# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Build specific energy consumption by carrier and industries.
"""

import pandas as pd
from _helpers import mute_print, set_scenario_config

# GWh/ktoe OR MWh/toe
toe_to_MWh = 11.630

eu28 = [
    "FR",
    "DE",
    "GB",
    "IT",
    "ES",
    "PL",
    "SE",
    "NL",
    "BE",
    "FI",
    "DK",
    "PT",
    "RO",
    "AT",
    "BG",
    "EE",
    "GR",
    "LV",
    "CZ",
    "HU",
    "IE",
    "SK",
    "LT",
    "HR",
    "LU",
    "SI",
    "CY",
    "MT",
]

sheet_names = {
    "Iron and steel": "ISI",
    "Chemicals Industry": "CHI",
    "Non-metallic mineral products": "NMM",
    "Pulp, paper and printing": "PPA",
    "Food, beverages and tobacco": "FBT",
    "Non Ferrous Metals": "NFM",
    "Transport Equipment": "TRE",
    "Machinery Equipment": "MAE",
    "Textiles and leather": "TEL",
    "Wood and wood products": "WWP",
    "Other Industrial Sectors": "OIS",
}


index = [
    "elec",
    "coal",
    "coke",
    "biomass",
    "methane",
    "hydrogen",
    "heat",
    "naphtha",
    "ammonia",
    "process emission",
    "process emission from feedstock",
]


def load_idees_data(sector, country="EU28"):
    suffixes = {"out": "", "fec": "_fec", "ued": "_ued", "emi": "_emi"}
    sheets = {k: sheet_names[sector] + v for k, v in suffixes.items()}

    def usecols(x):
        return isinstance(x, str) or x == year

    with mute_print():
        idees = pd.read_excel(
            f"{snakemake.input.idees}/JRC-IDEES-2015_Industry_{country}.xlsx",
            sheet_name=list(sheets.values()),
            index_col=0,
            header=0,
            usecols=usecols,
        )

    for k, v in sheets.items():
        idees[k] = idees.pop(v).squeeze()

    return idees


def iron_and_steel():
    # There are two different approaches to produce iron and steel:
    # i.e., integrated steelworks and electric arc.
    # Electric arc approach has higher efficiency and relies more on electricity.
    # We assume that integrated steelworks will be replaced by electric arc entirely.

    sector = "Iron and steel"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    ## Electric arc

    sector = "Electric arc"

    df[sector] = 0.0

    s_fec = idees["fec"][51:57]
    assert s_fec.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.at["elec", sector] += s_fec[sel].sum()

    df.at["heat", sector] += s_fec["Low enthalpy heat"]

    subsector = "Steel: Smelters"
    s_fec = idees["fec"][61:67]
    s_ued = idees["ued"][61:67]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    # efficiency changes due to transforming all the smelters into methane
    key = "Natural gas (incl. biogas)"
    eff_met = s_ued[key] / s_fec[key]

    df.at["methane", sector] += s_ued[subsector] / eff_met

    subsector = "Steel: Electric arc"
    s_fec = idees["fec"][67:68]
    assert s_fec.index[0] == subsector

    df.at["elec", sector] += s_fec[subsector]

    subsector = "Steel: Furnaces, Refining and Rolling"
    s_fec = idees["fec"][68:75]
    s_ued = idees["ued"][68:75]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Steel: Furnaces, Refining and Rolling - Electric"
    eff = s_ued[key] / s_fec[key]

    # assume fully electrified, other processes scaled by used energy
    df.at["elec", sector] += s_ued[subsector] / eff

    subsector = "Steel: Products finishing"
    s_fec = idees["fec"][75:92]
    s_ued = idees["ued"][75:92]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Steel: Products finishing - Electric"
    eff = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.at["elec", sector] += s_ued[subsector] / eff

    # Process emissions (per physical output)

    s_emi = idees["emi"][51:93]
    assert s_emi.index[0] == sector

    s_out = idees["out"][7:8]
    assert s_out.index[0] == sector

    # tCO2/t material
    df.loc["process emission", sector] += s_emi["Process emissions"] / s_out[sector]

    # final energy consumption MWh/t material
    sel = ["elec", "heat", "methane"]
    df.loc[sel, sector] = df.loc[sel, sector] * toe_to_MWh / s_out[sector]

    ## DRI + Electric arc
    # For primary route: DRI with H2 + EAF

    sector = "DRI + Electric arc"

    df[sector] = df["Electric arc"]

