def tst_weights(self):
from dials.algorithms.profile_model.modeller import CircleSampler
from math import exp, log
from scitbx import matrix
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 1
sampler = CircleSampler((width, height), scan_range, nz)
# Check the weight at the coord in 1.0
eps = 1e-7
for i in range(len(sampler)):
coord = sampler.coord(i)
weight = sampler.weight(i, 0, coord)
assert(abs(weight - 1.0) < eps)
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
r = r2 / (2.0*r1)
expected = exp(-4.0*r*r*log(2.0))
for i in range(1, 9):
coord = sampler.coord(i)
weight = sampler.weight(0, 0, coord)
assert(abs(weight - expected) < eps)
r = r2 / (2.0*(r2 - r1))
expected = exp(-4.0*r*r*log(2.0))
for i in range(1, 9):
coord = sampler.coord(0)
weight = sampler.weight(i, 0, coord)
assert(abs(weight - expected) < eps)
for i in range(1, 9):
coord1 = matrix.col(sampler.coord(0))
coord2 = matrix.col(sampler.coord(i))
coord = coord1 + r1 * (coord2 - coord1) / r2
weight = sampler.weight(0, 0, coord)
assert(abs(weight - 0.5) < eps)
weight = sampler.weight(i, 0, coord)
assert(abs(weight - 0.5) < eps)
print 'OK'
def tst_weights(self):
from dials.algorithms.profile_model.modeller import CircleSampler
from math import exp, log
from scitbx import matrix
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 1
sampler = CircleSampler((width, height), scan_range, nz)
# Check the weight at the coord in 1.0
eps = 1e-7
for i in range(len(sampler)):
coord = sampler.coord(i)
weight = sampler.weight(i, coord)
assert (abs(weight - 1.0) < eps)
r0 = sampler.r0()
r1 = sampler.r1()
r2 = sampler.r2()
r = r2 / (2.0 * r1)
expected = exp(-4.0 * r * r * log(2.0))
for i in range(1, 9):
coord = sampler.coord(i)
weight = sampler.weight(0, coord)
assert (abs(weight - expected) < eps)
r = r2 / (2.0 * (r2 - r1))
expected = exp(-4.0 * r * r * log(2.0))
for i in range(1, 9):
coord = sampler.coord(0)
weight = sampler.weight(i, coord)
assert (abs(weight - expected) < eps)
for i in range(1, 9):
coord1 = matrix.col(sampler.coord(0))
coord2 = matrix.col(sampler.coord(i))
coord = coord1 + r1 * (coord2 - coord1) / r2
weight = sampler.weight(0, coord)
assert (abs(weight - 0.5) < eps)
weight = sampler.weight(i, coord)
assert (abs(weight - 0.5) < eps)
print 'OK'
def test_weights():
from scitbx import matrix
from dials.algorithms.profile_model.modeller import CircleSampler
width = 1000
height = 1000
scan_range = (2, 12)
nz = 1
sampler = CircleSampler((width, height), scan_range, nz)
# Check the weight at the coord in 1.0
eps = 1e-7
for i in range(len(sampler)):
coord = sampler.coord(i)
weight = sampler.weight(i, 0, coord)
assert abs(weight - 1.0) < eps
r1 = sampler.r1()
r2 = sampler.r2()
r = r2 / (2.0 * r1)
expected = math.exp(-4.0 * r * r * math.log(2.0))
for i in range(1, 9):
coord = sampler.coord(i)
weight = sampler.weight(0, 0, coord)
assert abs(weight - expected) < eps
r = r2 / (2.0 * (r2 - r1))
expected = math.exp(-4.0 * r * r * math.log(2.0))
for i in range(1, 9):
coord = sampler.coord(0)
weight = sampler.weight(i, 0, coord)
assert abs(weight - expected) < eps
for i in range(1, 9):
coord1 = matrix.col(sampler.coord(0))
coord2 = matrix.col(sampler.coord(i))
coord = coord1 + r1 * (coord2 - coord1) / r2
weight = sampler.weight(0, 0, coord)
assert abs(weight - 0.5) < eps
weight = sampler.weight(i, 0, coord)
assert abs(weight - 0.5) < eps
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
