgIVd0uIiOAXVma1QNj0n4fDWxL/exne8MwjhNsKnDPYvbs2e2uv79u8THZcdlwHIdQqGcWxg/68zO9rqVndB+KPTcuTjh8+fFlAD3GvLqm9f99ajIwud22Xp/fRxqa+fyjSxk1sDe//MwU4PSi9M472Mi1jy2nckgF3/v4acBpRemds7+R659YzmnD+/LPHzsVOLUovbN3H+bm/17B1FH9uf2j44HxRelN39HA159ZyTnjBqXOv/bPwc7o60rVS74IXJv6+Vrgt1naLAHGi8jJIhIBPpfazu/2hmEcL9hUYSNP7CK/OIL+/Eyva+kZ3YNIOMSvPn8Wb27cx92vv1/q3elwjjWtxeE4Dt/703omjxzAweZWnl+5zXsjL70/rmfamIHsOhznD6u3F6WXcBy+98e1nDNuEHX7m3llfXFjcPGEw//50zpmVg7hvT1HeL22sNHgdL3v/3kdF40fSs22QyzeVNhocCbF9nUdNuLqwQ+Bp0XkeqAO+AyAiIwEHlTVj6tqQkS+BswHyoB5qrq6ve1TGpuA/kBERD4JfExV13TOYRmGkT9mTHsSQY0suTqmZ3rdSc/oPgT1fxoOwbzPnxXYyGtX/S64pvXHV5x+jG6h+3TbszX0j4b5/mWncaApwQ1PLgfgU1NGFqR3y9MrGdE/yncuPZXdR+Lc8t8rALjs9BF567nPtFYN7csdl4xnZ0OcW59O6l18at5le44+0zpl1AC+MesUthxo5h+eXQnABVVDC9K77ollnDtuMLfMPJlNexu5/fka/vljpzJj3OC89dIp9jwpiXFV1b3AxVle3wZ8PO33l4CX/G6fWjcusB01DKPjUcy49iBCoRALFixot008HvVs407B86Pnh+6kF/TnZ3qdp2d0H4I8NyIh+NXnz+KqB/9KXd2WogrkdMW+Lr16cCg02bdets8vPfLmooFxQqHTGdwnwgNXTePaRxezft06zhrclpeeWz34/CF7CIVOY3i/KPd+dirX/3oJa9asYerA7Jl9ufTc6sHTZR+h0KmMGBDlniuncsPjS6ipqeH0Af710iNvJrftJxQaz0mDK/jZp6fw1aeWsXJlnIn989NzqwdPaDlAKHQKlcP68tNPTebrv3mHT5wY55R+/vUyKbavK9WIa9eiLQH7DgQq6Rw+QnXvLwSr2XJ8pPyc1AGau247L3DNhsrgw0GdaPCabYeCryq4uml04JpFYTmthmEYhpGTSDh0tGATlJe8umtQ5Bt50x7t5bQO7hM5WrAJyGleM/VyRd4M7xc9WrAJ4jnNa6ZersibEQOiRws2QTyneU2nvZzWkwZXHC3YBLnNa6ZersibymF9jxZs+gS5zWtHY/NSDMMoOeI4nothGIZh9GS6W1ROZ5lWl/ZyXrPpeeW0tpfzmk3PK6e1vZzXTNozrS7t5bxm0/PKaW0v57WzMONqGEZpUcBR78UwDMMwejjdxbx2tml18WNe/ZhWFz/m1Y9pdfFjXv2YVhc/5tWPaXUptXk142oYRonxUVHYnoE1DMMwDOD4N6+lMq0u7ZnXfEyrS3vmNR/T6tKeec3HtLq0Z17zMa0upTSvZlwNwyg9ZlwNwzAMwzfHq3kN0rRqAabVJZt51QJMq0s286oFmFaXbOZVCzCtLtnMqxZgWl1KZV7NuBqGUXrMuBopYrEY8XicWCwWmN6iRYt6lF7Qn5/pdR09o/sQxLmRbl6f3XCky/dN96xqpLm5KRDTWlsbo8lRtKUxb9Pqkm5e/7j+AE2OUh5vyNu0uqSb1z+v30eTo/RpPZS3aXVJN6+vbNhDk6MMTBzI27S6pJvXhRt20+QowxL7Ci70lW5e/1q7s1P6OjOuhmGUFlVoa/NejG5PLBZj/vz5JBIJ5s+fX/QfQFevpqamR+kF/fmZXtfQM7oPQZ4b4RB8VNcSO9jGyrXvddm+6Q+LV3Ok4RAtr8wNRO+lZRvAcWh85YGi9PqG4RLWs6qpApw2Gl55qCi9/hG4WDbwTlNfcNo48Mq8ovQGRuAi2cDSxv7Q1sbeVx4uSm9IL7hQalnSOBDaEux65dGi9IZH4QJ5nzf3lndKXydqIxkMKB+m5w38dKCazuEjgerB8ROH0xH05DicDqE8+O/95uvuWKaq0/PdbkCv4XreiKs92/1p838VpG90DtOnT9elS5f6apsr523RokXU1NTQeO6XINKHcDhMJBLJ2jb99Xg8+932eDxOIpGA+JGepScCqqbXVfTiR6h482EmT57MrFmzsrb1k20oItYHdgGsr+tCel39u9/T9ALq6yB3f2c5roZhlBa3qnAAiMilwM+AMuBBVf1hxvqJwK+AacC/qOpP0tbNAy4HdqnqGYHskJEXY8eOZe3atVS8+TDhcJjq6moqKyuztp09+yNHf851cZg+qmF6pldqvbFjx2ZtZ/Q8rK8zve6s15F9nRlXwzBKTwAzP0SkDLgbmA3UA0tE5EVVXZPWbB9wG/DJLBIPA78EHi16Z4yCqKyspLq6mrq6OsaOHZvzD6npmV5P1DO6D139XDM90yulXnuYcTUMo/QE88jCh4FaVY0BiMhTwBXAUeOqqruAXSJy2d/ugr4uIuOC2BGjcCorKwP9o2d6pted9IzuQ1c/10zP9EqplwsrzmQYRokJLMd1FLAl7ff61GvdEhEZLCILROS91L+DcrS7VETWi0itiNzptb2IzBaRZSJSk/r3o511TIZhGJlYX2cYhosZV8MwSovit6rwUBFZmrbcmKEkOdS7K3cCr6jqeOCV1O/HkDZ9eg4wCbhKRCZ5bL8H+ISqTgauBX7doUdhGIbRPtbXGYYBmHE1DKMr4G/EdY+qTk9b5mao1ANj0n4fDWzrrEMoAVcAj6R+foTsz+0enT6tqnHAnT6dc3tVfUdV3c9tNRAVkV6B771hGIY/rK8zDAMw42oYRsnRZFVhr8WbJcB4ETlZRCLA54AXO3TXS8twVd0OkPr3hCxt2ps+7Wf7TwPvqGpLYHttGIaRH9bXGYYBWHEmwzBKjYJq8fm3qpoQka8B80nG4cxT1dUicnNq/X0iciKwFOgPOCLyDWCSqh4SkSeBWSSnJNcD31XVh4resSIQkZeBE7Os+he/Elle83UXQEROB34EfKydNjcCNwIW9WEYRsFYX2cYhh9Eg6nmmd+bigwG/hsYB2wCrlTV/VnaZc1kzLW9iMwGfghEgDjwj6q60Gt/BpQP0/MGfrro40rHOXwkUD0Ap6U5cM3jhV23nRe4ZkNl8WYpEycavGaHUB78937zdXdkDYv2YkB4mJ7b/5Oe7ebvf7Ag/e6KiKwHZqnqdhEZASxS1VMz2pwLfE9Vq1O//zOAqv57e9v//+3da4xdVRXA8f+qpCJQ+rAtlEB4SHgkmjRYSjCCUJRgMWji44MamkZDMJHoB8IzJBpJLGBCIH5QgtEmxhhsQEpAKA8lRkOxhZZSoQFkeBXBChWNEYKz/HD2yDC5c3tnuOfec2b+v2Tn7rnn7DNrdc7s6b7n7H0i4nDgAWBtZv6hl3hWrFiRW7Zs6Vt++zI6OsqcOf27acjjebymHy8iZmUfaF/X/nPX43m8qR5vsv5uWLcKO9FeUiWz18WZ9G4bqfo5yuvtHfbpdvt0x/YRsQC4E7i81//IDUM//5B6PI830443w9jXeTyP5/Gq9n2KY6qcaC/pHf15HM5ssw74VEQ8BYzdbUJEHBYRd0F1+zQwdvv0E8AtmbmzW/uy/7HAVRGxrZROc8IkaRDs6yQBw5vj+q6J8pN0FJ0m2p8yhfZOtJdaIkdbcot1g2Tm34GzOry/G1g97uu7gLum0P5q4Oq+BitJ02RfJ2lMbQPXNk2033/OQT2GJKn/vKIqSZKk7mobuGbmJyfbFhGvRMSycRPlX+2wW7dnMk7avky0vw04PzOf6RLfTcBNUC3O1Gtekvos6fVxN5IkSZqlhnWr8NhE+XX0MNEeeIlqov2Xu7Wf7kT7N97es+fuPT9+blqZNMdiqsWp2q5zHjdsGHwk781M+XlA77kcOe3v0IfH4UiSJGnmGtbAdR1wS0R8DXge+CJUE+2pHnuzerJnMnZrz7sn2l9V3js7Mztd0f2/zFzSx9yGIiK2zIRl8s2jeerOJTNJVw2WJElSF0MZuDrRXtJ46a3CkiRJ6mJYV1wlCYB/8vo9943esriHXWfKrdcz0tatW/dExGRTLtp+67zxD5fxV6Y/HUN9Y1/XaMY/XP2Mv2N/58B15rhp2AH0iXk0T625ZOY5dR5fg9FtykXbb503/uEyfjWJfV1zGf9wDSL+OXUeXINTVkluPfNonpmUiyRJktrJgaskSZIkqdEcuDZQRIxExI6I2BYRWzpsnx8Rd0TE9ojYGRFrp9D24ojIiOhlTmEj84iIiyJiV2lzbd151JVLRCyPiIfG3o+IlQ3PY0FEbIiIJyPiiYg4tby/KCLujYinyuvCuvNQ67T9qr3xD5fxqy3a/rM2/uEy/n2ITFfzbJqIGAFWZGbHCc4RcQUwPzMvjYglwC7g0Mx8q1vbiDgCuBk4AfjoZMfvlzryiIgzgSuBczPzzYhYuq/HHfVDTblsAq7PzN9ExGrgksw8o8F5rAd+n5k3R8Rc4IDM3Fs+PHgtM9dFxGXAwsy8tM48JEmSNLt4xbWdEpgXEQEcBLwGvN1Du+uBS0r7JphOHt8A1mXmmwCDGLT2aDq5JHBwqc8HdtcXXs865hERBwOnAz8ByMy3MnNvafNZYH2prwc+N8iAJUmSNPM5cG2mBDZFxNaIuKDD9h8CJ1INdHYA38rM0W5tI+I84KXM3F5z7OP1PQ/gOOC0iNgcEQ9GxMl1JjBOHbl8G7guIl4AfgBcXlv075huHscAfwN+GhGPRsTNEXFgaXNIZr4MUF6X1p6FJEmSZpfMtDSsAIeV16XAduD0Cdu/QHX1NIBjgWeBgydrCxwAbKa6BRRgBFjctjzK148DN5Y2K0ubaGkuNwKfL/UvAfc1NQ9gBdUV5FPKfjcA3yv1vROO8XrdeViGV0r/sQPYBmzpsH0+cEc5v3YCa/fVFrgOeBJ4DLgNWNCm+Mdtv5jqw6Ha+te64gcuopoasBO4tk3xA8uBh8beB1Y2NP4FwIZyrj8BnFreXwTcCzxVXhfWFb9l6Oeqfd2Q48e+bhDxL6Cmvq6WZC19PXG+A1w84b07gdPGff1Ap5N3rC3wEeDVchKOUA1Anqeau9iaPEr9buCMcdueAZa07WdS6v/gnXnmAbzR1DyAQ4GRce+fBtxZ6ruAZaW+DNg1yDwsgy3s44Mv4ArgmlJfQnW7+dxubYGzgf1K/Zqx9m2Jv2w7ArgHeK7b8ZsYP3AmcB/w/vL10pbFvwn4dKmvBn7X0PjXA18v9bmUQQtwLXBZqV9W5/lvGdjP2r6ugfHb1w0s/tr6Om8VbpiIODAi5o3VqTq5xyfs9jxwVtnnEOB44C+Ttc3MHZm5NDOPysyjgBeBkzLzr23Ko7T5NbCqbDuO6hei7kWm6splN/CJUl9F9QlUI/Mo58oLEXF82e8s4M+lvhFYU+prgNtrS0JtkExxvndmbsrMsX0eAg6vN8Suphx/cT3NWENgOvE3ae2A6cSfNGe9gI7xu07AjGRfN1z2dbOxr6trpG6Z9iccx1Bddh+79H5lef9C4MJSP4zqU5cdVAOPr3Zr2+F7jFDzrcJ15UE1UP152f8RYFVbfybAx4GtZdtmqpWeG5lH2bac6taUx6g+QFhY3v8gcD/VwPt+YNGwf48stZ5Hz5bfva3ABR22zwN+C7wM/ItqBfCe2pZ97hh/3rUhfuA84IZSr7V/rSn+bcB3Sz/0IHByy+I/kepDtxeAl4AjmxZ/6T8fBn4GPEq1wv+BZdveCcd4va74LcM9Vye0t68bfPz2dTXHX3dfV0uyFovFYpmZhRrme49reyXVvK/a5q33O34GvIZAHf/+DHDtgJriH9h6AdONH9cJaF2xr7Ova2D8s76v81ZhSVLPMnN3eX2V6j9eKyfssha4NStPU/0xO2FfbSNiDfAZ4CtZ/qK1JP4PAUcD28tzkg8HHomIQ1sSP1TTR8baPAyMAotbFP8a4NZS/1WHYzYh/heBFzNzc9lvA3BSqb8SEcsAymtTHvM2q9nX2dc1MP5Z39c5cJUk9aSu+d4RcQ5wKXBeZv67TfHnANcQaPvaAW1fL8B1AmYP+zr7uobGb1831Uu0FovFYpmdhfrmez9NNWdnWyk/alP8E77HCDXdPlfjv/9A1g6oMf6BrBfwXuIv25bjOgGtKDWeq/Z1w/33t6+rOf6ybTk19XVjj+KQJEmSJKmRvFVYkiRJktRoDlwlSZIkSY3mwFWSJEmS1GgOXNVoEXFyRDwWEfuXVc52RsSHhx2XJEmSpMFxcSY1XkRcDewPfIDq2VDfH3JIkiRJkgbIgasaLyLmAn8C/gN8LDP/O+SQJEmSJA2QtwqrDRYBBwHzqK68SpIkSZpFvOKqxouIjcAvgaOBZZn5zSGHJEmSJGmA9ht2AFI3EXE+8HZm/iIi3gf8MSJWZeYDw45NkiRJ0mB4xVWSJEmS1GjOcZUkSZIkNZoDV0mSJElSozlwlSRJkiQ1mgNXSZIkSVKjOXCVJEmSJDWaA1dJkiRJUqM5cJUkSZIkNZoDV0mSJElSo/0PZGKBUeQImgYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1152x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f,ax = plt.subplots(1,3,figsize=(4*4,4), subplot_kw=dict(aspect='equal'))\n",
+    "da_s.where(da_s.values>da_s.attrs[\"nodatavals\"][0]).plot(ax=ax[0])\n",
+    "ax[0].set_title(\"Sliced raster\")\n",
+    "splot.plot_spatial_weights(w_rook, data=da_s, ax=ax[1])\n",
+    "ax[1].set_title(\"Rook contiguity\")\n",
+    "splot.plot_spatial_weights(w_queen, data=da_s, ax=ax[2])\n",
+    "ax[2].set_title(\"Queen contiguity\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `higher_order` neighbors\n",
+    "\n",
+    "In some cases `Rook` and `Queen` contiguities don't provide sufficient neighbors when performing spatial analysis on a raster data, this is because `Rook` contiguity provides max 4 neighbors and `Queen` provides max 8.\n",
+    "\n",
+    "Therefore we've added `higher_order` functionality inside the builder method. We can now pass `k` value to the weight builder to obtain upto kth order neighbors. Since this can be computionally expensive we can take advantage of parallel processing using `n_jobs` argument. Now lets take a look at this functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Building a test DataArray \n",
+    "da_s = raster.testDataArray((1,5,10), rand=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we can see that builder selected all the neighbors of order less than equal to 2, with `rook` contiguity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda/lib/python3.8/site-packages/scipy/sparse/_index.py:124: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.\n",
+      "  self._set_arrayXarray(i, j, x)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1)\n",
+    "splot.plot_spatial_weights(w_rook2, data=da_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Few times we require the kth order neighbors for our analysis even if lower order neighbors are absent, hence we can use `include_nas` argument to do the same.\n",
+    "\n",
+    "We can also look in both the examples we used `n_jobs` parameter, and assigned -1 which equats to all the cores present in the computer for multithreading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 720x720 with 1 Axes>, <AxesSubplot:>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w_rook2 = raster.da2WSP(da_s, \"rook\", k=2, n_jobs=-1, include_nodata=True)\n",
+    "splot.plot_spatial_weights(w_rook2, data=da_s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Additional resources\n",
+    "\n",
+    "1. [Reading and writing files using Xarray](http://xarray.pydata.org/en/stable/io.html)\n",
+    "2. [Xarray Data Structures](http://xarray.pydata.org/en/stable/data-structures.html)\n",
+    "3. Dataset links:\n",
+    "    - [ECMWF_ERA-40_subset.nc](https://www.unidata.ucar.edu/software/netcdf/examples/files.html)\n",
+    "    - [lux_ppp_2019.tif](https://data.humdata.org/dataset/worldpop-population-counts-for-luxembourg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/examples.html b/notebooks/examples.html
new file mode 100644
index 000000000..39df8433a
--- /dev/null
+++ b/notebooks/examples.html
@@ -0,0 +1,1053 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Datasets for use with libpysal &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/pysal-styles.css?v=38b0ce36" />
+    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+    <script src="../_static/documentation_options.js?v=7a99edf7"></script>
+    <script src="../_static/doctools.js?v=888ff710"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>window.MathJax = {"TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": true}}, "tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="libpysal API reference" href="../api.html" />
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
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+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
+  <a role="button"
+     id="dLabelLocalToc"
+     data-toggle="dropdown"
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+     href="#">Page <b class="caret"></b></a>
+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">Datasets for use with libpysal</a></li>
+<li><a class="reference internal" href="#Backwards-compatibility-is-maintained">Backwards compatibility is maintained</a></li>
+<li><a class="reference internal" href="#Downloading-Remote-Datasets">Downloading Remote Datasets</a></li>
+<li><a class="reference internal" href="#Getting-remote-urls">Getting remote urls</a></li>
+<li><a class="reference internal" href="#Explaining-a-dataset">Explaining a dataset</a></li>
+<li><a class="reference internal" href="#Working-with-an-example-dataset">Working with an example dataset</a></li>
+<li><a class="reference internal" href="#Other-Remotes">Other Remotes</a></li>
+</ul>
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+    <div class="body col-md-12 content" role="main">
+      
+  <div class="admonition note">
+<p>This page was generated from <a class="reference external" href="https://github.com/pysal/libpysal/blob/master/notebooks/examples.ipynb">notebooks/examples.ipynb</a>.
