build_energy_totals: revision of eurostat report upgrade
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Fabian Neumann
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5b513f81db
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bf60da973b
@ -37,54 +37,6 @@ def reverse(dictionary):
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return {v: k for k, v in dictionary.items()}
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eurostat_codes = {
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"EU28": "EU",
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"EA19": "EA",
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"Belgium": "BE",
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"Bulgaria": "BG",
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"Czech Republic": "CZ",
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"Denmark": "DK",
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"Germany": "DE",
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"Estonia": "EE",
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"Ireland": "IE",
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"Greece": "GR",
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"Spain": "ES",
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"France": "FR",
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"Croatia": "HR",
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"Italy": "IT",
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"Cyprus": "CY",
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"Latvia": "LV",
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"Lithuania": "LT",
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"Luxembourg": "LU",
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"Hungary": "HU",
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"Malta": "MA",
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"Netherlands": "NL",
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"Austria": "AT",
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"Poland": "PL",
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"Portugal": "PT",
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"Romania": "RO",
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"Slovenia": "SI",
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"Slovakia": "SK",
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"Finland": "FI",
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"Sweden": "SE",
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"United Kingdom": "GB",
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"Iceland": "IS",
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"Norway": "NO",
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"Montenegro": "ME",
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"FYR of Macedonia": "MK",
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"Albania": "AL",
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"Serbia": "RS",
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"Turkey": "TU",
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"Bosnia and Herzegovina": "BA",
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"Kosovo\n(UNSCR 1244/99)": "KO", # 2017 version
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# 2016 version
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"Kosovo\n(under United Nations Security Council Resolution 1244/99)": "KO",
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"Moldova": "MO",
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"Ukraine": "UK",
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"Switzerland": "CH",
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}
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idees_rename = {"GR": "EL", "GB": "UK"}
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eu28 = cc.EU28as("ISO2").ISO2.tolist()
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@ -121,79 +73,54 @@ def build_eurostat(input_eurostat, countries, year):
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"""
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Return multi-index for all countries' energy data in TWh/a.
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"""
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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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df = {}
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countries = {idees_rename.get(country, country) for country in countries} - {"CH"}
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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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sheet_name=str(year),
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skiprows=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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# write 'International aviation' to the 2nd level of the multiindex
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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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# 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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# 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(
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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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# 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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# concatenate the dataframes
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eurostat = pd.concat([eurostat, df], axis=0)
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filename = (
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f"{input_eurostat}/{country}-Energy-balance-sheets-April-2023-edition.xlsb"
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)
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sheet = pd.read_excel(
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filename,
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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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)
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df[country] = sheet
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df = pd.concat(df, axis=0)
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# drop columns with all NaNs
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unnamed_cols = df.columns[df.columns.astype(str).str.startswith("Unnamed")]
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df.drop(unnamed_cols, axis=1, inplace=True)
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df.drop(year, axis=1, inplace=True)
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# make numeric values where possible
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df.replace("Z", 0, inplace=True)
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df = df.apply(pd.to_numeric, errors="coerce")
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df = df.select_dtypes(include=[np.number])
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# write 'International aviation' to the 2nd level of the multiindex
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int_avia = df.index.get_level_values(2) == "International aviation"
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temp = df.loc[int_avia]
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temp.index = pd.MultiIndex.from_frame(
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temp.index.to_frame().fillna("International aviation")
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)
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df = pd.concat([temp, df.loc[~int_avia]])
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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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index_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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"International maritime bunkers": "Bunkers",
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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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)
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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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columns_rename = {"Total": "Total all products", "UK": "GB"}
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df.rename(index=index_rename, columns=columns_rename, inplace=True)
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df.sort_index(inplace=True)
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df.index.names = [None] * len(df.index.names)
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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"), level=0
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)
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df = eurostat * 11.63 / 1e3
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# convert to TWh/a from ktoe/a
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df *= 11.63 / 1e3
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return df
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@ -776,25 +703,25 @@ 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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def rescale_idees_from_eurostat(
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idees_countries, energy, eurostat, input_eurostat, countries
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):
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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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main_cols = ["Total all products", "Electricity"]
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# read in the eurostat data for 2015
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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_year = eurostat[["Total all products", "Electricity"]]
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eurostat_2015 = build_eurostat(input_eurostat, countries, 2015)[main_cols]
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eurostat_year = eurostat[main_cols]
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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(
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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={"EL": "GR"}, level=0)
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cols_rename = {"Total all products": "total", "Electricity": "ele"}
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index_rename = {v: k for k, v in idees_rename.items()}
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ratio.rename(columns=cols_rename, index=index_rename, inplace=True)
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mappings = {
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"Residential": {
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@ -887,16 +814,16 @@ def rescale(idees_countries, energy, eurostat):
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for sector, mapping in mappings.items():
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sector_ratio = ratio.loc[(country, slice(None), sector)]
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energy.loc[country, mapping["total"]] *= sector_ratio[["total"]].iloc[0, 0]
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energy.loc[country, mapping["elec"]] *= sector_ratio[["ele"]].iloc[0, 0]
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energy.loc[country, mapping["total"]] *= sector_ratio["total"].iloc[0]
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energy.loc[country, mapping["elec"]] *= sector_ratio["ele"].iloc[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"), "total"]
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avi_i = ratio.loc[(country, "International aviation", slice(None)), "total"]
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energy.loc[country, avia_inter] *= avi_i.iloc[0]
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energy.loc[country, avia_domestic] *= avi_d.iloc[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"), "total"]
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energy.loc[country, navigation] *= nav.iloc[0]
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return energy
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@ -922,17 +849,16 @@ if __name__ == "__main__":
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input_eurostat = snakemake.input.eurostat
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eurostat = build_eurostat(input_eurostat, countries, data_year)
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swiss = build_swiss(data_year)
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# data from idees only exists from 2000-2015
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if data_year > 2015:
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# read in latest data and rescale later
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idees = build_idees(idees_countries, 2015)
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else:
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idees = build_idees(idees_countries, data_year)
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# data from idees only exists from 2000-2015. read in latest data and rescale later
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idees = build_idees(idees_countries, min(2015, data_year))
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energy = build_energy_totals(countries, eurostat, swiss, idees)
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if data_year > 2015:
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energy = rescale(idees_countries, energy, eurostat)
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logger.info("Data year is after 2015. Rescaling IDEES data based on eurostat.")
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energy = rescale_idees_from_eurostat(
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idees_countries, energy, eurostat, input_eurostat, countries
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)
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energy.to_csv(snakemake.output.energy_name)
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