Handle non-masked data in spatial plotting

src/CSET/operators/_utils.py

@@ -30,6 +30,7 @@
 import iris.cube
 import iris.exceptions
 import iris.util
+import numpy as np
 from iris.time import PartialDateTime
 
 from CSET._common import iter_maybe
@@ -583,3 +584,26 @@ def check_if_cylc_workflow() -> Path | None:
 
     # If ROSE_DATAC unset or its path does not exist, return None
     return None
+
+
+def calc_array_stats(
+    array: np.ndarray | np.ma.MaskedArray,
+) -> tuple[float, float, float]:
+    """Calculate the min, max, and mean of an array.
+
+    NaNs/Masked data is ignored.
+
+    Returns
+    -------
+    stats:
+        A tuple of (min, max, mean).
+    """
+    if np.ma.isMaskedArray(array):
+        array_min = array.min()
+        array_max = array.max()
+        array_mean = array.mean()
+    else:
+        array_min = np.nanmin(array)
+        array_max = np.nanmax(array)
+        array_mean = np.nanmean(array)
+    return array_min, array_max, array_mean

src/CSET/operators/plot.py

@@ -51,6 +51,7 @@
     get_model_colors_map,
 )
 from CSET.operators._utils import (
+    calc_array_stats,
     check_sequence_coordinate,
     check_single_cube,
     check_stamp_coordinate,
@@ -749,8 +750,9 @@ 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.
+    cube_min, cube_max, cube_mean = calc_array_stats(cube.data)
     axes.annotate(
-        f"Min: {np.nanmin(cube.data.filled(np.nan)):.3g} Max: {np.nanmax(cube.data.filled(np.nan)):.3g} Mean: {np.nanmean(cube.data.filled(np.nan)):.3g}",
+        f"Min: {cube_min:.3g} Max: {cube_max:.3g} Mean: {cube_mean:.3g}",
         xy=(0.025, yinfopad),
         xycoords="axes fraction",
         xytext=(-5, 5),
@@ -1478,8 +1480,9 @@ 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.
+    cube_min, cube_max, cube_mean = calc_array_stats(cube_vec_mag.data)
     axes.annotate(
-        f"Min: {np.nanmin(cube_vec_mag.data.filled(np.nan)):.3g} Max: {np.nanmax(cube_vec_mag.data.filled(np.nan)):.3g} Mean: {np.nanmean(cube_vec_mag.data.filled(np.nan)):.3g}",
+        f"Min: {cube_min:.3g} Max: {cube_max:.3g} Mean: {cube_mean:.3g}",
         xy=(0.05, -0.05),
         xycoords="axes fraction",
         xytext=(-5, 5),

tests/operators/test_utils.py

@@ -686,3 +686,30 @@ def test_check_if_cylc_workflow_no_dir(monkeypatch, tmp_path):
 def test_check_if_cylc_workflow_false(monkeypatch, tmp_path):
     """Test no ROSE_DATAC present."""
     assert operator_utils.check_if_cylc_workflow() is None
+
+
+def test_calc_array_stats():
+    """Array stats are calculated for a normal array."""
+    array = np.array((1.0, 2.0, 3.0))
+    array_min, array_max, array_mean = operator_utils.calc_array_stats(array)
+    assert array_min == 1.0
+    assert array_max == 3.0
+    assert array_mean == 2.0
+
+
+def test_calc_array_stats_nans():
+    """Array stats are calculated for an array containing NaNs."""
+    array = np.array((1.0, np.nan, 3.0))
+    array_min, array_max, array_mean = operator_utils.calc_array_stats(array)
+    assert array_min == 1.0
+    assert array_max == 3.0
+    assert array_mean == 2.0
+
+
+def test_calc_array_stats_masked_array():
+    """Array stats are calculated for a masked array."""
+    array = np.ma.MaskedArray((1.0, -9999, 3.0), mask=(False, True, False))
+    array_min, array_max, array_mean = operator_utils.calc_array_stats(array)
+    assert array_min == 1.0
+    assert array_max == 3.0
+    assert array_mean == 2.0