def test_nearest():
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(scan_range[0], scan_range[1])
r = math.sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = math.atan2(y - yc, x - xc)
ai = int(math.floor(t * 8 / (2 * math.pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z - scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest(0, (x, y, z))
assert index0 == index1
def test_self_consistent():
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
for i in range(len(sampler)):
coord = sampler.coord(i)
index = sampler.nearest(0, coord)
assert index == i
def tst_self_consistent(self):
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
for i in range(len(sampler)):
coord = sampler.coord(i)
index = sampler.nearest(0, coord)
assert(index == i)
print 'OK'
def test_z_index():
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
assert (sampler.nearest(0, (500, 500, 2.0)) / 9) == 0
assert (sampler.nearest(0, (500, 500, 3.0)) / 9) == 0
assert (sampler.nearest(0, (500, 500, 4.0)) / 9) == 0
assert (sampler.nearest(0, (500, 500, 5.0)) / 9) == 0
assert (sampler.nearest(0, (500, 500, 6.0)) / 9) == 0
assert (sampler.nearest(0, (500, 500, 6.5)) / 9) == 0
assert (sampler.nearest(0, (500, 500, 7.0)) / 9) == 1
assert (sampler.nearest(0, (500, 500, 7.5)) / 9) == 1
assert (sampler.nearest(0, (500, 500, 8.0)) / 9) == 1
assert (sampler.nearest(0, (500, 500, 9.0)) / 9) == 1
assert (sampler.nearest(0, (500, 500, 10.0)) / 9) == 1
assert (sampler.nearest(0, (500, 500, 11.0)) / 9) == 1
assert (sampler.nearest(0, (500, 500, 12.0)) / 9) == 1
def test_detector_area():
from dials.algorithms.profile_model.modeller import CircleSampler
from scitbx.array_family import flex
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
im = flex.int(flex.grid(height, width))
for j in range(height):
for i in range(width):
im[j, i] = sampler.nearest(0, (i, j, 0))
assert (im[height // 2, width // 2] == 0)
assert (im[height // 2, width - 1] == 1)
assert (im[height - 1, width - 1] == 2)
assert (im[height - 1, width // 2] == 3)
assert (im[height - 1, 0] == 4)
assert (im[height // 2, 0] == 5)
assert (im[0, 0] == 6)
assert (im[0, width // 2] == 7)
assert (im[0, width - 1] == 8)
def tst_nearest(self):
from math import sqrt, atan2, pi, floor
from random import uniform
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
xc, yc = sampler.image_centre()
r1 = sampler.r1()
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(scan_range[0], scan_range[1])
r = sqrt((x - xc)**2 + (y - yc)**2)
if r < r1:
index00 = 0
else:
t = atan2(y - yc, x - xc)
ai = int(floor(t * 8 / (2 * pi) + 0.5)) % 8
index00 = ai + 1
index01 = int((z-scan_range[0]) * nz / depth)
if index01 < 0:
index01 = 0
if index01 >= 2:
index01 = 1
index0 = index00 + index01 * 9
index1 = sampler.nearest(0, (x, y, z))
assert(index0 == index1)
print 'OK'
def tst_detector_area(self):
from dials.algorithms.profile_model.modeller import CircleSampler
from scitbx.array_family import flex
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
im = flex.int(flex.grid(height, width))
for j in range(height):
for i in range(width):
im[j,i] = sampler.nearest(0, (i, j, 0))
assert(im[height//2, width//2] == 0)
assert(im[height//2, width-1] == 1)
assert(im[height-1, width-1] == 2)
assert(im[height-1, width//2] == 3)
assert(im[height-1, 0] == 4)
assert(im[height//2, 0] == 5)
assert(im[0, 0] == 6)
assert(im[0, width//2] == 7)
assert(im[0, width-1] == 8)
print 'OK'
def tst_z_index(self):
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = CircleSampler((width, height), scan_range, nz)
assert((sampler.nearest(0, (500, 500, 2.0)) / 9) == 0)
assert((sampler.nearest(0, (500, 500, 3.0)) / 9) == 0)
assert((sampler.nearest(0, (500, 500, 4.0)) / 9) == 0)
