Merge commit '98056118202d8139005af098ec62a12743bd04dd' into runvl-pv

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.1.25
+current_version = 1.1.27
 commit = True
 tag = True
 message = Bump version: {current_version} → {new_version} [ci skip]

india_forecast_app/__init__.py

@@ -1,2 +1,2 @@
 """India Forecast App"""
-__version__ = "1.1.25"
+__version__ = "1.1.27"

india_forecast_app/models/all_models.yaml

@@ -60,6 +60,13 @@ models:
     client: ad
     asset_type: wind
     adjuster_average_minutes: 15
+  - name: windnet_ad_sites_generation_delay
+    type: pvnet
+    id: openclimatefix/windnet_ad_sites
+    version: bced479c5c3ab198bfce7d5cb4e02f8679be3297
+    client: ad
+    asset_type: wind
+    adjuster_average_minutes: 15
   # this is just a dummy one
   - name: dummy
     type: dummy

india_forecast_app/models/pvnet/model.py

@@ -11,7 +11,12 @@
 import numpy as np
 import pandas as pd
 import torch
-from ocf_datapipes.batch import batch_to_tensor, copy_batch_to_device, stack_np_examples_into_batch
+from ocf_datapipes.batch import (
+    BatchKey,
+    batch_to_tensor,
+    copy_batch_to_device,
+    stack_np_examples_into_batch,
+)
 from ocf_datapipes.training.pvnet_site import construct_sliced_data_pipeline as pv_base_pipeline
 from ocf_datapipes.training.windnet import DictDatasetIterDataPipe, split_dataset_dict_dp
 from ocf_datapipes.training.windnet import construct_sliced_data_pipeline as wind_base_pipeline
@@ -95,6 +100,10 @@ def predict(self, site_id: str, timestamp: dt.datetime):
             for i, batch in enumerate(self.dataloader):
                 log.info(f"Predicting for batch: {i}")
 
+                if self.name == "windnet_ad_sites_generation_delay":
+                    # this is a bit of hack, but it's important to do what was done in training
+                    batch[BatchKey.wind][:, int(batch[BatchKey.wind_t0_idx])] = -1
+
                 # save batch
                 save_batch(batch=batch, i=i, model_name=self.name, site_uuid=self.site_uuid)
 
@@ -242,7 +251,7 @@ def _prepare_data_sources(self):
                     source_nwp_path=os.environ["NWP_MO_GLOBAL_ZARR_PATH"],
                     dest_nwp_path=nwp_mo_global_path,
                     source="mo_global",
-                    config=self.config["input_data"]["nwp"]["mo_global"]
+                    config=self.config["input_data"]["nwp"]["mo_global"],
                 )
             )
 
@@ -264,7 +273,10 @@ def _prepare_data_sources(self):
             generation_da = self.generation_data["data"].to_xarray()
 
             # get the minimum timestamp in generation data
-            min_timestamp = generation_da.index.min().values
+            if len(generation_da.index) > 0:
+                min_timestamp = generation_da.index.min().values
+            else:
+                min_timestamp = self.t0 - pd.Timedelta(hours=24)
             # Add the forecast timesteps to the generation, with 0 values
             # 192 is 48 hours of 15 min intervals
             forecast_timesteps = pd.date_range(

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "india_forecast_app"
-version = "1.1.25"
+version = "1.1.27"
 description = "Runs wind and PV forecasts for India and saves to database"
 authors = ["Chris Briggs <[email protected]>"]
 readme = "README.md"

tests/test_app.py

@@ -210,13 +210,13 @@ def test_app_client_ad(
 
     app_run(timestamp=None, write_to_db=True)
 
-    # 2 pv models, 1 wind model
+    # 2 pv models, 2 wind model
     # x2 for adjuster
-    n_forecasts = 3 * 2
-    # one models is 8 hours, two model is 4 hours
+    n_forecasts = 4 * 2
+    # one models is 8 hours, three model is 4 hours
     # x 4 for each 15 minutes
     # x 2 for adjuster
-    n_forecast_values = (8 + 4 + 4) * 4 * 2
+    n_forecast_values = (8 + 4 + 4 + 4) * 4 * 2
 
     assert db_session.query(ForecastSQL).count() == init_n_forecasts + n_forecasts
     assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + n_forecast_values