def residential_developer(feasibility, households, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func):
kwargs = settings['residential_developer']
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size,
parcels.ave_sqft_per_unit,
parcels.total_residential_units,
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
**kwargs)
summary.add_parcel_output(new_buildings)
def residential_developer(feasibility, households, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func):
kwargs = settings['residential_developer']
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size,
parcels.ave_sqft_per_unit,
parcels.total_residential_units,
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
**kwargs)
summary.add_parcel_output(new_buildings)
def non_residential_developer(feasibility, jobs, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func):
kwargs = settings['non_residential_developer']
new_buildings = utils.run_developer(
["office", "retail", "industrial"],
jobs,
buildings,
"job_spaces",
parcels.parcel_size,
parcels.ave_sqft_per_unit,
parcels.total_job_spaces,
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
residential=False,
**kwargs)
summary.add_parcel_output(new_buildings)
def non_residential_developer(feasibility, jobs, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func):
kwargs = settings['non_residential_developer']
new_buildings = utils.run_developer(
["office", "retail", "industrial"],
jobs,
buildings,
"job_spaces",
parcels.parcel_size,
parcels.ave_sqft_per_unit,
parcels.total_job_spaces,
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
residential=False,
**kwargs)
summary.add_parcel_output(new_buildings)
def run_subsidized_developer(feasibility,
parcels,
buildings,
households,
acct_settings,
settings,
account,
year,
form_to_btype_func,
add_extra_columns_func,
summary,
create_deed_restricted=False,
policy_name="Unnamed"):
"""
The subsidized residential developer model.
Parameters
----------
feasibility : DataFrame
A DataFrame that is returned from run_feasibility for a given form
parcels : DataFrameWrapper
The standard parcels DataFrameWrapper (mostly just for run_developer)
buildings : DataFrameWrapper
The standard buildings DataFrameWrapper (passed to run_developer)
households : DataFrameWrapper
The households DataFrameWrapper (passed to run_developer)
acct_settings : Dict
A dictionary of settings to parameterize the model. Needs these keys:
sending_buildings_subaccount_def - maps buildings to subaccounts
receiving_buildings_filter - filter for eligible buildings
settings : Dict
The overall settings
account : Account
The Account object to use for subsidization
year : int
The current simulation year (will be added as metadata)
form_to_btype_func : function
Passed through to run_developer
add_extra_columns_func : function
Passed through to run_developer
summary : Summary
Used to add parcel summary information
create_deed_restricted : bool
Bool for whether to create deed restricted units with the subsidies
or not. The logic at the time of this writing is to keep track of
partial units so that when partial units sum to greater than a unit,
that unit will be deed restricted.
Returns
-------
Nothing
Subsidized residential developer is designed to run before the normal
residential developer - it will prioritize the developments we're
subsidizing (although this is not strictly required - running this model
after the market rate developer will just create a temporarily larger
supply of units, which will probably create less market rate development in
the next simulated year)
the steps for subsidizing are essentially these
1 run feasibility with only_built set to false so that the feasibility of
unprofitable units are recorded
2 temporarily filter to ONLY unprofitable units to check for possible
subsidized units (normal developer takes care of market-rate units)
3 compute the number of units in these developments
4 divide cost by number of units in order to get the subsidy per unit
5 filter developments to parcels in "receiving zone" similar to the way we
identified "sending zones"
6 iterate through subaccounts one at a time as subsidy will be limited
to available funds in the subaccount (usually by jurisdiction)
7 sort ascending by subsidy per unit so that we minimize subsidy (but total
subsidy is equivalent to total building cost)
8 cumsum the total subsidy in the buildings and locate the development
where the subsidy is less than or equal to the amount in the account -
filter to only those buildings (these will likely be built)
9 pass the results as "feasible" to run_developer - this is sort of a
boundary case of developer but should run OK
