def test_centroid_bias(self):
from dials.algorithms.image.centroid import centroid_image
from scitbx.array_family import flex
pixels = flex.double(flex.grid(5, 5), 0)
pixels[2, 2] = 10
centroid = centroid_image(pixels)
#assert abs(centroid.average_bias_estimate()[0] - 1/12.0) < 1e-7
#assert abs(centroid.average_bias_estimate()[1] - 1/12.0) < 1e-7
pixels = flex.double(flex.grid(5, 5), 0)
pixels[1, 2] = 5
pixels[2, 2] = 10
pixels[3, 2] = 5
pixels[2, 1] = 5
pixels[2, 3] = 5
centroid = centroid_image(pixels)
assert centroid.average_bias_estimate()[0] < 1e-7
assert centroid.average_bias_estimate()[1] < 1e-7
def tst_centroid_masked_image3d(self):
from dials.algorithms.image.centroid import centroid_image
from scitbx import matrix
centroid = centroid_image(self.pixels3d, self.mask3d)
assert(abs(self.goldmasked3d - matrix.col(centroid.mean())) < self.EPS)
assert(abs(self.goldmasked3dvar - matrix.col(centroid.variance())) < self.EPS)
assert(abs(self.goldmasked3dubvar - matrix.col(centroid.unbiased_variance())) < self.EPS)
print 'OK'
def test_centroid_image3d(self):
from scitbx import matrix
from dials.algorithms.image.centroid import centroid_image
centroid = centroid_image(self.pixels3d)
assert abs(self.gold3d - matrix.col(centroid.mean())) < self.EPS
assert abs(self.gold3dvar - matrix.col(centroid.variance())) < self.EPS
assert (abs(self.gold3dubvar -
matrix.col(centroid.unbiased_variance())) < self.EPS)
def tst_centroid_bias(self):
from dials.algorithms.image.centroid import centroid_image
from scitbx.array_family import flex
pixels = flex.double(flex.grid(5,5), 0)
pixels[2,2] = 10
centroid = centroid_image(pixels)
#assert abs(centroid.average_bias_estimate()[0] - 1/12.0) < 1e-7
#assert abs(centroid.average_bias_estimate()[1] - 1/12.0) < 1e-7
pixels = flex.double(flex.grid(5,5), 0)
pixels[1,2] = 5
pixels[2,2] = 10
pixels[3,2] = 5
pixels[2,1] = 5
pixels[2,3] = 5
centroid = centroid_image(pixels)
assert centroid.average_bias_estimate()[0] < 1e-7
assert centroid.average_bias_estimate()[1] < 1e-7
print 'OK'
def test_centroid_masked_image2d(self):
from dials.algorithms.image.centroid import centroid_image
from scitbx import matrix
centroid = centroid_image(self.pixels2d, self.mask2d)
assert (abs(self.goldmasked2d - matrix.col(centroid.mean())) <
self.EPS)
assert (abs(self.goldmasked2dvar - matrix.col(centroid.variance())) <
self.EPS)
assert (abs(self.goldmasked2dubvar -
matrix.col(centroid.unbiased_variance())) < self.EPS)
def check_reference(self, reference):
"""Check the reference spots."""
