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import geopandas as gpd
import numpy as np
import pytest
from libpysal.weights import Queen
from pandas.testing import assert_series_equal
from shapely.geometry import Point
import momepy as mm
class TestIntensity:
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_streets["nID"] = mm.unique_id(self.df_streets)
self.df_buildings["height"] = np.linspace(10.0, 30.0, 144)
self.df_tessellation["area"] = self.df_tessellation.geometry.area
self.df_buildings["area"] = self.df_buildings.geometry.area
self.df_buildings["fl_area"] = mm.FloorArea(self.df_buildings, "height").series
self.df_buildings["nID"] = mm.get_network_id(
self.df_buildings, self.df_streets, "nID"
)
blocks = mm.Blocks(
self.df_tessellation, self.df_streets, self.df_buildings, "bID", "uID"
)
self.blocks = blocks.blocks
self.df_buildings["bID"] = blocks.buildings_id
self.df_tessellation["bID"] = blocks.tessellation_id
def test_AreaRatio(self):
car = mm.AreaRatio(
self.df_tessellation, self.df_buildings, "area", "area", "uID"
).series
carlr = mm.AreaRatio(
self.df_tessellation,
self.df_buildings,
"area",
"area",
left_unique_id="uID",
right_unique_id="uID",
).series
check = 0.3206556897709747
assert car.mean() == pytest.approx(check)
assert carlr.mean() == pytest.approx(check)
far = mm.AreaRatio(
self.df_tessellation,
self.df_buildings,
self.df_tessellation.area,
self.df_buildings.fl_area,
"uID",
).series
check = 1.910949846262234
assert far.mean() == check
with pytest.raises(ValueError, match="Unique ID not correctly set."):
car = mm.AreaRatio(self.df_tessellation, self.df_buildings, "area", "area")
with pytest.raises(ValueError, match="Unique ID not correctly set."):
car = mm.AreaRatio(
self.df_tessellation,
self.df_buildings,
"area",
"area",
left_unique_id="uID",
)
with pytest.raises(ValueError, match="Unique ID not correctly set."):
car = mm.AreaRatio(
self.df_tessellation,
self.df_buildings,
"area",
"area",
right_unique_id="uID",
)
car_sel = mm.AreaRatio(
self.df_tessellation.iloc[10:20], self.df_buildings, "area", "area", "uID"
).series
assert (car_sel.index == self.df_tessellation.iloc[10:20].index).all()
self.blocks["area"] = self.blocks.geometry.area
car_block = mm.AreaRatio(self.blocks, self.df_buildings, "area", "area", "bID")
assert car_block.series.mean() == pytest.approx(0.27619743, rel=1e-8)
def test_Count(self):
eib = mm.Count(self.blocks, self.df_buildings, "bID", "bID").series
weib = mm.Count(
self.blocks, self.df_buildings, "bID", "bID", weighted=True
).series
weis = mm.Count(
self.df_streets, self.df_buildings, "nID", "nID", weighted=True
).series
check_eib = (
gpd.sjoin(self.df_buildings.drop(columns="bID"), self.blocks)["bID"]
.value_counts()
.sort_index()
)
check_weib = pytest.approx(0.00040170607189453996)
assert_series_equal(check_eib, eib, check_names=False)
assert weib.mean() == check_weib
assert weis.mean() == pytest.approx(0.020524232642849215)
point_gdf = gpd.GeoDataFrame(
{"nID": [0]}, geometry=[Point(1603569.010067892, 6464302.821695424)]
)
with pytest.raises(
TypeError, match="Geometry type does not support weighting."
