text:
import csv
import glob
import itertools
from inspect import currentframe, getframeinfo
from pathlib import Path

import numpy as np
import xarray as xr
from pypardiso import spsolve
from scipy import sparse

from . import DATA_DIR
from .background_systems import BackgroundSystemModel
from .export import ExportInventory
from .geomap import Geomap
from .utils import build_fleet_array

np.warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)

REMIND_FILES_DIR = DATA_DIR / "IAM"
DATASET_MAPPING = DATA_DIR / "mapping_datasets.csv"
ABRASION_EMISSIONS = DATA_DIR / "abrasion_emissions.csv"

def dataset_mapping():
    with open(DATASET_MAPPING) as f:
        csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()]
    header, *data = csv_list

    d = {}
    for row in data:
        d[(row[0], row[1], row[2],)] = {
            "name": row[3],
            "location": row[4],
            "unit": row[5],
            "reference product": row[6],
            "multiplication factor": float(row[7]),
        }

    return d

def abrasion_emissions():
    with open(ABRASION_EMISSIONS, encoding="latin1") as f:
        csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()]
    header, *data = csv_list

    d = {}
    for row in data:
        d[(row[0], row[1])] = {
            "tyre": float(row[2]),
            "brake": float(row[3]),
            "road": float(row[4]),
        }

    return d

class InventoryCalculation:
    """
    Build and solve the inventory for results characterization and inventory export

    Vehicles to be analyzed can be filtered by passing a `scope` dictionary.
    Some assumptions in the background system can also be adjusted by passing a `background_configuration` dictionary.

    .. code-block:: python

        scope = {
                        'powertrain':['BEV', 'Human', 'ICEV-p'],
                    }
        bc = {'country':'CH', # considers electricity network losses for Switzerland
              'custom electricity mix' : [[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
                                          [0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
                                          [0,0,1,0,0,0,0,0,0,0,0,0,0,0,0],
                                          [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0],
                                         ], # in this case, 100% nuclear for the second year
              'fuel blend':{
                 'petrol':{
                        'primary fuel':{
                            'type':'petrol',
                            'share':[0.9, 0.8, 0.7, 0.6]
                            },
                        'secondary fuel':{
                            'type':'bioethanol - wheat straw',
                            'share': [0.1, 0.2, 0.3, 0.4]
                            }
                        },
              'energy storage': {
                  'electric': {
                      'type':'NMC',
                      'origin': 'NO'
                  },
                  'hydrogen': {
                      'type':'carbon fiber'
                  }
              }
             }

        InventoryCalculation(CarModel.array,
                            background_configuration=background_configuration,
                            scope=scope,
                            scenario="RCP26")

    The `custom electricity mix` key in the background_configuration dictionary defines an electricity mix to apply,
    under the form of one or several array(s), depending on the number of years to analyze,
    that should total 1, of which the indices correspond to:

        - [0]: hydro-power
        - [1]: nuclear
        - [2]: natural gas
        - [3]: solar power
        - [4]: wind power
        - [5]: biomass
        - [6]: coal
        - [7]: oil
        - [8]: geothermal
        - [9]: waste incineration
        - [10]: biomass with CCS
        - [11]: biogas with CCS
        - [12]: coal with CCS
        - [13]: natural gas with CCS
        - [14]: wood with CCS

    If none is given, the electricity mix corresponding to the country specified in `country` will be selected.
    If no country is specified, Europe applies.

    The `primary` and `secondary` fuel keys contain an array with shares of alternative petrol fuel for each year, to create a custom blend.
    If none is provided, a blend provided by the Integrated Assessment model REMIND is used, which will depend on the REMIND energy scenario selected.

    Here is a list of available fuel pathways:

    Petrol technologies
    -------------------
    petrol
    bioethanol - wheat straw
    bioethanol - maize starch
    bioethanol - sugarbeet
    bioethanol - forest residues
    synthetic gasoline - economic allocation
    synthetic gasoline - energy allocation

    :ivar array: array from the CarModel class
    :vartype array: CarModel.array
    :ivar scope: dictionary that contains filters for narrowing the analysis
    :ivar background_configuration: dictionary that contains choices for background system
    :ivar scenario: REMIND energy scenario to use ("SSP2-Baseline": business-as-usual,
                                                    "SSP2-PkBudg1300": limits temperature increase by 2100 to 2 degrees Celsius,
                                                    "static": no forward-looking modification of the background inventories).
                    "SSP2-Baseline" selected by default.

    .. code-block:: python

    """

    def __init__(
        self,
        array,
        scope=None,
        background_configuration=None,
        scenario="SSP2-Base",
        method="recipe",
        method_type="midpoint",
    ):

        if scope is None:
            scope = {
                "size": array.coords["size"].values.tolist(),
                "powertrain": array.coords["powertrain"].values.tolist(),
                "year": array.coords["year"].values.tolist(),
                "fu": {"unit": "vkm", "quantity": 1},
            }
        else:
            scope["size"] = scope.get("size", array.coords["size"].values.tolist())
            scope["powertrain"] = scope.get(
                "powertrain", array.coords["powertrain"].values.tolist()
            )
            scope["year"] = scope.get("year", array.coords["year"].values.tolist())
            scope["fu"] = scope.get("fu", {"unit": "vkm", "quantity": 1})

            if "unit" not in scope["fu"]:
                scope["fu"]["unit"] = "vkm"
            else:
                if scope["fu"]["unit"] not in ["vkm", "pkm"]:
                    raise NameError(
                        "Incorrect specification of functional unit. Must be 'vkm' or 'pkm'."
                    )

            if "quantity" not in scope["fu"]:
                scope["fu"]["quantity"] = 1
            else:
                try:
                    float(scope["fu"]["quantity"])
                except ValueError:
                    raise ValueError(
                        "Incorrect quantity for the functional unit defined."
                    )

        self.scope = scope
        self.scenario = scenario
        self.geo = Geomap()

        self.mapping = dataset_mapping()
        self.abrasion_emissions = abrasion_emissions()

        # Check if a fleet composition is specified
        if "fleet" in self.scope["fu"]:

            if isinstance(self.scope["fu"]["fleet"], xr.DataArray):
                self.fleet = self.scope["fu"]["fleet"]
            else:

                # check if a path as string is provided
                if isinstance(self.scope["fu"]["fleet"], str):
                    fp = Path(self.scope["fu"]["fleet"])

                # check if instance of pathlib is provided instead
                elif isinstance(self.scope["fu"]["fleet"], Path):
                    fp = self.scope["fu"]["fleet"]

                else:
                    raise TypeError(
                        "The format used to specify fleet compositions is not valid."
                        "A file path that points to a CSV file is expected. "
                        "Or an array of type xarray.DataArray."
                    )

                if not fp.is_file():
                    raise FileNotFoundError(
                        "The CSV file that contains fleet composition could not be found."
                    )

                if fp.suffix != ".csv":
                    raise TypeError(
                        "A CSV file is expected to build the fleet composition."
                    )

                self.fleet = build_fleet_array(fp, self.scope)

        else:
            self.fleet = None

        array = array.sel(
            powertrain=self.scope["powertrain"],
            year=self.scope["year"],
            size=self.scope["size"],
        )

        self.array = array.stack(desired=["size", "powertrain", "year"])

        self.compliant_vehicles = array.sel(parameter="is_available")

        # store some important specs for inventory documentation
        self.specs = array.sel(
            parameter=[
                "combustion power",
                "electric power",
                "combustion power share",
                "lifetime kilometers",
                "kilometers per year",
                "range",
                "TtW efficiency",
                "TtW energy",
                "electric energy stored",
                "oxidation energy stored",
                "energy battery mass",
                "curb mass",
                "driving mass",
            ]
        )

        self.iterations = len(array.value.values)

        self.number_of_vehicles = (
            len(self.scope["size"])
            * len(self.scope["powertrain"])
            * len(self.scope["year"])
        )

        self.array_inputs = {
            x: i for i, x in enumerate(list(self.array.parameter.values), 0)
        }
        self.array_powertrains = {
            x: i for i, x in enumerate(list(self.array.powertrain.values), 0)
        }

        if not background_configuration is None:
            self.background_configuration = background_configuration
        else:
            self.background_configuration = {}

        if "energy storage" not in self.background_configuration:
            self.background_configuration["energy storage"] = {
                "electric": {"type": "NMC-111", "origin": "CN"}
            }
        else:
            if "electric" not in self.background_configuration["energy storage"]:
                self.background_configuration["energy storage"]["electric"] = {
                    "type": "NMC-111",
                    "origin": "CN",
                }
            else:
                if (
                    "origin"
                    not in self.background_configuration["energy storage"]["electric"]
                ):
                    self.background_configuration["energy storage"]["electric"][
                        "origin"
                    ] = "CN"
                if (
                    "type"
                    not in self.background_configuration["energy storage"]["electric"]
                ):
                    self.background_configuration["energy storage"]["electric"][
                        "type"
                    ] = "NMC-111"

        self.inputs = self.get_dict_input()
        self.bs = BackgroundSystemModel()
        self.country = self.get_country_of_use()
        self.add_additional_activities()
        self.rev_inputs = self.get_rev_dict_input()
        self.A = self.get_A_matrix()
        self.mix = self.define_electricity_mix_for_fuel_prep()
        self.fuel_blends = {}
        self.fuel_dictionary = self.create_fuel_dictionary()
        self.define_fuel_blends()
        self.set_actual_range()

        if "direct air capture" in self.background_configuration:
            if "heat source" in self.background_configuration["direct air capture"]:
                heat_source = self.background_configuration["direct air capture"][
                    "heat source"
                ]

                if heat_source != "waste heat":
                    self.select_heat_supplier(heat_source)

        self.map_non_fuel_emissions = {
            (
                "1-Pentene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "1-Pentene direct emissions, rural",
            (
                "1-Pentene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "1-Pentene direct emissions, suburban",
            (
                "1-Pentene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "1-Pentene direct emissions, urban",
            (
                "Acetaldehyde",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Acetaldehyde direct emissions, rural",
            (
                "Acetaldehyde",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Acetaldehyde direct emissions, suburban",
            (
                "Acetaldehyde",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Acetaldehyde direct emissions, urban",
            (
                "Acetone",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Acetone direct emissions, rural",
            (
                "Acetone",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Acetone direct emissions, suburban",
            (
                "Acetone",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Acetone direct emissions, urban",
            (
                "Acrolein",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Acrolein direct emissions, rural",
            (
                "Acrolein",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Acrolein direct emissions, suburban",
            (
                "Acrolein",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Acrolein direct emissions, urban",
            (
                "Ammonia",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Ammonia direct emissions, rural",
            (
                "Ammonia",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Ammonia direct emissions, suburban",
            (
                "Ammonia",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Ammonia direct emissions, urban",
            (
                "Arsenic",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Arsenic direct emissions, rural",
            (
                "Arsenic",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Arsenic direct emissions, suburban",
            (
                "Arsenic",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Arsenic direct emissions, urban",
            (
                "Benzaldehyde",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Benzaldehyde direct emissions, rural",
            (
                "Benzaldehyde",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Benzaldehyde direct emissions, suburban",
            (
                "Benzaldehyde",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Benzaldehyde direct emissions, urban",
            (
                "Benzene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Benzene direct emissions, rural",
            (
                "Benzene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Benzene direct emissions, suburban",
            (
                "Benzene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Benzene direct emissions, urban",
            (
                "Butane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Butane direct emissions, rural",
            (
                "Butane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Butane direct emissions, suburban",
            (
                "Butane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Butane direct emissions, urban",
            (
                "Cadmium",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Cadmium direct emissions, rural",
            (
                "Cadmium",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Cadmium direct emissions, suburban",
            (
                "Cadmium",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Cadmium direct emissions, urban",
            (
                "Carbon monoxide, fossil",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Carbon monoxide direct emissions, rural",
            (
                "Carbon monoxide, fossil",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Carbon monoxide direct emissions, suburban",
            (
                "Carbon monoxide, fossil",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Carbon monoxide direct emissions, urban",
            (
                "Chromium",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Chromium direct emissions, rural",
            (
                "Chromium",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Chromium direct emissions, suburban",
            (
                "Chromium",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Chromium direct emissions, urban",
            (
                "Chromium VI",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Chromium VI direct emissions, rural",
            (
                "Chromium VI",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Chromium VI direct emissions, suburban",
            (
                "Chromium VI",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Chromium VI direct emissions, urban",
            (
                "Copper",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Copper direct emissions, rural",
            (
                "Copper",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Copper direct emissions, suburban",
            (
                "Copper",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Copper direct emissions, urban",
            (
                "Cyclohexane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Cyclohexane direct emissions, rural",
            (
                "Cyclohexane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Cyclohexane direct emissions, suburban",
            (
                "Cyclohexane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Cyclohexane direct emissions, urban",
            (
                "Dinitrogen monoxide",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Dinitrogen oxide direct emissions, rural",
            (
                "Dinitrogen monoxide",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Dinitrogen oxide direct emissions, suburban",
            (
                "Dinitrogen monoxide",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Dinitrogen oxide direct emissions, urban",
            (
                "Ethane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Ethane direct emissions, rural",
            (
                "Ethane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Ethane direct emissions, suburban",
            (
                "Ethane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Ethane direct emissions, urban",
            (
                "Ethene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Ethene direct emissions, rural",
            (
                "Ethene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Ethene direct emissions, suburban",
            (
                "Ethene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Ethene direct emissions, urban",
            (
                "Formaldehyde",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Formaldehyde direct emissions, rural",
            (
                "Formaldehyde",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Formaldehyde direct emissions, suburban",
            (
                "Formaldehyde",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Formaldehyde direct emissions, urban",
            (
                "Heptane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Heptane direct emissions, rural",
            (
                "Heptane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Heptane direct emissions, suburban",
            (
                "Heptane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Heptane direct emissions, urban",
            (
                "Hexane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Hexane direct emissions, rural",
            (
                "Hexane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Hexane direct emissions, suburban",
            (
                "Hexane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Hexane direct emissions, urban",
            (
                "Mercury",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Mercury direct emissions, rural",
            (
                "Mercury",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Mercury direct emissions, suburban",
            (
                "Mercury",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Mercury direct emissions, urban",
            (
                "Methane, fossil",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Methane direct emissions, rural",
            (
                "Methane, fossil",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Methane direct emissions, suburban",
            (
                "Methane, fossil",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Methane direct emissions, urban",
            (
                "Methyl ethyl ketone",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Methyl ethyl ketone direct emissions, rural",
            (
                "Methyl ethyl ketone",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Methyl ethyl ketone direct emissions, suburban",
            (
                "Methyl ethyl ketone",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Methyl ethyl ketone direct emissions, urban",
            (
                "m-Xylene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "m-Xylene direct emissions, rural",
            (
                "m-Xylene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "m-Xylene direct emissions, suburban",
            (
                "m-Xylene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "m-Xylene direct emissions, urban",
            (
                "Nickel",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Nickel direct emissions, rural",
            (
                "Nickel",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Nickel direct emissions, suburban",
            (
                "Nickel",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Nickel direct emissions, urban",
            (
                "Nitrogen oxides",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Nitrogen oxides direct emissions, rural",
            (
                "Nitrogen oxides",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Nitrogen oxides direct emissions, suburban",
            (
                "Nitrogen oxides",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Nitrogen oxides direct emissions, urban",
            (
                "NMVOC, non-methane volatile organic compounds, unspecified origin",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "NMVOC direct emissions, rural",
            (
                "NMVOC, non-methane volatile organic compounds, unspecified origin",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "NMVOC direct emissions, suburban",
            (
                "NMVOC, non-methane volatile organic compounds, unspecified origin",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "NMVOC direct emissions, urban",
            (
                "o-Xylene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "o-Xylene direct emissions, rural",
            (
                "o-Xylene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "o-Xylene direct emissions, suburban",
            (
                "o-Xylene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "o-Xylene direct emissions, urban",
            (
                "PAH, polycyclic aromatic hydrocarbons",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "PAH, polycyclic aromatic hydrocarbons direct emissions, rural",
            (
                "PAH, polycyclic aromatic hydrocarbons",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "PAH, polycyclic aromatic hydrocarbons direct emissions, suburban",
            (
                "PAH, polycyclic aromatic hydrocarbons",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "PAH, polycyclic aromatic hydrocarbons direct emissions, urban",
            (
                "Particulates, < 2.5 um",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Particulate matters direct emissions, rural",
            (
                "Particulates, < 2.5 um",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Particulate matters direct emissions, suburban",
            (
                "Particulates, < 2.5 um",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Particulate matters direct emissions, urban",
            (
                "Pentane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Pentane direct emissions, rural",
            (
                "Pentane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Pentane direct emissions, suburban",
            (
                "Pentane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Pentane direct emissions, urban",
            (
                "Propane",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Propane direct emissions, rural",
            (
                "Propane",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Propane direct emissions, suburban",
            (
                "Propane",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Propane direct emissions, urban",
            (
                "Propene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Propene direct emissions, rural",
            (
                "Propene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Propene direct emissions, suburban",
            (
                "Propene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Propene direct emissions, urban",
            (
                "Selenium",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Selenium direct emissions, rural",
            (
                "Selenium",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Selenium direct emissions, suburban",
            (
                "Selenium",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Selenium direct emissions, urban",
            (
                "Styrene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Styrene direct emissions, rural",
            (
                "Styrene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Styrene direct emissions, suburban",
            (
                "Styrene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Styrene direct emissions, urban",
            (
                "Toluene",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Toluene direct emissions, rural",
            (
                "Toluene",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Toluene direct emissions, suburban",
            (
                "Toluene",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Toluene direct emissions, urban",
            (
                "Zinc",
                ("air", "low population density, long-term"),
                "kilogram",
            ): "Zinc direct emissions, rural",
            (
                "Zinc",
                ("air", "non-urban air or from high stacks"),
                "kilogram",
            ): "Zinc direct emissions, suburban",
            (
                "Zinc",
                ("air", "urban air close to ground"),
                "kilogram",
            ): "Zinc direct emissions, urban",
        }

