Fix for runs

india_forecast_app/adjuster.py

@@ -115,11 +115,14 @@ def get_me_values(
     # currently in 0, 60, 120,...
     # change to 0, 15, 30, 45, 60, 75, 90, 105, 120, ...
     me_df = me_df.set_index("horizon_minutes")
-    me_df = me_df.reindex(range(0, max(me_df.index), 15)).interpolate(limit=3)
+    me_df = me_df.reindex(range(0, max(me_df.index)+15, 15)).interpolate(limit=3)
 
-    # reset indiex
+    # reset index
     me_df = me_df.reset_index()
 
+    # log the maximum and minimum adjuster results
+    log.info(f"ME results: max={me_df['me_kw'].max()}, min={me_df['me_kw'].min()}")
+
     return me_df
 
 

india_forecast_app/app.py

@@ -391,9 +391,9 @@ def app_run(timestamp: dt.datetime | None, write_to_db: bool = False, log_level:
 
         log.info(f"Completed forecasts for {successful_runs} runs for "
                  f"{runs} model runs. This was for {len(sites)} sites")
-        if successful_runs == len(sites):
+        if successful_runs == runs:
             log.info("All forecasts completed successfully")
-        elif 0 < successful_runs < len(sites):
+        elif 0 < successful_runs < runs:
             raise Exception("Some forecasts failed")
         else:
             raise Exception("All forecasts failed")

tests/conftest.py

@@ -350,11 +350,14 @@ def nwp_mo_global_data(tmp_path_factory, time_before_present):
         f"{os.path.dirname(os.path.abspath(__file__))}/test_data/nwp-no-data_gfs.zarr"
     )
 
-    # Last t0 to at least 6 hours ago and floor to 3-hour interval
+    # rename dimension init_time_utc to init_time
+    ds = ds.rename({"init_time_utc": "init_time"})
+
+    # Last t0 to at least 4 hours ago and floor to 3-hour interval
     t0_datetime_utc = time_before_present(dt.timedelta(hours=0)).floor("3h")
-    t0_datetime_utc = t0_datetime_utc - dt.timedelta(hours=6)
-    ds.init_time_utc.values[:] = pd.date_range(
-        t0_datetime_utc - dt.timedelta(hours=12 * (len(ds.init_time_utc) - 1)),
+    t0_datetime_utc = t0_datetime_utc - dt.timedelta(hours=4)
+    ds.init_time.values[:] = pd.date_range(
+        t0_datetime_utc - dt.timedelta(hours=12 * (len(ds.init_time) - 1)),
         t0_datetime_utc,
         freq=dt.timedelta(hours=1),
     )
@@ -371,6 +374,13 @@ def nwp_mo_global_data(tmp_path_factory, time_before_present):
         if ds[v].dtype == object:
             ds[v].encoding.clear()
 
+    # change variables values to for MO global
+    ds.variable.values[0:3] = ["temperature_sl", "wind_u_component_10m", "wind_v_component_10m"]
+
+    # interpolate 3 hourly step to 1 hour steps
+    steps = pd.TimedeltaIndex(np.arange(49) * 3600 * 1e9, freq='infer')
+    ds = ds.interp(step=steps, method="linear")
+
     ds["mo_global"] = xr.DataArray(
         np.zeros([len(ds[c]) for c in ds.xindexes]),
         coords=[ds[c] for c in ds.xindexes],

tests/test_adjuster.py

@@ -21,7 +21,7 @@ def test_get_me_values(db_session, sites, generation_db_values, forecasts):
     me_df = get_me_values(db_session, hour, site_uuid=sites[0].site_uuid, ml_model_name="test")
 
     assert len(me_df) != 0
-    assert len(me_df) == 96
+    assert len(me_df) == 97
     assert me_df["me_kw"].sum() != 0
     assert me_df["horizon_minutes"][0] == 0
     assert me_df["horizon_minutes"][1] == 15

tests/test_app.py

@@ -161,9 +161,9 @@ def test_app(
     assert result.exit_code == 0
 
     if write_to_db:
-        assert db_session.query(ForecastSQL).count() == init_n_forecasts + 2 * 2
-        assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (2 * 2 * 192)
-        assert db_session.query(MLModelSQL).count() == 2 * 2
+        assert db_session.query(ForecastSQL).count() == init_n_forecasts + 3 * 2
+        assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (3 * 2 * 192)
+        assert db_session.query(MLModelSQL).count() == 3 * 2
     else:
         assert db_session.query(ForecastSQL).count() == init_n_forecasts
         assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values
@@ -183,8 +183,8 @@ 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 * 2
-    assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (2 * 2 * 192)
+    assert db_session.query(ForecastSQL).count() == init_n_forecasts + 2 * 3
+    assert db_session.query(ForecastValueSQL).count() == init_n_forecast_values + (2 * 3 * 192)
 
 
 @pytest.mark.requires_hf_token