Fix multi-metric output mislabeling in glcm_texture (#1106)

xrspatial/glcm.py

@@ -105,6 +105,11 @@ def glcm_texture(
                 f"must be one of {VALID_METRICS}"
             )
 
+    # Sort metrics to match the kernel's output order (VALID_METRICS order).
+    # Without this, coordinate labels would be wrong when the user requests
+    # metrics in a different order (e.g. ['entropy', 'contrast']).
+    metrics = _sorted_metrics(metrics)
+
     mapper = ArrayTypeFunctionMapping(
         numpy_func=_glcm_numpy,
         cupy_func=_glcm_cupy,
@@ -278,6 +283,16 @@ def _metric_flags(metrics):
     return flags
 
 
+def _sorted_metrics(metrics):
+    """Return *metrics* sorted in VALID_METRICS order.
+
+    The numba kernel always writes output slots in VALID_METRICS order,
+    so coordinate labels must follow the same ordering.
+    """
+    order = {m: i for i, m in enumerate(VALID_METRICS)}
+    return sorted(metrics, key=lambda m: order[m])
+
+
 def _run_glcm_on_quantized(quantized, metrics, window_size, levels,
                             distance, angle):
     """Run GLCM computation on pre-quantized int32 data.

xrspatial/tests/test_glcm_metric_order.py

@@ -0,0 +1,162 @@
+"""Tests for issue #1106: multi-metric output ordering bug.
+
+When glcm_texture() receives metrics in non-standard order, the coordinate
+labels must still match the actual data.  The kernel writes output slots in
+VALID_METRICS order, so the labels need to follow.
+"""
+try:
+    import dask.array as da
+except ImportError:
+    da = None
+
+import numpy as np
+import pytest
+import xarray as xr
+
+from xrspatial.glcm import glcm_texture, VALID_METRICS, _sorted_metrics
+from xrspatial.tests.general_checks import (
+    create_test_raster,
+    dask_array_available,
+)
+
+
+# ---- _sorted_metrics helper ----
+
+def test_sorted_metrics_standard_order():
+    assert _sorted_metrics(['contrast', 'entropy']) == ['contrast', 'entropy']
+
+
+def test_sorted_metrics_reversed():
+    assert _sorted_metrics(['entropy', 'contrast']) == ['contrast', 'entropy']
+
+
+def test_sorted_metrics_all():
+    shuffled = list(reversed(VALID_METRICS))
+    assert _sorted_metrics(shuffled) == list(VALID_METRICS)
+
+
+def test_sorted_metrics_single():
+    assert _sorted_metrics(['homogeneity']) == ['homogeneity']
+
+
+# ---- Core regression test: label matches data ----
+
+@pytest.fixture
+def random_8x8():
+    rng = np.random.default_rng(99)
+    return rng.random((8, 8))
+
+
+def test_reversed_order_matches_solo(random_8x8):
+    """Requesting metrics in reversed order should still label them correctly."""
+    agg = create_test_raster(random_8x8)
+
+    contrast_solo = glcm_texture(agg, metric='contrast', window_size=3,
+                                 levels=16, angle=0)
+    homogeneity_solo = glcm_texture(agg, metric='homogeneity', window_size=3,
+                                    levels=16, angle=0)
+
+    # Reversed order: homogeneity before contrast
+    multi = glcm_texture(agg, metric=['homogeneity', 'contrast'],
+                         window_size=3, levels=16, angle=0)
+
+    np.testing.assert_allclose(
+        multi.sel(metric='contrast').values,
+        contrast_solo.values,
+        rtol=1e-10,
+    )
+    np.testing.assert_allclose(
+        multi.sel(metric='homogeneity').values,
+        homogeneity_solo.values,
+        rtol=1e-10,
+    )
+
+
+def test_arbitrary_order_matches_solo(random_8x8):
+    """Any permutation of metrics should label outputs correctly."""
+    agg = create_test_raster(random_8x8)
+    metrics_order = ['entropy', 'contrast', 'homogeneity']
+
+    multi = glcm_texture(agg, metric=metrics_order, window_size=3,
+                         levels=16, angle=0)
+
+    for m in metrics_order:
+        solo = glcm_texture(agg, metric=m, window_size=3, levels=16, angle=0)
+        np.testing.assert_allclose(
+            multi.sel(metric=m).values,
+            solo.values,
+            rtol=1e-10, equal_nan=True,
+        )
+
+
+def test_all_metrics_reversed(random_8x8):
+    """All six metrics reversed should still produce correct labels."""
+    agg = create_test_raster(random_8x8)
+    reversed_metrics = list(reversed(VALID_METRICS))
+
+    multi = glcm_texture(agg, metric=reversed_metrics, window_size=3,
+                         levels=16, angle=0)
+
+    for m in VALID_METRICS:
+        solo = glcm_texture(agg, metric=m, window_size=3, levels=16, angle=0)
+        np.testing.assert_allclose(
+            multi.sel(metric=m).values,
+            solo.values,
+            rtol=1e-10, equal_nan=True,
+        )
+
+
+def test_standard_order_still_works(random_8x8):
+    """Standard order (which worked before the fix) should still be fine."""
+    agg = create_test_raster(random_8x8)
+    metrics = ['contrast', 'dissimilarity']
+
+    multi = glcm_texture(agg, metric=metrics, window_size=3,
+                         levels=16, angle=0)
+
+    for m in metrics:
+        solo = glcm_texture(agg, metric=m, window_size=3, levels=16, angle=0)
+        np.testing.assert_allclose(
+            multi.sel(metric=m).values,
+            solo.values,
+            rtol=1e-10, equal_nan=True,
+        )
+
+
+def test_angle_none_with_reversed_order(random_8x8):
+    """angle=None averaging should also respect metric ordering."""
+    agg = create_test_raster(random_8x8)
+    metrics = ['energy', 'contrast']
+
+    multi = glcm_texture(agg, metric=metrics, window_size=3, levels=16)
+
+    for m in metrics:
+        solo = glcm_texture(agg, metric=m, window_size=3, levels=16)
+        np.testing.assert_allclose(
+            multi.sel(metric=m).values,
+            solo.values,
+            rtol=1e-10, equal_nan=True,
+        )
+
+
+# ---- Dask backend ----
+
+@dask_array_available
+def test_dask_reversed_order_matches_numpy(random_8x8):
+    """Dask backend with reversed metric order should match numpy results."""
+    numpy_agg = create_test_raster(random_8x8)
+    dask_agg = create_test_raster(random_8x8, backend='dask+numpy',
+                                  chunks=(4, 4))
+    metrics = ['entropy', 'contrast']
+
+    np_result = glcm_texture(numpy_agg, metric=metrics, window_size=3,
+                             levels=16, angle=0)
+    da_result = glcm_texture(dask_agg, metric=metrics, window_size=3,
+                             levels=16, angle=0)
+
+    for m in metrics:
+        np.testing.assert_allclose(
+            np_result.sel(metric=m).values,
+            da_result.sel(metric=m).values,
+            rtol=1e-10, equal_nan=True,
+        )