+Interactive online version:
+<span class="raw-html"><a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath=notebooks/examples.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+</div>
+<section id="Datasets-for-use-with-libpysal">
+<h1>Datasets for use with libpysal<a class="headerlink" href="#Datasets-for-use-with-libpysal" title="Link to this heading">¶</a></h1>
+<p>As of version 4.2, libpysal has refactored the <code class="docutils literal notranslate"><span class="pre">examples</span></code> package to:</p>
+<ul class="simple">
+<li><p>reduce the size of the source installation</p></li>
+<li><p>allow the use of remote datasets from the <a class="reference external" href="https://spatial.uchicago.edu/">Center for Spatial Data Science at the Unversity of Chicago</a>, and other remotes</p></li>
+</ul>
+<p>This notebook highlights the new functionality</p>
+</section>
+<section id="Backwards-compatibility-is-maintained">
+<h1>Backwards compatibility is maintained<a class="headerlink" href="#Backwards-compatibility-is-maintained" title="Link to this heading">¶</a></h1>
+<p>If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work.</p>
+<p>For example:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import get_path
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>get_path(&quot;mexicojoin.dbf&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/serge/Documents/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf&#39;
+</pre></div></div>
+</div>
+<p>An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a <code class="docutils literal notranslate"><span class="pre">builtin</span></code> dataset.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import libpysal
+dbf = libpysal.io.open(get_path(&quot;mexicojoin.dbf&quot;))
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>dbf.header
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;POLY_ID&#39;,
+ &#39;AREA&#39;,
+ &#39;CODE&#39;,
+ &#39;NAME&#39;,
+ &#39;PERIMETER&#39;,
+ &#39;ACRES&#39;,
+ &#39;HECTARES&#39;,
+ &#39;PCGDP1940&#39;,
+ &#39;PCGDP1950&#39;,
+ &#39;PCGDP1960&#39;,
+ &#39;PCGDP1970&#39;,
+ &#39;PCGDP1980&#39;,
+ &#39;PCGDP1990&#39;,
+ &#39;PCGDP2000&#39;,
+ &#39;HANSON03&#39;,
+ &#39;HANSON98&#39;,
+ &#39;ESQUIVEL99&#39;,
+ &#39;INEGI&#39;,
+ &#39;INEGI2&#39;,
+ &#39;MAXP&#39;,
+ &#39;GR4000&#39;,
+ &#39;GR5000&#39;,
+ &#39;GR6000&#39;,
+ &#39;GR7000&#39;,
+ &#39;GR8000&#39;,
+ &#39;GR9000&#39;,
+ &#39;LPCGDP40&#39;,
+ &#39;LPCGDP50&#39;,
+ &#39;LPCGDP60&#39;,
+ &#39;LPCGDP70&#39;,
+ &#39;LPCGDP80&#39;,
+ &#39;LPCGDP90&#39;,
+ &#39;LPCGDP00&#39;,
+ &#39;TEST&#39;]
+</pre></div></div>
+</div>
+<p>The function <code class="docutils literal notranslate"><span class="pre">available</span></code> is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, <code class="docutils literal notranslate"><span class="pre">available</span></code> will report on what datasets are available, either as builtin or remotes.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import available
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>df = available()
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>df.shape
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(98, 3)
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.summary()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+98 datasets available, 27 installed, 71 remote.
+</pre></div></div>
+</div>
+<p>We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., <code class="docutils literal notranslate"><span class="pre">examples</span></code> has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed.</p>
+</section>
+<section id="Downloading-Remote-Datasets">
+<h1>Downloading Remote Datasets<a class="headerlink" href="#Downloading-Remote-Datasets" title="Link to this heading">¶</a></h1>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>df.head()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Name</th>
+      <th>Description</th>
+      <th>Installed</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>10740</td>
+      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>AirBnB</td>
+      <td>Airbnb rentals, socioeconomics, and crime in C...</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Atlanta</td>
+      <td>Atlanta, GA region homicide counts and rates</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Baltimore</td>
+      <td>Baltimore house sales prices and hedonics</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Bostonhsg</td>
+      <td>Boston housing and neighborhood data</td>
+      <td>False</td>
+    </tr>
+  </tbody>
+</table>
+</div></div>
+</div>
+<p>The remote <code class="docutils literal notranslate"><span class="pre">AirBnB</span></code> can be installed by calling <code class="docutils literal notranslate"><span class="pre">load_example</span></code>:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>airbnb = libpysal.examples.load_example(&quot;AirBnB&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading AirBnB to /home/serge/.local/share/pysal/AirBnB
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.summary()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+98 datasets available, 28 installed, 70 remote.
+</pre></div></div>
+</div>
+<p>And we see that the number of remotes as declined by one and the number of installed has increased by 1.</p>
+<p>Trying to load an example that doesn’t exist will return None and alert the user:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.load_example(&#39;dataset42&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Example not available: dataset42
+</pre></div></div>
+</div>
+</section>
+<section id="Getting-remote-urls">
+<h1>Getting remote urls<a class="headerlink" href="#Getting-remote-urls" title="Link to this heading">¶</a></h1>
+<p>If the url, rather than the dataset, is needed this can be obtained on a remote with <code class="docutils literal notranslate"><span class="pre">get_url</span></code>. As the <code class="docutils literal notranslate"><span class="pre">Baltimore</span></code> dataset has not yet been downloaded in this example, we can grab it’s url:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>balt_url = libpysal.examples.get_url(&#39;Baltimore&#39;)
+balt_url
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;https://geodacenter.github.io/data-and-lab//data/baltimore.zip&#39;
+</pre></div></div>
+</div>
+</section>
+<section id="Explaining-a-dataset">
+<h1>Explaining a dataset<a class="headerlink" href="#Explaining-a-dataset" title="Link to this heading">¶</a></h1>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;taz&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+taz
+===
+
+Dataset used for regionalization
+--------------------------------
+
+* taz.dbf: attribute data. (k=14)
+* taz.shp: Polygon shapefile. (n=4109)
+* taz.shx: spatial index.
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>taz = libpysal.examples.load_example(&#39;taz&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading taz to /home/serge/.local/share/pysal/taz
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>taz.get_file_list()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;/home/serge/.local/share/pysal/taz/taz-master/taz.dbf&#39;,
+ &#39;/home/serge/.local/share/pysal/taz/taz-master/taz.shp&#39;,
+ &#39;/home/serge/.local/share/pysal/taz/taz-master/README.md&#39;,
+ &#39;/home/serge/.local/share/pysal/taz/taz-master/taz.shx&#39;]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;Baltimore&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<iframe
+    width="700"
+    height="350"
+    src="https://geodacenter.github.io/data-and-lab//baltim/"
+    frameborder="0"
+    allowfullscreen
+
+></iframe></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>balt = libpysal.examples.load_example(&#39;Baltimore&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading Baltimore to /home/serge/.local/share/pysal/Baltimore
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.available()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Name</th>
+      <th>Description</th>
+      <th>Installed</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>10740</td>
+      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>AirBnB</td>
+      <td>Airbnb rentals, socioeconomics, and crime in C...</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Atlanta</td>
+      <td>Atlanta, GA region homicide counts and rates</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Baltimore</td>
+      <td>Baltimore house sales prices and hedonics</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Bostonhsg</td>
+      <td>Boston housing and neighborhood data</td>
+      <td>False</td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+    </tr>
+    <tr>
+      <th>93</th>
+      <td>taz</td>
+      <td>Traffic Analysis Zones in So. California</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>94</th>
+      <td>tokyo</td>
+      <td>Tokyo Mortality data</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>95</th>
+      <td>us_income</td>
+      <td>Per-capita income for the lower 48 US states 1...</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>96</th>
+      <td>virginia</td>
+      <td>Virginia counties shapefile</td>
+      <td>True</td>
+    </tr>
+    <tr>
+      <th>97</th>
+      <td>wmat</td>
+      <td>Datasets used for spatial weights testing</td>
+      <td>True</td>
+    </tr>
+  </tbody>
+</table>
+<p>98 rows × 3 columns</p>
+</div></div>
+</div>
+</section>
+<section id="Working-with-an-example-dataset">
+<h1>Working with an example dataset<a class="headerlink" href="#Working-with-an-example-dataset" title="Link to this heading">¶</a></h1>
+<p><code class="docutils literal notranslate"><span class="pre">explain</span></code> will render maps for an example if available</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import explain
+explain(&#39;Tampa1&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<iframe
+    width="700"
+    height="350"
+    src="https://geodacenter.github.io/data-and-lab//tampa1/"
+    frameborder="0"
+    allowfullscreen
+
+></iframe></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import load_example
+tampa1 = load_example(&#39;Tampa1&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading Tampa1 to /home/serge/.local/share/pysal/Tampa1
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tampa1.installed
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+True
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tampa1.get_file_list()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx&#39;,
+ &#39;/home/serge/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA&#39;]
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tampa_counties_shp = tampa1.load(&#39;tampa_counties.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tampa_counties_shp
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7febc3b1bd00&gt;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import geopandas
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>tampa_df = geopandas.read_file(tampa1.get_path(&#39;tampa_counties.shp&#39;))
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+tampa_df.plot()
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;AxesSubplot:&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_examples_40_1.png" src="../_images/notebooks_examples_40_1.png" />
+</div>
+</div>
+</section>
+<section id="Other-Remotes">
+<h1>Other Remotes<a class="headerlink" href="#Other-Remotes" title="Link to this heading">¶</a></h1>
+<p>In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;Rio Grande do Sul&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Rio_Grande_do_Sul
+======================
+
+Cities of the Brazilian State of Rio Grande do Sul
+-------------------------------------------------------
+
+* 43MUE250GC_SIR.dbf: attribute data (k=2)
+* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)
+* 43MUE250GC_SIR.shx: spatial index
+* 43MUE250GC_SIR.cpg: encoding file
+* 43MUE250GC_SIR.prj: projection information
+* map_RS_BR.dbf: attribute data (k=3)
+* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)
+* map_RS_BR.prj: projection information
+* map_RS_BR.shx: spatial index
+
+
+
+Source: Renan Xavier Cortes
+Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495
+
+
+</pre></div></div>
+</div>
+<p>Note that the <code class="docutils literal notranslate"><span class="pre">explain</span></code> function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rio = libpysal.examples.load_example(&#39;Rio Grande do Sul&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading Rio Grande do Sul to /home/serge/.local/share/pysal/Rio_Grande_do_Sul
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.remote_datasets.datasets # a listing of all remotes
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;AirBnB&#39;: &lt;libpysal.examples.base.Example at 0x7febc78c00d0&gt;,
+ &#39;Atlanta&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5efb0&gt;,
+ &#39;Baltimore&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5ed40&gt;,
+ &#39;Bostonhsg&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5ef50&gt;,
+ &#39;Buenosaires&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5efe0&gt;,
+ &#39;Charleston1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5ef80&gt;,
+ &#39;Charleston2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5ee90&gt;,
+ &#39;Chicago Health&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5ef20&gt;,
+ &#39;Chicago commpop&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f070&gt;,
+ &#39;Chicago parcels&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f0a0&gt;,
+ &#39;Chile Labor&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f100&gt;,
+ &#39;Chile Migration&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f130&gt;,
+ &#39;Cincinnati&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f1f0&gt;,
+ &#39;Cleveland&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f190&gt;,
+ &#39;Columbus&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f250&gt;,
+ &#39;Elections&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f0d0&gt;,
+ &#39;Grid100&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f220&gt;,
+ &#39;Groceries&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f160&gt;,
+ &#39;Guerry&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f280&gt;,
+ &#39;Health+&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f040&gt;,
+ &#39;Health Indicators&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f2b0&gt;,
+ &#39;Hickory1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f2e0&gt;,
+ &#39;Hickory2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f310&gt;,
+ &#39;Home Sales&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f340&gt;,
+ &#39;Houston&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f370&gt;,
+ &#39;Juvenile&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f3a0&gt;,
+ &#39;Lansing1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f3d0&gt;,
+ &#39;Lansing2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f400&gt;,
+ &#39;Laozone&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f430&gt;,
+ &#39;LasRosas&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f460&gt;,
+ &#39;Liquor Stores&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f490&gt;,
+ &#39;Malaria&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f4c0&gt;,
+ &#39;Milwaukee1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f4f0&gt;,
+ &#39;Milwaukee2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f520&gt;,
+ &#39;NCOVR&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f550&gt;,
+ &#39;Natregimes&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f580&gt;,
+ &#39;NDVI&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f5b0&gt;,
+ &#39;Nepal&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f5e0&gt;,
+ &#39;NYC&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f610&gt;,
+ &#39;NYC Earnings&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f640&gt;,
+ &#39;NYC Education&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f670&gt;,
+ &#39;NYC Neighborhoods&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f6a0&gt;,
+ &#39;NYC Socio-Demographics&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f6d0&gt;,
+ &#39;Ohiolung&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f700&gt;,
+ &#39;Orlando1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f730&gt;,
+ &#39;Orlando2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f760&gt;,
+ &#39;Oz9799&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f790&gt;,
+ &#39;Phoenix ACS&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f7c0&gt;,
+ &#39;Pittsburgh&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f7f0&gt;,
+ &#39;Police&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f820&gt;,
+ &#39;Sacramento1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f850&gt;,
+ &#39;Sacramento2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f880&gt;,
+ &#39;SanFran Crime&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f8b0&gt;,
+ &#39;Savannah1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f8e0&gt;,
+ &#39;Savannah2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f910&gt;,
+ &#39;Scotlip&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f940&gt;,
+ &#39;Seattle1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f970&gt;,
+ &#39;Seattle2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f9a0&gt;,
+ &#39;SIDS&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5f9d0&gt;,
+ &#39;SIDS2&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fa00&gt;,
+ &#39;Snow&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fa30&gt;,
+ &#39;South&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fa60&gt;,
+ &#39;Spirals&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fa90&gt;,
+ &#39;StLouis&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fac0&gt;,
+ &#39;Tampa1&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5faf0&gt;,
+ &#39;US SDOH&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fb20&gt;,
+ &#39;Rio Grande do Sul&#39;: &lt;libpysal.examples.base.Example at 0x7febc78c0130&gt;,
+ &#39;nyc_bikes&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fb80&gt;,
+ &#39;taz&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fc70&gt;,
+ &#39;clearwater&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5fbe0&gt;,
+ &#39;newHaven&#39;: &lt;libpysal.examples.base.Example at 0x7febc4a5eec0&gt;}
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/notebooks/examples.ipynb.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/notebooks/examples.ipynb b/notebooks/examples.ipynb
new file mode 100644
index 000000000..5905f568d
--- /dev/null
+++ b/notebooks/examples.ipynb
@@ -0,0 +1,1093 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Datasets for use with libpysal\n",
+    "As of version 4.2, libpysal has refactored the `examples` package to:\n",
+    "\n",
+    "- reduce the size of the source installation\n",
+    "- allow the use of remote datasets from the [Center for Spatial Data Science at the Unversity of Chicago](https://spatial.uchicago.edu/), and other remotes\n",
+    "\n",
+    "This notebook highlights the new functionality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Backwards compatibility is maintained"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you were familiar with previous versions of libpysal, the newest version maintains backwards compatibility so any code that relied on the previous API should work. \n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import get_path \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/serge/Documents/p/pysal/src/subpackages/libpysal/libpysal/examples/mexico/mexicojoin.dbf'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_path(\"mexicojoin.dbf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An important thing to note here is that the path to the file for this particular example is within the source distribution that was installed. Such an example data set is now referred to as a `builtin` dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import libpysal\n",
+    "dbf = libpysal.io.open(get_path(\"mexicojoin.dbf\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['POLY_ID',\n",
+       " 'AREA',\n",
+       " 'CODE',\n",
+       " 'NAME',\n",
+       " 'PERIMETER',\n",
+       " 'ACRES',\n",
+       " 'HECTARES',\n",
+       " 'PCGDP1940',\n",
+       " 'PCGDP1950',\n",
+       " 'PCGDP1960',\n",
+       " 'PCGDP1970',\n",
+       " 'PCGDP1980',\n",
+       " 'PCGDP1990',\n",
+       " 'PCGDP2000',\n",
+       " 'HANSON03',\n",
+       " 'HANSON98',\n",
+       " 'ESQUIVEL99',\n",
+       " 'INEGI',\n",
+       " 'INEGI2',\n",
+       " 'MAXP',\n",
+       " 'GR4000',\n",
+       " 'GR5000',\n",
+       " 'GR6000',\n",
+       " 'GR7000',\n",
+       " 'GR8000',\n",
+       " 'GR9000',\n",
+       " 'LPCGDP40',\n",
+       " 'LPCGDP50',\n",
+       " 'LPCGDP60',\n",
+       " 'LPCGDP70',\n",
+       " 'LPCGDP80',\n",
+       " 'LPCGDP90',\n",
+       " 'LPCGDP00',\n",