        self.index_emissions = [
            self.inputs[i] for i in self.map_non_fuel_emissions.keys()
        ]

        self.map_noise_emissions = {
            (
                "noise, octave 1, day time, urban",
                ("octave 1", "day time", "urban"),
                "joule",
            ): "noise, octave 1, day time, urban",
            (
                "noise, octave 2, day time, urban",
                ("octave 2", "day time", "urban"),
                "joule",
            ): "noise, octave 2, day time, urban",
            (
                "noise, octave 3, day time, urban",
                ("octave 3", "day time", "urban"),
                "joule",
            ): "noise, octave 3, day time, urban",
            (
                "noise, octave 4, day time, urban",
                ("octave 4", "day time", "urban"),
                "joule",
            ): "noise, octave 4, day time, urban",
            (
                "noise, octave 5, day time, urban",
                ("octave 5", "day time", "urban"),
                "joule",
            ): "noise, octave 5, day time, urban",
            (
                "noise, octave 6, day time, urban",
                ("octave 6", "day time", "urban"),
                "joule",
            ): "noise, octave 6, day time, urban",
            (
                "noise, octave 7, day time, urban",
                ("octave 7", "day time", "urban"),
                "joule",
            ): "noise, octave 7, day time, urban",
            (
                "noise, octave 8, day time, urban",
                ("octave 8", "day time", "urban"),
                "joule",
            ): "noise, octave 8, day time, urban",
            (
                "noise, octave 1, day time, suburban",
                ("octave 1", "day time", "suburban"),
                "joule",
            ): "noise, octave 1, day time, suburban",
            (
                "noise, octave 2, day time, suburban",
                ("octave 2", "day time", "suburban"),
                "joule",
            ): "noise, octave 2, day time, suburban",
            (
                "noise, octave 3, day time, suburban",
                ("octave 3", "day time", "suburban"),
                "joule",
            ): "noise, octave 3, day time, suburban",
            (
                "noise, octave 4, day time, suburban",
                ("octave 4", "day time", "suburban"),
                "joule",
            ): "noise, octave 4, day time, suburban",
            (
                "noise, octave 5, day time, suburban",
                ("octave 5", "day time", "suburban"),
                "joule",
            ): "noise, octave 5, day time, suburban",
            (
                "noise, octave 6, day time, suburban",
                ("octave 6", "day time", "suburban"),
                "joule",
            ): "noise, octave 6, day time, suburban",
            (
                "noise, octave 7, day time, suburban",
                ("octave 7", "day time", "suburban"),
                "joule",
            ): "noise, octave 7, day time, suburban",
            (
                "noise, octave 8, day time, suburban",
                ("octave 8", "day time", "suburban"),
                "joule",
            ): "noise, octave 8, day time, suburban",
            (
                "noise, octave 1, day time, rural",
                ("octave 1", "day time", "rural"),
                "joule",
            ): "noise, octave 1, day time, rural",
            (
                "noise, octave 2, day time, rural",
                ("octave 2", "day time", "rural"),
                "joule",
            ): "noise, octave 2, day time, rural",
            (
                "noise, octave 3, day time, rural",
                ("octave 3", "day time", "rural"),
                "joule",
            ): "noise, octave 3, day time, rural",
            (
                "noise, octave 4, day time, rural",
                ("octave 4", "day time", "rural"),
                "joule",
            ): "noise, octave 4, day time, rural",
            (
                "noise, octave 5, day time, rural",
                ("octave 5", "day time", "rural"),
                "joule",
            ): "noise, octave 5, day time, rural",
            (
                "noise, octave 6, day time, rural",
                ("octave 6", "day time", "rural"),
                "joule",
            ): "noise, octave 6, day time, rural",
            (
                "noise, octave 7, day time, rural",
                ("octave 7", "day time", "rural"),
                "joule",
            ): "noise, octave 7, day time, rural",
            (
                "noise, octave 8, day time, rural",
                ("octave 8", "day time", "rural"),
                "joule",
            ): "noise, octave 8, day time, rural",
        }

        self.elec_map = {
            "Hydro": (
                "electricity production, hydro, run-of-river",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Nuclear": (
                "electricity production, nuclear, pressure water reactor",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Gas": (
                "electricity production, natural gas, conventional power plant",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Solar": (
                "electricity production, photovoltaic, 3kWp slanted-roof installation, multi-Si, panel, mounted",
                "DE",
                "kilowatt hour",
                "electricity, low voltage",
            ),
            "Wind": (
                "electricity production, wind, 1-3MW turbine, onshore",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Biomass": (
                "heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Coal": (
                "electricity production, hard coal",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Oil": (
                "electricity production, oil",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Geo": (
                "electricity production, deep geothermal",
                "DE",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Waste": (
                "treatment of municipal solid waste, incineration",
                "DE",
                "kilowatt hour",
                "electricity, for reuse in municipal waste incineration only",
            ),
            "Biomass CCS": (
                "electricity production, at BIGCC power plant 450MW, pre, pipeline 200km, storage 1000m",
                "RER",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Coal CCS": (
                "electricity production, at power plant/hard coal, post, pipeline 200km, storage 1000m",
                "RER",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Gas CCS": (
                "electricity production, at power plant/natural gas, post, pipeline 200km, storage 1000m",
                "RER",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Biogas CCS": (
                "electricity production, at power plant/biogas, post, pipeline 200km, storage 1000m",
                "RER",
                "kilowatt hour",
                "electricity, high voltage",
            ),
            "Wood CCS": (
                "electricity production, at wood burning power plant 20 MW, truck 25km, post, pipeline 200km, storage 1000m",
                "RER",
                "kilowatt hour",
                "electricity, high voltage",
            ),
        }

        self.index_noise = [self.inputs[i] for i in self.map_noise_emissions.keys()]

        self.list_cat, self.split_indices = self.get_split_indices()

        self.method = method

        if self.method == "recipe":
            self.method_type = method_type
        else:
            self.method_type = "midpoint"

        self.impact_categories = self.get_dict_impact_categories()

        # Load the B matrix
        self.B = None

    def get_results_table(self, split, sensitivity=False):
        """
        Format an xarray.DataArray array to receive the results.

        :param sensitivity:
        :param split: "components" or "impact categories". Split by impact categories only applicable when "endpoint" level is applied.
        :return: xarrray.DataArray
        """

        if split == "components":
            cat = [
                "direct - exhaust",
                "direct - non-exhaust",
                "energy chain",
                "maintenance",
                "glider",
                "EoL",
                "powertrain",
                "energy storage",
                "road",
            ]

        dict_impact_cat = list(self.impact_categories.keys())

        sizes = self.scope["size"]

        if isinstance(self.fleet, xr.core.dataarray.DataArray):
            sizes += ["fleet average"]

        if not sensitivity:

            response = xr.DataArray(
                np.zeros(
                    (
                        self.B.shape[1],
                        len(sizes),
                        len(self.scope["powertrain"]),
                        len(self.scope["year"]),
                        len(cat),
                        self.iterations,
                    )
                ),
                coords=[
                    dict_impact_cat,
                    sizes,
                    self.scope["powertrain"],
                    self.scope["year"],
                    cat,
                    np.arange(0, self.iterations),
                ],
                dims=[
                    "impact_category",
                    "size",
                    "powertrain",
                    "year",
                    "impact",
                    "value",
                ],
            )

        else:
            params = [a for a in self.array.value.values]
            response = xr.DataArray(
                np.zeros(
                    (
                        self.B.shape[1],
                        len(sizes),
                        len(self.scope["powertrain"]),
                        len(self.scope["year"]),
                        self.iterations,
                    )
                ),
                coords=[
                    dict_impact_cat,
                    sizes,
                    self.scope["powertrain"],
                    self.scope["year"],
                    params,
                ],
                dims=["impact_category", "size", "powertrain", "year", "parameter"],
            )

        return response

    def get_split_indices(self):
        """
        Return list of indices to split the results into categories.

        :return: list of indices
        :rtype: list
        """
        filename = "dict_split.csv"
        filepath = DATA_DIR / filename
        if not filepath.is_file():
            raise FileNotFoundError("The dictionary of splits could not be found.")

        with open(filepath) as f:
            csv_list = [[val.strip() for val in r.split(";")] for r in f.readlines()]
        (_, _, *header), *data = csv_list

        csv_dict = {}
        for row in data:
            key, sub_key, *values = row

            if key in csv_dict:
                if sub_key in csv_dict[key]:
                    csv_dict[key][sub_key].append(
                        {"search by": values[0], "search for": values[1]}
                    )
                else:
                    csv_dict[key][sub_key] = [
                        {"search by": values[0], "search for": values[1]}
                    ]
            else:
                csv_dict[key] = {
                    sub_key: [{"search by": values[0], "search for": values[1]}]
                }

        flatten = itertools.chain.from_iterable

        d = {}
        l = []

        d["direct - exhaust"] = []
        d["direct - exhaust"].append(
            self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")]
        )
        d["direct - exhaust"].append(
            self.inputs[
                ("Carbon dioxide, from soil or biomass stock", ("air",), "kilogram")
            ]
        )
        d["direct - exhaust"].append(
            self.inputs[("Methane, fossil", ("air",), "kilogram")]
        )

        d["direct - exhaust"].extend(self.index_emissions)
        d["direct - exhaust"].extend(self.index_noise)

        l.append(d["direct - exhaust"])

        for cat in csv_dict["components"]:
            d[cat] = list(
                flatten(
                    [
                        self.get_index_of_flows([l["search for"]], l["search by"])
                        for l in csv_dict["components"][cat]
                    ]
                )
            )
            l.append(d[cat])

        list_ind = [d[x] for x in d]
        maxLen = max(map(len, list_ind))
        for row in list_ind:
            while len(row) < maxLen:
                row.extend([len(self.inputs) - 1])
        return list(d.keys()), list_ind

    def calculate_impacts(self, split="components", sensitivity=False):

        self.B = self.get_B_matrix()

        # Prepare an array to store the results
        results = self.get_results_table(split, sensitivity=sensitivity)

        # Create electricity and fuel market datasets
        self.create_electricity_market_for_fuel_prep()

        # Create electricity market dataset for battery production
        self.create_electricity_market_for_battery_production()

        # Fill in the A matrix with car parameters
        self.set_inputs_in_A_matrix(self.array.values)

        # Add rows for fleet vehicles, if any
        if isinstance(self.fleet, xr.core.dataarray.DataArray):
            self.build_fleet_vehicles()

            # Update number of cars
            self.number_of_vehicles += len(self.scope["year"]) * len(
                self.scope["powertrain"]
            )

            # Update dictionary
            self.get_rev_dict_input()

            # Update B matrix
            self.B = self.get_B_matrix()

        new_arr = np.float32(
            np.zeros((self.A.shape[1], self.B.shape[1], len(self.scope["year"])))
        )

        f = np.zeros((np.shape(self.A)[1]))

        # Collect indices of activities contributing to the first level
        ind_veh = [
            self.inputs[i]
            for i in self.inputs
            if "transport, " in i[0] and any(s in i[0] for s in self.scope["size"])
        ]

        arr = self.A[0, : -self.number_of_vehicles, ind_veh].sum(axis=0)
        ind = np.nonzero(arr)[0]

        if self.scenario != "static":
            B = self.B.interp(
                year=self.scope["year"], kwargs={"fill_value": "extrapolate"}
            ).values
        else:
            B = self.B[0].values

        for a in ind:
            f[:] = 0
            f[a] = 1
            X = np.float32(spsolve(sparse.csr_matrix(self.A[0]), f.T))

            if self.scenario == "static":
                new_arr[a] = np.float32(X * B).sum(axis=-1).T[..., None]
            else:
                new_arr[a] = np.float32(X * B).sum(axis=-1).T

        shape = (
            self.iterations,
            len(self.scope["size"]),
            len(self.scope["powertrain"]),
            len(self.scope["year"]),
            self.A.shape[1],
        )

        arr = (
            self.A[:, :, -self.number_of_vehicles :].transpose(0, 2, 1).reshape(shape)
            * new_arr.transpose(1, 2, 0)[:, None, None, None, ...]
            * -1
        )
        arr = arr[..., self.split_indices].sum(axis=-1)

        if sensitivity:
            results[...] = arr.transpose(0, 2, 3, 4, 5, 1).sum(axis=-2)
            results /= results.sel(parameter="reference")
        else:
            results[...] = arr.transpose(0, 2, 3, 4, 5, 1)

        print(results.shape)
        print(arr.shape)

        # If the FU is in passenger-km, we normalize the results by the number of passengers
        if self.scope["fu"]["unit"] == "vkm":
            load_factor = 1
        else:

            load_factor = np.resize(
                self.array[self.array_inputs["average passengers"]].values,
                (
                    1,
                    len(self.scope["size"]),
                    len(self.scope["powertrain"]),
                    len(self.scope["year"]),
                    1,
                    1,
                ),
            )

        return (
            results.astype("float32")
            / load_factor
            * int(self.scope["fu"]["quantity"])
            * self.compliant_vehicles.values[None, :, :, :, None, ...]
        )

    def add_additional_activities(self):
        # Add as many rows and columns as cars to consider
        # Also add additional columns and rows for electricity markets
        # for fuel preparation and energy battery production

        maximum = max(self.inputs.values())

        for y in self.scope["year"]:

            if {"ICEV-p"}.intersection(set(self.scope["powertrain"])):
                maximum += 1
                self.inputs[
                    (
                        "fuel supply for gasoline vehicles, " + str(y),
                        self.country,
                        "kilogram",
                        "fuel",
                    )
                ] = maximum

            if {"BEV"}.intersection(set(self.scope["powertrain"])):
                maximum += 1
                self.inputs[
                    (
                        "electricity supply for electric vehicles, " + str(y),
                        self.country,
                        "kilowatt hour",
                        "electricity, low voltage, for battery electric vehicles",
                    )
                ] = maximum

            maximum += 1
            self.inputs[
                (
                    "electricity market for fuel preparation, " + str(y),
                    self.country,
                    "kilowatt hour",
                    "electricity, low voltage",
                )
            ] = maximum

            maximum += 1
            self.inputs[
                (
                    "electricity market for energy storage production, " + str(y),
                    self.background_configuration["energy storage"]["electric"][
                        "origin"
                    ],
                    "kilowatt hour",
                    "electricity, low voltage, for energy storage production",
                )
            ] = maximum

        for s in self.scope["size"]:
            for pt in self.scope["powertrain"]:
                for y in self.scope["year"]:
                    maximum += 1

                    # European emission standards function of registration year
                    if y < 2003:
                        euro_class = "EURO-1"
                    if 2003 <= y < 2006:
                        euro_class = "EURO-2"
                    if 2006 <= y < 2016:
                        euro_class = "EURO-3"
                    if 2017 <= y < 2020:
                        euro_class = "EURO-4"
                    if y >= 2020:
                        euro_class = "EURO-5"

                    name = (
                        "transport, "
                        + s
                        + ", "
                        + pt
                        + ", "
                        + str(y)
                        + ", "
                        + euro_class
                    )

                    if self.scope["fu"]["unit"] == "vkm":
                        unit = "kilometer"
                    if self.scope["fu"]["unit"] == "pkm":
                        unit = "person kilometer"

                    self.inputs[
                        (
                            name,
                            self.background_configuration["country"],
                            unit,
                            "transport, " + s + ", " + euro_class,
                        )
                    ] = maximum

    def add_additional_activities_for_export(self):
        # Add as many rows and columns as cars to consider
        # Also add additional columns and rows for electricity markets
        # for fuel preparation and energy battery production

        maximum = max(self.inputs.values())

        for s in self.scope["size"]:
            for pt in self.scope["powertrain"]:
                for y in self.scope["year"]:
                    maximum += 1

                    # European emission standards function of registration year
                    if y < 2003:
                        euro_class = "EURO-1"
                    if 2003 <= y < 2006:
                        euro_class = "EURO-2"
                    if 2006 <= y < 2016:
                        euro_class = "EURO-3"
                    if 2017 <= y < 2020:
                        euro_class = "EURO-4"
                    if y >= 2020:
                        euro_class = "EURO-5"

                    name = s + ", " + pt + ", " + str(y) + ", " + euro_class

                    self.inputs[
                        (
                            name,
                            self.background_configuration["country"],
                            "unit",
                            s + ", " + euro_class,
                        )
                    ] = maximum

    def get_A_matrix(self):
        """
        Load the A matrix. The A matrix contains exchanges of products (rows) between activities (columns).

