# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Build total energy demands per country using JRC IDEES, eurostat, and EEA data.
"""

import logging
import multiprocessing as mp
import os
from functools import partial

import country_converter as coco
import geopandas as gpd
import numpy as np
import pandas as pd
from _helpers import configure_logging, mute_print, set_scenario_config
from tqdm import tqdm

cc = coco.CountryConverter()
logger = logging.getLogger(__name__)
idx = pd.IndexSlice


def cartesian(s1, s2):
    """
    Cartesian product of two pd.Series.
    """
    return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)


def reverse(dictionary):
    """
    Reverses a keys and values of a dictionary.
    """
    return {v: k for k, v in dictionary.items()}


idees_rename = {"GR": "EL", "GB": "UK"}

eu28 = cc.EU28as("ISO2").ISO2.tolist()

eu28_eea = eu28.copy()
eu28_eea.remove("GB")
eu28_eea.append("UK")


to_ipcc = {
    "electricity": "1.A.1.a - Public Electricity and Heat Production",
    "residential non-elec": "1.A.4.b - Residential",
    "services non-elec": "1.A.4.a - Commercial/Institutional",
    "rail non-elec": "1.A.3.c - Railways",
    "road non-elec": "1.A.3.b - Road Transportation",
    "domestic navigation": "1.A.3.d - Domestic Navigation",
    "international navigation": "1.D.1.b - International Navigation",
    "domestic aviation": "1.A.3.a - Domestic Aviation",
    "international aviation": "1.D.1.a - International Aviation",
    "total energy": "1 - Energy",
    "industrial processes": "2 - Industrial Processes and Product Use",
    "agriculture": "3 - Agriculture",
    "agriculture, forestry and fishing": "1.A.4.c - Agriculture/Forestry/Fishing",
    "LULUCF": "4 - Land Use, Land-Use Change and Forestry",
    "waste management": "5 - Waste management",
    "other": "6 - Other Sector",
    "indirect": "ind_CO2 - Indirect CO2",
    "total wL": "Total (with LULUCF)",
    "total woL": "Total (without LULUCF)",
}


def build_eurostat(input_eurostat, countries, year):
    """
    Return multi-index for all countries' energy data in TWh/a.
    """
    df = {}
    countries = {idees_rename.get(country, country) for country in countries} - {"CH"}
    for country in countries:
        filename = (
            f"{input_eurostat}/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
        )
        sheet = pd.read_excel(
            filename,
            engine="pyxlsb",
            sheet_name=str(year),
            skiprows=4,
            index_col=list(range(4)),
        )
        df[country] = sheet
    df = pd.concat(df, axis=0)

    # drop columns with all NaNs
    unnamed_cols = df.columns[df.columns.astype(str).str.startswith("Unnamed")]
    df.drop(unnamed_cols, axis=1, inplace=True)
    df.drop(year, axis=1, inplace=True)

    # make numeric values where possible
    df.replace("Z", 0, inplace=True)
    df = df.apply(pd.to_numeric, errors="coerce")
    df = df.select_dtypes(include=[np.number])

    # write 'International aviation' to the 2nd level of the multiindex
    int_avia = df.index.get_level_values(2) == "International aviation"
    temp = df.loc[int_avia]
    temp.index = pd.MultiIndex.from_frame(
        temp.index.to_frame().fillna("International aviation")
    )
    df = pd.concat([temp, df.loc[~int_avia]])

    # Renaming some indices
    index_rename = {
        "Households": "Residential",
        "Commercial & public services": "Services",
        "Domestic navigation": "Domestic Navigation",
        "International maritime bunkers": "Bunkers",
    }
    columns_rename = {"Total": "Total all products", "UK": "GB"}
    df.rename(index=index_rename, columns=columns_rename, inplace=True)
    df.sort_index(inplace=True)
    df.index.names = [None] * len(df.index.names)