assert((sampler.nearest(0, (500, 500, 5.0)) / 9) == 0)
assert((sampler.nearest(0, (500, 500, 6.0)) / 9) == 0)
assert((sampler.nearest(0, (500, 500, 6.5)) / 9) == 0)
assert((sampler.nearest(0, (500, 500, 7.0)) / 9) == 1)
assert((sampler.nearest(0, (500, 500, 7.5)) / 9) == 1)
assert((sampler.nearest(0, (500, 500, 8.0)) / 9) == 1)
assert((sampler.nearest(0, (500, 500, 9.0)) / 9) == 1)
assert((sampler.nearest(0, (500, 500, 10.0)) / 9) == 1)
assert((sampler.nearest(0, (500, 500, 11.0)) / 9) == 1)
assert((sampler.nearest(0, (500, 500, 12.0)) / 9) == 1)
print 'OK'
def compute_reference(experiments, reflections):
from dials.algorithms.profile_model.modeller import CircleSampler
from dials.array_family import flex
from dials.algorithms.profile_model.gaussian_rs.transform import TransformForward
from dials.algorithms.profile_model.gaussian_rs.transform import TransformSpec
from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem
reflections = select_strong(reflections)
print("Selected %d strong spots" % len(reflections))
sampler = CircleSampler(
experiments[0].detector[0].get_image_size(),
experiments[0].scan.get_array_range(),
1,
)
n_sigma = 4.0
grid_size = 25
spec = TransformSpec(
experiments[0].beam,
experiments[0].detector,
experiments[0].goniometer,
experiments[0].scan,
experiments[0].profile.sigma_b(deg=False),
experiments[0].profile.sigma_m(deg=False),
n_sigma,
grid_size,
)
m2 = experiments[0].goniometer.get_rotation_axis()
s0 = experiments[0].beam.get_s0()
reference = [
flex.double(
flex.grid(1 + 2 * grid_size, 1 + 2 * grid_size, 1 + 2 * grid_size))
for i in range(len(sampler))
]
count = [0] * len(sampler)
for r in reflections:
s1 = r["s1"]
phi = r["xyzcal.mm"][2]
xyz = r["xyzcal.px"]
bbox = r["bbox"]
panel = r["panel"]
image = r["shoebox"].data.as_double()
mask = r["shoebox"].mask.as_1d() == 5
mask.reshape(image.accessor())
cs = CoordinateSystem(m2, s0, s1, phi)
try:
transform = TransformForward(spec, cs, bbox, panel, image, mask)
d = transform.profile()
d /= flex.sum(d)
index = sampler.nearest(0, xyz)
indices = sampler.nearest_n(0, xyz)
for i in indices:
w = sampler.weight(i, 0, xyz)
reference[i] += w * d
count[i] += 1
except Exception:
pass
for i in range(len(reference)):
r = reference[i]
if flex.sum(r) > 0:
print(flex.max(r))
g = r.accessor()
r = r.as_1d()
s = r > 0.02 * flex.max(r)
r.set_selected(~s, flex.double(len(r), 0))
r = r / flex.sum(r)
r.reshape(g)
reference[i] = r
for i in range(len(reference)):
from matplotlib import pylab
print(count[i])
r = reference[i]
d = r.as_numpy_array()[11, :, :]
pylab.imshow(d, interpolation="None")
pylab.show()
return reference
def integrate(experiments, reflections, reference):
from dials.algorithms.profile_model.modeller import CircleSampler
from dials.array_family import flex
from dials.algorithms.profile_model.gaussian_rs.transform import TransformReverse
from dials.algorithms.profile_model.gaussian_rs.transform import TransformForward
from dials.algorithms.profile_model.gaussian_rs.transform import (
TransformReverseNoModel, )
from dials.algorithms.profile_model.gaussian_rs.transform import TransformSpec
from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem
selection = reflections.get_flags(reflections.flags.integrated_sum)
reflections = reflections.select(selection)
print("Selected %d reflections to integrate" % len(reflections))
sampler = CircleSampler(
experiments[0].detector[0].get_image_size(),
experiments[0].scan.get_array_range(),
1,
)
n_sigma = 4.0
grid_size = 25
spec = TransformSpec(
experiments[0].beam,
experiments[0].detector,
experiments[0].goniometer,
experiments[0].scan,
experiments[0].profile.sigma_b(deg=False),
experiments[0].profile.sigma_m(deg=False),
n_sigma,
grid_size,
)
m2 = experiments[0].goniometer.get_rotation_axis()