10 for those developments that get built, make sure to subtract from
account and keep a record (on the off chance that demand is less than
the subsidized units, run through the standard code path, although it's
very unlikely that there would be more subsidized housing than demand)
"""
# step 2
feasibility = feasibility.replace([np.inf, -np.inf], np.nan)
feasibility = feasibility[feasibility.max_profit < 0]
# step 3
feasibility['ave_sqft_per_unit'] = parcels.ave_sqft_per_unit
feasibility['residential_units'] = \
np.floor(feasibility.residential_sqft / feasibility.ave_sqft_per_unit)
# step 3B
# can only add units - don't subtract units - this is an approximation
# of the calculation that will be used to do this in the developer model
feasibility = feasibility[
feasibility.residential_units > feasibility.total_residential_units]
# step 3C
# towards the end, because we're about to sort by subsidy per unit, some
# large projects never get built, because it could be a 100 unit project
# times a 500k subsidy per unit. thus we're going to try filtering by
# the maximum subsidy for a single development here
feasibility = feasibility[feasibility.max_profit > -50 * 1000000]
# step 4
feasibility['subsidy_per_unit'] = \
-1 * feasibility['max_profit'] / feasibility['residential_units']
# assumption that even if the developer says this property is almost
# profitable, even the administration costs are likely to cost at least
# 10k / unit
feasibility['subsidy_per_unit'] = feasibility.subsidy_per_unit.clip(10000)
# step 5
if "receiving_buildings_filter" in acct_settings:
feasibility = feasibility.\
query(acct_settings["receiving_buildings_filter"])
else:
# otherwise all buildings are valid
pass
new_buildings_list = []
sending_bldgs = acct_settings["sending_buildings_subaccount_def"]
feasibility["regional"] = 1
feasibility["subaccount"] = feasibility.eval(sending_bldgs)
# step 6
for subacct, amount in account.iter_subaccounts():
print "Subaccount: ", subacct
df = feasibility[feasibility.subaccount == subacct]
print "Number of feasible projects in receiving zone:", len(df)
if len(df) == 0:
continue
# step 7
df = df.sort_values(['subsidy_per_unit'], ascending=True)
# df.to_csv('subsidized_units_%d_%s_%s.csv' %
# (orca.get_injectable("year"), account.name, subacct))
# step 8
print "Amount in subaccount: ${:,.2f}".format(amount)
num_bldgs = int((-1 * df.max_profit).cumsum().searchsorted(amount))
if num_bldgs == 0:
continue
# technically we only build these buildings if there's demand
# print "Building {:d} subsidized buildings".format(num_bldgs)
df = df.iloc[:int(num_bldgs)]
df.columns = pd.MultiIndex.from_tuples([("residential", col)
for col in df.columns])
# disable stdout since developer is a bit verbose for this use case
sys.stdout, old_stdout = StringIO(), sys.stdout
kwargs = settings['residential_developer']
# step 9
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size,
parcels.ave_sqft_per_unit,
parcels.total_residential_units,
orca.DataFrameWrapper("feasibility", df),
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
profit_to_prob_func=profit_to_prob_func,
**kwargs)
sys.stdout = old_stdout
buildings = orca.get_table("buildings")
if new_buildings is None:
continue
# keep track of partial subsidized untis so that we always get credit
# for a partial unit, even if it's not built in this specific building
partial_subsidized_units = 0
# step 10
for index, new_building in new_buildings.iterrows():
amt = new_building.max_profit
metadata = {
"description": "Developing subsidized building",
"year": year,
"residential_units": new_building.residential_units,
"building_id": index
}
account.add_transaction(amt, subaccount=subacct, metadata=metadata)
if create_deed_restricted:
revenue_per_unit = new_building.building_revenue / \
new_building.residential_units
total_subsidy = abs(new_building.max_profit)
subsidized_units = total_subsidy / revenue_per_unit + \
partial_subsidized_units
# right now there are inclusionary requirements
already_subsidized_units = new_building.deed_restricted_units
# get remainder
partial_subsidized_units = subsidized_units % 1
# round off for now
subsidized_units = int(subsidized_units) + \
already_subsidized_units
# cap at number of residential units
subsidized_units = min(subsidized_units,
new_building.residential_units)
buildings.local.loc[index, "deed_restricted_units"] =\
int(round(subsidized_units))
# also correct the debug output
new_buildings.loc[index, "deed_restricted_units"] =\
int(round(subsidized_units))
assert np.all(
buildings.local.deed_restricted_units.fillna(0) <=
buildings.local.residential_units.fillna(0))
