add missing ensure_spacing tests

* was previously only being tested indirectly via tests for peak_local_max

python/cucim/src/cucim/skimage/_shared/coord.py

@@ -106,6 +106,8 @@ def ensure_spacing(
     if len(coords):
         coords = cp.atleast_2d(coords)
         coords = cp.asnumpy(coords)
+        if not np.isscalar(spacing):
+            spacing = cp.asnumpy(spacing)
         if min_split_size is None:
             batch_list = [coords]
         else:

python/cucim/src/cucim/skimage/_shared/tests/test_coord.py

@@ -0,0 +1,98 @@
+import time
+
+import cupy as cp
+import numpy as np
+import pytest
+from scipy.spatial.distance import minkowski, pdist
+
+from cucim.skimage._shared.coord import ensure_spacing
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_trivial(p, size):
+    # --- Empty input
+    assert ensure_spacing(cp.asarray([]), p_norm=p).size == 0
+
+    # --- A unique point
+    coord = cp.random.randn(1, 2)
+    assert cp.array_equal(
+        coord, ensure_spacing(coord, p_norm=p, min_split_size=size)
+    )
+
+    # --- Verified spacing
+    coord = cp.random.randn(100, 2)
+
+    # --- 0 spacing
+    assert cp.array_equal(
+        coord, ensure_spacing(coord, spacing=0, p_norm=p, min_split_size=size)
+    )
+
+    # Spacing is chosen to be half the minimum distance
+    coord_cpu = cp.asnumpy(coord)
+    spacing = cp.asarray(pdist(coord_cpu, metric=minkowski, p=p).min() * 0.5)
+
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert cp.array_equal(coord, out)
+
+
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4, 5])
+@pytest.mark.parametrize("size", [2, 10, None])
+def test_ensure_spacing_nD(ndim, size):
+    coord = cp.ones((5, ndim))
+
+    expected = cp.ones((1, ndim))
+
+    assert cp.array_equal(ensure_spacing(coord, min_split_size=size), expected)
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [50, 100, None])
+def test_ensure_spacing_batch_processing(p, size):
+    coord_cpu = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = cp.asarray(np.median(pdist(coord_cpu, metric=minkowski, p=p)))
+    coord = cp.asarray(coord_cpu)
+
+    expected = ensure_spacing(coord, spacing=spacing, p_norm=p)
+
+    cp.testing.assert_array_equal(
+        ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size),
+        expected,
+    )
+
+
+def test_max_batch_size():
+    """Small batches are slow, large batches -> large allocations -> also slow.
+
+    https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
+    """
+    coords = cp.random.randint(low=0, high=1848, size=(40000, 2))
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=2000)
+    dur1 = time.time() - tstart
+
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=20000)
+    dur2 = time.time() - tstart
+
+    # Originally checked dur1 < dur2 to assert that the default batch size was
+    # faster than a much larger batch size. However, on rare occasion a CI test
+    # case would fail with dur1 ~5% larger than dur2. To be more robust to
+    # variable load or differences across architectures, we relax this here.
+    assert dur1 < 1.33 * dur2
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_p_norm(p, size):
+    coord_cpu = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = cp.asarray(np.median(pdist(coord_cpu, metric=minkowski, p=p)))
+    coord = cp.asarray(coord_cpu)
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert pdist(cp.asnumpy(out), metric=minkowski, p=p).min() > spacing