Issue/new ecmwf

india_forecast_app/models/all_models.yaml

@@ -32,12 +32,12 @@ models:
     version: d71104620f0b0bdd3eeb63cafecd2a49032ae0f7
     client: ruvnl
     asset_type: pv
-  - name: pvnet_india_ecmwf_mo_gfs
-    type: pvnet
-    id: openclimatefix/pvnet_india
-    version: 7a179d1f8349d99cb2a30a48e5be17d59b6f2b16
-    client: ruvnl
-    asset_type: pv
+#  - name: pvnet_india_ecmwf_mo_gfs
+#    type: pvnet
+#    id: openclimatefix/pvnet_india
+#    version: 7a179d1f8349d99cb2a30a48e5be17d59b6f2b16
+#    client: ruvnl
+#    asset_type: pv
   # Ad client solar
   - name: pvnet_ad_sites
     type: pvnet

india_forecast_app/models/pvnet/utils.py

@@ -133,6 +133,41 @@ def process_and_cache_nwp(nwp_config: NWPProcessAndCacheConfig):
             ds[v].encoding.clear()
 
     if nwp_config.source == "ecmwf":
+
+        if "HRES-IFS_india" in ds.data_vars:
+            # rename from HRES-IFS_india to ECMWF_INDIA
+            ds = ds.rename({"HRES-IFS_india": "ECMWF_INDIA"})
+
+            # rename variable names in the variable coordinate
+            # This is a renaming from ECMWF variables to what we use in the ML Model
+            # This change happened in the new nwp-consumer>=1.0.0
+            # Ideally we won't need this step in the future
+            variable_coords = ds.variable.values
+            rename = {'cloud_cover_high': 'hcc',
+                      'cloud_cover_low': 'lcc',
+                      'cloud_cover_medium': 'mcc',
+                      'cloud_cover_total': 'tcc',
+                      'snow_depth_gl': 'sde',
+                      'direct_shortwave_radiation_flux_gl': 'sr',
+                      'downward_longwave_radiation_flux_gl': 'dlwrf',
+                      'downward_shortwave_radiation_flux_gl': 'dswrf',
+                      'downward_ultraviolet_radiation_flux_gl': 'duvrs',
+                      'temperature_sl': 't',
+                      'total_precipitation_rate_gl': 'prate',
+                      'visibility_sl': 'vis',
+                      'wind_u_component_100m': 'u100',
+                      'wind_u_component_10m': 'u10',
+                      'wind_u_component_200m': 'u200',
+                      'wind_v_component_100m': 'v100',
+                      'wind_v_component_10m': 'v10',
+                      'wind_v_component_200m': 'v200'}
+
+            for k, v in rename.items():
+                variable_coords[variable_coords == k] = v
+
+            # assign the new variable names
+            ds = ds.assign_coords(variable=variable_coords)
+
         # Rename t variable to t2m
         variables = list(ds.variable.values)
         new_variables = []

tests/models/test_pydantic_models.py

@@ -5,7 +5,7 @@
 def test_get_all_models():
     """Test for getting all models"""
     models = get_all_models()
-    assert len(models.models) == 11
+    assert len(models.models) == 10
 
 
 def test_get_all_models_client():

tests/test_app.py

@@ -160,10 +160,12 @@ def test_app(
     result = run_click_script(app, args)
     assert result.exit_code == 0
 
+    n = 5
+
     if write_to_db:
-        assert db_session.query(ForecastSQL).count() == init_n_forecasts + 6 * 2
-        assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (6 * 2 * 192)
-        assert db_session.query(MLModelSQL).count() == 6 * 2
+        assert db_session.query(ForecastSQL).count() == init_n_forecasts + n * 2
+        assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (n * 2 * 192)
+        assert db_session.query(MLModelSQL).count() == n * 2
     else:
         assert db_session.query(ForecastSQL).count() == init_n_forecasts
         assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values
@@ -183,8 +185,10 @@ def test_app_no_pv_data(
     result = run_click_script(app, args)
     assert result.exit_code == 0
 
-    assert db_session.query(ForecastSQL).count() == init_n_forecasts + 2 * 6
-    assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (2 * 6 * 192)
+    n = 5
+
+    assert db_session.query(ForecastSQL).count() == init_n_forecasts + 2 * n
+    assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (2 * n * 192)
 
 
 @pytest.mark.requires_hf_token