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Read in more sector detail for road, rail, aviation and navigation

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Tom Brown 2019-07-18 15:38:37 +02:00
parent f3b6027cd5
commit b132193f7f
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109
scripts/build_energy_totals.py
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@ -179,6 +179,7 @@ def build_idees(year):
# TRANSPORT
filename = "{}/JRC-IDEES-2015_Transport_{}.xlsx".format(base_dir,rename.get(ct,ct))
df = pd.read_excel(filename,"TrRoad_ene")
assert df.iloc[2,0] == "by fuel (EUROSTAT DATA)"
@ -186,9 +187,31 @@ def build_idees(year):
assert df.iloc[13,0] == "Electricity"
totals.loc[ct,"electricity road"] = df.loc[13,year]
assert df.iloc[17,0] == "Powered 2-wheelers (Gasoline)"
totals.loc[ct,"total two-wheel"] = df.loc[17,year]
assert df.iloc[19,0] == "Passenger cars"
totals.loc[ct,"total passenger cars"] = df.loc[19,year]
assert df.iloc[30,0] == "Battery electric vehicles"
totals.loc[ct,"electricity passenger cars"] = df.loc[30,year]
assert df.iloc[31,0] == "Motor coaches, buses and trolley buses"
totals.loc[ct,"total other road passenger"] = df.loc[31,year]
assert df.iloc[39,0] == "Battery electric vehicles"
totals.loc[ct,"electricity other road passenger"] = df.loc[39,year]
assert df.iloc[41,0] == "Light duty vehicles"
totals.loc[ct,"total light duty road freight"] = df.loc[41,year]
assert df.iloc[49,0] == "Battery electric vehicles"
totals.loc[ct,"electricity light duty road freight"] = df.loc[49,year]
assert df.iloc[50,0] == "Heavy duty vehicles (Diesel oil incl. biofuels)"
totals.loc[ct,"total heavy duty road freight"] = df.loc[50,year]
assert df.iloc[61,0] == "Passenger cars"
totals.loc[ct,"passenger car efficiency"] = df.loc[61,year]
df = pd.read_excel(filename,"TrRail_ene")
assert df.iloc[2,0] == "by fuel (EUROSTAT DATA)"
@ -196,6 +219,45 @@ def build_idees(year):
assert df.iloc[12,0] == "Electricity"
totals.loc[ct,"electricity rail"] = df.loc[12,year]
assert df.iloc[15,0] == "Passenger transport"
totals.loc[ct,"total rail passenger"] = df.loc[15,year]
assert df.iloc[19,0] == "Electric"
totals.loc[ct,"electricity rail passenger"] = df.loc[19,year]
assert df.iloc[21,0] == "Freight transport"
totals.loc[ct,"total rail freight"] = df.loc[21,year]
assert df.iloc[23,0] == "Electric"
totals.loc[ct,"electricity rail freight"] = df.loc[23,year]
df = pd.read_excel(filename,"TrAvia_ene")
assert df.iloc[6,0] == "Passenger transport"
totals.loc[ct,"total aviation passenger"] = df.loc[6,year]
assert df.iloc[10,0] == "Freight transport"
totals.loc[ct,"total aviation freight"] = df.loc[10,year]
assert df.iloc[7,0] == "Domestic"
totals.loc[ct,"total domestic aviation passenger"] = df.loc[7,year]
assert df.iloc[8,0] == "International - Intra-EU"
assert df.iloc[9,0] == "International - Extra-EU"
totals.loc[ct,"total international aviation passenger"] = df.loc[[8,9],year].sum()
assert df.iloc[11,0] == "Domestic and International - Intra-EU"
totals.loc[ct,"total domestic aviation freight"] = df.loc[11,year]
assert df.iloc[12,0] == "International - Extra-EU"
totals.loc[ct,"total international aviation freight"] = df.loc[12,year]
totals.loc[ct,"total domestic aviation"] = totals.loc[ct,["total domestic aviation freight","total domestic aviation passenger"]].sum()
totals.loc[ct,"total international aviation"] = totals.loc[ct,["total international aviation freight","total international aviation passenger"]].sum()