+       " 'TEST']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dbf.header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function `available` is also available but has been updated to return a Pandas DataFrame. In addition to the builtin datasets, `available` will report on what datasets are available, either as builtin or remotes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98, 3)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "98 datasets available, 27 installed, 71 remote.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that there are 98 total datasets available for use with PySAL. On an initial install (i.e., `examples` has not been used yet), 27 of these are builtin datasets and 71 are remote. The latter can be downloaded and installed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Downloading Remote Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Installed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10740</td>\n",
+       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirBnB</td>\n",
+       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>Baltimore house sales prices and hedonics</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bostonhsg</td>\n",
+       "      <td>Boston housing and neighborhood data</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Name                                        Description  Installed\n",
+       "0      10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1     AirBnB  Airbnb rentals, socioeconomics, and crime in C...      False\n",
+       "2    Atlanta       Atlanta, GA region homicide counts and rates      False\n",
+       "3  Baltimore          Baltimore house sales prices and hedonics      False\n",
+       "4  Bostonhsg               Boston housing and neighborhood data      False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The remote `AirBnB` can be installed by calling `load_example`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading AirBnB to /home/serge/.local/share/pysal/AirBnB\n"
+     ]
+    }
+   ],
+   "source": [
+    "airbnb = libpysal.examples.load_example(\"AirBnB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "98 datasets available, 28 installed, 70 remote.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we see that the number of remotes as declined by one and the number of installed has increased by 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trying to load an example that doesn't exist will return None and alert the user:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Example not available: dataset42\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.load_example('dataset42')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting remote urls\n",
+    "\n",
+    "If the url, rather than the dataset, is needed this can be obtained on a remote with `get_url`. \n",
+    "As the `Baltimore` dataset has not yet been downloaded in this example, we can grab it's url:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://geodacenter.github.io/data-and-lab//data/baltimore.zip'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "balt_url = libpysal.examples.get_url('Baltimore')\n",
+    "balt_url"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explaining a dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "taz\n",
+      "===\n",
+      "\n",
+      "Dataset used for regionalization\n",
+      "--------------------------------\n",
+      "\n",
+      "* taz.dbf: attribute data. (k=14)\n",
+      "* taz.shp: Polygon shapefile. (n=4109)\n",
+      "* taz.shx: spatial index.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading taz to /home/serge/.local/share/pysal/taz\n"
+     ]
+    }
+   ],
+   "source": [
+    "taz = libpysal.examples.load_example('taz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/home/serge/.local/share/pysal/taz/taz-master/taz.dbf',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shp',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/README.md',\n",
+       " '/home/serge/.local/share/pysal/taz/taz-master/taz.shx']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "taz.get_file_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"700\"\n",
+       "            height=\"350\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//baltim/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7febc3b56c50>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('Baltimore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Baltimore to /home/serge/.local/share/pysal/Baltimore\n"
+     ]
+    }
+   ],
+   "source": [
+    "balt = libpysal.examples.load_example('Baltimore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Description</th>\n",
+       "      <th>Installed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10740</td>\n",
+       "      <td>Albuquerque, New Mexico, Census 2000 Tract Dat...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AirBnB</td>\n",
+       "      <td>Airbnb rentals, socioeconomics, and crime in C...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>Atlanta, GA region homicide counts and rates</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Baltimore</td>\n",
+       "      <td>Baltimore house sales prices and hedonics</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Bostonhsg</td>\n",
+       "      <td>Boston housing and neighborhood data</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>taz</td>\n",
+       "      <td>Traffic Analysis Zones in So. California</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>tokyo</td>\n",
+       "      <td>Tokyo Mortality data</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>us_income</td>\n",
+       "      <td>Per-capita income for the lower 48 US states 1...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>virginia</td>\n",
+       "      <td>Virginia counties shapefile</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>wmat</td>\n",
+       "      <td>Datasets used for spatial weights testing</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>98 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Name                                        Description  Installed\n",
+       "0       10740  Albuquerque, New Mexico, Census 2000 Tract Dat...       True\n",
+       "1      AirBnB  Airbnb rentals, socioeconomics, and crime in C...       True\n",
+       "2     Atlanta       Atlanta, GA region homicide counts and rates      False\n",
+       "3   Baltimore          Baltimore house sales prices and hedonics       True\n",
+       "4   Bostonhsg               Boston housing and neighborhood data      False\n",
+       "..        ...                                                ...        ...\n",
+       "93        taz           Traffic Analysis Zones in So. California       True\n",
+       "94      tokyo                               Tokyo Mortality data       True\n",
+       "95  us_income  Per-capita income for the lower 48 US states 1...       True\n",
+       "96   virginia                        Virginia counties shapefile       True\n",
+       "97       wmat          Datasets used for spatial weights testing       True\n",
+       "\n",
+       "[98 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with an example dataset\n",
+    "\n",
+    "`explain` will render maps for an example if available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"700\"\n",
+       "            height=\"350\"\n",
+       "            src=\"https://geodacenter.github.io/data-and-lab//tampa1/\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x7febc3b56890>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from libpysal.examples import explain\n",
+    "explain('Tampa1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Tampa1 to /home/serge/.local/share/pysal/Tampa1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from libpysal.examples import load_example\n",
+    "tampa1 = load_example('Tampa1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tampa1.installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shp',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.prj',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/2000 Census Data Variables_Documentation.pdf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.kml',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.dbf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.kml',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mif',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.prj',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sqlite',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.FDO_UUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000002.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByType.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByDestItemTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/timestamps',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByUUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.CatItemTypesByParentTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByType.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByForwardLabel.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.FDO_UUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByUUID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.TablesByName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000009.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByOriginID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000003.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a0000000a.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.CatItemsByPhysicalName.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbtablx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByOriginItemTypeID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.gdbtable',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/gdb',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000001.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000004.spx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000007.CatRelTypesByBackwardLabel.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000005.gdbindexes',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/TampaMSA.gdb/a00000006.CatRelsByDestinationID.atx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mid',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.geojson',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.mid',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.xlsx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.mif',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.dbf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.shp',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.gpkg',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.gpkg',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.xlsx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.shx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_final_census2.sqlite',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/TampaMSA/tampa_counties.geojson',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._2000 Census Data Variables_Documentation.pdf',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbn',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_counties.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/TampaMSA/._tampa_final_census2.sbx',\n",
+       " '/home/serge/.local/share/pysal/Tampa1/__MACOSX/._TampaMSA']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tampa1.get_file_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tampa_counties_shp = tampa1.load('tampa_counties.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<libpysal.io.iohandlers.pyShpIO.PurePyShpWrapper at 0x7febc3b1bd00>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tampa_counties_shp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tampa_df = geopandas.read_file(tampa1.get_path('tampa_counties.shp'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "tampa_df.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other Remotes\n",
+    "\n",
+    "In addition to the remote datasets from the GeoData Data Science Center, there are several large remotes available at github repositories. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rio_Grande_do_Sul\n",
+      "======================\n",
+      "\n",
+      "Cities of the Brazilian State of Rio Grande do Sul\n",
+      "-------------------------------------------------------\n",
+      "\n",
+      "* 43MUE250GC_SIR.dbf: attribute data (k=2)\n",
+      "* 43MUE250GC_SIR.shp: Polygon shapefile (n=499)\n",
+      "* 43MUE250GC_SIR.shx: spatial index\n",
+      "* 43MUE250GC_SIR.cpg: encoding file \n",
+      "* 43MUE250GC_SIR.prj: projection information \n",
+      "* map_RS_BR.dbf: attribute data (k=3)\n",
+      "* map_RS_BR.shp: Polygon shapefile (no lakes) (n=497)\n",
+      "* map_RS_BR.prj: projection information\n",
+      "* map_RS_BR.shx: spatial index\n",
+      "\n",
+      "\n",
+      "\n",
+      "Source: Renan Xavier Cortes \n",
+      "Reference: https://github.com/pysal/pysal/issues/889#issuecomment-396693495\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('Rio Grande do Sul')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the `explain` function generates a textual description of this example dataset - no rendering of the map is done as the source repository does not include that functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading Rio Grande do Sul to /home/serge/.local/share/pysal/Rio_Grande_do_Sul\n"
+     ]
+    }
+   ],
+   "source": [
+    "rio = libpysal.examples.load_example('Rio Grande do Sul')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'AirBnB': <libpysal.examples.base.Example at 0x7febc78c00d0>,\n",
+       " 'Atlanta': <libpysal.examples.base.Example at 0x7febc4a5efb0>,\n",
+       " 'Baltimore': <libpysal.examples.base.Example at 0x7febc4a5ed40>,\n",
+       " 'Bostonhsg': <libpysal.examples.base.Example at 0x7febc4a5ef50>,\n",
+       " 'Buenosaires': <libpysal.examples.base.Example at 0x7febc4a5efe0>,\n",
+       " 'Charleston1': <libpysal.examples.base.Example at 0x7febc4a5ef80>,\n",
+       " 'Charleston2': <libpysal.examples.base.Example at 0x7febc4a5ee90>,\n",
+       " 'Chicago Health': <libpysal.examples.base.Example at 0x7febc4a5ef20>,\n",
+       " 'Chicago commpop': <libpysal.examples.base.Example at 0x7febc4a5f070>,\n",
+       " 'Chicago parcels': <libpysal.examples.base.Example at 0x7febc4a5f0a0>,\n",
+       " 'Chile Labor': <libpysal.examples.base.Example at 0x7febc4a5f100>,\n",
+       " 'Chile Migration': <libpysal.examples.base.Example at 0x7febc4a5f130>,\n",
+       " 'Cincinnati': <libpysal.examples.base.Example at 0x7febc4a5f1f0>,\n",
+       " 'Cleveland': <libpysal.examples.base.Example at 0x7febc4a5f190>,\n",
+       " 'Columbus': <libpysal.examples.base.Example at 0x7febc4a5f250>,\n",
+       " 'Elections': <libpysal.examples.base.Example at 0x7febc4a5f0d0>,\n",
+       " 'Grid100': <libpysal.examples.base.Example at 0x7febc4a5f220>,\n",
+       " 'Groceries': <libpysal.examples.base.Example at 0x7febc4a5f160>,\n",
+       " 'Guerry': <libpysal.examples.base.Example at 0x7febc4a5f280>,\n",
+       " 'Health+': <libpysal.examples.base.Example at 0x7febc4a5f040>,\n",
+       " 'Health Indicators': <libpysal.examples.base.Example at 0x7febc4a5f2b0>,\n",
+       " 'Hickory1': <libpysal.examples.base.Example at 0x7febc4a5f2e0>,\n",
+       " 'Hickory2': <libpysal.examples.base.Example at 0x7febc4a5f310>,\n",
+       " 'Home Sales': <libpysal.examples.base.Example at 0x7febc4a5f340>,\n",
+       " 'Houston': <libpysal.examples.base.Example at 0x7febc4a5f370>,\n",
+       " 'Juvenile': <libpysal.examples.base.Example at 0x7febc4a5f3a0>,\n",
+       " 'Lansing1': <libpysal.examples.base.Example at 0x7febc4a5f3d0>,\n",
+       " 'Lansing2': <libpysal.examples.base.Example at 0x7febc4a5f400>,\n",
+       " 'Laozone': <libpysal.examples.base.Example at 0x7febc4a5f430>,\n",
+       " 'LasRosas': <libpysal.examples.base.Example at 0x7febc4a5f460>,\n",
+       " 'Liquor Stores': <libpysal.examples.base.Example at 0x7febc4a5f490>,\n",
+       " 'Malaria': <libpysal.examples.base.Example at 0x7febc4a5f4c0>,\n",
+       " 'Milwaukee1': <libpysal.examples.base.Example at 0x7febc4a5f4f0>,\n",
+       " 'Milwaukee2': <libpysal.examples.base.Example at 0x7febc4a5f520>,\n",
+       " 'NCOVR': <libpysal.examples.base.Example at 0x7febc4a5f550>,\n",
+       " 'Natregimes': <libpysal.examples.base.Example at 0x7febc4a5f580>,\n",
+       " 'NDVI': <libpysal.examples.base.Example at 0x7febc4a5f5b0>,\n",
+       " 'Nepal': <libpysal.examples.base.Example at 0x7febc4a5f5e0>,\n",
+       " 'NYC': <libpysal.examples.base.Example at 0x7febc4a5f610>,\n",
+       " 'NYC Earnings': <libpysal.examples.base.Example at 0x7febc4a5f640>,\n",
+       " 'NYC Education': <libpysal.examples.base.Example at 0x7febc4a5f670>,\n",
+       " 'NYC Neighborhoods': <libpysal.examples.base.Example at 0x7febc4a5f6a0>,\n",
+       " 'NYC Socio-Demographics': <libpysal.examples.base.Example at 0x7febc4a5f6d0>,\n",
+       " 'Ohiolung': <libpysal.examples.base.Example at 0x7febc4a5f700>,\n",
+       " 'Orlando1': <libpysal.examples.base.Example at 0x7febc4a5f730>,\n",
+       " 'Orlando2': <libpysal.examples.base.Example at 0x7febc4a5f760>,\n",
+       " 'Oz9799': <libpysal.examples.base.Example at 0x7febc4a5f790>,\n",
+       " 'Phoenix ACS': <libpysal.examples.base.Example at 0x7febc4a5f7c0>,\n",
+       " 'Pittsburgh': <libpysal.examples.base.Example at 0x7febc4a5f7f0>,\n",
+       " 'Police': <libpysal.examples.base.Example at 0x7febc4a5f820>,\n",
+       " 'Sacramento1': <libpysal.examples.base.Example at 0x7febc4a5f850>,\n",
+       " 'Sacramento2': <libpysal.examples.base.Example at 0x7febc4a5f880>,\n",
+       " 'SanFran Crime': <libpysal.examples.base.Example at 0x7febc4a5f8b0>,\n",
+       " 'Savannah1': <libpysal.examples.base.Example at 0x7febc4a5f8e0>,\n",
+       " 'Savannah2': <libpysal.examples.base.Example at 0x7febc4a5f910>,\n",
+       " 'Scotlip': <libpysal.examples.base.Example at 0x7febc4a5f940>,\n",
+       " 'Seattle1': <libpysal.examples.base.Example at 0x7febc4a5f970>,\n",
+       " 'Seattle2': <libpysal.examples.base.Example at 0x7febc4a5f9a0>,\n",
+       " 'SIDS': <libpysal.examples.base.Example at 0x7febc4a5f9d0>,\n",
+       " 'SIDS2': <libpysal.examples.base.Example at 0x7febc4a5fa00>,\n",
+       " 'Snow': <libpysal.examples.base.Example at 0x7febc4a5fa30>,\n",
+       " 'South': <libpysal.examples.base.Example at 0x7febc4a5fa60>,\n",
+       " 'Spirals': <libpysal.examples.base.Example at 0x7febc4a5fa90>,\n",
+       " 'StLouis': <libpysal.examples.base.Example at 0x7febc4a5fac0>,\n",
+       " 'Tampa1': <libpysal.examples.base.Example at 0x7febc4a5faf0>,\n",
+       " 'US SDOH': <libpysal.examples.base.Example at 0x7febc4a5fb20>,\n",
+       " 'Rio Grande do Sul': <libpysal.examples.base.Example at 0x7febc78c0130>,\n",
+       " 'nyc_bikes': <libpysal.examples.base.Example at 0x7febc4a5fb80>,\n",
+       " 'taz': <libpysal.examples.base.Example at 0x7febc4a5fc70>,\n",
+       " 'clearwater': <libpysal.examples.base.Example at 0x7febc4a5fbe0>,\n",
+       " 'newHaven': <libpysal.examples.base.Example at 0x7febc4a5eec0>}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.remote_datasets.datasets # a listing of all remotes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/fetch.html b/notebooks/fetch.html
new file mode 100644
index 000000000..084b7365d
--- /dev/null
+++ b/notebooks/fetch.html
@@ -0,0 +1,943 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>&lt;no title&gt; &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/pysal-styles.css?v=38b0ce36" />
+    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+    <script src="../_static/documentation_options.js?v=7a99edf7"></script>
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+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>window.MathJax = {"TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": true}}, "tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
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+<script type="text/javascript" src="../_static/js/jquery-1.12.4.min.js"></script>
+<script type="text/javascript" src="../_static/js/jquery-fix.js"></script>
+<script type="text/javascript" src="../_static/bootstrap-3.4.1/js/bootstrap.min.js"></script>
+<script type="text/javascript" src="../_static/bootstrap-sphinx.js"></script>
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+    <div class="container">
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+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
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+        </button>
+        <a class="navbar-brand" href="../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="../installation.html">Installation</a></li>
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+                <li><a href="../references.html">References</a></li>
+            
+            
+              <li class="dropdown globaltoc-container">
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+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
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+    </div>
+  </div>
+
+<div class="container">
+  <div class="row">
+    <div class="body col-md-12 content" role="main">
+      
+  <div class="admonition note">
+<p>This page was generated from <a class="reference external" href="https://github.com/pysal/libpysal/blob/master/notebooks/fetch.ipynb">notebooks/fetch.ipynb</a>.