    # convert to TWh/a from ktoe/a
    df *= 11.63 / 1e3

    return df


def build_swiss(year):
    """
    Return a pd.Series of Swiss energy data in TWh/a.
    """
    fn = snakemake.input.swiss

    df = pd.read_csv(fn, index_col=[0, 1]).loc["CH", str(year)]

    # convert PJ/a to TWh/a
    df /= 3.6

    return df


def idees_per_country(ct, year, base_dir):
    ct_idees = idees_rename.get(ct, ct)
    fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
    fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
    fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"

    # residential

    df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)[year]

    rows = ["Advanced electric heating", "Conventional electric heating"]
    ct_totals = {
        "total residential space": df["Space heating"],
        "electricity residential space": df[rows].sum(),
    }
    ct_totals["total residential water"] = df.at["Water heating"]

    assert df.index[23] == "Electricity"
    ct_totals["electricity residential water"] = df.iloc[23]

    ct_totals["total residential cooking"] = df["Cooking"]

    assert df.index[30] == "Electricity"
    ct_totals["electricity residential cooking"] = df.iloc[30]

    df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year]

    row = "Energy consumption by fuel - Eurostat structure (ktoe)"
    ct_totals["total residential"] = df[row]

    assert df.index[47] == "Electricity"
    ct_totals["electricity residential"] = df.iloc[47]

    assert df.index[46] == "Derived heat"
    ct_totals["derived heat residential"] = df.iloc[46]

    assert df.index[50] == "Thermal uses"
    ct_totals["thermal uses residential"] = df.iloc[50]

    # services

    df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]

    ct_totals["total services space"] = df["Space heating"]

    rows = ["Advanced electric heating", "Conventional electric heating"]
    ct_totals["electricity services space"] = df[rows].sum()

    ct_totals["total services water"] = df["Hot water"]

    assert df.index[24] == "Electricity"
    ct_totals["electricity services water"] = df.iloc[24]

    ct_totals["total services cooking"] = df["Catering"]

    assert df.index[31] == "Electricity"
    ct_totals["electricity services cooking"] = df.iloc[31]

    df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]

    row = "Energy consumption by fuel - Eurostat structure (ktoe)"
    ct_totals["total services"] = df[row]

    assert df.index[50] == "Electricity"
    ct_totals["electricity services"] = df.iloc[50]

    assert df.index[49] == "Derived heat"
    ct_totals["derived heat services"] = df.iloc[49]

    assert df.index[53] == "Thermal uses"
    ct_totals["thermal uses services"] = df.iloc[53]

    # agriculture, forestry and fishing

    start = "Detailed split of energy consumption (ktoe)"
    end = "Market shares of energy uses (%)"

    df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year]

    rows = [
        "Lighting",
        "Ventilation",
        "Specific electricity uses",
        "Pumping devices (electric)",
    ]
    ct_totals["total agriculture electricity"] = df[rows].sum()

    rows = ["Specific heat uses", "Low enthalpy heat"]
    ct_totals["total agriculture heat"] = df[rows].sum()

    rows = [
        "Motor drives",
        "Farming machine drives (diesel oil incl. biofuels)",
        "Pumping devices (diesel oil incl. biofuels)",
    ]
    ct_totals["total agriculture machinery"] = df[rows].sum()

    row = "Agriculture, forestry and fishing"
    ct_totals["total agriculture"] = df[row]

    # transport

    df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]

    ct_totals["total road"] = df["by fuel (EUROSTAT DATA)"]

    ct_totals["electricity road"] = df["Electricity"]

    ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"]

    assert df.index[19] == "Passenger cars"
    ct_totals["total passenger cars"] = df.iloc[19]

    assert df.index[30] == "Battery electric vehicles"
    ct_totals["electricity passenger cars"] = df.iloc[30]

    assert df.index[31] == "Motor coaches, buses and trolley buses"
    ct_totals["total other road passenger"] = df.iloc[31]

    assert df.index[39] == "Battery electric vehicles"
    ct_totals["electricity other road passenger"] = df.iloc[39]

    assert df.index[41] == "Light duty vehicles"
    ct_totals["total light duty road freight"] = df.iloc[41]

    assert df.index[49] == "Battery electric vehicles"
    ct_totals["electricity light duty road freight"] = df.iloc[49]

    row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
    ct_totals["total heavy duty road freight"] = df[row]

    assert df.index[61] == "Passenger cars"
    ct_totals["passenger car efficiency"] = df.iloc[61]

    df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year]

    ct_totals["total rail"] = df["by fuel (EUROSTAT DATA)"]

    ct_totals["electricity rail"] = df["Electricity"]

    assert df.index[15] == "Passenger transport"
    ct_totals["total rail passenger"] = df.iloc[15]

    assert df.index[16] == "Metro and tram, urban light rail"
    assert df.index[19] == "Electric"
    assert df.index[20] == "High speed passenger trains"
    ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()

    assert df.index[21] == "Freight transport"
    ct_totals["total rail freight"] = df.iloc[21]

    assert df.index[23] == "Electric"
    ct_totals["electricity rail freight"] = df.iloc[23]

    df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year]

    assert df.index[6] == "Passenger transport"
    ct_totals["total aviation passenger"] = df.iloc[6]

    assert df.index[10] == "Freight transport"
    ct_totals["total aviation freight"] = df.iloc[10]

    assert df.index[7] == "Domestic"
    ct_totals["total domestic aviation passenger"] = df.iloc[7]

    assert df.index[8] == "International - Intra-EU"
    assert df.index[9] == "International - Extra-EU"
    ct_totals["total international aviation passenger"] = df.iloc[[8, 9]].sum()

    assert df.index[11] == "Domestic and International - Intra-EU"
    ct_totals["total domestic aviation freight"] = df.iloc[11]

    assert df.index[12] == "International - Extra-EU"
    ct_totals["total international aviation freight"] = df.iloc[12]

    ct_totals["total domestic aviation"] = (
        ct_totals["total domestic aviation freight"]
        + ct_totals["total domestic aviation passenger"]
    )

    ct_totals["total international aviation"] = (
        ct_totals["total international aviation freight"]
        + ct_totals["total international aviation passenger"]
    )

    df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)[year]

    # coastal and inland
    ct_totals["total domestic navigation"] = df["by fuel (EUROSTAT DATA)"]

    df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year]

    assert df.index[85] == "Passenger cars"
    ct_totals["passenger cars"] = df.iloc[85]

    return pd.Series(ct_totals, name=ct)


def build_idees(countries, year):
    nprocesses = snakemake.threads
    disable_progress = snakemake.config["run"].get("disable_progressbar", False)

    func = partial(idees_per_country, year=year, base_dir=snakemake.input.idees)
    tqdm_kwargs = dict(
        ascii=False,
        unit=" country",
        total=len(countries),
        desc="Build from IDEES database",
        disable=disable_progress,
    )
    with mute_print():
        with mp.Pool(processes=nprocesses) as pool:
            totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))

    totals = pd.concat(totals_list, axis=1)

    # convert ktoe to TWh
    exclude = totals.index.str.fullmatch("passenger cars")
    totals.loc[~exclude] *= 11.63 / 1e3

    # convert TWh/100km to kWh/km
    totals.loc["passenger car efficiency"] *= 10

    return totals.T


def build_energy_totals(countries, eurostat, swiss, idees):
    eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}

    to_drop = ["passenger cars", "passenger car efficiency"]
    df = idees.reindex(countries).drop(to_drop, axis=1)

    eurostat_countries = eurostat.index.levels[0]
    in_eurostat = df.index.intersection(eurostat_countries)

    # add international navigation

    slicer = idx[in_eurostat, :, "Bunkers", :]
    fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
    df.loc[in_eurostat, "total international navigation"] = fill_values

