Allow processing of the dins table and its variants

xl2times/__main__.py

@@ -97,7 +97,7 @@ def convert_xl_to_times(
         transforms.remove_exreg_cols,
         transforms.generate_dummy_processes,
         transforms.process_time_slices,
-        transforms.process_transform_insert_variants,
+        transforms.process_transform_table_variants,
         transforms.process_transform_tables,
         transforms.process_tradelinks,
         transforms.process_processes,

xl2times/config/veda-tags.json

@@ -1008,7 +1008,7 @@
           "cset:cn",
           "cset: cn"
         ],
-        "use_name": "cset_cn",
+        "use_name": "commodity",
         "row_ignore_symbol": [
           "\\I:"
         ],
@@ -1072,7 +1072,7 @@
           "pset:pn",
           "pset: pn"
         ],
-        "use_name": "pset_pn",
+        "use_name": "process",
         "row_ignore_symbol": [
           "\\I:"
         ],

xl2times/transforms.py

@@ -1862,7 +1862,7 @@ def process_tradelinks(
     return result
 
 
-def process_transform_insert_variants(
+def process_transform_table_variants(
     config: datatypes.Config,
     tables: List[datatypes.EmbeddedXlTable],
     model: datatypes.TimesModel,
@@ -1886,7 +1886,12 @@ def is_year(col_name):
 
     result = []
     for table in tables:
-        if table.tag == datatypes.Tag.tfm_ins_ts:
+        tag = datatypes.Tag(table.tag.split(":")[0])
+        if tag in [
+            datatypes.Tag.tfm_dins_ts,
+            datatypes.Tag.tfm_ins_ts,
+            datatypes.Tag.tfm_upd_ts,
+        ]:
             # ~TFM_INS-TS: Gather columns whose names are years into a single "Year" column:
             df = table.dataframe
             query_columns = config.query_columns[datatypes.Tag(table.tag)]
@@ -1918,8 +1923,14 @@ def is_year(col_name):
             )
             # Convert the year column to integer
             df["year"] = df["year"].astype("int")
-            result.append(replace(table, dataframe=df, tag=datatypes.Tag.tfm_ins))
-        elif table.tag == datatypes.Tag.tfm_ins_at:
+            result.append(
+                replace(table, dataframe=df, tag=datatypes.Tag(tag.value.split("-")[0]))
+            )
+        elif tag in [
+            datatypes.Tag.tfm_dins_at,
+            datatypes.Tag.tfm_ins_at,
+            datatypes.Tag.tfm_upd_at,
+        ]:
             # ~TFM_INS-AT: Gather columns with attribute names into a single "Attribute" column
             df = table.dataframe
             if "attribute" in df.columns:
@@ -1938,7 +1949,9 @@ def is_year(col_name):
                 value_name="value",
                 ignore_index=False,
             )
-            result.append(replace(table, dataframe=df, tag=datatypes.Tag.tfm_ins))
+            result.append(
+                replace(table, dataframe=df, tag=datatypes.Tag(tag.value.split("-")[0]))
+            )
         else:
             result.append(table)
 
@@ -1955,9 +1968,9 @@ def process_transform_tables(
     """
     regions = model.internal_regions
     tfm_tags = [
+        datatypes.Tag.tfm_dins,
         datatypes.Tag.tfm_ins,
         datatypes.Tag.tfm_ins_txt,
-        datatypes.Tag.tfm_dins,
         datatypes.Tag.tfm_topins,
         datatypes.Tag.tfm_upd,
         datatypes.Tag.tfm_mig,
@@ -1971,6 +1984,7 @@ def process_transform_tables(
             result.append(table)
 
         elif table.tag in [
+            datatypes.Tag.tfm_dins,
             datatypes.Tag.tfm_ins,
             datatypes.Tag.tfm_ins_txt,
             datatypes.Tag.tfm_upd,
@@ -2345,7 +2359,7 @@ def eval_and_update(
         for _, row in tqdm(
             updates.iterrows(),
             total=len(updates),
-            desc=f"Processing wildcard for {datatypes.Tag.tfm_upd}",
+            desc=f"Processing wildcard for {datatypes.Tag.tfm_upd.value}",
         ):
             if row["value"] is None:  # TODO is this really needed?
                 continue
@@ -2378,7 +2392,7 @@ def eval_and_update(
         for _, row in tqdm(
             updates.iterrows(),
             total=len(updates),
-            desc=f"Processing wildcard for {datatypes.Tag.tfm_ins}",
+            desc=f"Processing wildcard for {datatypes.Tag.tfm_ins.value}",
         ):
             match = match_wildcards(row)
             # TODO perf: add matched procs/comms into column and use explode?
@@ -2395,6 +2409,12 @@ def eval_and_update(
         new_tables.append(tables[datatypes.Tag.fi_t])
         tables[datatypes.Tag.fi_t] = pd.concat(new_tables, ignore_index=True)
 
+    # TODO: Move this somewhere else (i.e. no wildcard processing)?
+    if datatypes.Tag.tfm_dins in tables:
+        table = tables[datatypes.Tag.fi_t]
+        updates = tables[datatypes.Tag.tfm_dins].filter(table.columns, axis=1)
+        tables[datatypes.Tag.fi_t] = pd.concat([table, updates], ignore_index=True)
+
     if datatypes.Tag.tfm_ins_txt in tables:
         updates = tables[datatypes.Tag.tfm_ins_txt]
 
@@ -2403,7 +2423,7 @@ def eval_and_update(
         for _, row in tqdm(
             updates.iterrows(),
             total=len(updates),
-            desc=f"Processing wildcard for {datatypes.Tag.tfm_ins_txt}",
+            desc=f"Processing wildcard for {datatypes.Tag.tfm_ins_txt.value}",
         ):
             match = match_wildcards(row)
             if match is None:
@@ -2434,7 +2454,7 @@ def eval_and_update(
         for _, row in tqdm(
             updates.iterrows(),
             total=len(updates),
-            desc=f"Processing wildcard for {datatypes.Tag.tfm_mig}",
+            desc=f"Processing wildcard for {datatypes.Tag.tfm_mig.value}",
         ):
             match = match_wildcards(row)
             processes, commodities = match if match is not None else (None, None)