Feature.track workflow loader

.gitignore

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

.pre-commit-config.yaml

@@ -11,7 +11,7 @@ repos:
         exclude: src/CSET/cset_workflow/app/metplus_point_stat/file/metoffice/PointStat_METplus_UKV.conf
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 3b3f7c3f57fe9925356faf5fe6230835138be230  # frozen: v0.15.17
+    rev: c59bba8fb259db0fec2bbb77ad8ba51ea7341b56  # frozen: v0.15.20
     hooks:
       - id: ruff-check
         args: [--fix, --show-fixes, --exit-non-zero-on-fix]

pyproject.toml

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

requirements/environment.yml

@@ -19,6 +19,7 @@ dependencies:
   - xarray # Required for scores verification now as used.
   - dask-core # Dask with minimal dependencies.
   - proj = 9.7.1 # Newer versions break plotting, see issue #2052.
+  - simple-track # For feature tracking and cell stats operators
 
   # Build dependencies
   - setuptools>=64

requirements/locks/sources

@@ -1 +1 @@
-3b279c2f279476b8a7ab53f290620f1f6b157fdd95a89eba4792ee0af6b5730e  requirements/environment.yml
+7cc3817dbb45d3ab5a22dba86386f620270a7f637d70978695c0fbc6f329c148  requirements/environment.yml

src/CSET/operators/__init__.py

@@ -33,6 +33,7 @@
     constraints,
     convection,
     ensembles,
+    feature,
     filters,
     humidity,
     imageprocessing,
@@ -60,6 +61,7 @@
     "convection",
     "ensembles",
     "execute_recipe",
+    "feature",
     "filters",
     "humidity",
     "get_operator",

src/CSET/operators/_colorbar_definition.json

@@ -178,6 +178,29 @@
     "max": 1.1,
     "min": 0.5
   },
+  "feature_id": {
+    "cmap": "viridis",
+    "max": 4000,
+    "min": 1,
+    "ymax": 1.0,
+    "ymin": 0.0
+  },
+  "feature_init": {
+    "cmap": "Blues",
+    "levels": [
+      0.5,
+      1
+    ],
+    "ymax": 1.0,
+    "ymin": 0.0
+  },
+  "feature_lifetime": {
+    "cmap": "YlGnBu",
+    "max": 150,
+    "min": 1,
+    "ymax": 1.0,
+    "ymin": 0.0
+  },
   "fog_fraction_at_screen_level": {
     "cmap": "viridis",
     "max": 1,
@@ -558,6 +581,26 @@
     "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/feature.py