    # add H2 consumption for DRI at 1.7 MWh H2 /ton steel
    df.at["hydrogen", sector] = params["H2_DRI"]

    # add electricity consumption in DRI shaft (0.322 MWh/tSl)
    df.at["elec", sector] += params["elec_DRI"]

    ## Integrated steelworks
    # could be used in combination with CCS)
    # Assume existing fuels are kept, except for furnaces, refining, rolling, finishing
    # Ignore 'derived gases' since these are top gases from furnaces

    sector = "Integrated steelworks"

    df[sector] = 0.0

    s_fec = idees["fec"][3:9]
    assert s_fec.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    subsector = "Steel: Sinter/Pellet making"

    s_fec = idees["fec"][13:19]
    s_ued = idees["ued"][13:19]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    df.loc["elec", sector] += s_fec["Electricity"]

    sel = ["Natural gas (incl. biogas)", "Residual fuel oil"]
    df.loc["methane", sector] += s_fec[sel].sum()

    df.loc["coal", sector] += s_fec["Solids"]

    subsector = "Steel: Blast /Basic oxygen furnace"

    s_fec = idees["fec"][19:25]
    s_ued = idees["ued"][19:25]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    sel = ["Natural gas (incl. biogas)", "Residual fuel oil"]
    df.loc["methane", sector] += s_fec[sel].sum()

    df.loc["coal", sector] += s_fec["Solids"]

    df.loc["coke", sector] = s_fec["Coke"]

    subsector = "Steel: Furnaces, Refining and Rolling"

    s_fec = idees["fec"][25:32]
    s_ued = idees["ued"][25:32]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Steel: Furnaces, Refining and Rolling - Electric"
    eff = s_ued[key] / s_fec[key]

    # assume fully electrified, other processes scaled by used energy
    df.loc["elec", sector] += s_ued[subsector] / eff

    subsector = "Steel: Products finishing"

    s_fec = idees["fec"][32:49]
    s_ued = idees["ued"][32:49]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Steel: Products finishing - Electric"
    eff = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff

    # Process emissions (per physical output)

    s_emi = idees["emi"][3:50]
    assert s_emi.index[0] == sector

    s_out = idees["out"][6:7]
    assert s_out.index[0] == sector

    # tCO2/t material
    df.loc["process emission", sector] = s_emi["Process emissions"] / s_out[sector]

    # final energy consumption MWh/t material
    sel = ["elec", "heat", "methane", "coke", "coal"]
    df.loc[sel, sector] = df.loc[sel, sector] * toe_to_MWh / s_out[sector]

    return df


def chemicals_industry():
    sector = "Chemicals Industry"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    # Basic chemicals

    sector = "Basic chemicals"

    df[sector] = 0.0

    s_fec = idees["fec"][3:9]
    assert s_fec.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    subsector = "Chemicals: Feedstock (energy used as raw material)"
    # There are Solids, Refinery gas, LPG, Diesel oil, Residual fuel oil,
    # Other liquids, Naphtha, Natural gas for feedstock.
    # Naphta represents 47%, methane 17%. LPG (18%) solids, refinery gas,
    # diesel oil, residual fuel oils and other liquids are assimilated to Naphtha

    s_fec = idees["fec"][13:22]
    assert s_fec.index[0] == subsector

    df.loc["naphtha", sector] += s_fec["Naphtha"]

    df.loc["methane", sector] += s_fec["Natural gas"]

    # LPG and other feedstock materials are assimilated to naphtha
    # since they will be produced through Fischer-Tropsh process
    sel = [
        "Solids",
        "Refinery gas",
        "LPG",
        "Diesel oil",
        "Residual fuel oil",
        "Other liquids",
    ]
    df.loc["naphtha", sector] += s_fec[sel].sum()

    subsector = "Chemicals: Steam processing"
    # All the final energy consumption in the steam processing is
    # converted to methane, since we need >1000 C temperatures here.
    # The current efficiency of methane is assumed in the conversion.

    s_fec = idees["fec"][22:33]
    s_ued = idees["ued"][22:33]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    # efficiency of natural gas
    eff_ch4 = s_ued["Natural gas (incl. biogas)"] / s_fec["Natural gas (incl. biogas)"]

