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import geopandas as gpd
import numpy as np
import pandas as pd
import pytest
from libpysal.graph import Graph
from packaging.version import Version
from pandas.testing import assert_series_equal
from shapely import Polygon
import momepy as mm
from .conftest import assert_result
GPD_013 = Version(gpd.__version__) >= Version("0.13")
class TestDimensions:
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.df_buildings["height"] = np.linspace(10.0, 30.0, 144)
self.graph = (
Graph.build_contiguity(self.df_tessellation)
.higher_order(k=3, lower_order=True)
.assign_self_weight()
)
def test_volume(self):
# pandas
expected = self.df_buildings.area * self.df_buildings["height"]
pd.testing.assert_series_equal(
mm.volume(self.df_buildings.area, self.df_buildings["height"]), expected
)
# numpy
expected = self.df_buildings.area.values * self.df_buildings["height"].values
np.testing.assert_array_equal(
mm.volume(
self.df_buildings.area.values, self.df_buildings["height"].values
),
expected,
)
def test_floor_area(self):
expected = self.df_buildings.area * (self.df_buildings["height"] // 3)
pd.testing.assert_series_equal(
mm.floor_area(self.df_buildings.area, self.df_buildings["height"]), expected
)
expected = self.df_buildings.area * (self.df_buildings["height"] // 5)
pd.testing.assert_series_equal(
mm.floor_area(
self.df_buildings.area, self.df_buildings["height"], floor_height=5
),
expected,
)
floor_height = np.repeat(np.array([3, 4]), 72)
expected = self.df_buildings.area * (
self.df_buildings["height"] // floor_height
)
pd.testing.assert_series_equal(
mm.floor_area(
self.df_buildings.area,
self.df_buildings["height"],
floor_height=floor_height,
),
expected,
)
def test_courtyard_area(self):
expected = self.df_buildings.geometry.apply(
lambda geom: Polygon(geom.exterior).area - geom.area
)
pd.testing.assert_series_equal(
mm.courtyard_area(self.df_buildings), expected, check_names=False
)
@pytest.mark.skipif(not GPD_013, reason="minimum_bounding_radius() not available")
def test_longest_axis_length(self):
expected = self.df_buildings.minimum_bounding_radius() * 2
pd.testing.assert_series_equal(
mm.longest_axis_length(self.df_buildings), expected, check_names=False
)
def test_perimeter_wall(self):
result = mm.perimeter_wall(self.df_buildings)
adj = Graph.build_contiguity(self.df_buildings)
result_given_graph = mm.perimeter_wall(self.df_buildings, adj)
pd.testing.assert_series_equal(result, result_given_graph)
assert result[0] == pytest.approx(137.210, rel=1e-3)
def test_perimeter_wall_buffer(self):
buildings = self.df_buildings.copy()
buildings["geometry"] = buildings.simplify(0.10)
adj = Graph.build_contiguity(self.df_buildings)
new_perimeter = mm.perimeter_wall(buildings, adj)
old_perimeter = mm.perimeter_wall(self.df_buildings, adj)
assert (new_perimeter.values != old_perimeter.values).any()
result = mm.perimeter_wall(buildings, adj, buffer=0.25)
assert result[0] == pytest.approx(137.210, rel=1e-3)
@pytest.mark.skipif(not GPD_013, reason="no attribute 'segmentize'")
def test_street_profile(self):
sp = mm.street_profile(
self.df_streets,
self.df_buildings,
tick_length=50,
distance=1,
height=self.df_buildings["height"],
)
expected_w = {
"count": 35,
"mean": 43.39405649921903,
"min": 31.017731525484447,
"max": 50.0,
}
expected_wd = {
"count": 22,
"mean": 1.1977356963898373,
"min": 0.09706119360586668,
"max": 5.154163996499861,
}
expected_o = {
"count": 35,
"mean": 0.7186966927475066,
"min": 0.15270935960591134,
"max": 1.0,
}
expected_h = {
"count": 22,