print "Amount left after subsidy: ${:,.2f}".\
format(account.total_transactions_by_subacct(subacct))
new_buildings_list.append(new_buildings)
total_len = reduce(lambda x, y: x + len(y), new_buildings_list, 0)
if total_len == 0:
print "No subsidized buildings"
return
new_buildings = pd.concat(new_buildings_list)
print "Built {} total subsidized buildings".format(len(new_buildings))
print " Total subsidy: ${:,.2f}".format(-1 *
new_buildings.max_profit.sum())
print " Total subsidized units: {:.0f}".\
format(new_buildings.residential_units.sum())
new_buildings["subsidized"] = True
new_buildings["policy_name"] = policy_name
summary.add_parcel_output(new_buildings)
def office_developer(feasibility, jobs, buildings, parcels, year,
settings, summary, form_to_btype_func, scenario,
add_extra_columns_func, parcels_geography,
limits_settings):
dev_settings = settings['non_residential_developer']
# I'm going to try a new way of computing this because the math the other
# way is simply too hard. Basically we used to try and apportion sectors
# into the demand for office, retail, and industrial, but there's just so
# much dirtyness to the data, for instance 15% of jobs are in residential
# buildings, and 15% in other buildings, it's just hard to know how much
# to build, we I think the right thing to do is to compute the number of
# job spaces that are required overall, and then to apportion that new dev
# into the three non-res types with a single set of coefficients
all_units = dev.compute_units_to_build(
len(jobs),
buildings.job_spaces.sum(),
dev_settings['kwargs']['target_vacancy'])
print "Total units to build = %d" % all_units
if all_units <= 0:
return
for typ in ["Office"]:
print "\nRunning for type: ", typ
num_units = all_units * float(dev_settings['type_splits'][typ])
targets = []
# now apply limits - limits are assumed to be yearly, apply to an
# entire jurisdiction and be in terms of residential_units or
# job_spaces
if year > 2015 and typ in limits_settings:
juris_name = parcels_geography.juris_name.\
reindex(parcels.index).fillna('Other')
juris_list = limits_settings[typ].keys()
for juris, limit in limits_settings[typ].items():
# the actual target is the limit times the number of years run
# so far in the simulation (plus this year), minus the amount
# built in previous years - in other words, you get rollover
# and development is lumpy
current_total = parcels.total_job_spaces[
(juris_name == juris) & (parcels.newest_building > 2015)]\
.sum()
target = (year - 2015 + 1) * limit - current_total
if target <= 0:
print "Already met target for juris = %s" % juris
print " target = %d, current_total = %d" %\
(target, current_total)
continue
targets.append((juris_name == juris, target, juris))
num_units -= target
# other cities not in the targets get the remaining target
targets.append((~juris_name.isin(juris_list), num_units, "none"))
else:
# otherwise use all parcels with total number of units
targets.append((parcels.index == parcels.index, num_units, "none"))
for parcel_mask, target, juris in targets:
print "Running developer for %s with target of %d" % \
(str(juris), target)
print "Parcels in play:\n", pd.Series(parcel_mask).value_counts()
# this was a fairly heinous bug - have to get the building wrapper
# again because the buildings df gets modified by the run_developer
# method below
buildings = orca.get_table('buildings')
new_buildings = utils.run_developer(
typ.lower(),
jobs,
buildings,
"job_spaces",
parcels.parcel_size[parcel_mask],
parcels.ave_sqft_per_unit[parcel_mask],
parcels.total_job_spaces[parcel_mask],
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
residential=False,
num_units_to_build=target,
profit_to_prob_func=subsidies.profit_to_prob_func,
**dev_settings['kwargs'])
if new_buildings is not None:
new_buildings["subsidized"] = False
summary.add_parcel_output(new_buildings)
def residential_developer(feasibility, households, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func, parcels_geography,
limits_settings, final_year):
kwargs = settings['residential_developer']
target_vacancy = pd.read_csv("data/regional_controls.csv",
index_col="year").loc[year].st_res_vac
num_units = dev.compute_units_to_build(
len(households),
buildings["residential_units"].sum(),
target_vacancy)
targets = []
typ = "Residential"
# now apply limits - limits are assumed to be yearly, apply to an
# entire jurisdiction and be in terms of residential_units or job_spaces
if typ in limits_settings:
juris_name = parcels_geography.juris_name.\
reindex(parcels.index).fillna('Other')
juris_list = limits_settings[typ].keys()
for juris, limit in limits_settings[typ].items():
