def test_Tessellation(self):
tes = mm.Tessellation(self.df_buildings, "uID", self.limit, segment=2)
tessellation = tes.tessellation
assert len(tessellation) == len(self.df_tessellation)
bands = mm.Tessellation(
self.df_streets, "nID", mm.buffered_limit(self.df_streets, 50), segment=5
).tessellation
assert len(bands) == len(self.df_streets)
queen_corners = tes.queen_corners(2)
w = libpysal.weights.Queen.from_dataframe(queen_corners)
assert w.neighbors[14] == [35, 36, 13, 15, 26, 27, 28, 30, 31]
def test_get_node_id(self):
nx = mm.gdf_to_nx(self.df_streets)
nodes, edges = mm.nx_to_gdf(nx)
self.df_buildings["nID"] = mm.get_network_id(self.df_buildings,
self.df_streets, "nID")
ids = mm.get_node_id(self.df_buildings, nodes, edges, "nodeID", "nID")
assert not ids.isna().any()
convex_hull = edges.unary_union.convex_hull
enclosures = mm.enclosures(edges, limit=gpd.GeoSeries([convex_hull]))
enclosed_tess = mm.Tessellation(self.df_buildings,
unique_id="uID",
enclosures=enclosures).tessellation
links = mm.get_network_ratio(enclosed_tess, edges)
enclosed_tess[links.columns] = links
ids = mm.get_node_id(
enclosed_tess,
nodes,
edges,
node_id="nodeID",
edge_keys="edgeID_keys",
edge_values="edgeID_values",
)
assert not ids.isna().any()
def test_get_network_ratio(self):
convex_hull = self.df_streets.unary_union.convex_hull
enclosures = mm.enclosures(self.df_streets, limit=gpd.GeoSeries([convex_hull]))
enclosed_tess = mm.Tessellation(
self.df_buildings, unique_id="uID", enclosures=enclosures
).tessellation
links = mm.get_network_ratio(enclosed_tess, self.df_streets, initial_buffer=10)
assert links.edgeID_values.apply(lambda x: sum(x)).sum() == len(enclosed_tess)
assert links.loc[149, "edgeID_keys"] == [13, 30, 27, 29, 28]
def test_Tessellation(self):
tes = mm.Tessellation(self.df_buildings, "uID", self.limit, segment=2)
tessellation = tes.tessellation
assert len(tessellation) == len(self.df_tessellation)
bands = mm.Tessellation(self.df_streets,
"nID",
mm.buffered_limit(self.df_streets, 50),
segment=5).tessellation
assert len(bands) == len(self.df_streets)
# test_enclosed_tessellation
enc1 = mm.Tessellation(self.df_buildings,
"uID",
enclosures=self.enclosures).tessellation
assert len(enc1) == 155
assert isinstance(enc1, gpd.GeoDataFrame)
enc1_loop = mm.Tessellation(self.df_buildings,
"uID",
enclosures=self.enclosures,
use_dask=False).tessellation
assert len(enc1) == 155
assert isinstance(enc1, gpd.GeoDataFrame)
assert len(enc1_loop) == 155
assert isinstance(enc1_loop, gpd.GeoDataFrame)
assert_geodataframe_equal(enc1, enc1_loop)
with pytest.raises(ValueError):
mm.Tessellation(self.df_buildings,
"uID",
limit=self.limit,
enclosures=self.enclosures)
enc1_loop = mm.Tessellation(self.df_buildings,
"uID",
enclosures=self.enclosures,
use_dask=False).tessellation
assert len(enc1) == 155
assert isinstance(enc1, gpd.GeoDataFrame)
# erroneous geometry
df = self.df_buildings
b = df.total_bounds
x = np.mean([b[0], b[2]])
y = np.mean([b[1], b[3]])
df.loc[144] = [145, Polygon([(x, y), (x, y + 1), (x + 1, y)])]
df.loc[145] = [146, MultiPoint([(x, y), (x + 1, y)]).buffer(0.55)]
df.loc[146] = [147, affinity.rotate(df.geometry.iloc[0], 12)]
tess = mm.Tessellation(df, "uID", self.limit)
assert tess.collapsed == {145}
assert len(tess.multipolygons) == 3
def tessellate(
bldgs: MultiPolygon,
block: Polygon,
) -> Tuple[gpd.GeoDataFrame, gpd.GeoDataFrame]:
""" Use momepy to do basic tesselation """
bldgs_gdf = gpd.GeoDataFrame({'geometry': bldgs})
bldgs_gdf['uID'] = np.arange(bldgs_gdf.shape[0])
tess_gdf = momepy.Tessellation(bldgs_gdf, unique_id='uID',
limit=block).tessellation
return tess_gdf, bldgs_gdf
buildings = mm.preprocess(buildings, size=30, compactness=False, islands=True)
buildings['uID'] = range(len(buildings))
buildings['blg_area'] = buildings.area
buildings.to_file('data/zurich.gpkg', layer='buildings', driver='GPKG')
buildings['geometry'] = buildings.simplify(0.2)
print('simplified')
buffers = [300, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 150, 200]
for buf in buffers:
print('Generating', buf)
limit = mm.buffered_limit(buildings, buf)
tessellation = mm.Tessellation(buildings, 'uID', limit).tessellation
tessellation.to_file('data/tessellation/{0}_tessellation.shp'.format(buf))
# In[ ]:
def gini(vals):
"""Calculate the Gini coefficient of a numpy array."""
# based on bottom eq:
# http://www.statsdirect.com/help/generatedimages/equations/equation154.svg
# from:
# http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
# All values are treated equally, arrays must be 1d:
vals = vals.flatten()
if np.amin(vals) < 0:
# Values cannot be negative:
def time_Tessellation(self):
mm.Tessellation(self.df_buildings, "uID", self.limit, segment=2)
