deploy: 73c4087

_sources/notebooks/03-uxarray-vis/03-polygons.ipynb

@@ -98,7 +98,7 @@
    "cell_type": "markdown",
    "source": [
     "## Conversion Methods\n",
-    "UXarray represents Unstructured Grids through a set of coordinate and connectivity variables (i.e. `node_lon`, `node_lat`, `face_node_connectivity`, etc.). These variables can be manipulated to obtain our Polygons for visualuzation"
+    "UXarray represents Unstructured Grids through a set of coordinate and connectivity variables (i.e. `node_lon`, `node_lat`, `face_node_connectivity`, etc.). These variables need to be manipulated to obtain geometries and data structures that are compatible with visualuzation libraries (i.e. HoloViews, Datashader, Matplotlib, etc.)"
    ],
    "metadata": {
     "collapsed": false
@@ -109,7 +109,7 @@
    "source": [
     "### Representation as a `GeoDataFrame`\n",
     "\n",
-    "Polygons are stored in a SpatialPandas `GeoDataFrame`, which is the expected data structured by the HoloViz stack of packages for visualizing polygons.\n",
+    "Each face is converted into a Polygon and stored within a SpatialPandas `GeoDataFrame`, which is the expected data structured by the HoloViz stack of packages for visualizing polygons.\n",
     "\n"
    ],
    "metadata": {
@@ -161,7 +161,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "If a data variable is not face-centered, it can be manipulated to get it to map to faces. For node-centered data, as is the case with our Geoflow dataset, we can perform a nodal-average operation, which takes the average all the nodes that saddle a face and use that value to shade the polygon.\n",
+    "If a data variable is not face-centered, it can be manipulated to become face-centered. For node-centered data, as is the case with our Geoflow dataset, we can perform a nodal-average operation, which takes the average all the nodes that surround a face and use that value to shade the polygon.\n",
     "\n",
     "Here we can also see that we need to index the `time` and `meshLayers` dimensions to obtain our 1D slice of data."
    ],

_sources/notebooks/03-uxarray-vis/06-performance.ipynb

@@ -305,6 +305,16 @@
     "collapsed": false
    }
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "is_executing": true
+   }
+  },
   {
    "cell_type": "markdown",
    "source": [

notebooks/01-intro/01-unstructured-grid-overview.html

@@ -507,7 +507,7 @@ <h2>Structured Grids<a class="headerlink" href="#structured-grids" title="Permal
   warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
 </pre></div>
 </div>
-<img alt="../../_images/3a34e4770a63f72fd719af55d9bfecd5b84ec70369edad335cfd7c5231423fbe.png" src="../../_images/3a34e4770a63f72fd719af55d9bfecd5b84ec70369edad335cfd7c5231423fbe.png" />
+<img alt="../../_images/7c0b6d48bbea5f3b94b1b12cac5455a4f19036f499cc2177c6f21add788bc255.png" src="../../_images/7c0b6d48bbea5f3b94b1b12cac5455a4f19036f499cc2177c6f21add788bc255.png" />
 </div>
 </div>
 </div>