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)