def get_edge_noises_df(edge_dicts):
edge_gdf_sub = nw.get_edge_gdf(edge_dicts, ['geometry', 'length', 'uvkey'])
# add noise split lines as list
edge_gdf_sub['split_lines'] = [
geom_utils.get_split_lines_list(line_geom, noise_polys)
for line_geom in edge_gdf_sub['geometry']
]
# explode new rows from split lines column
split_lines = geom_utils.explode_lines_to_split_lines(
edge_gdf_sub, 'uvkey')
# join noises to split lines
split_line_noises = exps.get_noise_attrs_to_split_lines(
split_lines, noise_polys)
# aggregate noises back to edges
edge_noises = exps.aggregate_line_noises(split_line_noises, 'uvkey')
return edge_noises
def test_add_exposures_to_edges():
graph_proj = files.get_network_kumpula()
edge_dicts = nw.get_all_edge_dicts(graph_proj)
edge_gdf = nw.get_edge_gdf(graph_proj,
attrs=['geometry', 'length', 'uvkey'],
subset=5)
edge_gdf['split_lines'] = [
geom_utils.get_split_lines_list(line_geom, noise_polys)
for line_geom in edge_gdf['geometry']
]
split_lines = geom_utils.explode_lines_to_split_lines(edge_gdf, 'uvkey')
split_line_noises = exps.get_noise_attrs_to_split_lines(
split_lines, noise_polys)
edge_noises = exps.aggregate_line_noises(split_line_noises, 'uvkey')
nw.update_edge_noises_to_graph(edge_noises, graph_proj)
edge_dicts = nw.get_all_edge_dicts(graph_proj)
edge_d = edge_dicts[0]
print(edge_d)
exp_len_sum = sum(edge_d['noises'].values())
assert (edge_d['noises'], round(exp_len_sum, 1)) == ({
65: 107.025,
70: 20.027
}, round(edge_d['length'], 1))
from shapely.geometry import Point
from multiprocessing import current_process, Pool
import utils.DT_API as DT_routing
import utils.DT_utils as DT_utils
import utils.geometry as geom_utils
import utils.times as times
import utils.files as files
import utils.utils as utils
import utils.commutes as commutes_utils
import utils.networks as nw
import ast
#%% read graph
graph = files.get_network_full_noise()
print('Graph of', graph.size(), 'edges read.')
edge_gdf = nw.get_edge_gdf(graph, attrs=['geometry', 'length', 'noises'])
node_gdf = nw.get_node_gdf(graph)
print('Network features extracted.')
edge_gdf = edge_gdf[['uvkey', 'geometry', 'noises']]
edges_sind = edge_gdf.sindex
nodes_sind = node_gdf.sindex
print('Spatial index built.')
#%% read YKR work commute data
commutes = pd.read_csv('data_ykr/T06_tma_e_TOL2008_2016_hel.csv')
# commutes['axyind'] = [int(xyind) for xyind in commutes['axyind']]
# commutes['txyind'] = [int(xyind) for xyind in commutes['txyind']]
# commutes = commutes.loc[commutes['akunta'] == 91]
# commutes = commutes.loc[commutes['sp'] == 0]
# commutes.to_csv('data_ykr/T06_tma_e_TOL2008_2016_hel.csv')
commutes['geom_home'] = commutes.apply(lambda row: Point(row['ax'], row['ay']),
graph, {
edge_d['uvkey']: {
'geometry': edge_d['geometry'],
'length': edge_d['length']
}
})
utils.print_duration(start_time, 'Missing edge geometries added.', round_n=1)
#%% 3.1 Remove unwalkable streets & tunnels from the graph [query graph for filtering]
print('Query unwalkable network...')
graph_filt = nw.get_unwalkable_network(extent_poly_wgs=aoi_poly)
filt_edge_dicts = nw.get_all_edge_dicts(graph_filt, by_nodes=False)
nw.add_missing_edge_geometries(graph_filt, filt_edge_dicts)
#%% 3.2 Remove unwalkable streets & tunnels from the graph [prepare networks for comparison]
filt_edge_gdf = nw.get_edge_gdf(graph_filt, by_nodes=True)
# add osmid as string to unwalkable (filter) edge gdfs
filt_edge_gdf['osmid_str'] = [
nw.osmid_to_string(osmid) for osmid in filt_edge_gdf['osmid']
]
print('Found', len(filt_edge_gdf), 'unwalkable edges within the extent.')
## save tunnel edge gdf to file
filt_edges_file = filt_edge_gdf.drop(
['oneway', 'access', 'osmid', 'uvkey', 'service', 'junction', 'lanes'],
axis=1,
errors='ignore')
filt_edges_filename = graph_name + '_tunnel_edges'
filt_edges_file.to_file('data/networks.gpkg',
layer=filt_edges_filename,
driver="GPKG")
print('exported', filt_edges_filename, 'to data/networks.gpkg')