datasources.py

import numpy as np
import pandas as pd
import os
from urbansim_defaults import datasources
from urbansim_defaults import utils
from urbansim.utils import misc
import urbansim.sim.simulation as sim

@sim.table('jobs', cache=True)
def jobs(store):
    df = store['jobs']
    df = df[df.building_id > 0]
    return df
    
@sim.table('fee_schedule', cache=True)
def fee_schedule(store):
    df = store['fee_schedule']
    return df
    
@sim.table('parcel_fee_schedule', cache=True)
def parcel_fee_schedule(store):
    df = store['parcel_fee_schedule']
    return df
    
@sim.table('zoning', cache=True)
def zoning(store):
    df = store['zoning']
    return df
    
@sim.table('zoning_allowed_uses', cache=True)
def zoning_allowed_uses(store, parcels):
    parcels_allowed = store['zoning_allowed_uses']
    parcels = sim.get_table('parcels').to_frame(columns = ['zoning_id',])
    
    allowed_df = pd.DataFrame(index = parcels.index)
    for devtype in np.unique(parcels_allowed.development_type_id):
        devtype_allowed = parcels_allowed[parcels_allowed.development_type_id == devtype].set_index('zoning_id')
        allowed = misc.reindex(devtype_allowed.development_type_id, parcels.zoning_id)
        df = pd.DataFrame(index=allowed.index)
        df['allowed'] = False
        df[~allowed.isnull()] = True
        allowed_df[devtype] = df.allowed

    return allowed_df
    
@sim.table('households', cache=True)
def households(store):
    df = store['households']
    df = df[df.building_id > 0]  ##Revisit the allocation and remove GQ from synthetic population?
    
    p = store['parcels']
    b = store['buildings']
    b['luz'] = misc.reindex(p.luz_id, b.parcel_id)
    df['base_luz'] = misc.reindex(b.luz, df.building_id)
    df['segmentation_col'] = 1
    
    return df
    
@sim.table('buildings', cache=True)
def buildings(store):
    df = store['buildings']
    df['res_price_per_sqft'] = 0.0
    df['nonres_rent_per_sqft'] = 0.0
    #df.residential_units = df.residential_units*2  ##For testing HLCM luz supply constraints only
    return df
    
@sim.table('parcels', cache=True)
def parcels(store):
    df = store['parcels']
    df['acres'] = df.parcel_acres
    
    #Delete duplicate index (parcel_id)
    df['rownum'] = df.index
    df = df.drop_duplicates(cols='rownum', take_last=True)
    del df['rownum']
    
    return df
    
@sim.table('annual_household_control_totals', cache=True)
def annual_household_control_totals():
    pecas_hh_controls = pd.read_csv('data/pecas_hh_controls.csv')
    pecas_hh_controls = pecas_hh_controls[pecas_hh_controls.activity_name.isin(['Households less than 25k annual income and 2 or less people',
                                                                                'Households 25 to 150k annual income and 2 or less people',
                                                                                'Households 150k or more annual income and 2 or less people',
                                                                                'Households less than 25k annual income and 3 or more people',
                                                                                'Households 25 to 150k annual income and 3 or more people',
                                                                                'Households 150k or more annual income and 3 or more people'])]
    pecas_hh_controls = pecas_hh_controls.rename(columns = {'yr':'year', 'Total_HH_Controls':'total_number_of_households', 'luz_id':'base_luz'})
    pecas_hh_controls.total_number_of_households = np.ceil(pecas_hh_controls.total_number_of_households).astype('int32')
    pecas_hh_controls = pecas_hh_controls[['year', 'activity_id', 'base_luz', 'total_number_of_households']].set_index('year')
    return pecas_hh_controls
    
@sim.injectable('building_sqft_per_job', cache=True)
def building_sqft_per_job(settings):
    return settings['building_sqft_per_job']
    
# non-residential rent data
@sim.table('costar', cache=True)
def costar(store):
    df = store['costar']
    return df
    
# residential price data
@sim.table('assessor_transactions', cache=True)
def assessor_transactions(store):
    df = store['assessor_transactions']
    df["index"] = df.index
    df.drop_duplicates(cols='index', take_last=True, inplace=True)
    del df["index"]
    return df

# luz price from pecas
@sim.table('pecas_prices', cache=True)
def pecas_prices(store):
    df = store['pecas_prices']
    return df


# this specifies the relationships between tables
sim.broadcast('nodes', 'costar', cast_index=True, onto_on='node_id')
sim.broadcast('parcels', 'costar', cast_index=True, onto_on='parcel_id')
sim.broadcast('nodes', 'assessor_transactions', cast_index=True, onto_on='node_id')
sim.broadcast('parcels', 'assessor_transactions', cast_index=True, onto_on='parcel_id')