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ENH: Improve random_noise with better param names & docfixes

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Josh Warner (Mac)
2013-07-03 23:54:26 -05:00
parent 647aacb316
commit 32f39eecbb
2 changed files with 47 additions and 31 deletions
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skimage/util/tests/test_random_noise.py
+14 -8
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@@ -1,4 +1,4 @@
from numpy.testing import assert_array_equal, assert_allclose
from numpy.testing import assert_array_equal, assert_allclose, assert_raises
import numpy as np
from skimage.data import camera
@@ -15,7 +15,7 @@ def test_set_seed():
def test_salt():
seed = 42
cam = img_as_float(camera())
cam_noisy = random_noise(cam, seed=seed, mode='salt', d=0.15)
cam_noisy = random_noise(cam, seed=seed, mode='salt', prop_replace=0.15)
saltmask = cam != cam_noisy
# Ensure all changes are to 1.0
@@ -29,7 +29,7 @@ def test_salt():
def test_pepper():
seed = 42
cam = img_as_float(camera())
cam_noisy = random_noise(cam, seed=seed, mode='pepper', d=0.15)
cam_noisy = random_noise(cam, seed=seed, mode='pepper', prop_replace=0.15)
peppermask = cam != cam_noisy
# Ensure all changes are to 1.0
@@ -43,7 +43,8 @@ def test_pepper():
def test_salt_and_pepper():
seed = 42
cam = img_as_float(camera())
cam_noisy = random_noise(cam, seed=seed, mode='s&p', d=0.15, p=0.25)
cam_noisy = random_noise(cam, seed=seed, mode='s&p', prop_replace=0.15,
prop_salt=0.25)
saltmask = np.logical_and(cam != cam_noisy, cam_noisy == 1.)
peppermask = np.logical_and(cam != cam_noisy, cam_noisy == 0.)
@@ -63,10 +64,10 @@ def test_salt_and_pepper():
def test_gaussian():
seed = 42
data = np.zeros((128, 128)) + 0.5
data_gaussian = random_noise(data, seed=seed, v=0.01)
data_gaussian = random_noise(data, seed=seed, var=0.01)
assert 0.008 < data_gaussian.var() < 0.012
data_gaussian = random_noise(data, seed=seed, m=0.3, v=0.015)
data_gaussian = random_noise(data, seed=seed, mean=0.3, var=0.015)
assert 0.28 < data_gaussian.mean() - 0.5 < 0.32
assert 0.012 < data_gaussian.var() < 0.018
@@ -78,10 +79,15 @@ def test_speckle():
noise = np.random.normal(0.1, 0.02 ** 0.5, (128, 128))
expected = np.clip(data + data * noise, 0, 1)
data_speckle = random_noise(data, mode='speckle', seed=seed, m=0.1,
v=0.02)
data_speckle = random_noise(data, mode='speckle', seed=seed, mean=0.1,
var=0.02)
assert_allclose(expected, data_speckle)
def test_bad_mode():
data = np.zeros((64, 64))
assert_raises(KeyError, random_noise, data, 'perlin')
if __name__ == '__main__':
np.testing.run_module_suite()