Adding feature tracking operator

.gitignore

@@ -138,3 +138,6 @@ dmypy.json
 
 # NFS synchronisation files
 .nfs*
+
+# MacOS temp files
+.DS_Store

pyproject.toml

@@ -35,6 +35,7 @@ dependencies = [
     "scores",
     "dask",
     "xarray",
+    "simple-track",
 ]
 
 [project.urls]

requirements/environment.yml

@@ -20,6 +20,7 @@ dependencies:
   - dask-core # Dask with minimal dependencies.
   - proj = 9.7.1 # Newer versions break plotting, see issue #2052.
   - matplotlib-base = 3.10.9 # Newer versions break plotting, see issue #2205.
+  - simple-track # For feature tracking and cell stats operators
 
   # Build dependencies
   - setuptools>=64

requirements/locks/py312-lock-linux-64.txt

@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: b250a7e669442d882266a2b36dbaf02ac22a344cf68c51b4909773b01bc8ed4e
+# input_hash: 18f693332aade4640da699cf921afdf4a4ddc6a3c923cf3a3b804f97de3cd919
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda#a9f577daf3de00bca7c3c76c0ecbd1de
 https://conda.anaconda.org/conda-forge/linux-64/aom-3.14.1-pl5321h039972f_1.conda#5a78a69eb3b50f24b379e9d2a93163ae
@@ -283,6 +283,7 @@ https://conda.anaconda.org/conda-forge/noarch/scores-2.5.0-pyhd8ed1ab_0.conda#b8
 https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda#8e194e7b992f99a5015edbd4ebd38efd
 https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-10.2.0-pyhcf101f3_0.conda#224418e442ea786882979fbd2b36061f
 https://conda.anaconda.org/conda-forge/noarch/setuptools_scm-10.2.0-he0c3440_0.conda#cc27f4be260e32227b41d00e87d16b32
+https://conda.anaconda.org/conda-forge/noarch/simple-track-2.2.1-pyhcf101f3_0.conda#9e6b5bb8d63c55901c51efcc534c7442
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3
 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.1.1-pyhd8ed1ab_0.conda#46b6abe31482f6bca064b965696ae807
 https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda#9e21f087f087f805debe877d88e00a14

requirements/locks/py313-lock-linux-64.txt

@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 9f964ad66b3ccd0df5e1897de428803405a71203d6fa546bb16f1fae4455533b
+# input_hash: a4926971a72ffdc7239622d764c9e59e4be6aa95391ad6dd37f42b5bacc22cae
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda#a9f577daf3de00bca7c3c76c0ecbd1de
 https://conda.anaconda.org/conda-forge/linux-64/aom-3.14.1-pl5321h039972f_1.conda#5a78a69eb3b50f24b379e9d2a93163ae
@@ -281,6 +281,7 @@ https://conda.anaconda.org/conda-forge/noarch/scores-2.5.0-pyhd8ed1ab_0.conda#b8
 https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda#8e194e7b992f99a5015edbd4ebd38efd
 https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-10.2.0-pyhcf101f3_0.conda#224418e442ea786882979fbd2b36061f
 https://conda.anaconda.org/conda-forge/noarch/setuptools_scm-10.2.0-he0c3440_0.conda#cc27f4be260e32227b41d00e87d16b32
+https://conda.anaconda.org/conda-forge/noarch/simple-track-2.2.1-pyhcf101f3_0.conda#9e6b5bb8d63c55901c51efcc534c7442
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3
 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.1.1-pyhd8ed1ab_0.conda#46b6abe31482f6bca064b965696ae807
 https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda#9e21f087f087f805debe877d88e00a14

requirements/locks/py314-lock-linux-64.txt

@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: ce6655aa6b01babac128d5359fbf8cc501c833139bc7a07ec3a3d72835aea244
+# input_hash: 78675034effda826d4616dd32d2b3cf2f7ed14e76b8b9f35c009a784a9e21f24
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda#a9f577daf3de00bca7c3c76c0ecbd1de
 https://conda.anaconda.org/conda-forge/linux-64/aom-3.14.1-pl5321h039972f_1.conda#5a78a69eb3b50f24b379e9d2a93163ae
@@ -282,6 +282,7 @@ https://conda.anaconda.org/conda-forge/noarch/scores-2.5.0-pyhd8ed1ab_0.conda#b8
 https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda#8e194e7b992f99a5015edbd4ebd38efd
 https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-10.2.0-pyhcf101f3_0.conda#224418e442ea786882979fbd2b36061f
 https://conda.anaconda.org/conda-forge/noarch/setuptools_scm-10.2.0-he0c3440_0.conda#cc27f4be260e32227b41d00e87d16b32
+https://conda.anaconda.org/conda-forge/noarch/simple-track-2.2.1-pyhcf101f3_0.conda#9e6b5bb8d63c55901c51efcc534c7442
 https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda#3339e3b65d58accf4ca4fb8748ab16b3
 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.1.1-pyhd8ed1ab_0.conda#46b6abe31482f6bca064b965696ae807
 https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda#9e21f087f087f805debe877d88e00a14