    # replace all fec by methane
    df.loc["methane", sector] += s_ued[subsector] / eff_ch4

    subsector = "Chemicals: Furnaces"

    s_fec = idees["fec"][33:41]
    s_ued = idees["ued"][33:41]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    # efficiency of electrification
    key = "Chemicals: Furnaces - Electric"
    eff_elec = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff_elec

    subsector = "Chemicals: Process cooling"

    s_fec = idees["fec"][41:55]
    s_ued = idees["ued"][41:55]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Chemicals: Process cooling - Electric"
    eff_elec = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff_elec

    subsector = "Chemicals: Generic electric process"

    s_fec = idees["fec"][55:56]
    assert s_fec.index[0] == subsector

    df.loc["elec", sector] += s_fec[subsector]

    # Process emissions

    # Correct everything by subtracting 2015's ammonia demand and
    # putting in ammonia demand for H2 and electricity separately

    s_emi = idees["emi"][3:57]
    assert s_emi.index[0] == sector

    # convert from MtHVC/a to ktHVC/a
    s_out = params["HVC_production_today"] * 1e3

    # tCO2/t material
    df.loc["process emission", sector] += (
        s_emi["Process emissions"]
        - params["petrochemical_process_emissions"] * 1e3
        - params["NH3_process_emissions"] * 1e3
    ) / s_out

    # emissions originating from feedstock, could be non-fossil origin
    # tCO2/t material
    df.loc["process emission from feedstock", sector] += (
        params["petrochemical_process_emissions"] * 1e3
    ) / s_out

    # convert from ktoe/a to GWh/a
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] *= toe_to_MWh

    # subtract ammonia energy demand (in ktNH3/a)
    ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
    ammonia_total = ammonia.loc[ammonia.index.intersection(eu28), str(year)].sum()
    df.loc["methane", sector] -= ammonia_total * params["MWh_CH4_per_tNH3_SMR"]
    df.loc["elec", sector] -= ammonia_total * params["MWh_elec_per_tNH3_SMR"]

    # subtract chlorine demand (in MtCl/a)
    chlorine_total = params["chlorine_production_today"]
    df.loc["hydrogen", sector] -= chlorine_total * params["MWh_H2_per_tCl"] * 1e3
    df.loc["elec", sector] -= chlorine_total * params["MWh_elec_per_tCl"] * 1e3

    # subtract methanol demand (in MtMeOH/a)
    methanol_total = params["methanol_production_today"]
    df.loc["methane", sector] -= methanol_total * params["MWh_CH4_per_tMeOH"] * 1e3
    df.loc["elec", sector] -= methanol_total * params["MWh_elec_per_tMeOH"] * 1e3

    # MWh/t material
    df.loc[sources, sector] = df.loc[sources, sector] / s_out

    df.rename(columns={sector: "HVC"}, inplace=True)

    # HVC mechanical recycling

    sector = "HVC (mechanical recycling)"
    df[sector] = 0.0
    df.loc["elec", sector] = params["MWh_elec_per_tHVC_mechanical_recycling"]

    # HVC chemical recycling

    sector = "HVC (chemical recycling)"
    df[sector] = 0.0
    df.loc["elec", sector] = params["MWh_elec_per_tHVC_chemical_recycling"]

    # Ammonia

    sector = "Ammonia"
    df[sector] = 0.0
    if snakemake.params.ammonia:
        df.loc["ammonia", sector] = params["MWh_NH3_per_tNH3"]
    else:
        df.loc["hydrogen", sector] = params["MWh_H2_per_tNH3_electrolysis"]
        df.loc["elec", sector] = params["MWh_elec_per_tNH3_electrolysis"]

    # Chlorine

    sector = "Chlorine"
    df[sector] = 0.0
    df.loc["hydrogen", sector] = params["MWh_H2_per_tCl"]
    df.loc["elec", sector] = params["MWh_elec_per_tCl"]

    # Methanol

    sector = "Methanol"
    df[sector] = 0.0
    df.loc["methane", sector] = params["MWh_CH4_per_tMeOH"]
    df.loc["elec", sector] = params["MWh_elec_per_tMeOH"]

    # Other chemicals

    sector = "Other chemicals"

    df[sector] = 0.0

    s_fec = idees["fec"][58:64]
    assert s_fec.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    subsector = "Chemicals: High enthalpy heat  processing"

    s_fec = idees["fec"][68:81]
    s_ued = idees["ued"][68:81]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "High enthalpy heat  processing - Electric (microwave)"
    eff_elec = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff_elec

    subsector = "Chemicals: Furnaces"

    s_fec = idees["fec"][81:89]
    s_ued = idees["ued"][81:89]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Chemicals: Furnaces - Electric"
    eff_elec = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff_elec

    subsector = "Chemicals: Process cooling"

    s_fec = idees["fec"][89:103]
    s_ued = idees["ued"][89:103]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Chemicals: Process cooling - Electric"
    eff = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff

    subsector = "Chemicals: Generic electric process"

    s_fec = idees["fec"][103:104]
    assert s_fec.index[0] == subsector

    df.loc["elec", sector] += s_fec[subsector]