@@ -0,0 +1,222 @@
+# © Crown copyright, Met Office (2022-2026) and CSET contributors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Operators for identifying and tracking features."""
+
+import logging
+import os
+
+import iris
+import iris.cube
+import iris.util
+import numpy as np
+from simpletrack.track import Tracker
+
+
+def track(
+    cube: iris.cube.Cube,
+    threshold: float,
+    under_threshold: bool = False,
+    min_size: int = 4,
+    retain_lifetime_on_split: bool = True,
+    tracking_nbhood: int = 5,
+    overlap_threshold: float = 0.3,
+    save_data: bool = False,
+):
+    """Track features between subsequent timesteps.
+
+    Parameters
+    ----------
+    cube: iris.cube.Cube
+        The cube to identify features in. The cube must be 3D and contain a time coordinate
+        and horizontal coordinates of xy type (not latitude/longitude).
+    threshold: float
+        The threshold value for feature detection.
+    under_threshold: bool, optional
+        If set to True, features are identified where the data is below the threshold.
+        If set to False, features are identified where the data is above the threshold.
+        Default is False.
+    min_size: int, optional
+        The minimum number of contiguous grid points required for a feature to be tracked.
+        Default is 4.
+    retain_lifetime_on_split: bool, optional
+        If set to True, the lifetime of a feature is retained when it splits into
+        multiple features. If set to False, the lifetime is reset when a feature splits.
+        Default is True.
+    tracking_nbhood: int, optional
+        The size of the neighbourhood used for tracking features between timesteps.
+        This dictates the maximum pixel radius from a feature centroid at which new features could
+        reasonably be spawned.
+        Default is 5.
+    overlap_threshold: float, optional
+        The minimum overlap required between features in consecutive timesteps for
+        them to be considered the same feature.
+        Default is 0.3.
+    save_data: bool, optional
+        If set to True, all tracking data is saved to disk for further analysis (including csv
+        and txt files containing feature properties that are not returned in output cubes).
+        Default is False.
+
+    Returns
+    -------
+    tracking_cubes: iris.cube.CubeList
+        A list of iris cubes containing tracking data, including feature ID, lifetime,
+        and locations of initiating features.
+
+    Notes
+    -----
+    This operator uses the Simple-Track package to track features between timesteps. Simple-Track is a
+    data-agnostic, threshold-based object tracking algorithm for 2D data. Features are tracked between
+    consecutive frames of data by projecting feature fields onto common timeframes and matching
+    between them based on the degree of overlap. Matched features retain the same identification
+    between all tracked fields, while new features are assigned a unique label.
+    Thus, Simple-Track compiles comprehensive information about feature merging, splitting, accretion,
+    initiation and dissipation.
+
+    Currently outputs three cubes containing the following data:
+        "feature_id":
+            A 2D field containing the unique label assigned to each feature, which is retained
+            if the feature is tracked across multiple timesteps. This cube can be used as a mask
+            to identify the location of the tracked feature throughout the evaluation period.
+        "feature_lifetime":
+            A 2D field containing the lifetime of each feature in terms of the number of
+            timesteps it has been tracked for. This cube can be used to distinguish between
+            mature and fresh features.
+        "feature_init":
+            A 2D binary field indicating the location of newly initiated features at each timestep.
+            These features are identified as having a lifetime of 1 AND have initiated sufficiently
+            far from other, existing features that they are not considered to have spawned from them.
+
+    Links
+    ----------
+    .. https://github.com/ParaChute-UK/simple-track
+
+    Examples
+    --------
+    >>> tracking_cubes = feature.track(threshold=2)
+    >>> lifetime_cube = tracking_cubes.extract_cube("feature_lifetime")
+    # Plot the final timestep of lifetime cube. This will show
+    # the lifetime of features that have been tracked for multiple previous
+    # timesteps, as well as new features that have just been initiated.
+    >>> iplt.pcolormesh(lifetime_cube[-1,:,:],cmap=mpl.cm.bwr)
+    >>> plt.gca().coastlines('10m')
+    >>> plt.clim(-5,5)
+    >>> plt.colorbar()
+    >>> plt.show()
+
+    """
+    # Check that the input cube has horizontal coordinates of xy type, not latitude/longitude
+    _check_xy_coords(cube)
+
+    # Setup config
+    tracker_config = {
+        "FEATURE": {
+            "threshold": threshold,
+            "under_threshold": under_threshold,
+            "min_size": min_size,
+        },
+        "TRACKING": {
+            "retain_lifetime_on_split": retain_lifetime_on_split,
+            "overlap_nbhood": tracking_nbhood,
+            "overlap_threshold": overlap_threshold,
+        },
+        "OUTPUT": {
+            "save_data": save_data,
+            "experiment_name": "feature_tracking",
+            "path": f"{os.getcwd()}/tracking_data",
+        },
+    }
+    logging.debug(f"Tracker config: {tracker_config}")
+
+    # Get cube data into a dict to pass to Tracker
+    times = cube.coord("time").points
+    time_units = cube.coord("time").units
+    times_dt = [time_units.num2pydate(t) for t in times]
+    cube_dict = {
+        time: cube_slice.data
+        for time, cube_slice in zip(times_dt, cube.slices_over("time"), strict=True)
+    }
+
+    # Run tracking, returning Timeline object
+    timeline = Tracker(tracker_config).run(cube_dict)
+    logging.debug("Tracking completed")
+
+    # Use input cube as template to make returned cube
+    # By iterating over all cube times, this will ensure all data is present
+    # If a Frame at the given time is not contained in the timeline, error is raised
+    output_type_and_methods = {
+        "lifetime": {
+            "getter": "lifetime_field",
+            "cube_name": "feature_lifetime",
+        },
+        "feature": {
+            "getter": "feature_field",
+            "cube_name": "feature_id",
+        },
+        "init": {
+            "getter": "get_init_field",
+            "cube_name": "feature_init",
+        },
+    }
+
+    tracking_cubelist = iris.cube.CubeList()
+    for output_type in output_type_and_methods:
+        tracking_data = []
+        for time in times_dt:
+            frame = timeline.get_frame(time)
+            getter = getattr(frame, output_type_and_methods[output_type]["getter"])
+            if callable(getter):
+                tracking_data.append(getter())
+            else:
+                tracking_data.append(getter)
+
+        # Convert to numpy arrays
+        tracking_data = np.stack(tracking_data, axis=0)
+
+        # Create cubes
+        tracking_cube = cube.copy(data=tracking_data)
+        tracking_cube.long_name = output_type_and_methods[output_type]["cube_name"]
+        tracking_cube.standard_name = None
+        tracking_cube.var_name = None
+        tracking_cube.units = "1"
+        tracking_cubelist.append(tracking_cube)
+
+    return tracking_cubelist
+
+
+def _check_xy_coords(cube: iris.cube.Cube) -> None:
+    """Check that the input cube has horizontal coordinates of xy type, not latitude/longitude.
+
+    Parameters
+    ----------
+    cube: iris.cube.Cube
+        An iris cube containing horizontal coordinates.
+
+    Raises
+    ------
+    ValueError
+        If the input cube has horizontal coordinates of latitude/longitude type.
+    """
+    hzntl_coords = [
+        coord
+        for coord in cube.coords()
+        if iris.util.guess_coord_axis(coord) in ["X", "Y"]
+    ]
+    invalid_coord_names = ["latitude", "longitude", "grid_latitude", "grid_longitude"]
+    for coord in hzntl_coords:
+        if coord.name() in invalid_coord_names:
+            raise ValueError(
+                f"Input cube {cube} has horizontal coordinate {coord}, "
+                "which is not of xy type. Please provide a cube with horizontal "
+                "coordinates of xy type."
+            )

