def build_mining_output(factor, gross_output, elec, fuels):
output_by_factor = gross_output.multiply(factor)
elec_intensity = elec.divide(output_by_factor)
elec_intensity = standard_interpolation(elec_intensity).ffill()
fuels_intensity = fuels.divide(output_by_factor)
fuels_intensity = standard_interpolation(fuels_intensity).ffill()
electricity_final = elec_intensity.multiply(elec_intensity)
fuels_final = fuels_intensity.multiply(fuels_intensity)
data_dict = {'energy': {'elec': electricity_final, 'fuels': fuels_final},
'activity': {'gross_output': gross_output}}
return data_dict
def construction(self):
"""https://www.census.gov/data/tables/2017/econ/economic-census/naics-sector-23.html
https://www.census.gov/data/tables/2012/econ/census/construction.html
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23I1&prodType=table
http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2002_US_23I04A&prodType=table
http://www.census.gov/epcd/www/97EC23.HTM
http://www.census.gov/prod/www/abs/cciview.html
"""
value_added, gross_output = self.indicators_nonman_2018_bea() # NonMan_output_data / M, Y
value_added = value_added['Construction']
gross_output = gross_output['Construction']
electricity, fuels = self.construction_raw_data()
elec_intensity = electricity.divide(gross_output * 0.0001)
elec_intensity = elec_intensity(fuels_intensity).fillna(method='bfill')
fuels_intensity = fuels.divide(gross_output * 0.0001)
fuels_intensity.iloc[1982] = np.nan
fuels_intensity.iloc[2002] = np.nan
fuels_intensity = standard_interpolation(fuels_intensity).fillna(method='bfill')
final_electricity = elec_intensity.multiply(gross_output * 0.0001)
final_fuels = fuels_intensity.multiply(gross_output * 0.0001)
data_dict = {'energy':
{'elec': final_electricity, 'fuels': final_fuels},
'activity':
{'gross_output': gross_output, 'value_added': value_added}}
return data_dict
def mecs_annual_fuel(mecs_fuel):
"""TODO: Do NAICS codes 324, 325 have different data?
Args:
mecs_fuel ([type]): [description]
"""
mecs_annual_fuel = standard_interpolation(mecs_fuel, axis=1)
def interpolate_mecs(mecs_fuel, ASMdata_010220_xlsx_data):
"""
Between-MECS-year interpolations are made in MECS_Annual_Fuel1
and MECS_Annual_Fuel2 tabs in Ind_hap3 spreadsheet.
Interpolations are also based on estimates developed in
ASMdata_010220.xlsx[3DNAICS], which ultimately tie back to MECS fuel data
from Table 4.2 and Table 3.2
"""
standard_interpolation(dataframe=, name_to_interp= , axis=)
# in ASMdata_010220.xlsx[Final_quantities_w_ASM_85]
mecs_data_sic =
ratio_fuel_offsite_pre98 = standard_interpolation(dataframe=mecs_data_sic, name_to_interp= , axis=)
data_98 =
mecs_tables_31_32 =
mecs_table42 =
ratio_fuel_offsite =
def expend_ratios_revised_85_97(self, NAICS3D):
mecs = # from MECS_prices_101116b.xlsx[MECS_data_SIC]/BL
asm_data = # from Ind_hap3_102316.xlsx[ASM_Fuel_Cost_1985-88]/ R
asm_data = asm_data.iloc[[1985, 1986, 1987]]
NAICS3D = NAICS3D.loc[1988:, ['EXPFUEL']]
asm = pd.concat([asm_data, NAICS3D])
ratio = mecs.divide(asm)
interpolated_ratio = standard_interpolation(ratio)
mecs_based_expenditure = asm.multiply(interpolated_ratio * 1000)
return mecs_based_expenditure
def expenditure_ratios_revised(NAICS3D):
mecs = # E from MECS_prices_122419.xlsx[MECS_data]/AN and NAICS3D/AW (also called EXPFUEL)
asm = NAICS3D[['EXPFUEL']] # F
mecs_asm_ratio = mecs.divide(asm).multiply(1000) # G
interpolated_ratio = standard_interpolation(mecs_asm_ratio) # H
mecs_based_expenditure = # I depends on MECS year/not
return mecs_based_expenditure