        :return: A matrix with three dimensions of shape (number of values, number of products, number of activities).
        :rtype: numpy.ndarray

        """
        filename = "A_matrix.csv"
        filepath = (
            Path(getframeinfo(currentframe()).filename)
            .resolve()
            .parent.joinpath("data/" + filename)
        )
        if not filepath.is_file():
            raise FileNotFoundError("The technology matrix could not be found.")

        # build matrix A from coordinates
        A_coords = np.genfromtxt(filepath, delimiter=";")
        I = A_coords[:, 0].astype(int)
        J = A_coords[:, 1].astype(int)
        initial_A = sparse.csr_matrix((A_coords[:, 2], (I, J))).toarray()

        new_A = np.identity(len(self.inputs))

        new_A[0 : np.shape(initial_A)[0], 0 : np.shape(initial_A)[0]] = initial_A

        # Resize the matrix to fit the number of iterations in `array`

        new_A = np.resize(
            new_A,
            (self.array.shape[1], new_A.shape[0], new_A.shape[1]),
        )
        return new_A

    def build_fleet_vehicles(self):

        # additional cars
        n_cars = len(self.scope["year"]) * len(self.scope["powertrain"]) + len(
            self.scope["year"]
        )
        self.A = np.pad(self.A, ((0, 0), (0, n_cars), (0, n_cars)))
        maximum = max(self.inputs.values())

        for pt in self.scope["powertrain"]:

            for y in self.scope["year"]:

                # share of the powertrain that year, all sizes
                share_pt = self.fleet.sel(powertrain=pt, variable=y).sum().values

                name = "transport, two-wheeler, fleet average, " + pt + ", " + str(y)

                maximum += 1

                if self.scope["fu"]["unit"] == "vkm":
                    unit = "kilometer"
                else:
                    unit = "person kilometer"

                self.inputs[
                    (
                        name,
                        self.background_configuration["country"],
                        unit,
                        "transport, two-wheeler, fleet average",
                    )
                ] = maximum

                self.A[:, maximum, maximum] = 1

                if share_pt > 0:
                    for s in self.fleet.coords["size"].values:
                        for vin_year in range(min(self.scope["year"]), y + 1):

                            if vin_year in self.fleet.vintage_year:

                                fleet_share = (
                                    self.fleet.sel(
                                        powertrain=pt,
                                        vintage_year=vin_year,
                                        size=s,
                                        variable=y,
                                    )
                                    .sum()
                                    .values
                                    / share_pt
                                )

                                if fleet_share > 0:

                                    car_index = [
                                        self.inputs[i]
                                        for i in self.inputs
                                        if all(
                                            [
                                                item in i[0]
                                                for item in [
                                                    pt,
                                                    str(vin_year),
                                                    s,
                                                    "transport",
                                                ]
                                            ]
                                        )
                                    ][0]

                                    car_inputs = (
                                        self.A[:, : car_index - 1, car_index]
                                        * fleet_share
                                    )

                                    self.A[:, : car_index - 1, maximum] += car_inputs

                    # Fuel and electricity supply must be from the fleet year, not the vintage year

                    d_map_fuel = {
                        "ICEV-p": "gasoline",
                        "BEV": "electric",
                    }

                    ind_supply = [
                        self.inputs[i]
                        for i in self.inputs
                        if "supply for " + d_map_fuel[pt] + " vehicles, " in i[0]
                    ]
                    amount_fuel = self.A[:, ind_supply, maximum].sum(axis=1)

                    # zero out initial fuel inputs
                    self.A[:, ind_supply, maximum] = 0

                    # set saved amount to current fuel supply provider
                    current_provider = [
                        self.inputs[i]
                        for i in self.inputs
                        if "supply for " + d_map_fuel[pt] + " vehicles, " + str(y)
                        in i[0]
                    ]
                    self.A[:, current_provider, maximum] = amount_fuel

        # We also want to produce a fleet average vehicle, with all powertrain types

        for y in self.scope["year"]:

            # share of that year, all sizes and powertrains
            share_pt = self.fleet.sel(variable=y).sum().values

            name = "transport, two-wheeler, fleet average, all powertrains, " + str(y)

            maximum += 1

            if self.scope["fu"]["unit"] == "vkm":
                unit = "kilometer"
            else:
                unit = "person kilometer"

            self.inputs[
                (
                    name,
                    self.background_configuration["country"],
                    unit,
                    "transport, two-wheeler, fleet average",
                )
            ] = maximum

            self.A[:, maximum, maximum] = 1

            if share_pt > 0:
                for pt in self.fleet.coords["powertrain"].values:
                    for s in self.fleet.coords["size"].values:
                        for vin_year in range(min(self.scope["year"]), y + 1):

                            if vin_year in self.fleet.vintage_year:

                                fleet_share = (
                                    self.fleet.sel(
                                        powertrain=pt,
                                        vintage_year=vin_year,
                                        size=s,
                                        variable=y,
                                    )
                                    .sum()
                                    .values
                                    / share_pt
                                )

                                if fleet_share > 0:
                                    car_index = [
                                        self.inputs[i]
                                        for i in self.inputs
                                        if all(
                                            [
                                                item in i[0]
                                                for item in [
                                                    pt,
                                                    str(vin_year),
                                                    s,
                                                    "transport",
                                                ]
                                            ]
                                        )
                                    ][0]

                                    car_inputs = (
                                        self.A[:, : car_index - 1, car_index]
                                        * fleet_share
                                    )

                                    self.A[:, : car_index - 1, maximum] += car_inputs

            # Fuel and electricity supply must be from the fleet year, not the vintage year
            d_map_fuel = {
                "ICEV-p": "gasoline",
                "BEV": "electric",
            }

            for fuel_type in set(d_map_fuel.values()):

                ind_supply = [
                    self.inputs[i]
                    for i in self.inputs
                    if "supply for " + fuel_type + " vehicles, " in i[0]
                ]
                amount_fuel = self.A[:, ind_supply, maximum].sum(axis=1)

                if amount_fuel > 0:

                    # zero out initial fuel inputs
                    self.A[:, ind_supply, maximum] = 0

                    # set saved amount to current fuel supply provider
                    current_provider = [
                        self.inputs[i]
                        for i in self.inputs
                        if "supply for " + fuel_type + " vehicles, " + str(y) in i[0]
                    ]

                    self.A[:, current_provider, maximum] = amount_fuel

    def get_B_matrix(self):
        """
        Load the B matrix. The B matrix contains impact assessment figures for a give impact assessment method,
        per unit of activity. Its length column-wise equals the length of the A matrix row-wise.
        Its length row-wise equals the number of impact assessment methods.

        :return: an array with impact values per unit of activity for each method.
        :rtype: numpy.ndarray

        """

        if self.method == "recipe":
            if self.method_type == "midpoint":
                list_file_names = glob.glob(
                    str(REMIND_FILES_DIR)
                    + "/*recipe_midpoint*{}*.csv".format(self.scenario)
                )
                list_file_names = sorted(list_file_names)
                B = np.zeros((len(list_file_names), 21, len(self.inputs)))
            elif self.method_type == "endpoint":
                list_file_names = glob.glob(
                    str(REMIND_FILES_DIR)
                    + "/*recipe_endpoint*{}*.csv".format(self.scenario)
                )
                list_file_names = sorted(list_file_names)
                B = np.zeros((len(list_file_names), 4, len(self.inputs)))
            else:
                raise TypeError(
                    "The LCIA method type should be either 'midpoint' or 'endpoint'."
                )

        else:
            list_file_names = glob.glob(
                str(REMIND_FILES_DIR) + "/*ilcd*{}*.csv".format(self.scenario)
            )
            list_file_names = sorted(list_file_names)
            B = np.zeros((len(list_file_names), 19, len(self.inputs)))

        for f, fp in enumerate(list_file_names):
            initial_B = np.genfromtxt(fp, delimiter=";")
            new_B = np.zeros(
                (
                    np.shape(initial_B)[0],
                    len(self.inputs),
                )
            )
            new_B[0 : np.shape(initial_B)[0], 0 : np.shape(initial_B)[1]] = initial_B
            B[f, :, :] = new_B

        list_impact_categories = list(self.impact_categories.keys())

        if self.scenario != "static":
            response = xr.DataArray(
                B,
                coords=[
                    [2005, 2010, 2020, 2030, 2040, 2050],
                    list_impact_categories,
                    list(self.inputs.keys()),
                ],
                dims=["year", "category", "activity"],
            )
        else:
            response = xr.DataArray(
                B,
                coords=[[2020], list_impact_categories, list(self.inputs.keys())],
                dims=["year", "category", "activity"],
            )

        return response

    @staticmethod
    def get_dict_input():
        """
        Load a dictionary with tuple ("name of activity", "location", "unit", "reference product") as key, row/column
        indices as values.

        :return: dictionary with `label:index` pairs.
        :rtype: dict

        """
        filename = "dict_inputs_A_matrix.csv"
        filepath = DATA_DIR / filename
        if not filepath.is_file():
            raise FileNotFoundError(
                "The dictionary of activity labels could not be found."
            )
        csv_dict = {}
        count = 0
        with open(filepath) as f:
            input_dict = csv.reader(f, delimiter=";")
            for row in input_dict:
                if "(" in row[1]:
                    new_str = row[1].replace("(", "")
                    new_str = new_str.replace(")", "")
                    new_str = [s.strip() for s in new_str.split(",") if s]
                    t = ()
                    for s in new_str:
                        if "low population" in s:
                            s = "low population density, long-term"
                            t += (s,)
                            break
                        else:
                            t += (s.replace("'", ""),)
                    csv_dict[(row[0], t, row[2])] = count
                else:
                    csv_dict[(row[0], row[1], row[2], row[3])] = count
                count += 1

        return csv_dict

    def get_dict_impact_categories(self):
        """
        Load a dictionary with available impact assessment methods as keys, and assessment level and categories as values.

        ..code-block:: python

            {'recipe': {'midpoint': ['freshwater ecotoxicity',
                                   'human toxicity',
                                   'marine ecotoxicity',
                                   'terrestrial ecotoxicity',
                                   'metal depletion',
                                   'agricultural land occupation',
                                   'climate change',
                                   'fossil depletion',
                                   'freshwater eutrophication',
                                   'ionising radiation',
                                   'marine eutrophication',
                                   'natural land transformation',
                                   'ozone depletion',
                                   'particulate matter formation',
                                   'photochemical oxidant formation',
                                   'terrestrial acidification',
                                   'urban land occupation',
                                   'water depletion',
                                   'human noise',
                                   'primary energy, non-renewable',
                                   'primary energy, renewable']
                       }
           }

        :return: dictionary
        :rtype: dict
        """
        filename = "dict_impact_categories.csv"
        filepath = DATA_DIR / filename
        if not filepath.is_file():
            raise FileNotFoundError(
                "The dictionary of impact categories could not be found."
            )

        csv_dict = {}

        with open(filepath) as f:
            input_dict = csv.reader(f, delimiter=";")
            for row in input_dict:
                if row[0] == self.method and row[3] == self.method_type:
                    csv_dict[row[2]] = {
                        "method": row[1],
                        "category": row[2],
                        "type": row[3],
                        "abbreviation": row[4],
                        "unit": row[5],
                        "source": row[6],
                    }

        return csv_dict

    def get_rev_dict_input(self):
        """
        Reverse the self.inputs dictionary.

        :return: reversed dictionary
        :rtype: dict
        """
        return {v: k for k, v in self.inputs.items()}

    def get_index_vehicle_from_array(
        self, items_to_look_for, items_to_look_for_also=None, method="or"
    ):
        """
        Return list of row/column indices of self.array of labels that contain the string defined in `items_to_look_for`.

        :param items_to_look_for_also:
        :param method:
        :param items_to_look_for: string to search for
        :return: list
        """
        if not isinstance(items_to_look_for, list):
            items_to_look_for = [items_to_look_for]

        if not items_to_look_for_also is None:
            if not isinstance(items_to_look_for_also, list):
                items_to_look_for_also = [items_to_look_for_also]

        list_vehicles = self.array.desired.values

        if method == "or":
            return [
                c
                for c, v in enumerate(list_vehicles)
                if set(items_to_look_for).intersection(v)
            ]

        if method == "and":
            return [
                c
                for c, v in enumerate(list_vehicles)
                if set(items_to_look_for).intersection(v)
                and set(items_to_look_for_also).intersection(v)
            ]

    def get_index_of_flows(self, items_to_look_for, search_by="name"):
        """
        Return list of row/column indices of self.A of labels that contain the string defined in `items_to_look_for`.

        :param items_to_look_for: string
        :param search_by: "name" or "compartment" (for elementary flows)
        :return: list of row/column indices
        :rtype: list
        """
        if search_by == "name":
            return [
                int(self.inputs[c])
                for c in self.inputs
                if all(ele in c[0].lower() for ele in items_to_look_for)
            ]
        if search_by == "compartment":
            return [
                int(self.inputs[c])
                for c in self.inputs
                if all(ele in c[1] for ele in items_to_look_for)
            ]

    def resize_A_matrix_for_export(self):
        """Removes some vehicles if they do not comply with
        requirements in terms of range"""

        indices_to_remove = []

        for i in self.inputs:
            if (
                any(s.lower() in i[0].lower() for s in self.scope["size"])
                and "fleet average" not in i[0].lower()
                and "market" not in i[0].lower()
            ):

                if "transport" in i[0]:
                    _, size, pt, year, _ = [x.strip() for x in i[0].split(", ")]
                else:
                    size, pt, year, _ = i[0].split(", ")

                if (
                    self.compliant_vehicles.sel(
                        powertrain=pt, size=size, year=int(year)
                    )
                    == 0
                ):
                    indices_to_remove.append(self.inputs[i])
                    self.rev_inputs.pop(self.inputs[i])

        indices_to_preserve = [
            i for i in range(self.A.shape[1]) if i not in indices_to_remove
        ]

        self.A = self.A[
            np.ix_(range(self.A.shape[0]), indices_to_preserve, indices_to_preserve)
        ]

        self.rev_inputs = {v: k for v, k in enumerate(self.rev_inputs.values())}

    def export_lci(
        self,
        presamples=True,
        ecoinvent_compatibility=True,
        ecoinvent_version="3.7",
        db_name="carculator_two_wheeler db",
        forbidden_activities=None,
        create_vehicle_datasets=True,
    ):
        """
        Export the inventory as a dictionary. Also return a list of arrays that contain pre-sampled random values if
        :param db_name:
        :param forbidden_activities:
        :meth:`stochastic` of :class:`CarModel` class has been called.

