def setup(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
self.swb = Queen.from_dataframe(self.df_buildings)
self.sw5 = mm.sw_high(k=5, gdf=self.df_tessellation, ids="uID")
self.sw3 = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.sws = mm.sw_high(k=2, gdf=self.df_streets)
nx = mm.gdf_to_nx(self.df_streets)
nx = mm.node_degree(nx)
self.nodes, self.edges, W = mm.nx_to_gdf(nx, spatial_weights=True)
self.swn = mm.sw_high(k=3, weights=W)
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() == approx(check)
assert dens2.mean() == 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() == approx(1.656420)
def test_CoveredArea(self):
sw = sw_high(gdf=self.df_tessellation, k=1, ids="uID")
covered_sw = mm.CoveredArea(self.df_tessellation, sw, "uID").series
assert covered_sw[0] == approx(24115.667, rel=1e-3)
sw_drop = sw_high(k=3, gdf=self.df_tessellation[2:], ids="uID")
assert mm.CoveredArea(self.df_tessellation, sw_drop,
"uID").series.isna().any()
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.df_tessellation["area"] = mm.Area(self.df_tessellation).series
self.sw = sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.sw.neighbors[100] = []
self.sw_drop = sw_high(k=3, gdf=self.df_tessellation[2:], ids="uID")
def setup(self, *args):
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.df_tessellation["area"] = mm.Area(self.df_tessellation).series
self.sw = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
def test_MeanInterbuildingDistance(self):
sw = Queen.from_dataframe(self.df_tessellation, ids="uID")
swh = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.df_buildings["m_dist_sw"] = mm.MeanInterbuildingDistance(
self.df_buildings, sw, "uID", swh).series
self.df_buildings["m_dist"] = mm.MeanInterbuildingDistance(
self.df_buildings, sw, "uID", order=3).series
check = 29.305457092042744
assert self.df_buildings["m_dist_sw"][0] == check
assert self.df_buildings["m_dist"][0] == check
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) == 18085.45897711331
assert max(count_sw) == 138
assert max(mean) == 1808.5458977113315
assert max(std) == 3153.7019229524785
assert max(area_v) == 79169.31385861784
assert max(mean_v) == 7916.931385861784
assert max(std_v) == 8995.18003493457
def test_BuildingAdjacencyy(self):
sw = Queen.from_dataframe(self.df_buildings, ids="uID")
swh = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.df_buildings["adj_sw"] = mm.BuildingAdjacency(
self.df_buildings,
spatial_weights=sw,
unique_id="uID",
spatial_weights_higher=swh,
).series
self.df_buildings["adj_sw_none"] = mm.BuildingAdjacency(
self.df_buildings, unique_id="uID",
spatial_weights_higher=swh).series
check = 0.2613824113909074
assert self.df_buildings["adj_sw"].mean() == check
assert self.df_buildings["adj_sw_none"].mean() == check
swh_drop = mm.sw_high(k=3, gdf=self.df_tessellation[2:], ids="uID")
assert (mm.BuildingAdjacency(
self.df_buildings,
unique_id="uID",
spatial_weights_higher=swh_drop).series.isna().any())
def __init__(
self,
gdf,
spatial_weights,
unique_id,
spatial_weights_higher=None,
order=3,
verbose=True,
):
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
if spatial_weights_higher is None:
print(
f"Generating weights matrix (Queen) of {order} topological steps..."
) if verbose else None
self.order = order
from momepy import sw_high
# matrix to define area of analysis (more steps)
spatial_weights_higher = sw_high(k=order, weights=spatial_weights)
self.sw_higher = spatial_weights_higher
data = gdf.set_index(unique_id).geometry
# define empty list for results
results_list = []
# define adjacency list from lipysal
adj_list = spatial_weights.to_adjlist()
adj_list["distance"] = (
data.loc[adj_list.focal]
.reset_index()
.distance(data.loc[adj_list.neighbor].reset_index())
)
print("Computing mean interbuilding distances...")
