def lcm_simulate(cfg, choosers, buildings, join_tbls, out_fname,
supply_fname, vacant_fname,
enable_supply_correction=None):
"""
Simulate the location choices for the specified choosers
Parameters
----------
cfg : string
The name of the yaml config file from which to read the location
choice model
choosers : DataFrameWrapper
A dataframe of agents doing the choosing
buildings : DataFrameWrapper
A dataframe of buildings which the choosers are locating in and which
have a supply
join_tbls : list of strings
A list of land use dataframes to give neighborhood info around the
buildings - will be joined to the buildings using existing broadcasts.
out_fname : string
The column name to write the simulated location to
supply_fname : string
The string in the buildings table that indicates the amount of
available units there are for choosers, vacant or not
vacant_fname : string
The string in the buildings table that indicates the amount of vacant
units there will be for choosers
enable_supply_correction : Python dict
Should contain keys "price_col" and "submarket_col" which are set to
the column names in buildings which contain the column for prices and
an identifier which segments buildings into submarkets
"""
cfg = misc.config(cfg)
choosers_df = utils.to_frame(choosers, [], cfg, additional_columns=[out_fname])
additional_columns = [supply_fname, vacant_fname]
if enable_supply_correction is not None and \
"submarket_col" in enable_supply_correction:
additional_columns += [enable_supply_correction["submarket_col"]]
if enable_supply_correction is not None and \
"price_col" in enable_supply_correction:
additional_columns += [enable_supply_correction["price_col"]]
locations_df = utils.to_frame(buildings, join_tbls, cfg,
additional_columns=additional_columns)
available_units = buildings[supply_fname]
vacant_units = buildings[vacant_fname]
print("There are %d total available units" % available_units.sum())
print(" and %d total choosers" % len(choosers))
print(" but there are %d overfull buildings" %
len(vacant_units[vacant_units < 0]))
vacant_units = vacant_units[vacant_units > 0]
# sometimes there are vacant units for buildings that are not in the
# locations_df, which happens for reasons explained in the warning below
indexes = np.repeat(vacant_units.index.values,
vacant_units.values.astype('int'))
isin = pd.Series(indexes).isin(locations_df.index)
missing = len(isin[isin == False])
indexes = indexes[isin.values]
units = locations_df.loc[indexes].reset_index()
utils.check_nas(units)
print(" for a total of %d temporarily empty units" % vacant_units.sum())
print(" in %d buildings total in the region" % len(vacant_units))
if missing > 0:
print("WARNING: %d indexes aren't found in the locations df -" %
missing)
print(" this is usually because of a few records that don't join ")
print(" correctly between the locations df and the aggregations tables")
movers = choosers_df[choosers_df[out_fname] == -1]
print("There are %d total movers for this LCM" % len(movers))
if enable_supply_correction is not None:
assert isinstance(enable_supply_correction, dict)
assert "price_col" in enable_supply_correction
price_col = enable_supply_correction["price_col"]
assert "submarket_col" in enable_supply_correction
submarket_col = enable_supply_correction["submarket_col"]
lcm = utils.yaml_to_class(cfg).from_yaml(str_or_buffer=cfg)
if enable_supply_correction.get("warm_start", False) is True:
raise NotImplementedError()
multiplier_func = enable_supply_correction.get("multiplier_func", None)
if multiplier_func is not None:
multiplier_func = sim.get_injectable(multiplier_func)
kwargs = enable_supply_correction.get('kwargs', {})
new_prices, submarkets_ratios = supply_and_demand(
lcm,
movers,
units,
submarket_col,
price_col,
base_multiplier=None,
multiplier_func=multiplier_func,
**kwargs)
# we will only get back new prices for those alternatives
# that pass the filter - might need to specify the table in
# order to get the complete index of possible submarkets
submarket_table = enable_supply_correction.get("submarket_table", None)
if submarket_table is not None:
submarkets_ratios = submarkets_ratios.reindex(
sim.get_table(submarket_table).index).fillna(1)
# write final shifters to the submarket_table for use in debugging
sim.get_table(submarket_table)["price_shifters"] = submarkets_ratios
print("Running supply and demand")
print("Simulated Prices")
print(buildings[price_col].describe())
print("Submarket Price Shifters")
print(submarkets_ratios.describe())
# we want new prices on the buildings, not on the units, so apply
# shifters directly to buildings and ignore unit prices
sim.add_column(buildings.name,
price_col+"_hedonic", buildings[price_col])
new_prices = buildings[price_col] * \
submarkets_ratios.loc[buildings[submarket_col]].values
buildings.update_col_from_series(price_col, new_prices)
print("Adjusted Prices")
print(buildings[price_col].describe())
#if len(movers) > vacant_units.sum():
