mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 01:46:21 +08:00
Stefan van der Walt
2df22d2fc5
Dask is not yet packaged on all platforms, so make it optional for now.
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
from __future__ import absolute_import
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from numpy.testing.decorators import skipif
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from skimage.filters import threshold_adaptive, gaussian
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from skimage.util.apply_parallel import apply_parallel, dask_available
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@skipif(not dask_available)
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def test_apply_parallel():
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# data
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a = np.arange(144).reshape(12, 12).astype(float)
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# apply the filter
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expected1 = threshold_adaptive(a, 3)
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result1 = apply_parallel(threshold_adaptive, a, chunks=(6, 6), depth=5,
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extra_arguments=(3,),
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extra_keywords={'mode': 'reflect'})
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assert_array_almost_equal(result1, expected1)
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def wrapped_gauss(arr):
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return gaussian(arr, 1, mode='reflect')
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expected2 = gaussian(a, 1, mode='reflect')
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result2 = apply_parallel(wrapped_gauss, a, chunks=(6, 6), depth=5)
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assert_array_almost_equal(result2, expected2)
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@skipif(not dask_available)
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def test_no_chunks():
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a = np.ones(1 * 4 * 8 * 9).reshape(1, 4, 8, 9)
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def add_42(arr):
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return arr + 42
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expected = add_42(a)
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result = apply_parallel(add_42, a)
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assert_array_almost_equal(result, expected)
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@skipif(not dask_available)
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def test_apply_parallel_wrap():
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def wrapped(arr):
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return gaussian(arr, 1, mode='wrap')
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a = np.arange(144).reshape(12, 12).astype(float)
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expected = gaussian(a, 1, mode='wrap')
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result = apply_parallel(wrapped, a, chunks=(6, 6), depth=5, mode='wrap')
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assert_array_almost_equal(result, expected)
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@skipif(not dask_available)
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def test_apply_parallel_nearest():
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def wrapped(arr):
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return gaussian(arr, 1, mode='nearest')
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a = np.arange(144).reshape(12, 12).astype(float)
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expected = gaussian(a, 1, mode='nearest')
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result = apply_parallel(wrapped, a, chunks=(6, 6), depth={0: 5, 1: 5},
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mode='nearest')
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assert_array_almost_equal(result, expected)
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