        :param presamples: boolean.
        :param ecoinvent_compatibility: bool. If True, compatible with ecoinvent. If False, compatible with REMIND-ecoinvent.
        :param ecoinvent_version: str. "3.5", "3.6" or "uvek"
        :param create_vehicle_datasets: bool. Whether vehicles datasets (as structured in ecoinvent) should be created too.
        :return: inventory, and optionally, list of arrays containing pre-sampled values.
        :rtype: list
        """

        self.inputs = self.get_dict_input()
        self.bs = BackgroundSystemModel()
        self.country = self.get_country_of_use()
        self.add_additional_activities()
        self.rev_inputs = self.get_rev_dict_input()
        self.A = self.get_A_matrix()

        if create_vehicle_datasets:

            # add vehicles datasets
            self.add_additional_activities_for_export()

            # Update dictionary
            self.rev_inputs = self.get_rev_dict_input()

            # resize A matrix
            self.A = self.get_A_matrix()

            # Create electricity and fuel market datasets
            self.create_electricity_market_for_fuel_prep()

            # Create electricity market dataset for battery production
            self.create_electricity_market_for_battery_production()

            # Create fuel markets
            self.fuel_blends = {}
            self.fuel_dictionary = self.create_fuel_dictionary()
            self.define_fuel_blends()
            self.set_actual_range()

            self.set_inputs_in_A_matrix_for_export(self.array.values)

        else:

            # Create electricity and fuel market datasets
            self.create_electricity_market_for_fuel_prep()

            # Create electricity market dataset for battery production
            self.create_electricity_market_for_battery_production()

            # Create fuel markets
            self.fuel_blends = {}
            self.fuel_dictionary = self.create_fuel_dictionary()
            self.define_fuel_blends()
            self.set_actual_range()

            self.set_inputs_in_A_matrix(self.array.values)

        # Add rows for fleet vehicles, if any
        if isinstance(self.fleet, xr.core.dataarray.DataArray):
            print("Building fleet average vehicles...")
            self.build_fleet_vehicles()

            # Update dictionary
            self.rev_inputs = self.get_rev_dict_input()

            # Update number of cars
            self.number_of_vehicles += len(self.scope["year"]) * len(
                self.scope["powertrain"]
            )

        # if the inventories are meant to link to `premise` databases
        # we need to remove the additional electricity input
        # in the fuel market datasets
        if not ecoinvent_compatibility:
            fuel_markets = [
                self.inputs[a] for a in self.inputs if "fuel market for" in a[0]
            ]
            electricity_inputs = [
                self.inputs[a] for a in self.inputs if "electricity market for" in a[0]
            ]
            self.A[np.ix_(range(self.A.shape[0]), electricity_inputs, fuel_markets)] = 0

        # Remove vehicles not compliant or available
        self.resize_A_matrix_for_export()

        if presamples:
            lci, array = ExportInventory(
                self.A, self.rev_inputs, db_name=db_name
            ).write_lci(
                presamples=presamples,
                ecoinvent_compatibility=ecoinvent_compatibility,
                ecoinvent_version=ecoinvent_version,
                forbidden_activities=forbidden_activities,
                vehicle_specs=self.specs,
            )
            return lci, array
        else:
            lci = ExportInventory(self.A, self.rev_inputs, db_name=db_name).write_lci(
                presamples=presamples,
                ecoinvent_compatibility=ecoinvent_compatibility,
                ecoinvent_version=ecoinvent_version,
                forbidden_activities=forbidden_activities,
                vehicle_specs=self.specs,
            )
            return lci

    def export_lci_to_bw(
        self,
        presamples=True,
        ecoinvent_compatibility=True,
        ecoinvent_version="3.7",
        db_name="carculator_two_wheeler db",
        forbidden_activities=None,
        create_vehicle_datasets=True,
    ):
        """
        Export the inventory as a `brightway2` bw2io.importers.base_lci.LCIImporter object
        with the inventory in the `data` attribute.

        .. code-block:: python

            # get the inventory
            i, _ = ic.export_lci_to_bw()

            # import it in a Brightway2 project
            i.match_database('ecoinvent 3.6 cutoff', fields=('name', 'unit', 'location', 'reference product'))
            i.match_database("biosphere3", fields=('name', 'unit', 'categories'))
            i.match_database(fields=('name', 'unit', 'location', 'reference product'))
            i.match_database(fields=('name', 'unit', 'categories'))

            # Create an additional biosphere database for the few flows that do not
            # exist in "biosphere3"
            i.create_new_biosphere("additional_biosphere", relink=True)

            # Check if all exchanges link
            i.statistics()

            # Register the database
            i.write_database()

        :return: LCIImport object that can be directly registered in a `brightway2` project.
        :rtype: bw2io.importers.base_lci.LCIImporter
        """

        self.inputs = self.get_dict_input()
        self.bs = BackgroundSystemModel()
        self.country = self.get_country_of_use()
        self.add_additional_activities()
        self.rev_inputs = self.get_rev_dict_input()
        self.A = self.get_A_matrix()

        if create_vehicle_datasets:

            # add vehicles datasets
            self.add_additional_activities_for_export()

            # Update dictionary
            self.rev_inputs = self.get_rev_dict_input()

            # resize A matrix
            self.A = self.get_A_matrix()

            # Create electricity and fuel market datasets
            self.create_electricity_market_for_fuel_prep()

            # Create electricity market dataset for battery production
            self.create_electricity_market_for_battery_production()

            # Create fuel markets
            self.fuel_blends = {}
            self.fuel_dictionary = self.create_fuel_dictionary()
            self.define_fuel_blends()
            self.set_actual_range()

            self.set_inputs_in_A_matrix_for_export(self.array.values)

        else:

            # Create electricity and fuel market datasets
            self.create_electricity_market_for_fuel_prep()

            # Create electricity market dataset for battery production
            self.create_electricity_market_for_battery_production()

            # Create fuel markets
            self.fuel_blends = {}
            self.fuel_dictionary = self.create_fuel_dictionary()
            self.define_fuel_blends()
            self.set_actual_range()

            self.set_inputs_in_A_matrix(self.array.values)

        # Add rows for fleet vehicles, if any
        if isinstance(self.fleet, xr.core.dataarray.DataArray):
            print("Building fleet average vehicles...")
            self.build_fleet_vehicles()

            # Update dictionary
            self.rev_inputs = self.get_rev_dict_input()

            # Update number of cars
            self.number_of_vehicles += len(self.scope["year"]) * len(
                self.scope["powertrain"]
            )

        # if the inventories are meant to link to `premise` databases
        # we need to remove the additional electricity input
        # in the fuel market datasets
        if not ecoinvent_compatibility:
            fuel_markets = [
                self.inputs[a] for a in self.inputs if "fuel supply for" in a[0]
            ]
            electricity_inputs = [
                self.inputs[a] for a in self.inputs if "electricity market for" in a[0]
            ]
            self.A[np.ix_(range(self.A.shape[0]), electricity_inputs, fuel_markets)] = 0

        # Remove vehicles not compliant or available
        self.resize_A_matrix_for_export()

        if presamples:
            lci, array = ExportInventory(
                self.A, self.rev_inputs, db_name=db_name
            ).write_lci_to_bw(
                presamples=presamples,
                ecoinvent_compatibility=ecoinvent_compatibility,
                ecoinvent_version=ecoinvent_version,
                forbidden_activities=forbidden_activities,
                vehicle_specs=self.specs,
            )
            return lci, array
        else:

            lci = ExportInventory(
                self.A, self.rev_inputs, db_name=db_name
            ).write_lci_to_bw(
                presamples=presamples,
                ecoinvent_compatibility=ecoinvent_compatibility,
                ecoinvent_version=ecoinvent_version,
                forbidden_activities=forbidden_activities,
                vehicle_specs=self.specs,
            )

            return lci

    def export_lci_to_excel(
        self,
        directory=None,
        ecoinvent_compatibility=True,
        ecoinvent_version="3.7",
        software_compatibility="brightway2",
        filename=None,
        forbidden_activities=None,
        create_vehicle_datasets=True,
        export_format="file",
    ):
        """
        Export the inventory as an Excel file (if the destination software is Brightway2) or a CSV file (if the destination software is Simapro) file.
        Also return the file path where the file is stored.

        :param filename:
        :param forbidden_activities:
        :param create_vehicle_datasets:
        :param export_format:
        :param directory: directory where to save the file.
        :type directory: str
        :param ecoinvent_compatibility: If True, compatible with ecoinvent. If False, compatible with REMIND-ecoinvent.
        :param ecoinvent_version: "3.6", "3.5" or "uvek"
        :param software_compatibility: "brightway2" or "simapro"
        :return: file path where the file is stored.
        :rtype: str
        """

        if software_compatibility not in ("brightway2", "simapro"):
            raise NameError(
                "The destination software argument is not valid. Choose between 'brightway2' or 'simapro'."
            )

        # Simapro inventory only for ecoinvent 3.5 or UVEK
        if software_compatibility == "simapro":
            if ecoinvent_version == "3.7":
                print(
                    "Simapro-compatible inventory export is only available for ecoinvent 3.5, 3.6 or UVEK."
                )
                return
            ecoinvent_compatibility = True

        self.inputs = self.get_dict_input()
        self.bs = BackgroundSystemModel()
        self.country = self.get_country_of_use()
        self.add_additional_activities()
        self.rev_inputs = self.get_rev_dict_input()
        self.A = self.get_A_matrix()

        if create_vehicle_datasets:

            # add vehicles datasets
            self.add_additional_activities_for_export()

            # Update dictionary
            self.rev_inputs = self.get_rev_dict_input()

            # resize A matrix
            self.A = self.get_A_matrix()

            # Create electricity and fuel market datasets
            self.create_electricity_market_for_fuel_prep()

            # Create fuel markets
            self.fuel_blends = {}
            self.fuel_dictionary = self.create_fuel_dictionary()
            self.define_fuel_blends()
            self.set_actual_range()

            # Create electricity market dataset for battery production
            self.create_electricity_market_for_battery_production()

            self.set_inputs_in_A_matrix_for_export(self.array.values)

        else:

            # Create electricity and fuel market datasets
            self.create_electricity_market_for_fuel_prep()

            # Create electricity market dataset for battery production
            self.create_electricity_market_for_battery_production()

            # Create fuel markets
            self.fuel_blends = {}
            self.fuel_dictionary = self.create_fuel_dictionary()
            self.define_fuel_blends()
            self.set_actual_range()

            self.set_inputs_in_A_matrix(self.array.values)

        # Add rows for fleet vehicles, if any
        if isinstance(self.fleet, xr.core.dataarray.DataArray):
            print("Building fleet average vehicles...")
            self.build_fleet_vehicles()

            # Update dictionary
            self.rev_inputs = self.get_rev_dict_input()

            # Update number of cars
            self.number_of_vehicles += len(self.scope["year"]) * len(
                self.scope["powertrain"]
            )

        # if the inventories are meant to link to `premise` databases
        # we need to remove the additional electricity input
        # in the fuel market datasets
        if not ecoinvent_compatibility:
            fuel_markets = [
                self.inputs[a] for a in self.inputs if "fuel market for" in a[0]
            ]
            electricity_inputs = [
                self.inputs[a] for a in self.inputs if "electricity market for" in a[0]
            ]
            self.A[np.ix_(range(self.A.shape[0]), electricity_inputs, fuel_markets)] = 0

        # Remove vehicles not compliant or available
        self.resize_A_matrix_for_export()

        fp = ExportInventory(
            self.A, self.rev_inputs, db_name=filename or "carculator_two_wheeler db"
        ).write_lci_to_excel(
            directory=directory,
            ecoinvent_compatibility=ecoinvent_compatibility,
            ecoinvent_version=ecoinvent_version,
            software_compatibility=software_compatibility,
            filename=filename,
            forbidden_activities=forbidden_activities,
            export_format=export_format,
            vehicle_specs=self.specs,
        )
        return fp

    def get_country_of_use(self):

        if "country" not in self.background_configuration:
            self.background_configuration["country"] = "RER"

        return self.background_configuration["country"]

    def define_electricity_mix_for_fuel_prep(self):
        """
        This function defines a fuel mix based either on user-defined mix, or on default mixes for a given country.
        The mix is calculated as the average mix, weighted by the distribution of annually driven kilometers.
        :return:
        """
        try:
            losses_to_low = float(self.bs.losses[self.country]["LV"])
        except KeyError:
            # If losses for the country are not found, assume EU average
            losses_to_low = float(self.bs.losses["RER"]["LV"])

        if "custom electricity mix" in self.background_configuration:
            # If a special electricity mix is specified, we use it
            mix = self.background_configuration["custom electricity mix"]

            if np.shape(mix)[0] != len(self.scope["year"]):
                raise ValueError(
                    "The number of electricity mixes ({}) must match with the "
                    "number of years ({}).".format(
                        np.shape(mix)[0], len(self.scope["year"])
                    )
                )

            if not np.allclose(np.sum(mix, 1), np.ones(len(self.scope["year"]))):
                print(
                    "The sum of the electricity mix share does "
                    "not equal to 1 for each year."
                )

        else:
            use_year = (
                (
                    self.array.values[self.array_inputs["lifetime kilometers"]]
                    / self.array.values[self.array_inputs["kilometers per year"]]
                )
                .reshape(
                    self.iterations,
                    len(self.scope["powertrain"]),
                    len(self.scope["size"]),
                    len(self.scope["year"]),
                )
                .mean(axis=(0, 1, 2))
            )

            if self.country not in self.bs.electricity_mix.country.values:
                print(
                    "The electricity mix for {} could not be found. Average European electricity mix is used instead.".format(
                        self.country
                    )
                )
                country = "RER"
            else:
                country = self.country

            mix = [
                self.bs.electricity_mix.sel(
                    country=country,
                    variable=[
                        "Hydro",
                        "Nuclear",
                        "Gas",
                        "Solar",
                        "Wind",
                        "Biomass",
                        "Coal",
                        "Oil",
                        "Geothermal",
                        "Waste",
                        "Biogas CCS",
                        "Biomass CCS",
                        "Coal CCS",
                        "Gas CCS",
                        "Wood CCS",
                    ],
                )
                .interp(
                    year=np.arange(year, year + use_year[y]),
                    kwargs={"fill_value": "extrapolate"},
                )
                .mean(axis=0)
                .values
                if y + use_year[y] <= 2050
                else self.bs.electricity_mix.sel(
                    country=country,
                    variable=[
                        "Hydro",
                        "Nuclear",
                        "Gas",
                        "Solar",
                        "Wind",
                        "Biomass",
                        "Coal",
                        "Oil",
                        "Geothermal",
                        "Waste",
                        "Biogas CCS",
                        "Biomass CCS",
                        "Coal CCS",
                        "Gas CCS",
                        "Wood CCS",
                    ],
                )
                .interp(
                    year=np.arange(year, 2051), kwargs={"fill_value": "extrapolate"}
                )
                .mean(axis=0)
                .values
                for y, year in enumerate(self.scope["year"])
            ]

        return np.clip(mix, 0, 1) / np.clip(mix, 0, 1).sum(axis=1)[:, None]

    def define_renewable_rate_in_mix(self):

        try:
            losses_to_low = float(self.bs.losses[self.country]["LV"])
        except KeyError:
            # If losses for the country are not found, assume EU average
            losses_to_low = float(self.bs.losses["RER"]["LV"])

        category_name = (
            "climate change"
            if self.method == "recipe"
            else "climate change - climate change total"
        )

        if self.method_type != "endpoint":
            if self.scenario != "static":
                year = self.scope["year"]
                co2_intensity_tech = (
                    self.B.sel(
                        category=category_name,
                        activity=list(self.elec_map.values()),
                    )
                    .interp(year=year, kwargs={"fill_value": "extrapolate"})
                    .values
                    * losses_to_low
                ) * 1000
            else:
                year = 2020
                co2_intensity_tech = np.resize(
                    (
                        self.B.sel(
                            category=category_name,
                            activity=list(self.elec_map.values()),
                            year=year,
                        ).values
                        * losses_to_low
                        * 1000
                    ),
                    (len(self.scope["year"]), 15),
                )
        else:
            co2_intensity_tech = np.zeros((len(self.scope["year"]), 15))

        sum_renew = [
            np.sum([self.mix[x][i] for i in [0, 3, 4, 5, 8]])
            for x in range(0, len(self.mix))
        ]

        return sum_renew, co2_intensity_tech

    def create_electricity_market_for_fuel_prep(self):
        """This function fills the electricity market that supplies battery charging operations
        and hydrogen production through electrolysis.
        """

        try:
            losses_to_low = float(self.bs.losses[self.country]["LV"])
        except KeyError:
            # If losses for the country are not found, assume EU average
            losses_to_low = float(self.bs.losses["RER"]["LV"])

        # Fill the electricity markets for battery charging and hydrogen production
        for y, year in enumerate(self.scope["year"]):
            m = np.array(self.mix[y], dtype=object).reshape((-1, 15, 1))
            # Add electricity technology shares
            self.A[
                np.ix_(
                    np.arange(self.iterations),
                    [self.inputs[self.elec_map[t]] for t in self.elec_map],
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(year) in i[0]
                        and "electricity market for fuel preparation" in i[0]
                    ],
                )
            ] = (
                m * -1 * losses_to_low
            )

            # Add transmission network for high and medium voltage
            self.A[
                :,
                self.inputs[
                    (
                        "transmission network construction, electricity, high voltage",
                        "CH",
                        "kilometer",
                        "transmission network, electricity, high voltage",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for fuel preparation" in i[0]
                ],
            ] = (
                6.58e-9 * -1 * losses_to_low
            )

            self.A[
                :,
                self.inputs[
                    (
                        "transmission network construction, electricity, medium voltage",
                        "CH",
                        "kilometer",
                        "transmission network, electricity, medium voltage",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for fuel preparation" in i[0]
                ],
            ] = (
                1.86e-8 * -1 * losses_to_low
            )

            self.A[
                :,
                self.inputs[
                    (
                        "transmission network construction, long-distance",
                        "UCTE",
                        "kilometer",
                        "transmission network, long-distance",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for fuel preparation" in i[0]
                ],
            ] = (
                3.17e-10 * -1 * losses_to_low
            )

            # Add distribution network, low voltage
            self.A[
                :,
                self.inputs[
                    (
                        "distribution network construction, electricity, low voltage",
                        "CH",
                        "kilometer",
                        "distribution network, electricity, low voltage",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for fuel preparation" in i[0]
                ],
            ] = (
                8.74e-8 * -1 * losses_to_low
            )