+Interactive online version:
+<span class="raw-html"><a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath=notebooks/fetch.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import libpysal
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.fetch_all()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+downloading dataset from https://s3.amazonaws.com/geoda/data/guerry.zip to /home/jovyan/pysal_data
+Extracting files....
+downloading dataset from https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip to /home/jovyan/pysal_data
+Extracting files....
+downloading dataset from https://s3.amazonaws.com/geoda/data/ncovr.zip to /home/jovyan/pysal_data
+Extracting files....
+downloading dataset from https://github.com/sjsrey/nyc_bikes/archive/master.zip to /home/jovyan/pysal_data
+Extracting files....
+downloading dataset from https://s3.amazonaws.com/geoda/data/SacramentoMSA2.zip to /home/jovyan/pysal_data
+Extracting files....
+downloading dataset from https://s3.amazonaws.com/geoda/data/south.zip to /home/jovyan/pysal_data
+Extracting files....
+downloading dataset from https://github.com/sjsrey/taz/archive/master.zip to /home/jovyan/pysal_data
+Extracting files....
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import nat
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>nat.fetch_nat()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+already exists, not downloading
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from os import environ
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>environ.get(&quot;PYSALDATA&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/pysal_data&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import south
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>sd = south.fetch_south()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+already exists, not downloading
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples import guerry
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>guerry.fetch_guerry()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+already exists, not downloading
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;guerry&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+guerry
+======
+
+Andre-Michel Guerry data on &#34;moral statistics&#34; 1930 crime, suicide, literacy and other “moral statistics” in 1830s France.
+
+- Observations = 85
+- Variables = 23
+- Years = 1915-1934
+- Support = polygon
+
+Files
+-----
+Guerry.dbf  Guerry_documentation.html  Guerry.geojson  Guerry.prj  Guerry.shp  Guerry.shx  README.md
+
+
+Variables
+---------
+
+dept, code_de   Department ID: Standard numbers for the departments
+region  Region of France (‘N’=’North’, ‘S’=’South’, ‘E’=’East’, ‘W’=’West’, ‘C’=’Central’). Corsica is coded as NA.
+dprtmnt         Department name: Departments are named according to usage in 1830, but without accents. A factor with levels Ain Aisne Allier … Vosges Yonne
+crm_prs         Population per Crime against persons.   A2. Compte général, 1825-1830
+crm_prp         Population per Crime against property.  Compte général, 1825-1830
+litercy         Percent of military conscripts who can read and write.  A2
+donatns         Donations to the poor.  A2. Bulletin des lois
+infants         Population per illegitimate birth.      A2. Bureau des Longitudes, 1817-1821
+suicids         Population per suicide.         A2. Compte général, 1827-1830
+maincty         Size of principal city (‘1:Sm’, ‘2:Med’, ‘3:Lg’), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium.      A1. An ordered factor with levels: 1:Sm &lt; 2:Med &lt; 3:Lg
+wealth  Per capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant.     A1
+commerc         Commerce and Industry, measured by the rank of the number of patents / population.      A1
+clergy  Distribution of clergy, measured by the rank of the number of Catholic priests in active service population.    A1. Almanach officiel du clergy, 1829
+crim_prn        Crimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830.    A1. Compte général
+infntcd         Infanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830.      A1. Compte général
+dntn_cl         Donations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824.   A1. Bull. des lois, ordunn. d’autorisation
+lottery         Per capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826.       A1. Compte rendu par le ministre des finances
+desertn         Military desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827.      A1. Compte du ministère du guerre, 1829 état V
+instrct         Instruction. Ranks recorded from Guerry’s map of Instruction. Note: this is inversely related to Literacy
+Prsttts         Number of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth        Parent-Duchatelet (1836), De la prostitution en Paris
+distanc         Distance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris)       Calculated from department centroids
+area    Area (1000 km^2).       Angeville (1836)
+pop1831         Population in 1831, in 1000s    Taken from Angeville (1836), Essai sur la Statistique de la Population français
+Details
+
+Note that most of the variables (e.g., Crime_pers) are scaled so that more is “better”.
+
+Values for the quantitative variables displayed on Guerry’s maps were taken from Table A2 in the English translation of Guerry (1833) by Whitt and Reinking. Values for the ranked variables were taken from Table A1, with some corrections applied. The maximum is indicated by rank 1, and the minimum by rank 86.
+Sources
+
+Angeville, A. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour.
+
+Guerry, A.-M. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: Hugh P. Whitt and Victor W. Reinking, Lewiston, N.Y. : Edwin Mellen Press, 2002.
+
+Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36
+References
+
+Dray, S. and Jombart, T. (2011). A Revisit Of Guerry’s Data: Introducing Spatial Constraints In Multivariate Analysis. The Annals of Applied Statistics, Vol. 5, No. 4, 2278-2299., DOI: 10.1214/10-AOAS356.
+
+Brunsdon, C. and Dykes, J. (2007). Geographically weighted visualization: interactive graphics for scale-varying exploratory analysis. Geographical Information Science Research Conference (GISRUK 07), NUI Maynooth, Ireland, April, 2007.
+
+Friendly, M. (2007). A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis. Statistical Science, 22, 368-399.
+
+Friendly, M. (2007). Data from A.-M. Guerry, Essay on the Moral Statistics of France (1833).
+See Also
+
+The Guerry package for maps of France: gfrance and related data.
+
+Prepared by Center for Spatial Data Science. Last updated July 3, 2017. Data provided “as is,” no warranties.
+
+
+
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&#39;Guerry.geojson&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/pysal_data/guerry/Guerry.geojson&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;south&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+south
+=====
+
+Homicides and selected socio-economic characteristics for Southern U.S. counties.
+---------------------------------------------------------------------------------
+
+- Observations = 1,412
+- Variables = 69
+- Years = 1960-90s
+- Support = polygon
+
+Files
+-----
+south.gdb     README.md  south.dbf      south.gpkg  south.kml  south.mif  south.shp  south.sqlite
+codebook.pdf  south.csv  south.geojson  south.html  south.mid  south.prj  south.shx  south.xlsx
+
+Variables
+---------
+NAME    county name
+STATE_NAME      state name
+STATE_FIPS      state fips code (character)
+CNTY_FIPS       county fips code (character)
+FIPS    combined state and county fips code (character)
+STFIPS  state fips code (numeric)
+COFIPS  county fips code (numeric)
+FIPSNO  fips code as numeric variable
+SOUTH   dummy variable for Southern counties (South = 1)
+HR**    homicide rate per 100,000 (1960, 1970, 1980, 1990)
+HC**    homicide count, three year average centered on 1960, 1970, 1980, 1990
+PO**    county population, 1960, 1970, 1980, 1990
+RD**    resource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)
+PS**    population structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)
+UE**    unemployment rate 1960, 1970, 1980, 1990
+DV**    divorce rate 1960, 1970, 1980, 1990 (% males over 14 divorced)
+MA**    median age 1960, 1970, 1980, 1990
+POL**   log of population 1960, 1970, 1980, 1990
+DNL**   log of population density 1960, 1970, 1980, 1990
+MFIL**  log of median family income 1960, 1970, 1980, 1990
+FP**    % families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)
+BLK**   % black 1960, 1970, 1980, 1990
+GI**    Gini index of family income inequality 1960, 1970, 1980, 1990
+FH**    % female headed households 1960, 1970, 1980, 1990
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&#39;south.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/pysal_data/south/south.shp&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&#39;missing.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+missing.shp not found.
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>pth = libpysal.examples.get_path(&#39;south.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>pth
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/pysal_data/south/south.shp&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import geopandas as gpd
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>df = gpd.read_file(pth)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>df.head()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>NAME</th>
+      <th>STATE_NAME</th>
+      <th>STATE_FIPS</th>
+      <th>CNTY_FIPS</th>
+      <th>FIPS</th>
+      <th>STFIPS</th>
+      <th>COFIPS</th>
+      <th>FIPSNO</th>
+      <th>SOUTH</th>
+      <th>HR60</th>
+      <th>...</th>
+      <th>BLK90</th>
+      <th>GI59</th>
+      <th>GI69</th>
+      <th>GI79</th>
+      <th>GI89</th>
+      <th>FH60</th>
+      <th>FH70</th>
+      <th>FH80</th>
+      <th>FH90</th>
+      <th>geometry</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Hancock</td>
+      <td>West Virginia</td>
+      <td>54</td>
+      <td>029</td>
+      <td>54029</td>
+      <td>54</td>
+      <td>29</td>
+      <td>54029</td>
+      <td>1</td>
+      <td>1.682864</td>
+      <td>...</td>
+      <td>2.557262</td>
+      <td>0.223645</td>
+      <td>0.295377</td>
+      <td>0.332251</td>
+      <td>0.363934</td>
+      <td>9.981297</td>
+      <td>7.8</td>
+      <td>9.785797</td>
+      <td>12.604552</td>
+      <td>POLYGON ((-80.6280517578125 40.39815902709961,...</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Brooke</td>
+      <td>West Virginia</td>
+      <td>54</td>
+      <td>009</td>
+      <td>54009</td>
+      <td>54</td>
+      <td>9</td>
+      <td>54009</td>
+      <td>1</td>
+      <td>4.607233</td>
+      <td>...</td>
+      <td>0.748370</td>
+      <td>0.220407</td>
+      <td>0.318453</td>
+      <td>0.314165</td>
+      <td>0.350569</td>
+      <td>10.929337</td>
+      <td>8.0</td>
+      <td>10.214990</td>
+      <td>11.242293</td>
+      <td>POLYGON ((-80.52625274658203 40.16244888305664...</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Ohio</td>
+      <td>West Virginia</td>
+      <td>54</td>
+      <td>069</td>
+      <td>54069</td>
+      <td>54</td>
+      <td>69</td>
+      <td>54069</td>
+      <td>1</td>
+      <td>0.974132</td>
+      <td>...</td>
+      <td>3.310334</td>
+      <td>0.272398</td>
+      <td>0.358454</td>
+      <td>0.376963</td>
+      <td>0.390534</td>
+      <td>15.621643</td>
+      <td>12.9</td>
+      <td>14.716681</td>
+      <td>17.574021</td>
+      <td>POLYGON ((-80.52516937255859 40.02275085449219...</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Marshall</td>
+      <td>West Virginia</td>
+      <td>54</td>
+      <td>051</td>
+      <td>54051</td>
+      <td>54</td>
+      <td>51</td>
+      <td>54051</td>
+      <td>1</td>
+      <td>0.876248</td>
+      <td>...</td>
+      <td>0.546097</td>
+      <td>0.227647</td>
+      <td>0.319580</td>
+      <td>0.320953</td>
+      <td>0.377346</td>
+      <td>11.962834</td>
+      <td>8.8</td>
+      <td>8.803253</td>
+      <td>13.564159</td>
+      <td>POLYGON ((-80.52446746826172 39.72112655639648...</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>New Castle</td>
+      <td>Delaware</td>
+      <td>10</td>
+      <td>003</td>
+      <td>10003</td>
+      <td>10</td>
+      <td>3</td>
+      <td>10003</td>
+      <td>1</td>
+      <td>4.228385</td>
+      <td>...</td>
+      <td>16.480294</td>
+      <td>0.256106</td>
+      <td>0.329678</td>
+      <td>0.365830</td>
+      <td>0.332703</td>
+      <td>12.035714</td>
+      <td>10.7</td>
+      <td>15.169480</td>
+      <td>16.380903</td>
+      <td>POLYGON ((-75.77269744873047 39.38300704956055...</td>
+    </tr>
+  </tbody>
+</table>
+<p>5 rows × 70 columns</p>
+</div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples.sacramento2 import fetch_sacramento2
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fetch_sacramento2()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+already exists, not downloading
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;sacramento2&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+sacramento2
+===========
+
+2000 Census Tract Data for Sacramento MSA
+-----------------------------------------
+
+- Observations = 83
+- Variables = 66
+- Years = 1998, 2001
+- Support = polygon
+
+Files
+-----
+ SacramentoMSA2.gdb       SacramentoMSA2.kml   SacramentoMSA2.shp
+ README.md                SacramentoMSA2.mid   SacramentoMSA2.shx
+ SacramentoMSA2.csv       SacramentoMSA2.mif   SacramentoMSA2.sqlite
+ SacramentoMSA2.dbf       SacramentoMSA2.prj   SacramentoMSA2.xlsx
+ SacramentoMSA2.geojson   SacramentoMSA2.sbn  &#39;Variable Info for Zip Code File.pdf&#39;
+ SacramentoMSA2.gpkg      SacramentoMSA2.sbx
+
+Variables
+---------
+ZIP ZIP code
+PO_NAME         Name of ZIP code area
+STATE   STATE
+MSA     MSA
+CBSA_CODE       CBSA code
+MAN98   1998 total manufacturing establishments (MSA)
+MAN98_12        1998 total manufacturing establishments, 1-9 employees (MSA)
+MAN98_39        1998 total manufacturing establishments 10+ employees (MSA)
+MAN01   2001 total manufacturing establishments (MSA)
+MAN01_12        2001 total manufacturing establishments, 1-9 employees (MSA)
+MAN01_39        2001 total manufacturing establishments, 10+ employees (MSA)
+MAN98US         1998 total manufacturing establishments (US)
+MAN98US12       1998 total manufacturing establishments, 1-9 employees (US)
+MAN98US39       1998 total manufacturing establishments 10+ employees (US)
+MAN01US         2001 total manufacturing establishments (US)
+MAN01US_12      2001 total manufacturing establishments, 1-9 employees (US)
+MAN01US_39      2001 total manufacturing establishments, 10+ employees (US)
+OFF98   1998 total office establishments (MSA)
+OFF98_12        1998 total office establishments, 1-9 employees (MSA)
+OFF98_39        1998 total office establishments, 10+ employees (MSA)
+OFF01   2001 total office establishments (MSA)
+OFF01_12        2001 total office establishments, 1-9 employees (MSA)
+OFF01_39        2001 total office establishments, 10+ employees (MSA)
+OFF98US         1998 total office establishments (US)
+OFF98US12       1998 total office establishments, 1-9 employees (US)
+OFF98US39       1998 total office establishments, 10+ employees (US)
+OFF01US         2001 total office establishments (US)
+OFFUS01_12      2001 total office establishments, 1-9 employees (US)
+OFFUS01_39      2001 total office establishments, 10+ employees (US)
+INFO98  1998 total information establishments (MSA)
+INFO98_12       1998 total information establishments, 1-9 employees (MSA)
+INFO98_39       1998 total information establishments, 10+ employees (MSA)
+INFO01  2001 total information establishments (MSA)
+INFO01_12       2001 total information establishments, 1-9 employees (MSA)
+INFO01_39       2001 total information establishments, 10+ employees (MSA)
+INFO98US        1998 total information establishments (US)
+INFO98US12      1998 total information establishments, 1-9 employees (US)
+INFO98US39      1998 total information establishments, 10+ employees (US)
+INFO01US        2001 total information establishments (US)
+INFO01US_1      2001 total information establishments, 1-9 employees (US)
+INFO01US_3      2001 total information establishments, 10+ employees (US)
+INDEX   Index
+NUMSEC  Number of sectors represented in ZIP code
+EST98   Total establishments in ZIP code, 1998
+EST01   Total establishments in ZIP code, 2001
+PCTNGE  National growth effect, percent (N)
+PCTIME  Industry mix effect, percent (M)
+PCTCSE  Competitive shift effect, percent (S)
+PCTGRO  Percent growth establishments, 1998-2001 (R)
+ID      Unique ZIP code ID for ID variables in weights matrix creation window
+
+Source: US Census Bureau, 2000 Census (Summary File 3). Extracted from http://factfinder.census.gov in April 2004.