    # add swiss energy data

    df.loc["CH"] = swiss

    # get values for missing countries based on Eurostat EnergyBalances
    # divide cooking/space/water according to averages in EU28

    missing = df.index[df["total residential"].isna()]
    to_fill = missing.intersection(eurostat_countries)
    uses = ["space", "cooking", "water"]

    for sector in ["residential", "services", "road", "rail"]:
        eurostat_sector = sector.capitalize()

        # fuel use

        for fuel in ["electricity", "total"]:
            slicer = idx[to_fill, :, :, eurostat_sector]
            fill_values = (
                eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).sum()
            )
            df.loc[to_fill, f"{fuel} {sector}"] = fill_values

    for sector in ["residential", "services"]:
        # electric use

        for use in uses:
            fuel_use = df[f"electricity {sector} {use}"]
            fuel = df[f"electricity {sector}"]
            avg = fuel_use.div(fuel).mean()
            logger.debug(
                f"{sector}: average fraction of electricity for {use} is {avg:.3f}"
            )
            df.loc[to_fill, f"electricity {sector} {use}"] = (
                avg * df.loc[to_fill, f"electricity {sector}"]
            )

        # non-electric use

        for use in uses:
            nonelectric_use = (
                df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
            )
            nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"]
            avg = nonelectric_use.div(nonelectric).mean()
            logger.debug(
                f"{sector}: average fraction of non-electric for {use} is {avg:.3f}"
            )
            electric_use = df.loc[to_fill, f"electricity {sector} {use}"]
            nonelectric = (
                df.loc[to_fill, f"total {sector}"]
                - df.loc[to_fill, f"electricity {sector}"]
            )
            df.loc[to_fill, f"total {sector} {use}"] = electric_use + avg * nonelectric

    # Fix Norway space and water heating fractions
    # http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14
    # The main heating source for about 73 per cent of the households is based on electricity
    # => 26% is non-electric

    if "NO" in df.index:
        elec_fraction = 0.73

        no_norway = df.drop("NO")

        for sector in ["residential", "services"]:
            # assume non-electric is heating
            nonelectric = (
                df.loc["NO", f"total {sector}"] - df.loc["NO", f"electricity {sector}"]
            )
            total_heating = nonelectric / (1 - elec_fraction)

            for use in uses:
                nonelectric_use = (
                    no_norway[f"total {sector} {use}"]
                    - no_norway[f"electricity {sector} {use}"]
                )
                nonelectric = (
                    no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
                )
                fraction = nonelectric_use.div(nonelectric).mean()
                df.loc["NO", f"total {sector} {use}"] = total_heating * fraction
                df.loc["NO", f"electricity {sector} {use}"] = (
                    total_heating * fraction * elec_fraction
                )

    # Missing aviation

    slicer = idx[to_fill, :, :, "Domestic aviation"]
    fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
    df.loc[to_fill, "total domestic aviation"] = fill_values

    slicer = idx[to_fill, :, :, "International aviation"]
    fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
    df.loc[to_fill, "total international aviation"] = fill_values

    # missing domestic navigation

    slicer = idx[to_fill, :, :, "Domestic Navigation"]
    fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
    df.loc[to_fill, "total domestic navigation"] = fill_values

    # split road traffic for non-IDEES
    missing = df.index[df["total passenger cars"].isna()]
    for fuel in ["total", "electricity"]:
        selection = [
            f"{fuel} passenger cars",
            f"{fuel} other road passenger",
            f"{fuel} light duty road freight",
        ]
        if fuel == "total":
            selection.extend([f"{fuel} two-wheel", f"{fuel} heavy duty road freight"])
        road = df[selection].sum()
        road_fraction = road / road.sum()
        fill_values = cartesian(df.loc[missing, f"{fuel} road"], road_fraction)
        df.loc[missing, road_fraction.index] = fill_values

