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test.py
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test.py
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#!/usr/bin/env python
import orca
import shutil
import models, utils
from urbansim.utils import misc, networks
import variables
data_out = utils.get_run_filename()
orca.run([
"refiner",
'build_networks',
"neighborhood_vars"] +
orca.get_injectable('repm_step_names') + # In place of ['nrh_simulate', 'rsh_simulate']
["increase_property_values"]
) # increase feasibility based on projected income
orca.run([
"neighborhood_vars",
"scheduled_demolition_events",
"random_demolition_events",
"scheduled_development_events",
"refiner",
"households_transition",
"fix_lpr",
"households_relocation",
"jobs_transition",
"jobs_relocation",
"feasibility",
"residential_developer",
"non_residential_developer"
] +
orca.get_injectable('repm_step_names') + # In place of ['nrh_simulate', 'rsh_simulate']
["increase_property_values"] + # Hack to make more feasibility
orca.get_injectable('hlcm_step_names') +
orca.get_injectable('elcm_step_names') +
[
"elcm_home_based",
"jobs_scaling_model",
"gq_pop_scaling_model",
],
iter_vars=list(range(2020, 2022)),
data_out=data_out,
out_base_tables=['jobs', 'base_job_space', 'employment_sectors', 'annual_relocation_rates_for_jobs',
'households', 'persons', 'annual_relocation_rates_for_households',
'buildings', 'parcels', 'zones', 'semmcds', 'counties',
'target_vacancies', 'building_sqft_per_job',
'annual_employment_control_totals',
'travel_data', 'zoning', 'large_areas', 'building_types', 'land_use_types',
'workers_labor_participation_rates', 'workers_employment_rates_by_large_area_age',
'workers_employment_rates_by_large_area',
'transit_stops', 'crime_rates', 'schools', 'poi',
'group_quarters', 'group_quarters_control_totals',
'annual_household_control_totals',
'events_addition', 'events_deletion', 'refiner_events'],
out_run_tables=['buildings', 'jobs', 'base_job_space', 'parcels', 'households', 'persons', 'group_quarters', 'dropped_buildings'],
out_interval=1,
compress=True)