In [1]:

    

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from os.path import join
import sys
import json


    

In [2]:

    

# Load the "autoreload" extension
%load_ext autoreload

# always reload modules marked with "%aimport"
%autoreload 1


    

In [4]:

    

# add the 'src' directory as one where we can import modules
cwd = os.getcwd()
src_dir = join(cwd, os.pardir, 'src')
sys.path.append(src_dir)


    

In [26]:

    

from util.utils import rename_cols


    

In [31]:

    

#EPA CEMS data
path = join(cwd, '..', 'Data storage', 'Derived data',
            'Monthly EPA emissions 2017-08-31.csv')
epa = pd.read_csv(path)
rename_cols(epa)
epa = epa.groupby(['year', 'month', 'plant id']).sum()


    

In [6]:

    

#EIA facility data
path = join(cwd, '..', 'Data storage',
            'Facility gen fuels and CO2 2017-08-31.zip')
eia_fac = pd.read_csv(path)
rename_cols(eia_fac)
eia_fac = eia_fac.groupby(['year', 'month', 'plant id', 'fuel']).sum()


    

In [10]:

    

idx = pd.IndexSlice


    

In [37]:

    

example_eia = eia_fac.loc[idx[2016, 6, 1897, :], :]
example_eia


    

    
Out[37]:







  

    

      

      

      

      

      
total fuel (mmbtu)
      
generation (mwh)
      
elec fuel (mmbtu)
      
lat
      
lon
      
quarter
      
all fuel fossil co2 (kg)
      
elec fuel fossil co2 (kg)
      
all fuel total co2 (kg)
      
elec fuel total co2 (kg)
    
    

      
year
      
month
      
plant id
      
fuel
      

      

      

      

      

      

      

      

      

      

    
  
  

    

      
2016
      
6
      
1897
      
NG
      
909.0
      
3.547
      
56.0
      
46.735331
      
-92.151711
      
2
      
48240.63
      
2971.92
      
48240.63
      
2971.92
    
    

      
SUB
      
19577.0
      
76.387
      
1199.0
      
46.735331
      
-92.151711
      
2
      
1902884.40
      
116542.80
      
1902884.40
      
116542.80
    
    

      
WDS
      
218373.0
      
852.066
      
13372.0
      
46.735331
      
-92.151711
      
2
      
0.00
      
0.00
      
20483387.40
      
1254293.60
    
  

In [38]:

    

example_eia.sum()


    

    
Out[38]:





total fuel (mmbtu)           2.388590e+05
generation (mwh)             9.320000e+02
elec fuel (mmbtu)            1.462700e+04
lat                          1.402060e+02
lon                         -2.764551e+02
quarter                      6.000000e+00
all fuel fossil co2 (kg)     1.951125e+06
elec fuel fossil co2 (kg)    1.195147e+05
all fuel total co2 (kg)      2.243451e+07
elec fuel total co2 (kg)     1.373808e+06
dtype: float64

In [23]:

    

example_eia.to_clipboard()


    

In [35]:

    

example_epa = epa.loc[idx[2016, 6, 1897], :]
example_epa


    

    
Out[35]:





co2_mass (kg)         2.311098e+07
gload (mw)                     NaN
heat_input (mmbtu)    2.445270e+05
op_time               7.909800e+02
sload (1000lb/hr)     1.278900e+05
Name: (2016, 6, 1897), dtype: float64

In [39]:

    

co2_factor = (example_eia.sum()['elec fuel fossil co2 (kg)']
              / example_eia.sum()['all fuel total co2 (kg)'])
co2_factor


    

    
Out[39]:





0.0053272706671432662

In [40]:

    

co2_factor * example_epa['co2_mass (kg)']


    

    
Out[40]:





123118.46621921226