def accessory_units(year, buildings, parcels):
add_units = pd.read_csv("data/accessory_units.csv",
index_col="juris")[str(year)]
buildings_juris = misc.reindex(parcels.juris, buildings.parcel_id)
res_buildings = buildings_juris[buildings.general_type == "Residential"]
add_buildings = groupby_random_choice(res_buildings, add_units)
add_buildings = pd.Series(add_buildings.index).value_counts()
buildings.local.loc[add_buildings.index, "residential_units"] += \
add_buildings.values
def accessory_units(year, buildings, parcels):
add_units = pd.read_csv("data/accessory_units.csv",
index_col="juris")[str(year)]
buildings_juris = misc.reindex(parcels.juris, buildings.parcel_id)
res_buildings = buildings_juris[buildings.general_type == "Residential"]
add_buildings = groupby_random_choice(res_buildings, add_units)
add_buildings = pd.Series(add_buildings.index).value_counts()
buildings.local.loc[add_buildings.index, "residential_units"] += \
add_buildings.values
def _proportional_jobs_model(
target_ratio, # ratio of jobs of this sector to households
sector, # empsix sector
groupby_col, # ratio will be matched at this level of geog
hh_df,
jobs_df,
locations_series,
target_jobs=None # pass this if you want to compute target jobs
):
if target_jobs is None:
# compute it if not passed
target_jobs = hh_df[groupby_col].value_counts() * target_ratio
target_jobs = target_jobs.astype('int')
current_jobs = jobs_df[
jobs_df.empsix == sector][groupby_col].value_counts()
need_more_jobs = target_jobs - current_jobs
need_more_jobs = need_more_jobs[need_more_jobs > 0]
need_more_jobs_total = int(need_more_jobs.sum())
available_jobs = \
jobs_df.query("empsix == '%s' and building_id == -1" % sector)
print "Need more jobs total: %d" % need_more_jobs_total
print "Available jobs: %d" % len(available_jobs)
if len(available_jobs) == 0:
# corner case
return pd.Series()
if len(available_jobs) >= need_more_jobs_total:
# have enough jobs to assign, truncate available jobs
available_jobs = available_jobs.head(need_more_jobs_total)
else:
# don't have enough jobs - random sample locations to partially
# match the need (won't succed matching the entire need)
need_more_jobs = round_series_match_target(
need_more_jobs, len(available_jobs), 0)
need_more_jobs_total = need_more_jobs.sum()
assert need_more_jobs_total == len(available_jobs)
if need_more_jobs_total <= 0:
return pd.Series()
print "Need more jobs\n", need_more_jobs
choices = groupby_random_choice(locations_series, need_more_jobs)
# choose random locations within jurises to match need_more_jobs totals
return pd.Series(choices.index, available_jobs.index)
def vacant_market_rate_units_minus_structural_vacancy(buildings,
baseyear_taz_controls):
# this will take vacant_market_rate_units above and remove the number of
# units that we require to be vacant because of the structural vacancy rate
# first sum the residential units by zone and multiply by structural
# vacancy rate in order to get the required vacancies
residential_units_by_zone = \
buildings.residential_units.groupby(buildings.zone_id).sum()
required_vacant_units_by_zone = \
(residential_units_by_zone *
baseyear_taz_controls.target_ltvacancy).astype("int")
# repeat building ids according to the number of vacant units
unit_zone_ids = \
buildings.zone_id.repeat(
buildings.vacant_market_rate_units.astype("int"))