src/CSET/operators/plot.py

@@ -474,6 +474,9 @@ def _plot_and_save_spatial_plot(
     # Specify the color bar
     cmap, levels, norm = colorbar_map_levels(cube)
 
+    if "feature" in cube.long_name:
+        cmap.set_under("white")
+
     # If overplotting, set required colorbars
     if overlay_cube:
         over_cmap, over_levels, over_norm = colorbar_map_levels(overlay_cube)

src/CSET/recipes/example_recipes/example_feature_track.yaml

@@ -0,0 +1,26 @@
+category: Quick Look
+title: Precipitation flux feature lifetime spatial plot
+description: |
+  Uses the feature.track operator to identify and track features in a cube with "time" series coordinate, 
+  and then plots the lifetime of the identified features as a spatial plot. 
+
+steps:
+  - operator: read.read_cubes
+    file_paths: $INPUT_PATHS
+
+  - operator: filters.filter_cubes
+    constraint:
+      operator: constraints.generate_var_constraint
+      varname: precipitation_flux
+
+  - operator: feature.track
+    threshold: 3
+    save_data: False # Whether to save raw tracking data for further analysis 
+
+  # Filter tracking cubelist to just one of "feature_lifetime", "feature_id" or "feature_init"
+  - operator: filters.filter_cubes
+    constraint:
+      operator: constraints.generate_var_constraint
+      varname: feature_lifetime
+
+  - operator: plot.spatial_pcolormesh_plot