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@@ -175,6 +175,18 @@ if __name__ == "__main__":
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aggregate_cats_N2O = {
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"3.A.2": {"sources": ["3.A.2.b"], "orig_cat_name": "3A2 Manure Management"},
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"3.A": {"sources": ["3.A.2"], "orig_cat_name": "3A Livestock"},
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+ "3": {
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+ "sources": ["3.A", "3.B", "3.C", "3.D"],
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+ "orig_cat_name": "3 AGRICULTURE, FORESTRY AND OTHER LAND USE",
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+ },
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+ }
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+
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+ aggregate_cats_CH4 = {
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+ "3.A": {"sources": ["3.A.1", "3.A.2"], "orig_cat_name": "3A Livestock"},
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+ "3": {
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+ "sources": ["3.A", "3.B", "3.C", "3.D"],
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+ "orig_cat_name": "3 AGRICULTURE, FORESTRY AND OTHER LAND USE",
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+ },
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}
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aggregate_cats_CO2CH4N2O = {
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@@ -308,6 +320,7 @@ if __name__ == "__main__":
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]
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).sum(min_count=1)
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+ df_combine = df_combine.drop(columns=[cat_label, "orig_cat_name"])
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df_combine.insert(0, cat_label, cat_to_agg)
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df_combine.insert(
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1, "orig_cat_name", aggregate_cats[cat_to_agg]["orig_cat_name"]
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@@ -320,10 +333,11 @@ if __name__ == "__main__":
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print(f"no data to aggregate category {cat_to_agg}")
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# delete cat 3 for N2O as it's wrong
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- index_3A_N2O = data_if[
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- (data_if[cat_label] == "3") & (data_if["entity"] == "N2O")
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+ index_3_N2O = data_if[
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+ (data_if[cat_label].isin(["3", "3.A", "3.A.2"]))
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+ & (data_if["entity"].isin(["N2O"]))
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].index
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- data_if = data_if.drop(index_3A_N2O)
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+ data_if = data_if.drop(index_3_N2O)
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# aggregate cat 3 for N2O
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for cat_to_agg in aggregate_cats_N2O:
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@@ -356,6 +370,55 @@ if __name__ == "__main__":
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]
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).sum(min_count=1)
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+ df_combine = df_combine.drop(columns=[cat_label, "orig_cat_name"])
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+ df_combine.insert(0, cat_label, cat_to_agg)
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+ df_combine.insert(
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+ 1, "orig_cat_name", aggregate_cats_N2O[cat_to_agg]["orig_cat_name"]
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+ )
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+
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+ df_combine = df_combine.reset_index()
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+
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+ data_if = pd.concat([data_if, df_combine])
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+ else:
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+ print(f"no data to aggregate category {cat_to_agg}")
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+
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+ index_3_CH4 = data_if[
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+ (data_if[cat_label].isin(["3", "3.A"])) & (data_if["entity"].isin(["CH4"]))
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+ ].index
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+ data_if = data_if.drop(index_3_CH4)
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+
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+ # aggregate cat 3 for CH4
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+ for cat_to_agg in aggregate_cats_CH4:
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+ mask = data_if[cat_label].isin(aggregate_cats_CH4[cat_to_agg]["sources"])
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+ df_test = data_if[mask]
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+ df_test = df_test[df_test["entity"] == "CH4"]
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+
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+ if len(df_test) > 0:
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+ print(f"Aggregating category {cat_to_agg}")
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+ df_combine = df_test.copy(deep=True)
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+
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+ time_format = "%Y"
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+ time_columns = [
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+ col
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+ for col in df_combine.columns.to_numpy()
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+ if matches_time_format(col, time_format)
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+ ]
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+
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+ for col in time_columns:
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+ df_combine[col] = pd.to_numeric(df_combine[col], errors="coerce")
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+
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+ df_combine = df_combine.groupby(
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+ by=[
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+ "source",
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+ "scenario (PRIMAP)",
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+ "provenance",
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+ "area (ISO3)",
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+ "entity",
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+ "unit",
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+ ]
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+ ).sum(min_count=1)
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+
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+ df_combine = df_combine.drop(columns=[cat_label, "orig_cat_name"])
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df_combine.insert(0, cat_label, cat_to_agg)
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df_combine.insert(
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1, "orig_cat_name", aggregate_cats_N2O[cat_to_agg]["orig_cat_name"]
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@@ -369,11 +432,11 @@ if __name__ == "__main__":
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# delete cat 3.A.2 for CO2CH4N2O as it's wrong
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index_3A2_CO2CH4N2O = data_if[
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- (data_if[cat_label] == "3.A.2") & (data_if["entity"] == "CH4CO2N2O (SARGWP100)")
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+ (data_if[cat_label] == "3.A.2") & (data_if["entity"] == "CO2CH4N2O (SARGWP100)")
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].index
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data_if = data_if.drop(index_3A2_CO2CH4N2O)
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- # aggregate cat 3 for N2O
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+ # aggregate cat 3 for CO2CH4N2O
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for cat_to_agg in aggregate_cats_CO2CH4N2O:
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mask = data_if[cat_label].isin(aggregate_cats_CO2CH4N2O[cat_to_agg]["sources"])
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df_test = data_if[mask]
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@@ -404,6 +467,7 @@ if __name__ == "__main__":
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]
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).sum(min_count=1)
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+ df_combine = df_combine.drop(columns=[cat_label, "orig_cat_name"])
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df_combine.insert(0, cat_label, cat_to_agg)
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df_combine.insert(
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1,
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@@ -417,6 +481,12 @@ if __name__ == "__main__":
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else:
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print(f"no data to aggregate category {cat_to_agg}")
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+ # Fix 4.B.1 for CH4 as it's wrong
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+ index_4B1_CH4 = data_if[
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+ (data_if[cat_label] == "4.B.1") & (data_if["entity"] == "CH4")
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+ ].index
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+ data_if.loc[index_4B1_CH4]["2019"] = data_if.loc[index_4B1_CH4]["2019"] / 100
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+
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data_if.attrs = attrs
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data_pm2 = pm2.pm2io.from_interchange_format(data_if)
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