update

.CondaPkg/env/Lib/site-packages/skimage/measure/tests/test_block.py

@@ -0,0 +1,130 @@
+import numpy as np
+from skimage.measure import block_reduce
+
+from skimage._shared import testing
+from skimage._shared.testing import assert_equal
+
+
+def test_block_reduce_sum():
+    image1 = np.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3))
+    expected1 = np.array([[24, 42], [96, 114]])
+    assert_equal(expected1, out1)
+
+    image2 = np.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (3, 3))
+    expected2 = np.array([[81, 108, 87], [174, 192, 138]])
+    assert_equal(expected2, out2)
+
+
+def test_block_reduce_mean():
+    image1 = np.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=np.mean)
+    expected1 = np.array([[4.0, 7.0], [16.0, 19.0]])
+    assert_equal(expected1, out1)
+
+    image2 = np.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=np.mean)
+    expected2 = np.array([[14.0, 10.8], [8.5, 5.7]])
+    assert_equal(expected2, out2)
+
+
+def test_block_reduce_median():
+    image1 = np.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=np.median)
+    expected1 = np.array([[4.0, 7.0], [16.0, 19.0]])
+    assert_equal(expected1, out1)
+
+    image2 = np.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=np.median)
+    expected2 = np.array([[14.0, 6.5], [0.0, 0.0]])
+    assert_equal(expected2, out2)
+
+    image3 = np.array([[1, 5, 5, 5], [5, 5, 5, 1000]])
+    out3 = block_reduce(image3, (2, 4), func=np.median)
+    assert_equal(5, out3)
+
+
+def test_block_reduce_min():
+    image1 = np.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=np.min)
+    expected1 = np.array([[0, 3], [12, 15]])
+    assert_equal(expected1, out1)
+
+    image2 = np.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=np.min)
+    expected2 = np.array([[0, 0], [0, 0]])
+    assert_equal(expected2, out2)
+
+
+def test_block_reduce_max():
+    image1 = np.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=np.max)
+    expected1 = np.array([[8, 11], [20, 23]])
+    assert_equal(expected1, out1)
+
+    image2 = np.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=np.max)
+    expected2 = np.array([[28, 31], [36, 39]])
+    assert_equal(expected2, out2)
+
+
+def test_invalid_block_size():
+    image = np.arange(4 * 6).reshape(4, 6)
+
+    with testing.raises(ValueError):
+        block_reduce(image, [1, 2, 3])
+    with testing.raises(ValueError):
+        block_reduce(image, [1, 0.5])
+
+
+def test_default_block_size():
+    image = np.arange(4 * 6).reshape(4, 6)
+    out = block_reduce(image, func=np.min)
+    expected = np.array([[0, 2, 4], [12, 14, 16]])
+    assert_equal(expected, out)
+
+
+def test_scalar_block_size():
+    image = np.arange(6 * 6).reshape(6, 6)
+    out = block_reduce(image, 3, func=np.min)
+    expected1 = np.array([[0, 3], [18, 21]])
+    assert_equal(expected1, out)
+    expected2 = block_reduce(image, (3, 3), func=np.min)
+    assert_equal(expected2, out)
+
+
+def test_func_kwargs_same_dtype():
+    image = np.array(
+        [
+            [97, 123, 173, 227],
+            [217, 241, 221, 214],
+            [211, 11, 170, 53],
+            [214, 205, 101, 57],
+        ],
+        dtype=np.uint8,
+    )
+
+    out = block_reduce(image, (2, 2), func=np.mean, func_kwargs={'dtype': np.uint8})
+    expected = np.array([[41, 16], [32, 31]], dtype=np.uint8)
+
+    assert_equal(out, expected)
+    assert out.dtype == expected.dtype
+
+
+def test_func_kwargs_different_dtype():
+    image = np.array(
+        [
+            [0.45745366, 0.67479345, 0.20949775, 0.3147348],
+            [0.7209286, 0.88915504, 0.66153409, 0.07919526],
+            [0.04640037, 0.54008495, 0.34664343, 0.56152301],
+            [0.58085003, 0.80144708, 0.87844473, 0.29811511],
+        ],
+        dtype=np.float64,
+    )
+
+    out = block_reduce(image, (2, 2), func=np.mean, func_kwargs={'dtype': np.float16})
+    expected = np.array([[0.6855, 0.3164], [0.4922, 0.521]], dtype=np.float16)
+
+    assert_equal(out, expected)
+    assert out.dtype == expected.dtype