requirements/locks/sources

@@ -1 +1 @@
-97e468837e6c5cfff450fcad0310548b6d211f1148e4370f1e11130c3eb0de45  requirements/environment.yml
+ef70f8db8d41711660879766b126d46c6b03d4e416e6ebec3598b64622a526c4  requirements/environment.yml

src/CSET/operators/__init__.py

@@ -33,6 +33,7 @@
     constraints,
     convection,
     ensembles,
+    feature,
     filters,
     humidity,
     imageprocessing,
@@ -61,6 +62,7 @@
     "convection",
     "ensembles",
     "execute_recipe",
+    "feature",
     "filters",
     "humidity",
     "get_operator",

src/CSET/operators/_colorbar_definition.json

@@ -558,6 +558,26 @@
     "max": 1e-05,
     "min": -1e-05
   },
+  "precipitation_flux": {
+    "cmap": "cividis",
+    "levels": [
+      0,
+      0.125,
+      0.25,
+      0.5,
+      1,
+      2,
+      4,
+      8,
+      16,
+      32,
+      64,
+      128,
+      256
+    ],
+    "ymax": 1.0,
+    "ymin": 0.0
+  },
   "radar_reflectivity_at_1km_above_the_surface": {
     "cmap": "cubehelix_r",
     "max": 70.0,

src/CSET/operators/_colormaps.py

@@ -240,6 +240,7 @@ def colorbar_map_levels(cube: iris.cube.Cube, axis: Literal["x", "y"] | None = N
         cmap, levels, norm = custom_colormap_precipitation(cube, cmap, levels, norm)
         cmap, levels, norm = custom_colormap_visibility_in_air(cube, cmap, levels, norm)
         cmap, levels, norm = custom_colormap_celsius(cube, cmap, levels, norm)
+        cmap, levels, norm = custom_colormap_feature_tracking(cube, cmap, levels, norm)
         return cmap, levels, norm
 
 
@@ -597,3 +598,67 @@ def custom_colormap_scores(cube: iris.cube.Cube):
     if any("RMSE_" in name for name in varnames):
         cmap = plt.get_cmap("PuRd", 51)
     return cmap, levels, norm
+
+
+def custom_colormap_feature_tracking(cube: iris.cube.Cube, cmap, levels, norm):
+    """Return altered colormap for feature tracking.
+
+    Parameters
+    ----------
+    cube: Cube
+        Cube of variable for which the colorbar information is desired.
+
+    Returns
+    -------
+    cmap: Matplotlib colormap.
+    levels: List
+        List of levels to use for plotting. For continuous plots the min and max
+        should be taken as the range.
+    norm: BoundaryNorm.
+    """
+    varnames = list(filter(None, [cube.long_name, cube.standard_name, cube.var_name]))
+    if (
+        any("feature_id" in name for name in varnames)
+        and "difference" not in cube.long_name
+        and "mask" not in cube.long_name
+    ):
+        # Define the levels and colors
+        levels = np.linspace(1, np.ma.max(cube.data), 10)
+        cmap = plt.get_cmap("viridis")
+        # Normalize the levels
+        norm = mcolors.BoundaryNorm(levels, cmap.N)
+        logging.info("change colormap for feature id variable colorbar.")
+    elif (
+        any("feature_lifetime" in name for name in varnames)
+        and "difference" not in cube.long_name
+        and "mask" not in cube.long_name
+    ):
+        # Define the levels and colors
+        levels = np.linspace(1, np.ma.max(cube.data), 10)
+        cmap = plt.get_cmap("YlGnBu")
+        # Normalize the levels
+        norm = mcolors.BoundaryNorm(levels, cmap.N)
+        logging.info("change colormap for feature lifetime variable colorbar.")
+    elif (
+        any("feature_init" in name for name in varnames)
+        and "difference" not in cube.long_name
+        and "mask" not in cube.long_name
+    ):
+        # Define the levels and colors
+        levels = np.array([0.5, 1])
+        cmap = plt.get_cmap("Blues")
+        # Normalize the levels
+        norm = mcolors.BoundaryNorm(levels, cmap.N)
+        logging.info("change colormap for feature init variable colorbar.")
+
+    else:
+        # do nothing and keep existing colorbar attributes
+        cmap = cmap
+        levels = levels
+        norm = norm
+
+    # Set all non-feature data to white
+    if any("feature" in name for name in varnames):
+        cmap.with_extremes(under="white")
+
+    return cmap, levels, norm