|
|
@@ -286,7 +286,6 @@ def read_data( # noqa: PLR0915 PLR0912
|
|
|
result_proc_if = result_proc.pr.to_interchange_format()
|
|
|
|
|
|
output_filename = f"FAOSTAT_Agrifood_system_emissions_{release_name}"
|
|
|
- # output_folder = extracted_data_path / release_name
|
|
|
|
|
|
if not output_folder.exists():
|
|
|
output_folder.mkdir()
|
|
|
@@ -322,43 +321,22 @@ def process(ds: xarray.Dataset) -> xarray.Dataset:
|
|
|
The processed dataset
|
|
|
|
|
|
"""
|
|
|
- # make categorisation A from yaml
|
|
|
- # categorisation_a = cc.FAO
|
|
|
- # make categorisation B from yaml
|
|
|
- # categorisation_b = cc.IPCC2006_PRIMAP
|
|
|
-
|
|
|
- # category FAOSTAT not yet part of climate categories, so we need to add it manually
|
|
|
- # cats = {
|
|
|
- # "FAO": categorisation_a,
|
|
|
- # "IPCC2006_PRIMAP": categorisation_b,
|
|
|
- # }
|
|
|
-
|
|
|
# drop UNFCCC data
|
|
|
ds = ds.drop_sel(source="UNFCCC")
|
|
|
|
|
|
# consistency check in original categorisation
|
|
|
ds_checked = ds.pr.add_aggregates_coordinates(agg_info=agg_info_fao) # noqa: F841
|
|
|
|
|
|
- # We need a conversion CSV file for each entity
|
|
|
- # That's a temporary workaround until the filter function in climate categories works
|
|
|
- # conv_old = {}
|
|
|
gases = ["CO2", "CH4", "N2O"]
|
|
|
- #
|
|
|
- # for var in gases:
|
|
|
- # conversion_path = root_path / f"conv_FAO_IPPCC2006_PRIMAP_{var}.csv"
|
|
|
- # conv_old[var] = cc.Conversion.from_csv(
|
|
|
- # conversion_path,
|
|
|
- # cats=cats, # type: ignore
|
|
|
- # )
|
|
|
+
|
|
|
conv = cc.FAO.conversion_to(cc.IPCC2006_PRIMAP)
|
|
|
+
|
|
|
# convert for each entity
|
|
|
da_dict = {}
|
|
|
for var in gases:
|
|
|
conv_for_gas = conv.filter(aux_dim="gas", values=[var])
|
|
|
- # conv_for_gas_old = conv_old[var]
|
|
|
da_dict[var] = ds[var].pr.convert(
|
|
|
dim="category (FAO)",
|
|
|
- # conversion=conv[var],
|
|
|
conversion=conv_for_gas,
|
|
|
)
|
|
|
|