+
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&quot;10740.shx&quot;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/libpysal/examples/10740/10740.shx&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples.nyc_bikes import fetch_bikes
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fetch_bikes()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+already exists, not downloading
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&#39;nyct2010.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/pysal_data/nyc_bikes/nyct2010.shp&#39;
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples.rio_grande_do_sul import fetch_rio
+fetch_rio()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+already exists, not downloading
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&#39;map_RS_BR.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/jovyan/pysal_data/rio_grande_do_sul/map_RS_BR.shp&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.examples.taz import fetch_taz
+fetch_taz()
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.get_path(&#39;taz.dbf&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/notebooks/fetch.ipynb.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/notebooks/fetch.ipynb b/notebooks/fetch.ipynb
new file mode 100644
index 000000000..c8f002b9d
--- /dev/null
+++ b/notebooks/fetch.ipynb
@@ -0,0 +1,887 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/guerry.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://github.com/sjsrey/rio_grande_do_sul/archive/master.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/ncovr.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://github.com/sjsrey/nyc_bikes/archive/master.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/SacramentoMSA2.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://s3.amazonaws.com/geoda/data/south.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n",
+      "downloading dataset from https://github.com/sjsrey/taz/archive/master.zip to /home/jovyan/pysal_data\n",
+      "Extracting files....\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.fetch_all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import nat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "nat.fetch_nat()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from os import environ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "environ.get(\"PYSALDATA\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import south"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "sd = south.fetch_south()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples import guerry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "guerry.fetch_guerry()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "guerry\n",
+      "======\n",
+      "\n",
+      "Andre-Michel Guerry data on \"moral statistics\" 1930 crime, suicide, literacy and other “moral statistics” in 1830s France.\n",
+      "\n",
+      "- Observations = 85\n",
+      "- Variables = 23\n",
+      "- Years = 1915-1934\n",
+      "- Support = polygon\n",
+      "\n",
+      "Files\n",
+      "-----\n",
+      "Guerry.dbf  Guerry_documentation.html  Guerry.geojson  Guerry.prj  Guerry.shp  Guerry.shx  README.md\n",
+      "\n",
+      "\n",
+      "Variables\n",
+      "---------\n",
+      "\n",
+      "dept, code_de \tDepartment ID: Standard numbers for the departments \t \n",
+      "region \tRegion of France (‘N’=’North’, ‘S’=’South’, ‘E’=’East’, ‘W’=’West’, ‘C’=’Central’). Corsica is coded as NA. \t \n",
+      "dprtmnt \tDepartment name: Departments are named according to usage in 1830, but without accents. A factor with levels Ain Aisne Allier … Vosges Yonne \t \n",
+      "crm_prs \tPopulation per Crime against persons. \tA2. Compte général, 1825-1830\n",
+      "crm_prp \tPopulation per Crime against property. \tCompte général, 1825-1830\n",
+      "litercy \tPercent of military conscripts who can read and write. \tA2 \n",
+      "donatns \tDonations to the poor. \tA2. Bulletin des lois\n",
+      "infants \tPopulation per illegitimate birth. \tA2. Bureau des Longitudes, 1817-1821\n",
+      "suicids \tPopulation per suicide. \tA2. Compte général, 1827-1830\n",
+      "maincty \tSize of principal city (‘1:Sm’, ‘2:Med’, ‘3:Lg’), used as a surrogate for population density. Large refers to the top 10, small to the bottom 10; all the rest are classed Medium. \tA1. An ordered factor with levels: 1:Sm < 2:Med < 3:Lg\n",
+      "wealth \tPer capita tax on personal property. A ranked index based on taxes on personal and movable property per inhabitant. \tA1\n",
+      "commerc \tCommerce and Industry, measured by the rank of the number of patents / population. \tA1\n",
+      "clergy \tDistribution of clergy, measured by the rank of the number of Catholic priests in active service population. \tA1. Almanach officiel du clergy, 1829\n",
+      "crim_prn \tCrimes against parents, measured by the rank of the ratio of crimes against parents to all crimes – Average for the years 1825-1830. \tA1. Compte général\n",
+      "infntcd \tInfanticides per capita. A ranked ratio of number of infanticides to population – Average for the years 1825-1830. \tA1. Compte général\n",
+      "dntn_cl \tDonations to the clergy. A ranked ratio of the number of bequests and donations inter vivios to population – Average for the years 1815-1824. \tA1. Bull. des lois, ordunn. d’autorisation\n",
+      "lottery \tPer capita wager on Royal Lottery. Ranked ratio of the proceeds bet on the royal lottery to population — Average for the years 1822-1826. \tA1. Compte rendu par le ministre des finances\n",
+      "desertn \tMilitary desertion, ratio of number of young soldiers accused of desertion to the force of the military contingent, minus the deficit produced by the insufficiency of available billets – Average of the years 1825-1827. \tA1. Compte du ministère du guerre, 1829 état V\n",
+      "instrct \tInstruction. Ranks recorded from Guerry’s map of Instruction. Note: this is inversely related to Literacy \t \n",
+      "Prsttts \tNumber of prostitutes registered in Paris from 1816 to 1834, classified by the department of their birth \tParent-Duchatelet (1836), De la prostitution en Paris\n",
+      "distanc \tDistance to Paris (km). Distance of each department centroid to the centroid of the Seine (Paris) \tCalculated from department centroids\n",
+      "area \tArea (1000 km^2). \tAngeville (1836)\n",
+      "pop1831 \tPopulation in 1831, in 1000s \tTaken from Angeville (1836), Essai sur la Statistique de la Population français\n",
+      "Details\n",
+      "\n",
+      "Note that most of the variables (e.g., Crime_pers) are scaled so that more is “better”. \n",
+      "\n",
+      "Values for the quantitative variables displayed on Guerry’s maps were taken from Table A2 in the English translation of Guerry (1833) by Whitt and Reinking. Values for the ranked variables were taken from Table A1, with some corrections applied. The maximum is indicated by rank 1, and the minimum by rank 86. \n",
+      "Sources\n",
+      "\n",
+      "Angeville, A. (1836). Essai sur la Statistique de la Population française Paris: F. Doufour. \n",
+      "\n",
+      "Guerry, A.-M. (1833). Essai sur la statistique morale de la France Paris: Crochard. English translation: Hugh P. Whitt and Victor W. Reinking, Lewiston, N.Y. : Edwin Mellen Press, 2002. \n",
+      "\n",
+      "Parent-Duchatelet, A. (1836). De la prostitution dans la ville de Paris, 3rd ed, 1857, p. 32, 36 \n",
+      "References\n",
+      "\n",
+      "Dray, S. and Jombart, T. (2011). A Revisit Of Guerry’s Data: Introducing Spatial Constraints In Multivariate Analysis. The Annals of Applied Statistics, Vol. 5, No. 4, 2278-2299., DOI: 10.1214/10-AOAS356. \n",
+      "\n",
+      "Brunsdon, C. and Dykes, J. (2007). Geographically weighted visualization: interactive graphics for scale-varying exploratory analysis. Geographical Information Science Research Conference (GISRUK 07), NUI Maynooth, Ireland, April, 2007. \n",
+      "\n",
+      "Friendly, M. (2007). A.-M. Guerry’s Moral Statistics of France: Challenges for Multivariable Spatial Analysis. Statistical Science, 22, 368-399. \n",
+      "\n",
+      "Friendly, M. (2007). Data from A.-M. Guerry, Essay on the Moral Statistics of France (1833). \n",
+      "See Also\n",
+      "\n",
+      "The Guerry package for maps of France: gfrance and related data. \n",
+      "\n",
+      "Prepared by Center for Spatial Data Science. Last updated July 3, 2017. Data provided “as is,” no warranties.\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('guerry')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/guerry/Guerry.geojson'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('Guerry.geojson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "south\n",
+      "=====\n",
+      "\n",
+      "Homicides and selected socio-economic characteristics for Southern U.S. counties.\n",
+      "---------------------------------------------------------------------------------\n",
+      "\n",
+      "- Observations = 1,412\n",
+      "- Variables = 69\n",
+      "- Years = 1960-90s\n",
+      "- Support = polygon\n",
+      "\n",
+      "Files\n",
+      "-----\n",
+      "south.gdb     README.md  south.dbf      south.gpkg  south.kml  south.mif  south.shp  south.sqlite\n",
+      "codebook.pdf  south.csv  south.geojson  south.html  south.mid  south.prj  south.shx  south.xlsx\n",
+      "\n",
+      "Variables\n",
+      "---------\n",
+      "NAME \tcounty name\n",
+      "STATE_NAME \tstate name\n",
+      "STATE_FIPS \tstate fips code (character)\n",
+      "CNTY_FIPS \tcounty fips code (character)\n",
+      "FIPS \tcombined state and county fips code (character)\n",
+      "STFIPS \tstate fips code (numeric)\n",
+      "COFIPS \tcounty fips code (numeric)\n",
+      "FIPSNO \tfips code as numeric variable\n",
+      "SOUTH \tdummy variable for Southern counties (South = 1)\n",
+      "HR** \thomicide rate per 100,000 (1960, 1970, 1980, 1990)\n",
+      "HC** \thomicide count, three year average centered on 1960, 1970, 1980, 1990\n",
+      "PO** \tcounty population, 1960, 1970, 1980, 1990\n",
+      "RD** \tresource deprivation 1960, 1970, 1980, 1990 (principal component, see Codebook for details)\n",
+      "PS** \tpopulation structure 1960, 1970, 1980, 1990 (principal component, see Codebook for details)\n",
+      "UE** \tunemployment rate 1960, 1970, 1980, 1990\n",
+      "DV** \tdivorce rate 1960, 1970, 1980, 1990 (% males over 14 divorced)\n",
+      "MA** \tmedian age 1960, 1970, 1980, 1990\n",
+      "POL** \tlog of population 1960, 1970, 1980, 1990\n",
+      "DNL** \tlog of population density 1960, 1970, 1980, 1990\n",
+      "MFIL** \tlog of median family income 1960, 1970, 1980, 1990\n",
+      "FP** \t% families below poverty 1960, 1970, 1980, 1990 (see Codebook for details)\n",
+      "BLK** \t% black 1960, 1970, 1980, 1990\n",
+      "GI** \tGini index of family income inequality 1960, 1970, 1980, 1990\n",
+      "FH** \t% female headed households 1960, 1970, 1980, 1990\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('south')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/south/south.shp'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('south.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "missing.shp not found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('missing.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = libpysal.examples.get_path('south.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/south/south.shp'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = gpd.read_file(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>STATE_NAME</th>\n",
+       "      <th>STATE_FIPS</th>\n",
+       "      <th>CNTY_FIPS</th>\n",
+       "      <th>FIPS</th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>COFIPS</th>\n",
+       "      <th>FIPSNO</th>\n",
+       "      <th>SOUTH</th>\n",
+       "      <th>HR60</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BLK90</th>\n",
+       "      <th>GI59</th>\n",
+       "      <th>GI69</th>\n",
+       "      <th>GI79</th>\n",
+       "      <th>GI89</th>\n",
+       "      <th>FH60</th>\n",
+       "      <th>FH70</th>\n",
+       "      <th>FH80</th>\n",
+       "      <th>FH90</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Hancock</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>029</td>\n",
+       "      <td>54029</td>\n",
+       "      <td>54</td>\n",
+       "      <td>29</td>\n",
+       "      <td>54029</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.682864</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.557262</td>\n",
+       "      <td>0.223645</td>\n",
+       "      <td>0.295377</td>\n",
+       "      <td>0.332251</td>\n",
+       "      <td>0.363934</td>\n",
+       "      <td>9.981297</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>9.785797</td>\n",
+       "      <td>12.604552</td>\n",
+       "      <td>POLYGON ((-80.6280517578125 40.39815902709961,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooke</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>009</td>\n",
+       "      <td>54009</td>\n",
+       "      <td>54</td>\n",
+       "      <td>9</td>\n",
+       "      <td>54009</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.607233</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.748370</td>\n",
+       "      <td>0.220407</td>\n",
+       "      <td>0.318453</td>\n",
+       "      <td>0.314165</td>\n",
+       "      <td>0.350569</td>\n",
+       "      <td>10.929337</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>10.214990</td>\n",
+       "      <td>11.242293</td>\n",
+       "      <td>POLYGON ((-80.52625274658203 40.16244888305664...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>069</td>\n",
+       "      <td>54069</td>\n",
+       "      <td>54</td>\n",
+       "      <td>69</td>\n",
+       "      <td>54069</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.974132</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.310334</td>\n",
+       "      <td>0.272398</td>\n",
+       "      <td>0.358454</td>\n",
+       "      <td>0.376963</td>\n",
+       "      <td>0.390534</td>\n",
+       "      <td>15.621643</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>14.716681</td>\n",
+       "      <td>17.574021</td>\n",
+       "      <td>POLYGON ((-80.52516937255859 40.02275085449219...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Marshall</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>54</td>\n",
+       "      <td>051</td>\n",
+       "      <td>54051</td>\n",
+       "      <td>54</td>\n",
+       "      <td>51</td>\n",
+       "      <td>54051</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.876248</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.546097</td>\n",
+       "      <td>0.227647</td>\n",
+       "      <td>0.319580</td>\n",
+       "      <td>0.320953</td>\n",
+       "      <td>0.377346</td>\n",
+       "      <td>11.962834</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>8.803253</td>\n",
+       "      <td>13.564159</td>\n",
+       "      <td>POLYGON ((-80.52446746826172 39.72112655639648...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>New Castle</td>\n",
+       "      <td>Delaware</td>\n",
+       "      <td>10</td>\n",
+       "      <td>003</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.228385</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.480294</td>\n",
+       "      <td>0.256106</td>\n",
+       "      <td>0.329678</td>\n",
+       "      <td>0.365830</td>\n",
+       "      <td>0.332703</td>\n",
+       "      <td>12.035714</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>15.169480</td>\n",
+       "      <td>16.380903</td>\n",
+       "      <td>POLYGON ((-75.77269744873047 39.38300704956055...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         NAME     STATE_NAME STATE_FIPS CNTY_FIPS   FIPS  STFIPS  COFIPS  \\\n",
+       "0     Hancock  West Virginia         54       029  54029      54      29   \n",
+       "1      Brooke  West Virginia         54       009  54009      54       9   \n",
+       "2        Ohio  West Virginia         54       069  54069      54      69   \n",
+       "3    Marshall  West Virginia         54       051  54051      54      51   \n",
+       "4  New Castle       Delaware         10       003  10003      10       3   \n",
+       "\n",
+       "   FIPSNO  SOUTH      HR60  ...      BLK90      GI59      GI69      GI79  \\\n",
+       "0   54029      1  1.682864  ...   2.557262  0.223645  0.295377  0.332251   \n",
+       "1   54009      1  4.607233  ...   0.748370  0.220407  0.318453  0.314165   \n",
+       "2   54069      1  0.974132  ...   3.310334  0.272398  0.358454  0.376963   \n",
+       "3   54051      1  0.876248  ...   0.546097  0.227647  0.319580  0.320953   \n",
+       "4   10003      1  4.228385  ...  16.480294  0.256106  0.329678  0.365830   \n",
+       "\n",
+       "       GI89       FH60  FH70       FH80       FH90  \\\n",
+       "0  0.363934   9.981297   7.8   9.785797  12.604552   \n",
+       "1  0.350569  10.929337   8.0  10.214990  11.242293   \n",
+       "2  0.390534  15.621643  12.9  14.716681  17.574021   \n",
+       "3  0.377346  11.962834   8.8   8.803253  13.564159   \n",
+       "4  0.332703  12.035714  10.7  15.169480  16.380903   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((-80.6280517578125 40.39815902709961,...  \n",
+       "1  POLYGON ((-80.52625274658203 40.16244888305664...  \n",
+       "2  POLYGON ((-80.52516937255859 40.02275085449219...  \n",
+       "3  POLYGON ((-80.52446746826172 39.72112655639648...  \n",
+       "4  POLYGON ((-75.77269744873047 39.38300704956055...  \n",
+       "\n",
+       "[5 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples.sacramento2 import fetch_sacramento2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "fetch_sacramento2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "sacramento2\n",
+      "===========\n",
+      "\n",
+      "2000 Census Tract Data for Sacramento MSA\n",
+      "-----------------------------------------\n",
+      "\n",
+      "- Observations = 83\n",
+      "- Variables = 66\n",
+      "- Years = 1998, 2001\n",
+      "- Support = polygon\n",
+      "\n",
+      "Files\n",
+      "-----\n",
+      " SacramentoMSA2.gdb       SacramentoMSA2.kml   SacramentoMSA2.shp\n",
+      " README.md                SacramentoMSA2.mid   SacramentoMSA2.shx\n",
+      " SacramentoMSA2.csv       SacramentoMSA2.mif   SacramentoMSA2.sqlite\n",
+      " SacramentoMSA2.dbf       SacramentoMSA2.prj   SacramentoMSA2.xlsx\n",
+      " SacramentoMSA2.geojson   SacramentoMSA2.sbn  'Variable Info for Zip Code File.pdf'\n",
+      " SacramentoMSA2.gpkg      SacramentoMSA2.sbx\n",
+      "\n",
+      "Variables\n",
+      "---------\n",
+      "ZIP ZIP code\n",
+      "PO_NAME \tName of ZIP code area\n",
+      "STATE \tSTATE\n",
+      "MSA \tMSA\n",
+      "CBSA_CODE \tCBSA code\n",
+      "MAN98 \t1998 total manufacturing establishments (MSA)\n",
+      "MAN98_12 \t1998 total manufacturing establishments, 1-9 employees (MSA)\n",
+      "MAN98_39 \t1998 total manufacturing establishments 10+ employees (MSA)\n",
+      "MAN01 \t2001 total manufacturing establishments (MSA)\n",
+      "MAN01_12 \t2001 total manufacturing establishments, 1-9 employees (MSA)\n",
+      "MAN01_39 \t2001 total manufacturing establishments, 10+ employees (MSA)\n",
+      "MAN98US \t1998 total manufacturing establishments (US)\n",
+      "MAN98US12 \t1998 total manufacturing establishments, 1-9 employees (US)\n",
+      "MAN98US39 \t1998 total manufacturing establishments 10+ employees (US)\n",
+      "MAN01US \t2001 total manufacturing establishments (US)\n",
+      "MAN01US_12 \t2001 total manufacturing establishments, 1-9 employees (US)\n",
+      "MAN01US_39 \t2001 total manufacturing establishments, 10+ employees (US)\n",
+      "OFF98 \t1998 total office establishments (MSA)\n",
+      "OFF98_12 \t1998 total office establishments, 1-9 employees (MSA)\n",
+      "OFF98_39 \t1998 total office establishments, 10+ employees (MSA)\n",
+      "OFF01 \t2001 total office establishments (MSA)\n",
+      "OFF01_12 \t2001 total office establishments, 1-9 employees (MSA)\n",
+      "OFF01_39 \t2001 total office establishments, 10+ employees (MSA)\n",
+      "OFF98US \t1998 total office establishments (US)\n",
+      "OFF98US12 \t1998 total office establishments, 1-9 employees (US)\n",
+      "OFF98US39 \t1998 total office establishments, 10+ employees (US)\n",
+      "OFF01US \t2001 total office establishments (US)\n",
+      "OFFUS01_12 \t2001 total office establishments, 1-9 employees (US)\n",
+      "OFFUS01_39 \t2001 total office establishments, 10+ employees (US)\n",
+      "INFO98 \t1998 total information establishments (MSA)\n",
+      "INFO98_12 \t1998 total information establishments, 1-9 employees (MSA)\n",
+      "INFO98_39 \t1998 total information establishments, 10+ employees (MSA)\n",
+      "INFO01 \t2001 total information establishments (MSA)\n",
+      "INFO01_12 \t2001 total information establishments, 1-9 employees (MSA)\n",
+      "INFO01_39 \t2001 total information establishments, 10+ employees (MSA)\n",
+      "INFO98US \t1998 total information establishments (US)\n",
+      "INFO98US12 \t1998 total information establishments, 1-9 employees (US)\n",
+      "INFO98US39 \t1998 total information establishments, 10+ employees (US)\n",
+      "INFO01US \t2001 total information establishments (US)\n",
+      "INFO01US_1 \t2001 total information establishments, 1-9 employees (US)\n",
+      "INFO01US_3 \t2001 total information establishments, 10+ employees (US)\n",
+      "INDEX \tIndex\n",
+      "NUMSEC \tNumber of sectors represented in ZIP code\n",
+      "EST98 \tTotal establishments in ZIP code, 1998\n",
+      "EST01 \tTotal establishments in ZIP code, 2001\n",
+      "PCTNGE \tNational growth effect, percent (N)\n",
+      "PCTIME \tIndustry mix effect, percent (M)\n",
+      "PCTCSE \tCompetitive shift effect, percent (S)\n",
+      "PCTGRO \tPercent growth establishments, 1998-2001 (R)\n",
+      "ID \tUnique ZIP code ID for ID variables in weights matrix creation window\n",
+      "\n",
+      "Source: US Census Bureau, 2000 Census (Summary File 3). Extracted from http://factfinder.census.gov in April 2004.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('sacramento2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/libpysal/examples/10740/10740.shx'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path(\"10740.shx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples.nyc_bikes import fetch_bikes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "fetch_bikes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/nyc_bikes/nyct2010.shp'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('nyct2010.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "already exists, not downloading\n"
+     ]
+    }
+   ],
+   "source": [
+    "from libpysal.examples.rio_grande_do_sul import fetch_rio\n",
+    "fetch_rio()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/jovyan/pysal_data/rio_grande_do_sul/map_RS_BR.shp'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.get_path('map_RS_BR.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.examples.taz import fetch_taz\n",
+    "fetch_taz()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "libpysal.examples.get_path('taz.dbf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/io.html b/notebooks/io.html
new file mode 100644
index 000000000..e64be2bed
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+    <script>window.MathJax = {"TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": true}}, "tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+<p>This page was generated from <a class="reference external" href="https://github.com/pysal/libpysal/blob/master/notebooks/io.ipynb">notebooks/io.ipynb</a>.
