Add Cell stats operator

pyproject.toml

@@ -34,6 +34,7 @@ dependencies = [
     "scikit-image",
     "scores",
     "dask",
+    "simple-track",
     "xarray",
     "simple-track",
 ]

src/CSET/operators/__init__.py

@@ -121,6 +121,7 @@ def get_operator(name: str):
         operator = CSET.operators
         for section in name_sections:
             operator = getattr(operator, section)
+
         if callable(operator):
             return operator
         else:

src/CSET/operators/_colorbar_definition.json

@@ -199,6 +199,50 @@
     "max": 1.1,
     "min": 0.5
   },
+      "feature_id": {
+        "cmap": "viridis",
+        "levels": [
+            1,
+            50,
+            100,
+            150,
+            200,
+            250,
+            300,
+            350,
+            400
+        ],
+        "ymax": 1.0,
+        "ymin": 0.0
+    },
+    "feature_lifetime": {
+        "cmap": "YlGnBu",
+        "levels": [
+            1,
+            2,
+            3,
+            4,
+            5,
+            6,
+            7,
+            8,
+            9,
+            10,
+            11,
+            12
+        ],
+        "ymax": 1.0,
+        "ymin": 0.0
+    },
+    "feature_init": {
+        "cmap": "Blues",
+        "levels": [
+            0.5,
+            1
+        ],
+        "ymax": 1.0,
+        "ymin": 0.0
+    },
   "fog_fraction_at_screen_level": {
     "cmap": "viridis",
     "max": 1,

src/CSET/operators/collapse.py

@@ -75,18 +75,24 @@ def collapse(
         raise ValueError("Must specify additional_percent")
 
     # Retain only common time points between different models if multiple model inputs.
+    # Do this only if "forecast_reference_time" and "forecast_period" are present in the cubes.
     if isinstance(cubes, iris.cube.CubeList) and len(cubes) > 1:
-        logging.debug(
-            "Extracting common time points as multiple model inputs detected."
-        )
-        for cube in cubes:
-            cube.coord("forecast_reference_time").bounds = None
-            cube.coord("forecast_period").bounds = None
-        cubes = cubes.extract_overlapping(
-            ["forecast_reference_time", "forecast_period"]
+        fcst_ref_time_check = all(
+            "forecast_reference_time" in cube.coords() for cube in cubes
         )
-        if len(cubes) == 0:
-            raise ValueError("No overlapping times detected in input cubes.")
+        fcst_period_check = all("forecast_period" in cube.coords() for cube in cubes)
+        if fcst_ref_time_check and fcst_period_check:
+            logging.debug(
+                "Extracting common time points as multiple model inputs detected."
+            )
+            for cube in cubes:
+                cube.coord("forecast_reference_time").bounds = None
+                cube.coord("forecast_period").bounds = None
+            cubes = cubes.extract_overlapping(
+                ["forecast_reference_time", "forecast_period"]
+            )
+            if len(cubes) == 0:
+                raise ValueError("No overlapping times detected in input cubes.")
 
     collapsed_cubes = iris.cube.CubeList([])
     with warnings.catch_warnings():