# print "WARNING: Not enough locations for movers"
# print " reducing locations to size of movers for performance gain"
# movers = movers.head(vacant_units.sum())
new_units, _ = utils.yaml_to_class(cfg).predict_from_cfg(movers, units, cfg)
# new_units returns nans when there aren't enough units,
# get rid of them and they'll stay as -1s
new_units = new_units.dropna()
# go from units back to buildings
new_buildings = pd.Series(units.loc[new_units.values][out_fname].values,
index=new_units.index)
choosers.update_col_from_series(out_fname, new_buildings)
utils._print_number_unplaced(choosers, out_fname)
if enable_supply_correction is not None:
new_prices = buildings[price_col]
if "clip_final_price_low" in enable_supply_correction:
new_prices = new_prices.clip(lower=enable_supply_correction[
"clip_final_price_low"])
if "clip_final_price_high" in enable_supply_correction:
new_prices = new_prices.clip(upper=enable_supply_correction[
"clip_final_price_high"])
buildings.update_col_from_series(price_col, new_prices)
vacant_units = buildings[vacant_fname]
print(" and there are now %d empty units" % vacant_units.sum())
print(" and %d overfull buildings" % len(vacant_units[vacant_units < 0]))
def run_developer(forms, agents, buildings, buildings_all, supply_fname, parcel_size,
ave_unit_size, total_units, feasibility, year=None,
target_vacancy=.1, form_to_btype_callback=None,
add_more_columns_callback=None, max_parcel_size=34647265,
residential=True, bldg_sqft_per_job=400.0,
min_unit_size=400, remove_developed_buildings=True,
unplace_agents=['households', 'jobs']):
"""
Run the developer model to pick and build buildings
Parameters
----------
forms : string or list of strings
Passed directly dev.pick
agents : DataFrame Wrapper
Used to compute the current demand for units/floorspace in the area
buildings : DataFrame Wrapper
Used to compute the current supply of units/floorspace in the area
buildings_all:
Buildings for the entire region, used to write back to buildings table
supply_fname : string
Identifies the column in buildings which indicates the supply of
units/floorspace
parcel_size : Series
Passed directly to dev.pick
ave_unit_size : Series
Passed directly to dev.pick - average residential unit size
total_units : Series
Passed directly to dev.pick - total current residential_units /
job_spaces
feasibility : DataFrame Wrapper
The output from feasibility above (the table called 'feasibility')
year : int
The year of the simulation - will be assigned to 'year_built' on the
new buildings
target_vacancy : float
The target vacancy rate - used to determine how much to build
form_to_btype_callback : function
Will be used to convert the 'forms' in the pro forma to
'building_type_id' in the larger model
add_more_columns_callback : function
Takes a dataframe and returns a dataframe - is used to make custom
modifications to the new buildings that get added
max_parcel_size : float
Passed directly to dev.pick - max parcel size to consider
min_unit_size : float
Passed directly to dev.pick - min unit size that is valid
residential : boolean
Passed directly to dev.pick - switches between adding/computing
residential_units and job_spaces
bldg_sqft_per_job : float
Passed directly to dev.pick - specified the multiplier between
floor spaces and job spaces for this form (does not vary by parcel
as ave_unit_size does)
remove_redeveloped_buildings : optional, boolean (default True)
Remove all buildings on the parcels which are being developed on
unplace_agents : optional : list of strings (default ['households', 'jobs'])
For all tables in the list, will look for field building_id and set
it to -1 for buildings which are removed - only executed if
remove_developed_buildings is true
Returns
-------
Writes the result back to the buildings table and returns the new
buildings with available debugging information on each new building
"""
dev = developer.Developer(feasibility.to_frame())
#dev = WFRCDeveloper.WFRCDeveloper(feasibility.to_frame())
target_units = dev.\
compute_units_to_build(len(agents),
buildings[supply_fname].sum(),
target_vacancy)
print("{:,} feasible buildings before running developer".format(
len(dev.feasibility)))
new_buildings = dev.pick(forms,
target_units,
parcel_size,
ave_unit_size,
total_units,
max_parcel_size=max_parcel_size,
min_unit_size=min_unit_size,
drop_after_build=True,
residential=residential,
bldg_sqft_per_job=bldg_sqft_per_job)
sim.add_table("feasibility", dev.feasibility)
year = sim.get_injectable('year')
if new_buildings is None:
return
if len(new_buildings) == 0:
return new_buildings
if not isinstance(forms, list):
# form gets set only if forms is a list
new_buildings["form"] = forms
if form_to_btype_callback is not None:
new_buildings["building_type_id"] = new_buildings.\
apply(form_to_btype_callback, axis=1)
new_buildings["stories"] = new_buildings.stories.apply(np.ceil)
new_buildings["note"] = "simulated"
ret_buildings = new_buildings
if add_more_columns_callback is not None:
new_buildings = add_more_columns_callback(new_buildings)
if year is not None:
new_buildings["year_built"] = year
print("Adding {:,} buildings with {:,} {}".
