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samakinensamakinen
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properly handle missing geopandas
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‎Scripts/events/standard_modules/geodata_helpers.py‎

Lines changed: 96 additions & 92 deletions
Original file line numberDiff line numberDiff line change
@@ -1,110 +1,114 @@
11
from typing import Any, Dict, List, TYPE_CHECKING
22
import pandas as pd
3-
import geopandas as gpd
4-
from shapely import LineString, Point
3+
from utils import log
54
if TYPE_CHECKING:
65
from assignment.emme_bindings.mock_project import Network
76
from assignment.emme_bindings.mock_project import Scenario
7+
try:
8+
import geopandas as gpd
9+
from shapely import LineString, Point
810

9-
def get_links(network: 'Network', scen: 'Scenario') -> gpd.GeoDataFrame:
10-
"""
11-
Extracts link data from a network and scenario and returns it as a GeoDataFrame.
11+
def get_links(network: 'Network', scen: 'Scenario') -> gpd.GeoDataFrame:
12+
"""
13+
Extracts link data from a network and scenario and returns it as a GeoDataFrame.
1214
13-
Args:
14-
network (Network): The network object containing link data.
15-
scen (Scenario): The scenario object containing attributes and ID.
15+
Args:
16+
network (Network): The network object containing link data.
17+
scen (Scenario): The scenario object containing attributes and ID.
1618
17-
Returns:
18-
gpd.GeoDataFrame: A GeoDataFrame containing link data with the following columns:
19-
- 'scenario': The scenario ID.
20-
- 'id': The link ID.
21-
- 'geom': The geometry of the link as a LineString.
22-
- 'inode': The ID of the starting node of the link.
23-
- 'jnode': The ID of the ending node of the link.
24-
- Additional columns for each attribute in the scenario's "LINK" attributes.
25-
"""
26-
attributes = scen.attributes("LINK")
27-
obj = network.links()
28-
values = [dict(zip(
29-
['scenario', 'id', 'geom', 'inode', 'jnode'] + attributes,
30-
[int(scen.id), o.id, LineString(o.shape), o.i_node.id, o.j_node.id] + [o[a] for a in attributes]))
31-
for o in obj]
32-
return gpd.GeoDataFrame(values, geometry='geom').set_crs('epsg:3879')
19+
Returns:
20+
gpd.GeoDataFrame: A GeoDataFrame containing link data with the following columns:
21+
- 'scenario': The scenario ID.
22+
- 'id': The link ID.
23+
- 'geom': The geometry of the link as a LineString.
24+
- 'inode': The ID of the starting node of the link.
25+
- 'jnode': The ID of the ending node of the link.
26+
- Additional columns for each attribute in the scenario's "LINK" attributes.
27+
"""
28+
attributes = scen.attributes("LINK")
29+
obj = network.links()
30+
values = [dict(zip(
31+
['scenario', 'id', 'geom', 'inode', 'jnode'] + attributes,
32+
[int(scen.id), o.id, LineString(o.shape), o.i_node.id, o.j_node.id] + [o[a] for a in attributes]))
33+
for o in obj]
34+
return gpd.GeoDataFrame(values, geometry='geom').set_crs('epsg:3879')
3335

34-
def get_nodes(network: 'Network', scen: 'Scenario') -> gpd.GeoDataFrame:
35-
"""
36-
Extracts node data from a network and scenario, and returns it as a GeoDataFrame.
36+
def get_nodes(network: 'Network', scen: 'Scenario') -> gpd.GeoDataFrame:
37+
"""
38+
Extracts node data from a network and scenario, and returns it as a GeoDataFrame.
3739
38-
Args:
39-
network (Network): The network object containing node information.
40-
scen (Scenario): The scenario object containing attributes and scenario ID.
40+
Args:
41+
network (Network): The network object containing node information.
42+
scen (Scenario): The scenario object containing attributes and scenario ID.
4143
42-
Returns:
43-
gpd.GeoDataFrame: A GeoDataFrame containing node data with the following columns:
44-
- 'scenario': The scenario ID.
45-
- 'id': The node ID.
46-
- 'geom': The geometry of the node as a Point.
47-
- Additional columns for each attribute in the scenario's "NODE" attributes.
48-
"""
49-
attributes = scen.attributes("NODE")
50-
obj = network.nodes()
51-
values = [dict(zip(
52-
['scenario', 'id', 'geom'] + attributes,
53-
[int(scen.id), o.id, Point(o.x, o.y)] + [o[a] for a in attributes]))
54-
for o in obj]
55-
return gpd.GeoDataFrame(values, geometry='geom').set_crs('epsg:3879')
44+
Returns:
45+
gpd.GeoDataFrame: A GeoDataFrame containing node data with the following columns:
46+
- 'scenario': The scenario ID.
47+
- 'id': The node ID.
48+
- 'geom': The geometry of the node as a Point.
49+
- Additional columns for each attribute in the scenario's "NODE" attributes.
50+
"""
51+
attributes = scen.attributes("NODE")
52+
obj = network.nodes()
53+
values = [dict(zip(
54+
['scenario', 'id', 'geom'] + attributes,
55+
[int(scen.id), o.id, Point(o.x, o.y)] + [o[a] for a in attributes]))
56+
for o in obj]
57+
return gpd.GeoDataFrame(values, geometry='geom').set_crs('epsg:3879')
5658

