|
|
@@ -46,7 +46,6 @@ def test_conversion_from_FAO_to_IPCC2006_PRIMAP():
|
|
|
da_dict[var] = ds[var].pr.convert(
|
|
|
dim="category (FAOSTAT)",
|
|
|
conversion=conv[var],
|
|
|
- # auxiliary_dimensions={"gas": "entity"},
|
|
|
)
|
|
|
|
|
|
result = xr.Dataset(da_dict)
|
|
|
@@ -57,17 +56,17 @@ def test_conversion_from_FAO_to_IPCC2006_PRIMAP():
|
|
|
|
|
|
result = pm2.pm2io.from_interchange_format(result_if)
|
|
|
|
|
|
- country_processing_step1 = {
|
|
|
- "tolerance": 0.01,
|
|
|
- "aggregate_cats": {
|
|
|
- # "M.3.D.AG" : {"sources" : ["3.D.2"]}, we don't have 3.D.2
|
|
|
+ agg_info = {
|
|
|
+ "category (IPCC2006_PRIMAP)": {
|
|
|
+ "3.C.1": {
|
|
|
+ "sources": ["3.C.1.a", "3.C.1.b", "3.C.1.c"],
|
|
|
+ },
|
|
|
"M.3.C.AG": {
|
|
|
"sources": ["3.C.1", "3.C.4", "3.C.5"],
|
|
|
},
|
|
|
"M.AG.ELV": {
|
|
|
"sources": ["M.3.C.AG"], # "M.3.D.AG" is zero
|
|
|
},
|
|
|
- "3.C.1": {"sources": ["3.C.1.a", "3.C.1.b"]},
|
|
|
"3.C": {
|
|
|
"sources": [
|
|
|
"3.C.1",
|
|
|
@@ -80,12 +79,12 @@ def test_conversion_from_FAO_to_IPCC2006_PRIMAP():
|
|
|
]
|
|
|
},
|
|
|
# "3.D" : {"sources" : ["3.D.1", "3.D.2"]}, # we don't have it
|
|
|
- "3.A.1.a": {
|
|
|
+ "3.A.1.a": { # cattle (dairy) + cattle (non-dairy)
|
|
|
"sources": [
|
|
|
"3.A.1.a.i",
|
|
|
"3.A.1.a.ii",
|
|
|
]
|
|
|
- }, # cattle (dairy) + cattle (non-dairy)
|
|
|
+ },
|
|
|
"3.A.1": {
|
|
|
"sources": [
|
|
|
"3.A.1.a",
|
|
|
@@ -95,16 +94,16 @@ def test_conversion_from_FAO_to_IPCC2006_PRIMAP():
|
|
|
"3.A.1.e",
|
|
|
"3.A.1.f",
|
|
|
"3.A.1.g",
|
|
|
- "3.A.1.h", # what happened to 3.A.1.i
|
|
|
+ "3.A.1.h", # 3.A.1.i poultry left out because it is a group of categories
|
|
|
"3.A.1.j",
|
|
|
]
|
|
|
},
|
|
|
- "3.A.2.a": {
|
|
|
+ "3.A.2.a": { # decomposition of manure cattle (dairy) + cattle (non-dairy)
|
|
|
"sources": [
|
|
|
"3.A.2.a.i",
|
|
|
"3.A.2.a.ii",
|
|
|
]
|
|
|
- }, # cattle (dairy) + cattle (non-dairy)
|
|
|
+ },
|
|
|
"3.A.2": {
|
|
|
"sources": [
|
|
|
"3.A.2.a",
|
|
|
@@ -121,38 +120,17 @@ def test_conversion_from_FAO_to_IPCC2006_PRIMAP():
|
|
|
},
|
|
|
"3.A": {"sources": ["3.A.1", "3.A.2"]},
|
|
|
"M.AG": {"sources": ["3.A", "M.AG.ELV"]},
|
|
|
- # M.AG.ELV
|
|
|
- # AFOLU
|
|
|
# "M.3.D.LU": {"sources": ["3.D.1"]},
|
|
|
- "M.LULUCF": {"sources": ["3.B"]}, # , "M.3.D.LU"]},
|
|
|
- },
|
|
|
+ # Forest Land + Cropland + Grassland, all we have
|
|
|
+ "M.LULUCF": {"sources": ["3.B.1", "3.B.2", "3.B.3"]},
|
|
|
+ "M.AFOLU": {"sources": ["M.AG", "M.LULUCF"]},
|
|
|
+ }
|
|
|
}
|
|
|
|
|
|
- # prep input to add_aggregates_coordinates
|
|
|
- agg_info = {"category": country_processing_step1["aggregate_cats"]}
|
|
|
-
|
|
|
- agg_tolerance = country_processing_step1["tolerance"]
|
|
|
-
|
|
|
- df_list = []
|
|
|
- for iso3_code in result.coords["area (ISO3)"].to_numpy():
|
|
|
- result_per_country = result.pr.loc[
|
|
|
- {
|
|
|
- "area (ISO3)": iso3_code,
|
|
|
- }
|
|
|
- ]
|
|
|
-
|
|
|
- result_per_country = result_per_country.pr.add_aggregates_coordinates(
|
|
|
- agg_info=agg_info,
|
|
|
- tolerance=agg_tolerance,
|
|
|
- skipna=True,
|
|
|
- min_count=1,
|
|
|
- )
|
|
|
-
|
|
|
- result_per_country_if = result_per_country.pr.to_interchange_format()
|
|
|
-
|
|
|
- df_list.append(result_per_country_if)
|
|
|
+ result_proc = result.pr.add_aggregates_coordinates(agg_info=agg_info)
|
|
|
|
|
|
- # df_all = pd.concat(df_list, axis=0, join="outer", ignore_index=True)
|
|
|
+ result_proc_if = result_proc.pr.to_interchange_format()
|
|
|
+ assert result_proc_if
|
|
|
|
|
|
# df_all = pd.concat([ds_if, result_if], axis=0, join="outer", ignore_index=True)
|
|
|
#
|
