add back in pvnet ecmwf old model

pvnet_app/app.py

@@ -107,6 +107,7 @@ def app(
         - SENTRY_DSN, optional link to sentry
         - ENVIRONMENT, the environment this is running in, defaults to local
         - USE_ECMWF_ONLY, option to use ecmwf only model, defaults to false
+        - USE_OCF_DATA_SAMPLER, option to use ocf_data_sampler, defaults to true
 
     Args:
         t0 (datetime): Datetime at which forecast is made
@@ -127,18 +128,20 @@ def app(
     use_day_ahead_model = os.getenv("DAY_AHEAD_MODEL", "false").lower() == "true"
     use_ecmwf_only = os.getenv("USE_ECMWF_ONLY", "false").lower() == "true"
     run_extra_models = os.getenv("RUN_EXTRA_MODELS", "false").lower() == "true"
+    use_ocf_data_sampler = os.getenv("USE_OCF_DATA_SAMPLER", "true").lower() == "true"
 
     logger.info(f"Using `pvnet` library version: {pvnet.__version__}")
     logger.info(f"Using `pvnet_app` library version: {pvnet_app.__version__}")
     logger.info(f"Using {num_workers} workers")
     logger.info(f"Using day ahead model: {use_day_ahead_model}")
-    logger.info(f"Using ecwmwf only: {use_ecmwf_only}")
+    logger.info(f"Using ecmwf only: {use_ecmwf_only}")
     logger.info(f"Running extra models: {run_extra_models}")
 
     # load models
     model_configs = get_all_models(get_ecmwf_only=use_ecmwf_only,
                                    get_day_ahead_only=use_day_ahead_model,
-                                   run_extra_models=run_extra_models)
+                                   run_extra_models=run_extra_models,
+                                   use_ocf_data_sampler=use_ocf_data_sampler)
 
     logger.info(f"Using adjuster: {model_configs[0].use_adjuster}")
     logger.info(f"Saving GSP sum: {model_configs[0].save_gsp_sum}")

pvnet_app/model_configs/all_models.yaml

@@ -47,6 +47,29 @@ models:
         version: 4fe6b1441b6dd549292c201ed85eee156ecc220c
     ecmwf_only: True
     uses_satellite_data: False
+# This is the old model for pvnet and pvnet_ecmwf
+  - name: pvnet_v2
+    pvnet:
+      repo: openclimatefix/pvnet_uk_region
+      version: 62e5e20ab793cee7cf94eadac870d2199501a730
+    summation:
+      repo: openclimatefix/pvnet_v2_summation
+      version: ffac655f9650b81865d96023baa15839f3ce26ec
+    use_adjuster: True
+    save_gsp_sum: False
+    verbose: True
+    save_gsp_to_recent: True
+    uses_ocf_data_sampler: False
+  - name: pvnet_ecmwf # this name is important as it used for blending
+    pvnet:
+        repo: openclimatefix/pvnet_uk_region
+        version: 35d55181a82440bdd087f380d650bfd0b64bd322
+    summation:
+        repo: openclimatefix/pvnet_v2_summation
+        version: 9002baf1e9dc1ec141f3c4a1fa8447b6316a4558
+    ecmwf_only: True
+    uses_satellite_data: False
+    uses_ocf_data_sampler: False
 # The day ahead model has not yet been re-trained with data-sampler.
 # It will be run with the legacy dataloader using ocf_datapipes
   - name: pvnet_day_ahead
@@ -61,4 +84,5 @@ models:
     verbose: True
     save_gsp_to_recent: True
     day_ahead: True
+    uses_ocf_data_sampler: False
 

pvnet_app/model_configs/pydantic_models.py

@@ -49,6 +49,12 @@ class Model(BaseModel):
         True, title="Uses Satellite Data", description="If this model uses satellite data"
     )
 
+    uses_ocf_data_sampler: Optional[bool] = Field(
+        True, title="Uses OCF Data Sampler", description="If this model uses data sampler, old one uses ocf_datapipes"
+    )
+
+
+
 
 class Models(BaseModel):
     """A group of ml models"""
@@ -60,8 +66,8 @@ class Models(BaseModel):
     @field_validator("models")
     @classmethod
     def name_must_be_unique(cls, v: List[Model]) -> List[Model]:
-        """Ensure that all model names are unique"""
-        names = [model.name for model in v]
+        """Ensure that all model names are unique, respect to using ocf_data_sampler or not"""
+        names = [(model.name,model.uses_ocf_data_sampler) for model in v]
         unique_names = set(names)
 
         if len(names) != len(unique_names):
@@ -73,6 +79,7 @@ def get_all_models(
     get_ecmwf_only: Optional[bool] = False,
     get_day_ahead_only: Optional[bool] = False,
     run_extra_models: Optional[bool] = False,
+    use_ocf_data_sampler: Optional[bool] = True,
 ) -> List[Model]:
     """
     Returns all the models for a given client
@@ -81,6 +88,7 @@ def get_all_models(
         get_ecmwf_only: If only the ECMWF model should be returned
         get_day_ahead_only: If only the day ahead model should be returned
         run_extra_models: If extra models should be run
+        use_ocf_data_sampler: If the OCF Data Sampler should be used
     """
 
     # load models from yaml file
@@ -107,6 +115,13 @@ def get_all_models(
         log.info("Not running extra models")
         models.models = [model for model in models.models if model.name == "pvnet_v2"]
 
+    if use_ocf_data_sampler:
+        log.info("Using OCF Data Sampler")
+        models.models = [model for model in models.models if model.uses_ocf_data_sampler]
+    else:
+        log.info("Not using OCF Data Sampler, using ocf_datapipes")
+        models.models = [model for model in models.models if not model.uses_ocf_data_sampler]
+
     return models.models
 
