Edit to the interface to remove the ignore_intensity argument and use…

… the presence of the intensity_categories dictionary instead. Also a few minor edits.
gavinevans · Aug 14, 2024 · 06e9090 · 06e9090
1 parent 55aaaa0
commit 06e9090
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Showing 4 changed files with 43 additions and 49 deletions.
diff --git a/improver/cli/weather_symbol_modes.py b/improver/cli/weather_symbol_modes.py
@@ -44,7 +44,6 @@ def process(
     day_start: int = 6,
     day_end: int = 18,
     wet_bias: int = 1,
-    ignore_intensity: bool = False,
     model_id_attr: str = None,
     record_run_attr: str = None,
 ):
@@ -62,9 +61,14 @@ def process(
             A JSON file containing a definition for a broad category grouping.
             The expected categories are wet and dry.
         wet_categories (dict):
-            A JSON file containing a definition for a wet category grouping.
+            A JSON file containing a definition for a wet category grouping. No specific
+            names for the keys are required. Key and values within the dictionary
+            should both be ordered in terms of descending priority.
         intensity_categories (dict):
             A JSON file containing a definition for an intensity category grouping.
+            Values should be ordered in terms of descending priority. The most common
+            weather code from the options available representing different intensities
+            will be used as the representative weather code.
         day_weighting:
             Weighting to provide day time weather codes. A weighting of 1 indicates
             the default weighting. A weighting of 2 indicates that the weather codes
@@ -81,12 +85,6 @@ def process(
             only a quarter of codes are required to be wet, in order to generate
             a wet symbol. To generate a wet symbol, the fraction of wet symbols
             therefore need to be greater than or equal to 1 / (1 + wet_bias).
-        ignore_intensity:
-            Boolean indicating whether weather codes of different intensities
-            should be grouped together when establishing the most representative
-            weather code. The most common weather code from the options available
-            representing different intensities will be used as the representative
-            weather code.
         model_id_attr (str):
             Name of attribute recording source models that should be
             inherited by the output cube. The source models are expected as
@@ -112,7 +110,6 @@ def process(
         day_start,
         day_end,
         wet_bias,
-        ignore_intensity,
         model_id_attr=model_id_attr,
         record_run_attr=record_run_attr,
     )(cubes)
diff --git a/improver/wxcode/modal_code.py b/improver/wxcode/modal_code.py
@@ -361,12 +361,11 @@ def __init__(
         self,
         broad_categories: Dict[str, int],
         wet_categories: Dict[str, int],
-        intensity_categories: Dict[str, int],
+        intensity_categories: Optional[Dict[str, int]] = None,
         day_weighting: int = 1,
         day_start: int = 6,
         day_end: int = 18,
         wet_bias: int = 1,
-        ignore_intensity: bool = False,
         model_id_attr: Optional[str] = None,
         record_run_attr: Optional[str] = None,
     ):
@@ -383,7 +382,9 @@ def __init__(
                 should both be ordered in terms of descending priority.
             intensity_categories:
                 Dictionary defining intensity groupings. Values should be ordered in
-                terms of descending priority.
+                terms of descending priority. The most common weather code from the
+                options available representing different intensities will be used as the
+                representative weather code.
             day_weighting:
                 Weighting to provide day time weather codes. A weighting of 1 indicates
                 the default weighting. A weighting of 2 indicates that the weather codes
@@ -400,12 +401,6 @@ def __init__(
                 only a quarter of codes are required to be wet, in order to generate
                 a wet symbol. To generate a wet symbol, the fraction of wet symbols
                 therefore need to be greater than or equal to 1 / (1 + wet_bias).
-            ignore_intensity:
-                Boolean indicating whether weather codes of different intensities
-                should be grouped together when establishing the most representative
-                weather code. The most common weather code from the options available
-                representing different intensities will be used as the representative
-                weather code.
             model_id_attr:
                 Name of attribute recording source models that should be
                 inherited by the output cube. The source models are expected as
@@ -421,7 +416,6 @@ def __init__(
         self.day_start = day_start
         self.day_end = day_end
         self.wet_bias = wet_bias
-        self.ignore_intensity = ignore_intensity
         self.model_id_attr = model_id_attr
         self.record_run_attr = record_run_attr
 
@@ -433,10 +427,10 @@ def _consolidate_intensity_categories(self, cube: Cube) -> Cube:
             cube: Weather codes cube.
 
