Simulation.py

import shutil
import sys
import os

# check disk space, need at least 10GB
total, used, free = [round(s/(2 ** 30),1) for s in shutil.disk_usage(".")]
print(f"Disk space: {total} GB;   Used: {used} GB;   Free: {free} GB" )
if free < 10:
    print(f"Free space is too small. Only {free} GB available. Stop running")
    sys.exit()

import orca
import models, utils
from urbansim.utils import misc, networks
import output_indicators

data_out = utils.get_run_filename()
print(data_out)

orca.run(
    ["refiner", "build_networks_2050", "neighborhood_vars"]
    + orca.get_injectable("repm_step_names")
    + ["increase_property_values"]  # In place of ['nrh_simulate', 'rsh_simulate']
)  # increase feasibility based on projected income

orca.run(
    [
        "build_networks_2050",
        "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")
    + ["increase_property_values"]  # In place of ['nrh_simulate', 'rsh_simulate']
    + orca.get_injectable("hlcm_step_names")
    + orca.get_injectable("elcm_step_names")
    + [
        "elcm_home_based",
        "jobs_scaling_model",
        "gq_pop_scaling_model",
        # "travel_model", Fixme: on hold
    ],
    iter_vars=list(range(2016, 2045 + 1)),
    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,
)

# output_indicators.main(data_out)
# dir_out = data_out.replace('.h5', '')
# shutil.copytree(dir_out, '/mnt/hgfs/U/RDF2045/model_runs/' + os.path.basename(os.path.normpath(dir_out)))
# shutil.copy(data_out, '/mnt/hgfs/J')