    # split rail traffic for non-IDEES
    missing = df.index[df["total rail passenger"].isna()]
    for fuel in ["total", "electricity"]:
        selection = [f"{fuel} rail passenger", f"{fuel} rail freight"]
        rail = df[selection].sum()
        rail_fraction = rail / rail.sum()
        fill_values = cartesian(df.loc[missing, f"{fuel} rail"], rail_fraction)
        df.loc[missing, rail_fraction.index] = fill_values

    # split aviation traffic for non-IDEES
    missing = df.index[df["total domestic aviation passenger"].isna()]
    for destination in ["domestic", "international"]:
        selection = [
            f"total {destination} aviation passenger",
            f"total {destination} aviation freight",
        ]
        aviation = df[selection].sum()
        aviation_fraction = aviation / aviation.sum()
        fill_values = cartesian(
            df.loc[missing, f"total {destination} aviation"], aviation_fraction
        )
        df.loc[missing, aviation_fraction.index] = fill_values

    for purpose in ["passenger", "freight"]:
        attrs = [
            f"total domestic aviation {purpose}",
            f"total international aviation {purpose}",
        ]
        df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(
            axis=1
        )

    if "BA" in df.index:
        # fill missing data for BA (services and road energy data)
        # proportional to RS with ratio of total residential demand
        missing = df.loc["BA"] == 0.0
        ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
        df.loc["BA", missing] = ratio * df.loc["RS", missing]

    return df


def build_district_heat_share(countries, idees):
    # district heating share
    district_heat = idees[["derived heat residential", "derived heat services"]].sum(
        axis=1
    )
    total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
        axis=1
    )

    district_heat_share = district_heat / total_heat

    district_heat_share = district_heat_share.reindex(countries)

    # Missing district heating share
    dh_share = (
        pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1])
        .div(100)
        .squeeze()
    )
    # make conservative assumption and take minimum from both data sets
    district_heat_share = pd.concat(
        [district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1
    ).min(axis=1)

    district_heat_share.name = "district heat share"

    return district_heat_share


def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
    # https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
    # downloaded 201228 (modified by EEA last on 201221)
    df = pd.read_csv(input_co2, encoding="latin-1", low_memory=False)

    df.replace(dict(Year="1985-1987"), 1986, inplace=True)
    df.Year = df.Year.astype(int)
    index_col = ["Country_code", "Pollutant_name", "Year", "Sector_name"]
    df = df.set_index(index_col).sort_index()

    emissions_scope = emissions_scope

    cts = ["CH", "EUA", "NO"] + eu28_eea

    slicer = idx[cts, emissions_scope, year, to_ipcc.values()]
    emissions = (
        df.loc[slicer, "emissions"]
        .unstack("Sector_name")
        .rename(columns=reverse(to_ipcc))
        .droplevel([1, 2])
    )

    emissions.rename(index={"EUA": "EU28", "UK": "GB"}, inplace=True)

    to_subtract = [
        "electricity",
        "services non-elec",
        "residential non-elec",
        "road non-elec",
        "rail non-elec",
        "domestic aviation",
        "international aviation",
        "domestic navigation",
        "international navigation",
        "agriculture, forestry and fishing",
    ]
    emissions["industrial non-elec"] = emissions["total energy"] - emissions[
        to_subtract
    ].sum(axis=1)

    emissions["agriculture"] += emissions["agriculture, forestry and fishing"]

    to_drop = [
        "total energy",
        "total wL",
        "total woL",
        "agriculture, forestry and fishing",
    ]
    emissions.drop(columns=to_drop, inplace=True)

    # convert from Gg to Mt
    return emissions / 1e3


def build_eurostat_co2(input_eurostat, countries, year=1990):
    eurostat = build_eurostat(input_eurostat, countries, year)

    specific_emissions = pd.Series(index=eurostat.columns, dtype=float)

    # emissions in tCO2_equiv per MWh_th
    specific_emissions["Solid fuels"] = 0.36  # Approximates coal
    specific_emissions["Oil (total)"] = 0.285  # Average of distillate and residue
    specific_emissions["Gas"] = 0.2  # For natural gas