            # Add supply of sulfur hexafluoride for transformers
            self.A[
                :,
                self.inputs[
                    (
                        "market for sulfur hexafluoride, liquid",
                        "RER",
                        "kilogram",
                        "sulfur hexafluoride, liquid",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for fuel preparation" in i[0]
                ],
            ] = (
                (5.4e-8 + 2.99e-9) * -1 * losses_to_low
            )

            # Add SF_6 leakage

            self.A[
                :,
                self.inputs[("Sulfur hexafluoride", ("air",), "kilogram")],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for fuel preparation" in i[0]
                ],
            ] = (
                (5.4e-8 + 2.99e-9) * -1 * losses_to_low
            )

    def create_electricity_market_for_battery_production(self):
        """
        This function fills in the column in `self.A` concerned with the electricity mix used for manufacturing battery cells
        :return:
        """

        battery_origin = self.background_configuration["energy storage"]["electric"][
            "origin"
        ]

        if battery_origin != "custom electricity mix":

            try:
                losses_to_low = float(self.bs.losses[battery_origin]["LV"])
            except KeyError:
                losses_to_low = float(self.bs.losses["CN"]["LV"])

            if battery_origin not in self.bs.electricity_mix.country.values:
                print(
                    "The electricity mix for {} could not be found. Average Chinese electricity mix is used for "
                    "battery manufacture instead.".format(self.country)
                )
                battery_origin = "CN"

            mix_battery_manufacturing = (
                self.bs.electricity_mix.sel(
                    country=battery_origin,
                    variable=[
                        "Hydro",
                        "Nuclear",
                        "Gas",
                        "Solar",
                        "Wind",
                        "Biomass",
                        "Coal",
                        "Oil",
                        "Geothermal",
                        "Waste",
                        "Biogas CCS",
                        "Biomass CCS",
                        "Coal CCS",
                        "Gas CCS",
                        "Wood CCS",
                    ],
                )
                .interp(year=self.scope["year"], kwargs={"fill_value": "extrapolate"})
                .values
            )
        else:
            # electricity mix for battery manufacturing same as `custom electricity mix`
            mix_battery_manufacturing = self.mix
            losses_to_low = 1.1

        # Fill the electricity markets for battery production
        for y, year in enumerate(self.scope["year"]):
            m = np.array(mix_battery_manufacturing[y], dtype=object).reshape(
                (-1, 15, 1)
            )

            self.A[
                np.ix_(
                    np.arange(self.iterations),
                    [self.inputs[self.elec_map[t]] for t in self.elec_map],
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(year) in i[0]
                        and "electricity market for energy storage production" in i[0]
                    ],
                )
            ] = (
                m * losses_to_low * -1
            )

            # Add transmission network for high and medium voltage
            self.A[
                :,
                self.inputs[
                    (
                        "transmission network construction, electricity, high voltage",
                        "CH",
                        "kilometer",
                        "transmission network, electricity, high voltage",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for energy storage production" in i[0]
                ],
            ] = (
                6.58e-9 * -1 * losses_to_low
            )

            self.A[
                :,
                self.inputs[
                    (
                        "transmission network construction, electricity, medium voltage",
                        "CH",
                        "kilometer",
                        "transmission network, electricity, medium voltage",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for energy storage production" in i[0]
                ],
            ] = (
                1.86e-8 * -1 * losses_to_low
            )

            self.A[
                :,
                self.inputs[
                    (
                        "transmission network construction, long-distance",
                        "UCTE",
                        "kilometer",
                        "transmission network, long-distance",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for energy storage production" in i[0]
                ],
            ] = (
                3.17e-10 * -1 * losses_to_low
            )

            # Add distribution network, low voltage
            self.A[
                :,
                self.inputs[
                    (
                        "distribution network construction, electricity, low voltage",
                        "CH",
                        "kilometer",
                        "distribution network, electricity, low voltage",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for energy storage production" in i[0]
                ],
            ] = (
                8.74e-8 * -1 * losses_to_low
            )

            # Add supply of sulfur hexafluoride for transformers
            self.A[
                :,
                self.inputs[
                    (
                        "market for sulfur hexafluoride, liquid",
                        "RER",
                        "kilogram",
                        "sulfur hexafluoride, liquid",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for energy storage production" in i[0]
                ],
            ] = (
                (5.4e-8 + 2.99e-9) * -1 * losses_to_low
            )

            # Add SF_6 leakage

            self.A[
                :,
                self.inputs[("Sulfur hexafluoride", ("air",), "kilogram")],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "electricity market for energy storage production" in i[0]
                ],
            ] = (
                (5.4e-8 + 2.99e-9) * -1 * losses_to_low
            )

    def get_share_biogasoline(self):
        """Returns average share of biogasoline according to historical IEA stats"""
        share_biofuel = np.squeeze(
            np.clip(
                self.bs.biogasoline.sel(country=self.country)
                .interp(year=self.scope["year"], kwargs={"fill_value": "extrapolate"})
                .values,
                0,
                0.2,
            )
        )
        return share_biofuel

    def get_share_biofuel(self):
        """Returns average share of biofuel in blend according to IAM REMIND"""
        try:
            region = self.bs.region_map[self.country]["RegionCode"]
        except KeyError:
            print(
                "Could not find biofuel share information for the region/country specified. "
                "Will use European biofuel share instead."
            )
            region = "EUR"
        scenario = self.scenario if self.scenario != "static" else "SSP2-Base"

        share_biofuel = (
            self.bs.biofuel.sel(
                region=region,
                value=0,
                fuel_type="Biomass fuel",
                scenario=scenario,
            )
            .interp(year=self.scope["year"], kwargs={"fill_value": "extrapolate"})
            .values
        )
        return share_biofuel

    def find_fuel_shares(self, fuel_type):

        default_fuels = {
            "petrol": {
                "primary": "petrol",
                "secondary": "bioethanol - wheat straw",
                "third": "bioethanol - maize starch",
            },
        }

        tertiary = None
        tertiary_share = None

        if "fuel blend" in self.background_configuration:
            if fuel_type in self.background_configuration["fuel blend"]:
                primary = self.background_configuration["fuel blend"][fuel_type][
                    "primary fuel"
                ]["type"]

                if (
                    "secondary fuel"
                    in self.background_configuration["fuel blend"][fuel_type]
                ):
                    secondary = self.background_configuration["fuel blend"][fuel_type][
                        "secondary fuel"
                    ]["type"]
                else:

                    if default_fuels[fuel_type]["secondary"] != primary:
                        secondary = default_fuels[fuel_type]["secondary"]
                        self.background_configuration["fuel blend"][fuel_type][
                            "secondary fuel"
                        ] = {"type": secondary}
                    else:
                        secondary = default_fuels[fuel_type]["third"]
                        self.background_configuration["fuel blend"][fuel_type][
                            "secondary fuel"
                        ] = {"type": secondary}

                if (
                    "tertiary fuel"
                    in self.background_configuration["fuel blend"][fuel_type]
                ):
                    tertiary = self.background_configuration["fuel blend"][fuel_type][
                        "tertiary fuel"
                    ]["type"]

                primary_share = self.background_configuration["fuel blend"][fuel_type][
                    "primary fuel"
                ]["share"]

                if (
                    "share"
                    not in self.background_configuration["fuel blend"][fuel_type][
                        "secondary fuel"
                    ]
                ):
                    secondary_share = 1 - np.asarray(primary_share)
                else:
                    secondary_share = self.background_configuration["fuel blend"][
                        fuel_type
                    ]["secondary fuel"]["share"]

                if tertiary:
                    tertiary_share = self.background_configuration["fuel blend"][
                        fuel_type
                    ]["tertiary fuel"]["share"]

            else:
                primary = default_fuels[fuel_type]["primary"]
                secondary = default_fuels[fuel_type]["secondary"]

                if fuel_type == "petrol":
                    if self.country in self.bs.biogasoline.country.values:
                        secondary_share = self.get_share_biogasoline()
                    else:
                        secondary_share = self.get_share_biofuel()

                primary_share = 1 - np.array(secondary_share, dtype=object)

        else:
            primary = default_fuels[fuel_type]["primary"]
            secondary = default_fuels[fuel_type]["secondary"]

            if fuel_type == "petrol":
                if self.country in self.bs.biogasoline.country.values:
                    secondary_share = self.get_share_biogasoline()
                else:
                    secondary_share = self.get_share_biofuel()

            primary_share = 1 - np.array(secondary_share, dtype=object)

        return (
            primary,
            secondary,
            primary_share,
            secondary_share,
            tertiary,
            tertiary_share,
        )

    def set_actual_range(self):
        """
        Set the actual range considering the blend.
        Liquid bio-fuels and synthetic fuels typically have a lower calorific value. Hence, the need to recalculate
        the vehicle range.
        Modifies parameter `range` of `array` in place
        """

        if {"ICEV-p"}.intersection(set(self.scope["powertrain"])):
            for y, year in enumerate(self.scope["year"]):

                share_primary = self.fuel_blends["petrol"]["primary"]["share"][y]
                lhv_primary = self.fuel_blends["petrol"]["primary"]["lhv"]
                share_secondary = self.fuel_blends["petrol"]["secondary"]["share"][y]
                lhv_secondary = self.fuel_blends["petrol"]["secondary"]["lhv"]

                if "tertiary" in self.fuel_blends["petrol"]:
                    share_tertiary = self.fuel_blends["petrol"]["tertiary"]["share"][y]
                    lhv_tertiary = self.fuel_blends["petrol"]["tertiary"]["lhv"]
                else:
                    share_tertiary = 0
                    lhv_tertiary = 0

                index = self.get_index_vehicle_from_array(
                    ["ICEV-p"], year, method="and"
                )

                self.array.values[self.array_inputs["range"], :, index] = (
                    (
                        (
                            self.array.values[self.array_inputs["fuel mass"], :, index]
                            * share_primary
                            * lhv_primary
                        )
                        + (
                            self.array.values[self.array_inputs["fuel mass"], :, index]
                            * share_secondary
                            * lhv_secondary
                        )
                        + (
                            self.array.values[self.array_inputs["fuel mass"], :, index]
                            * share_tertiary
                            * lhv_tertiary
                        )
                        + (
                            self.array.values[
                                self.array_inputs["electric energy stored"], :, index
                            ]
                            * 3.6
                        )
                    )
                    * 1000
                    / self.array.values[self.array_inputs["TtW energy"], :, index]
                )

                self.array.values[self.array_inputs["LHV fuel MJ per kg"], :, index] = (
                    (share_primary * lhv_primary)
                    + (share_secondary * lhv_secondary)
                    + (share_tertiary * lhv_tertiary)
                )

    def define_fuel_blends(self):
        """
        This function defines fuel blends from what is passed in `background_configuration`.
        It populates a dictionary `self.fuel_blends` that contains the respective shares, lower heating values
        and CO2 emission factors of the fuels used.

        Source for LHV: https://www.bafu.admin.ch/bafu/en/home/topics/climate/state/data/climate-reporting/references.html

        :return:
        """

        fuels_lhv = {
            "petrol": 42.6,
            "bioethanol - wheat straw": 26.5,
            "bioethanol - maize starch": 26.5,
            "bioethanol - sugarbeet": 26.5,
            "bioethanol - forest residues": 26.5,
            "synthetic gasoline - economic allocation": 42.4,
            "synthetic gasoline - energy allocation": 42.4,
        }

        fuels_CO2 = {
            "petrol": 3.14,
            "bioethanol - wheat straw": 1.96,
            "bioethanol - maize starch": 1.96,
            "bioethanol - sugarbeet": 1.96,
            "bioethanol - forest residues": 1.96,
            "synthetic gasoline - economic allocation": 3.14,
            "synthetic gasoline - energy allocation": 3.14,
        }

        if {"ICEV-p"}.intersection(set(self.scope["powertrain"])):
            fuel_type = "petrol"
            (
                primary,
                secondary,
                primary_share,
                secondary_share,
                tertiary,
                tertiary_share,
            ) = self.find_fuel_shares(fuel_type)
            self.create_fuel_markets(
                fuel_type,
                primary,
                secondary,
                tertiary,
                primary_share,
                secondary_share,
                tertiary_share,
            )
            self.fuel_blends[fuel_type] = {
                "primary": {
                    "type": primary,
                    "share": primary_share,
                    "lhv": fuels_lhv[primary],
                    "CO2": fuels_CO2[primary],
                },
                "secondary": {
                    "type": secondary,
                    "share": secondary_share,
                    "lhv": fuels_lhv[secondary],
                    "CO2": fuels_CO2[secondary],
                },
            }

            if tertiary:
                self.fuel_blends[fuel_type]["tertiary"] = {
                    "type": tertiary,
                    "share": tertiary_share,
                    "lhv": fuels_lhv[tertiary],
                    "CO2": fuels_CO2[tertiary],
                }

        if {"BEV"}.intersection(set(self.scope["powertrain"])):
            fuel_type = "electricity"
            self.create_fuel_markets(fuel_type)

    def get_sulfur_content(self, location, fuel, year):
        """
        Return the sulfur content in the fuel.
        If a region is passed, the average sulfur content over
        the countries the region contains is returned.
        :param year:
        :param location: str. A country or region ISO code
        :param fuel: str. "diesel" or "gasoline
        :return: float. Sulfur content in ppm.
        """

        try:
            int(year)
        except ValueError:
            raise ValueError(
                "The year for which to fetch sulfur concentration values is not valid."
            )

        if location in self.bs.sulfur.country.values:
            sulfur_concentration = (
                self.bs.sulfur.sel(country=location, year=year, fuel=fuel).sum().values
            )
        else:
            # If the geography is in fact a region,
            # we need to calculate the average sulfur content
            # across the region

            list_countries = self.geo.iam_to_ecoinvent_location(location)
            list_countries = [
                c for c in list_countries if c in self.bs.sulfur.country.values
            ]

            if len(list_countries) > 0:

                sulfur_concentration = (
                    self.bs.sulfur.sel(
                        country=list_countries,
                        year=year,
                        fuel=fuel,
                    )
                    .mean()
                    .values
                )

            else:
                # if we do not have the sulfur concentration for the required country, we pick Europe
                print(
                    "The sulfur content for {} fuel in {} could not be found. European average sulfur content is used instead.".format(
                        fuel, location
                    )
                )
                sulfur_concentration = (
                    self.bs.sulfur.sel(country="RER", year=year, fuel=fuel).sum().values
                )
        return sulfur_concentration

    def create_fuel_dictionary(self):

        d_fuels = {
            "petrol": {
                "name": (
                    "market for petrol, low-sulfur",
                    "Europe without Switzerland",
                    "kilogram",
                    "petrol, low-sulfur",
                )
            },
            "bioethanol - wheat straw": {
                "name": (
                    "Ethanol, from wheat straw pellets, at fuelling station",
                    "RER",
                    "kilogram",
                    "ethanol, without water, in 99.7% solution state, vehicle grade",
                )
            },
            "bioethanol - forest residues": {
                "name": (
                    "Ethanol, from forest residues, at fuelling station",
                    "RER",
                    "kilogram",
                    "ethanol, without water, in 99.7% solution state, vehicle grade",
                )
            },
            "bioethanol - sugarbeet": {
                "name": (
                    "Ethanol, from sugarbeet, at fuelling station",
                    "RER",
                    "kilogram",
                    "ethanol, without water, in 99.7% solution state, vehicle grade",
                )
            },
            "bioethanol - maize starch": {
                "name": (
                    "Ethanol, from maize starch, at fuelling station",
                    "RER",
                    "kilogram",
                    "ethanol, without water, in 99.7% solution state, vehicle grade",
                )
            },
            "synthetic gasoline - economic allocation": {
                "name": (
                    "gasoline production, synthetic, from methanol, hydrogen from electrolysis, CO2 from DAC, economic allocation, at fuelling station",
                    "RER",
                    "kilogram",
                    "gasoline, synthetic, vehicle grade",
                )
            },
            "synthetic gasoline - energy allocation": {
                "name": (
                    "gasoline production, synthetic, from methanol, hydrogen from electrolysis, CO2 from DAC, energy allocation, at fuelling station",
                    "RER",
                    "kilogram",
                    "gasoline, synthetic, vehicle grade",
                )
            },
        }

        for d in d_fuels:
            if any(
                i in d_fuels[d]["name"][0].lower()
                for i in ("synthetic", "ethanol", "biodiesel")
            ):
                d_fuels[d]["additional electricity"] = self.find_inputs(
                    "kilowatt hour", d_fuels[d]["name"][0], "unit"
                )
            else:
                d_fuels[d]["additional electricity"] = 0

        for d in d_fuels:
            if any(i in d_fuels[d]["name"][0].lower() for i in ("synthetic", "bio")):
                self.find_inputs(
                    "kilowatt hour", d_fuels[d]["name"][0], "unit", zero_out_input=True
                )

