Additional tests for realization collapse and for percentile extracti…

…on directly from realization data. Modifies the test in spot_manipulation for selecting the percentile method to check if data is actually masked, rather than just of masked type. This will enable the fast method to be used in more cases.
bayliffe · May 16, 2024 · 839a422 · 839a422
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commit 839a422
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Showing 3 changed files with 60 additions and 15 deletions.
diff --git a/improver/cli/spot_extract.py b/improver/cli/spot_extract.py
@@ -67,13 +67,13 @@ def process(
             And the neighbour cube is a cube of spot-data neighbours and
             the spot site information.
         apply_lapse_rate_correction (bool):
-            Use to apply a lapse-rate correction to screen temperature data so
-            that the data are a better match the altitude of the spot site for
-            which they have been extracted. This lapse rate will be applied for
-            a fixed orographic difference between the site and gridpoint
-            altitude. Differences in orography in excess of this fixed limit
-            will use the Environmental Lapse Rate (also known as the Standard
-            Atmosphere Lapse Rate).
+            Use to apply a lapse-rate correction to screen temperature
+            forecasts so that they better represent the altitude of the
+            spot site for which they have been extracted. This lapse rate
+            will be applied for a fixed orographic difference between the
+            site and grid point altitude. Differences in orography in
+            excess of this fixed limit will use the Environmental Lapse
+            Rate (also known as the Standard Atmosphere Lapse Rate).
         fixed_lapse_rate (float):
             If provided, use this fixed value as a lapse-rate for adjusting
             the forecast values if apply_lapse_rate_correction is True. This

diff --git a/improver/spotdata/spot_manipulation.py b/improver/spotdata/spot_manipulation.py
@@ -80,13 +80,13 @@ def __init__(
 
         Args:
             apply_lapse_rate_correction (bool):
-                Use to apply a lapse-rate correction to screen temperature data so
-                that the data are a better match the altitude of the spot site for
-                which they have been extracted. This lapse rate will be applied for
-                a fixed orographic difference between the site and gridpoint
-                altitude. Differences in orography in excess of this fixed limit
-                will use the Environmental Lapse Rate (also known as the Standard
-                Atmosphere Lapse Rate).
+                Use to apply a lapse-rate correction to screen temperature
+                forecasts so that they better represent the altitude of the
+                spot site for which they have been extracted. This lapse rate
+                will be applied for a fixed orographic difference between the
+                site and grid point altitude. Differences in orography in
+                excess of this fixed limit will use the Environmental Lapse
+                Rate (also known as the Standard Atmosphere Lapse Rate).
             fixed_lapse_rate (float):
                 If provided, use this fixed value as a lapse-rate for adjusting
                 the forecast values if apply_lapse_rate_correction is True. This
@@ -197,7 +197,7 @@ def process(self, cubes: CubeList) -> Cube:
                     )(result, percentiles=extract_percentiles)
                     result = iris.util.squeeze(result)
                 elif result.coords("realization", dim_coords=True):
-                    fast_percentile_method = not np.ma.isMaskedArray(result.data)
+                    fast_percentile_method = not np.ma.is_masked(result.data)
                     result = PercentileConverter(
                         "realization",
                         percentiles=extract_percentiles,

diff --git a/improver_tests/spotdata/test_SpotManipulation.py b/improver_tests/spotdata/test_SpotManipulation.py
@@ -204,6 +204,51 @@ def add_grid_hash(target, source):
             {"extract_percentiles": [40, 60]},
             np.array([[277, 277], [279, 279]]),
         ),
+        # Extraction including realization collapse
+        (
+            gridded_variable,
+            np.arange(273, 291).reshape(2, 3, 3),
+            np.array([[[1, 2], [0, 0], [5, 10]]]),
+            {"realization_collapse": True},
+            np.array([278.5, 279.5]),
+        ),
+        # Percentile extraction derived from realizations
+        (
+            gridded_variable,
+            np.arange(273, 300).reshape(3, 3, 3),
+            np.array([[[1, 2], [0, 0], [5, 10]]]),
+            {"extract_percentiles": [50]},
+            np.array([283, 284]),
+        ),
+        # Percentile extraction derived from realizations; masked type, no mask
+        (
+            gridded_variable,
+            np.ma.arange(273, 300).reshape(3, 3, 3),
+            np.array([[[1, 2], [0, 0], [5, 10]]]),
+            {"extract_percentiles": [50]},
+            np.array([283, 284]),
+        ),
+        # Percentile extraction derived from realizations; masked type, masking
+        (
+            gridded_variable,
+            np.ma.masked_array(
+                [
+                    np.arange(273, 282).reshape(3, 3),
+                    np.arange(282, 291).reshape(3, 3),
+                    np.arange(291, 300).reshape(3, 3),
+                    np.arange(300, 309).reshape(3, 3),
+                ],
+                mask=[
+                    np.zeros((3, 3)),
+                    np.zeros((3, 3)),
+                    np.zeros((3, 3)),
+                    np.ones((3, 3)),
+                ],
+            ),
+            np.array([[[1, 2], [0, 0], [5, 10]]]),
+            {"extract_percentiles": [50]},
+            np.array([283, 284]),
+        ),
     ],
 )
 def test_extraction(ftype, forecast_data, neighbour_cube, kwargs, expected):