Support for unstructured regional ML gridded data

src/CSET/operators/plot.py

@@ -253,7 +253,7 @@ def _setup_spatial_map(
 
         # Add coastlines and borderlines if cube contains x and y map coordinates.
         # Avoid adding lines for 2D masked data or specific fixed ancillary spatial plots.
-        if (cube.ndim > 1 and iris.util.is_masked(cube.data)) or any(
+        if cube.ndim > 1 and any(
             name in cube.name() for name in ["land_", "orography", "altitude"]
         ):
             pass
@@ -747,7 +747,7 @@ def _plot_and_save_spatial_plot(
     # Add watermark with min/max/mean. Currently not user togglable.
     # In the bbox dictionary, fc and ec are hex colour codes for grey shade.
     axes.annotate(
-        f"Min: {np.min(cube.data):.3g} Max: {np.max(cube.data):.3g} Mean: {np.mean(cube.data):.3g}",
+        f"Min: {np.nanmin(np.asarray(cube.data)):.3g} Max: {np.nanmax(np.asarray(cube.data)):.3g} Mean: {np.nanmean(np.asarray(cube.data)):.3g}",
         xy=(0.025, yinfopad),
         xycoords="axes fraction",
         xytext=(-5, 5),
@@ -1093,6 +1093,8 @@ def _plot_and_save_line_power_spectrum_series(
         xname = xcoord.points
 
         yfield = cube.data  # power spectrum
+        if np.all(np.isnan(yfield)):
+            yfield = np.zeros_like(yfield)
         label = None
         color = "black"
         if model_colors_map:
@@ -1469,7 +1471,7 @@ def _plot_and_save_vector_plot(
     # Add watermark with min/max/mean. Currently not user togglable.
     # In the bbox dictionary, fc and ec are hex colour codes for grey shade.
     axes.annotate(
-        f"Min: {np.min(cube_vec_mag.data):.3g} Max: {np.max(cube_vec_mag.data):.3g} Mean: {np.mean(cube_vec_mag.data):.3g}",
+        f"Min: {np.nanmin(np.asarray(cube_vec_mag.data)):.3g} Max: {np.nanmax(np.asarray(cube_vec_mag.data)):.3g} Mean: {np.nanmean(np.asarray(cube_vec_mag.data)):.3g}",
         xy=(0.05, -0.05),
         xycoords="axes fraction",
         xytext=(-5, 5),
@@ -1605,7 +1607,10 @@ def _plot_and_save_histogram_series(
         ax.set_ylabel(
             f"Contribution to mean ({iter_maybe(cubes)[0].units})", fontsize=14
         )
-    ax.set_xlim(vmin, vmax)
+    try:
+        ax.set_xlim(vmin, vmax)
+    except ValueError:
+        pass
     ax.tick_params(axis="both", labelsize=12)
 
     # Overlay grid-lines onto histogram plot.

src/CSET/operators/read.py

@@ -40,6 +40,7 @@
     get_cube_yxcoordname,
     is_spatialdim,
 )
+from CSET.operators.regrid import restructure_ugrid
 
 logger = logging.getLogger(__name__)
 
@@ -234,9 +235,24 @@ def _load_model(
     """Load a single model's data into a CubeList."""
     input_files = _check_input_files(paths)
     # If unset, a constraint of None lets everything be loaded.
-    logger.debug("Constraint: %s", constraint)
-    cubes = iris.load(input_files, constraint, callback=_loading_callback)
-    # If required, compute wind_speed from components.
+
+    logging.debug("Constraint: %s", constraint)
+
+    cubes = iris.load(input_files)
+
+    # If a cube called latitude exists, chances are its unstructured/flattened. If
+    # so, then pass through restructure_ugrid to make rectilinear.
+    if len(cubes.extract("latitude")) > 0:
+        cubes = restructure_ugrid(cubes, constraint)
+
+    for cube in cubes:
+        _loading_callback(cube, None, None)
+
+    # Extract required cubes based on constraint
+    cubes = cubes.extract(constraint)
+
+    # cubes = iris.load(input_files, constraint, callback=_loading_callback)
+    # Make the UM's winds consistent with LFRic.
     cubes = _compute_winds(cubes)
 
     # Add model_name attribute to each cube to make it available at any further
@@ -454,6 +470,8 @@ def _um_normalise_callback(cube: iris.cube.Cube):
                 f"Unknown STASH code: {stash}. Please check file stash_to_lfric.py to update.",
                 level=logging.WARNING,
             )
+    if cube.standard_name == "lwe_thickness_of_precipitation_amount":
+        cube.long_name = "surface_microphysical_rainfall_rate"
 
 
 def _lfric_normalise_callback(cube: iris.cube.Cube):