format(len(new_buildings),
int(new_buildings[supply_fname].sum()),
supply_fname))
print("{:,} feasible buildings after running developer".format(
len(dev.feasibility)))
old_buildings = buildings.to_frame(buildings.local_columns)
old_buildings_all = buildings_all.to_frame(buildings.local_columns)
new_buildings = new_buildings[buildings.local_columns]
if remove_developed_buildings:
redev_buildings = old_buildings.parcel_id.isin(new_buildings.parcel_id)
redev_buildings_all = old_buildings_all.parcel_id.isin(new_buildings.parcel_id)
l = len(old_buildings)
drop_buildings = old_buildings[redev_buildings]
drop_buildings_all = old_buildings_all[redev_buildings_all]
old_buildings = old_buildings[np.logical_not(redev_buildings)]
old_buildings_all = old_buildings_all[np.logical_not(redev_buildings_all)]
l2 = len(old_buildings)
print("before dropped l:" + str(l))
print("after dropped l2: " + str(l2))
#print redev_buildings
#print drop_buildings
if l2-l > 0:
print("Dropped {} buildings because they were redeveloped".
format(l2 - l))
for tbl in unplace_agents:
agents = sim.get_table(tbl)
agents = agents.to_frame(agents.local_columns)
#displaced_agents = agents.building_id.isin(drop_buildings.index)
displaced_agents = agents.building_id.isin(drop_buildings_all.index)
print("Unplaced {} before: {}".format(tbl, len(agents.query(
"building_id == -1"))))
agents.building_id[displaced_agents] = -1
print("Unplaced {} after: {}".format(tbl, len(agents.query(
"building_id == -1"))))
sim.add_table(tbl, agents)
all_buildings = dev.merge(old_buildings_all, new_buildings)
sim.add_table("buildings", all_buildings)
return ret_buildings
def get_run_no():
if 'run_no' not in sim.list_injectables():
sim.add_injectable("run_no", misc.get_run_number())
return sim.get_injectable("run_no")
def get_run_filename():
if 'run_no' not in sim.list_injectables():
get_run_no()
return os.path.join(misc.runs_dir(), "run%d.h5" % sim.get_injectable("run_no"))
import datasources, models, variables, utils
import orca_wfrc.orca as sim
import pandas as pd
import numpy as np
import os
remm_years = sim.get_injectable('settings')['remm']
#sim.run(["travel_time_reset"])
sim.run(
[
"clear_cache",
"utility_restriction", # run utility restriction for UT County
"travel_time_import",
"neighborhood_vars", # neighborhood variables
"households_transition", # households transition
"jobs_transition", # jobs transition
"feasibility", # compute feasibility
"residential_developer_slc", # build buildings Salt Lake County
"residential_developer_davis", # build buildings Davis County
"residential_developer_weber", # build buildings Weber County
"residential_developer_utah", # build buildings Utah County
"office_developer_slc",
"office_developer_utah",
"office_developer_davis",
"office_developer_weber",
"retail_developer_slc",
"retail_developer_utah",
"retail_developer_davis",
"retail_developer_weber",
"industrial_developer_slc",