         Returns:
-            Weather codes cube with intensity categories consolidated, if the
-            ignore_intensity option is enabled.
+            Weather codes cube with intensity categories consolidated,
+            if intensity categories are provided.
         """
-        if self.ignore_intensity:
+        if self.intensity_categories:
             # Ignore intensities, so that weather codes representing different
             # intensities can be grouped.
             for values in self.intensity_categories.values():
@@ -781,7 +775,7 @@ def process(self, cubes: CubeList) -> Cube:
                 categorise_using_modal=False,
             )
 
-            if self.ignore_intensity:
+            if self.intensity_categories:
                 intensity_indices = self._find_intensity_indices(result)
                 result = self._get_most_likely_following_grouping(
                     original_cube,

diff --git a/improver_tests/acceptance/SHA256SUMS b/improver_tests/acceptance/SHA256SUMS
@@ -753,7 +753,7 @@ c44a00d49912f13fb17256fa0e01d9425977b5c94f2d41c602c657d39deb74cc  ./weather-symb
 a981f6238828b63d7a9f7c597dd234dcc86dd35e2e3c4ba6c3b6feb491ec872e  ./weather-symbol-modes/blend_mismatch_inputs/20201209T1700Z-weather_symbols-PT01H.nc
 de4a49bca4cd930328942cf6bb9a1bc1bdd2589120a57a2335ee4ef2449dbd5d  ./weather-symbol-modes/blend_mismatch_inputs/20201209T1800Z-weather_symbols-PT01H.nc
 9ee56eeaf451b360120a1e52b95435bc5fa1089f6447d92244afcb0ca84aa781  ./weather-symbol-modes/blend_mismatch_inputs/kgo.nc
-bfefb60f52d72e81ddcf6d775c83762cf0894355601a3519d286571525eed813  ./weather-symbol-modes/broad_categories.json
+bdaffce463e5d9e30ea6cfae58d4decfa266600cbb51480161d4170f46bb6ba3  ./weather-symbol-modes/broad_categories.json
 deb7f4effb821b2808b647e02ac955c91adae4baa33765b16378cff40e3ec5e8  ./weather-symbol-modes/gridded_input/20201209T0700Z-weather_symbols-PT01H.nc
 a61a70b0ce9e70577ba177462b9f1bfbda2457cc3975f0e9a562e1311e86e671  ./weather-symbol-modes/gridded_input/20201209T0800Z-weather_symbols-PT01H.nc
 64fc223da6c516a1eef11a61119547fb33e899ce86eb570a2917be27a119b517  ./weather-symbol-modes/gridded_input/20201209T0900Z-weather_symbols-PT01H.nc
@@ -766,7 +766,7 @@ bcd90ab1d28fd736d4a3d9e481374348438d7716b543f9c7d435b003ba10c344  ./weather-symb
 973c60900aa526818e7119ed016997170055017ee1bbda279b9e640750f96f61  ./weather-symbol-modes/gridded_input/20201209T1600Z-weather_symbols-PT01H.nc
 571bff58be29197e5f946745ed565889ec81499521c38d8f7286488079afb46d  ./weather-symbol-modes/gridded_input/20201209T1700Z-weather_symbols-PT01H.nc
 2af4455b0ba7c4124e49eb1ff004e770b6239a9e2e1513f60ba4db3f0beb02cf  ./weather-symbol-modes/gridded_input/20201209T1800Z-weather_symbols-PT01H.nc
-16d5a73082db5212d4f0039dd216edc9b3ecd948c00e828212a998a5d7b21e4c  ./weather-symbol-modes/gridded_input/kgo.nc
+c00571c03d331d661066607cbf51cb53e9a3e9c573c9c782361e41e9a643275f  ./weather-symbol-modes/gridded_input/kgo.nc
 96a8462af571f06dbd8b91a7a90aaef403eefd2b73929a5c6d8a3fbb01159aca  ./weather-symbol-modes/gridded_ties/20201209T0700Z-weather_symbols-PT01H.nc
 9f64c7a8aa7cf0e87799f96ebffe1e449e1f5174fb583d44f2479e085672dc84  ./weather-symbol-modes/gridded_ties/20201209T0800Z-weather_symbols-PT01H.nc
 c698b9599219fe89374a2565e55a374d9236904c9dd99a2ae61b5416506e98d3  ./weather-symbol-modes/gridded_ties/20201209T0900Z-weather_symbols-PT01H.nc
@@ -780,7 +780,7 @@ c39ea98f6fe64788c4ea7ea242111a5c8bbeacfaf52b2ead0cf0aed0007d46ab  ./weather-symb
 6a5b04644ab11d077809f615bac2829656127a0eeee3843940f8d33673bd70c8  ./weather-symbol-modes/gridded_ties/20201209T1700Z-weather_symbols-PT01H.nc
 39d0fa291798366a00ecae79a65de0b0692d5b4db17ac98a97d48e54b75e5dd4  ./weather-symbol-modes/gridded_ties/20201209T1800Z-weather_symbols-PT01H.nc
 fc922ede9e118dea3e7e3ba354664151f09407b32450688ed5c9870de5307c14  ./weather-symbol-modes/gridded_ties/kgo.nc
-e50db92fb58503b6a7f7fa88cd96124bf005cf8df3924fefb007b41274dd2d74  ./weather-symbol-modes/intensity_categories.json
+a534bf31735a0b4a00755447b35d9531da01eb5feb1a09d1e2cc68cdd93e5d09  ./weather-symbol-modes/intensity_categories.json
 89ba47a99c53d23b5490254366211a7cc0a5c8633c9faee97c091ee48a366b87  ./weather-symbol-modes/single_input/20201210T0000Z-weather_symbols-PT01H.nc
 d64efaa75b03aa4ba1fb16caa31891492e9fc5f967a584de42a4a59dc2f54237  ./weather-symbol-modes/single_input/kgo.nc
 f4e13dec400ec945ba5bd03a286780b183665c22d707c038c0990ef3491e888e  ./weather-symbol-modes/spot_input/20201209T0700Z-weather_symbols-PT01H.nc
@@ -809,7 +809,7 @@ ea67ae7a7f5363ae3692b29c93fb9989449d96e49dc613a1297d98ddb12c578a  ./weather-symb
 51f636314a6d1fa894ab98cf750493503b191a779c67d6a15081aab2a3612a31  ./weather-symbol-modes/spot_ties/20201209T1700Z-weather_symbols-PT01H.nc
 134fb1750cf47e868ee67801b1ea5b1f17120e6f58a787a69e54638d7d88ff82  ./weather-symbol-modes/spot_ties/20201209T1800Z-weather_symbols-PT01H.nc
 391a9bd50824318fb8f147db3b14bcd56f5b0f0e0d5479b3579e08d70ef6ba77  ./weather-symbol-modes/spot_ties/kgo.nc
-681fa8df3ee66828499311b94c47dba5bfff43c9e4c6ea2d2f81ed49f86edad6  ./weather-symbol-modes/wet_categories.json
+2d66678569a1af9b3357cd1c9d6b75dfedd912a99c3ed77135b2a2169b12687e  ./weather-symbol-modes/wet_categories.json
 2ec32b8654824a633fcc9d7405d4d3a8b487514bddb9e8ec6aae66c9a96c2565  ./weather-symbol-modes/wx_decision_tree.json
 bd51a8596838e355c78509b8b3b1215a3f1e3b798d5b300d4aff8032a26507b7  ./weighted_blending/accum_cycle_blend/kgo.nc
 c465dc56d83e9e866c1140033c65f07a02db154792b505d4a704d2773c3744e4  ./weighted_blending/accum_cycle_blend/ukv_prob_accum_PT3H.nc

