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Merge pull request #632 from ahojnnes/local-blocks

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Refactor N-dimensial array resampling and add additional functionality
This commit is contained in:
Josh Warner
2013-07-31 14:10:18 -07:00
parent 33a3a12936 109f500d4b
commit a3bb1098d1
10 changed files with 222 additions and 175 deletions
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skimage/transform/tests/test_warps.py
+7 -7
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@@ -6,7 +6,7 @@ from skimage.transform import (warp, warp_coords, rotate, resize, rescale,
AffineTransform,
ProjectiveTransform,
SimilarityTransform,
downscale_local_means)
downscale_local_mean)
from skimage import transform as tf, data, img_as_float
from skimage.color import rgb2gray
@@ -195,15 +195,15 @@ def test_warp_coords_example():
map_coordinates(image[:, :, 0], coords[:2])
def test_downscale_local_means():
"""Verifying downsampling of an array with expected result in mean mode"""
image1 = np.arange(4*6).reshape(4, 6)
out1 = downscale_local_means(image1, (2, 3))
def test_downscale_local_mean():
image1 = np.arange(4 * 6).reshape(4, 6)
out1 = downscale_local_mean(image1, (2, 3))
expected1 = np.array([[ 4., 7.],
[ 16., 19.]])
assert_array_equal(expected1, out1)
image2 = np.arange(5*8).reshape(5, 8)
out2 = downscale_local_means(image2, (4, 5))
image2 = np.arange(5 * 8).reshape(5, 8)
out2 = downscale_local_mean(image2, (4, 5))
expected2 = np.array([[ 14. , 10.8],
[ 8.5, 5.7]])
assert_array_equal(expected2, out2)