# the actual target is the limit times the number of years run
# so far in the simulation (plus this year), minus the amount
# built in previous years - in other words, you get rollover
# and development is lumpy
current_total = parcels.total_residential_units[
(juris_name == juris) & (parcels.newest_building >= 2010)]\
.sum()
target = (year - 2010 + 1) * limit - current_total
# make sure we don't overshoot the total development of the limit
# for the horizon year - for instance, in Half Moon Bay we have
# a very low limit and a single development in a far out year can
# easily build over the limit for the total simulation
max_target = (final_year - 2010 + 1) * limit - current_total
if target <= 0:
continue
targets.append((juris_name == juris, target, max_target, juris))
num_units -= target
# other cities not in the targets get the remaining target
targets.append((~juris_name.isin(juris_list), num_units, None, "none"))
else:
# otherwise use all parcels with total number of units
targets.append((parcels.index == parcels.index,
num_units, None, "none"))
for parcel_mask, target, final_target, juris in targets:
print "Running developer for %s with target of %d" % \
(str(juris), target)
# this was a fairly heinous bug - have to get the building wrapper
# again because the buildings df gets modified by the run_developer
# method below
buildings = orca.get_table('buildings')
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size[parcel_mask],
parcels.ave_sqft_per_unit[parcel_mask],
parcels.total_residential_units[parcel_mask],
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
num_units_to_build=target,
profit_to_prob_func=subsidies.profit_to_prob_func,
**kwargs)
buildings = orca.get_table('buildings')
if new_buildings is not None:
new_buildings["subsidized"] = False
if final_target is not None and new_buildings is not None:
# make sure we don't overbuild the target for the whole simulation
overshoot = new_buildings.net_units.sum() - final_target
if overshoot > 0:
index = new_buildings.tail(1).index[0]
index = int(index)
# make sure we don't get into a negative unit situation
overshoot = min(overshoot,
buildings.local.loc[index,
"residential_units"])
buildings.local.loc[index, "residential_units"] -= overshoot
summary.add_parcel_output(new_buildings)
def office_developer(feasibility, jobs, buildings, parcels, year,
settings, summary, form_to_btype_func, scenario,
add_extra_columns_func, parcels_geography,
limits_settings):
dev_settings = settings['non_residential_developer']
# I'm going to try a new way of computing this because the math the other
# way is simply too hard. Basically we used to try and apportion sectors
# into the demand for office, retail, and industrial, but there's just so
# much dirtyness to the data, for instance 15% of jobs are in residential
# buildings, and 15% in other buildings, it's just hard to know how much
# to build, we I think the right thing to do is to compute the number of
# job spaces that are required overall, and then to apportion that new dev
# into the three non-res types with a single set of coefficients
all_units = dev.compute_units_to_build(
len(jobs),
buildings.job_spaces.sum(),
dev_settings['kwargs']['target_vacancy'])
print "Total units to build = %d" % all_units
if all_units <= 0:
return
for typ in ["Office"]:
print "\nRunning for type: ", typ
num_units = all_units * float(dev_settings['type_splits'][typ])
targets = []
# now apply limits - limits are assumed to be yearly, apply to an
# entire jurisdiction and be in terms of residential_units or
# job_spaces
if year > 2015 and typ in limits_settings:
juris_name = parcels_geography.juris_name.\
reindex(parcels.index).fillna('Other')
juris_list = limits_settings[typ].keys()
for juris, limit in limits_settings[typ].items():
# the actual target is the limit times the number of years run
# so far in the simulation (plus this year), minus the amount
# built in previous years - in other words, you get rollover
# and development is lumpy
current_total = parcels.total_job_spaces[
(juris_name == juris) & (parcels.newest_building > 2015)]\
.sum()
target = (year - 2015 + 1) * limit - current_total
if target <= 0:
print "Already met target for juris = %s" % juris
print " target = %d, current_total = %d" %\
(target, current_total)
continue
targets.append((juris_name == juris, target, juris))
num_units -= target
# other cities not in the targets get the remaining target
targets.append((~juris_name.isin(juris_list), num_units, "none"))
else:
# otherwise use all parcels with total number of units
targets.append((parcels.index == parcels.index, num_units, "none"))
