CO2 AFOLU handling

src/gcages/cmip7_scenariomip/pre_processing/reaggregation/basic.py

@@ -104,15 +104,8 @@ def to_required_variables(self, all_species: tuple[str, ...]) -> tuple[str, ...]
     GriddingSectorComponentsReporting(
         gridding_sector="Agriculture",
         spatial_resolution=SpatialResolutionOption.MODEL_REGION,
-        input_sectors=("AFOLU|Agriculture",),
-        input_sectors_optional=(),
-        input_species_optional=(),
-    ),
-    GriddingSectorComponentsReporting(
-        gridding_sector="Agricultural Waste Burning",
-        spatial_resolution=SpatialResolutionOption.MODEL_REGION,
         input_sectors=(
-            "AFOLU|Agricultural Waste Burning",
+            "AFOLU|Agriculture",
             "AFOLU|Land|Harvested Wood Products",
             "AFOLU|Land|Land Use and Land-Use Change",
             "AFOLU|Land|Other",
@@ -131,6 +124,13 @@ def to_required_variables(self, all_species: tuple[str, ...]) -> tuple[str, ...]
             "Sulfur",
         ),
     ),
+    GriddingSectorComponentsReporting(
+        gridding_sector="Agricultural Waste Burning",
+        spatial_resolution=SpatialResolutionOption.MODEL_REGION,
+        input_sectors=("AFOLU|Agricultural Waste Burning",),
+        input_sectors_optional=(),
+        input_species_optional=(),
+    ),
     GriddingSectorComponentsReporting(
         gridding_sector="Aircraft",
         spatial_resolution=SpatialResolutionOption.WORLD,
@@ -1004,23 +1004,31 @@ def to_gridding_sectors(
         {"Energy|Demand|Transportation": "Transportation Sector"},
         axis="columns",
     )
-    # Do other compilations.
-    # We can do this here with confidence
-    # because we assume that the users have used `to_complete`
-    # before calling this function.
+
+    # To handle CO2 differently
+    # Get boolean masks for CO2 and non-CO2 species
+    is_co2 = region_sector_df_gridding.index.get_level_values("species") == "CO2"
+    # Special treatment for CO2
+    region_sector_df_gridding_co2 = region_sector_df_gridding[is_co2]
+    # Select rows for non-CO2
+    region_sector_df_gridding = region_sector_df_gridding[~is_co2]
+
+    # # Do other compilations.
+    # # We can do this here with confidence
+    # # because we assume that the users have used `to_complete`
+    # # before calling this function.
     for gridding_sector, components in (
-        ("Agriculture", ["AFOLU|Agriculture"]),
         (
-            "Agricultural Waste Burning",
+            "Agriculture",
             [
-                "AFOLU|Agricultural Waste Burning",
-                # Hmmm, almost definitely wrong
+                "AFOLU|Agriculture",
                 "AFOLU|Land|Harvested Wood Products",
                 "AFOLU|Land|Land Use and Land-Use Change",
                 "AFOLU|Land|Other",
                 "AFOLU|Land|Wetlands",
             ],
         ),
+        ("Agricultural Waste Burning", ["AFOLU|Agricultural Waste Burning"]),
         ("Energy Sector", ["Energy|Supply"]),
         ("Forest Burning", ["AFOLU|Land|Fires|Forest Burning"]),
         ("Grassland Burning", ["AFOLU|Land|Fires|Grassland Burning"]),
@@ -1042,13 +1050,32 @@ def to_gridding_sectors(
         ("Solvents Production and Application", ["Product Use"]),
         ("Waste", ["Waste"]),
     ):
+        if any("AFOLU" in c for c in components):
+            region_sector_df_gridding_co2 = region_sector_df_gridding_co2.drop(
+                list(set(components)), axis="columns", errors="ignore"
+            )
+        else:
+            region_sector_df_gridding_co2[gridding_sector] = (
+                region_sector_df_gridding_co2[components].sum(axis="columns")
+            )  # type: ignore # pandas-stubs confused
+            region_sector_df_gridding_co2 = region_sector_df_gridding_co2.drop(
+                list(set(components) - {gridding_sector}), axis="columns"
+            )
+
         region_sector_df_gridding[gridding_sector] = region_sector_df_gridding[
             components
         ].sum(axis="columns")  # type: ignore # pandas-stubs confused
+
         region_sector_df_gridding = region_sector_df_gridding.drop(
             list(set(components) - {gridding_sector}), axis="columns"
         )
 
+    region_sector_df_gridding = (
+        pd.concat([region_sector_df_gridding, region_sector_df_gridding_co2])
+        .sort_index()
+        .fillna(0)
+    )
+
     sector_df_gridding_like_input = combine_sectors(
         set_new_single_value_levels(
             sector_df_gridding.unstack().stack("sectors", future_stack=True),  # type: ignore # pandas-stubs confused

tests/integration/cmip7-scenariomip/pre-processing/reaggregation/test_integration_cmip7_scenariomip_pre_processing_reaggregation_basic.py

@@ -121,15 +121,8 @@ def to_required_variables(self, all_species: tuple[str, ...]) -> tuple[str, ...]
         GriddingSectorComponentsReporting(
             gridding_sector="Agriculture",
             spatial_resolution="model region",
-            input_sectors=("AFOLU|Agriculture",),
-            input_sectors_optional=(),
-            input_species_optional=(),
-        ),
-        GriddingSectorComponentsReporting(
-            gridding_sector="Agricultural Waste Burning",
-            spatial_resolution="model region",
             input_sectors=(
-                "AFOLU|Agricultural Waste Burning",
+                "AFOLU|Agriculture",
                 "AFOLU|Land|Harvested Wood Products",
                 "AFOLU|Land|Land Use and Land-Use Change",
                 "AFOLU|Land|Other",
@@ -148,6 +141,13 @@ def to_required_variables(self, all_species: tuple[str, ...]) -> tuple[str, ...]
                 "Sulfur",
             ),
         ),
+        GriddingSectorComponentsReporting(
+            gridding_sector="Agricultural Waste Burning",
+            spatial_resolution="model region",
+            input_sectors=("AFOLU|Agricultural Waste Burning",),
+            input_sectors_optional=(),
+            input_species_optional=(),
+        ),
         GriddingSectorComponentsReporting(
             gridding_sector="Aircraft",
             spatial_resolution="world",