move import down (#144)

move import down

segregation/network/network.py

@@ -8,14 +8,6 @@
 from segregation.util import project_gdf
 import os
 import sys
-try:
-    import pandana as pdna
-    from urbanaccess.osm.load import ua_network_from_bbox
-except ImportError:
-    warn(
-        "You need pandana and urbanaccess to work with segregation's network module\n"
-        "You can install them with  `pip install urbanaccess pandana` "
-        "or `conda install -c udst pandana urbanaccess`")
 
 
 # This class allows us to hide the diagnostic messages from urbanaccess if the `quiet` flag is set
@@ -54,6 +46,15 @@ def get_osm_network(geodataframe, maxdist=5000, quiet=True, **kwargs):
         spatial segregation measures that include a distance decay
 
     """
+    try:
+        import pandana as pdna
+        from urbanaccess.osm.load import ua_network_from_bbox
+    except ImportError:
+        raise ImportError(
+            "You need pandana and urbanaccess to work with segregation's network module\n"
+            "You can install them with  `pip install urbanaccess pandana` "
+            "or `conda install -c udst pandana urbanaccess`")
+
     gdf = geodataframe.copy()
     gdf = project_gdf(gdf)
     gdf = gdf.buffer(maxdist)

segregation/tests/test_absolute_centralization.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import AbsoluteCentralization
 
 
 class Absolute_Centralization_Tester(unittest.TestCase):
     def test_Absolute_Centralization(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = AbsoluteCentralization(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.6891422368736286)

segregation/tests/test_absolute_clustering.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import AbsoluteClustering
 
 
 class Absolute_Clustering_Tester(unittest.TestCase):
     def test_Absolute_Clustering(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = AbsoluteClustering(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.005189287311955573)

segregation/tests/test_absolute_concentration.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import AbsoluteConcentration
 
 
 class Absolute_Concentration_Tester(unittest.TestCase):
     def test_Absolute_Concentration(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = AbsoluteConcentration(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.8512824549657465)

segregation/tests/test_atkinson.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Atkinson
 
 
 class Atkinson_Tester(unittest.TestCase):
     def test_Atkinson(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = Atkinson(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.15079259382667654)

segregation/tests/test_bias_corrected_dissimilarity.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import BiasCorrectedDissim
 
 
 class Bias_Corrected_Dissim_Tester(unittest.TestCase):
     def test_Bias_Corrected_Dissim(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         np.random.seed(1234)
         index = BiasCorrectedDissim(df, 'HISP_', 'TOT_POP')

segregation/tests/test_boundary_spatial_dissimilarity.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import BoundarySpatialDissim
 
 
 class Boundary_Spatial_Dissim_Tester(unittest.TestCase):
     def test_Boundary_Spatial_Dissim(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = BoundarySpatialDissim(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.2667626367289605)

segregation/tests/test_compute_all.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.compute_all import ComputeAllAspatialSegregation, ComputeAllSpatialSegregation, ComputeAllSegregation
 
 
 class ComputeAll_Tester(unittest.TestCase):
     def test_ComputeAll(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
 
         np.random.seed(123)
         res = ComputeAllAspatialSegregation(s_map, 'HISP_', 'TOT_POP')

segregation/tests/test_conprof.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import ConProf
 
 
 class Con_Prof_Tester(unittest.TestCase):
     def test_Con_Prof(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = ConProf(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.1376874794741899)

segregation/tests/test_correlationr.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import CorrelationR
 
 
 class Correlation_R_Tester(unittest.TestCase):
     def test_Correlation_R(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = CorrelationR(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.09164042012926693)

segregation/tests/test_decomposition.py

@@ -1,5 +1,5 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Dissim
@@ -8,7 +8,7 @@
 
 class Decomposition_Tester(unittest.TestCase):
     def test_Decomposition(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         index1 = Dissim(s_map, 'HISP_', 'TOT_POP')
         index2 = Dissim(s_map, 'BLACK_', 'TOT_POP')
         res = DecomposeSegregation(index1, index2, counterfactual_approach = "composition")