from dials.array_family import flex
from dials.algorithms.image.centroid import centroid_image
from math import sqrt
# Get a load of stuff
I_sim = reference["intensity.sim"]
I_exp = reference["intensity.exp"]
I_cal = reference["intensity.sum.value"]
I_var = reference["intensity.sum.variance"]
# Get the transformed shoeboxes
profiles = reference["rs_shoebox"]
n_sigma = 3
grid_size = 4
step_size = n_sigma / (grid_size + 0.5)
eps = 1e-7
for i in range(1): # len(profiles)):
import numpy
data = profiles[i].data
# dmax = flex.max(data)
# data = 100 * data / dmax
# p = data.as_numpy_array()
# p = p.astype(numpy.int)
# print p
print(flex.sum(data), I_exp[i], I_cal[i])
# assert(abs(flex.sum(data) - I_exp[i]) < eps)
centroid = centroid_image(data)
m = centroid.mean()
v = centroid.variance()
s1 = tuple(sqrt(vv) for vv in v)
s2 = tuple(ss * step_size for ss in s1)
assert all(abs(mm - (grid_size + 0.5)) < 0.25 for mm in m)
assert all(abs(ss2 - 1.0) < 0.25 for ss2 in s2)
# Calculate Z
Z = (I_cal - I_exp) / flex.sqrt(I_var)
mv = flex.mean_and_variance(Z)
Z_mean = mv.mean()
Z_var = mv.unweighted_sample_variance()
print("Z: mean: %f, var: %f" % (Z_mean, Z_var))
from matplotlib import pylab
pylab.hist((I_cal - I_exp) / I_exp)
pylab.show()
def check_reference(self, reference):
''' Check the reference spots. '''
from dials.array_family import flex
from dials.algorithms.image.centroid import centroid_image
from math import sqrt
# Get a load of stuff
I_sim = reference['intensity.sim']
I_exp = reference['intensity.exp']
I_cal = reference['intensity.prf.value']
I_var = reference['intensity.prf.variance']
# Get the transformed shoeboxes
profiles = reference['rs_shoebox']
n_sigma = 3
n_sigma2 = 5
grid_size = 4
step_size = n_sigma2 / (grid_size + 0.5)
eps = 1e-7
for i in range(len(profiles)):
data = profiles[i].data
#dmax = flex.max(data)
#data = 100 * data / dmax
#p = data.as_numpy_array()
#p = p.astype(numpy.int)
#print p
print flex.sum(data), I_exp[i], I_cal[i]
#assert(abs(flex.sum(data) - I_exp[i]) < eps)
centroid = centroid_image(data)
m = centroid.mean()
v = centroid.variance()
s1 = tuple(sqrt(vv) for vv in v)
s2 = tuple(ss * step_size for ss in s1)
assert(all(abs(mm - (grid_size + 0.5)) < 0.25 for mm in m))
assert(all(abs(ss2 - n_sigma / n_sigma2) < 0.25 for ss2 in s2))
# Calculate Z
Z = (I_cal - I_exp) / flex.sqrt(I_var)
mv = flex.mean_and_variance(Z)
Z_mean = mv.mean()
Z_var = mv.unweighted_sample_variance()
print "Z: mean: %f, var: %f" % (Z_mean, Z_var)
from matplotlib import pylab
pylab.hist((I_cal - I_exp) / I_exp)
pylab.show()
def check_profiles(self, learner):
''' Check the reference profiles. '''
from dials.array_family import flex
from dials.algorithms.image.centroid import centroid_image
from math import sqrt
# Get the reference locator
locator = learner.locate()
np = locator.size()
assert (np == 9)
assert (flex.sum(learner.counts()) == 10000)
#profile = locator.profile(0)
#pmax = flex.max(profile)
#profile = 100 * profile / pmax
#profile = profile.as_numpy_array()
#import numpy
#profile = profile.astype(numpy.int)
#print profile
# Check all the profiles
eps = 1e-7
n_sigma = 3
n_sigma2 = 5
grid_size = 4
step_size = n_sigma2 / (grid_size + 0.5)
for i in range(np):
profile = locator.profile(i)
assert (abs(flex.sum(profile) - 1.0) < eps)
centroid = centroid_image(profile)
m = centroid.mean()
v = centroid.variance()
s1 = tuple(sqrt(vv) for vv in v)
s2 = tuple(ss * step_size for ss in s1)
assert (all(abs(mm - (grid_size + 0.5)) < 0.25 for mm in m))