    # Process emissions

    s_emi = idees["emi"][58:105]
    s_out = idees["out"][9:10]
    assert s_emi.index[0] == sector
    assert sector in str(s_out.index)

    # tCO2/t material
    df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    # Pharmaceutical products

    sector = "Pharmaceutical products etc."

    df[sector] = 0.0

    s_fec = idees["fec"][106:112]
    assert s_fec.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    subsector = "Chemicals: High enthalpy heat  processing"

    s_fec = idees["fec"][116:129]
    s_ued = idees["ued"][116:129]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "High enthalpy heat  processing - Electric (microwave)"
    eff_elec = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff_elec

    subsector = "Chemicals: Furnaces"

    s_fec = idees["fec"][129:137]
    s_ued = idees["ued"][129:137]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Chemicals: Furnaces - Electric"
    eff = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff

    subsector = "Chemicals: Process cooling"

    s_fec = idees["fec"][137:151]
    s_ued = idees["ued"][137:151]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    key = "Chemicals: Process cooling - Electric"
    eff_elec = s_ued[key] / s_fec[key]

    # assume fully electrified
    df.loc["elec", sector] += s_ued[subsector] / eff_elec

    subsector = "Chemicals: Generic electric process"

    s_fec = idees["fec"][151:152]
    s_out = idees["out"][10:11]
    assert s_fec.index[0] == subsector
    assert sector in str(s_out.index)

    df.loc["elec", sector] += s_fec[subsector]

    # tCO2/t material
    df.loc["process emission", sector] += 0.0

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    return df


def nonmetalic_mineral_products():
    # This includes cement, ceramic and glass production.
    # This includes process emissions related to the fabrication of clinker.

    sector = "Non-metallic mineral products"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    # Cement

    # This sector has process-emissions.
    # Includes three subcategories:
    # (a) Grinding, milling of raw material,
    # (b) Pre-heating and pre-calcination,
    # (c) clinker production (kilns),
    # (d) Grinding, packaging.
    # (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
    # Temperatures above 1400C are required for processing limestone and sand into clinker.
    # Everything (except current electricity and heat consumption and existing biomass)
    # is transformed into methane for high T.

    sector = "Cement"

    df[sector] = 0.0

    s_fec = idees["fec"][3:25]
    s_ued = idees["ued"][3:25]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # pre-processing: keep existing elec and biomass, rest to methane
    df.loc["elec", sector] += s_fec["Cement: Grinding, milling of raw material"]
    df.loc["biomass", sector] += s_fec["Biomass"]
    df.loc["methane", sector] += (
        s_fec["Cement: Pre-heating and pre-calcination"] - s_fec["Biomass"]
    )

    subsector = "Cement: Clinker production (kilns)"

    s_fec = idees["fec"][34:43]
    s_ued = idees["ued"][34:43]
    assert s_fec.index[0] == subsector
    assert s_ued.index[0] == subsector

    df.loc["biomass", sector] += s_fec["Biomass"]
    df.loc["methane", sector] += (
        s_fec["Cement: Clinker production (kilns)"] - s_fec["Biomass"]
    )
    df.loc["elec", sector] += s_fec["Cement: Grinding, packaging"]

    # Process emissions

    # come from calcination of limestone to chemically reactive calcium oxide (lime).
    # Calcium carbonate -> lime + CO2
    # CaCO3  -> CaO + CO2

    s_emi = idees["emi"][3:44]
    assert s_emi.index[0] == sector

    s_out = idees["out"][7:8]
    assert sector in str(s_out.index)

