137 lines
3.9 KiB
Python
137 lines
3.9 KiB
Python
import math
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import subprocess
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import xmltodict
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import geopandas as gpd
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from shapely.geometry import Point
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from matsim_activity_to_matsim_schedule import MatsimActivityToMatsimSchedule
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from hub_function_to_matsim_activity import HubFunctionToMatsimActivity
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class MatSimEngine:
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def __init__(self, city, output_file_path):
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self._city = city
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self._output_file_path = output_file_path
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facilities_dict = {
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'facilities': {
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'@name': 'Montreal Facilities',
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'facility': []
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}
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}
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hub_function_to_matsim = HubFunctionToMatsimActivity()
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matsim_schedule = MatsimActivityToMatsimSchedule()
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buildings_shape_data = {
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'id': [],
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'geometry': []
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}
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# 1- Facilities
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# TODO: this should come from the factories, please check idf generation
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for building in city.buildings:
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activity = hub_function_to_matsim.dictionary[building.function].split(',')
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for surface in building.grounds:
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for coord in surface.solid_polygon.coordinates:
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buildings_shape_data['id'].append(f"{building.name}")
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buildings_shape_data['geometry'].append(Point(coord[0], coord[1]))
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facility = {
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'@id': building.name,
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'@x': str(building.centroid[0]),
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'@y': str(building.centroid[1]),
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'activity': []
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}
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if len(building.thermal_zones_from_internal_zones) > 1:
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raise NotImplementedError("multi-zone buildings aren't yet supported")
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building_schedules = []
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capacity = 0
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for thermal_zone in building.thermal_zones_from_internal_zones:
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capacity = thermal_zone.occupancy.occupancy_density * building.floor_area * building.storeys_above_ground
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for schedule in thermal_zone.occupancy.occupancy_schedules:
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building_schedules.append(schedule)
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for new_activity in activity:
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activity_info = {
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'@type': new_activity,
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'capacity': {
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'@value': math.ceil(capacity)
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},
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'opentime': []
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}
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for schedule in building_schedules:
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opening_hour = 0
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closing_hour = 0
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# Find opening hour (first hour > 0)
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for i, value in enumerate(schedule.values):
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if value > 0:
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opening_hour = i
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break
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for i, value in reversed(list(enumerate(schedule.values))):
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if value > 0:
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closing_hour = i
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break
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for day in schedule.day_types:
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if day[0:3] != 'hol':
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activity_info['opentime'].append({
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'@day': day[0:3],
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'@start_time': opening_hour,
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'@end_time': closing_hour
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})
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facility['activity'].append(activity_info)
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facilities_dict['facilities']['facility'].append(facility)
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gdf = gpd.GeoDataFrame(
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buildings_shape_data,
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crs=city.srs_name
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)
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gdf.to_file("input_files/buildings_shapefile.shp")
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# Convert the Python dictionary to an XML string
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xml_content = xmltodict.unparse(facilities_dict, pretty=True, short_empty_elements=True)
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# Write the XML to the file
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with open(f"{output_file_path}/{city.name}_facilities.xml", 'w') as file:
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file.write(xml_content)
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# 2- Network
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# First get only the part of the network necessary for the simulation
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java_path = "java"
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jar_path = "matsim-network-from-osm.jar"
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command = [
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java_path,
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"-jar", jar_path,
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"input_files/merged-network.osm.pbf",
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"input_files/buildings_shapefile.shp",
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f"{output_file_path}/network.xml.gz"
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]
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subprocess.run(command)
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# 3- Population
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# 3.1 - Public Transport
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# 4- Config Generation
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def run(self):
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java_path = "java"
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jar_path = "matsim.jar"
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command = [java_path, "-jar", jar_path]
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# Must generate this config file first.
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# command.append(config_file_path)
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subprocess.run(command)
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