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config.yml
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config.yml
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## Synthetic population pipeline for California
## based on the synpp package
# This is the path to a directory where the pipeline can store temporary data
working_directory: /nas/balacm/California/cache_sf_1pct_test
# This section defines which parts of the pipeline should be run
run:
#- data.spatial.zones
#- data.census.cleaned
#- data.osm.add_pt_variable
#- data.education.education_facilities
#- data.hts.cleaned
#- data.od.raw
#- synthesis.population.sampled
#- synthesis.population.matching
#- synthesis.population.trips
#- synthesis.population.spatial.by_person.primary_zones
#- synthesis.population.spatial.by_person.primary_locations
#- synthesis.population.spatial.by_person.secondary.locations
- synthesis.output # To create the output population in the output_path (see below)
#- synthesis.population.spatial.locations
#- synthesis.population.sociodemographics
#- matsim.scenario.population
#- matsim.scenario.supply.gtfs_merger_sf
#- matsim.runtime.pt2matsim
#- matsim.scenario.supply.osm
#- matsim.scenario.supply.processed
#- synthesis.population.spatial.by_person.secondary.distance_distributions
#- synthesis.destinations
#- matsim.simulation.run
#- matsim.scenario.supply.gtfs
#- matsim.runtime.eqasim
- matsim.simulation.prepare
#- temp.cleaned
#- data.census.popgen_prepare
#- matsim.output # Uncomment, if you want to run the full simulation (you'll need Java for that)
#- analysis.analysis
# Here the configuraiton of the pipeline starts
config:
# Some general configuration
processes: 24
# Define sampling rate and random seed for the output population
sampling_rate: 0.01
random_seed: 1234
# define regions to be synthesized
region: "sf"
counties: [1.0, 13.0, 41.0, 55.0, 75.0, 81.0, 85.0, 95.0, 97.0] #San Francisco Bay Area
#counties: [111.0, 37.0, 71.0, 65.0, 59.0] #Los Angeles
#counties: [73.0]
county_names: ["Alameda","Contra Costa","Marin","Napa","San Francisco","San Mateo","Santa Clara","Solano","Sonoma"] #San Francisco Bay Area
#county_names: ["Los Angeles", "Orange", "Ventura", "Riverside", "San Bernardino"] #Los Angeles
#county_names: ["San Diego"]
zones: ['001', '075', '085', '081', '095', '097', '013', '041', '055'] #San Francisco Bay Area
#zones: ['111', '037', '071', '065', '059'] #Los Angeles
#zones: ['073'] #San Diego
minimum_source_samples: 4
#spatial_file: "tl_2017_06_tract_cleaned_v2.shp"
spatial_file: "SF_Bay_Area_cleaned.shp" #San Francisco Bay Area
spatial_imputation_file: "SF_InnerCity.shp" #San Francisco Bay Area
spatial_imputation_file_la: "LA_area_downtowns_2227_reduced2.shp" #Los Angeles downtown
spatial_imputation_file_orange: "Orange_County_2227.shp" #Orange County
#osm_file: "la5counties.osm.pbf"
osm_file: "sf_bay.osm.pbf" #San Francisco Bay Area
osm_file_pt2matsim: "sf_bay.osm.gz" #San Francisco Bay Area
#osm_file_pt2matsim: "la5counties.osm.gz" #Los Angeles
eqasim_java_package: "san_francisco" #San Francisco Bay Area
#eqasim_java_package: "los_angeles" #Los Angeles
# Paths to the input data and where the output should be stored
data_path: /nas/balacm/California/Data/SF
output_path: /nas/balacm/California/output/SF/1pct
popgen_input_path: /nas/balacm/California/Data/SF/PopGen
# Only interesting if you run the simulation
java_memory: 100G
# Only interesting if the analysis script is run
analysis_path: /nas/balacm/California/Analysis/SF