    # tCO2/t material
    df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    # Ceramics & other NMM

    # This sector has process emissions.
    # Includes four subcategories:
    # (a) Mixing of raw material,
    # (b) Drying and sintering of raw material,
    # (c) Primary production process,
    # (d) Product finishing.
    # (b) represents 65% of fec and (a) 4%. So (a) is joined to (b).
    # Everything is electrified

    sector = "Ceramics & other NMM"

    df[sector] = 0.0

    s_fec = idees["fec"][45:94]
    s_ued = idees["ued"][45:94]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Ceramics: Microwave drying and sintering"
    eff_elec = s_ued[key] / s_fec[key]

    sel = [
        "Ceramics: Mixing of raw material",
        "Ceramics: Drying and sintering of raw material",
    ]
    df.loc["elec", sector] += s_ued[sel].sum() / eff_elec

    key = "Ceramics: Electric kiln"
    eff_elec = s_ued[key] / s_fec[key]

    df.loc["elec", sector] += s_ued["Ceramics: Primary production process"] / eff_elec

    key = "Ceramics: Electric furnace"
    eff_elec = s_ued[key] / s_fec[key]

    df.loc["elec", sector] += s_ued["Ceramics: Product finishing"] / eff_elec

    s_emi = idees["emi"][45:94]
    assert s_emi.index[0] == sector

    s_out = idees["out"][8:9]
    assert sector in str(s_out.index)

    # tCO2/t material
    df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    # Glass production

    # This sector has process emissions.
    # Includes four subcategories:
    # (a) Melting tank
    # (b) Forming
    # (c) Annealing
    # (d) Finishing processes.
    # (a) represents 73%. (b), (d) are joined to (c).
    # Everything is electrified.

    sector = "Glass production"

    df[sector] = 0.0

    s_fec = idees["fec"][95:123]
    s_ued = idees["ued"][95:123]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Glass: Electric melting tank"
    eff_elec = s_ued[key] / s_fec[key]

    df.loc["elec", sector] += s_ued["Glass: Melting tank"] / eff_elec

    key = "Glass: Annealing - electric"
    eff_elec = s_ued[key] / s_fec[key]

    sel = ["Glass: Forming", "Glass: Annealing", "Glass: Finishing processes"]
    df.loc["elec", sector] += s_ued[sel].sum() / eff_elec

    s_emi = idees["emi"][95:124]
    assert s_emi.index[0] == sector

    s_out = idees["out"][9:10]
    assert sector in str(s_out.index)

    # tCO2/t material
    df.loc["process emission", sector] += s_emi["Process emissions"] / s_out.values

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    return df


def pulp_paper_printing():
    # Pulp, paper and printing can be completely electrified.
    # There are no process emissions associated to this sector.

    sector = "Pulp, paper and printing"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    # Pulp production

    # Includes three subcategories:
    # (a) Wood preparation, grinding;
    # (b) Pulping;
    # (c) Cleaning.
    #
    # (b) Pulping is either biomass or electric; left like this (dominated by biomass).
    # (a) Wood preparation, grinding and (c) Cleaning represent only 10% of their current
    # energy consumption is assumed to be electrified without any change in efficiency

    sector = "Pulp production"

    df[sector] = 0.0

    s_fec = idees["fec"][3:28]
    s_ued = idees["ued"][3:28]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Industry-specific
    sel = [
        "Pulp: Wood preparation, grinding",
        "Pulp: Cleaning",
        "Pulp: Pulping electric",
    ]
    df.loc["elec", sector] += s_fec[sel].sum()

    # Efficiency changes due to biomass
    eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
    df.loc["biomass", sector] += s_ued["Pulp: Pulping thermal"] / eff_bio

    s_out = idees["out"][8:9]
    assert sector in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Pulp production (kt)"]
    )

    # Paper production

    # Includes three subcategories:
    # (a) Stock preparation;
    # (b) Paper machine;
    # (c) Product finishing.
    #
    # (b) Paper machine and (c) Product finishing are left electric
    # and thermal is moved to biomass. The efficiency is calculated
    # from the pulping process that is already biomass.
    #
    # (a) Stock preparation represents only 7% and its current energy
    # consumption is assumed to be electrified without any change in efficiency.

    sector = "Paper production"

    df[sector] = 0.0

    s_fec = idees["fec"][29:78]
    s_ued = idees["ued"][29:78]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Industry-specific
    df.loc["elec", sector] += s_fec["Paper: Stock preparation"]

    # add electricity from process that is already electrified
    df.loc["elec", sector] += s_fec["Paper: Paper machine - Electricity"]

    # add electricity from process that is already electrified
    df.loc["elec", sector] += s_fec["Paper: Product finishing - Electricity"]

    s_fec = idees["fec"][53:64]
    s_ued = idees["ued"][53:64]
    assert s_fec.index[0] == "Paper: Paper machine - Steam use"
    assert s_ued.index[0] == "Paper: Paper machine - Steam use"

