rename columns

india_forecast_app/app.py

@@ -127,7 +127,10 @@ def get_generation_data(
         # as this is current what ocf_datapipes expects
         # This is because we normalize by the watts amount
         col = generation_df.columns[0]
-        generation_df[col] = generation_df[col].astype(float) * 1e3
+        generation_df[col] = generation_df[col].astype(float) * 1e3\
+
+        # rename columns 0 to power_kw
+        generation_df.rename({col:"power_kw"}, inplace=True)
 
     # Site metadata dataframe
     sites_df = pd.DataFrame(

india_forecast_app/models/pvnet/model.py

@@ -139,7 +139,7 @@ def predict(self, site_id: str, timestamp: dt.datetime):
                 final_gen_points = 0
                 final_gen_index = 0
                 for gen_idx in range(len(generation_da.index.values) - 1, -1, -1):
-                    current_gen = generation_da.isel(index=gen_idx)["0"].values
+                    current_gen = generation_da.isel(index=gen_idx)["power_kw"].values
                     if not np.isnan(current_gen) and current_gen > 0:
                         final_gen_points = current_gen * 1000.0
                         # Convert to KW back from MW

tests/test_app.py

@@ -51,7 +51,7 @@ def test_get_generation_data(db_session, sites, generation_db_values, init_times
 
     # Check for 5 (non-null) generation values
     assert len(gen_df) == 5
-    assert not gen_df["0"].isnull().any()  # 0 is the ml_id/system_id of the wind site
+    assert not gen_df["power_kw"].isnull().any()  # 0 is the ml_id/system_id of the wind site
 
     # Check first and last timestamps are correct
     assert gen_df.index[0] == init_timestamp - dt.timedelta(hours=1)