diff --git a/improver_tests/wxcode/wxcode/test_ModalFromGroupings.py b/improver_tests/wxcode/wxcode/test_ModalFromGroupings.py
@@ -46,14 +46,14 @@
 RECORD_RUN_ATTR = "mosg__model_run"
 TARGET_TIME = dt(2020, 6, 15, 18)
 BROAD_CATEGORIES = {
-    "wet": np.arange(9, 31),
-    "dry": np.arange(0, 9),
+    "wet": [10, 11, 12, 14, 15, 17, 18, 20, 21, 23, 24, 26, 27, 29, 30],
+    "dry": [1, 3, 4, 5, 6, 7, 8],
 }
 # Priority ordered categories (keys) in case of ties
 WET_CATEGORIES = {
-    "extreme_convection": [30, 29, 28, 21, 20, 19],
-    "frozen": [27, 26, 25, 24, 23, 22, 18, 17, 16],
-    "liquid": [15, 14, 13, 12, 11, 10, 9],
+    "extreme_convection": [30, 29, 21, 20],
+    "frozen": [27, 26, 24, 23, 18, 17],
+    "liquid": [15, 14, 12, 11, 10],
 }
 INTENSITY_CATEGORIES = {
     "rain_shower": [14, 10],
@@ -140,9 +140,7 @@
 def test_expected_values(wxcode_series, expected):
     """Test that the expected period representative symbol is returned."""
     _, _, _, _, wxcode_cubes = wxcode_series
-    result = ModalFromGroupings(BROAD_CATEGORIES, WET_CATEGORIES, INTENSITY_CATEGORIES)(
-        wxcode_cubes
-    )
+    result = ModalFromGroupings(BROAD_CATEGORIES, WET_CATEGORIES)(wxcode_cubes)
     expected = [expected] if not isinstance(expected, list) else expected
     for index in range(len(expected)):
         assert result.data.flatten()[index] == expected[index]
@@ -212,9 +210,9 @@ def test_expected_values_wet_bias(
     if reverse_wet_keys:
         wet_categories = dict(reversed(list(wet_categories.items())))
 