# iterate over objects to get the final values
for uid in tqdm(data.index, total=data.shape[0], disable=not verbose):
# define neighbours based on weights matrix defining analysis area
if uid in spatial_weights_higher.neighbors.keys():
neighbours = spatial_weights_higher.neighbors[uid].copy()
neighbours.append(uid)
if neighbours:
selection = adj_list[adj_list.focal.isin(neighbours)][
adj_list.neighbor.isin(neighbours)
]
results_list.append(np.nanmean(selection.distance))
else:
results_list.append(np.nan)
self.series = pd.Series(results_list, index=gdf.index)
def test_WeightedCharacter(self):
sw = sw_high(k=3, gdf=self.df_tessellation, ids="uID")
weighted = mm.WeightedCharacter(self.df_buildings, "height", sw,
"uID").series
assert weighted[38] == approx(18.301, rel=1e-3)
self.df_buildings["area"] = self.df_buildings.geometry.area
sw = sw_high(k=3, gdf=self.df_tessellation, ids="uID")
weighted = mm.WeightedCharacter(self.df_buildings, "height", sw, "uID",
"area").series
assert weighted[38] == approx(18.301, rel=1e-3)
area = self.df_buildings.geometry.area
sw = sw_high(k=3, gdf=self.df_tessellation, ids="uID")
weighted = mm.WeightedCharacter(self.df_buildings,
self.df_buildings.height, sw, "uID",
area).series
assert weighted[38] == approx(18.301, rel=1e-3)
sw_drop = sw_high(k=3, gdf=self.df_tessellation[2:], ids="uID")
assert (mm.WeightedCharacter(self.df_buildings, "height", sw_drop,
"uID").series.isna().any())
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() == 0.012690163074599968
assert weighted.mean() == 0.023207675994368446
assert array.mean() == 0.008554067995928158
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() == approx(check)
assert dens2.mean() == approx(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):
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 setup(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.df_buildings["volume"] = mm.Volume(self.df_buildings, "height").series
self.df_streets["nID"] = mm.unique_id(self.df_streets)
self.df_buildings["nID"] = mm.get_network_id(
self.df_buildings, self.df_streets, "nID"
)
self.df_buildings["orient"] = mm.Orientation(self.df_buildings).series
self.df_tessellation["orient"] = mm.Orientation(self.df_tessellation).series
self.sw = Queen.from_dataframe(self.df_tessellation, ids="uID")
self.swh = mm.sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.swb = Queen.from_dataframe(self.df_buildings, ids="uID")
def test_sw_high(self):
first_order = libpysal.weights.Queen.from_dataframe(
self.df_tessellation)
from_sw = mm.sw_high(2, gdf=None, weights=first_order)
from_df = mm.sw_high(2, gdf=self.df_tessellation)
rook = mm.sw_high(2, gdf=self.df_tessellation, contiguity="rook")
check = sorted([133, 134, 111, 112, 113, 114, 115, 121, 125])
assert sorted(from_sw.neighbors[0]) == check
assert sorted(from_df.neighbors[0]) == check
assert sorted(rook.neighbors[0]) == check
with pytest.raises(AttributeError):
mm.sw_high(2, gdf=None, weights=None)
with pytest.raises(ValueError):
mm.sw_high(2, gdf=self.df_tessellation, contiguity="nonexistent")
def test_AverageCharacter(self):
spatial_weights = sw_high(k=3, gdf=self.df_tessellation, ids="uID")
self.df_tessellation[
"area"] = area = self.df_tessellation.geometry.area
self.df_tessellation["mesh_ar"] = mm.AverageCharacter(
self.df_tessellation,
values="area",
spatial_weights=spatial_weights,