    # oil values from https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11
    # Distillate oil (No. 2)  0.276
    # Residual oil (No. 6)  0.298
    # https://www.eia.gov/electricity/annual/html/epa_a_03.html

    return eurostat.multiply(specific_emissions).sum(axis=1)


def build_co2_totals(countries, eea_co2, eurostat_co2):
    co2 = eea_co2.reindex(countries)

    for ct in pd.Index(countries).intersection(["BA", "RS", "AL", "ME", "MK"]):
        mappings = {
            "electricity": (ct, "+", "Electricity & heat generation", np.nan),
            "residential non-elec": (ct, "+", "+", "Residential"),
            "services non-elec": (ct, "+", "+", "Services"),
            "road non-elec": (ct, "+", "+", "Road"),
            "rail non-elec": (ct, "+", "+", "Rail"),
            "domestic navigation": (ct, "+", "+", "Domestic Navigation"),
            "international navigation": (ct, "-", "Bunkers"),
            "domestic aviation": (ct, "+", "+", "Domestic aviation"),
            "international aviation": (ct, "-", "International aviation"),
            # does not include industrial process emissions or fuel processing/refining
            "industrial non-elec": (ct, "+", "Industry sector"),
            # does not include non-energy emissions
            "agriculture": (eurostat_co2.index.get_level_values(0) == ct)
            & eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
        }

        for i, mi in mappings.items():
            co2.at[ct, i] = eurostat_co2.loc[mi].sum()

    return co2


def build_transport_data(countries, population, idees):
    transport_data = pd.DataFrame(index=countries)

    # collect number of cars

    transport_data["number cars"] = idees["passenger cars"]

    # CH from http://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EU#Luxembourg_has_the_highest_number_of_passenger_cars_per_inhabitant
    if "CH" in countries:
        transport_data.at["CH", "number cars"] = 4.136e6

    missing = transport_data.index[transport_data["number cars"].isna()]
    if not missing.empty:
        logger.info(
            f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data."
        )

        cars_pp = transport_data["number cars"] / population
        transport_data.loc[missing, "number cars"] = cars_pp.mean() * population

    # collect average fuel efficiency in kWh/km

    transport_data["average fuel efficiency"] = idees["passenger car efficiency"]

    missing = transport_data.index[transport_data["average fuel efficiency"].isna()]
    if not missing.empty:
        logger.info(
            f"Missing data on fuel efficiency from:\n{list(missing)}\nFilling gapswith averaged data."
        )

        fill_values = transport_data["average fuel efficiency"].mean()
        transport_data.loc[missing, "average fuel efficiency"] = fill_values

    return transport_data


def rescale_idees_from_eurostat(
    idees_countries, energy, eurostat, input_eurostat, countries
):
    """
    Takes JRC IDEES data from 2015 and rescales it by the ratio of the eurostat
    data and the 2015 eurostat data.

    missing data: ['passenger car efficiency', 'passenger cars']
    """
    main_cols = ["Total all products", "Electricity"]
    # read in the eurostat data for 2015
    eurostat_2015 = build_eurostat(input_eurostat, countries, 2015)[main_cols]
    eurostat_year = eurostat[main_cols]
    # calculate the ratio of the two data sets
    ratio = eurostat_year / eurostat_2015
    ratio = ratio.droplevel([1, 4])
    cols_rename = {"Total all products": "total", "Electricity": "ele"}
    index_rename = {v: k for k, v in idees_rename.items()}
    ratio.rename(columns=cols_rename, index=index_rename, inplace=True)