"mean": 16.72499857958264,
"min": 10.0,
"max": 28.1969381969382,
}
expected_hd = {
"count": 22,
"mean": 1.2372251098113227,
"min": 0.0,
"max": 7.947097088834963,
}
expected_p = {
"count": 22,
"mean": 0.43257459410448046,
"min": 0.20379941361273096,
"max": 0.7432052069071473,
}
assert_result(sp["width"], expected_w, self.df_streets)
assert_result(sp["width_deviation"], expected_wd, self.df_streets)
assert_result(sp["openness"], expected_o, self.df_streets)
assert_result(sp["height"], expected_h, self.df_streets)
assert_result(sp["height_deviation"], expected_hd, self.df_streets)
assert_result(sp["hw_ratio"], expected_p, self.df_streets)
@pytest.mark.skipif(not GPD_013, reason="no attribute 'segmentize'")
def test_street_profile_infinity(self):
# avoid infinity
from shapely import LineString, Point
blg = gpd.GeoDataFrame(
{"height": [2, 5]},
geometry=[
Point(0, 0).buffer(10, cap_style=3),
Point(30, 0).buffer(10, cap_style=3),
],
)
lines = gpd.GeoDataFrame(
geometry=[LineString([(-8, -8), (8, 8)]), LineString([(15, -10), (15, 10)])]
)
assert mm.street_profile(lines, blg, height=blg["height"], distance=2)[
"hw_ratio"
].equals(pd.Series([np.nan, 0.35]))
def test_weighted_char(self):
weighted = mm.weighted_character(
self.df_buildings.height, self.df_buildings.area, self.graph
)
weighted_expected = {
"sum": 2703.7438005082204,
"mean": 18.775998614640418,
"min": 11.005171818893885,
"max": 25.424162063245504,
}
assert_result(
weighted,
weighted_expected,
self.df_tessellation,
check_names=False,
exact=False,
)
class TestDimensionEquivalence:
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_buildings["height"] = np.linspace(10.0, 30.0, 144)
self.df_tessellation = gpd.read_file(test_file_path, layer="tessellation")
self.sw = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.graph = (
Graph.build_contiguity(self.df_tessellation)
.higher_order(k=3, lower_order=True)
.assign_self_weight()
)
@pytest.mark.skipif(not GPD_013, reason="no attribute 'segmentize'")
def test_street_profile(self):
sp_new = mm.street_profile(
self.df_streets,
self.df_buildings,
tick_length=50,
distance=1,
height=self.df_buildings["height"],
)
sp_old = mm.StreetProfile(
self.df_streets,
self.df_buildings,
tick_length=50,
distance=1,
heights=self.df_buildings["height"],
)
## needs tolerance because the generate ticks are different
assert_series_equal(sp_new["width"], sp_old.w, rtol=1e-2, check_names=False)
assert_series_equal(
sp_new["width_deviation"].replace(np.nan, 0),
sp_old.wd,
rtol=1,
check_names=False,
)
assert_series_equal(sp_new["openness"], sp_old.o, rtol=1e-1, check_names=False)
assert_series_equal(
sp_new["height"].replace(np.nan, 0), sp_old.h, check_names=False, rtol=1e-1
)
assert_series_equal(
sp_new["height_deviation"].replace(np.nan, 0),
sp_old.hd,
check_names=False,
rtol=1e-1,
)
assert_series_equal(
sp_new["hw_ratio"].replace(np.nan, 0), sp_old.p, check_names=False, rtol=1
)
def test_covered_area(self):
covered_sw_new = self.graph.describe(
self.df_tessellation.area, statistics=["sum"]
)["sum"]
covered_sw_old = mm.CoveredArea(self.df_tessellation, self.sw, "uID").series
assert_series_equal(
covered_sw_new, covered_sw_old, check_names=False, check_index_type=False
)
def test_weighted_char(self):
weighted_new = mm.weighted_character(
self.df_buildings.height, self.df_buildings.area, self.graph
)
weighted_old = mm.WeightedCharacter(
self.df_buildings, "height", self.sw, "uID"
).series
assert_series_equal(
weighted_new, weighted_old, check_names=False, check_index_type=False
)
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