[pre-commit.ci] auto fixes from pre-commit.com hooks
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@ -137,6 +137,7 @@ def input_eurostat(w):
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else:
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return "data/bundle-sector/eurostat-energy_balances-april_2023_edition"
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def solved_previous_horizon(w):
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planning_horizons = config_provider("scenario", "planning_horizons")(w)
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i = planning_horizons.index(int(w.planning_horizons))
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@ -8,6 +8,7 @@ Build total energy demands per country using JRC IDEES, eurostat, and EEA data.
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import logging
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import multiprocessing as mp
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import os
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from functools import partial
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import country_converter as coco
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@ -16,7 +17,6 @@ import numpy as np
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import pandas as pd
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from _helpers import configure_logging, mute_print, set_scenario_config
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from tqdm import tqdm
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import os
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cc = coco.CountryConverter()
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logger = logging.getLogger(__name__)
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@ -155,35 +155,47 @@ def build_eurostat(input_eurostat, countries, report_year, year):
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else:
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# read in every country file in countries
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eurostat = pd.DataFrame()
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countries = [country if country != 'GB' else 'UK' for country in countries]
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countries = [country if country != 'GR' else 'EL' for country in countries]
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countries = [country if country != "GB" else "UK" for country in countries]
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countries = [country if country != "GR" else "EL" for country in countries]
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for country in countries:
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filename = f"/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
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if os.path.exists(input_eurostat + filename):
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df = pd.read_excel(
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input_eurostat + filename,
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engine='pyxlsb',
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engine="pyxlsb",
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sheet_name=str(year),
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skiprows=4,
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index_col=list(range(4)))
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index_col=list(range(4)),
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)
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# replace entry 'Z' with 0
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df.replace('Z', 0, inplace=True)
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df.replace("Z", 0, inplace=True)
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# write 'International aviation' to the 2nd level of the multiindex
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index_number = (df.index.get_level_values(1) == 'International aviation').argmax()
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new_index = ('-', 'International aviation', 'International aviation', 'ktoe')
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index_number = (
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df.index.get_level_values(1) == "International aviation"
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).argmax()
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new_index = (
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"-",
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"International aviation",
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"International aviation",
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"ktoe",
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)
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modified_index = list(df.index)
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modified_index[index_number] = new_index
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df.index = pd.MultiIndex.from_tuples(modified_index, names=df.index.names)
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df.index = pd.MultiIndex.from_tuples(
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modified_index, names=df.index.names
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)
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# drop the annoying subhead line
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df.drop(df[df[year] == year].index, inplace=True)
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# replace 'Z' with 0
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df = df.replace('Z', 0)
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df = df.replace("Z", 0)
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# add country to the multiindex
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new_tuple = [(country, *idx) for idx in df.index]
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new_mindex = pd.MultiIndex.from_tuples(new_tuple, names=['country', None, 'name', None, 'unit'])
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new_mindex = pd.MultiIndex.from_tuples(
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new_tuple, names=["country", None, "name", None, "unit"]
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)
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df.index = new_mindex
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# make numeric values where possible
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df = df.apply(pd.to_numeric, errors='coerce')
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df = df.apply(pd.to_numeric, errors="coerce")
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# drop non-numeric columns
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non_numeric_cols = df.columns[df.dtypes != float]
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df.drop(non_numeric_cols, axis=1, inplace=True)
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@ -193,19 +205,30 @@ def build_eurostat(input_eurostat, countries, report_year, year):
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eurostat.drop(["Unnamed: 4", year, "Unnamed: 6"], axis=1, inplace=True)
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# Renaming some indices
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rename = {
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'Households': 'Residential',
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'Commercial & public services': 'Services',
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'Domestic navigation': 'Domestic Navigation'
