def tst_mean_and_variance_filter(self):
from dials.algorithms.image.filter import mean_and_variance_filter
from scitbx.array_family import flex
from random import randint
# Create an image
image = flex.random_double(2000 * 2000)
image.reshape(flex.grid(2000, 2000))
# Calculate the summed area table
mean_and_variance = mean_and_variance_filter(image, (3, 3))
mean = mean_and_variance.mean()
variance = mean_and_variance.variance()
sample_variance = mean_and_variance.sample_variance()
# For a selection of random points, ensure that the value is the
# sum of the area under the kernel
eps = 1e-7
for i in range(10000):
i = randint(10, 1990)
j = randint(10, 1990)
m1 = mean[j, i]
v1 = variance[j, i]
sv1 = sample_variance[j, i]
p = image[j - 3:j + 4, i - 3:i + 4]
mv = flex.mean_and_variance(p.as_1d())
m2 = mv.mean()
sv2 = mv.unweighted_sample_variance()
assert (abs(m1 - m2) <= eps)
assert (abs(sv1 - sv2) <= eps)
# Test passed
print 'OK'
def tst_mean_and_variance_filter(self):
from dials.algorithms.image.filter import mean_and_variance_filter
from scitbx.array_family import flex
from random import randint
# Create an image
image = flex.random_double(2000 * 2000)
image.reshape(flex.grid(2000, 2000))
# Calculate the summed area table
mean_and_variance = mean_and_variance_filter(image, (3, 3))
mean = mean_and_variance.mean()
variance = mean_and_variance.variance()
sample_variance = mean_and_variance.sample_variance()
# For a selection of random points, ensure that the value is the
# sum of the area under the kernel
eps = 1e-7
for i in range(10000):
i = randint(10, 1990)
j = randint(10, 1990)
m1 = mean[j,i]
v1 = variance[j,i]
sv1 = sample_variance[j,i]
p = image[j-3:j+4,i-3:i+4]
mv = flex.mean_and_variance(p.as_1d())
m2 = mv.mean()
sv2 = mv.unweighted_sample_variance()
assert(abs(m1 - m2) <= eps)
assert(abs(sv1 - sv2) <= eps)
# Test passed
print 'OK'
def tst_masked_mean_and_variance_filter(self):
from dials.algorithms.image.filter import mean_and_variance_filter
from scitbx.array_family import flex
from random import randint
# Create an image
image = flex.random_double(2000 * 2000)
image.reshape(flex.grid(2000, 2000))
mask = flex.random_bool(2000 * 2000, 0.99).as_int()
mask.reshape(flex.grid(2000, 2000))
# Calculate the summed area table
mask2 = mask.deep_copy()
mv = mean_and_variance_filter(image, mask2, (3, 3), 2)
mean = mv.mean()
var = mv.sample_variance()
# For a selection of random points, ensure that the value is the
# sum of the area under the kernel
eps = 1e-7
for i in range(10000):
i = randint(10, 1990)
j = randint(10, 1990)
m1 = mean[j, i]
v1 = var[j, i]
p = image[j - 3:j + 4, i - 3:i + 4]
m = mask[j - 3:j + 4, i - 3:i + 4]
if mask[j, i] == 0:
m2 = 0.0
v2 = 0.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
assert (abs(m1 - m2) <= eps)
assert (abs(v1 - v2) <= eps)
# Test passed
print 'OK'
def tst_masked_mean_and_variance_filter(self):
from dials.algorithms.image.filter import mean_and_variance_filter
from scitbx.array_family import flex
from random import randint
# Create an image
image = flex.random_double(2000 * 2000)
image.reshape(flex.grid(2000, 2000))
mask = flex.random_bool(2000 * 2000, 0.99).as_int()
mask.reshape(flex.grid(2000, 2000))
# Calculate the summed area table
mask2 = mask.deep_copy()
mv = mean_and_variance_filter(image, mask2, (3, 3), 2)
mean = mv.mean()
var = mv.sample_variance()
# For a selection of random points, ensure that the value is the
# sum of the area under the kernel
eps = 1e-7
for i in range(10000):
i = randint(10, 1990)
j = randint(10, 1990)
m1 = mean[j,i]
v1 = var[j,i]
p = image[j-3:j+4,i-3:i+4]
m = mask[j-3:j+4,i-3:i+4]
if mask[j,i] == 0:
m2 = 0.0
v2 = 0.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
assert(abs(m1 - m2) <= eps)
assert(abs(v1 - v2) <= eps)
# Test passed
print 'OK'