unique_id="uID",
mode="mode",
).mode
self.df_tessellation["mesh_array"] = mm.AverageCharacter(
self.df_tessellation,
values=area,
spatial_weights=spatial_weights,
unique_id="uID",
mode="median",
).median
self.df_tessellation["mesh_id"] = mm.AverageCharacter(
self.df_tessellation,
spatial_weights=spatial_weights,
values="area",
rng=(10, 90),
unique_id="uID",
).mean
self.df_tessellation["mesh_iq"] = mm.AverageCharacter(
self.df_tessellation,
spatial_weights=spatial_weights,
values="area",
rng=(25, 75),
unique_id="uID",
).series
all_m = mm.AverageCharacter(
self.df_tessellation,
spatial_weights=spatial_weights,
values="area",
unique_id="uID",
)
two = mm.AverageCharacter(
self.df_tessellation,
spatial_weights=spatial_weights,
values="area",
unique_id="uID",
mode=["mean", "median"],
)
with pytest.raises(ValueError):
self.df_tessellation["mesh_ar"] = mm.AverageCharacter(
self.df_tessellation,
values="area",
spatial_weights=spatial_weights,
unique_id="uID",
mode="nonexistent",
)
with pytest.raises(ValueError):
self.df_tessellation["mesh_ar"] = mm.AverageCharacter(
self.df_tessellation,
values="area",
spatial_weights=spatial_weights,
unique_id="uID",
mode=["nonexistent", "mean"],
)
assert self.df_tessellation["mesh_ar"][0] == approx(249.503, rel=1e-3)
assert self.df_tessellation["mesh_array"][0] == approx(2623.996,
rel=1e-3)
assert self.df_tessellation["mesh_id"][38] == approx(2250.224,
rel=1e-3)
assert self.df_tessellation["mesh_iq"][38] == approx(2118.609,
rel=1e-3)
assert all_m.mean[0] == approx(2922.957, rel=1e-3)
assert all_m.median[0] == approx(2623.996, rel=1e-3)
assert all_m.mode[0] == approx(249.503, rel=1e-3)
assert all_m.series[0] == approx(2922.957, rel=1e-3)
assert two.mean[0] == approx(2922.957, rel=1e-3)
assert two.median[0] == approx(2623.996, rel=1e-3)
sw_drop = sw_high(k=3, gdf=self.df_tessellation[2:], ids="uID")
assert (mm.AverageCharacter(
self.df_tessellation,
values="area",
spatial_weights=sw_drop,
unique_id="uID",
).series.isna().any())
def time_sw_high(self):
mm.sw_high(5, gdf=None, weights=self.first_order)
def __init__(self,
gdf,
spatial_weights,
unique_id,
spatial_weights_higher=None,
order=3):
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
if spatial_weights_higher is None:
print(
"Generating weights matrix (Queen) of {} topological steps...".
format(order))
self.order = order
from momepy import sw_high
# matrix to define area of analysis (more steps)
spatial_weights_higher = sw_high(k=order, weights=spatial_weights)
self.sw_higher = spatial_weights_higher
data = gdf.set_index(unique_id).geometry
# define empty list for results
results_list = []
print("Generating adjacency matrix based on weights matrix...")
# define adjacency list from lipysal
adj_list = spatial_weights.to_adjlist()
adj_list["distance"] = -1
print("Computing interbuilding distances...")
# measure each interbuilding distance of neighbours and save them to adjacency list
for row in tqdm(adj_list.itertuples(), total=adj_list.shape[0]):
inverted = adj_list[(adj_list.focal == row.neighbor)][(
adj_list.neighbor == row.focal)].iloc[0]["distance"]
if inverted == -1:
building_object = data.loc[row.focal]
building_neighbour = data.loc[row.neighbor]
adj_list.loc[row.Index,
"distance"] = building_neighbour.distance(
building_object)
else:
adj_list.at[row.Index, "distance"] = inverted
print("Computing mean interbuilding distances...")
