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models.py
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models.py
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import urbansim.sim.simulation as sim
from urbansim.utils import misc
import random
import os
import utils
import dataset
import variables
import time
import numpy as np
@sim.model('rsh_estimate')
def rsh_estimate(buildings, zones):
return utils.hedonic_estimate("rsh.yaml", buildings, zones)
@sim.model('rsh_simulate')
def rsh_simulate(buildings, zones):
ret = utils.hedonic_simulate("rsh.yaml", buildings, zones,
"residential_sales_price")
s = buildings.residential_sales_price
s[s > 1200] = 1200
s[s < 250] = 250
buildings.update_col_from_series("residential_sales_price", s)
return ret
@sim.model('nrh_estimate')
def nrh_estimate(buildings, zones):
return utils.hedonic_estimate("nrh.yaml", buildings, zones)
@sim.model('nrh_simulate')
def nrh_simulate(buildings, zones):
return utils.hedonic_simulate("nrh.yaml", buildings, zones,
"non_residential_rent")
@sim.model('hlcm_estimate')
def hlcm_estimate(households, buildings, zones):
return utils.lcm_estimate("hlcm.yaml", households, "building_id",
buildings, zones)
@sim.model('hlcm_simulate')
def hlcm_simulate(households, buildings, zones):
return utils.lcm_simulate("hlcm.yaml", households, buildings, zones,
"building_id", "residential_units",
"vacant_residential_units")
@sim.model('elcm_estimate')
def elcm_estimate(jobs, buildings, zones):
return utils.lcm_estimate("elcm.yaml", jobs, "building_id",
buildings, zones)
@sim.model('elcm_simulate')
def elcm_simulate(jobs, buildings, zones):
return utils.lcm_simulate("elcm.yaml", jobs, buildings, zones,
"building_id", "job_spaces", "vacant_job_spaces")
@sim.model('households_relocation')
def households_relocation(households):
return utils.simple_relocation(households, .05, "building_id")
@sim.model('jobs_relocation')
def jobs_relocation(jobs):
return utils.simple_relocation(jobs, .05, "building_id")
@sim.model('households_transition')
def households_transition(households):
return utils.simple_transition(households, .02, "building_id")
@sim.model('jobs_transition')
def jobs_transition(jobs):
return utils.simple_transition(jobs, .02, "building_id")
@sim.model('feasibility')
def feasibility(parcels):
utils.run_feasibility(parcels,
variables.parcel_average_price,
variables.parcel_is_allowed,
historic_preservation='oldest_building > 1940 and '
'oldest_building < 2000',
residential_to_yearly=True,
pass_through=["oldest_building", "total_sqft",
"max_far", "max_dua", "land_cost",
"residential", "min_max_fars",
"max_far_from_dua", "max_height",
"max_far_from_heights",
"building_purchase_price",
"building_purchase_price_sqft"])
def add_extra_columns(df):
for col in ["residential_sales_price", "non_residential_rent"]:
df[col] = 0
return df
@sim.model('residential_developer')
def residential_developer(feasibility, households, buildings, parcels, year):
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size,
parcels.ave_unit_size,
parcels.total_units,
feasibility,
year=year,
target_vacancy=.06,
min_unit_size=800,
form_to_btype_callback=sim.get_injectable("form_to_btype_f"),
add_more_columns_callback=add_extra_columns,
bldg_sqft_per_job=400.0)
utils.add_parcel_output(new_buildings)
@sim.model('non_residential_developer')
def non_residential_developer(feasibility, jobs, buildings, parcels, year):
new_buildings = utils.run_developer(
["office", "retail", "industrial"],
jobs,
buildings,
"job_spaces",
parcels.parcel_size,
parcels.ave_unit_size,
parcels.total_job_spaces,
feasibility,
year=year,
target_vacancy=.63,
form_to_btype_callback=sim.get_injectable("form_to_btype_f"),
add_more_columns_callback=add_extra_columns,
residential=False,
bldg_sqft_per_job=400.0)
utils.add_parcel_output(new_buildings)
@sim.model("clear_cache")
def clear_cache():
sim.clear_cache()
# this method is used to push messages from urbansim to websites for live
# exploration of simulation results
@sim.model("pusher")
def pusher(year, run_number, uuid):
try:
import pusher
except:
# if pusher not installed, just return
return
import socket
p = pusher.Pusher(
app_id='90082',
key='2fb2b9562f4629e7e87c',
secret='2f0a7b794ec38d16d149'
)
host = "http://localhost:8765/"
sim_output = host+"runs/run{}_simulation_output.json".format(run_number)
parcel_output = host+"runs/run{}_parcel_output.csv".format(run_number)
p['urbansim'].trigger('simulation_year_completed',
{'year': year,
'region': 'sanfrancisco',
'run_number': run_number,
'hostname': socket.gethostname(),
'uuid': uuid,
'time': time.ctime(),
'sim_output': sim_output,
'field_name': 'residential_units',
'table': 'diagnostic_outputs',
'scale': 'jenks',
'parcel_output': parcel_output})
@sim.model("diagnostic_output")
def diagnostic_output(households, buildings, zones, year):
households = households.to_frame()
buildings = buildings.to_frame()
zones = zones.to_frame()
zones['residential_units'] = buildings.groupby('zone_id').\
residential_units.sum()
zones['non_residential_sqft'] = buildings.groupby('zone_id').\
non_residential_sqft.sum()
zones['retail_sqft'] = buildings.query('general_type == "Retail"').\
groupby('zone_id').non_residential_sqft.sum()
zones['office_sqft'] = buildings.query('general_type == "Office"').\
groupby('zone_id').non_residential_sqft.sum()
zones['industrial_sqft'] = buildings.query('general_type == "Industrial"').\
groupby('zone_id').non_residential_sqft.sum()
zones['average_income'] = households.groupby('zone_id').income.quantile()
zones['household_size'] = households.groupby('zone_id').persons.quantile()
zones['residential_sales_price'] = buildings.\
query('general_type == "Residential"').groupby('zone_id').\
residential_sales_price.quantile()
zones['retail_rent'] = buildings[buildings.general_type == "Retail"].\
groupby('zone_id').non_residential_rent.quantile()
zones['office_rent'] = buildings[buildings.general_type == "Office"].\
groupby('zone_id').non_residential_rent.quantile()
zones['industrial_rent'] = \
buildings[buildings.general_type == "Industrial"].\
groupby('zone_id').non_residential_rent.quantile()
utils.add_simulation_output(zones, "diagnostic_outputs", year)
utils.write_simulation_output(os.path.join(misc.runs_dir(),
"run{}_simulation_output.json"))
utils.write_parcel_output(os.path.join(misc.runs_dir(),
"run{}_parcel_output.csv"))