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import geopandas as gpd
from libpysal.graph import Graph
from pandas.testing import assert_series_equal
import momepy as mm
from .conftest import assert_result
class TestDistribution:
def setup_method(self):
test_file_path = mm.datasets.get_path("bubenec")
self.df_buildings = gpd.read_file(test_file_path, layer="buildings")
self.df_streets = gpd.read_file(test_file_path, layer="streets")
self.df_tessellation = gpd.read_file(test_file_path, layer="tessellation")
self.graph = Graph.build_knn(self.df_buildings.centroid, k=5)
self.contiguity = Graph.build_contiguity(self.df_buildings)
self.neighborhood_graph = self.graph.higher_order(3, lower_order=True)
self.tess_contiguity = Graph.build_contiguity(self.df_tessellation)
def test_orientation(self):
expected = {
"mean": 20.983859394267952,
"sum": 3021.6757527745854,
"min": 7.968673890244247,
"max": 42.329365250279125,
}
r = mm.orientation(self.df_buildings)
assert_result(r, expected, self.df_buildings, rel=1e-4)
expected = {
"mean": 21.176405050561755,
"sum": 741.1741767696615,
"min": 0.834911325974133,
"max": 44.83357900046826,
}
r = mm.orientation(self.df_streets)
assert_result(r, expected, self.df_streets)
def test_shared_walls(self):
expected = {
"mean": 36.87618331446485,
"sum": 5310.17039728293,
"min": 0,
"max": 106.20917523555639,
}
r = mm.shared_walls(self.df_buildings)
assert_result(r, expected, self.df_buildings)
def test_shared_walls_approx(self):
expected = {
"mean": 36.87618331446485,
"sum": 5310.17039728293,
"min": 0,
"max": 106.20917523555639,
}
tolerance = 0.1
geometry = self.df_buildings.buffer(-tolerance)
r = mm.shared_walls(geometry)
assert (r == 0).all()
r = mm.shared_walls(geometry, strict=False, tolerance=tolerance + 0.001)
# check that values are equal to strict version up to 10cm
assert_result(r, expected, self.df_buildings, rel=1e-1)
def test_alignment(self):
orientation = mm.orientation(self.df_buildings)
expected = {
"mean": 2.90842367974375,
"sum": 418.8130098831,
"min": 0.03635249292455285,
"max": 21.32311946014944,
}
r = mm.alignment(orientation, self.graph)
assert_result(r, expected, self.df_buildings, check_names=False, rel=1e-4)
def test_neighbor_distance(self):
expected = {
"mean": 14.254601392635818,
"sum": 2052.662600539558,
"min": 2.0153493186952085,
"max": 42.164831456311475,
}
r = mm.neighbor_distance(self.df_buildings, self.graph)
assert_result(r, expected, self.df_buildings, check_names=False)
def test_mean_interbuilding_distance(self):
expected = {
"mean": 13.018190603684694,
"sum": 1874.6194469305958,
"min": 6.623582625492466,
"max": 22.513464171665948,
}
r = mm.mean_interbuilding_distance(
self.df_buildings, self.graph, self.neighborhood_graph
)
assert_result(r, expected, self.df_buildings)
def test_building_adjacency(self):
expected = {
"mean": 0.3784722222222222,
"sum": 54.5,
"min": 0.16666666666666666,
"max": 0.8333333333333334,
}
r = mm.building_adjacency(self.contiguity, self.graph)
assert_result(r, expected, self.df_buildings, exact=False)
def test_neighbors(self):
expected = {
"mean": 5.180555555555555,
"sum": 746,
"min": 2,
"max": 12,
}
r = mm.neighbors(self.df_tessellation, self.tess_contiguity, weighted=False)
assert_result(r, expected, self.df_buildings, exact=False, check_names=False)
expected = {
"mean": 0.029066398893536072,
"sum": 4.185561440669194,
"min": 0.008659386154613532,
"max": 0.08447065801729325,
}
r = mm.neighbors(self.df_tessellation, self.tess_contiguity, weighted=True)
assert_result(r, expected, self.df_buildings, exact=False, check_names=False)
def test_street_alignment(self):
building_orientation = mm.orientation(self.df_buildings)
street_orientation = mm.orientation(self.df_streets)
street_index = mm.get_nearest_street(self.df_buildings, self.df_streets)
expected = {
"mean": 2.024707906317863,
"sum": 291.5579385097722,
"min": 0.0061379200252815,
"max": 20.357934749623894,
}
r = mm.street_alignment(building_orientation, street_orientation, street_index)
assert_result(r, expected, self.df_buildings, rel=1e-3)
def test_cell_alignment(self):