assert (all(abs(ss2 - n_sigma / n_sigma2) < 0.25 for ss2 in s2))
# Check all the profiles have good correlation coefficients
cor = locator.correlations()
assert (all(cor > 0.99))
# Test passed
print 'OK'
def check_profiles(self, learner):
''' Check the reference profiles. '''
from dials.array_family import flex
from dials.algorithms.image.centroid import centroid_image
from math import sqrt
# Get the reference locator
locator = learner.locate()
np = locator.size()
assert(np == 9)
assert(flex.sum(learner.counts()) == 10000)
#profile = locator.profile(0)
#pmax = flex.max(profile)
#profile = 100 * profile / pmax
#profile = profile.as_numpy_array()
#import numpy
#profile = profile.astype(numpy.int)
#print profile
# Check all the profiles
eps = 1e-7
n_sigma = 3
n_sigma2 = 5
grid_size = 4
step_size = n_sigma2 / (grid_size + 0.5)
for i in range(np):
profile = locator.profile(i)
assert(abs(flex.sum(profile) - 1.0) < eps)
centroid = centroid_image(profile)
m = centroid.mean()
v = centroid.variance()
s1 = tuple(sqrt(vv) for vv in v)
s2 = tuple(ss * step_size for ss in s1)
assert(all(abs(mm - (grid_size + 0.5)) < 0.25 for mm in m))
assert(all(abs(ss2 - n_sigma / n_sigma2) < 0.25 for ss2 in s2))
# Check all the profiles have good correlation coefficients
cor = locator.correlations()
assert(all(cor > 0.99))
# Test passed
print 'OK'
def check_profiles(self, learner):
''' Check the reference profiles. '''
from dials.array_family import flex
from dials.algorithms.image.centroid import centroid_image
from math import sqrt
# Get the reference locator
locator = learner.locate()
np = locator.size()
assert(np == 9)
cexp = [10000, 3713, 3817, 5023, 3844, 3723, 3768, 4886, 3781]
assert(all(c1 == c2 for c1, c2 in zip(cexp, learner.counts())))
#profile = locator.profile(0)
#pmax = flex.max(profile)
#profile = 100 * profile / pmax
#profile = profile.as_numpy_array()
#import numpy
#profile = profile.astype(numpy.int)
#print profile
# Check all the profiles
eps = 1e-7
n_sigma = 3
grid_size = 4
step_size = n_sigma / (grid_size + 0.5)
for i in range(np):
profile = locator.profile(i)
assert(abs(flex.sum(profile) - 1.0) < eps)
centroid = centroid_image(profile)
m = centroid.mean()
v = centroid.variance()
s1 = tuple(sqrt(vv) for vv in v)
s2 = tuple(ss * step_size for ss in s1)
assert(all(abs(mm - (grid_size + 0.5)) < 0.25 for mm in m))
assert(all(abs(ss2 - 1.0) < 0.25 for ss2 in s2))
# Test passed
print 'OK'
def check_profiles(self, learner):
"""Check the reference profiles."""
from dials.array_family import flex
from dials.algorithms.image.centroid import centroid_image
from math import sqrt
# Get the reference locator
locator = learner.locate()
np = locator.size()
assert np == 9
cexp = [10000, 3713, 3817, 5023, 3844, 3723, 3768, 4886, 3781]
assert all(c1 == c2 for c1, c2 in zip(cexp, learner.counts()))
# profile = locator.profile(0)
# pmax = flex.max(profile)
# profile = 100 * profile / pmax
# profile = profile.as_numpy_array()
# import numpy
# profile = profile.astype(numpy.int)
# print profile
# Check all the profiles
eps = 1e-7
n_sigma = 3
grid_size = 4
step_size = n_sigma / (grid_size + 0.5)
for i in range(np):
profile = locator.profile(i)
assert abs(flex.sum(profile) - 1.0) < eps
centroid = centroid_image(profile)
m = centroid.mean()
v = centroid.variance()
s1 = tuple(sqrt(vv) for vv in v)
s2 = tuple(ss * step_size for ss in s1)
assert all(abs(mm - (grid_size + 0.5)) < 0.25 for mm in m)
assert all(abs(ss2 - 1.0) < 0.25 for ss2 in s2)
# Test passed
print("OK")