for parcel_mask, target, juris in targets:
print "Running developer for %s with target of %d" % \
(str(juris), target)
print "Parcels in play:\n", pd.Series(parcel_mask).value_counts()
# this was a fairly heinous bug - have to get the building wrapper
# again because the buildings df gets modified by the run_developer
# method below
buildings = orca.get_table('buildings')
new_buildings = utils.run_developer(
typ.lower(),
jobs,
buildings,
"job_spaces",
parcels.parcel_size[parcel_mask],
parcels.ave_sqft_per_unit[parcel_mask],
parcels.total_job_spaces[parcel_mask],
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
residential=False,
num_units_to_build=target,
profit_to_prob_func=subsidies.profit_to_prob_func,
**dev_settings['kwargs'])
if new_buildings is not None:
new_buildings["subsidized"] = False
summary.add_parcel_output(new_buildings)
def residential_developer(feasibility, households, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func, parcels_geography,
limits_settings, final_year,
regional_controls):
kwargs = settings['residential_developer']
rc = regional_controls.to_frame()
target_vacancy = rc.loc[year].st_res_vac
num_units = dev.compute_units_to_build(
len(households),
buildings["residential_units"].sum(),
target_vacancy)
targets = []
typ = "Residential"
# now apply limits - limits are assumed to be yearly, apply to an
# entire jurisdiction and be in terms of residential_units or job_spaces
if typ in limits_settings:
juris_name = parcels_geography.juris_name.\
reindex(parcels.index).fillna('Other')
juris_list = limits_settings[typ].keys()
for juris, limit in limits_settings[typ].items():
# the actual target is the limit times the number of years run
# so far in the simulation (plus this year), minus the amount
# built in previous years - in other words, you get rollover
# and development is lumpy
current_total = parcels.total_residential_units[
(juris_name == juris) & (parcels.newest_building >= 2010)]\
.sum()
target = (year - 2010 + 1) * limit - current_total
# make sure we don't overshoot the total development of the limit
# for the horizon year - for instance, in Half Moon Bay we have
# a very low limit and a single development in a far out year can
# easily build over the limit for the total simulation
max_target = (final_year - 2010 + 1) * limit - current_total
if target <= 0:
continue
targets.append((juris_name == juris, target, max_target, juris))
num_units -= target
# other cities not in the targets get the remaining target
targets.append((~juris_name.isin(juris_list), num_units, None, "none"))
else:
# otherwise use all parcels with total number of units
targets.append((parcels.index == parcels.index,
num_units, None, "none"))
for parcel_mask, target, final_target, juris in targets:
print "Running developer for %s with target of %d" % \
(str(juris), target)
# this was a fairly heinous bug - have to get the building wrapper
# again because the buildings df gets modified by the run_developer
# method below
buildings = orca.get_table('buildings')
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size[parcel_mask],
parcels.ave_sqft_per_unit[parcel_mask],
parcels.total_residential_units[parcel_mask],
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
num_units_to_build=target,
profit_to_prob_func=subsidies.profit_to_prob_func,
**kwargs)
buildings = orca.get_table('buildings')
if new_buildings is not None:
new_buildings["subsidized"] = False
if final_target is not None and new_buildings is not None:
# make sure we don't overbuild the target for the whole simulation
overshoot = new_buildings.net_units.sum() - final_target
if overshoot > 0:
index = new_buildings.tail(1).index[0]
index = int(index)
# make sure we don't get into a negative unit situation
current_units = buildings.local.loc[index, "residential_units"]
# only can reduce by as many units as we have
overshoot = min(overshoot, current_units)
# used below - this is the pct we need to reduce the building
overshoot_pct = \
(current_units - overshoot) / float(current_units)
buildings.local.loc[index, "residential_units"] -= overshoot
# we also need to fix the other columns so they make sense
for col in ["residential_sqft", "building_sqft",
"deed_restricted_units"]:
val = buildings.local.loc[index, col]
# reduce by pct but round to int
buildings.local.loc[index, col] = int(val * overshoot_pct)
summary.add_parcel_output(new_buildings)
def run_subsidized_developer(feasibility, parcels, buildings, households,
acct_settings, settings, account, year,
form_to_btype_func, add_extra_columns_func,
summary, create_deed_restricted=False):
"""
The subsidized residential developer model.