 

tests/model_configs/test_pydantic_models.py

@@ -18,7 +18,7 @@ def test_get_all_models_get_ecmwf_only():
 
 def test_get_all_models_get_day_ahead_only():
     """Test for getting all models with ecmwf_only"""
-    models = get_all_models(get_day_ahead_only=True)
+    models = get_all_models(get_day_ahead_only=True, use_ocf_data_sampler=False)
     assert len(models) == 1
     assert models[0].day_ahead
 
@@ -28,3 +28,15 @@ def test_get_all_models_run_extra_models():
     models = get_all_models(run_extra_models=True)
     assert len(models) == 5
 
+
+def test_get_all_models_ocf_data_sampler():
+    """Test for getting all models with ecmwf_only"""
+    models = get_all_models(use_ocf_data_sampler=True, run_extra_models=True)
+    assert len(models) == 5
+
+    models = get_all_models(use_ocf_data_sampler=False, run_extra_models=True)
+    assert len(models) == 2
+
+    models = get_all_models(use_ocf_data_sampler=False, run_extra_models=True, get_day_ahead_only=True)
+    assert len(models) == 1
+

tests/test_app.py

@@ -111,14 +111,15 @@ def test_app_day_ahead_model(
 
         os.environ["DAY_AHEAD_MODEL"] = "True"
         os.environ["RUN_EXTRA_MODELS"] = "False"
+        os.environ["USE_OCF_DATA_SAMPLER"] = "False"
 
         # Run prediction
         # Thes import needs to come after the environ vars have been set
         from pvnet_app.app import app
 
         app(gsp_ids=list(range(1, 318)), num_workers=2)
 
-    all_models = get_all_models(get_day_ahead_only=True)
+    all_models = get_all_models(get_day_ahead_only=True, use_ocf_data_sampler=False)
 
     # Check correct number of forecasts have been made
     # (317 GSPs + 1 National + maybe GSP-sum) = 318 or 319 forecasts
@@ -174,6 +175,7 @@ def test_app_no_sat(
         os.environ["RUN_EXTRA_MODELS"] = "True"
         os.environ["SAVE_GSP_SUM"] = "True"
         os.environ["DAY_AHEAD_MODEL"] = "False"
+        os.environ["USE_OCF_DATA_SAMPLER"] = "True"
 
         # Run prediction
         # Thes import needs to come after the environ vars have been set
@@ -213,4 +215,72 @@ def test_app_no_sat(
         expected_forecast_results += 317 * model_config.save_gsp_to_recent
         expected_forecast_results += model_config.save_gsp_sum  # optional Sum of GSPs
 
-    assert len(db_session.query(ForecastValueSevenDaysSQL).all()) == expected_forecast_results * 16
+    assert len(db_session.query(ForecastValueSevenDaysSQL).all()) == expected_forecast_results * 16
+
+
+# test legacy models
+# Its nice to have this here, so we can run the latest version in production, but still use the old models
+# Once we have re trained PVnet summation models we can remove this
+def test_app_ocf_datapipes(
+        db_session, nwp_ukv_data, nwp_ecmwf_data, sat_5_data, gsp_yields_and_systems, me_latest
+):
+    """Test the app running the day ahead model"""
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        os.chdir(tmpdirname)
+
+        temp_nwp_path = "temp_nwp_ukv.zarr"
+        os.environ["NWP_UKV_ZARR_PATH"] = temp_nwp_path
+        nwp_ukv_data.to_zarr(temp_nwp_path)
+
+        temp_nwp_path = "temp_nwp_ecmwf.zarr"
+        os.environ["NWP_ECMWF_ZARR_PATH"] = temp_nwp_path
+        nwp_ecmwf_data.to_zarr(temp_nwp_path)
+
+        temp_sat_path = "temp_sat.zarr.zip"
+        os.environ["SATELLITE_ZARR_PATH"] = temp_sat_path
+        with zarr.storage.ZipStore(temp_sat_path, mode="x") as store:
+            sat_5_data.to_zarr(store)
+
+        os.environ["DAY_AHEAD_MODEL"] = "False"
+        os.environ["RUN_EXTRA_MODELS"] = "False"
+        os.environ["USE_OCF_DATA_SAMPLER"] = "False"
+
+        # Run prediction
+        # Thes import needs to come after the environ vars have been set
+        from pvnet_app.app import app
+
+        app(gsp_ids=list(range(1, 318)), num_workers=2)
+
+    all_models = get_all_models(use_ocf_data_sampler=False)
+
+    # Check correct number of forecasts have been made
+    # (317 GSPs + 1 National + maybe GSP-sum) = 318 or 319 forecasts
+    # Forecast made with multiple models
+    expected_forecast_results = 0
+    for model_config in all_models:
+        expected_forecast_results += 318 + model_config.save_gsp_sum
+
+    forecasts = db_session.query(ForecastSQL).all()
+    # Doubled for historic and forecast
+    assert len(forecasts) == expected_forecast_results * 2
+
+    # Check probabilistic added
+    assert "90" in forecasts[0].forecast_values[0].properties
+    assert "10" in forecasts[0].forecast_values[0].properties
+
+    # 72 time steps in forecast
+    expected_forecast_timesteps = 16
+
+    assert (
+            len(db_session.query(ForecastValueSQL).all())
+            == expected_forecast_results * expected_forecast_timesteps
+    )
+    assert (
+            len(db_session.query(ForecastValueLatestSQL).all())
+            == expected_forecast_results * expected_forecast_timesteps
+    )
+    assert (
+            len(db_session.query(ForecastValueSevenDaysSQL).all())
+            == expected_forecast_results * expected_forecast_timesteps
+    )