def alt_feasibility(parcels, settings, parcel_sales_price_sqft_func,
parcel_is_allowed_func):
kwargs = settings['feasibility']
config = sqftproforma.SqFtProFormaConfig()
config.parking_rates["office"] = 1.5
config.parking_rates["retail"] = 1.5
utils.run_feasibility(parcels,
parcel_sales_price_sqft_func,
parcel_is_allowed_func,
config=config,
**kwargs)
f = subsidies.policy_modifications_of_profit(
orca.get_table('feasibility').to_frame(), parcels)
orca.add_table("feasibility", f)
def nonresidential_proforma(form, devtype_id, use, parking_rate):
print form
parcels = sim.get_table('parcels').to_frame()
residential_to_yearly = False
parcel_filter = settings['feasibility']['parcel_filter']
#parcel_filter = None
pfc = sqftproforma.SqFtProFormaConfig()
pfc.forms = {form: {use: 1.0}}
pfc.uses = [use]
pfc.residential_uses = [False]
pfc.parking_rates = {use: parking_rate}
if use == 'retail':
pfc.costs = {use: [160.0, 175.0, 200.0, 230.0]}
elif use == 'industrial':
pfc.costs = {use: [140.0, 175.0, 200.0, 230.0]}
else: #office
pfc.costs = {use: [160.0, 175.0, 200.0, 230.0]}
#Fees
fee_schedule_devtype = fee_schedule[fee_schedule.development_type_id ==
devtype_id]
parcel_fee_schedule_devtype = pd.merge(parcel_fee_schedule,
fee_schedule_devtype,
left_on='fee_schedule_id',
right_on='fee_schedule_id')
parcel_fee_schedule_devtype[
'development_fee_per_unit'] = parcel_fee_schedule_devtype.development_fee_per_unit_space_initial * parcel_fee_schedule_devtype.portion
parcel_fees_processed = parcel_fee_schedule_devtype.groupby(
'parcel_id').development_fee_per_unit.sum()
fees = pd.Series(data=parcel_fees_processed,
index=parcels.index).fillna(0)
pf = sqftproforma.SqFtProForma(pfc)
fees = fees * pf.config.cap_rate
return run_proforma_lookup(parcels,
fees,
pf,
use,
form,
residential_to_yearly,
parcel_filter=parcel_filter)
def alt_feasibility(parcels, settings, parcel_sales_price_sqft_func,
parcel_is_allowed_func):
kwargs = settings['feasibility']
config = sqftproforma.SqFtProFormaConfig()
config.parking_rates["office"] = 1.5
config.parking_rates["retail"] = 1.5
config.building_efficiency = .85
config.parcel_coverage = .85
# use the cap rate from settings.yaml
config.cap_rate = settings["cap_rate"]
utils.run_feasibility(parcels,
parcel_sales_price_sqft_func,
parcel_is_allowed_func,
config=config,
**kwargs)
f = subsidies.policy_modifications_of_profit(
orca.get_table('feasibility').to_frame(), parcels)
orca.add_table("feasibility", f)
def subsidized_residential_feasibility(parcels, settings,
add_extra_columns_func,
parcel_sales_price_sqft_func,
parcel_is_allowed_func,
parcels_geography):
kwargs = settings['feasibility'].copy()
kwargs["only_built"] = False
kwargs["forms_to_test"] = ["residential"]
config = sqftproforma.SqFtProFormaConfig()
# use the cap rate from settings.yaml
config.cap_rate = settings["cap_rate"]
# step 1
utils.run_feasibility(parcels,
parcel_sales_price_sqft_func,
parcel_is_allowed_func,
config=config,
**kwargs)
feasibility = orca.get_table("feasibility").to_frame()
# get rid of the multiindex that comes back from feasibility
feasibility = feasibility.stack(level=0).reset_index(level=1, drop=True)
# join to parcels_geography for filtering
feasibility = feasibility.join(parcels_geography.to_frame())
# add the multiindex back
feasibility.columns = pd.MultiIndex.from_tuples([
("residential", col) for col in feasibility.columns
])
feasibility = policy_modifications_of_profit(feasibility, parcels)
orca.add_table("feasibility", feasibility)
df = orca.get_table("feasibility").to_frame()
df = df.stack(level=0).reset_index(level=1, drop=True)
df.to_csv("runs/run{}_feasibility_{}.csv".format(
orca.get_injectable("run_number"), orca.get_injectable("year")))