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"Households": "Residential",
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"Commercial & public services": "Services",
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"Domestic navigation": "Domestic Navigation",
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}
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for name, rename in rename.items():
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eurostat.index = eurostat.index.set_levels(
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eurostat.index.levels[3].where(eurostat.index.levels[3] != name, rename),
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level=3)
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new_index = eurostat.index.set_levels(eurostat.index.levels[2].where(eurostat.index.levels[2] != 'International maritime bunkers', 'Bunkers'), level=2)
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eurostat.index.levels[3].where(
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eurostat.index.levels[3] != name, rename
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),
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level=3,
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)
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new_index = eurostat.index.set_levels(
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eurostat.index.levels[2].where(
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eurostat.index.levels[2] != "International maritime bunkers", "Bunkers"
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),
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level=2,
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)
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eurostat.index = new_index
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eurostat.rename(columns={'Total': 'Total all products'}, inplace=True)
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eurostat.index = eurostat.index.set_levels(eurostat.index.levels[0].where(eurostat.index.levels[0] != 'UK', 'GB'), level=0)
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eurostat.rename(columns={"Total": "Total all products"}, inplace=True)
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eurostat.index = eurostat.index.set_levels(
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eurostat.index.levels[0].where(eurostat.index.levels[0] != "UK", "GB"),
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level=0,
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)
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df = eurostat * 11.63 / 1e3
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@ -751,7 +774,9 @@ def build_co2_totals(countries, eea_co2, eurostat_co2, report_year):
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"industrial non-elec": (ct, "+", "Industry"),
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# does not include non-energy emissions
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"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
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& eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
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& eurostat_co2.index.isin(
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["Agriculture / Forestry", "Fishing"], level=3
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),
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}
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else:
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mappings = {
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@ -768,7 +793,9 @@ def build_co2_totals(countries, eea_co2, eurostat_co2, report_year):
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"industrial non-elec": (ct, "+", "Industry sector"),
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# does not include non-energy emissions
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"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
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& eurostat_co2.index.isin(["Agriculture & forestry", "Fishing"], level=3),
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& eurostat_co2.index.isin(
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["Agriculture & forestry", "Fishing"], level=3
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),
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}
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for i, mi in mappings.items():
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@ -812,20 +839,26 @@ def build_transport_data(countries, population, idees):
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return transport_data
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def rescale(idees_countries, energy, eurostat):
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'''
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Takes JRC IDEES data from 2015 and rescales it by the ratio of the
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eurostat data and the 2015 eurostat data.
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"""
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Takes JRC IDEES data from 2015 and rescales it by the ratio of the eurostat
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data and the 2015 eurostat data.
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missing data: ['passenger car efficiency', 'passenger cars']
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'''
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"""
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# read in the eurostat data for 2015
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eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[["Total all products", "Electricity"]]
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eurostat_2015 = build_eurostat(input_eurostat, countries, 2023, 2015)[
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["Total all products", "Electricity"]
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]
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# eurostat_2015 = eurostat_2015.rename(index={'GB': 'UK'}, level=0)
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eurostat_year = eurostat[["Total all products", "Electricity"]]
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# calculate the ratio of the two data sets
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ratio = eurostat_year / eurostat_2015
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ratio = ratio.droplevel([1,4])
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ratio.rename(columns={"Total all products": "total", "Electricity": "ele"}, inplace=True)
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ratio = ratio.droplevel([1, 4])
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ratio.rename(
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columns={"Total all products": "total", "Electricity": "ele"}, inplace=True
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)
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ratio = ratio.rename(index={"GB": "UK"}, level=0)
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residential_total = [
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@ -892,54 +925,55 @@ def rescale(idees_countries, energy, eurostat):
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]
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avia_inter = [
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'total aviation passenger',
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'total aviation freight',