    # Efficiency changes due to biomass
    eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
    df.loc["biomass", sector] += s_ued["Paper: Paper machine - Steam use"] / eff_bio

    s_fec = idees["fec"][66:77]
    s_ued = idees["ued"][66:77]
    assert s_fec.index[0] == "Paper: Product finishing - Steam use"
    assert s_ued.index[0] == "Paper: Product finishing - Steam use"

    # Efficiency changes due to biomass
    eff_bio = s_ued["Biomass"] / s_fec["Biomass"]
    df.loc["biomass", sector] += s_ued["Paper: Product finishing - Steam use"] / eff_bio

    s_out = idees["out"][9:10]
    assert sector in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    # Printing and media reproduction

    # (a) Printing and publishing is assumed to be
    # electrified without any change in efficiency.

    sector = "Printing and media reproduction"

    df[sector] = 0.0

    s_fec = idees["fec"][79:90]
    s_ued = idees["ued"][79:90]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()
    df.loc["elec", sector] += s_ued[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]
    df.loc["heat", sector] += s_ued["Low enthalpy heat"]

    # Industry-specific
    df.loc["elec", sector] += s_fec["Printing and publishing"]
    df.loc["elec", sector] += s_ued["Printing and publishing"]

    s_out = idees["out"][10:11]
    assert sector in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = df.loc[sources, sector] * toe_to_MWh / s_out.values

    return df


def food_beverages_tobacco():
    # Food, beverages and tobaco can be completely electrified.
    # There are no process emissions associated to this sector.

    sector = "Food, beverages and tobacco"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    df[sector] = 0.0

    s_fec = idees["fec"][3:78]
    s_ued = idees["ued"][3:78]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification

    key = "Food: Direct Heat - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Food: Oven (direct heat)"] / eff_elec

    key = "Food: Process Heat - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Food: Specific process heat"] / eff_elec

    key = "Food: Electric drying"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Food: Drying"] / eff_elec

    key = "Food: Electric cooling"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += (
        s_ued["Food: Process cooling and refrigeration"] / eff_elec
    )

    # Steam processing goes all to biomass without change in efficiency
    df.loc["biomass", sector] += s_fec["Food: Steam processing"]

    # add electricity from process that is already electrified
    df.loc["elec", sector] += s_fec["Food: Electric machinery"]

    s_out = idees["out"][3:4]
    assert "Physical output" in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
    )

    return df


def non_ferrous_metals():
    sector = "Non Ferrous Metals"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    # Alumina

    # High enthalpy heat is converted to methane.
    # Process heat at T>500C is required here.
    # Refining is electrified.
    # There are no process emissions associated to Alumina manufacturing.

    sector = "Alumina production"

    df[sector] = 0.0

    s_fec = idees["fec"][3:31]
    s_ued = idees["ued"][3:31]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # High-enthalpy heat is transformed into methane

    s_fec = idees["fec"][13:24]
    s_ued = idees["ued"][13:24]
    assert s_fec.index[0] == "Alumina production: High enthalpy heat"
    assert s_ued.index[0] == "Alumina production: High enthalpy heat"

    eff_met = s_ued["Natural gas (incl. biogas)"] / s_fec["Natural gas (incl. biogas)"]
    df.loc["methane", sector] += (
        s_fec["Alumina production: High enthalpy heat"] / eff_met
    )

    # Efficiency changes due to electrification

    s_fec = idees["fec"][24:30]
    s_ued = idees["ued"][24:30]
    assert s_fec.index[0] == "Alumina production: Refining"
    assert s_ued.index[0] == "Alumina production: Refining"

    eff_elec = s_ued["Electricity"] / s_fec["Electricity"]
    df.loc["elec", sector] += s_ued["Alumina production: Refining"] / eff_elec

    s_out = idees["out"][9:10]
    assert sector in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Alumina production (kt)"]
    )

    # Aluminium primary route

    # Production through the primary route is divided into 50% remains
    # as today and 50% is transformed into secondary route.

    sector = "Aluminium - primary production"

    df[sector] = 0.0

    s_fec = idees["fec"][31:66]
    s_ued = idees["ued"][31:66]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Add aluminium  electrolysis (smelting
    df.loc["elec", sector] += s_fec["Aluminium electrolysis (smelting)"]

