def assert_geometries_equal(actual, expected):
actual = np.asarray(actual)
expected = np.broadcast_to(expected, actual.shape)
mask = pygeos.is_geometry(expected)
if np.any(mask):
assert pygeos.equals(actual[mask], expected[mask]).all()
if np.any(~mask):
assert pygeos.is_missing(actual[~mask])
def _cast(collection):
"""
Cast a collection to a pygeos geometry array.
"""
if isinstance(collection, (geopandas.GeoSeries, geopandas.GeoDataFrame)):
return collection.geometry.values.data.squeeze()
elif pygeos.is_geometry(collection).all():
if isinstance(collection, (numpy.ndarray, list)):
return numpy.asarray(collection)
else:
return numpy.array([collection])
elif isinstance(collection, (numpy.ndarray, list)):
return pygeos.from_shapely(collection).squeeze()
else:
return numpy.array([pygeos.from_shapely(collection)])
def _cast(collection):
"""
Cast a collection to a pygeos geometry array.
"""
try:
import pygeos, geopandas
except (ImportError, ModuleNotFoundError) as exception:
raise type(exception)(
"pygeos and geopandas are required for map comparison statistics.")
if isinstance(collection, (geopandas.GeoSeries, geopandas.GeoDataFrame)):
return collection.geometry.values.data.squeeze()
elif pygeos.is_geometry(collection).all():
if isinstance(collection, (numpy.ndarray, list)):
return numpy.asarray(collection)
else:
return numpy.array([collection])
elif isinstance(collection, (numpy.ndarray, list)):
return pygeos.from_shapely(collection).squeeze()
else:
return numpy.array([pygeos.from_shapely(collection)])
def test_subclass_is_geometry(with_point_in_registry):
assert pygeos.is_geometry(Point("POINT (1 1)"))
def test_from_wkt_empty(wkt):
geom = pygeos.from_wkt(wkt)
assert pygeos.is_geometry(geom).all()
assert pygeos.is_empty(geom).all()
assert pygeos.to_wkt(geom) == wkt
def test_from_wkb_empty(wkt):
wkb = pygeos.to_wkb(pygeos.Geometry(wkt))
geom = pygeos.from_wkb(wkb)
assert pygeos.is_geometry(geom).all()
assert pygeos.is_empty(geom).all()
assert pygeos.to_wkb(geom) == wkb
if not filename.exists():
print(f"WARNING: {filename} not found")
continue
# Extract and merge lakes and wetlands
df = read_dataframe(
f"/vsizip/{filename}/HU8_{huc8}_Watershed/HU8_{huc8}_Wetlands.shp",
columns=["ATTRIBUTE", "WETLAND_TY"],
where="WETLAND_TY in ('Lake', 'Pond', 'Riverine')",
).rename(columns={
"ATTRIBUTE": "nwi_code",
"WETLAND_TY": "nwi_type"
})
# some geometries are invalid, filter them out
df = df.loc[pg.is_geometry(df.geometry.values.data)].copy()
if not len(df):
continue
df = df.to_crs(CRS)
# Mark structurally altered types where
# codes with x (excavated), d (ditched), r (artificial substrate), h (diked)
# strip any terminal numbers then take last character
df["modifier"] = df.nwi_code.str.rstrip("123456789").str[-1:]
df["altered"] = df.modifier.isin(MODIFIERS)
waterbodies = append(waterbodies,
df.loc[df.nwi_type.isin(["Lake", "Pond"])])