Parameters
----------
feasibility : DataFrame
A DataFrame that is returned from run_feasibility for a given form
parcels : DataFrameWrapper
The standard parcels DataFrameWrapper (mostly just for run_developer)
buildings : DataFrameWrapper
The standard buildings DataFrameWrapper (passed to run_developer)
households : DataFrameWrapper
The households DataFrameWrapper (passed to run_developer)
acct_settings : Dict
A dictionary of settings to parameterize the model. Needs these keys:
sending_buildings_subaccount_def - maps buildings to subaccounts
receiving_buildings_filter - filter for eligible buildings
settings : Dict
The overall settings
account : Account
The Account object to use for subsidization
year : int
The current simulation year (will be added as metadata)
form_to_btype_func : function
Passed through to run_developer
add_extra_columns_func : function
Passed through to run_developer
summary : Summary
Used to add parcel summary information
create_deed_restricted : bool
Bool for whether to create deed restricted units with the subsidies
or not. The logic at the time of this writing is to keep track of
partial units so that when partial units sum to greater than a unit,
that unit will be deed restricted.
Returns
-------
Nothing
Subsidized residential developer is designed to run before the normal
residential developer - it will prioritize the developments we're
subsidizing (although this is not strictly required - running this model
after the market rate developer will just create a temporarily larger
supply of units, which will probably create less market rate development in
the next simulated year)
the steps for subsidizing are essentially these
1 run feasibility with only_built set to false so that the feasibility of
unprofitable units are recorded
2 temporarily filter to ONLY unprofitable units to check for possible
subsidized units (normal developer takes care of market-rate units)
3 compute the number of units in these developments
4 divide cost by number of units in order to get the subsidy per unit
5 filter developments to parcels in "receiving zone" similar to the way we
identified "sending zones"
6 iterate through subaccounts one at a time as subsidy will be limited
to available funds in the subaccount (usually by jurisdiction)
7 sort ascending by subsidy per unit so that we minimize subsidy (but total
subsidy is equivalent to total building cost)
8 cumsum the total subsidy in the buildings and locate the development
where the subsidy is less than or equal to the amount in the account -
filter to only those buildings (these will likely be built)
9 pass the results as "feasible" to run_developer - this is sort of a
boundary case of developer but should run OK
10 for those developments that get built, make sure to subtract from
account and keep a record (on the off chance that demand is less than
the subsidized units, run through the standard code path, although it's
very unlikely that there would be more subsidized housing than demand)
"""
# step 2
feasibility = feasibility.replace([np.inf, -np.inf], np.nan)
feasibility = feasibility[feasibility.max_profit < 0]
# step 3
feasibility['ave_sqft_per_unit'] = parcels.ave_sqft_per_unit
feasibility['residential_units'] = \
np.floor(feasibility.residential_sqft / feasibility.ave_sqft_per_unit)
# step 3B
# can only add units - don't subtract units - this is an approximation
# of the calculation that will be used to do this in the developer model
feasibility = feasibility[
feasibility.residential_units > feasibility.total_residential_units]
# step 3C
# towards the end, because we're about to sort by subsidy per unit, some
# large projects never get built, because it could be a 100 unit project
# times a 500k subsidy per unit. thus we're going to try filtering by
# the maximum subsidy for a single development here
feasibility = feasibility[feasibility.max_profit > -50*1000000]
# step 4
feasibility['subsidy_per_unit'] = \
-1 * feasibility['max_profit'] / feasibility['residential_units']
