Merge pull request #12 from gavinevans/mobt_877_implement_qrf3

Update QRF acceptance test data, return cubes with perc dim if they had one before and return none if fp or cycletime is not present in the tables.

Author: Kat-90

improver/calibration/load_and_apply_quantile_regression_random_forest.py

@@ -22,6 +22,7 @@
 )
 from improver.ensemble_copula_coupling.ensemble_copula_coupling import (
     RebadgePercentilesAsRealizations,
+    RebadgeRealizationsAsPercentiles,
 )
 from improver.ensemble_copula_coupling.utilities import choose_set_of_percentiles
 from improver.utilities.cube_checker import assert_spatial_coords_match
@@ -185,8 +186,7 @@ def _percentiles_to_realizations(cube_inputs: CubeList) -> CubeList:
             if feature_cube.coords("percentile"):
                 feature_cube = RebadgePercentilesAsRealizations()(feature_cube)
             realization_cube_inputs.append(feature_cube)
-        cube_inputs = realization_cube_inputs
-        return cube_inputs
+        return realization_cube_inputs
 
     @staticmethod
     def _organise_cubes(
@@ -250,14 +250,19 @@ def process(
         cube_inputs, forecast_cube, qrf_model = self._get_inputs(file_paths)
         if not qrf_model:
             return forecast_cube
+
+        template_forecast_cube = forecast_cube.copy()
         if forecast_cube.coords("percentile"):
-            percentiles = self._compute_percentiles(forecast_cube, "percentile")
-            cube_inputs = self._percentiles_to_realizations(cube_inputs)
+            percentiles = self._compute_percentiles(forecast_cube.copy(), "percentile")
+            cube_inputs = self._percentiles_to_realizations(cube_inputs.copy())
+            template_forecast_cube = RebadgePercentilesAsRealizations()(
+                template_forecast_cube
+            )
         elif forecast_cube.coords("realization"):
-            percentiles = self._compute_percentiles(forecast_cube, "realization")
+            percentiles = self._compute_percentiles(forecast_cube.copy(), "realization")
 
         cube_inputs, template_forecast_cube = self._organise_cubes(
-            cube_inputs, forecast_cube
+            cube_inputs, template_forecast_cube
         )
 
         result = ApplyQuantileRegressionRandomForests(
@@ -266,4 +271,7 @@ def process(
             transformation=self.transformation,
             pre_transform_addition=self.pre_transform_addition,
         )(qrf_model, cube_inputs, template_forecast_cube)
+
+        if forecast_cube.coords("percentile"):
+            result = RebadgeRealizationsAsPercentiles()(result)
         return result

improver/calibration/load_and_train_quantile_regression_random_forest.py

@@ -271,6 +271,9 @@ def _dataframe_to_cubes(
                 truth_slice = iris.util.new_axis(truth_slice, "time")
                 truth_cubes.append(truth_slice)
 
+        if len(forecast_cubes) == 0 or len(truth_cubes) == 0:
+            return None, None
+
         truth_cube = truth_cubes.concatenate_cube()
         forecast_cube = forecast_cubes.concatenate()
 
@@ -417,6 +420,8 @@ def process(
         forecast_cube, truth_cube = self._dataframe_to_cubes(
             forecast_df, truth_df, forecast_periods
         )
+        if forecast_cube is None or truth_cube is None:
+            return None
 
         # If target_forecast is also a dynamic feature in the feature config then
         # add it to cube_inputs