57-
def get_transit_lines(network: 'Network', scen: 'Scenario') -> pd.DataFrame:
58-
"""
59-
Retrieve transit line data from a given network and scenario.
59+
def get_transit_lines(network: 'Network', scen: 'Scenario') -> pd.DataFrame:
60+
"""
61+
Retrieve transit line data from a given network and scenario.
6062
61-
This function extracts transit line information from the provided network
62-
and scenario, including attributes such as scenario ID, transit line ID,
63-
mode, vehicle, and any additional attributes defined for transit lines
64-
in the scenario.
63+
This function extracts transit line information from the provided network
64+
and scenario, including attributes such as scenario ID, transit line ID,
65+
mode, vehicle, and any additional attributes defined for transit lines
66+
in the scenario.
6567
66-
Args:
67-
network (Network): The network object containing transit line data.
68-
scen (Scenario): The scenario object containing attributes and context.
68+
Args:
69+
network (Network): The network object containing transit line data.
70+
scen (Scenario): The scenario object containing attributes and context.
6971
70-
Returns:
71-
pd.DataFrame: A DataFrame containing transit line data with the following columns:
72-
- 'scenario': The ID of the scenario.
73-
- 'id': The ID of the transit line.
74-
- 'mode': The mode of the transit line.
75-
- 'vehicle': The vehicle type of the transit line.
76-
- Additional columns for each attribute defined in the scenario for transit lines.
77-
"""
78-
attributes = scen.attributes("TRANSIT_LINE")
79-
obj = network.transit_lines()
80-
values = [dict(zip(
81-
['scenario', 'id', 'mode', 'vehicle'] + attributes,
82-
[int(scen.id), o.id, o.mode.id, o.vehicle.id] + [o[a] for a in attributes]))
83-
for o in obj]
84-
return pd.DataFrame(values)
72+
Returns:
73+
pd.DataFrame: A DataFrame containing transit line data with the following columns:
74+
- 'scenario': The ID of the scenario.
75+
- 'id': The ID of the transit line.
76+
- 'mode': The mode of the transit line.
77+
- 'vehicle': The vehicle type of the transit line.
78+
- Additional columns for each attribute defined in the scenario for transit lines.
79+
"""
80+
attributes = scen.attributes("TRANSIT_LINE")
81+
obj = network.transit_lines()
82+
values = [dict(zip(
83+
['scenario', 'id', 'mode', 'vehicle'] + attributes,
84+
[int(scen.id), o.id, o.mode.id, o.vehicle.id] + [o[a] for a in attributes]))
85+
for o in obj]
86+
return pd.DataFrame(values)
8587

86-
def get_transit_segments(network: 'Network', scen: 'Scenario') -> pd.DataFrame:
87-
"""
88-
Extracts transit segment data from a given network and scenario and returns it as a pandas DataFrame.
88+
def get_transit_segments(network: 'Network', scen: 'Scenario') -> pd.DataFrame:
89+
"""
90+
Extracts transit segment data from a given network and scenario and returns it as a pandas DataFrame.
8991
90-
Args:
91-
network (Network): The transit network object containing transit lines and segments.
92-
scen (Scenario): The scenario object containing attributes and scenario-specific data.
92+
Args:
93+
network (Network): The transit network object containing transit lines and segments.
94+
scen (Scenario): The scenario object containing attributes and scenario-specific data.
9395
94-
Returns:
95-
pd.DataFrame: A DataFrame containing transit segment data with the following columns:
96-
- 'scenario': The ID of the scenario.
97-
- 'id': The ID of the transit segment.
98-
- 'line_id': The ID of the transit line to which the segment belongs.
99-
- 'link_id': The ID of the link associated with the segment.
100-
- Additional columns for each attribute defined in the scenario's "TRANSIT_SEGMENT" attributes.
101-
"""
102-
attributes = scen.attributes("TRANSIT_SEGMENT")
103-
tls = network.transit_lines()
104-
values: List[Dict[str, Any]] = []
105-
for tl in tls:
106-
for ts in tl.segments():
107-
values.append(dict(zip(
108-
['scenario', 'id', 'line_id', 'link_id'] + attributes,
109-
[int(scen.id), ts.id, tl.id, ts.link.id] + [ts[a] for a in attributes])))
110-
return pd.DataFrame(values)
96+
Returns:
97+
pd.DataFrame: A DataFrame containing transit segment data with the following columns:
98+
- 'scenario': The ID of the scenario.
99+
- 'id': The ID of the transit segment.
100+
- 'line_id': The ID of the transit line to which the segment belongs.
101+
- 'link_id': The ID of the link associated with the segment.
102+
- Additional columns for each attribute defined in the scenario's "TRANSIT_SEGMENT" attributes.
103+
"""
104+
attributes = scen.attributes("TRANSIT_SEGMENT")
105+
tls = network.transit_lines()
106+
values: List[Dict[str, Any]] = []
107+
for tl in tls:
108+
for ts in tl.segments():
109+
values.append(dict(zip(
110+
['scenario', 'id', 'line_id', 'link_id'] + attributes,
111+
[int(scen.id), ts.id, tl.id, ts.link.id] + [ts[a] for a in attributes])))
112+
return pd.DataFrame(values)
113+
except ImportError:
114+
log.warn("geopandas is not installed. Please install it to use this feature to export GPKG data.")

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