Process ANSWER-style defaults in apply_composite_tag

xl2times/__main__.py

@@ -122,11 +122,11 @@ def convert_xl_to_times(
         transforms.generate_dummy_processes,
         transforms.process_time_slices,
         transforms.process_transform_table_variants,
+        transforms.apply_tag_specified_defaults,
         transforms.process_transform_tables,
         transforms.process_tradelinks,
         transforms.process_processes,
         transforms.process_topology,
-        transforms.apply_tag_specified_defaults,
         transforms.process_flexible_import_tables,  # slow
         transforms.process_user_constraint_tables,
         transforms.process_commodity_emissions,

xl2times/utils.py

@@ -51,13 +51,38 @@ def apply_composite_tag(table: datatypes.EmbeddedXlTable) -> datatypes.EmbeddedX
         and table tag simplified.
     """
     if table.defaults:
-        varname = table.defaults
+        defaults = table.defaults
         df = table.dataframe
-        if "attribute" in df.columns:
-            df["attribute"] = df["attribute"].fillna(varname)
+        # Check for ANSWER-style defaults
+        if "=" in defaults:
+            # Split multiple comma-separated defaults
+            if "," in defaults:
+                defaults = defaults.split(",")
+            else:
+                # Make it a list
+                defaults = list([defaults])
+            # Check whether there are invalid values on the list
+            invalid_defaults = [default for default in defaults if "=" not in default]
+            if invalid_defaults:
+                logger.warning(
+                    f"Expected ANSWER-style defaults, got {invalid_defaults}"
+                )
+            defaults = [default.split("=") for default in defaults]
+            # TODO: check whether a column is allowed in a particular table type
+            for col, val in defaults:
+                colname = col.lower()
+                if colname in df.columns:
+                    df[colname] = df[colname].fillna(val.upper())
+                else:
+                    df[colname] = val.upper()
+            return replace(table, dataframe=df)
         else:
-            df["attribute"] = varname
-        return replace(table, dataframe=df)
+            # TODO: Resolve the default value (it doesn't have to be an attribute)
+            if "attribute" in df.columns:
+                df["attribute"] = df["attribute"].fillna(defaults)
+            else:
+                df["attribute"] = defaults
+            return replace(table, dataframe=df)
     else:
         return table