# assumption that even if the developer says this property is almost
# profitable, even the administration costs are likely to cost at least
# 10k / unit
feasibility['subsidy_per_unit'] = feasibility.subsidy_per_unit.clip(10000)
# step 5
if "receiving_buildings_filter" in acct_settings:
feasibility = feasibility.\
query(acct_settings["receiving_buildings_filter"])
else:
# otherwise all buildings are valid
pass
new_buildings_list = []
sending_bldgs = acct_settings["sending_buildings_subaccount_def"]
feasibility["regional"] = 1
feasibility["subaccount"] = feasibility.eval(sending_bldgs)
# step 6
for subacct, amount in account.iter_subaccounts():
print "Subaccount: ", subacct
df = feasibility[feasibility.subaccount == subacct]
print "Number of feasible projects in receiving zone:", len(df)
if len(df) == 0:
continue
# step 7
df = df.sort(columns=['subsidy_per_unit'], ascending=True)
# df.to_csv('subsidized_units_%d_%s_%s.csv' %
# (orca.get_injectable("year"), account.name, subacct))
# step 8
print "Amount in subaccount: ${:,.2f}".format(amount)
num_bldgs = int((-1*df.max_profit).cumsum().searchsorted(amount))
if num_bldgs == 0:
continue
# technically we only build these buildings if there's demand
# print "Building {:d} subsidized buildings".format(num_bldgs)
df = df.iloc[:int(num_bldgs)]
df.columns = pd.MultiIndex.from_tuples(
[("residential", col) for col in df.columns])
# disable stdout since developer is a bit verbose for this use case
sys.stdout, old_stdout = StringIO(), sys.stdout
kwargs = settings['residential_developer']
# step 9
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size,
parcels.ave_sqft_per_unit,
parcels.total_residential_units,
orca.DataFrameWrapper("feasibility", df),
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
**kwargs)
sys.stdout = old_stdout
buildings = orca.get_table("buildings")
if new_buildings is None:
continue
# keep track of partial subsidized untis so that we always get credit
# for a partial unit, even if it's not built in this specific building
partial_subsidized_units = 0
# step 10
for index, new_building in new_buildings.iterrows():
amt = new_building.max_profit
metadata = {
"description": "Developing subsidized building",
"year": year,
"residential_units": new_building.residential_units,
"building_id": index
}
account.add_transaction(amt, subaccount=subacct,
metadata=metadata)
if create_deed_restricted:
revenue_per_unit = new_building.building_revenue / \
new_building.residential_units
total_subsidy = abs(new_building.max_profit)
subsidized_units = total_subsidy / revenue_per_unit + \
partial_subsidized_units
# right now there are inclusionary requirements
already_subsidized_units = new_building.deed_restricted_units
# get remainder
partial_subsidized_units = subsidized_units % 1
# round off for now
subsidized_units = int(subsidized_units) + \
already_subsidized_units
buildings.local.loc[index, "deed_restricted_units"] =\
int(round(subsidized_units))
print "Amount left after subsidy: ${:,.2f}".\
format(account.total_transactions_by_subacct(subacct))
new_buildings_list.append(new_buildings)
total_len = reduce(lambda x, y: x+len(y), new_buildings_list, 0)
if total_len == 0:
print "No subsidized buildings"
return
new_buildings = pd.concat(new_buildings_list)
print "Built {} total subsidized buildings".format(len(new_buildings))
print " Total subsidy: ${:,.2f}".format(
-1*new_buildings.max_profit.sum())
print " Total subsidized units: {:.0f}".\
format(new_buildings.residential_units.sum())
new_buildings["subsidized"] = True
summary.add_parcel_output(new_buildings)
def residential_developer(feasibility, households, buildings, parcels, year,
settings, summary, form_to_btype_func,
add_extra_columns_func, parcels_geography,
limits_settings, final_year,
regional_controls):
kwargs = settings['residential_developer']
rc = regional_controls.to_frame()
target_vacancy = rc.loc[year].st_res_vac
num_units = dev.compute_units_to_build(
len(households),
buildings["residential_units"].sum(),
target_vacancy)
targets = []
typ = "Residential"