df_buildings = self.df_buildings.reset_index()
df_tessellation = self.df_tessellation.reset_index()
blgori = mm.orientation(df_buildings)
tessori = mm.orientation(df_tessellation)
align = mm.cell_alignment(blgori, tessori)
align2 = mm.cell_alignment(blgori.values, tessori.values)
align_expected = {
"count": 144,
"mean": 2.604808585700,
"max": 33.201625570390746,
"min": 1.722848278973288e-05,
}
assert_result(align, align_expected, df_buildings, abs=1e-2)
assert_series_equal(align, align2, check_names=False)
class TestEquality:
def setup_method(self):
test_file_path = mm.datasets.get_path("bubenec")
self.df_buildings = gpd.read_file(test_file_path, layer="buildings").set_index(
"uID"
)
self.df_tessellation = gpd.read_file(
test_file_path, layer="tessellation"
).set_index("uID")
self.df_streets = gpd.read_file(test_file_path, layer="streets")
self.graph = Graph.build_knn(self.df_buildings.centroid, k=5)
self.df_buildings["orientation"] = mm.orientation(self.df_buildings)
self.df_streets["orientation"] = mm.orientation(self.df_streets)
self.contiguity = Graph.build_contiguity(self.df_buildings)
self.tessellation_contiguity = Graph.build_contiguity(self.df_tessellation)
self.neighborhood_graph = self.tessellation_contiguity.higher_order(
3, lower_order=True
)
def test_alignment(self):
new = mm.alignment(self.df_buildings["orientation"], self.graph)
old = mm.Alignment(
self.df_buildings.reset_index(),
self.graph.to_W(),
"uID",
"orientation",
verbose=False,
).series
assert_series_equal(new, old, check_names=False, check_index=False)
def test_neighbor_distance(self):
new = mm.neighbor_distance(self.df_buildings, self.graph)
old = mm.NeighborDistance(
self.df_buildings.reset_index(), self.graph.to_W(), "uID", verbose=False
).series
assert_series_equal(new, old, check_names=False, check_index=False)
def test_mean_interbuilding_distance(self):
new = mm.mean_interbuilding_distance(
self.df_buildings, self.tessellation_contiguity, self.neighborhood_graph
)
old = mm.MeanInterbuildingDistance(
self.df_buildings.reset_index(),
self.tessellation_contiguity.to_W(),
"uID",
verbose=False,
).series
assert_series_equal(new, old, check_names=False, check_index=False)
def test_building_adjacency(self):
new = mm.building_adjacency(self.contiguity, self.graph)
old = mm.BuildingAdjacency(
self.df_buildings.reset_index(), self.graph.to_W(), "uID", verbose=False
).series
assert_series_equal(new, old, check_names=False, check_index=False)
def test_neighbors(self):
new = mm.neighbors(
self.df_tessellation, self.tessellation_contiguity, weighted=False
)
old = mm.Neighbors(
self.df_tessellation.reset_index(),
self.tessellation_contiguity.to_W(),
"uID",
weighted=False,
verbose=False,
).series
assert_series_equal(new, old, check_names=False, check_index=False)
new = mm.neighbors(
self.df_tessellation, self.tessellation_contiguity, weighted=True
)
old = mm.Neighbors(
self.df_tessellation.reset_index(),
self.tessellation_contiguity.to_W(),
"uID",
weighted=True,
verbose=False,
).series
assert_series_equal(new, old, check_names=False, check_index=False)
def test_street_alignment(self):
street_index = mm.get_nearest_street(self.df_buildings, self.df_streets)
self.df_buildings["nID"] = street_index
new = mm.street_alignment(
self.df_buildings["orientation"],
self.df_streets["orientation"],
street_index,
)
old = mm.StreetAlignment(
self.df_buildings.reset_index(),
self.df_streets.reset_index(),
"orientation",
left_network_id="nID",
right_network_id="index",
).series
assert_series_equal(new, old, check_names=False, check_index=False)
def test_cell_alignment(self):
df_buildings = self.df_buildings.reset_index()
df_tessellation = self.df_tessellation.reset_index()
df_buildings["orient"] = blgori = mm.orientation(df_buildings)
df_tessellation["orient"] = tessori = mm.orientation(df_tessellation)
align_new = mm.cell_alignment(blgori, tessori)
align_old = mm.CellAlignment(
df_buildings, df_tessellation, "orient", "orient", "uID", "uID"
).series
assert_series_equal(align_new, align_old, check_names=False, check_dtype=False)
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