energy_totals: fetch all years 2000-2015 from IDEES
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Fabian Neumann
parent
d47a9aac72
commit
eee6651d12
@ -147,90 +147,89 @@ def build_swiss(year=None):
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return df
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def idees_per_country(ct, year):
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def idees_per_country(country):
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base_dir = snakemake.input.idees
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ct_totals = {}
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ct_idees = idees_rename.get(ct, ct)
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ct_idees = idees_rename.get(country, country)
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fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
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fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
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fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
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# residential
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df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)[year]
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df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)
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ct_totals["total residential space"] = df["Space heating"]
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ct_totals["total residential space"] = df.loc["Space heating"]
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rows = ["Advanced electric heating", "Conventional electric heating"]
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ct_totals["electricity residential space"] = df[rows].sum()
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ct_totals["electricity residential space"] = df.loc[rows].sum()
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ct_totals["total residential water"] = df.at["Water heating"]
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ct_totals["total residential water"] = df.loc["Water heating"]
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assert df.index[23] == "Electricity"
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ct_totals["electricity residential water"] = df[23]
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ct_totals["electricity residential water"] = df.iloc[23]
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ct_totals["total residential cooking"] = df["Cooking"]
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ct_totals["total residential cooking"] = df.loc["Cooking"]
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assert df.index[30] == "Electricity"
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ct_totals["electricity residential cooking"] = df[30]
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ct_totals["electricity residential cooking"] = df.iloc[30]
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df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year]
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df = pd.read_excel(fn_residential, "RES_summary", index_col=0)
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total residential"] = df[row]
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ct_totals["total residential"] = df.loc[row]
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assert df.index[47] == "Electricity"
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ct_totals["electricity residential"] = df[47]
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ct_totals["electricity residential"] = df.iloc[47]
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assert df.index[46] == "Derived heat"
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ct_totals["derived heat residential"] = df[46]
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ct_totals["derived heat residential"] = df.iloc[46]
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assert df.index[50] == 'Thermal uses'
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ct_totals["thermal uses residential"] = df[50]
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ct_totals["thermal uses residential"] = df.iloc[50]
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# services
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df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
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df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)
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ct_totals["total services space"] = df["Space heating"]
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ct_totals["total services space"] = df.loc["Space heating"]
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rows = ["Advanced electric heating", "Conventional electric heating"]
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ct_totals["electricity services space"] = df[rows].sum()
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ct_totals["electricity services space"] = df.loc[rows].sum()
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ct_totals["total services water"] = df["Hot water"]
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ct_totals["total services water"] = df.loc["Hot water"]
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assert df.index[24] == "Electricity"
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ct_totals["electricity services water"] = df[24]
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ct_totals["electricity services water"] = df.iloc[24]
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ct_totals["total services cooking"] = df["Catering"]
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ct_totals["total services cooking"] = df.loc["Catering"]
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assert df.index[31] == "Electricity"
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ct_totals["electricity services cooking"] = df[31]
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ct_totals["electricity services cooking"] = df.iloc[31]
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df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
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df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)
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row = "Energy consumption by fuel - Eurostat structure (ktoe)"
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ct_totals["total services"] = df[row]
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ct_totals["total services"] = df.loc[row]
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assert df.index[50] == "Electricity"
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ct_totals["electricity services"] = df[50]
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ct_totals["electricity services"] = df.iloc[50]
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assert df.index[49] == "Derived heat"
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ct_totals["derived heat services"] = df[49]
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ct_totals["derived heat services"] = df.iloc[49]
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assert df.index[53] == 'Thermal uses'
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ct_totals["thermal uses services"] = df[53]
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ct_totals["thermal uses services"] = df.iloc[53]
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# agriculture, forestry and fishing
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start = "Detailed split of energy consumption (ktoe)"
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end = "Market shares of energy uses (%)"
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df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year]
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df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end]
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rows = [
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"Lighting",
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@ -238,142 +237,143 @@ def idees_per_country(ct, year):
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"Specific electricity uses",
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"Pumping devices (electric)"
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]
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ct_totals["total agriculture electricity"] = df[rows].sum()
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ct_totals["total agriculture electricity"] = df.loc[rows].sum()
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rows = ["Specific heat uses", "Low enthalpy heat"]
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ct_totals["total agriculture heat"] = df[rows].sum()
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ct_totals["total agriculture heat"] = df.loc[rows].sum()
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rows = [
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"Motor drives",
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"Farming machine drives (diesel oil incl. biofuels)",
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"Pumping devices (diesel oil incl. biofuels)",
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]
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ct_totals["total agriculture machinery"] = df[rows].sum()
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ct_totals["total agriculture machinery"] = df.loc[rows].sum()
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row = "Agriculture, forestry and fishing"
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ct_totals["total agriculture"] = df[row]
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ct_totals["total agriculture"] = df.loc[row]
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# transport
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df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
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df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)
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ct_totals["total road"] = df["by fuel (EUROSTAT DATA)"]
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ct_totals["total road"] = df.loc["by fuel (EUROSTAT DATA)"]
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ct_totals["electricity road"] = df["Electricity"]
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ct_totals["electricity road"] = df.loc["Electricity"]
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ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"]
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ct_totals["total two-wheel"] = df.loc["Powered 2-wheelers (Gasoline)"]
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assert df.index[19] == "Passenger cars"
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ct_totals["total passenger cars"] = df[19]
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ct_totals["total passenger cars"] = df.iloc[19]
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assert df.index[30] == "Battery electric vehicles"
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ct_totals["electricity passenger cars"] = df[30]
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ct_totals["electricity passenger cars"] = df.iloc[30]