+Interactive online version:
+<span class="raw-html"><a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath=notebooks/io.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import sys
+import os
+
+sys.path.append(os.path.abspath(&#39;..&#39;))
+import libpysal
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w = libpysal.weights.lat2W(5,5)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.n
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+25
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.pct_nonzero
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+12.8
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.neighbors[0]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[5, 1]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.neighbors[5]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[0, 10, 6]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.available()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;georgia&#39;,
+ &#39;__pycache__&#39;,
+ &#39;tests&#39;,
+ &#39;newHaven&#39;,
+ &#39;Polygon_Holes&#39;,
+ &#39;nat&#39;,
+ &#39;Polygon&#39;,
+ &#39;10740&#39;,
+ &#39;berlin&#39;,
+ &#39;rio_grande_do_sul&#39;,
+ &#39;sids2&#39;,
+ &#39;sacramento2&#39;,
+ &#39;burkitt&#39;,
+ &#39;arcgis&#39;,
+ &#39;calemp&#39;,
+ &#39;stl&#39;,
+ &#39;virginia&#39;,
+ &#39;geodanet&#39;,
+ &#39;desmith&#39;,
+ &#39;book&#39;,
+ &#39;nyc_bikes&#39;,
+ &#39;Line&#39;,
+ &#39;south&#39;,
+ &#39;snow_maps&#39;,
+ &#39;Point&#39;,
+ &#39;street_net_pts&#39;,
+ &#39;guerry&#39;,
+ &#39;__pycache__&#39;,
+ &#39;baltim&#39;,
+ &#39;networks&#39;,
+ &#39;us_income&#39;,
+ &#39;taz&#39;,
+ &#39;columbus&#39;,
+ &#39;tokyo&#39;,
+ &#39;mexico&#39;,
+ &#39;__pycache__&#39;,
+ &#39;chicago&#39;,
+ &#39;wmat&#39;,
+ &#39;juvenile&#39;,
+ &#39;clearwater&#39;]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;baltim&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;name&#39;: &#39;baltim&#39;,
+ &#39;description&#39;: &#39;Baltimore house sales prices and hedonics 1978&#39;,
+ &#39;explanation&#39;: [&#39;* baltim.dbf: attribute data. (k=17)&#39;,
+  &#39;* baltim.shp: Point shapefile. (n=211)&#39;,
+  &#39;* baltim.shx: spatial index.&#39;,
+  &#39;* baltim.tri.k12.kwt: kernel weights using a triangular kernel with 12 nearest neighbors in KWT format.&#39;,
+  &#39;* baltim_k4.gwt: nearest neighbor weights (4nn) in GWT format.&#39;,
+  &#39;* baltim_q.gal: queen contiguity weights in GAL format.&#39;,
+  &#39;* baltimore.geojson: spatial weights in geojson format.&#39;]}
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>pth = libpysal.examples.get_path(&#39;baltim.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>pth
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&#39;/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/baltim/baltim.shp&#39;
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>shp_file = libpysal.io.open(pth)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>shapes = [shp for shp in shp_file]
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>shapes[0]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(907.0, 534.0)
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w = libpysal.io.open(libpysal.examples.get_path(&#39;baltim_q.gal&#39;)).read()
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.n
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+211
+</pre></div></div>
+</div>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/notebooks/io.ipynb.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/notebooks/io.ipynb b/notebooks/io.ipynb
new file mode 100644
index 000000000..76f11e123
--- /dev/null
+++ b/notebooks/io.ipynb
@@ -0,0 +1,310 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "sys.path.append(os.path.abspath('..'))\n",
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = libpysal.weights.lat2W(5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.8"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 1]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.neighbors[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 10, 6]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.neighbors[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['georgia',\n",
+       " '__pycache__',\n",
+       " 'tests',\n",
+       " 'newHaven',\n",
+       " 'Polygon_Holes',\n",
+       " 'nat',\n",
+       " 'Polygon',\n",
+       " '10740',\n",
+       " 'berlin',\n",
+       " 'rio_grande_do_sul',\n",
+       " 'sids2',\n",
+       " 'sacramento2',\n",
+       " 'burkitt',\n",
+       " 'arcgis',\n",
+       " 'calemp',\n",
+       " 'stl',\n",
+       " 'virginia',\n",
+       " 'geodanet',\n",
+       " 'desmith',\n",
+       " 'book',\n",
+       " 'nyc_bikes',\n",
+       " 'Line',\n",
+       " 'south',\n",
+       " 'snow_maps',\n",
+       " 'Point',\n",
+       " 'street_net_pts',\n",
+       " 'guerry',\n",
+       " '__pycache__',\n",
+       " 'baltim',\n",
+       " 'networks',\n",
+       " 'us_income',\n",
+       " 'taz',\n",
+       " 'columbus',\n",
+       " 'tokyo',\n",
+       " 'mexico',\n",
+       " '__pycache__',\n",
+       " 'chicago',\n",
+       " 'wmat',\n",
+       " 'juvenile',\n",
+       " 'clearwater']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'baltim',\n",
+       " 'description': 'Baltimore house sales prices and hedonics 1978',\n",
+       " 'explanation': ['* baltim.dbf: attribute data. (k=17)',\n",
+       "  '* baltim.shp: Point shapefile. (n=211)',\n",
+       "  '* baltim.shx: spatial index.',\n",
+       "  '* baltim.tri.k12.kwt: kernel weights using a triangular kernel with 12 nearest neighbors in KWT format.',\n",
+       "  '* baltim_k4.gwt: nearest neighbor weights (4nn) in GWT format.',\n",
+       "  '* baltim_q.gal: queen contiguity weights in GAL format.',\n",
+       "  '* baltimore.geojson: spatial weights in geojson format.']}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('baltim')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = libpysal.examples.get_path('baltim.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/examples/baltim/baltim.shp'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shp_file = libpysal.io.open(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes = [shp for shp in shp_file]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(907.0, 534.0)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shapes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = libpysal.io.open(libpysal.examples.get_path('baltim_q.gal')).read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "211"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/voronoi.html b/notebooks/voronoi.html
new file mode 100644
index 000000000..05050d3cd
--- /dev/null
+++ b/notebooks/voronoi.html
@@ -0,0 +1,536 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Voronoi Polygons for 2-D Point Sets &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/pysal-styles.css?v=38b0ce36" />
+    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+    <script src="../_static/documentation_options.js?v=7a99edf7"></script>
+    <script src="../_static/doctools.js?v=888ff710"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>window.MathJax = {"TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": true}}, "tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Datasets for use with libpysal" href="examples.html" />
+    <link rel="prev" title="Weights from GeoDataFrames" href="weights.html" />
+<meta charset='utf-8'>
+<meta http-equiv='X-UA-Compatible' content='IE=edge,chrome=1'>
+<meta name='viewport' content='width=device-width, initial-scale=1.0, maximum-scale=1'>
+<meta name="apple-mobile-web-app-capable" content="yes">
+<script type="text/javascript" src="../_static/js/jquery-1.12.4.min.js"></script>
+<script type="text/javascript" src="../_static/js/jquery-fix.js"></script>
+<script type="text/javascript" src="../_static/bootstrap-3.4.1/js/bootstrap.min.js"></script>
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+
+  </head><body>
+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
+    <div class="container">
+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
+        <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".nav-collapse">
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+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
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+<li class="toctree-l2 current"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial.html#example-datasets">Example Datasets</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../api.html">API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#io">io</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#examples">examples</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#experimental">Experimental</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#graph">graph</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
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+  <a role="button"
+     id="dLabelLocalToc"
+     data-toggle="dropdown"
+     data-target="#"
+     href="#">Page <b class="caret"></b></a>
+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">Voronoi Polygons for 2-D Point Sets</a><ul>
+<li><a class="reference internal" href="#Basic-Usage">Basic Usage</a></li>
+<li><a class="reference internal" href="#Larger-Problem">Larger Problem</a></li>
+<li><a class="reference internal" href="#Trimming">Trimming</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Voronoi-Weights">Voronoi Weights</a></li>
+</ul>
+</ul>
+</li>
+              
+            
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+            
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+          </ul>
+
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+    <div class="body col-md-12 content" role="main">
+      
+  <div class="admonition note">
+<p>This page was generated from <a class="reference external" href="https://github.com/pysal/libpysal/blob/master/notebooks/voronoi.ipynb">notebooks/voronoi.ipynb</a>.
+Interactive online version:
+<span class="raw-html"><a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath=notebooks/voronoi.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+</div>
+<section id="Voronoi-Polygons-for-2-D-Point-Sets">
+<h1>Voronoi Polygons for 2-D Point Sets<a class="headerlink" href="#Voronoi-Polygons-for-2-D-Point-Sets" title="Link to this heading">¶</a></h1>
+<p>Author: Serge Rey (<a class="reference external" href="http://github.com/sjsrey">http://github.com/sjsrey</a>)</p>
+<section id="Basic-Usage">
+<h2>Basic Usage<a class="headerlink" href="#Basic-Usage" title="Link to this heading">¶</a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import sys
+import os
+sys.path.append(os.path.abspath(&#39;..&#39;))
+import libpysal
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.cg.voronoi  import voronoi, voronoi_frames
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>regions, vertices = voronoi(points)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>regions
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>vertices
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[  4.21783296,   4.08408578],
+       [  7.51956025,   3.51807539],
+       [  9.4642193 ,  19.3994576 ],
+       [ 14.98210684, -10.63503022],
+       [ -9.22691341,  -4.58994414],
+       [ 14.98210684, -10.63503022],
+       [  1.78491801,  19.89803294],
+       [  9.4642193 ,  19.3994576 ],
+       [  1.78491801,  19.89803294],
+       [ -9.22691341,  -4.58994414]])
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>region_df, point_df = voronoi_frames(points)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+import matplotlib
+import numpy as np
+import matplotlib.pyplot as plt
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+region_df.plot(ax=ax, color=&#39;blue&#39;,edgecolor=&#39;black&#39;, alpha=0.3)
+point_df.plot(ax=ax, color=&#39;red&#39;)
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f85e38b94a8&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_voronoi_10_1.png" src="../_images/notebooks_voronoi_10_1.png" />
+</div>
+</div>
+</section>
+<section id="Larger-Problem">
+<h2>Larger Problem<a class="headerlink" href="#Larger-Problem" title="Link to this heading">¶</a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>n_points = 200
+np.random.seed(12345)
+points = np.random.random((n_points,2))*10 + 10
+results = voronoi(points)
+mins = points.min(axis=0)
+maxs = points.max(axis=0)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[34]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>regions, vertices = voronoi(points)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>regions_df, points_df = voronoi_frames(points)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+points_df.plot(ax=ax, color=&#39;red&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f85e39dae10&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_voronoi_15_1.png" src="../_images/notebooks_voronoi_15_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[37]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+regions_df.plot(ax=ax, color=&#39;blue&#39;,edgecolor=&#39;black&#39;, alpha=0.3)
+points_df.plot(ax=ax, color=&#39;red&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[37]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f85e1fbd240&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_voronoi_16_1.png" src="../_images/notebooks_voronoi_16_1.png" />
+</div>
+</div>
+</section>
+<section id="Trimming">
+<h2>Trimming<a class="headerlink" href="#Trimming" title="Link to this heading">¶</a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>points = np.array(points)
+maxs = points.max(axis=0)
+mins = points.min(axis=0)
+xr = maxs[0] - mins[0]
+yr = maxs[1] - mins[1]
+buff = 0.05
+r = max(yr, xr) * buff
+minx = mins[0] - r
+miny = mins[1] - r
+maxx = maxs[0] + r
+maxy = maxs[1] + r
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>fig, ax = plt.subplots()
+regions_df.plot(ax=ax, edgecolor=&#39;black&#39;, facecolor=&#39;blue&#39;, alpha=0.2 )
+points_df.plot(ax=ax, color=&#39;red&#39;)
+plt.xlim(minx, maxx)
+plt.ylim(miny, maxy)
+plt.title(&quot;buffer: %f, n: %d&quot;%(r,n_points))
+plt.show()
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_voronoi_19_0.png" src="../_images/notebooks_voronoi_19_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Voronoi-Weights">
+<h1>Voronoi Weights<a class="headerlink" href="#Voronoi-Weights" title="Link to this heading">¶</a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.weights.contiguity import Voronoi as Vornoi_weights
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w = Vornoi_weights(points)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.n
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+200
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[43]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.pct_nonzero
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[43]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+2.915
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.histogram
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[(3, 3),
+ (4, 28),
+ (5, 52),
+ (6, 65),
+ (7, 34),
+ (8, 10),
+ (9, 5),
+ (10, 2),
+ (11, 0),
+ (12, 1)]
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>idx = [i for i in range(w.n) if w.cardinalities[i]==12]
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[46]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>points[idx]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[46]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[16.50851787, 13.12932895]])
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/notebooks/voronoi.ipynb.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
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+  </body>
+</html>
\ No newline at end of file
diff --git a/notebooks/voronoi.ipynb b/notebooks/voronoi.ipynb
new file mode 100644
index 000000000..27276c168
--- /dev/null
+++ b/notebooks/voronoi.ipynb
@@ -0,0 +1,476 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voronoi Polygons for 2-D Point Sets\n",
+    "\n",
+    "Author: Serge Rey (http://github.com/sjsrey)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.abspath('..'))\n",
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.cg.voronoi  import voronoi, voronoi_frames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = [(10.2, 5.1), (4.7, 2.2), (5.3, 5.7), (2.7, 5.3)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regions, vertices = voronoi(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 3, 2], [4, 5, 1, 0], [0, 1, 7, 6], [9, 0, 8]]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  4.21783296,   4.08408578],\n",
+       "       [  7.51956025,   3.51807539],\n",
+       "       [  9.4642193 ,  19.3994576 ],\n",
+       "       [ 14.98210684, -10.63503022],\n",
+       "       [ -9.22691341,  -4.58994414],\n",
+       "       [ 14.98210684, -10.63503022],\n",
+       "       [  1.78491801,  19.89803294],\n",
+       "       [  9.4642193 ,  19.3994576 ],\n",
+       "       [  1.78491801,  19.89803294],\n",
+       "       [ -9.22691341,  -4.58994414]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vertices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "region_df, point_df = voronoi_frames(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85e38b94a8>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "region_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
+    "point_df.plot(ax=ax, color='red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Larger Problem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_points = 200\n",
+    "np.random.seed(12345)\n",
+    "points = np.random.random((n_points,2))*10 + 10\n",
+    "results = voronoi(points)\n",
+    "mins = points.min(axis=0)\n",
+    "maxs = points.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regions, vertices = voronoi(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regions_df, points_df = voronoi_frames(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85e39dae10>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "points_df.plot(ax=ax, color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f85e1fbd240>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "regions_df.plot(ax=ax, color='blue',edgecolor='black', alpha=0.3)\n",
+    "points_df.plot(ax=ax, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trimming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "points = np.array(points)\n",
+    "maxs = points.max(axis=0)\n",
+    "mins = points.min(axis=0)\n",
+    "xr = maxs[0] - mins[0]\n",
+    "yr = maxs[1] - mins[1]\n",
+    "buff = 0.05\n",
+    "r = max(yr, xr) * buff\n",
+    "minx = mins[0] - r\n",
+    "miny = mins[1] - r\n",
+    "maxx = maxs[0] + r\n",
+    "maxy = maxs[1] + r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "regions_df.plot(ax=ax, edgecolor='black', facecolor='blue', alpha=0.2 )\n",
+    "points_df.plot(ax=ax, color='red')\n",
+    "plt.xlim(minx, maxx)\n",
+    "plt.ylim(miny, maxy)\n",
+    "plt.title(\"buffer: %f, n: %d\"%(r,n_points))\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Voronoi Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.weights.contiguity import Voronoi as Vornoi_weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = Vornoi_weights(points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.915"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(3, 3),\n",
+       " (4, 28),\n",
+       " (5, 52),\n",
+       " (6, 65),\n",
+       " (7, 34),\n",
+       " (8, 10),\n",
+       " (9, 5),\n",
+       " (10, 2),\n",
+       " (11, 0),\n",
+       " (12, 1)]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "idx = [i for i in range(w.n) if w.cardinalities[i]==12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16.50851787, 13.12932895]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "points[idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/weights.html b/notebooks/weights.html
new file mode 100644
index 000000000..4053c792c
--- /dev/null
+++ b/notebooks/weights.html
@@ -0,0 +1,1174 @@
+<!DOCTYPE html>
+
+<html lang="en" data-content_root="../">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+    <title>Weights from GeoDataFrames &#8212; libpysal v4.9.0 Manual</title>
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../_static/bootstrap-sphinx.css?v=9afac83c" />
+    <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
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+    <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+    <script src="../_static/documentation_options.js?v=7a99edf7"></script>
+    <script src="../_static/doctools.js?v=888ff710"></script>
+    <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script>window.MathJax = {"TeX": {"equationNumbers": {"autoNumber": "AMS", "useLabelIds": true}}, "tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+    <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <link rel="icon" href="../_static/pysal_favicon.ico"/>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="Voronoi Polygons for 2-D Point Sets" href="voronoi.html" />
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+
+  <div id="navbar" class="navbar navbar-default navbar-fixed-top">
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+      <div class="navbar-header">
+        <!-- .btn-navbar is used as the toggle for collapsed navbar content -->
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+        <a class="navbar-brand" href="../index.html">
+          libpysal</a>
+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
+      </div>
+
+        <div class="collapse navbar-collapse nav-collapse">
+          <ul class="nav navbar-nav">
+            
+                <li><a href="../installation.html">Installation</a></li>
+                <li><a href="../tutorial.html">Tutorial</a></li>
+                <li><a href="../api.html">API</a></li>
+                <li><a href="../references.html">References</a></li>
+            
+            
+              <li class="dropdown globaltoc-container">
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+     id="dLabelGlobalToc"
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+  <ul class="dropdown-menu globaltoc"
+      role="menu"
+      aria-labelledby="dLabelGlobalToc"><p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-released-version">Installing released version</a></li>
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+<li class="toctree-l2 current"><a class="reference internal" href="../tutorial.html#spatial-weights">Spatial Weights</a></li>
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+</li>
+<li class="toctree-l1"><a class="reference internal" href="../api.html">API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#spatial-weights">Spatial Weights</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#cg-computational-geometry">cg: Computational Geometry</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#io">io</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#examples">examples</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#experimental">Experimental</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../api.html#graph">graph</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../references.html">References</a></li>
+</ul>
+</ul>
+</li>
+              
+                <li class="dropdown">
+  <a role="button"
+     id="dLabelLocalToc"
+     data-toggle="dropdown"
+     data-target="#"
+     href="#">Page <b class="caret"></b></a>
+  <ul class="dropdown-menu localtoc"
+      role="menu"
+      aria-labelledby="dLabelLocalToc"><ul>
+<li><a class="reference internal" href="#">Weights from GeoDataFrames</a><ul>
+<li><a class="reference internal" href="#Contiguity-Weights">Contiguity Weights</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#Weights-from-shapefiles-(without-geopandas)">Weights from shapefiles (without geopandas)</a></li>
+<li><a class="reference internal" href="#Lattice-Weights">Lattice Weights</a></li>
+<li><a class="reference internal" href="#Handling-nonplanar-geometries">Handling nonplanar geometries</a></li>
+</ul>
+</ul>
+</li>
+              
+            
+            
+            
+            
+            
+          </ul>
+
+          
+            
+<form class="navbar-form navbar-right" action="../search.html" method="get">
+ <div class="form-group">
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+<div class="container">
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+    <div class="body col-md-12 content" role="main">
+      
+  <div class="admonition note">
+<p>This page was generated from <a class="reference external" href="https://github.com/pysal/libpysal/blob/master/notebooks/weights.ipynb">notebooks/weights.ipynb</a>.