    # Efficiency changes due to electrification
    key = "Aluminium processing - Electric"
    eff_elec = s_ued[key] / s_fec[key]

    key = "Aluminium processing  (metallurgy e.g. cast house, reheating)"
    df.loc["elec", sector] += s_ued[key] / eff_elec

    key = "Aluminium finishing - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Aluminium finishing"] / eff_elec

    s_emi = idees["emi"][31:67]
    assert s_emi.index[0] == sector

    s_out = idees["out"][11:12]
    assert sector in str(s_out.index)

    # tCO2/t material
    df.loc["process emission", sector] = (
        s_emi["Process emissions"] / s_out["Aluminium - primary production"]
    )

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Aluminium - primary production"]
    )

    # Aluminium secondary route

    # All is converted into secondary route fully electrified.

    sector = "Aluminium - secondary production"

    df[sector] = 0.0

    s_fec = idees["fec"][68:109]
    s_ued = idees["ued"][68:109]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Secondary aluminium - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    key = "Secondary aluminium (incl. pre-treatment, remelting)"
    df.loc["elec", sector] += s_ued[key] / eff_elec

    key = "Aluminium processing - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    key = "Aluminium processing  (metallurgy e.g. cast house, reheating)"
    df.loc["elec", sector] += s_ued[key] / eff_elec

    key = "Aluminium finishing - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Aluminium finishing"] / eff_elec

    s_out = idees["out"][12:13]
    assert sector in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Aluminium - secondary production"]
    )

    # Other non-ferrous metals

    sector = "Other non-ferrous metals"

    df[sector] = 0.0

    s_fec = idees["fec"][110:152]
    s_ued = idees["ued"][110:152]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Metal production - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Other Metals: production"] / eff_elec

    key = "Metal processing - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    key = "Metal processing  (metallurgy e.g. cast house, reheating)"
    df.loc["elec", sector] += s_ued[key] / eff_elec

    key = "Metal finishing - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Metal finishing"] / eff_elec

    s_emi = idees["emi"][110:153]
    assert s_emi.index[0] == sector

    s_out = idees["out"][13:14]
    assert sector in str(s_out.index)

    # tCO2/t material
    df.loc["process emission", sector] = (
        s_emi["Process emissions"] / s_out["Other non-ferrous metals (kt lead eq.)"]
    )

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector]
        * toe_to_MWh
        / s_out["Other non-ferrous metals (kt lead eq.)"]
    )

    return df


def transport_equipment():
    sector = "Transport Equipment"
    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    df[sector] = 0.0

    s_fec = idees["fec"][3:45]
    s_ued = idees["ued"][3:45]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Trans. Eq.: Electric Foundries"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Trans. Eq.: Foundries"] / eff_elec

    key = "Trans. Eq.: Electric connection"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Trans. Eq.: Connection techniques"] / eff_elec

    key = "Trans. Eq.: Heat treatment - Electric"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Trans. Eq.: Heat treatment"] / eff_elec

    df.loc["elec", sector] += s_fec["Trans. Eq.: General machinery"]
    df.loc["elec", sector] += s_fec["Trans. Eq.: Product finishing"]

    # Steam processing is supplied with biomass
    eff_biomass = s_ued["Biomass"] / s_fec["Biomass"]
    df.loc["biomass", sector] += s_ued["Trans. Eq.: Steam processing"] / eff_biomass

    s_out = idees["out"][3:4]
    assert "Physical output" in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
    )

    return df


def machinery_equipment():
    sector = "Machinery Equipment"

    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    df[sector] = 0.0

    s_fec = idees["fec"][3:45]
    s_ued = idees["ued"][3:45]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Mach. Eq.: Electric Foundries"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Mach. Eq.: Foundries"] / eff_elec

    key = "Mach. Eq.: Electric connection"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Mach. Eq.: Connection techniques"] / eff_elec

    key = "Mach. Eq.: Heat treatment - Electric"
    eff_elec = s_ued[key] / s_fec[key]

    df.loc["elec", sector] += s_ued["Mach. Eq.: Heat treatment"] / eff_elec

    df.loc["elec", sector] += s_fec["Mach. Eq.: General machinery"]
    df.loc["elec", sector] += s_fec["Mach. Eq.: Product finishing"]

    # Steam processing is supplied with biomass
    eff_biomass = s_ued["Biomass"] / s_fec["Biomass"]
    df.loc["biomass", sector] += s_ued["Mach. Eq.: Steam processing"] / eff_biomass

    s_out = idees["out"][3:4]
    assert "Physical output" in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
    )

    return df


def textiles_and_leather():
    sector = "Textiles and leather"