df = pd.read_excel(filename,"TrNavi_ene")
#coastal and inland
assert df.iloc[2,0] == "by fuel (EUROSTAT DATA)"
totals.loc[ct,"total domestic navigation"] = df.loc[2,year]
df = pd.read_excel(filename,"TrRoad_act")
assert df.iloc[85,0] == "Passenger cars"
@ -215,6 +277,10 @@ def build_energy_totals():
clean_df = idees.reindex(population.index).drop(["passenger cars","passenger car efficiency"],axis=1)
print("International navigation")
in_eurostat = clean_df.index.intersection(eurostat.index.levels[0])
clean_df.loc[in_eurostat,"total international navigation"] = eurostat.loc[idx[in_eurostat,:,"Bunkers",:],"Total all products"].groupby(level=0).sum()
clean_df.loc["CH"] = swiss
#get values for missing countries based on Eurostat EnergyBalances
@ -268,6 +334,49 @@ def build_energy_totals():
clean_df.loc["NO","{} {} {}".format("total",sector,use)] = total_heating*fraction
clean_df.loc["NO","{} {} {}".format("electricity",sector,use)] = total_heating*fraction*elec_fraction
#Missing aviation
print("Aviation")
clean_df.loc[missing_in_eurostat,"total domestic aviation"] = eurostat.loc[idx[missing_in_eurostat,:,:,"Domestic aviation"],"Total all products"].groupby(level=0).sum()
clean_df.loc[missing_in_eurostat,"total international aviation"] = eurostat.loc[idx[missing_in_eurostat,:,:,"International aviation"],"Total all products"].groupby(level=0).sum()
print("Domestic navigation")
clean_df.loc[missing_in_eurostat,"total domestic navigation"] = eurostat.loc[idx[missing_in_eurostat,:,:,"Domestic Navigation"],"Total all products"].groupby(level=0).sum()
#split road traffic for non-IDEES
missing = clean_df.index[clean_df["total passenger cars"].isnull()]
for fuel in ["total","electricity"]:
selection = [fuel+" passenger cars",fuel+" other road passenger",fuel+" light duty road freight"]
if fuel == "total":
selection = [fuel+" two-wheel"] + selection + [fuel+" heavy duty road freight"]
road = clean_df[selection].sum()
road_fraction = road/road.sum()
for i in road_fraction.index:
clean_df.loc[missing,i] = road_fraction[i]*clean_df.loc[missing,fuel+" road"]
#split rail traffic for non-IDEES
missing = clean_df.index[clean_df["total rail passenger"].isnull()]
for fuel in ["total","electricity"]:
selection = [fuel+" rail passenger",fuel+" rail freight"]
rail = clean_df[selection].sum()
rail_fraction = rail/rail.sum()
for i in rail_fraction.index:
clean_df.loc[missing,i] = rail_fraction[i]*clean_df.loc[missing,fuel+" rail"].values
#split aviation traffic for non-IDEES
missing = clean_df.index[clean_df["total domestic aviation passenger"].isnull()]
for destination in ["domestic","international"]:
selection = ["total " + destination+" aviation passenger","total " + destination+" aviation freight"]
aviation = clean_df[selection].sum()
aviation_fraction = aviation/aviation.sum()
for i in aviation_fraction.index:
clean_df.loc[missing,i] = aviation_fraction[i]*clean_df.loc[missing,"total "+ destination + " aviation"].values
clean_df.loc[missing,"total aviation passenger"] = clean_df.loc[missing,["total domestic aviation passenger","total international aviation passenger"]].sum(axis=1)
clean_df.loc[missing,"total aviation freight"] = clean_df.loc[missing,["total domestic aviation freight","total international aviation freight"]].sum(axis=1)
#fix missing data for BA (services and road energy data)
missing = (clean_df.loc["BA"] == 0.)