Correction for handling missing parquet inputs

improver/calibration/load_and_train_quantile_regression_random_forest.py

@@ -270,13 +270,13 @@ def process(
 
         # If there are no parquet files, return None.
         if not parquets:
-            return None
+            return None, None, None
 
         forecast_table_path, truth_table_path = identify_parquet_type(parquets)
 
         # If either the forecast or truth parquet files are missing, return None.
         if not forecast_table_path or not truth_table_path:
-            return None
+            return None, None, None
 
         forecast_periods = self._parse_forecast_periods()
         forecast_df, truth_df = self._read_parquet_files(

improver/cli/train_quantile_regression_random_forest.py

@@ -122,6 +122,8 @@ def process(
         training_length=training_length,
         unique_site_id_keys=unique_site_id_keys,
     )(file_paths)
+    if forecast_df is None or truth_df is None or cube_inputs is None:
+        return None
     result = PrepareAndTrainQRF(
         feature_config=feature_config,
         target_cf_name=target_cf_name,

improver_tests/acceptance/SHA256SUMS

@@ -84,6 +84,7 @@ eeb021922eb3f61c4dcc4c5096140d458d8b91c3f431ee3e54cdd06c08029f12  ./apply-night-
 3a6b4b6e2931e4b58d970c4b034247269a96c372e5a4f97a8ba62a895071fa95  ./apply-night-mask/uk_prob/invalid_input.nc
 ed8ab78a9f55b54bf0a49f191d3eb33daae30e0d05b9051275a70c0e697aac71  ./apply-night-mask/uk_prob/kgo.nc
 809a446327626d007ac288b8277520730863bc596e98116bca5c4afb0d531e96  ./apply-night-mask/uk_prob/valid_input.nc
+0f74c2d6e3caf30c4f971da2dc757b1ae659062e31194e4da8e5266a1f23d2af  ./apply-quantile-regression-random-forest/added_comment_kgo.nc
 d2f7d8389b33cde359dd253def2aaf31afbc27557f389bf0f744a28b24e145dd  ./apply-quantile-regression-random-forest/config.json
 8db1b35bde734c16340a6e42454b9ac146ea32f7c012c17c5e64815069af41bf  ./apply-quantile-regression-random-forest/input_forecast.nc
 0f74c2d6e3caf30c4f971da2dc757b1ae659062e31194e4da8e5266a1f23d2af  ./apply-quantile-regression-random-forest/with_transformation_kgo.nc

improver_tests/acceptance/test_apply_quantile_regression_random_forest.py

@@ -18,7 +18,8 @@
     ["without_transformation", "with_transformation"],
 )
 def test_basic(tmp_path, transformation):
-    """Test"""
+    """Test apply-quantile-regression-random-forest CLI with and without a
+    transformation applied."""
     kgo_dir = acc.kgo_root() / "apply-quantile-regression-random-forest/"
     kgo_path = kgo_dir / f"{transformation}_kgo.nc"
     qrf_path = kgo_dir / f"{transformation}_input.pickle"
@@ -38,3 +39,28 @@ def test_basic(tmp_path, transformation):
 
     run_cli(args)
     acc.compare(output_path, kgo_path, atol=LOOSE_TOLERANCE)
+
+
+def test_missing_qrf_model(tmp_path):
+    """Test that if no QRF model is provided, the result matches the input forecast
+    with the exception of the comment attribute."""
+    kgo_dir = acc.kgo_root() / "apply-quantile-regression-random-forest/"
+    kgo_path = kgo_dir / "added_comment_kgo.nc"
+    forecast_path = kgo_dir / "input_forecast.nc"
+    config_path = kgo_dir / "config.json"
+    output_path = tmp_path / "output.nc"
+    args = [
+        forecast_path,
+        "--feature-config",
+        config_path,
+        "--target-cf-name",
+        "air_temperature",
+        "--output",
+        output_path,
+    ]
+
+    run_cli(args)
+    acc.compare(
+        output_path, forecast_path, atol=LOOSE_TOLERANCE, exclude_attributes=["comment"]
+    )
+    acc.compare(output_path, kgo_path, atol=LOOSE_TOLERANCE)

improver_tests/acceptance/test_train_quantile_regression_random_forest.py

@@ -74,3 +74,43 @@ def test_basic(
 
     run_cli(compulsory_args + named_args)
     acc.compare(output_path, kgo_path, file_type="pickled_forest")
+
+
+def test_missing_inputs(
+    tmp_path,
+):
+    """
+    Test train-quantile-regression-random-forest CLI with missing parquet inputs.
+    """
+    kgo_dir = acc.kgo_root() / CLI
+    config_path = kgo_dir / "config.json"
+    output_path = tmp_path / "output.pickle"
+    compulsory_args = []
+    named_args = [
+        "--feature-config",
+        config_path,
+        "--parquet-diagnostic-names",
+        "temperature_at_screen_level",
+        "--target-cf-name",
+        "air_temperature",
+        "--forecast-periods",
+        "6:18:6",
+        "--cycletime",
+        "20250804T0000Z",
+        "--training-length",
+        "2",
+        "--experiment",
+        "mix-latestblend",
+        "--n-estimators",
+        "10",
+        "--max-depth",
+        "5",
+        "--random-state",
+        "42",
+        "--compression-level",
+        "5",
+        "--output",
+        output_path,
+    ]
+
+    assert run_cli(compulsory_args + named_args) is None

improver_tests/calibration/quantile_regression_random_forests_calibration/test_load_and_train_quantile_regression_random_forest.py

@@ -529,7 +529,7 @@ def test_load_for_qrf_no_paths(tmp_path, make_files):
     )
     result = plugin(file_paths)
     # Expecting None since no valid paths are provided
-    assert result is None
+    assert result == (None, None, None)
 
 
 @pytest.mark.parametrize(