Adjusted test

tests/unit/bm3dornl/test_utils.py

@@ -44,54 +44,29 @@ def test_is_within_threshold():
     result = is_within_threshold(ref_patch, cmp_patch_close, threshold)
     assert not result, "Failed: Slightly different patches should not be within very small distance of 0.1"
 
-
-@pytest.mark.skip(reason="Changed function signature")
 def test_horizontal_binning():
+    size_x , size_y = 64, 64
+    k = 6
     # Initial setup: Create a test image
-    Z = np.random.rand(64, 64)
-
-    # Number of binning iterations
-    k = 3
-
-    # Perform the binning
-    binned_images = horizontal_binning(Z, k)
-
-    # Assert we have the correct number of images
-    assert len(binned_images) == k + 1, "Incorrect number of binned images returned"
+    Z = np.random.rand(size_y, size_y)
 
     # Assert that each image has the correct dimensions
-    expected_width = 64
-    for i, img in enumerate(binned_images):
-        assert img.shape[0] == 64, f"Height of image {i} is incorrect"
-        assert img.shape[1] == expected_width, f"Width of image {i} is incorrect"
+    expected_width = size_x
+    for i in range(k):
+        # Perform the binning
         expected_width = (expected_width + 1) // 2  # Calculate the next expected width
-
-
-@pytest.mark.skip(reason="Changed function signature")
-def test_horizontal_binning_k_zero():
-    Z = np.random.rand(64, 64)
-    binned_images = horizontal_binning(Z, 0)
-    assert len(binned_images) == 1 and np.array_equal(
-        binned_images[0], Z
-    ), "Binning with k=0 should return only the original image"
-
-
-@pytest.mark.skip(reason="Changed function signature")
-def test_horizontal_binning_large_k():
-    Z = np.random.rand(64, 64)
-    binned_images = horizontal_binning(Z, 6)
-    assert len(binned_images) == 7, "Incorrect number of images for large k"
-    assert binned_images[-1].shape[1] == 1, "Final image width should be 1 for large k"
-
+        binned_image = horizontal_binning(Z, fac=2)
+        assert binned_image.shape[0] == 64, f"Height of image {i} is incorrect"
+        assert binned_image.shape[1] == expected_width, f"Width of image {i} is incorrect"
+        Z = binned_image
 
 @pytest.mark.parametrize(
     "original_width, target_width", [(32, 64), (64, 128), (128, 256)]
 )
-@pytest.mark.skip(reason="Changed function signature")
 def test_horizontal_debinning_scaling(original_width, target_width):
     original_image = np.random.rand(64, original_width)
     target_shape = (64, target_width)
-    debinned_image = horizontal_debinning(original_image, np.empty(target_shape))
+    debinned_image = horizontal_debinning(original_image, target_width, fac=2, dim=1, n_iter=1)
     assert (
         debinned_image.shape == target_shape
     ), f"Failed to scale from {original_width} to {target_width}"