    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    df[sector] = 0.0

    s_fec = idees["fec"][3:57]
    s_ued = idees["ued"][3:57]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Textiles: Electric drying"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Textiles: Drying"] / eff_elec

    df.loc["elec", sector] += s_fec["Textiles: Electric general machinery"]
    df.loc["elec", sector] += s_fec["Textiles: Finishing Electric"]

    # Steam processing is supplied with biomass
    eff_biomass = s_ued[15:26]["Biomass"] / s_fec[15:26]["Biomass"]
    df.loc["biomass", sector] += (
        s_ued["Textiles: Pretreatment with steam"] / eff_biomass
    )
    df.loc["biomass", sector] += (
        s_ued["Textiles: Wet processing with steam"] / eff_biomass
    )

    s_out = idees["out"][3:4]
    assert "Physical output" in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
    )

    return df


def wood_and_wood_products():
    sector = "Wood and wood products"

    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    df[sector] = 0.0

    s_fec = idees["fec"][3:46]
    s_ued = idees["ued"][3:46]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Wood: Electric drying"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Wood: Drying"] / eff_elec

    df.loc["elec", sector] += s_fec["Wood: Electric mechanical processes"]
    df.loc["elec", sector] += s_fec["Wood: Finishing Electric"]

    # Steam processing is supplied with biomass
    eff_biomass = s_ued[15:25]["Biomass"] / s_fec[15:25]["Biomass"]
    df.loc["biomass", sector] += (
        s_ued["Wood: Specific processes with steam"] / eff_biomass
    )

    s_out = idees["out"][3:4]
    assert "Physical output" in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
    )

    return df


def other_industrial_sectors():
    sector = "Other Industrial Sectors"

    idees = load_idees_data(sector)

    df = pd.DataFrame(index=index)

    df[sector] = 0.0

    s_fec = idees["fec"][3:67]
    s_ued = idees["ued"][3:67]
    assert s_fec.index[0] == sector
    assert s_ued.index[0] == sector

    sel = ["Lighting", "Air compressors", "Motor drives", "Fans and pumps"]
    df.loc["elec", sector] += s_fec[sel].sum()

    df.loc["heat", sector] += s_fec["Low enthalpy heat"]

    # Efficiency changes due to electrification
    key = "Other Industrial sectors: Electric processing"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += (
        s_ued["Other Industrial sectors: Process heating"] / eff_elec
    )

    key = "Other Industries: Electric drying"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += s_ued["Other Industrial sectors: Drying"] / eff_elec

    key = "Other Industries: Electric cooling"
    eff_elec = s_ued[key] / s_fec[key]
    df.loc["elec", sector] += (
        s_ued["Other Industrial sectors: Process Cooling"] / eff_elec
    )

    # Diesel motors are electrified
    key = "Other Industrial sectors: Diesel motors (incl. biofuels)"
    df.loc["elec", sector] += s_fec[key]
    key = "Other Industrial sectors: Electric machinery"
    df.loc["elec", sector] += s_fec[key]

    # Steam processing is supplied with biomass
    eff_biomass = s_ued[15:25]["Biomass"] / s_fec[15:25]["Biomass"]
    df.loc["biomass", sector] += (
        s_ued["Other Industrial sectors: Steam processing"] / eff_biomass
    )

    s_out = idees["out"][3:4]
    assert "Physical output" in str(s_out.index)

    # MWh/t material
    sources = ["elec", "biomass", "methane", "hydrogen", "heat", "naphtha"]
    df.loc[sources, sector] = (
        df.loc[sources, sector] * toe_to_MWh / s_out["Physical output (index)"]
    )

    return df


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake("build_industry_sector_ratios")
    set_scenario_config(snakemake)

    # TODO make params option
    year = 2015

    params = snakemake.params.industry

    df = pd.concat(
        [
            iron_and_steel(),
            chemicals_industry(),
            nonmetalic_mineral_products(),
            pulp_paper_printing(),
            food_beverages_tobacco(),
            non_ferrous_metals(),
            transport_equipment(),
            machinery_equipment(),
            textiles_and_leather(),
            wood_and_wood_products(),
            other_industrial_sectors(),
        ],
        axis=1,
    )

    df.index.name = "MWh/tMaterial"
    df.to_csv(snakemake.output.industry_sector_ratios)