):
mm.Count(point_gdf, self.blocks, "nID", "bID", weighted=True).series # noqa: B018
def test_Courtyards(self):
courtyards = mm.Courtyards(self.df_buildings).series
sw = Queen.from_dataframe(
self.df_buildings, silence_warnings=True, use_index=False
)
courtyards_wm = mm.Courtyards(self.df_buildings, sw).series
check = 0.6805555555555556
assert courtyards.mean() == check
assert courtyards_wm.mean() == check
def test_BlocksCount(self):
sw = mm.sw_high(k=5, gdf=self.df_tessellation, ids="uID")
count = mm.BlocksCount(self.df_tessellation, "bID", sw, "uID").series
count2 = mm.BlocksCount(
self.df_tessellation, self.df_tessellation.bID, sw, "uID"
).series
unweigthed = mm.BlocksCount(
self.df_tessellation, "bID", sw, "uID", weighted=False
).series
check = 3.142437439120778e-05
check2 = 5.222222222222222
assert count.mean() == check
assert count2.mean() == check
assert unweigthed.mean() == check2
with pytest.raises(
ValueError, match="Attribute 'weighted' needs to be True or False."
):
count = mm.BlocksCount(
self.df_tessellation, "bID", sw, "uID", weighted="yes"
)
sw_drop = mm.sw_high(k=5, gdf=self.df_tessellation[2:], ids="uID")
assert (
mm.BlocksCount(self.df_tessellation, "bID", sw_drop, "uID")
.series.isna()
.any()
)
def test_Reached(self):
count = mm.Reached(self.df_streets, self.df_buildings, "nID", "nID").series
area = mm.Reached(
self.df_streets,
self.df_buildings,
self.df_streets.nID,
self.df_buildings.nID,
mode="sum",
).series
mean = mm.Reached(
self.df_streets, self.df_buildings, "nID", "nID", mode="mean"
).series
std = mm.Reached(
self.df_streets, self.df_buildings, "nID", "nID", mode="std"
).series
area_v = mm.Reached(
self.df_streets,
self.df_buildings,
"nID",
"nID",
mode="sum",
values="fl_area",
).series
mean_v = mm.Reached(
self.df_streets,
self.df_buildings,
"nID",
"nID",
mode="mean",
values="fl_area",
).series
std_v = mm.Reached(
self.df_streets,
self.df_buildings,
"nID",
"nID",
mode="std",
values="fl_area",
).series
sw = mm.sw_high(k=2, gdf=self.df_streets)
count_sw = mm.Reached(
self.df_streets, self.df_buildings, "nID", "nID", sw
).series
assert max(count) == 18
assert max(area) == pytest.approx(18085.45897711331)
assert max(count_sw) == 138
assert max(mean) == pytest.approx(1808.5458977113315)
assert max(std) == pytest.approx(3153.7019229524785)
assert max(area_v) == pytest.approx(79169.31385861784)
assert max(mean_v) == pytest.approx(7916.931385861784)
assert max(std_v) == pytest.approx(8995.18003493457)
def test_NodeDensity(self):
nx = mm.gdf_to_nx(self.df_streets)
nx = mm.node_degree(nx)
nodes, edges, W = mm.nx_to_gdf(nx, spatial_weights=True)
sw = mm.sw_high(k=3, weights=W)
density = mm.NodeDensity(nodes, edges, sw).series
weighted = mm.NodeDensity(
nodes, edges, sw, weighted=True, node_degree="degree"
).series
array = mm.NodeDensity(nodes, edges, W).series
assert density.mean() == pytest.approx(0.005534125924228438)
assert weighted.mean() == pytest.approx(0.010090861332429164)
assert array.mean() == 0.01026753724860306
def test_Density(self):
sw = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
dens = mm.Density(
self.df_tessellation,
self.df_buildings["fl_area"],
sw,
"uID",
self.df_tessellation.area,
).series
dens2 = mm.Density(
self.df_tessellation, self.df_buildings["fl_area"], sw, "uID"
).series
check = 1.661587
assert dens.mean() == pytest.approx(check)
assert dens2.mean() == pytest.approx(check)
sw_drop = mm.sw_high(k=3, gdf=self.df_tessellation[2:], ids="uID")
assert (
mm.Density(
self.df_tessellation, self.df_buildings["fl_area"], sw_drop, "uID"
)
.series.isna()
.any()
)
# island
sw.neighbors[1] = []
dens3 = mm.Density(
self.df_tessellation,
self.df_buildings["fl_area"],
sw,
"uID",
self.df_tessellation.area,
).series
assert dens3.mean() == pytest.approx(1.656420)
|