+Interactive online version:
+<span class="raw-html"><a href="https://mybinder.org/v2/gh/pysal/libpysal/master?filepath=notebooks/weights.ipynb"><img alt="Binder badge" src="https://mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"></a></span></p>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import sys
+import os
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>sys.path.append(os.path.abspath(&#39;..&#39;))
+import libpysal
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.available()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&#39;georgia&#39;,
+ &#39;__pycache__&#39;,
+ &#39;tests&#39;,
+ &#39;newHaven&#39;,
+ &#39;Polygon_Holes&#39;,
+ &#39;nat&#39;,
+ &#39;Polygon&#39;,
+ &#39;10740&#39;,
+ &#39;berlin&#39;,
+ &#39;rio_grande_do_sul&#39;,
+ &#39;sids2&#39;,
+ &#39;sacramento2&#39;,
+ &#39;burkitt&#39;,
+ &#39;arcgis&#39;,
+ &#39;calemp&#39;,
+ &#39;stl&#39;,
+ &#39;virginia&#39;,
+ &#39;geodanet&#39;,
+ &#39;desmith&#39;,
+ &#39;book&#39;,
+ &#39;nyc_bikes&#39;,
+ &#39;Line&#39;,
+ &#39;south&#39;,
+ &#39;snow_maps&#39;,
+ &#39;Point&#39;,
+ &#39;street_net_pts&#39;,
+ &#39;guerry&#39;,
+ &#39;__pycache__&#39;,
+ &#39;baltim&#39;,
+ &#39;networks&#39;,
+ &#39;us_income&#39;,
+ &#39;taz&#39;,
+ &#39;columbus&#39;,
+ &#39;tokyo&#39;,
+ &#39;mexico&#39;,
+ &#39;__pycache__&#39;,
+ &#39;chicago&#39;,
+ &#39;wmat&#39;,
+ &#39;juvenile&#39;,
+ &#39;clearwater&#39;]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>libpysal.examples.explain(&#39;mexico&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{&#39;name&#39;: &#39;mexico&#39;,
+ &#39;description&#39;: &#39;Decennial per capita incomes of Mexican states 1940-2000&#39;,
+ &#39;explanation&#39;: [&#39;* mexico.csv: attribute data. (n=32, k=13)&#39;,
+  &#39;* mexico.gal: spatial weights in GAL format.&#39;,
+  &#39;* mexicojoin.shp: Polygon shapefile. (n=32)&#39;,
+  &#39;Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) &#34;Interregional inequality dynamics in Mexico.&#34; Spatial Economic Analysis, 5: 277-298.&#39;]}
+</pre></div></div>
+</div>
+<section id="Weights-from-GeoDataFrames">
+<h1>Weights from GeoDataFrames<a class="headerlink" href="#Weights-from-GeoDataFrames" title="Link to this heading">¶</a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import geopandas
+pth = libpysal.examples.get_path(&quot;mexicojoin.shp&quot;)
+gdf = geopandas.read_file(pth)
+
+from libpysal.weights import Queen, Rook, KNN
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>%matplotlib inline
+import matplotlib.pyplot as plt
+<br/></pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = gdf.plot()
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_7_0.png" src="../_images/notebooks_weights_7_0.png" />
+</div>
+</div>
+<section id="Contiguity-Weights">
+<h2>Contiguity Weights<a class="headerlink" href="#Contiguity-Weights" title="Link to this heading">¶</a></h2>
+<p>The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. <strong>Rook</strong> neighbors are those states that share an edge on their respective borders:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook = Rook.from_dataframe(gdf)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook.n
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+32
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook.pct_nonzero
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+12.6953125
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = w_rook.plot(gdf, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_12_0.png" src="../_images/notebooks_weights_12_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>gdf.head()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area rendered_html docutils container">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>POLY_ID</th>
+      <th>AREA</th>
+      <th>CODE</th>
+      <th>NAME</th>
+      <th>PERIMETER</th>
+      <th>ACRES</th>
+      <th>HECTARES</th>
+      <th>PCGDP1940</th>
+      <th>PCGDP1950</th>
+      <th>PCGDP1960</th>
+      <th>...</th>
+      <th>GR9000</th>
+      <th>LPCGDP40</th>
+      <th>LPCGDP50</th>
+      <th>LPCGDP60</th>
+      <th>LPCGDP70</th>
+      <th>LPCGDP80</th>
+      <th>LPCGDP90</th>
+      <th>LPCGDP00</th>
+      <th>TEST</th>
+      <th>geometry</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>7.252751e+10</td>
+      <td>MX02</td>
+      <td>Baja California Norte</td>
+      <td>2040312.385</td>
+      <td>1.792187e+07</td>
+      <td>7252751.376</td>
+      <td>22361.0</td>
+      <td>20977.0</td>
+      <td>17865.0</td>
+      <td>...</td>
+      <td>0.05</td>
+      <td>4.35</td>
+      <td>4.32</td>
+      <td>4.25</td>
+      <td>4.40</td>
+      <td>4.47</td>
+      <td>4.43</td>
+      <td>4.48</td>
+      <td>1.0</td>
+      <td>(POLYGON ((-113.1397171020508 29.0177764892578...</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>2</td>
+      <td>7.225988e+10</td>
+      <td>MX03</td>
+      <td>Baja California Sur</td>
+      <td>2912880.772</td>
+      <td>1.785573e+07</td>
+      <td>7225987.769</td>
+      <td>9573.0</td>
+      <td>16013.0</td>
+      <td>16707.0</td>
+      <td>...</td>
+      <td>0.00</td>
+      <td>3.98</td>
+      <td>4.20</td>
+      <td>4.22</td>
+      <td>4.39</td>
+      <td>4.46</td>
+      <td>4.41</td>
+      <td>4.42</td>
+      <td>2.0</td>
+      <td>(POLYGON ((-111.2061233520508 25.8027763366699...</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>3</td>
+      <td>2.731957e+10</td>
+      <td>MX18</td>
+      <td>Nayarit</td>
+      <td>1034770.341</td>
+      <td>6.750785e+06</td>
+      <td>2731956.859</td>
+      <td>4836.0</td>
+      <td>7515.0</td>
+      <td>7621.0</td>
+      <td>...</td>
+      <td>-0.05</td>
+      <td>3.68</td>
+      <td>3.88</td>
+      <td>3.88</td>
+      <td>4.04</td>
+      <td>4.13</td>
+      <td>4.11</td>
+      <td>4.06</td>
+      <td>3.0</td>
+      <td>(POLYGON ((-106.6210784912109 21.5653114318847...</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>4</td>
+      <td>7.961008e+10</td>
+      <td>MX14</td>
+      <td>Jalisco</td>
+      <td>2324727.436</td>
+      <td>1.967200e+07</td>
+      <td>7961008.285</td>
+      <td>5309.0</td>
+      <td>8232.0</td>
+      <td>9953.0</td>
+      <td>...</td>
+      <td>0.03</td>
+      <td>3.73</td>
+      <td>3.92</td>
+      <td>4.00</td>
+      <td>4.21</td>
+      <td>4.32</td>
+      <td>4.30</td>
+      <td>4.33</td>
+      <td>4.0</td>
+      <td>POLYGON ((-101.52490234375 21.85663986206055, ...</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>5</td>
+      <td>5.467030e+09</td>
+      <td>MX01</td>
+      <td>Aguascalientes</td>
+      <td>313895.530</td>
+      <td>1.350927e+06</td>
+      <td>546702.985</td>
+      <td>10384.0</td>
+      <td>6234.0</td>
+      <td>8714.0</td>
+      <td>...</td>
+      <td>0.13</td>
+      <td>4.02</td>
+      <td>3.79</td>
+      <td>3.94</td>
+      <td>4.21</td>
+      <td>4.32</td>
+      <td>4.32</td>
+      <td>4.44</td>
+      <td>5.0</td>
+      <td>POLYGON ((-101.8461990356445 22.01176071166992...</td>
+    </tr>
+  </tbody>
+</table>
+<p>5 rows × 35 columns</p>
+</div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[1, 22]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>gdf[&#39;NAME&#39;][[0, 1,22]]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+0     Baja California Norte
+1       Baja California Sur
+22                   Sonora
+Name: NAME, dtype: object
+</pre></div></div>
+</div>
+<p>So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora.</p>
+<p><strong>Queen</strong> neighbors are based on a more inclusive condition that requires only a shared vertex between two states:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_queen = Queen.from_dataframe(gdf)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_queen.n == w_rook.n
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+True
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>(w_queen.pct_nonzero &gt; w_rook.pct_nonzero) == (w_queen.n == w_rook.n)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+True
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = w_queen.plot(gdf, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_21_0.png" src="../_images/notebooks_weights_21_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_queen.histogram
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook.histogram
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>gdf[&#39;NAME&#39;][c9]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+28    San Luis Potosi
+Name: NAME, dtype: object
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook.neighbors[28]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[5, 6, 7, 27, 29, 30, 31]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_queen.neighbors[28]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[3, 5, 6, 7, 24, 27, 29, 30, 31]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import numpy as np
+f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect=&#39;equal&#39;))
+gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;, ax=ax[0])
+w_rook.plot(gdf, ax=ax[0],
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax[0].set_title(&#39;Rook&#39;)
+ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))
+
+ax[0].axis(&#39;off&#39;)
+gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;, ax=ax[1])
+w_queen.plot(gdf, ax=ax[1],
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax[1].set_title(&#39;Queen&#39;)
+ax[1].axis(&#39;off&#39;)
+ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([-105.,  -95.,   21.,   26.])
