## Register output table.
@sim.table(cache=True)
def parcels_out(parcels_in):
index = pd.Series(parcels_in.index).dropna().unique()
df = pd.DataFrame(index=index)
df.index.name = 'apn'
return df
## Register output columns.
out = sim.column('parcels_out', cache=True)
@out
def county_id():
return '081'
@out
def parcel_id_local():
pass
@out
def land_use_type_id(code='situs.USE_CODE'):
return code
## Register intermediate table and columns.
# The purpose of this intermediate table is to compute certain fields,
# like non_residential_sqft and residential_units, before grouping together
# records with the same parc_py_id. Thus, single-family condominium units
# would each be assumed to have one residential unit, and this count would
# be summed when grouping later.
@sim.table()
def parcels_in2(parcels_in):
return pd.DataFrame(index=parcels_in.index)
in2 = sim.column('parcels_in2', cache=True)
@in2
def res_type2(land_use_type_id='parcels_in.use_code'):
return utils.get_res_type(land_use_type_id, res_codes)
@in2
def building_sqft2(bldg_sqft='parcels_in.bldg_sqft', tla='parcels_in.tla'):
# Alternate inputs:
# - "NET_RNT_AR"
sqft = pd.concat([bldg_sqft, tla])
return sqft.groupby(level=0).max()
@in2