# now apply limits - limits are assumed to be yearly, apply to an
# entire jurisdiction and be in terms of residential_units or job_spaces
if typ in limits_settings:
juris_name = parcels_geography.juris_name.\
reindex(parcels.index).fillna('Other')
juris_list = limits_settings[typ].keys()
for juris, limit in limits_settings[typ].items():
# the actual target is the limit times the number of years run
# so far in the simulation (plus this year), minus the amount
# built in previous years - in other words, you get rollover
# and development is lumpy
current_total = parcels.total_residential_units[
(juris_name == juris) & (parcels.newest_building >= 2010)]\
.sum()
target = (year - 2010 + 1) * limit - current_total
# make sure we don't overshoot the total development of the limit
# for the horizon year - for instance, in Half Moon Bay we have
# a very low limit and a single development in a far out year can
# easily build over the limit for the total simulation
max_target = (final_year - 2010 + 1) * limit - current_total
if target <= 0:
continue
targets.append((juris_name == juris, target, max_target, juris))
num_units -= target
# other cities not in the targets get the remaining target
targets.append((~juris_name.isin(juris_list), num_units, None, "none"))
else:
# otherwise use all parcels with total number of units
targets.append((parcels.index == parcels.index,
num_units, None, "none"))
for parcel_mask, target, final_target, juris in targets:
print("Running developer for %s with target of %d" %
(str(juris), target))
# this was a fairly heinous bug - have to get the building wrapper
# again because the buildings df gets modified by the run_developer
# method below
buildings = orca.get_table('buildings')
print('Stats of buildings before run_developer(): \n{}'.format(
buildings.to_frame()[['deed_restricted_units','preserved_units','inclusionary_units']].sum()))
new_buildings = utils.run_developer(
"residential",
households,
buildings,
"residential_units",
parcels.parcel_size[parcel_mask],
parcels.ave_sqft_per_unit[parcel_mask],
parcels.total_residential_units[parcel_mask],
feasibility,
year=year,
form_to_btype_callback=form_to_btype_func,
add_more_columns_callback=add_extra_columns_func,
num_units_to_build=int(target),
profit_to_prob_func=subsidies.profit_to_prob_func,
**kwargs)
print('Stats of buildings before run_developer(): \n{}'.format(
buildings.to_frame()[['deed_restricted_units','preserved_units','inclusionary_units']].sum()))
buildings = orca.get_table('buildings')
if new_buildings is not None:
new_buildings["subsidized"] = False
if final_target is not None and new_buildings is not None:
# make sure we don't overbuild the target for the whole simulation
overshoot = new_buildings.net_units.sum() - final_target
if overshoot > 0:
index = new_buildings.tail(1).index[0]
index = int(index)
# make sure we don't get into a negative unit situation
current_units = buildings.local.loc[index, "residential_units"]
# only can reduce by as many units as we have
overshoot = min(overshoot, current_units)
# used below - this is the pct we need to reduce the building
overshoot_pct = \
(current_units - overshoot) / float(current_units)
buildings.local.loc[index, "residential_units"] -= overshoot
# we also need to fix the other columns so they make sense
for col in ["residential_sqft", "building_sqft",
"deed_restricted_units", "inclusionary_units",
"subsidized_units"]:
val = buildings.local.loc[index, col]
# reduce by pct but round to int
buildings.local.loc[index, col] = int(val * overshoot_pct)
# also fix the corresponding columns in new_buildings
for col in ["residential_sqft","building_sqft",
"residential_units", "deed_restricted_units",
"inclusionary_units", "subsidized_units"]:
val = new_buildings.loc[index, col]
new_buildings.loc[index, col] = int(val * overshoot_pct)
for col in ["policy_based_revenue_reduction",
"max_profit"]:
val = new_buildings.loc[index, col]
new_buildings.loc[index, col] = val * overshoot_pct
summary.add_parcel_output(new_buildings)