        return d_fuels

    def create_fuel_markets(
        self,
        fuel_type,
        primary=None,
        secondary=None,
        tertiary=None,
        primary_share=None,
        secondary_share=None,
        tertiary_share=None,
    ):
        """
        This function creates markets for fuel, considering a given blend, a given fuel type and a given year.
        It also adds separate electricity input in case hydrogen from electrolysis is needed somewhere in the fuel supply chain.
        :return:
        """

        d_dataset_name = {
            "petrol": "fuel supply for gasoline vehicles, ",
            "electricity": "electricity supply for electric vehicles, ",
        }

        if fuel_type != "electricity":
            for y, year in enumerate(self.scope["year"]):
                dataset_name = d_dataset_name[fuel_type] + str(year)
                fuel_market_index = [
                    self.inputs[i] for i in self.inputs if i[0] == dataset_name
                ][0]

                try:
                    primary_fuel_activity_index = self.inputs[
                        self.fuel_dictionary[primary]["name"]
                    ]
                    secondary_fuel_activity_index = self.inputs[
                        self.fuel_dictionary[secondary]["name"]
                    ]
                except KeyError:
                    raise KeyError(
                        "One of the primary or secondary fuels specified in "
                        "the fuel blend for {} is not valid.".format(fuel_type)
                    )

                if tertiary:

                    if ~np.isclose(
                        primary_share[y] + secondary_share[y] + tertiary_share[y],
                        1,
                        rtol=1e-3,
                    ):
                        sum_blend = (
                            primary_share[y] + secondary_share[y] + tertiary_share[y]
                        )
                        print(
                            f"The fuel blend for {fuel_type} in {year} is not equal to 1, but {sum_blend}."
                            f"The primary fuel share is adjusted so that the fuel blend equals 1."
                        )
                        primary_share[y] = 1 - (secondary_share[y] + tertiary_share[y])
                else:

                    if ~np.isclose(primary_share[y] + secondary_share[y], 1, rtol=1e-3):
                        sum_blend = primary_share[y] + secondary_share[y]
                        print(
                            f"The fuel blend for {fuel_type} in {year} is not equal to 1, but {sum_blend}."
                            f"The primary fuel share is adjusted so that the fuel blend equals 1."
                        )
                        primary_share[y] = 1 - secondary_share[y]

                self.A[:, primary_fuel_activity_index, fuel_market_index] = (
                    -1 * primary_share[y]
                )
                self.A[:, secondary_fuel_activity_index, fuel_market_index] = (
                    -1 * secondary_share[y]
                )

                def learning_rate_fuel(fuel, year, share, val):
                    if fuel == "synthetic gasoline - energy allocation":
                        # apply some learning rate for electrolysis
                        h2 = 0.338
                        electrolysis = -0.3538 * (float(year) - 2010) + 58.589
                        electricity = val - (h2 * 58)
                        electricity += electrolysis * h2
                        electricity *= share

                    elif fuel == "synthetic gasoline - economic allocation":
                        # apply some learning rate for electrolysis
                        h2 = 0.6385
                        electrolysis = -0.3538 * (float(year) - 2010) + 58.589
                        electricity = val - (h2 * 58)
                        electricity += electrolysis * h2
                        electricity *= share

                    else:
                        electricity = val * share
                    return electricity

                additional_electricity_primary = learning_rate_fuel(
                    primary,
                    year,
                    primary_share[y],
                    self.fuel_dictionary[primary]["additional electricity"],
                )

                additional_electricity_secondary = learning_rate_fuel(
                    secondary,
                    year,
                    secondary_share[y],
                    self.fuel_dictionary[secondary]["additional electricity"],
                )

                additional_electricity = (
                    additional_electricity_primary + additional_electricity_secondary
                )

                if tertiary:
                    tertiary_fuel_activity_index = self.inputs[
                        self.fuel_dictionary[tertiary]["name"]
                    ]
                    self.A[:, tertiary_fuel_activity_index, fuel_market_index] = (
                        -1 * tertiary_share[y]
                    )
                    additional_electricity += learning_rate_fuel(
                        tertiary,
                        year,
                        tertiary_share[y],
                        self.fuel_dictionary[tertiary]["additional electricity"],
                    )

                if additional_electricity > 0:
                    electricity_mix_index = [
                        self.inputs[i]
                        for i in self.inputs
                        if i[0]
                        == "electricity market for fuel preparation, " + str(year)
                    ][0]
                    self.A[:, electricity_mix_index, fuel_market_index] = (
                        -1 * additional_electricity
                    )
        else:
            for year in self.scope["year"]:
                dataset_name = d_dataset_name[fuel_type] + str(year)
                electricity_market_index = [
                    self.inputs[i] for i in self.inputs if i[0] == dataset_name
                ][0]
                electricity_mix_index = [
                    self.inputs[i]
                    for i in self.inputs
                    if i[0] == "electricity market for fuel preparation, " + str(year)
                ][0]
                self.A[:, electricity_mix_index, electricity_market_index] = -1

    def find_inputs(
        self, value_in, value_out, find_input_by="name", zero_out_input=False
    ):
        """
        Finds the exchange inputs to a specified functional unit
        :param zero_out_input:
        :param find_input_by: can be 'name' or 'unit'
        :param value_in: value to look for
        :param value_out: functional unit output
        :return: indices of all inputs to FU, indices of inputs of intereste
        :rtype: tuple
        """

        if isinstance(value_out, str):
            value_out = [value_out]

        index_output = [
            self.inputs[i]
            for val in value_out
            for i in self.inputs
            if val.lower() in i[0].lower()
        ]

        f = np.zeros((np.shape(self.A)[1]))

        f[index_output] = 1

        X = np.float32(spsolve(sparse.csr_matrix(self.A[0]), f.T))

        ind_inputs = np.nonzero(X)[0]

        if find_input_by == "name":
            ins = [
                i
                for i in ind_inputs
                if value_in.lower() in self.rev_inputs[i][0].lower()
            ]

        if find_input_by == "unit":
            ins = [
                i
                for i in ind_inputs
                if value_in.lower() in self.rev_inputs[i][2].lower()
            ]

        outs = [i for i in ind_inputs if i not in ins]

        sum_supplied = X[ins].sum()

        if zero_out_input:
            # zero out initial inputs
            self.A[np.ix_(np.arange(0, self.A.shape[0]), ins, outs)] *= 0
        else:
            return sum_supplied

    def set_inputs_in_A_matrix(self, array):
        """
        Fill-in the A matrix. Does not return anything. Modifies in place.
        Shape of the A matrix (values, products, activities).

        :param array: :attr:`array` from :class:`CarModel` class
        """

        # Glider
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "glider") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "glider"]["name"],
                                self.mapping[s, pt, "glider"]["location"],
                                self.mapping[s, pt, "glider"]["unit"],
                                self.mapping[s, pt, "glider"]["reference product"],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        array[
                            self.array_inputs["glider base mass"],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ]
                        / array[
                            self.array_inputs["lifetime kilometers"],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ]
                        * self.mapping[s, pt, "glider"]["multiplication factor"]
                    ).T * -1

        self.A[
            :,
            self.inputs[
                ("Glider lightweighting", "GLO", "kilogram", "Glider lightweighting")
            ],
            -self.number_of_vehicles :,
        ] = (
            (
                array[self.array_inputs["lightweighting"], :]
                * array[self.array_inputs["glider base mass"], :]
            )
            / array[self.array_inputs["lifetime kilometers"], :]
            * -1
        )

        # Maintenance
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "maintenance") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "maintenance"]["name"],
                                self.mapping[s, pt, "maintenance"]["location"],
                                self.mapping[s, pt, "maintenance"]["unit"],
                                self.mapping[s, pt, "maintenance"]["reference product"],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        (
                            array[
                                self.array_inputs["lifetime kilometers"],
                                :,
                                self.get_index_vehicle_from_array(pt, s, method="and"),
                            ]
                            / self.mapping[s, pt, "maintenance"][
                                "multiplication factor"
                            ]
                        )
                        / array[
                            self.array_inputs["lifetime kilometers"],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ]
                    ).T * -1

        # Glider EoL
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "glider disposal") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "glider disposal"]["name"],
                                self.mapping[s, pt, "glider disposal"]["location"],
                                self.mapping[s, pt, "glider disposal"]["unit"],
                                self.mapping[s, pt, "glider disposal"][
                                    "reference product"
                                ],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        array[
                            self.array_inputs["glider base mass"],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ]
                        / array[
                            self.array_inputs["lifetime kilometers"],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ]
                        * self.mapping[s, pt, "glider disposal"][
                            "multiplication factor"
                        ]
                    ).T * -1

        # Powertrain disposal
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "powertrain disposal") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "powertrain disposal"]["name"],
                                self.mapping[s, pt, "powertrain disposal"]["location"],
                                self.mapping[s, pt, "powertrain disposal"]["unit"],
                                self.mapping[s, pt, "powertrain disposal"][
                                    "reference product"
                                ],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        array[
                            [
                                self.array_inputs[i]
                                for i in [
                                    "mechanical powertrain mass",
                                    "electrical powertrain mass",
                                    "converter mass",
                                    "inverter mass",
                                    "power distribution unit mass",
                                    "electric engine mass",
                                ]
                            ],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ].sum(axis=0)
                        / array[
                            self.array_inputs["lifetime kilometers"],
                            :,
                            self.get_index_vehicle_from_array(pt, s, method="and"),
                        ]
                        * self.mapping[s, pt, "powertrain disposal"][
                            "multiplication factor"
                        ]
                    ).T * -1

        # Battery EoL
        self.A[
            :,
            self.inputs[
                (
                    "market for used Li-ion battery",
                    "GLO",
                    "kilogram",
                    "used Li-ion battery",
                )
            ],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            array[
                self.array_inputs["energy battery mass"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
        ).T * -1

        # Powertrain components
        if (
            any(
                x in self.scope["size"]
                for x in [
                    "Kick-scooter",
                ]
            )
            and "BEV" in self.scope["powertrain"]
        ):

            self.A[
                :,
                self.inputs[
                    (
                        "charging station, 100W",
                        "GLO",
                        "unit",
                        "charging station, 100W",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if "BEV" in i[0] and any(x in i[0] for x in ["Kick-scooter"])
                ],
            ] = (
                1
                / array[
                    self.array_inputs["lifetime kilometers"],
                    :,
                    self.get_index_vehicle_from_array(
                        ["BEV"], ["Kick-scooter"], method="and"
                    ),
                ]
            ).T * -1

        if (
            any(x in self.scope["size"] for x in ["Bicycle <25", "Bicycle <45"])
            and "BEV" in self.scope["powertrain"]
        ):
            self.A[
                :,
                self.inputs[
                    (
                        "charging station, 500W",
                        "GLO",
                        "unit",
                        "charging station, 500W",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if "BEV" in i[0]
                    and any(x in i[0] for x in ["Bicycle <25", "Bicycle <45"])
                ],
            ] = (
                1
                / array[
                    self.array_inputs["lifetime kilometers"],
                    :,
                    self.get_index_vehicle_from_array(
                        ["BEV"], ["Bicycle <25", "Bicycle <45"], method="and"
                    ),
                ]
                * -1
            ).T

        if (
            any(
                x in self.scope["size"]
                for x in [
                    "Scooter <4kW",
                    "Scooter 4-11kW",
                    "Motorcycle 4-11kW",
                    "Motorcycle 11-35kW",
                    "Motorcycle >35kW",
                ]
            )
            and "BEV" in self.scope["powertrain"]
        ):
            self.A[
                :,
                self.inputs[
                    (
                        "charging station, 3kW",
                        "GLO",
                        "unit",
                        "charging station, 3kW",
                    )
                ],
                [
                    self.inputs[i]
                    for i in self.inputs
                    if "BEV" in i[0]
                    and any(
                        x in i[0]
                        for x in [
                            "Scooter <4kW",
                            "Scooter 4-11kW",
                            "Motorcycle 4-11kW",
                            "Motorcycle 11-35kW",
                            "Motorcycle >35kW",
                        ]
                    )
                ],
            ] = (
                1
                / array[
                    self.array_inputs["lifetime kilometers"],
                    :,
                    self.get_index_vehicle_from_array(
                        ["BEV"],
                        [
                            "Scooter <4kW",
                            "Scooter 4-11kW",
                            "Motorcycle 4-11kW",
                            "Motorcycle 11-35kW",
                            "Motorcycle >35kW",
                        ],
                        method="and",
                    ),
                ]
                * -1
            ).T

        self.A[
            :,
            self.inputs[
                (
                    "market for converter, for electric passenger car",
                    "GLO",
                    "kilogram",
                    "converter, for electric passenger car",
                )
            ],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            array[
                self.array_inputs["converter mass"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            * -1
        ).T

        self.A[
            :,
            self.inputs[
                (
                    "market for electric motor, electric passenger car",
                    "GLO",
                    "kilogram",
                    "electric motor, electric passenger car",
                )
            ],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            array[
                self.array_inputs["electric engine mass"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            * -1
        ).T

        self.A[
            :,
            self.inputs[
                (
                    "market for inverter, for electric passenger car",
                    "GLO",
                    "kilogram",
                    "inverter, for electric passenger car",
                )
            ],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            array[
                self.array_inputs["inverter mass"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            * -1
        ).T

        self.A[
            :,
            self.inputs[
                (
                    "market for power distribution unit, for electric passenger car",
                    "GLO",
                    "kilogram",
                    "power distribution unit, for electric passenger car",
                )
            ],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            array[
                self.array_inputs["power distribution unit mass"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
            * -1
        ).T

        self.A[
            :,
            self.inputs[
                (
                    "market for internal combustion engine, passenger car",
                    "GLO",
                    "kilogram",
                    "internal combustion engine, for passenger car",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(
                    x in i[0]
                    for x in [
                        "Scooter <4kW",
                        "Scooter 4-11kW",
                        "Moped <4kW",
                        "Motorcycle 4-11kW",
                        "Motorcycle 11-35kW",
                        "Motorcycle >35kW",
                    ]
                )
                and "Human" not in i[0]
            ],
        ] = (
            (
                array[
                    np.ix_(
                        [
                            self.array_inputs[l]
                            for l in [
                                "combustion engine mass",
                                "mechanical powertrain mass",
                            ]
                        ],
                        np.arange(self.iterations),
                        self.get_index_vehicle_from_array(
                            [
                                "Scooter <4kW",
                                "Scooter 4-11kW",
                                "Moped <4kW",
                                "Motorcycle 4-11kW",
                                "Motorcycle 11-35kW",
                                "Motorcycle >35kW",
                            ],
                            ["BEV", "ICEV-p"],
                            method="and",
                        ),
                    )
                ].sum(axis=0)
            )
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(
                    [
                        "Scooter <4kW",
                        "Scooter 4-11kW",
                        "Moped <4kW",
                        "Motorcycle 4-11kW",
                        "Motorcycle 11-35kW",
                        "Motorcycle >35kW",
                    ],
                    ["BEV", "ICEV-p"],
                    method="and",
                ),
            ].T
            * -1
        )

        # Start of printout

        print(
            "****************** IMPORTANT BACKGROUND PARAMETERS ******************",
            end="\n * ",
        )

        # Energy storage

        print(
            "The country of use is " + self.country,
            end="\n * ",
        )

        battery_tech = self.background_configuration["energy storage"]["electric"][
            "type"
        ]
        battery_origin = self.background_configuration["energy storage"]["electric"][
            "origin"
        ]

        print(
            "Power and energy batteries produced in "
            + battery_origin
            + " using "
            + battery_tech
            + " chemistry.",
            end="\n * ",
        )

        # Use the NMC inventory of Schmidt et al. 2019
        self.A[
            :,
            self.inputs[("Battery BoP", "GLO", "kilogram", "Battery BoP")],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            (
                array[
                    self.array_inputs["battery BoP mass"],
                    :,
                    self.get_index_vehicle_from_array(["BEV"]),
                ]
                * (
                    1
                    + array[
                        self.array_inputs["battery lifetime replacements"],
                        :,
                        self.get_index_vehicle_from_array(["BEV"]),
                    ]
                )
            )
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
        ).T * -1

        battery_cell_label = (
            "Battery cell, " + battery_tech,
            "GLO",
            "kilogram",
            "Battery cell",
        )

        self.A[
            :,
            self.inputs[battery_cell_label],
            [self.inputs[i] for i in self.inputs if "BEV" in i[0]],
        ] = (
            (
                array[
                    self.array_inputs["battery cell mass"],
                    :,
                    self.get_index_vehicle_from_array(["BEV"]),
                ]
                * (
                    1
                    + array[
                        self.array_inputs["battery lifetime replacements"],
                        :,
                        self.get_index_vehicle_from_array(["BEV"]),
                    ]
                )
            )
            / array[
                self.array_inputs["lifetime kilometers"],
                :,
                self.get_index_vehicle_from_array(["BEV"]),
            ]
        ).T * -1

