def sort(self):
perm = flex.sort_permutation(
data=flex.abs(flex.double(self.array_of_a())),
reverse=True)
return sum(
flex.select(self.array_of_a(), perm),
flex.select(self.array_of_b(), perm),
self.c(),
self.use_c())
def fake_it(self, size):
selection_set = self.non_para_bootstrap.draw(size)
isel = flex.int()
for element in selection_set:
isel.append(int(element))
new_x = flex.double(flex.select(self.x_data, isel))
new_y = flex.double(flex.select(self.y_data, isel))
return new_x, new_y
def test_masked_mean_filter():
from scitbx.array_family import flex
from dials.algorithms.image.filter import mean_filter
# 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()
mean = mean_filter(image, mask2, (3, 3), 1)
# 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 = random.randint(10, 1990)
j = random.randint(10, 1990)
m1 = mean[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
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
assert m1 == pytest.approx(m2, abs=eps)
def is_correct(self, shoebox, mask, n_sigma, min_pixels):
from scitbx.array_family import flex
flags = [m & (1 << 0) for m in mask]
pixels = flex.select(shoebox, flags=flags)
assert (len(pixels) >= min_pixels)
meanp = flex.mean_and_variance(flex.double(pixels))
maxp = flex.max(flex.double(pixels))
m = meanp.mean()
s = meanp.unweighted_sample_standard_deviation()
assert (maxp <= m + n_sigma * s)
def is_correct(self, shoebox, mask, n_sigma, min_pixels): from scitbx.array_family import flex flags = [m & (1 << 0) for m in mask] pixels = flex.select(shoebox, flags=flags) assert(len(pixels) >= min_pixels) meanp = flex.mean_and_variance(flex.double(pixels)) maxp = flex.max(flex.double(pixels)) m = meanp.mean() s = meanp.unweighted_sample_standard_deviation() assert(maxp <= m + n_sigma * s)
def show_minimize_multi_histogram(f=None, reset=True):
global minimize_multi_histogram
minimizer_types = list(minimize_multi_histogram.keys())
counts = flex.double(list(minimize_multi_histogram.values()))
perm = flex.sort_permutation(data=counts, reverse=True)
minimizer_types = flex.select(minimizer_types, perm)
counts = counts.select(perm)
n_total = flex.sum(counts)
for m, c in zip(minimizer_types, counts):
print("%-39s %5.3f %6d" % (m, c / max(1, n_total), c), file=f)
print(file=f)
if (reset):
minimize_multi_histogram = {"None": 0}
def show_minimize_multi_histogram(f=None, reset=True):
global minimize_multi_histogram
minimizer_types = minimize_multi_histogram.keys()
counts = flex.double(minimize_multi_histogram.values())
perm = flex.sort_permutation(data=counts, reverse=True)
minimizer_types = flex.select(minimizer_types, perm)
counts = counts.select(perm)
n_total = flex.sum(counts)
for m,c in zip(minimizer_types, counts):
print >> f, "%-39s %5.3f %6d" % (m, c/max(1,n_total), c)
print >> f
if (reset):
minimize_multi_histogram = {"None": 0}
def run(self):
from dials.algorithms.image.filter import fano_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()
fano_filter = fano_filter(image, mask2, (3, 3), 2)
mean = fano_filter.mean()
var = fano_filter.sample_variance()
fano = fano_filter.fano()
# 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]
f1 = fano[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
f2 = 1.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
f2 = v2 / m2
assert (abs(m1 - m2) <= eps)
assert (abs(v1 - v2) <= eps)
assert (abs(f1 - f2) <= eps)
# Test passed
print 'OK'
def run(self):
from dials.algorithms.image.filter import fano_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()
fano_filter = fano_filter(image, mask2, (3, 3), 2)
mean = fano_filter.mean()
var = fano_filter.sample_variance()
fano = fano_filter.fano()
# 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]
f1 = fano[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
f2 = 1.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
f2 = v2 / m2
assert(abs(m1 - m2) <= eps)
assert(abs(v1 - v2) <= eps)
assert(abs(f1 - f2) <= eps)
# Test passed
print 'OK'
def test():
from scitbx.array_family import flex
from dials.algorithms.image.filter import index_of_dispersion_filter
# 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()
index_of_dispersion_filter = index_of_dispersion_filter(
image, mask2, (3, 3), 2)
mean = index_of_dispersion_filter.mean()
var = index_of_dispersion_filter.sample_variance()
index_of_dispersion = index_of_dispersion_filter.index_of_dispersion()
# 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 = random.randint(10, 1990)
j = random.randint(10, 1990)
m1 = mean[j, i]
v1 = var[j, i]
f1 = index_of_dispersion[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
f2 = 1.0
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
v2 = mv.unweighted_sample_variance()
f2 = v2 / m2
assert m1 == pytest.approx(m2, abs=eps)
assert v1 == pytest.approx(v2, abs=eps)
assert f1 == pytest.approx(f2, abs=eps)
def tst_masked_mean_filter(self):
from dials.algorithms.image.filter import mean_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()
mean = mean_filter(image, mask2, (3, 3), 1)
# 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]
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
else:
p = flex.select(p, flags=m)
mv = flex.mean_and_variance(flex.double(p))
m2 = mv.mean()
s1 = flex.sum(flex.double(p))
s2 = flex.sum(m.as_1d())
assert(abs(m1 - m2) <= eps)
# Test passed
print 'OK'
def random_permutation(s): from scitbx.array_family import flex return flex.select(s, flex.random_permutation(size=len(s)))
from dials.algorithms.background import IndexOfDispersionDiscriminator
from dials.algorithms.background import normal_expected_n_sigma
print normal_expected_n_sigma(30 * 30)
discriminate_normal = NormalDiscriminator(n_sigma = normal_expected_n_sigma(30 * 30))
discriminate_poisson = IndexOfDispersionDiscriminator(n_sigma = 2)
grid_i = flex.int(flex.grid(grid.all()))
for i, g in enumerate(grid):
grid_i[i] = int(g)
mask_normal = discriminate_normal(grid_i)
mask_poisson = discriminate_poisson(grid_i)
normal_pixels = flex.select(grid, flags=(mask_normal == 1))
poisson_pixels = flex.select(grid, flags=(mask_poisson == 1))
all_pixels = list(grid)
line = grid.as_numpy_array()[15,:]
from matplotlib import pylab
pylab.subplot2grid((3, 3), (0, 0))
pylab.hist(all_pixels, bins=20)
pylab.subplot2grid((3, 3), (0, 1))
pylab.hist(normal_pixels, bins=20)
pylab.subplot2grid((3, 3), (0, 2))
pylab.hist(poisson_pixels, bins=20)
pylab.subplot2grid((3, 3), (1, 0), colspan=3)
pylab.plot(line)
pylab.subplot2grid((3, 3), (2, 0))