/usr/lib/python2.7/dist-packages/dipy/denoise/tests/test_nlmeans.py is in python-dipy 0.10.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | import numpy as np
from numpy.testing import (run_module_suite,
assert_,
assert_equal,
assert_array_almost_equal)
from dipy.denoise.nlmeans import nlmeans
from dipy.denoise.denspeed import (add_padding_reflection, remove_padding,
cpu_count)
from time import time
def test_nlmeans_padding():
S0 = 100 + 2 * np.random.standard_normal((50, 50, 50))
S0 = S0.astype('f8')
S0n = add_padding_reflection(S0, 5)
S0n2 = remove_padding(S0n, 5)
assert_equal(S0.shape, S0n2.shape)
def test_nlmeans_static():
S0 = 100 * np.ones((20, 20, 20), dtype='f8')
S0n = nlmeans(S0, sigma=np.ones((20, 20, 20)), rician=False)
assert_array_almost_equal(S0, S0n)
def test_nlmeans_random_noise():
S0 = 100 + 2 * np.random.standard_normal((22, 23, 30))
S0n = nlmeans(S0, sigma=np.ones((22, 23, 30)) * np.std(S0), rician=False)
print(S0.mean(), S0.min(), S0.max())
print(S0n.mean(), S0n.min(), S0n.max())
assert_(S0n.min() > S0.min())
assert_(S0n.max() < S0.max())
assert_equal(np.round(S0n.mean()), 100)
def test_nlmeans_boundary():
# nlmeans preserves boundaries
S0 = 100 + np.zeros((20, 20, 20))
noise = 2 * np.random.standard_normal((20, 20, 20))
S0 += noise
S0[:10, :10, :10] = 300 + noise[:10, :10, :10]
S0n = nlmeans(S0, sigma=np.ones((20, 20, 20)) * np.std(noise),
rician=False)
print(S0[9, 9, 9])
print(S0[10, 10, 10])
assert_(S0[9, 9, 9] > 290)
assert_(S0[10, 10, 10] < 110)
def test_nlmeans_4D_and_mask():
S0 = 200 * np.ones((20, 20, 20, 3), dtype='f8')
mask = np.zeros((20, 20, 20))
mask[10, 10, 10] = 1
S0n = nlmeans(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.shape, S0n.shape)
assert_equal(np.round(S0n[10, 10, 10]), 200)
assert_equal(S0n[8, 8, 8], 0)
def test_nlmeans_dtype():
S0 = 200 * np.ones((20, 20, 20, 3), dtype='f4')
mask = np.zeros((20, 20, 20))
mask[10:14, 10:14, 10:14] = 1
S0n = nlmeans(S0, sigma=1, mask=mask, rician=True)
assert_equal(S0.dtype, S0n.dtype)
S0 = 200 * np.ones((20, 20, 20), dtype=np.uint16)
mask = np.zeros((20, 20, 20))
mask[10:14, 10:14, 10:14] = 1
S0n = nlmeans(S0, sigma=np.ones((20, 20, 20)), mask=mask, rician=True)
assert_equal(S0.dtype, S0n.dtype)
def test_nlmeans_4d_3dsigma_and_threads():
# Input is 4D data and 3D sigma
data = np.ones((50, 50, 50, 5))
sigma = np.ones(data.shape[:3])
mask = np.zeros(data.shape[:3])
# mask[25-10:25+10] = 1
mask[:] = 1
print('cpu count %d' % (cpu_count(),))
print('1')
t = time()
new_data = nlmeans(data, sigma, mask, num_threads=1)
duration_1core = time() - t
print(duration_1core)
print('All')
t = time()
new_data2 = nlmeans(data, sigma, mask, num_threads=None)
duration_all_core = time() - t
print(duration_all_core)
print('2')
t = time()
new_data3 = nlmeans(data, sigma, mask, num_threads=2)
duration_2core = time() - t
print(duration_all_core)
assert_array_almost_equal(new_data, new_data2)
assert_array_almost_equal(new_data2, new_data3)
if cpu_count() > 2:
assert_equal(duration_all_core < duration_2core, True)
assert_equal(duration_2core < duration_1core, True)
if cpu_count() == 2:
assert_equal(duration_2core < duration_1core, True)
if __name__ == '__main__':
# test_nlmeans_4d_3dsigma_and_threads()
run_module_suite()
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