# this is some convoluted pandas for the next two lines!
# but the concept is simple:
# can't require more vacancy units than we have
s = unit_zone_ids.value_counts().reindex(
required_vacant_units_by_zone.index).fillna(0)
required_vacant_units_by_zone = \
required_vacant_units_by_zone.clip(upper=s).astype('int')
# select among units to remove from the choice and leave vacant
remove_unit_zone_ids =\
groupby_random_choice(unit_zone_ids,
required_vacant_units_by_zone,
replace=False)
# the building ids are the index, so count em up
remove_building_zone_ids = pd.Series(
remove_unit_zone_ids.index).value_counts()
# subtract the ones we want to stay vacant from the vacant ones
s = buildings.vacant_market_rate_units.sub(remove_building_zone_ids,
fill_value=0)
return s
def _proportional_jobs_model(
target_ratio, # ratio of jobs of this sector to households
sector, # empsix sector
groupby_col, # ratio will be matched at this level of geog
hh_df,
jobs_df,
locations_series,
target_jobs=None # pass this if you want to compute target jobs
):
if target_jobs is None:
# compute it if not passed
target_jobs = hh_df[groupby_col].value_counts() * target_ratio
target_jobs = target_jobs.astype('int')
current_jobs = jobs_df[
jobs_df.empsix == sector][groupby_col].value_counts()
need_more_jobs = target_jobs - current_jobs
need_more_jobs = need_more_jobs[need_more_jobs > 0]
need_more_jobs_total = int(need_more_jobs.sum())
available_jobs = \
jobs_df.query("empsix == '%s' and building_id == -1" % sector)
print "Need more jobs total: %d" % need_more_jobs_total
print "Available jobs: %d" % len(available_jobs)
if len(available_jobs) == 0:
# corner case
return pd.Series()
if len(available_jobs) >= need_more_jobs_total:
# have enough jobs to assign, truncate available jobs
available_jobs = available_jobs.head(need_more_jobs_total)
else:
# don't have enough jobs - random sample locations to partially
# match the need (won't succed matching the entire need)
need_more_jobs = round_series_match_target(
need_more_jobs, len(available_jobs), 0)
need_more_jobs_total = need_more_jobs.sum()
assert need_more_jobs_total == len(available_jobs)
if need_more_jobs_total <= 0:
return pd.Series()
print "Need more jobs\n", need_more_jobs
excess = need_more_jobs.sub(locations_series.value_counts(), fill_value=0)
print "Excess demand\n", excess[excess > 0]
# there's an issue with groupby_random_choice where it can't choose from
# a set of locations that don't exist - e.g. we have 2 jobs in a certain
# city but not locations to put them in. we need to drop this demand
drop = need_more_jobs.index.difference(locations_series.unique())
print "We don't have any locations for these locations:\n", drop
need_more_jobs = need_more_jobs.drop(drop)
# choose random locations within jurises to match need_more_jobs totals
choices = groupby_random_choice(locations_series, need_more_jobs,
replace=True)
# these might not be the same length after dropping a few lines above
available_jobs = available_jobs.head(len(choices))
return pd.Series(choices.index, available_jobs.index)
def _proportional_jobs_model(
target_ratio, # ratio of jobs of this sector to households
sector, # empsix sector
groupby_col, # ratio will be matched at this level of geog
hh_df,
jobs_df,
locations_series,
target_jobs=None # pass this if you want to compute target jobs
):
if target_jobs is None:
# compute it if not passed
target_jobs = hh_df[groupby_col].value_counts() * target_ratio
target_jobs = target_jobs.astype('int')
current_jobs = jobs_df[
jobs_df.empsix == sector][groupby_col].value_counts()
need_more_jobs = target_jobs - current_jobs
need_more_jobs = need_more_jobs[need_more_jobs > 0]
need_more_jobs_total = int(need_more_jobs.sum())
available_jobs = \
jobs_df.query("empsix == '%s' and building_id == -1" % sector)
print "Need more jobs total: %d" % need_more_jobs_total
print "Available jobs: %d" % len(available_jobs)
if len(available_jobs) == 0:
# corner case
return pd.Series()
if len(available_jobs) >= need_more_jobs_total:
# have enough jobs to assign, truncate available jobs
available_jobs = available_jobs.head(need_more_jobs_total)
else:
# don't have enough jobs - random sample locations to partially
# match the need (won't succed matching the entire need)
need_more_jobs = round_series_match_target(
need_more_jobs, len(available_jobs), 0)
need_more_jobs_total = need_more_jobs.sum()
assert need_more_jobs_total == len(available_jobs)
if need_more_jobs_total <= 0:
return pd.Series()
print "Need more jobs\n", need_more_jobs
excess = need_more_jobs.sub(locations_series.value_counts(), fill_value=0)
print "Excess demand\n", excess[excess > 0]
# there's an issue with groupby_random_choice where it can't choose from
# a set of locations that don't exist - e.g. we have 2 jobs in a certain
# city but not locations to put them in. we need to drop this demand
drop = need_more_jobs.index.difference(locations_series.unique())
print "We don't have any locations for these locations:\n", drop
need_more_jobs = need_more_jobs.drop(drop)
# choose random locations within jurises to match need_more_jobs totals
choices = groupby_random_choice(locations_series, need_more_jobs,
replace=True)
# these might not be the same length after dropping a few lines above
available_jobs = available_jobs.head(len(choices))
return pd.Series(choices.index, available_jobs.index)