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'total international aviation passenger',
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'total international aviation freight',
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'total international aviation'
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"total aviation passenger",
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"total aviation freight",
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"total international aviation passenger",
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"total international aviation freight",
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"total international aviation",
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]
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avia_domestic = [
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'total domestic aviation passenger',
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'total domestic aviation freight',
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'total domestic aviation',
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"total domestic aviation passenger",
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"total domestic aviation freight",
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"total domestic aviation",
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]
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navigation = [
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"total domestic navigation",
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]
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idees_countries = idees_countries.repalce({'GB': 'UK', 'GR': 'EL'})
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idees_countries = idees_countries.repalce({"GB": "UK", "GR": "EL"})
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for country in idees_countries:
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res = ratio.loc[(country, slice(None), 'Residential')]
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energy.loc[country, residential_total] *= res[['total']].iloc[0,0]
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energy.loc[country, residential_ele] *= res[['ele']].iloc[0,0]
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res = ratio.loc[(country, slice(None), "Residential")]
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energy.loc[country, residential_total] *= res[["total"]].iloc[0, 0]
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energy.loc[country, residential_ele] *= res[["ele"]].iloc[0, 0]
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ser = ratio.loc[(country, slice(None), 'Services')]
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energy.loc[country, service_total] *= ser[['total']].iloc[0,0]
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energy.loc[country, service_ele] *= ser[['ele']].iloc[0,0]
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ser = ratio.loc[(country, slice(None), "Services")]
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energy.loc[country, service_total] *= ser[["total"]].iloc[0, 0]
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energy.loc[country, service_ele] *= ser[["ele"]].iloc[0, 0]
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agri = ratio.loc[(country, slice(None), 'Agriculture & forestry')]
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energy.loc[country, agri_total] *= agri[['total']].iloc[0,0]
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energy.loc[country, agri_ele] *= agri[['ele']].iloc[0,0]
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agri = ratio.loc[(country, slice(None), "Agriculture & forestry")]
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energy.loc[country, agri_total] *= agri[["total"]].iloc[0, 0]
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energy.loc[country, agri_ele] *= agri[["ele"]].iloc[0, 0]
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road = ratio.loc[(country, slice(None), 'Road')]
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energy.loc[country, road_total] *= road[['total']].iloc[0,0]
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energy.loc[country, road_ele] *= road[['ele']].iloc[0,0]
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road = ratio.loc[(country, slice(None), "Road")]
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energy.loc[country, road_total] *= road[["total"]].iloc[0, 0]
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energy.loc[country, road_ele] *= road[["ele"]].iloc[0, 0]
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rail = ratio.loc[(country, slice(None), 'Rail')]
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energy.loc[country, rail_total] *= rail[['total']].iloc[0,0]
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energy.loc[country, rail_ele] *= rail[['ele']].iloc[0,0]
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rail = ratio.loc[(country, slice(None), "Rail")]
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energy.loc[country, rail_total] *= rail[["total"]].iloc[0, 0]
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energy.loc[country, rail_ele] *= rail[["ele"]].iloc[0, 0]
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avi_d = ratio.loc[(country, slice(None), 'Domestic aviation')]
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avi_i = ratio.loc[(country, 'International aviation', slice(None))]
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energy.loc[country, avia_inter] *= avi_i[['total']].iloc[0,0]
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energy.loc[country, avia_domestic] *= avi_d[['total']].iloc[0,0]
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avi_d = ratio.loc[(country, slice(None), "Domestic aviation")]
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avi_i = ratio.loc[(country, "International aviation", slice(None))]
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energy.loc[country, avia_inter] *= avi_i[["total"]].iloc[0, 0]
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energy.loc[country, avia_domestic] *= avi_d[["total"]].iloc[0, 0]
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nav = ratio.loc[(country, slice(None), 'Domestic Navigation')]
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energy.loc[country, navigation] *= nav[['total']].iloc[0,0]
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nav = ratio.loc[(country, slice(None), "Domestic Navigation")]
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energy.loc[country, navigation] *= nav[["total"]].iloc[0, 0]
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return energy
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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@ -977,7 +1011,9 @@ if __name__ == "__main__":
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energy.to_csv(snakemake.output.energy_name)
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# use rescaled idees data to calculate district heat share
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district_heat_share = build_district_heat_share(countries, energy.loc[idees_countries])
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district_heat_share = build_district_heat_share(
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countries, energy.loc[idees_countries]
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)
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district_heat_share.to_csv(snakemake.output.district_heat_share)
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base_year_emissions = params["base_emissions_year"]
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