        # Set an input of electricity, given the country of manufacture
        self.A[
            :,
            self.inputs[
                (
                    "market group for electricity, medium voltage",
                    "World",
                    "kilowatt hour",
                    "electricity, medium voltage",
                )
            ],
            self.inputs[battery_cell_label],
        ] = 0

        for y in self.scope["year"]:
            index = self.get_index_vehicle_from_array(y, ["BEV"], method="and")

            self.A[
                np.ix_(
                    np.arange(self.iterations),
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(y) in i[0]
                        and "electricity market for energy storage production" in i[0]
                    ],
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(y) in i[0] and "transport, " in i[0] and "BEV" in i[0]
                    ],
                )
            ] = (
                array[
                    self.array_inputs["battery cell production electricity"], :, index
                ].T
                * self.A[
                    :,
                    self.inputs[battery_cell_label],
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(y) in i[0] and "transport, " in i[0] and "BEV" in i[0]
                    ],
                ]
            ).reshape(
                self.iterations, 1, -1
            )

        index_A = [
            self.inputs[c]
            for c in self.inputs
            if any(
                ele in c[0]
                for ele in ["ICEV-d", "ICEV-p", "HEV-p", "PHEV-p", "PHEV-d", "HEV-d"]
            )
        ]
        index = self.get_index_vehicle_from_array(["ICEV-p"])

        self.A[
            :,
            self.inputs[
                (
                    "polyethylene production, high density, granulate",
                    "RER",
                    "kilogram",
                    "polyethylene, high density, granulate",
                )
            ],
            index_A,
        ] = (
            array[self.array_inputs["fuel tank mass"], :, index]
            / array[self.array_inputs["lifetime kilometers"], :, index]
        ).T * -1

        try:
            sum_renew, co2_intensity_tech = self.define_renewable_rate_in_mix()

        except AttributeError:
            sum_renew = [0] * len(self.scope["year"])
            co2_intensity_tech = [0] * len(self.scope["year"])

        for y, year in enumerate(self.scope["year"]):

            if y + 1 == len(self.scope["year"]):
                end_str = "\n * "
            else:
                end_str = "\n \t * "

            print(
                "in "
                + str(year)
                + ", % of renewable: "
                + str(np.round(sum_renew[y] * 100, 0))
                + "%"
                + ", GHG intensity per kWh: "
                + str(int(np.sum(co2_intensity_tech[y] * self.mix[y])))
                + " g. CO2-eq.",
                end=end_str,
            )

        if any(True for x in ["BEV"] if x in self.scope["powertrain"]):
            for y in self.scope["year"]:
                index = self.get_index_vehicle_from_array(["BEV"], y, method="and")

                self.A[
                    np.ix_(
                        np.arange(self.iterations),
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if str(y) in i[0]
                            and "electricity supply for electric vehicles" in i[0]
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if str(y) in i[0]
                            and "transport, " in i[0]
                            and any(True for x in ["BEV"] if x in i[0])
                        ],
                    )
                ] = (
                    array[self.array_inputs["electricity consumption"], :, index] * -1
                ).T.reshape(
                    self.iterations, 1, -1
                )

        if [i for i in self.scope["powertrain"] if i in ["ICEV-p"]]:
            index = self.get_index_vehicle_from_array(["ICEV-p"])

            if "tertiary" in self.fuel_blends["petrol"]:
                print(
                    "{} is completed by {} and {}.".format(
                        self.fuel_blends["petrol"]["primary"]["type"],
                        self.fuel_blends["petrol"]["secondary"]["type"],
                        self.fuel_blends["petrol"]["tertiary"]["type"],
                    ),
                    end="\n \t * ",
                )

            else:

                print(
                    "{} is completed by {}.".format(
                        self.fuel_blends["petrol"]["primary"]["type"],
                        self.fuel_blends["petrol"]["secondary"]["type"],
                    ),
                    end="\n \t * ",
                )

            for y, year in enumerate(self.scope["year"]):
                if y + 1 == len(self.scope["year"]):
                    end_str = "\n * "
                else:
                    end_str = "\n \t * "

                if "tertiary" in self.fuel_blends["petrol"]:
                    print(
                        "in "
                        + str(year)
                        + " _________________ "
                        + str(
                            np.round(
                                self.fuel_blends["petrol"]["secondary"]["share"][y]
                                * 100,
                                0,
                            )
                        )
                        + "%"
                        + " _________________ "
                        + str(
                            np.round(
                                self.fuel_blends["petrol"]["tertiary"]["share"][y]
                                * 100,
                                0,
                            )
                        )
                        + "%",
                        end=end_str,
                    )
                else:
                    print(
                        "in "
                        + str(year)
                        + " _________________________________________ "
                        + str(
                            np.round(
                                self.fuel_blends["petrol"]["secondary"]["share"][y]
                                * 100,
                                0,
                            )
                        )
                        + "%",
                        end=end_str,
                    )

                ind_A = [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and "transport, " in i[0]
                    and any(x in i[0] for x in ["ICEV-p"])
                ]
                ind_array = [
                    x for x in self.get_index_vehicle_from_array(year) if x in index
                ]

                # Fuel supply

                fuel_amount = (
                    array[self.array_inputs["TtW energy"], :, ind_array]
                    / (
                        self.array.values[
                            self.array_inputs["LHV fuel MJ per kg"], :, ind_array
                        ]
                        * 1000
                    )
                    * -1
                ).T

                self.A[
                    :,
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(year) in i[0]
                        and "fuel supply for gasoline vehicles" in i[0]
                    ],
                    ind_A,
                ] = fuel_amount

                share_fossil = 0
                CO2_fossil = 0

                # Fuel-based CO2 emission from conventional petrol
                if self.fuel_blends["petrol"]["primary"]["type"] == "petrol":
                    share_fossil = self.fuel_blends["petrol"]["primary"]["share"][y]
                    CO2_fossil += (
                        self.fuel_blends["petrol"]["primary"]["CO2"]
                        * self.fuel_blends["petrol"]["primary"]["share"][y]
                    )

                if self.fuel_blends["petrol"]["secondary"]["type"] == "petrol":
                    share_fossil += self.fuel_blends["petrol"]["secondary"]["share"][y]
                    CO2_fossil += (
                        self.fuel_blends["petrol"]["secondary"]["CO2"]
                        * self.fuel_blends["petrol"]["secondary"]["share"][y]
                    )

                if "tertiary" in self.fuel_blends["petrol"]:
                    if self.fuel_blends["petrol"]["tertiary"]["type"] == "petrol":
                        share_fossil += self.fuel_blends["petrol"]["tertiary"]["share"][
                            y
                        ]
                        CO2_fossil += (
                            self.fuel_blends["petrol"]["tertiary"]["CO2"]
                            * self.fuel_blends["petrol"]["tertiary"]["share"][y]
                        )

                self.A[
                    :,
                    self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")],
                    ind_A,
                ] = (
                    fuel_amount * CO2_fossil
                )

                # Fuel-based SO2 emissions
                # Sulfur concentration value for a given country, a given year, as a concentration ratio

                sulfur_concentration = self.get_sulfur_content(
                    self.country, "petrol", year
                )

                self.A[
                    :,
                    self.inputs[("Sulfur dioxide", ("air",), "kilogram")],
                    ind_A,
                ] = (
                    fuel_amount
                    * sulfur_concentration
                    * (64 / 32)  # molar mass of SO2/molar mass of O2
                )

                share_non_fossil = 0
                CO2_non_fossil = 0

                # Fuel-based CO2 emission from alternative petrol
                # The share of non-fossil fuel in the blend is retrieved
                # As well as the CO2 emission factor of the fuel
                if self.fuel_blends["petrol"]["primary"]["type"] != "petrol":
                    share_non_fossil += self.fuel_blends["petrol"]["primary"]["share"][
                        y
                    ]
                    CO2_non_fossil += (
                        self.fuel_blends["petrol"]["primary"]["CO2"]
                        * self.fuel_blends["petrol"]["primary"]["share"][y]
                    )

                if self.fuel_blends["petrol"]["secondary"]["type"] != "petrol":
                    share_non_fossil += self.fuel_blends["petrol"]["secondary"][
                        "share"
                    ][y]
                    CO2_non_fossil += (
                        self.fuel_blends["petrol"]["secondary"]["CO2"]
                        * self.fuel_blends["petrol"]["secondary"]["share"][y]
                    )

                if "tertiary" in self.fuel_blends["petrol"]:
                    if self.fuel_blends["petrol"]["tertiary"]["type"] != "petrol":
                        share_non_fossil += self.fuel_blends["petrol"]["tertiary"][
                            "share"
                        ][y]
                        CO2_non_fossil += (
                            self.fuel_blends["petrol"]["tertiary"]["share"][y]
                            * self.fuel_blends["petrol"]["tertiary"]["CO2"]
                        )

                self.A[
                    :,
                    self.inputs[
                        (
                            "Carbon dioxide, from soil or biomass stock",
                            ("air",),
                            "kilogram",
                        )
                    ],
                    ind_A,
                ] = (
                    fuel_amount * CO2_non_fossil
                )

        # Non-exhaust emissions
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt) in self.abrasion_emissions:

                    self.A[
                        :,
                        self.inputs[
                            (
                                "treatment of road wear emissions, passenger car",
                                "RER",
                                "kilogram",
                                "road wear emissions, passenger car",
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        self.abrasion_emissions[(s, pt)]["road"] * -1
                    )

                    self.A[
                        :,
                        self.inputs[
                            (
                                "treatment of tyre wear emissions, passenger car",
                                "RER",
                                "kilogram",
                                "tyre wear emissions, passenger car",
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        self.abrasion_emissions[(s, pt)]["tyre"] * -1
                    )

                    self.A[
                        :,
                        self.inputs[
                            (
                                "treatment of brake wear emissions, passenger car",
                                "RER",
                                "kilogram",
                                "brake wear emissions, passenger car",
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0] and pt in i[0]
                        ],
                    ] = (
                        self.abrasion_emissions[(s, pt)]["brake"] * -1
                    )

        # Infrastructure
        self.A[
            :,
            self.inputs[("road construction", "CH", "meter-year", "road")],
            -self.number_of_vehicles :,
        ] = (
            5.37e-7 * array[self.array_inputs["driving mass"], :] * -1
        )

        # Infrastructure maintenance
        self.A[
            :,
            self.inputs[("road maintenance", "CH", "meter-year", "road maintenance")],
            -self.number_of_vehicles :,
        ] = (
            1.29e-3 * array[self.array_inputs["is_available"], :] * -1
        )

        # Exhaust emissions
        # Non-fuel based emissions
        self.A[:, self.index_emissions, -self.number_of_vehicles :] = (
            array[
                [
                    self.array_inputs[self.map_non_fuel_emissions[self.rev_inputs[x]]]
                    for x in self.index_emissions
                ]
            ]
            * -1
        ).transpose([1, 0, 2])

        # Noise emissions
        self.A[:, self.index_noise, -self.number_of_vehicles :] = (
            array[
                [
                    self.array_inputs[self.map_noise_emissions[self.rev_inputs[x]]]
                    for x in self.index_noise
                ]
            ]
            * -1
        ).transpose([1, 0, 2])

        # Transport to market from China

        # 15'900 km by ship
        self.A[
            :,
            self.inputs[
                (
                    "transport, freight, sea, container ship",
                    "GLO",
                    "ton kilometer",
                    "transport, freight, sea, container ship",
                )
            ],
            -self.number_of_vehicles :,
        ] = (
            array[self.array_inputs["curb mass"]]
            / array[self.array_inputs["lifetime kilometers"]]
            / 1000
            * 15900
        ) * -1

        # 1'000 km by truck
        self.A[
            :,
            self.inputs[
                (
                    "transport, freight, lorry, fleet average, 40t",
                    "EUR",
                    "ton kilometer",
                    "transport, freight, lorry, fleet average",
                )
            ],
            -self.number_of_vehicles :,
        ] = (
            array[self.array_inputs["curb mass"]]
            / array[self.array_inputs["lifetime kilometers"]]
            / 1000
            * 1000
        ) * -1

        self.A = np.nan_to_num(self.A, 0)

        print("*********************************************************************")

    def set_inputs_in_A_matrix_for_export(self, array):
        """
        Fill-in the A matrix. Does not return anything. Modifies in place.
        Shape of the A matrix (values, products, activities).

        :param array: :attr:`array` from :class:`CarModel` class
        """

        # Glider
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "glider") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "glider"]["name"],
                                self.mapping[s, pt, "glider"]["location"],
                                self.mapping[s, pt, "glider"]["unit"],
                                self.mapping[s, pt, "glider"]["reference product"],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if i[0].startswith(s) and pt in i[0]
                        ],
                    ] = (
                        array[self.array_inputs["glider base mass"], :]
                        * self.mapping[s, pt, "glider"]["multiplication factor"]
                        * -1
                    )

        self.A[
            :,
            self.inputs[
                ("Glider lightweighting", "GLO", "kilogram", "Glider lightweighting")
            ],
            -self.number_of_vehicles :,
        ] = (
            array[self.array_inputs["lightweighting"], :]
            * array[self.array_inputs["glider base mass"], :]
        ) * -1

        # Maintenance
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "maintenance") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "maintenance"]["name"],
                                self.mapping[s, pt, "maintenance"]["location"],
                                self.mapping[s, pt, "maintenance"]["unit"],
                                self.mapping[s, pt, "maintenance"]["reference product"],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if i[0].startswith(s) and pt in i[0]
                        ],
                    ] = (
                        self.mapping[s, pt, "maintenance"]["multiplication factor"]
                    ) * -1

        # Glider EoL
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "glider disposal") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "glider disposal"]["name"],
                                self.mapping[s, pt, "glider disposal"]["location"],
                                self.mapping[s, pt, "glider disposal"]["unit"],
                                self.mapping[s, pt, "glider disposal"][
                                    "reference product"
                                ],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if i[0].startswith(s) and pt in i[0]
                        ],
                    ] = (
                        array[self.array_inputs["glider base mass"]]
                        * self.mapping[s, pt, "glider disposal"][
                            "multiplication factor"
                        ]
                    ) * -1

        # Powertrain disposal
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt, "powertrain disposal") in self.mapping:
                    self.A[
                        :,
                        self.inputs[
                            (
                                self.mapping[s, pt, "powertrain disposal"]["name"],
                                self.mapping[s, pt, "powertrain disposal"]["location"],
                                self.mapping[s, pt, "powertrain disposal"]["unit"],
                                self.mapping[s, pt, "powertrain disposal"][
                                    "reference product"
                                ],
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if i[0].startswith(s) and pt in i[0]
                        ],
                    ] = (
                        array[
                            [
                                self.array_inputs[i]
                                for i in [
                                    "mechanical powertrain mass",
                                    "electrical powertrain mass",
                                    "converter mass",
                                    "inverter mass",
                                    "power distribution unit mass",
                                    "electric engine mass",
                                ]
                            ]
                        ].sum(axis=0)
                        * self.mapping[s, pt, "powertrain disposal"][
                            "multiplication factor"
                        ]
                    ) * -1

        # Battery EoL
        self.A[
            :,
            self.inputs[
                (
                    "market for used Li-ion battery",
                    "GLO",
                    "kilogram",
                    "used Li-ion battery",
                )
            ],
            [self.inputs[i] for i in self.inputs if i[0].startswith(s) and pt in i[0]],
        ] = array[self.array_inputs["energy battery mass"], :]

        # Powertrain components

        self.A[
            :,
            self.inputs[
                (
                    "market for converter, for electric passenger car",
                    "GLO",
                    "kilogram",
                    "converter, for electric passenger car",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[self.array_inputs["converter mass"], :] * -1
        )

        self.A[
            :,
            self.inputs[
                (
                    "market for electric motor, electric passenger car",
                    "GLO",
                    "kilogram",
                    "electric motor, electric passenger car",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[self.array_inputs["electric engine mass"], :] * -1
        )

        self.A[
            :,
            self.inputs[
                (
                    "market for inverter, for electric passenger car",
                    "GLO",
                    "kilogram",
                    "inverter, for electric passenger car",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[self.array_inputs["inverter mass"], :] * -1
        )

        self.A[
            :,
            self.inputs[
                (
                    "market for power distribution unit, for electric passenger car",
                    "GLO",
                    "kilogram",
                    "power distribution unit, for electric passenger car",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[self.array_inputs["power distribution unit mass"], :] * -1
        )

        self.A[
            :,
            self.inputs[
                (
                    "market for internal combustion engine, passenger car",
                    "GLO",
                    "kilogram",
                    "internal combustion engine, for passenger car",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[
                [
                    self.array_inputs[l]
                    for l in ["combustion engine mass", "mechanical powertrain mass"]
                ],
                :,
            ].sum(axis=0)
        ) * -1

        # Start of printout

        print(
            "****************** IMPORTANT BACKGROUND PARAMETERS ******************",
            end="\n * ",
        )

