bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

fix bugs and clean up

Browse Source
This commit is contained in:
lisazeyen 2024-07-22 21:40:47 +02:00
parent 53bbc28908
commit b957f88645
1 changed files with 37 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

54
scripts/build_energy_totals.py
View File

@ -55,21 +55,6 @@ logger = logging.getLogger(__name__)
idx = pd.IndexSlice
# from JRC-2021 methodology p.58
agriculture_idees_eurostat_mapping = {
"Solids": ["C0000X0350-0370", "P1000", "S2000"],
"LPG": ["O4630"],
"Diesel oil and liquid biofuels": ["O4671XR5220B", "R5210P", "R5210B", "R5220P", "R5220B", "R5230P", "R5230B", "R5290"],
"Fuel oil and other liquids": ["O4680", "O4100_TOT_4200-4500XBIO", "O4652XR5210B", "O4651", "O4653", "O4661XR5230B", "O4669", "O4640", "O4691", "O4692", "O4695", "O4694", "O4693", "O4699"],
"Natural gas and biogas": ["G3000", "C0350-0370", "R5300"],
"Biomass and waste": ["R5110-5150_W6000RI", "R5160", "W6210", "W6100_6220"],
"Solar and geothermal": ["RA200", "RA410"],
"Ambient heat": ["RA600"],
"Distributed heat": ["H8000"],
"Electricity": ["E7000"]
}
def cartesian(s1: pd.Series, s2: pd.Series) -> pd.DataFrame:
"""
Compute the Cartesian product of two pandas Series.
@ -603,6 +588,31 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
return totals
def fill_missing_years(fill_values: pd.Series) -> pd.Series:
"""
Fill missing years for some countries by mean over the other years.
Parameters
----------
fill_values : pd.Series
A pandas Series with a MultiIndex (levels: country and year) representing
energy values, where some values may be zero and need to be filled.
Returns
-------
pd.Series
A pandas Series with zero values replaced by the mean value of the corresponding
country.
Notes
-----
- The function groups the data by the 'country' level and computes the mean for each group.
- Zero values in the original Series are replaced by the mean value of their respective country group.
"""
means = fill_values.groupby(level='country').transform('mean')
return fill_values.where(fill_values != 0, means)
def build_energy_totals(
countries: List[str],
eurostat: pd.DataFrame,
@ -656,6 +666,8 @@ def build_energy_totals(
slicer = idx[in_eurostat, :, :, "Bunkers", :]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
# fill missing years for some countries by mean over the other years
fill_values = fill_missing_years(fill_values)
df.loc[in_eurostat, "total international navigation"] = fill_values
# add swiss energy data
@ -679,8 +691,8 @@ def build_energy_totals(
fill_values = eurostat.loc[slicer]["Total all products"].groupby(level=[0,1]).sum()
# fill missing years for some countries by mean over the other years
means = fill_values.groupby(level='country').transform('mean')
fill_values = fill_values.where(fill_values != 0, means)
fill_values = fill_missing_years(fill_values)
df.loc[to_fill, "total agriculture"] = fill_values
# split into end uses by average EU data from IDEES
uses = ["electricity", "heat", "machinery"]
@ -711,6 +723,8 @@ def build_energy_totals(
fill_values = (
eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=[0, 1]).sum()
)
# fill missing years for some countries by mean over the other years
fill_values = fill_missing_years(fill_values)
df.loc[to_fill, f"{fuel} {sector}"] = fill_values
for sector in ["residential", "services"]:
@ -786,16 +800,22 @@ def build_energy_totals(
slicer = idx[c, y, :, :, "Domestic aviation"]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
# fill missing years for some countries by mean over the other years
fill_values = fill_missing_years(fill_values)
df.loc[to_fill, "total domestic aviation"] = fill_values
slicer = idx[c, y, :, :, "International aviation"]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
# fill missing years for some countries by mean over the other years
fill_values = fill_missing_years(fill_values)
df.loc[to_fill, "total international aviation"] = fill_values
# missing domestic navigation
slicer = idx[c, y, :, :, "Domestic Navigation"]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=[0, 1]).sum()
# fill missing years for some countries by mean over the other years
fill_values = fill_missing_years(fill_values)
df.loc[to_fill, "total domestic navigation"] = fill_values
# split road traffic for non-IDEES