s0 = experiments[0].beam.get_s0()
Iprf = flex.double(len(reflections))
Vprf = flex.double(len(reflections))
Cprf = flex.double(len(reflections))
Fprf = flex.bool(len(reflections))
for i, r in enumerate(reflections):
s1 = r["s1"]
phi = r["xyzcal.mm"][2]
xyz = r["xyzcal.px"]
bbox = r["bbox"]
panel = r["panel"]
image = r["shoebox"].data.as_double()
background = r["shoebox"].background.as_double()
mask = r["shoebox"].mask.as_1d(
) == 5 # | (r['shoebox'].mask.as_1d() == 3)
mask.reshape(image.accessor())
cs = CoordinateSystem(m2, s0, s1, phi)
index = sampler.nearest(0, xyz)
profile = reference[index]
# print flex.sum(profile)
# print r['partiality']
if False:
from dials.algorithms.integration.maximum_likelihood import (
ProfileFittingDouble as ProfileFitting, )
transform = TransformReverseNoModel(spec, cs, bbox, panel, profile)
p = transform.profile()
d = image
m = mask
b = background
# print flex.sum(p)
ysize, xsize = p.all()[1:3]
p1 = flex.double(flex.grid(1, ysize, xsize))
d1 = flex.double(flex.grid(1, ysize, xsize))
b1 = flex.double(flex.grid(1, ysize, xsize))
m1 = flex.double(flex.grid(1, ysize, xsize))
for k in range(p.all()[0]):
p1 += p[k:k + 1, :, :]
d1 += d[k:k + 1, :, :]
b1 += b[k:k + 1, :, :]
m1 = m[k:k + 1, :, :]
try:
fit = ProfileFitting(p1, m1, d1, b1, 1e-3, 1000)
assert fit.niter() < 1000
Iprf[i] = fit.intensity()
Vprf[i] = fit.variance()
Cprf[i] = fit.correlation()
Fprf[i] = True
print(i, fit.intensity(), flex.sum(p1))
# from matplotlib import pylab
# pylab.imshow(p1.as_numpy_array()[0,:,:], interpolation='none')
# pylab.show()
except Exception:
pass
else:
from dials.algorithms.integration.fit import (
ProfileFittingDouble as ProfileFitting, )
try:
transform = TransformForward(spec, cs, bbox, panel, image,
background, mask)
index = sampler.nearest(0, xyz)
p = reference[index]
d = transform.profile()
b = transform.background()
m = p > 0
fit = ProfileFitting(p, m, d, b, 1e-3, 1000)
assert fit.niter() < 1000
Iprf[i] = fit.intensity()
Vprf[i] = fit.variance()
Cprf[i] = fit.correlation()
Fprf[i] = True
print(i, fit.intensity(), flex.sum(p))
# from matplotlib import pylab
# pylab.imshow(p1.as_numpy_array()[0,:,:], interpolation='none')
# pylab.show()
except Exception:
pass
reflections["intensity.prf.value"] = Iprf
reflections["intensity.prf.variance"] = Vprf
reflections["intensity.prf.correlation"] = Cprf
reflections.set_flags(Fprf, reflections.flags.integrated_prf)
return reflections
def integrate_job(block,
experiments,
reflections,
reference,
grid_size=5,
detector_space=False):
from dials.algorithms.profile_model.modeller import CircleSampler
from dials.array_family import flex
from dials.algorithms.profile_model.gaussian_rs.transform import TransformReverse
from dials.algorithms.profile_model.gaussian_rs.transform import TransformForward
from dials.algorithms.profile_model.gaussian_rs.transform import (
TransformReverseNoModel, )
from dials.algorithms.profile_model.gaussian_rs.transform import TransformSpec
from dials.algorithms.profile_model.gaussian_rs import CoordinateSystem
from dials.algorithms.integration.fit import ProfileFitter
from dials.array_family import flex
from dials.model.data import make_image
reflections["shoebox"] = flex.shoebox(reflections["panel"],
reflections["bbox"],
allocate=True)
frame0, frame1 = experiments[0].scan.get_array_range()
frame0 = frame0 + block[0]
frame1 = frame0 + block[1]
reflections["shoebox"] = flex.shoebox(reflections["panel"],
reflections["bbox"],
allocate=True)
extractor = flex.ShoeboxExtractor(reflections, 1, frame0, frame1)
iset = experiments[0].imageset[block[0]:block[1]]
for i in range(len(iset)):
print("Reading image %d" % i)
data = iset.get_raw_data(i)
mask = iset.get_mask(i)
extractor.next(make_image(data, mask))
print("Computing mask")