        # Energy storage

        print(
            "The country of use is " + self.country,
            end="\n * ",
        )

        battery_tech = self.background_configuration["energy storage"]["electric"][
            "type"
        ]
        battery_origin = self.background_configuration["energy storage"]["electric"][
            "origin"
        ]

        print(
            "Power and energy batteries produced in "
            + battery_origin
            + " using "
            + battery_tech
            + " chemistry.",
            end="\n * ",
        )

        # Use the NMC inventory of Schmidt et al. 2019
        self.A[
            :,
            self.inputs[("Battery BoP", "GLO", "kilogram", "Battery BoP")],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[self.array_inputs["battery BoP mass"], :]
            * (1 + array[self.array_inputs["battery lifetime replacements"], :])
        ) * -1

        battery_cell_label = (
            "Battery cell, " + battery_tech,
            "GLO",
            "kilogram",
            "Battery cell",
        )

        self.A[
            :,
            self.inputs[battery_cell_label],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            array[self.array_inputs["battery cell mass"], :]
            * (1 + array[self.array_inputs["fuel cell lifetime replacements"], :])
        ) * -1

        # Set an input of electricity, given the country of manufacture
        self.A[
            :,
            self.inputs[
                (
                    "market group for electricity, medium voltage",
                    "World",
                    "kilowatt hour",
                    "electricity, medium voltage",
                )
            ],
            self.inputs[battery_cell_label],
        ] = 0

        for y in self.scope["year"]:
            index = self.get_index_vehicle_from_array(y)

            self.A[
                np.ix_(
                    np.arange(self.iterations),
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(y) in i[0]
                        and "electricity market for energy storage production" in i[0]
                    ],
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if any(i[0].startswith(x) for x in self.scope["size"])
                    ],
                )
            ] = (
                array[
                    self.array_inputs["battery cell production electricity"], :, index
                ].T
                * self.A[
                    :,
                    self.inputs[battery_cell_label],
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if any(i[0].startswith(x) for x in self.scope["size"])
                    ],
                ]
            ).reshape(
                self.iterations, 1, -1
            )

        index_A = [
            self.inputs[c]
            for c in self.inputs
            if any(ele in c[0] for ele in ["ICEV-p"])
            and any(c[0].startswith(x) for x in self.scope["size"])
        ]
        index = self.get_index_vehicle_from_array(["ICEV-p"])

        self.A[
            :,
            self.inputs[
                (
                    "polyethylene production, high density, granulate",
                    "RER",
                    "kilogram",
                    "polyethylene, high density, granulate",
                )
            ],
            index_A,
        ] = (array[self.array_inputs["fuel tank mass"], :, index] * -1).T

        # END of vehicle building

        self.A[
            :,
            [
                self.inputs[c]
                for c in self.inputs
                if any(c[0].startswith(x) for x in self.scope["size"])
            ],
            [
                self.inputs[c]
                for c in self.inputs
                if c[0].startswith("transport, ")
                and any(c[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (
            -1 / array[self.array_inputs["lifetime kilometers"]]
        )

        try:
            sum_renew, co2_intensity_tech = self.define_renewable_rate_in_mix()

        except AttributeError:
            sum_renew = [0] * len(self.scope["year"])
            co2_intensity_tech = [0] * len(self.scope["year"])

        for y, year in enumerate(self.scope["year"]):

            if y + 1 == len(self.scope["year"]):
                end_str = "\n * "
            else:
                end_str = "\n \t * "

            print(
                "in "
                + str(year)
                + ", % of renewable: "
                + str(np.round(sum_renew[y] * 100, 0))
                + "%"
                + ", GHG intensity per kWh: "
                + str(int(np.sum(co2_intensity_tech[y] * self.mix[y])))
                + " g. CO2-eq.",
                end=end_str,
            )

        if any(True for x in ["BEV"] if x in self.scope["powertrain"]):
            for y in self.scope["year"]:
                index = self.get_index_vehicle_from_array(["BEV"], y, method="and")

                self.A[
                    np.ix_(
                        np.arange(self.iterations),
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if str(y) in i[0]
                            and "electricity supply for electric vehicles" in i[0]
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if str(y) in i[0]
                            and i[0].startswith("transport, ")
                            and any(True for x in ["BEV"] if x in i[0])
                        ],
                    )
                ] = (
                    array[self.array_inputs["electricity consumption"], :, index] * -1
                ).T.reshape(
                    self.iterations, 1, -1
                )

        if [i for i in self.scope["powertrain"] if i in ["ICEV-p"]]:
            index = self.get_index_vehicle_from_array(["ICEV-p"])

            if "tertiary" in self.fuel_blends["petrol"]:
                print(
                    "{} is completed by {} and {}.".format(
                        self.fuel_blends["petrol"]["primary"]["type"],
                        self.fuel_blends["petrol"]["secondary"]["type"],
                        self.fuel_blends["petrol"]["tertiary"]["type"],
                    ),
                    end="\n \t * ",
                )

            else:

                print(
                    "{} is completed by {}.".format(
                        self.fuel_blends["petrol"]["primary"]["type"],
                        self.fuel_blends["petrol"]["secondary"]["type"],
                    ),
                    end="\n \t * ",
                )

            for y, year in enumerate(self.scope["year"]):
                if y + 1 == len(self.scope["year"]):
                    end_str = "\n * "
                else:
                    end_str = "\n \t * "

                if "tertiary" in self.fuel_blends["petrol"]:
                    print(
                        "in "
                        + str(year)
                        + " _________________ "
                        + str(
                            np.round(
                                self.fuel_blends["petrol"]["secondary"]["share"][y]
                                * 100,
                                0,
                            )
                        )
                        + "%"
                        + " _________________ "
                        + str(
                            np.round(
                                self.fuel_blends["petrol"]["tertiary"]["share"][y]
                                * 100,
                                0,
                            )
                        )
                        + "%",
                        end=end_str,
                    )
                else:
                    print(
                        "in "
                        + str(year)
                        + " _________________________________________ "
                        + str(
                            np.round(
                                self.fuel_blends["petrol"]["secondary"]["share"][y]
                                * 100,
                                0,
                            )
                        )
                        + "%",
                        end=end_str,
                    )

                ind_A = [
                    self.inputs[i]
                    for i in self.inputs
                    if str(year) in i[0]
                    and i[0].startswith("transport, ")
                    and any(x in i[0] for x in ["ICEV-p"])
                ]
                ind_array = [
                    x for x in self.get_index_vehicle_from_array(year) if x in index
                ]

                # Fuel supply

                fuel_amount = (
                    array[
                        self.array_inputs["TtW energy, combustion mode"], :, ind_array
                    ]
                    / (
                        self.array.values[
                            self.array_inputs["LHV fuel MJ per kg"], :, ind_array
                        ]
                        * 1000
                    )
                    * (
                        1
                        - self.array.values[
                            self.array_inputs["electric utility factor"], :, ind_array
                        ]
                    )
                    * -1
                ).T

                self.A[
                    :,
                    [
                        self.inputs[i]
                        for i in self.inputs
                        if str(year) in i[0]
                        and "fuel supply for gasoline vehicles" in i[0]
                    ],
                    ind_A,
                ] = fuel_amount

                share_fossil = 0
                CO2_fossil = 0

                # Fuel-based CO2 emission from conventional petrol
                if self.fuel_blends["petrol"]["primary"]["type"] == "petrol":
                    share_fossil = self.fuel_blends["petrol"]["primary"]["share"][y]
                    CO2_fossil += (
                        self.fuel_blends["petrol"]["primary"]["CO2"]
                        * self.fuel_blends["petrol"]["primary"]["share"][y]
                    )

                if self.fuel_blends["petrol"]["secondary"]["type"] == "petrol":
                    share_fossil += self.fuel_blends["petrol"]["secondary"]["share"][y]
                    CO2_fossil += (
                        self.fuel_blends["petrol"]["secondary"]["CO2"]
                        * self.fuel_blends["petrol"]["secondary"]["share"][y]
                    )

                if "tertiary" in self.fuel_blends["petrol"]:
                    if self.fuel_blends["petrol"]["tertiary"]["type"] == "petrol":
                        share_fossil += self.fuel_blends["petrol"]["tertiary"]["share"][
                            y
                        ]
                        CO2_fossil += (
                            self.fuel_blends["petrol"]["tertiary"]["CO2"]
                            * self.fuel_blends["petrol"]["tertiary"]["share"][y]
                        )

                self.A[
                    :,
                    self.inputs[("Carbon dioxide, fossil", ("air",), "kilogram")],
                    ind_A,
                ] = (
                    fuel_amount * CO2_fossil
                )

                # Fuel-based SO2 emissions
                # Sulfur concentration value for a given country, a given year, as a concentration ratio

                sulfur_concentration = self.get_sulfur_content(
                    self.country, "petrol", year
                )

                self.A[
                    :,
                    self.inputs[("Sulfur dioxide", ("air",), "kilogram")],
                    ind_A,
                ] = (
                    fuel_amount
                    * sulfur_concentration
                    * (64 / 32)  # molar mass of SO2/molar mass of O2
                )

                share_non_fossil = 0
                CO2_non_fossil = 0

                # Fuel-based CO2 emission from alternative petrol
                # The share of non-fossil fuel in the blend is retrieved
                # As well as the CO2 emission factor of the fuel
                if self.fuel_blends["petrol"]["primary"]["type"] != "petrol":
                    share_non_fossil += self.fuel_blends["petrol"]["primary"]["share"][
                        y
                    ]
                    CO2_non_fossil += (
                        self.fuel_blends["petrol"]["primary"]["CO2"]
                        * self.fuel_blends["petrol"]["primary"]["share"][y]
                    )

                if self.fuel_blends["petrol"]["secondary"]["type"] != "petrol":
                    share_non_fossil += self.fuel_blends["petrol"]["secondary"][
                        "share"
                    ][y]
                    CO2_non_fossil += (
                        self.fuel_blends["petrol"]["secondary"]["CO2"]
                        * self.fuel_blends["petrol"]["secondary"]["share"][y]
                    )

                if "tertiary" in self.fuel_blends["petrol"]:
                    if self.fuel_blends["petrol"]["tertiary"]["type"] != "petrol":
                        share_non_fossil += self.fuel_blends["petrol"]["tertiary"][
                            "share"
                        ][y]
                        CO2_non_fossil += (
                            self.fuel_blends["petrol"]["tertiary"]["share"][y]
                            * self.fuel_blends["petrol"]["tertiary"]["CO2"]
                        )

                self.A[
                    :,
                    self.inputs[
                        (
                            "Carbon dioxide, from soil or biomass stock",
                            ("air",),
                            "kilogram",
                        )
                    ],
                    ind_A,
                ] = (
                    fuel_amount * CO2_non_fossil
                )

        # Non-exhaust emissions
        for pt in self.scope["powertrain"]:
            for s in self.scope["size"]:
                if (s, pt) in self.abrasion_emissions:
                    self.A[
                        :,
                        self.inputs[
                            (
                                "treatment of road wear emissions, passenger car",
                                "RER",
                                "kilogram",
                                "road wear emissions, passenger car",
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0]
                            and pt in i[0]
                            and i[0].startswith("transport, ")
                        ],
                    ] = (
                        self.abrasion_emissions[(s, pt)]["road"] * -1
                    )

                    self.A[
                        :,
                        self.inputs[
                            (
                                "treatment of tyre wear emissions, passenger car",
                                "RER",
                                "kilogram",
                                "tyre wear emissions, passenger car",
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0]
                            and pt in i[0]
                            and i[0].startswith("transport, ")
                        ],
                    ] = (
                        self.abrasion_emissions[(s, pt)]["tyre"] * -1
                    )

                    self.A[
                        :,
                        self.inputs[
                            (
                                "treatment of brake wear emissions, passenger car",
                                "RER",
                                "kilogram",
                                "brake wear emissions, passenger car",
                            )
                        ],
                        [
                            self.inputs[i]
                            for i in self.inputs
                            if s in i[0]
                            and pt in i[0]
                            and i[0].startswith("transport, ")
                        ],
                    ] = (
                        self.abrasion_emissions[(s, pt)]["brake"] * -1
                    )

        # Infrastructure
        self.A[
            :,
            self.inputs[("road construction", "CH", "meter-year", "road")],
            [self.inputs[i] for i in self.inputs if i[0].startswith("transport, ")],
        ] = (
            5.37e-7 * array[self.array_inputs["driving mass"], :] * -1
        )

        # Infrastructure maintenance
        self.A[
            :,
            self.inputs[("road maintenance", "CH", "meter-year", "road maintenance")],
            [self.inputs[i] for i in self.inputs if i[0].startswith("transport, ")],
        ] = (
            1.29e-3 * -1
        )

        # Exhaust emissions
        # Non-fuel based emissions

        self.A[
            np.ix_(
                np.arange(self.iterations),
                self.index_emissions,
                [self.inputs[i] for i in self.inputs if i[0].startswith("transport, ")],
            )
        ] = (
            array[
                [
                    self.array_inputs[self.map_non_fuel_emissions[self.rev_inputs[x]]]
                    for x in self.index_emissions
                ]
            ]
            * -1
        ).transpose(
            [1, 0, 2]
        )

        # Noise emissions
        self.A[
            np.ix_(
                np.arange(self.iterations),
                self.index_noise,
                [self.inputs[i] for i in self.inputs if i[0].startswith("transport, ")],
            )
        ] = (
            array[
                [
                    self.array_inputs[self.map_noise_emissions[self.rev_inputs[x]]]
                    for x in self.index_noise
                ]
            ]
            * -1
        ).transpose(
            [1, 0, 2]
        )

        # Transport to market from China

        # 15'900 km by ship
        self.A[
            :,
            self.inputs[
                (
                    "transport, freight, sea, container ship",
                    "GLO",
                    "ton kilometer",
                    "transport, freight, sea, container ship",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (array[self.array_inputs["curb mass"], :, index] / 1000 * 15900) * -1

        # 1'000 km by truck
        self.A[
            :,
            self.inputs[
                (
                    "transport, freight, lorry, fleet average, 40t",
                    "EUR",
                    "ton kilometer",
                    "transport, freight, lorry, fleet average",
                )
            ],
            [
                self.inputs[i]
                for i in self.inputs
                if any(i[0].startswith(x) for x in self.scope["size"])
            ],
        ] = (array[self.array_inputs["curb mass"], :, index] / 1000 * 1000) * -1

        print("*********************************************************************")

    def select_heat_supplier(self, heat_supplier):
        """
        The heat supply is an important aspect of direct air capture.
        Here, we can change the supplier of heat.
        :param heat_supplier: by default "waste heat". Must be one of "waste heat", "biomass heat",
        "natural gas heat", "market heat".
        :type heat_supplier: str
        :return:
        """

        d_heat_suppliers = {
            "waste heat": (
                "heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas",
                "CH",
                "megajoule",
                "heat, district or industrial, other than natural gas",
            ),
            "biomass heat": (
                "heat production, hardwood chips from forest, at furnace 1000kW, state-of-the-art 2014",
                "CH",
                "megajoule",
                "heat, district or industrial, other than natural gas",
            ),
            "natural gas heat": (
                "market group for heat, central or small-scale, natural gas",
                "RER",
                "megajoule",
                "heat, central or small-scale, natural gas",
            ),
            "market heat": (
                "market for heat, from steam, in chemical industry",
                "RER",
                "megajoule",
                "heat, from steam, in chemical industry",
            ),
        }

        air_capture = self.inputs[
            (
                "carbon dioxide, captured from atmosphere",
                "RER",
                "kilogram",
                "carbon dioxide, captured from the atmosphere",
            )
        ]

        methanol_distillation = self.inputs[
            ("Methanol distillation", "RER", "kilogram", "Purified methanol")
        ]

        all_inds = [self.inputs[i] for i in list(d_heat_suppliers.values())]

        # DAC
        heat_amount = self.A[
            np.ix_(range(self.A.shape[0]), all_inds, [air_capture])
        ].sum()
        # zero out the heat input
        self.A[np.ix_(range(self.A.shape[0]), all_inds, [air_capture])] = 0
        # find index of the new supplier and set the amount
        ind = self.inputs[d_heat_suppliers[heat_supplier]]
        self.A[np.ix_(range(self.A.shape[0]), [ind], [air_capture])] = heat_amount

        # Methanol distillation
        heat_amount = self.A[
            np.ix_(range(self.A.shape[0]), all_inds, [methanol_distillation])
        ].sum()
        # zero out the heat input
        self.A[np.ix_(range(self.A.shape[0]), all_inds, [methanol_distillation])] = 0
        # find index of the new supplier and set the amount
        ind = self.inputs[d_heat_suppliers[heat_supplier]]
        self.A[
            np.ix_(range(self.A.shape[0]), [ind], [methanol_distillation])
        ] = heat_amount

ext: py

reason: import numpy,from scipy