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assert df.index[31] == "Motor coaches, buses and trolley buses"
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ct_totals["total other road passenger"] = df[31]
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ct_totals["total other road passenger"] = df.iloc[31]
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assert df.index[39] == "Battery electric vehicles"
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ct_totals["electricity other road passenger"] = df[39]
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ct_totals["electricity other road passenger"] = df.iloc[39]
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assert df.index[41] == "Light duty vehicles"
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ct_totals["total light duty road freight"] = df[41]
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ct_totals["total light duty road freight"] = df.iloc[41]
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assert df.index[49] == "Battery electric vehicles"
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ct_totals["electricity light duty road freight"] = df[49]
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ct_totals["electricity light duty road freight"] = df.iloc[49]
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row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
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ct_totals["total heavy duty road freight"] = df[row]
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ct_totals["total heavy duty road freight"] = df.loc[row]
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assert df.index[61] == "Passenger cars"
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ct_totals["passenger car efficiency"] = df[61]
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ct_totals["passenger car efficiency"] = df.iloc[61]
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df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year]
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df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)
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ct_totals["total rail"] = df["by fuel (EUROSTAT DATA)"]
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ct_totals["total rail"] = df.loc["by fuel (EUROSTAT DATA)"]
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ct_totals["electricity rail"] = df["Electricity"]
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ct_totals["electricity rail"] = df.loc["Electricity"]
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assert df.index[15] == "Passenger transport"
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ct_totals["total rail passenger"] = df[15]
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ct_totals["total rail passenger"] = df.iloc[15]
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assert df.index[16] == "Metro and tram, urban light rail"
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assert df.index[19] == "Electric"
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assert df.index[20] == "High speed passenger trains"
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ct_totals["electricity rail passenger"] = df[[16, 19, 20]].sum()
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ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()
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assert df.index[21] == "Freight transport"
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ct_totals["total rail freight"] = df[21]
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ct_totals["total rail freight"] = df.iloc[21]
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assert df.index[23] == "Electric"
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ct_totals["electricity rail freight"] = df[23]
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ct_totals["electricity rail freight"] = df.iloc[23]
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df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year]
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df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)
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assert df.index[6] == "Passenger transport"
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ct_totals["total aviation passenger"] = df[6]
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ct_totals["total aviation passenger"] = df.iloc[6]
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assert df.index[10] == "Freight transport"
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ct_totals["total aviation freight"] = df[10]
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ct_totals["total aviation freight"] = df.iloc[10]
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assert df.index[7] == "Domestic"
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ct_totals["total domestic aviation passenger"] = df[7]
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ct_totals["total domestic aviation passenger"] = df.iloc[7]
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assert df.index[8] == "International - Intra-EU"
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assert df.index[9] == "International - Extra-EU"
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ct_totals["total international aviation passenger"] = df[[8,9]].sum()
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ct_totals["total international aviation passenger"] = df.iloc[[8,9]].sum()
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assert df.index[11] == "Domestic and International - Intra-EU"
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ct_totals["total domestic aviation freight"] = df[11]
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ct_totals["total domestic aviation freight"] = df.iloc[11]
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assert df.index[12] == "International - Extra-EU"
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ct_totals["total international aviation freight"] = df[12]
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ct_totals["total international aviation freight"] = df.iloc[12]
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ct_totals["total domestic aviation"] = ct_totals["total domestic aviation freight"] \
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+ ct_totals["total domestic aviation passenger"]
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+ ct_totals["total domestic aviation passenger"]
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ct_totals["total international aviation"] = ct_totals["total international aviation freight"] \
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+ ct_totals["total international aviation passenger"]
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+ ct_totals["total international aviation passenger"]
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df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)[year]
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df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)
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# coastal and inland
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ct_totals["total domestic navigation"] = df["by fuel (EUROSTAT DATA)"]
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ct_totals["total domestic navigation"] = df.loc["by fuel (EUROSTAT DATA)"]
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df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year]
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df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)
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assert df.index[85] == "Passenger cars"
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ct_totals["passenger cars"] = df[85]
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ct_totals["passenger cars"] = df.iloc[85]
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return pd.Series(ct_totals, name=ct)
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return pd.DataFrame(ct_totals)
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def build_idees(countries, year):
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def build_idees(countries, year=None):
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nprocesses = snakemake.threads
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func = partial(idees_per_country, year=year)
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tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
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desc='Build from IDEES database')
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with mp.Pool(processes=nprocesses) as pool:
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totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
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dfs = list(tqdm(pool.imap(idees_per_country, countries), **tqdm_kwargs))
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totals = pd.concat(totals_list, axis=1)
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df = pd.concat(dfs, keys=countries, names=['country', 'year'])
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# convert ktoe to TWh
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exclude = totals.index.str.fullmatch("passenger cars")
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totals.loc[~exclude] *= 11.63 / 1e3
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exclude = df.columns.str.fullmatch("passenger cars")
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df.loc[:,~exclude] *= 11.63 / 1e3
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# convert TWh/100km to kWh/km
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totals.loc["passenger car efficiency"] *= 10
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df["passenger car efficiency"] *= 10
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# district heating share
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district_heat = totals.loc[["derived heat residential",
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"derived heat services"]].sum()
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total_heat = totals.loc[["thermal uses residential",
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"thermal uses services"]].sum()
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totals.loc["district heat share"] = district_heat.div(total_heat)
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subset = ["derived heat residential", "derived heat services"]
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district_heat = df[subset].sum(axis=1)
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subset = ["thermal uses residential", "thermal uses services"]
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total_heat = df[subset].sum(axis=1)
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df["district heat share"] = district_heat.div(total_heat)
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return totals.T
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if year:
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df = df.xs(int(year), level='year')
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return df
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def build_energy_totals(countries, eurostat, swiss, idees):
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