%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from Index_Calculations import facility_index_gen, index_and_generation
import os
import glob
import numpy as np
from joblib import Parallel, delayed
states = ["AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE",
"FL", "GA", "HI", "ID", "IL", "IN", "IA", "KS",
"KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS",
"MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY",
"NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
"SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY"]
facility_path = os.path.join('Data storage', 'Facility gen fuels and CO2 2017-05-25.zip')
facility_df = pd.read_csv(facility_path)
epa_path = os.path.join('Data storage', 'Monthly EPA emissions 2017-05-25.csv')
epa_df = pd.read_csv(epa_path)
ef_path = os.path.join('Data storage', 'Final emission factors.csv')
out_folder = os.path.join('Data storage', 'final state data')
NERC_path = os.path.join('Data storage', 'Facility NERC labels.csv')
all_fuel_paths = [os.path.join('Data storage',
'state gen data',
state + ' fuels gen.csv') for state in states]
if __name__ == '__main__':
Parallel(n_jobs=-1, verbose=3)(delayed(index_and_generation)
(eia_facility_df=facility_df,
all_fuel_path = fuel_path,
epa_df=epa_df,
emission_factor_path=ef_path,
export_folder=out_folder, export_path_ext=' '
+ state, state=state) for state, fuel_path in zip(states, all_fuel_paths))
[Parallel(n_jobs=-1)]: Done 36 out of 50 | elapsed: 1.7min remaining: 40.3s [Parallel(n_jobs=-1)]: Done 50 out of 50 | elapsed: 2.3min finished