reflections.compute_mask(experiments)
print("Computing background")
reflections.compute_background(experiments)
print("Computing centroid")
reflections.compute_centroid(experiments)
print("Computing summed intensity")
reflections.compute_summed_intensity()
assert len(reference) % 9 == 0
num_scan_points = len(reference) // 9
sampler = CircleSampler(
experiments[0].detector[0].get_image_size(),
experiments[0].scan.get_array_range(),
num_scan_points,
)
spec = TransformSpec(
experiments[0].beam,
experiments[0].detector,
experiments[0].goniometer,
experiments[0].scan,
experiments[0].profile.sigma_b(deg=False),
experiments[0].profile.sigma_m(deg=False),
experiments[0].profile.n_sigma() * 1.5,
grid_size,
)
m2 = experiments[0].goniometer.get_rotation_axis()
s0 = experiments[0].beam.get_s0()
Iprf = flex.double(len(reflections))
Vprf = flex.double(len(reflections))
Cprf = flex.double(len(reflections))
Fprf = flex.bool(len(reflections))
Part = reflections["partiality"]
reflections["intensity.prf_old.value"] = reflections["intensity.prf.value"]
reflections["intensity.prf_old.variance"] = reflections[
"intensity.prf.variance"]
selection = reflections.get_flags(reflections.flags.integrated_prf)
reflections.unset_flags(~Fprf, reflections.flags.integrated_prf)
for i, r in enumerate(reflections):
if selection[i] == False:
continue
s1 = r["s1"]
phi = r["xyzcal.mm"][2]
xyz = r["xyzcal.px"]
bbox = r["bbox"]
panel = r["panel"]
image = r["shoebox"].data.as_double()
background = r["shoebox"].background.as_double()
mask = r["shoebox"].mask.as_1d(
) == 5 # | (r['shoebox'].mask.as_1d() == 3)
mask.reshape(image.accessor())
cs = CoordinateSystem(m2, s0, s1, phi)
index = sampler.nearest(0, xyz)
profile, profile_mask = reference[index]
# print flex.sum(profile)
# print r['partiality']
if detector_space:
transform = TransformReverseNoModel(spec, cs, bbox, panel, profile)
p = transform.profile()
d = image
m = mask
b = background
# print flex.sum(p)
Part[i] = flex.sum(p)
# ysize, xsize = p.all()[1:3]
# p1 = flex.double(flex.grid(1, ysize , xsize))
# d1 = flex.double(flex.grid(1, ysize , xsize))
# b1 = flex.double(flex.grid(1, ysize , xsize))
# m1 = flex.double(flex.grid(1, ysize , xsize))
# for k in range(p.all()[0]):
# p1 += p[k:k+1,:,:]
# d1 += d[k:k+1,:,:]
# b1 += b[k:k+1,:,:]
# m1 = m[k:k+1,:,:]
try:
fit = ProfileFitter(d, b, m, p, 1e-3, 100)
assert fit.niter() < 100
Iprf[i] = fit.intensity()
Vprf[i] = fit.variance()
Cprf[i] = fit.correlation()
Fprf[i] = True
# if r['intensity.sum.value'] > 10 and abs(fit.intensity()) < 1e-3:
print(
r["miller_index"],
i,
fit.intensity(),
r["intensity.sum.value"],
r["intensity.prf_old.value"],
Part[i],
fit.niter(),
)
# from matplotlib import pylab
# pylab.imshow(p1.as_numpy_array()[0,:,:], interpolation='none')
# pylab.show()
except Exception as e:
print(e)
pass
else:
try:
transform = TransformForward(spec, cs, bbox, panel, image,
background, mask)
p = profile
d = transform.profile()
b = transform.background()
m = transform.mask() & profile_mask
# if r['miller_index'] == (9, -25, 74):
# print list(p)
# print list(m)
# print list(b)
# print list(d)
# exit(0)
fit = ProfileFitter(d, b, m, p, 1e-3, 100)
assert fit.niter() < 100
Iprf[i] = fit.intensity()[0]
Vprf[i] = fit.variance()[0]
Cprf[i] = fit.correlation()
Fprf[i] = True
print(r["miller_index"], i, fit.intensity(),
r["intensity.prf_old.value"])
# from matplotlib import pylab
# pylab.imshow(p1.as_numpy_array()[0,:,:], interpolation='none')
# pylab.show()
except Exception as e:
pass
reflections["intensity.prf.value"] = Iprf
reflections["intensity.prf.variance"] = Vprf
reflections["intensity.prf.correlation"] = Cprf
reflections.set_flags(Fprf, reflections.flags.integrated_prf)
del reflections["shoebox"]
return reflections