# iterate over objects to get the final values
for uid in tqdm(data.index, total=data.shape[0]):
# define neighbours based on weights matrix defining analysis area
if uid in spatial_weights_higher.neighbors.keys():
neighbours = spatial_weights_higher.neighbors[uid].copy()
neighbours.append(uid)
if neighbours:
selection = adj_list[adj_list.focal.isin(neighbours)][
adj_list.neighbor.isin(neighbours)]
results_list.append(np.nanmean(selection.distance))
else:
results_list.append(np.nan)
self.series = pd.Series(results_list, index=gdf.index)
def test_PerimeterWall(self):
sw = sw_high(gdf=self.df_buildings, k=1)
wall = mm.PerimeterWall(self.df_buildings).series
wall_sw = mm.PerimeterWall(self.df_buildings, sw).series
assert wall[0] == wall_sw[0]
assert wall[0] == approx(137.210, rel=1e-3)
#
# nodes, edges, sw = mm.nx_to_gdf(graph, spatial_weights=True)
#
# fo = libpysal.io.open("/Users/martin/Dropbox/Academia/Data/Geo/Prague/Redo/nodes.gal", "w")
# fo.write(sw)
# fo.close()
#
# nodes.to_file("/Users/martin/Dropbox/Academia/Data/Geo/Prague/Redo/elements.gpkg", layer="nodes", driver="GPKG")
# edges.to_file("/Users/martin/Dropbox/Academia/Data/Geo/Prague/Redo/elements.gpkg", layer="edges", driver="GPKG")
nodes = gpd.read_file(
"/Users/martin/Dropbox/Academia/Data/Geo/Prague/Redo/elements.gpkg", layer="nodes"
)
edges = gpd.read_file(
"/Users/martin/Dropbox/Academia/Data/Geo/Prague/Redo/elements.gpkg", layer="edges"
)
edges_w3 = mm.sw_high(k=3, gdf=edges)
edges["ldsMSL"] = mm.segments_length(edges, spatial_weights=edges_w3, mean=True)
edges["ldsRea"] = mm.reached(edges, tess, "nID", "nID", spatial_weights=edges_w3)
edges["ldsRea"] = mm.reached(
edges, tess, "nID", "nID", spatial_weights=edges_w3, mode="sum", values="sdcAre"
)
# error below
# Traceback (most recent call last):
# File "03_fix_streets.py", line 99, in <module>
# nodes_w5 = mm.sw_high(k=5, weights=sw)
# File "/home/ubuntu/momepy/utils.py", line 94, in sw_high
# first_order, k=i, silence_warnings=silent
# File "/home/ubuntu/miniconda3/envs/mmp/lib/python3.7/site-packages/libpysal/weights/util.py", line 427, in higher_order
# return higher_order_sp(w, k, **kwargs)
# File "/home/ubuntu/miniconda3/envs/mmp/lib/python3.7/site-packages/libpysal/weights/util.py", line 486, in higher_order_sp
def test_PerimeterWall(self):
sw = sw_high(gdf=self.df_buildings, k=1)
wall = mm.PerimeterWall(self.df_buildings).series
wall_sw = mm.PerimeterWall(self.df_buildings, sw).series
assert wall[0] == wall_sw[0]
assert wall[0] == 137.2106961418436
import momepy as mm
import geopandas as gpd
import libpysal
tess = gpd.read_file('files/elements.gpkg', layer='tessellation')
blg = gpd.read_file('files/elements.gpkg', layer='buildings')
blocks = gpd.read_file('files/elements.gpkg', layer='blocks')
streets = gpd.read_file('files/elements.gpkg', layer='streets')
queen1 = mm.sw_high(k=1, gdf=tess, ids='uID')
queen3 = mm.sw_high(k=3, weights=queen1)
blg_queen = mm.sw_high(k=1, gdf=blg, ids='uID')
blg['ltbIBD'] = mm.MeanInterbuildingDistance(blg, queen1, 'uID', queen3).series
blg['ltcBuA'] = mm.BuildingAdjacency(blg, queen3, 'uID', blg_queen).series
# blg['temp_fa'] = mm.floor_area(blg, 'sdbHei', 'sdbAre')
# tess = tess.merge(blg[['temp_fa', 'uID']], on='uID', how='left')
# tess['licGDe'] = mm.density(tess, 'temp_fa', queen3, 'uID', 'sdcAre')
tess['ltcWRB'] = mm.BlocksCount(tess, 'bID', queen3, 'uID').series
tess.to_file('files/elements.gpkg', layer='tessellation', driver='GPKG')
blg.to_file('files/elements.gpkg', layer='buildings', driver='GPKG')
fo = libpysal.io.open('files/GRqueen1.gal', 'w')
fo.write(queen1)
fo.close()
fo = libpysal.io.open('files/GRqueen3.gal', 'w')
fo.write(queen3)
fo.close()