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_28_1.png" src="../_images/notebooks_weights_28_1.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_knn = KNN.from_dataframe(gdf, k=4)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_knn.histogram
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[(4, 32)]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = w_knn.plot(gdf, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_31_0.png" src="../_images/notebooks_weights_31_0.png" />
+</div>
+</div>
+</section>
+</section>
+<section id="Weights-from-shapefiles-(without-geopandas)">
+<h1>Weights from shapefiles (without geopandas)<a class="headerlink" href="#Weights-from-shapefiles-(without-geopandas)" title="Link to this heading">¶</a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>pth = libpysal.examples.get_path(&quot;mexicojoin.shp&quot;)
+from libpysal.weights import Queen, Rook, KNN
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_queen = Queen.from_shapefile(pth)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_rook = Rook.from_shapefile(pth)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_knn1 = KNN.from_shapefile(pth)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 2 components
+  warnings.warn(&#34;The weights matrix is not fully connected. There are %d components&#34; % self.n_components)
+</pre></div></div>
+</div>
+<p>The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = w_knn1.plot(gdf, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_38_0.png" src="../_images/notebooks_weights_38_0.png" />
+</div>
+</div>
+<p>The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[34]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w_knn3 = KNN.from_shapefile(pth,k=3)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>ax = gdf.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = w_knn3.plot(gdf, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_41_0.png" src="../_images/notebooks_weights_41_0.png" />
+</div>
+</div>
+</section>
+<section id="Lattice-Weights">
+<h1>Lattice Weights<a class="headerlink" href="#Lattice-Weights" title="Link to this heading">¶</a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>from libpysal.weights import lat2W
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[37]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w = lat2W(4,3)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.n
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+12
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.pct_nonzero
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+23.61111111111111
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>w.neighbors
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{0: [3, 1],
+ 3: [0, 6, 4],
+ 1: [0, 4, 2],
+ 4: [1, 3, 7, 5],
+ 2: [1, 5],
+ 5: [2, 4, 8],
+ 6: [3, 9, 7],
+ 7: [4, 6, 10, 8],
+ 8: [5, 7, 11],
+ 9: [6, 10],
+ 10: [7, 9, 11],
+ 11: [8, 10]}
+</pre></div></div>
+</div>
+</section>
+<section id="Handling-nonplanar-geometries">
+<h1>Handling nonplanar geometries<a class="headerlink" href="#Handling-nonplanar-geometries" title="Link to this heading">¶</a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rs = libpysal.examples.get_path(&#39;map_RS_BR.shp&#39;)
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import geopandas as gpd
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[43]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>rs_df = gpd.read_file(rs)
+wq = libpysal.weights.Queen.from_dataframe(rs_df)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:168: UserWarning: There are 29 disconnected observations
+  Island ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374
+  &#34; Island ids: %s&#34; % &#39;, &#39;.join(str(island) for island in self.islands))
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>len(wq.islands)
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+29
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>wq[0]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{}
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[46]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>wf = libpysal.weights.fuzzy_contiguity(rs_df)
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>wf.islands
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[]
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[48]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>wf[0]
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[48]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[49]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>plt.rcParams[&quot;figure.figsize&quot;] = (20,15)
+ax = rs_df.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = wq.plot(rs_df, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+
+ax.set_axis_off()
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_57_0.png" src="../_images/notebooks_weights_57_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[53]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span>ax = rs_df.plot(edgecolor=&#39;grey&#39;, facecolor=&#39;w&#39;)
+f,ax = wf.plot(rs_df, ax=ax,
+        edge_kws=dict(color=&#39;r&#39;, linestyle=&#39;:&#39;, linewidth=1),
+        node_kws=dict(marker=&#39;&#39;))
+ax.set_title(&#39;Rio Grande do Sul: Nonplanar Weights&#39;)
+ax.set_axis_off()
+plt.savefig(&#39;rioGrandeDoSul.png&#39;)
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_weights_58_0.png" src="../_images/notebooks_weights_58_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+
+
+    </div>
+      
+  </div>
+</div>
+<footer class="footer">
+  <div class="container">
+    <p class="pull-right">
+      <a href="#">Back to top</a>
+      
+        <br/>
+        
+<div id="sourcelink">
+  <a href="../_sources/notebooks/weights.ipynb.txt"
+     rel="nofollow">Source</a>
+</div>
+      
+    </p>
+    <p>
+        &copy; Copyright 2018-, pysal developers.<br/>
+      Created using <a href="http://sphinx-doc.org/">Sphinx</a> 7.2.6.<br/>
+    </p>
+  </div>
+</footer>
+  </body>
+</html>
\ No newline at end of file
diff --git a/notebooks/weights.ipynb b/notebooks/weights.ipynb
new file mode 100644
index 000000000..bb2e9fc6d
--- /dev/null
+++ b/notebooks/weights.ipynb
@@ -0,0 +1,1263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.append(os.path.abspath('..'))\n",
+    "import libpysal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['georgia',\n",
+       " '__pycache__',\n",
+       " 'tests',\n",
+       " 'newHaven',\n",
+       " 'Polygon_Holes',\n",
+       " 'nat',\n",
+       " 'Polygon',\n",
+       " '10740',\n",
+       " 'berlin',\n",
+       " 'rio_grande_do_sul',\n",
+       " 'sids2',\n",
+       " 'sacramento2',\n",
+       " 'burkitt',\n",
+       " 'arcgis',\n",
+       " 'calemp',\n",
+       " 'stl',\n",
+       " 'virginia',\n",
+       " 'geodanet',\n",
+       " 'desmith',\n",
+       " 'book',\n",
+       " 'nyc_bikes',\n",
+       " 'Line',\n",
+       " 'south',\n",
+       " 'snow_maps',\n",
+       " 'Point',\n",
+       " 'street_net_pts',\n",
+       " 'guerry',\n",
+       " '__pycache__',\n",
+       " 'baltim',\n",
+       " 'networks',\n",
+       " 'us_income',\n",
+       " 'taz',\n",
+       " 'columbus',\n",
+       " 'tokyo',\n",
+       " 'mexico',\n",
+       " '__pycache__',\n",
+       " 'chicago',\n",
+       " 'wmat',\n",
+       " 'juvenile',\n",
+       " 'clearwater']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.available()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'mexico',\n",
+       " 'description': 'Decennial per capita incomes of Mexican states 1940-2000',\n",
+       " 'explanation': ['* mexico.csv: attribute data. (n=32, k=13)',\n",
+       "  '* mexico.gal: spatial weights in GAL format.',\n",
+       "  '* mexicojoin.shp: Polygon shapefile. (n=32)',\n",
+       "  'Data used in Rey, S.J. and M.L. Sastre Gutierrez. (2010) \"Interregional inequality dynamics in Mexico.\" Spatial Economic Analysis, 5: 277-298.']}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "libpysal.examples.explain('mexico')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weights from GeoDataFrames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
+    "gdf = geopandas.read_file(pth)\n",
+    "\n",
+    "from libpysal.weights import Queen, Rook, KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contiguity Weights\n",
+    "\n",
+    "The first set of spatial weights we illustrate use notions of contiguity to define neighboring observations. **Rook** neighbors are those states that share an edge on their respective borders:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_rook = Rook.from_dataframe(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.6953125"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_rook.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>POLY_ID</th>\n",
+       "      <th>AREA</th>\n",
+       "      <th>CODE</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>PERIMETER</th>\n",
+       "      <th>ACRES</th>\n",
+       "      <th>HECTARES</th>\n",
+       "      <th>PCGDP1940</th>\n",
+       "      <th>PCGDP1950</th>\n",
+       "      <th>PCGDP1960</th>\n",
+       "      <th>...</th>\n",
+       "      <th>GR9000</th>\n",
+       "      <th>LPCGDP40</th>\n",
+       "      <th>LPCGDP50</th>\n",
+       "      <th>LPCGDP60</th>\n",
+       "      <th>LPCGDP70</th>\n",
+       "      <th>LPCGDP80</th>\n",
+       "      <th>LPCGDP90</th>\n",
+       "      <th>LPCGDP00</th>\n",
+       "      <th>TEST</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7.252751e+10</td>\n",
+       "      <td>MX02</td>\n",
+       "      <td>Baja California Norte</td>\n",
+       "      <td>2040312.385</td>\n",
+       "      <td>1.792187e+07</td>\n",
+       "      <td>7252751.376</td>\n",
+       "      <td>22361.0</td>\n",
+       "      <td>20977.0</td>\n",
+       "      <td>17865.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4.35</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.25</td>\n",
+       "      <td>4.40</td>\n",
+       "      <td>4.47</td>\n",
+       "      <td>4.43</td>\n",
+       "      <td>4.48</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>(POLYGON ((-113.1397171020508 29.0177764892578...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>7.225988e+10</td>\n",
+       "      <td>MX03</td>\n",
+       "      <td>Baja California Sur</td>\n",
+       "      <td>2912880.772</td>\n",
+       "      <td>1.785573e+07</td>\n",
+       "      <td>7225987.769</td>\n",
+       "      <td>9573.0</td>\n",
+       "      <td>16013.0</td>\n",
+       "      <td>16707.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.98</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>4.22</td>\n",
+       "      <td>4.39</td>\n",
+       "      <td>4.46</td>\n",
+       "      <td>4.41</td>\n",
+       "      <td>4.42</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>(POLYGON ((-111.2061233520508 25.8027763366699...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.731957e+10</td>\n",
+       "      <td>MX18</td>\n",
+       "      <td>Nayarit</td>\n",
+       "      <td>1034770.341</td>\n",
+       "      <td>6.750785e+06</td>\n",
+       "      <td>2731956.859</td>\n",
+       "      <td>4836.0</td>\n",
+       "      <td>7515.0</td>\n",
+       "      <td>7621.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.05</td>\n",
+       "      <td>3.68</td>\n",
+       "      <td>3.88</td>\n",
+       "      <td>3.88</td>\n",
+       "      <td>4.04</td>\n",
+       "      <td>4.13</td>\n",
+       "      <td>4.11</td>\n",
+       "      <td>4.06</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>(POLYGON ((-106.6210784912109 21.5653114318847...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>7.961008e+10</td>\n",
+       "      <td>MX14</td>\n",
+       "      <td>Jalisco</td>\n",
+       "      <td>2324727.436</td>\n",
+       "      <td>1.967200e+07</td>\n",
+       "      <td>7961008.285</td>\n",
+       "      <td>5309.0</td>\n",
+       "      <td>8232.0</td>\n",
+       "      <td>9953.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>4.21</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.30</td>\n",
+       "      <td>4.33</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>POLYGON ((-101.52490234375 21.85663986206055, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.467030e+09</td>\n",
+       "      <td>MX01</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>313895.530</td>\n",
+       "      <td>1.350927e+06</td>\n",
+       "      <td>546702.985</td>\n",
+       "      <td>10384.0</td>\n",
+       "      <td>6234.0</td>\n",
+       "      <td>8714.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>4.02</td>\n",
+       "      <td>3.79</td>\n",
+       "      <td>3.94</td>\n",
+       "      <td>4.21</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>4.44</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>POLYGON ((-101.8461990356445 22.01176071166992...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   POLY_ID          AREA  CODE                   NAME    PERIMETER  \\\n",
+       "0        1  7.252751e+10  MX02  Baja California Norte  2040312.385   \n",
+       "1        2  7.225988e+10  MX03    Baja California Sur  2912880.772   \n",
+       "2        3  2.731957e+10  MX18                Nayarit  1034770.341   \n",
+       "3        4  7.961008e+10  MX14                Jalisco  2324727.436   \n",
+       "4        5  5.467030e+09  MX01         Aguascalientes   313895.530   \n",
+       "\n",
+       "          ACRES     HECTARES  PCGDP1940  PCGDP1950  PCGDP1960  \\\n",
+       "0  1.792187e+07  7252751.376    22361.0    20977.0    17865.0   \n",
+       "1  1.785573e+07  7225987.769     9573.0    16013.0    16707.0   \n",
+       "2  6.750785e+06  2731956.859     4836.0     7515.0     7621.0   \n",
+       "3  1.967200e+07  7961008.285     5309.0     8232.0     9953.0   \n",
+       "4  1.350927e+06   546702.985    10384.0     6234.0     8714.0   \n",
+       "\n",
+       "                         ...                          GR9000  LPCGDP40  \\\n",
+       "0                        ...                            0.05      4.35   \n",
+       "1                        ...                            0.00      3.98   \n",
+       "2                        ...                           -0.05      3.68   \n",
+       "3                        ...                            0.03      3.73   \n",
+       "4                        ...                            0.13      4.02   \n",
+       "\n",
+       "   LPCGDP50  LPCGDP60  LPCGDP70  LPCGDP80  LPCGDP90  LPCGDP00  TEST  \\\n",
+       "0      4.32      4.25      4.40      4.47      4.43      4.48   1.0   \n",
+       "1      4.20      4.22      4.39      4.46      4.41      4.42   2.0   \n",
+       "2      3.88      3.88      4.04      4.13      4.11      4.06   3.0   \n",
+       "3      3.92      4.00      4.21      4.32      4.30      4.33   4.0   \n",
+       "4      3.79      3.94      4.21      4.32      4.32      4.44   5.0   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  (POLYGON ((-113.1397171020508 29.0177764892578...  \n",
+       "1  (POLYGON ((-111.2061233520508 25.8027763366699...  \n",
+       "2  (POLYGON ((-106.6210784912109 21.5653114318847...  \n",
+       "3  POLYGON ((-101.52490234375 21.85663986206055, ...  \n",
+       "4  POLYGON ((-101.8461990356445 22.01176071166992...  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 22]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.neighbors[0] # the first location has two neighbors at locations 1 and 22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Baja California Norte\n",
+       "1       Baja California Sur\n",
+       "22                   Sonora\n",
+       "Name: NAME, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf['NAME'][[0, 1,22]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, Baja California Norte has 2 rook neighbors: Baja California Sur and Sonora."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Queen** neighbors are based on a more inclusive condition that requires only a shared vertex between two states:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_queen = Queen.from_dataframe(gdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.n == w_rook.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(w_queen.pct_nonzero > w_rook.pct_nonzero) == (w_queen.n == w_rook.n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_queen.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1), (2, 6), (3, 6), (4, 6), (5, 5), (6, 2), (7, 3), (8, 2), (9, 1)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1, 1), (2, 6), (3, 7), (4, 7), (5, 3), (6, 4), (7, 3), (8, 1)]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c9 = [idx for idx,c in w_queen.cardinalities.items() if c==9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28    San Luis Potosi\n",
+       "Name: NAME, dtype: object"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf['NAME'][c9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 6, 7, 27, 29, 30, 31]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_rook.neighbors[28]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[3, 5, 6, 7, 24, 27, 29, 30, 31]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_queen.neighbors[28]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-105.,  -95.,   21.,   26.])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "f,ax = plt.subplots(1,2,figsize=(10, 6), subplot_kw=dict(aspect='equal'))\n",
+    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[0])\n",
+    "w_rook.plot(gdf, ax=ax[0], \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax[0].set_title('Rook')\n",
+    "ax[0].axis(np.asarray([-105.0, -95.0, 21, 26]))\n",
+    "\n",
+    "ax[0].axis('off')\n",
+    "gdf.plot(edgecolor='grey', facecolor='w', ax=ax[1])\n",
+    "w_queen.plot(gdf, ax=ax[1], \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax[1].set_title('Queen')\n",
+    "ax[1].axis('off')\n",
+    "ax[1].axis(np.asarray([-105.0, -95.0, 21, 26]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_knn = KNN.from_dataframe(gdf, k=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(4, 32)]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w_knn.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_knn.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Weights from shapefiles (without geopandas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = libpysal.examples.get_path(\"mexicojoin.shp\")\n",
+    "from libpysal.weights import Queen, Rook, KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_queen = Queen.from_shapefile(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_rook = Rook.from_shapefile(pth)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:170: UserWarning: The weights matrix is not fully connected. There are 2 components\n",
+      "  warnings.warn(\"The weights matrix is not fully connected. There are %d components\" % self.n_components)\n"
+     ]
+    }
+   ],
+   "source": [
+    "w_knn1 = KNN.from_shapefile(pth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The warning alerts us to the fact that using a first nearest neighbor criterion to define the neighbors results in a connectivity graph that has more than a single component. In this particular case there are 2 components which can be seen in the following plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_knn1.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The two components are separated in the southern part of the country, with the smaller component to the east and the larger component running through the rest of the country to the west. For certain types of spatial analytical methods, it is necessary to have a adjacency structure that consists of a single component. To ensure this for the case of Mexican states, we can increase the number of nearest neighbors to three:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w_knn3 = KNN.from_shapefile(pth,k=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = gdf.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = w_knn3.plot(gdf, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lattice Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from libpysal.weights import lat2W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = lat2W(4,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.61111111111111"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: [3, 1],\n",
+       " 3: [0, 6, 4],\n",
+       " 1: [0, 4, 2],\n",
+       " 4: [1, 3, 7, 5],\n",
+       " 2: [1, 5],\n",
+       " 5: [2, 4, 8],\n",
+       " 6: [3, 9, 7],\n",
+       " 7: [4, 6, 10, 8],\n",
+       " 8: [5, 7, 11],\n",
+       " 9: [6, 10],\n",
+       " 10: [7, 9, 11],\n",
+       " 11: [8, 10]}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.neighbors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling nonplanar geometries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rs = libpysal.examples.get_path('map_RS_BR.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/serge/Dropbox/p/pysal/src/subpackages/libpysal/libpysal/weights/weights.py:168: UserWarning: There are 29 disconnected observations \n",
+      "  Island ids: 0, 4, 23, 27, 80, 94, 101, 107, 109, 119, 122, 139, 169, 175, 223, 239, 247, 253, 254, 255, 256, 261, 276, 291, 294, 303, 321, 357, 374\n",
+      "  \" Island ids: %s\" % ', '.join(str(island) for island in self.islands))\n"
+     ]
+    }
+   ],
+   "source": [
+    "rs_df = gpd.read_file(rs)\n",
+    "wq = libpysal.weights.Queen.from_dataframe(rs_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(wq.islands)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wq[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wf = libpysal.weights.fuzzy_contiguity(rs_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wf.islands"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{239: 1.0, 59: 1.0, 152: 1.0, 23: 1.0, 107: 1.0}"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wf[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.rcParams[\"figure.figsize\"] = (20,15)\n",
+    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = wq.plot(rs_df, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "ax = rs_df.plot(edgecolor='grey', facecolor='w')\n",
+    "f,ax = wf.plot(rs_df, ax=ax, \n",
+    "        edge_kws=dict(color='r', linestyle=':', linewidth=1),\n",
+    "        node_kws=dict(marker=''))\n",
+    "ax.set_title('Rio Grande do Sul: Nonplanar Weights')\n",
+    "ax.set_axis_off()\n",
+    "plt.savefig('rioGrandeDoSul.png')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/objects.inv b/objects.inv
index 8c8a34fe028d0d8118c92d3cd44d32e53008dc3f..c2c9d2d4a12d8c4c03c7d17f52f79bfe71c6bed0 100644
GIT binary patch
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+  <section id="references">
+<h1>References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h1>
+<div class="docutils container" id="id1">
+<div role="list" class="citation-list">
+<div class="citation" id="id2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>AS96<span class="fn-bracket">]</span></span>
+<p>Luc Anselin and Oleg Smirnov. Efficient algorithms for constructing proper higher order spatial lag operators. <em>Journal of Regional Science</em>, 36(1):67–89, 1996. <a class="reference external" href="https://arxiv.org/abs/https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x">arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x</a>, <a class="reference external" href="https://doi.org/10.1111/j.1467-9787.1996.tb01101.x">doi:10.1111/j.1467-9787.1996.tb01101.x</a>.</p>
+</div>
+<div class="citation" id="id3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>RA07<span class="fn-bracket">]</span></span>
+<p>Sergio J. Rey and Luc Anselin. PySAL: A Python Library of Spatial Analytical Methods. <em>The Review of Regional Studies</em>, 37(1):5–27, 2007.</p>
+</div>
+<div class="citation" id="id4" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>WS98<span class="fn-bracket">]</span></span>
+<p>D.J. Watts and S.H. Strogatz. Collective dynamics of 'small-world' networks. <em>Nature</em>, 393:440–442, 1998.</p>
+</div>
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diff --git a/searchindex.js b/searchindex.js
index 2ba3b8680..bff2af42d 100644
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diff --git a/tutorial.html b/tutorial.html
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+        <span class="navbar-text navbar-version pull-left"><b>4.9.0</b></span>
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+      
+  <section id="libpysal-tutorial">
+<h1>libpysal Tutorial<a class="headerlink" href="#libpysal-tutorial" title="Link to this heading">¶</a></h1>
+<section id="spatial-weights">
+<h2>Spatial Weights<a class="headerlink" href="#spatial-weights" title="Link to this heading">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html">Weights from GeoDataFrames</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/weights.html#Contiguity-Weights">Contiguity Weights</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html#Weights-from-shapefiles-(without-geopandas)">Weights from shapefiles (without geopandas)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html#Lattice-Weights">Lattice Weights</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/weights.html#Handling-nonplanar-geometries">Handling nonplanar geometries</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/voronoi.html">Voronoi Polygons for 2-D Point Sets</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/voronoi.html#Basic-Usage">Basic Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/voronoi.html#Larger-Problem">Larger Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/voronoi.html#Trimming">Trimming</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="notebooks/voronoi.html#Voronoi-Weights">Voronoi Weights</a></li>
+</ul>
+</div>
+</section>
+<section id="example-datasets">
+<h2>Example Datasets<a class="headerlink" href="#example-datasets" title="Link to this heading">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html">Datasets for use with libpysal</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Backwards-compatibility-is-maintained">Backwards compatibility is maintained</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Downloading-Remote-Datasets">Downloading Remote Datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Getting-remote-urls">Getting remote urls</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Explaining-a-dataset">Explaining a dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Working-with-an-example-dataset">Working with an example dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/examples.html#Other-Remotes">Other Remotes</a></li>
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