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np.random.seed is not thread safe for test_parallel

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This commit is contained in:
François Boulogne
2016-08-01 22:03:57 +02:00
parent d4dbc598c8
commit 103a17384c
1 changed files with 74 additions and 78 deletions
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skimage/filters/rank/tests/test_rank.py
+74 -78
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@@ -8,90 +8,86 @@ from skimage import data, util, morphology
from skimage.morphology import grey, disk
from skimage.filters import rank
from skimage._shared._warnings import expected_warnings
from skimage._shared.testing import test_parallel
def test_all():
with expected_warnings(['precision loss', 'non-integer|\A\Z']):
check_all()
@test_parallel()
def check_all():
np.random.seed(0)
image = np.random.rand(25, 25)
selem = morphology.disk(1)
refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"],
rank.autolevel(image, selem))
assert_equal(refs["autolevel_percentile"],
rank.autolevel_percentile(image, selem))
assert_equal(refs["bottomhat"],
rank.bottomhat(image, selem))
assert_equal(refs["equalize"],
rank.equalize(image, selem))
assert_equal(refs["gradient"],
rank.gradient(image, selem))
assert_equal(refs["gradient_percentile"],
rank.gradient_percentile(image, selem))
assert_equal(refs["maximum"],
rank.maximum(image, selem))
assert_equal(refs["mean"],
rank.mean(image, selem))
assert_equal(refs["geometric_mean"],
rank.geometric_mean(image, selem)),
assert_equal(refs["mean_percentile"],
rank.mean_percentile(image, selem))
assert_equal(refs["mean_bilateral"],
rank.mean_bilateral(image, selem))
assert_equal(refs["subtract_mean"],
rank.subtract_mean(image, selem))
assert_equal(refs["subtract_mean_percentile"],
rank.subtract_mean_percentile(image, selem))
assert_equal(refs["median"],
rank.median(image, selem))
assert_equal(refs["minimum"],
rank.minimum(image, selem))
assert_equal(refs["modal"],
rank.modal(image, selem))
assert_equal(refs["enhance_contrast"],
rank.enhance_contrast(image, selem))
assert_equal(refs["enhance_contrast_percentile"],
rank.enhance_contrast_percentile(image, selem))
assert_equal(refs["pop"],
rank.pop(image, selem))
assert_equal(refs["pop_percentile"],
rank.pop_percentile(image, selem))
assert_equal(refs["pop_bilateral"],
rank.pop_bilateral(image, selem))
assert_equal(refs["sum"],
rank.sum(image, selem))
assert_equal(refs["sum_bilateral"],
rank.sum_bilateral(image, selem))
assert_equal(refs["sum_percentile"],
rank.sum_percentile(image, selem))
assert_equal(refs["threshold"],
rank.threshold(image, selem))
assert_equal(refs["threshold_percentile"],
rank.threshold_percentile(image, selem))
assert_equal(refs["tophat"],
rank.tophat(image, selem))
assert_equal(refs["noise_filter"],
rank.noise_filter(image, selem))
assert_equal(refs["entropy"],
rank.entropy(image, selem))
assert_equal(refs["otsu"],
rank.otsu(image, selem))
assert_equal(refs["percentile"],
rank.percentile(image, selem))
assert_equal(refs["windowed_histogram"],
rank.windowed_histogram(image, selem))
class TestRank():
def setup(self):
np.random.seed(0)
def test_all(self):
def check_all():
image = np.random.rand(25, 25)
selem = morphology.disk(1)
refs = np.load(os.path.join(skimage.data_dir, "rank_filter_tests.npz"))
assert_equal(refs["autolevel"],
rank.autolevel(image, selem))
assert_equal(refs["autolevel_percentile"],
rank.autolevel_percentile(image, selem))
assert_equal(refs["bottomhat"],
rank.bottomhat(image, selem))
assert_equal(refs["equalize"],
rank.equalize(image, selem))
assert_equal(refs["gradient"],
rank.gradient(image, selem))
assert_equal(refs["gradient_percentile"],
rank.gradient_percentile(image, selem))
assert_equal(refs["maximum"],
rank.maximum(image, selem))
assert_equal(refs["mean"],
rank.mean(image, selem))
assert_equal(refs["geometric_mean"],
rank.geometric_mean(image, selem)),
assert_equal(refs["mean_percentile"],
rank.mean_percentile(image, selem))
assert_equal(refs["mean_bilateral"],
rank.mean_bilateral(image, selem))
assert_equal(refs["subtract_mean"],
rank.subtract_mean(image, selem))
assert_equal(refs["subtract_mean_percentile"],
rank.subtract_mean_percentile(image, selem))
assert_equal(refs["median"],
rank.median(image, selem))
assert_equal(refs["minimum"],
rank.minimum(image, selem))
assert_equal(refs["modal"],
rank.modal(image, selem))
assert_equal(refs["enhance_contrast"],
rank.enhance_contrast(image, selem))
assert_equal(refs["enhance_contrast_percentile"],
rank.enhance_contrast_percentile(image, selem))
assert_equal(refs["pop"],
rank.pop(image, selem))
assert_equal(refs["pop_percentile"],
rank.pop_percentile(image, selem))
assert_equal(refs["pop_bilateral"],
rank.pop_bilateral(image, selem))
assert_equal(refs["sum"],
rank.sum(image, selem))
assert_equal(refs["sum_bilateral"],
rank.sum_bilateral(image, selem))
assert_equal(refs["sum_percentile"],
rank.sum_percentile(image, selem))
assert_equal(refs["threshold"],
rank.threshold(image, selem))
assert_equal(refs["threshold_percentile"],
rank.threshold_percentile(image, selem))
assert_equal(refs["tophat"],
rank.tophat(image, selem))
assert_equal(refs["noise_filter"],
rank.noise_filter(image, selem))
assert_equal(refs["entropy"],
rank.entropy(image, selem))
assert_equal(refs["otsu"],
rank.otsu(image, selem))
assert_equal(refs["percentile"],
rank.percentile(image, selem))
assert_equal(refs["windowed_histogram"],
rank.windowed_histogram(image, selem))
with expected_warnings(['precision loss', 'non-integer|\A\Z']):
check_all()
def test_random_sizes(self):
# make sure the size is not a problem