    mappings = {
        "Residential": {
            "total": [
                "total residential space",
                "total residential water",
                "total residential cooking",
                "total residential",
                "derived heat residential",
                "thermal uses residential",
            ],
            "elec": [
                "electricity residential space",
                "electricity residential water",
                "electricity residential cooking",
                "electricity residential",
            ],
        },
        "Services": {
            "total": [
                "total services space",
                "total services water",
                "total services cooking",
                "total services",
                "derived heat services",
                "thermal uses services",
            ],
            "elec": [
                "electricity services space",
                "electricity services water",
                "electricity services cooking",
                "electricity services",
            ],
        },
        "Agriculture & forestry": {
            "total": [
                "total agriculture heat",
                "total agriculture machinery",
                "total agriculture",
            ],
            "elec": [
                "total agriculture electricity",
            ],
        },
        "Road": {
            "total": [
                "total road",
                "total passenger cars",
                "total other road passenger",
                "total light duty road freight",
            ],
            "elec": [
                "electricity road",
                "electricity passenger cars",
                "electricity other road passenger",
                "electricity light duty road freight",
            ],
        },
        "Rail": {
            "total": [
                "total rail",
                "total rail passenger",
                "total rail freight",
            ],
            "elec": [
                "electricity rail",
                "electricity rail passenger",
                "electricity rail freight",
            ],
        },
    }

    avia_inter = [
        "total aviation passenger",
        "total aviation freight",
        "total international aviation passenger",
        "total international aviation freight",
        "total international aviation",
    ]
    avia_domestic = [
        "total domestic aviation passenger",
        "total domestic aviation freight",
        "total domestic aviation",
    ]
    navigation = [
        "total domestic navigation",
    ]

    for country in idees_countries:
        for sector, mapping in mappings.items():
            sector_ratio = ratio.loc[(country, slice(None), sector)]

            energy.loc[country, mapping["total"]] *= sector_ratio["total"].iloc[0]
            energy.loc[country, mapping["elec"]] *= sector_ratio["ele"].iloc[0]

        avi_d = ratio.loc[(country, slice(None), "Domestic aviation"), "total"]
        avi_i = ratio.loc[(country, "International aviation", slice(None)), "total"]
        energy.loc[country, avia_inter] *= avi_i.iloc[0]
        energy.loc[country, avia_domestic] *= avi_d.iloc[0]

        nav = ratio.loc[(country, slice(None), "Domestic Navigation"), "total"]
        energy.loc[country, navigation] *= nav.iloc[0]

    return energy


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

        snakemake = mock_snakemake("build_energy_totals")

    configure_logging(snakemake)
    set_scenario_config(snakemake)

    params = snakemake.params.energy

    nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
    population = nuts3["pop"].groupby(nuts3.country).sum()

    countries = snakemake.params.countries
    idees_countries = pd.Index(countries).intersection(eu28)

    data_year = params["energy_totals_year"]
    input_eurostat = snakemake.input.eurostat
    eurostat = build_eurostat(input_eurostat, countries, data_year)
    swiss = build_swiss(data_year)
    # data from idees only exists from 2000-2015. read in latest data and rescale later
    idees = build_idees(idees_countries, min(2015, data_year))

    energy = build_energy_totals(countries, eurostat, swiss, idees)

    if data_year > 2015:
        logger.info("Data year is after 2015. Rescaling IDEES data based on eurostat.")
        energy = rescale_idees_from_eurostat(
            idees_countries, energy, eurostat, input_eurostat, countries
        )

    energy.to_csv(snakemake.output.energy_name)

    # use rescaled idees data to calculate district heat share
    district_heat_share = build_district_heat_share(
        countries, energy.loc[idees_countries]
    )
    district_heat_share.to_csv(snakemake.output.district_heat_share)

    base_year_emissions = params["base_emissions_year"]
    emissions_scope = snakemake.params.energy["emissions"]
    eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
    eurostat_co2 = build_eurostat_co2(input_eurostat, countries, base_year_emissions)

    co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
    co2.to_csv(snakemake.output.co2_name)

    transport = build_transport_data(countries, population, idees)
    transport.to_csv(snakemake.output.transport_name)