-    result = ModalFromGroupings(
-        BROAD_CATEGORIES, wet_categories, INTENSITY_CATEGORIES, wet_bias=wet_bias,
-    )(wxcode_cubes)
+    result = ModalFromGroupings(BROAD_CATEGORIES, wet_categories, wet_bias=wet_bias,)(
+        wxcode_cubes
+    )
     expected = [expected] if not isinstance(expected, list) else expected
     for index in range(len(expected)):
         assert result.data.flatten()[index] == expected[index]
@@ -278,7 +276,6 @@ def test_expected_values_day_weighting(
     result = ModalFromGroupings(
         BROAD_CATEGORIES,
         WET_CATEGORIES,
-        INTENSITY_CATEGORIES,
         day_weighting=day_weighting,
         day_start=day_start,
         day_end=day_end,
@@ -332,11 +329,10 @@ def test_expected_values_ignore_intensity(
         intensity_categories = {}
         for key in INTENSITY_CATEGORIES.keys():
             intensity_categories[key] = [i for i in reversed(INTENSITY_CATEGORIES[key])]
+    if not ignore_intensity:
+        intensity_categories = None
     result = ModalFromGroupings(
-        BROAD_CATEGORIES,
-        WET_CATEGORIES,
-        intensity_categories,
-        ignore_intensity=ignore_intensity,
+        BROAD_CATEGORIES, WET_CATEGORIES, intensity_categories=intensity_categories,
     )(wxcode_cubes)
     expected = [expected] if not isinstance(expected, list) else expected
     for index in range(len(expected)):
@@ -398,15 +394,19 @@ def test_expected_values_interactions(
     _, _, _, _, wxcode_cubes = wxcode_series
     class_instance = ModalFromGroupings
     class_instance.DAY_LENGTH = day_length
+    if ignore_intensity:
+        intensity_categories = INTENSITY_CATEGORIES.copy()
+    else:
+        intensity_categories = None
+
     result = class_instance(
         BROAD_CATEGORIES,
         WET_CATEGORIES,
-        INTENSITY_CATEGORIES,
+        intensity_categories=intensity_categories,
         wet_bias=wet_bias,
         day_weighting=day_weighting,
         day_start=day_start,
         day_end=day_end,
-        ignore_intensity=ignore_intensity,
     )(wxcode_cubes)
     expected = [expected] if not isinstance(expected, list) else expected
     for index in range(len(expected)):
@@ -448,7 +448,10 @@ def as_utc_timestamp(time):
         kwargs.update({"record_run_attr": RECORD_RUN_ATTR})
 
     result = ModalFromGroupings(
-        BROAD_CATEGORIES, WET_CATEGORIES, INTENSITY_CATEGORIES, **kwargs,
+        BROAD_CATEGORIES,
+        WET_CATEGORIES,
+        intensity_categories=INTENSITY_CATEGORIES,
+        **kwargs,
     )(wxcode_cubes)
 
     n_times = len(wxcode_cubes)
@@ -513,6 +516,6 @@ def test_unmatching_bounds_exception(wxcode_series):
     with pytest.raises(
         ValueError, match="Input diagnostics do not have consistent periods."
     ):
-        ModalFromGroupings(BROAD_CATEGORIES, WET_CATEGORIES, INTENSITY_CATEGORIES,)(
-            wxcode_cubes
-        )
+        ModalFromGroupings(
+            BROAD_CATEGORIES, WET_CATEGORIES, intensity_categories=INTENSITY_CATEGORIES,
+        )(wxcode_cubes)