segregation/tests/test_delta.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import Delta
 
 
 class Delta_Tester(unittest.TestCase):
     def test_Delta(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = Delta(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.8044969214141899)

segregation/tests/test_density_corrected_dissimilarity.py

@@ -1,5 +1,5 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import DensityCorrectedDissim
@@ -8,7 +8,7 @@
 class Density_Corrected_Dissim_Tester(unittest.TestCase):
 
     def test_Density_Corrected_Dissim(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = DensityCorrectedDissim(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.295205155464069)

segregation/tests/test_dissim.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Dissim
 
 
 class Dissim_Tester(unittest.TestCase):
     def test_Dissim(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = Dissim(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.32184656076566864)

segregation/tests/test_distance_decay_exposure.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import DistanceDecayExposure
 
 
 class Distance_Decay_Exposure_Tester(unittest.TestCase):
     def test_Distance_Decay_Exposure(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = DistanceDecayExposure(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.8396583368412371)

segregation/tests/test_distance_decay_isolation.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.spatial import DistanceDecayIsolation
 
 
 class Distance_Decay_Isolation_Tester(unittest.TestCase):
     def test_Distance_Decay_Isolation(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = DistanceDecayIsolation(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.1562162475606278)

segregation/tests/test_entropy.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Entropy
 
 
 class Entropy_Tester(unittest.TestCase):
     def test_Entropy(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = Entropy(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.09459760633014454)

segregation/tests/test_exposure.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Exposure
 
 
 class Exposure_Tester(unittest.TestCase):
     def test_Exposure(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = Exposure(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.7680384513540848)

segregation/tests/test_gini_seg.py

@@ -1,5 +1,5 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import GiniSeg
@@ -8,7 +8,7 @@
 class Gini_Seg_Tester(unittest.TestCase):
 
     def test_Gini_Seg(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = GiniSeg(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.43506510676886234)

segregation/tests/test_inference.py

@@ -1,5 +1,5 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Dissim, MultiDissim
@@ -8,7 +8,7 @@
 
 class Inference_Tester(unittest.TestCase):
     def test_Inference(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         index1 = Dissim(s_map, 'HISP_', 'TOT_POP')
         index2 = Dissim(s_map, 'BLACK_', 'TOT_POP')
 

segregation/tests/test_isolation.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.aspatial import Isolation
 
 
 class Isolation_Tester(unittest.TestCase):
     def test_Isolation(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         df = s_map[['geometry', 'HISP_', 'TOT_POP']]
         index = Isolation(df, 'HISP_', 'TOT_POP')
         np.testing.assert_almost_equal(index.statistic, 0.2319615486459151)

segregation/tests/test_local_multi_local_diversity.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.local import MultiLocalDiversity
 
 
 class Multi_Local_Diversity_Tester(unittest.TestCase):
     def test_Multi_Local_Diversity(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
         df = s_map[groups_list]
         index = MultiLocalDiversity(df, groups_list)

segregation/tests/test_local_multi_local_entropy.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.local import MultiLocalEntropy
 
 
 class Multi_Local_Entropy_Tester(unittest.TestCase):
     def test_Multi_Local_Entropy(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
         df = s_map[groups_list]
         index = MultiLocalEntropy(df, groups_list)

segregation/tests/test_local_multi_local_simpson_concentration.py

@@ -1,13 +1,13 @@
 import unittest
-import libpysal
+from libpysal.examples import load_example
 import geopandas as gpd
 import numpy as np
 from segregation.local import MultiLocalSimpsonConcentration
 
 
 class Multi_Local_Simpson_Concentration_Tester(unittest.TestCase):
     def test_Multi_Local_Simpson_Concentration(self):
-        s_map = gpd.read_file(libpysal.examples.get_path("sacramentot2.shp"))
+        s_map = gpd.read_file(load_example("Sacramento1").get_path("sacramentot2.shp"))
         groups_list = ['WHITE_', 'BLACK_', 'ASIAN_','HISP_']
         df = s_map[groups_list]
         index = MultiLocalSimpsonConcentration(df, groups_list)