def tst_nearest_n(self):
from random import uniform
from dials.algorithms.profile_model.modeller import SingleSampler
from math import floor
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(*scan_range)
k = int(floor((z - scan_range[0]) * nz / depth))
if k >= nz:
k = nz - 1
index0 = k
index1 = sampler.nearest_n(0, (x, y, z))
assert (len(set(index1)) == len(index1))
assert (index0 == index1[-1])
for ind in index1:
assert (abs(ind - k) <= 1)
print 'OK'
def tst_nearest_n(self):
from random import uniform
from dials.algorithms.profile_model.modeller import SingleSampler
from math import floor
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(*scan_range)
k = int(floor((z-scan_range[0]) * nz / depth))
if k >= nz:
k = nz - 1
index0 = k
index1 = sampler.nearest_n((x, y, z))
assert(len(set(index1)) == len(index1))
assert(index0 == index1[-1])
for ind in index1:
assert(abs(ind - k) <= 1)
print 'OK'
def test_pickle():
from dials.algorithms.profile_model.modeller import SingleSampler
scan_range = (2, 12)
nz = 2
sampler = SingleSampler(scan_range, nz)
sampler2 = pickle.loads(pickle.dumps(sampler))
assert sampler.grid_size() == sampler2.grid_size()
def test_self_consistent():
from dials.algorithms.profile_model.modeller import SingleSampler
scan_range = (2, 12)
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(len(sampler)):
coord = sampler.coord(i)
index = sampler.nearest(0, coord)
assert index == i
def test_pickle():
from dials.algorithms.profile_model.modeller import SingleSampler
import six.moves.cPickle as pickle
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
sampler2 = pickle.loads(pickle.dumps(sampler))
assert sampler.grid_size() == sampler2.grid_size()
def test_self_consistent():
from dials.algorithms.profile_model.modeller import SingleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(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 SingleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(len(sampler)):
coord = sampler.coord(i)
index = sampler.nearest(coord)
assert(index == i)
print 'OK'
def test_getters():
from dials.algorithms.profile_model.modeller import SingleSampler
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
scan_range = sampler.scan_range()
grid_size = sampler.grid_size()
step_size = sampler.step_size()
size = len(sampler)
assert scan_range[0] == scan_range[0]
assert scan_range[1] == scan_range[1]
assert nz == grid_size
assert step_size == depth / nz
assert nz == size
def test_nearest():
from dials.algorithms.profile_model.modeller import SingleSampler
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(*scan_range)
k = int(math.floor((z - scan_range[0]) / (depth / nz)))
if k >= nz:
k = nz - 1
index0 = k
index1 = sampler.nearest(0, (x, y, z))
assert index0 == index1
def tst_getters(self):
from dials.algorithms.profile_model.modeller import SingleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
scan_range = sampler.scan_range()
grid_size = sampler.grid_size()
step_size = sampler.step_size()
size = len(sampler)
assert (scan_range[0] == scan_range[0])
assert (scan_range[1] == scan_range[1])
assert (nz == grid_size)
assert (step_size == depth / nz)
assert (nz == size)
print 'OK'
def tst_getters(self):
from dials.algorithms.profile_model.modeller import SingleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
scan_range = sampler.scan_range()
grid_size = sampler.grid_size()
step_size = sampler.step_size()
size = len(sampler)
assert(scan_range[0] == scan_range[0])
assert(scan_range[1] == scan_range[1])
assert(nz == grid_size)
assert(step_size == depth / nz)
assert(nz == size)
print 'OK'
def tst_pickle(self):
from dials.algorithms.profile_model.modeller import SingleSampler
import tempfile
import cPickle as pickle
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
tf = tempfile.TemporaryFile()
pickle.dump(sampler, tf)
tf.flush()
tf.seek(0)
sampler2 = pickle.load(tf)
assert(sampler.grid_size() == sampler2.grid_size())
print 'OK'
def tst_pickle(self):
from dials.algorithms.profile_model.modeller import SingleSampler
import tempfile
import cPickle as pickle
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
tf = tempfile.TemporaryFile()
pickle.dump(sampler, tf)
tf.flush()
tf.seek(0)
sampler2 = pickle.load(tf)
assert (sampler.grid_size() == sampler2.grid_size())
print 'OK'
def test_indexing():
from dials.algorithms.profile_model.modeller import SingleSampler
width = 1000
height = 1000
scan_range = (2, 12)
nz = 2
sampler = SingleSampler(scan_range, nz)
zstep = sampler.step_size()
zind = list(range(nz))
xp = [width * 0.5 for k in range(nz)]
yp = [height * 0.5 for k in range(nz)]
zp = [(z + 0.5) * zstep + scan_range[0] for z in zind]
eps = 1e-10
for x0, y0, z0, i in zip(xp, yp, zp, range(len(sampler))):
x1, y1, z1 = sampler.coord(i)
assert abs(z0 - z1) <= eps
def test_nearest_n():
from dials.algorithms.profile_model.modeller import SingleSampler
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(1000):
x = random.uniform(0, 1000)
y = random.uniform(0, 1000)
z = random.uniform(*scan_range)
k = int(math.floor((z - scan_range[0]) * nz / depth))
if k >= nz:
k = nz - 1
index0 = k
index1 = sampler.nearest_n(0, (x, y, z))
assert len(set(index1)) == len(index1)
assert index0 == index1[-1]
for ind in index1:
assert abs(ind - k) <= 1
def tst_indexing(self):
from dials.algorithms.profile_model.modeller import SingleSampler
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
zstep = sampler.step_size()
zind = [k for k in range(nz)]
xp = [width *0.5 for k in range(nz)]
yp = [height *0.5 for k in range(nz)]
zp = [(z + 0.5) * zstep + scan_range[0] for z in zind]
eps = 1e-10
for x0, y0, z0, i in zip(xp, yp, zp, range(len(sampler))):
x1, y1, z1 = sampler.coord(i)
assert(abs(z0 - z1) <= eps)
print 'OK'
def tst_weights(self):
from dials.algorithms.profile_model.modeller import SingleSampler
from scitbx import matrix
from math import log, exp
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(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
# Ensure we get the expected weight at the next grid point at half way
# between grid points
expected = exp(-4.0 * log(2.0))
for k in range(nz):
coord1 = matrix.col(sampler.coord(k))
if k > 0:
coord = matrix.col(sampler.coord(k - 1))
weight = sampler.weight(k, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(k, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
if k < nz - 1:
coord = matrix.col(sampler.coord(k + 1))
weight = sampler.weight(k, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(k, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
print "OK"
def tst_nearest(self):
from random import uniform
from dials.algorithms.profile_model.modeller import SingleSampler
from math import floor
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(scan_range, nz)
for i in range(1000):
x = uniform(0, 1000)
y = uniform(0, 1000)
z = uniform(*scan_range)
k = int(floor((z - scan_range[0]) / (depth / nz)))
if k >= nz:
k = nz - 1
index0 = k
index1 = sampler.nearest(0, (x, y, z))
assert index0 == index1
print "OK"
def create(cls,
experiments,
grid_size=5,
scan_step=5,
grid_method="circular_grid"):
"""
Create the intensity calculator
"""
from dials.algorithms.integration.parallel_integrator import (
GaussianRSReferenceCalculator, )
from dials.algorithms.profile_model.modeller import SingleSampler
from dials.algorithms.profile_model.modeller import CircleSampler
from dials.algorithms.profile_model.modeller import GridSampler
from dials.algorithms.profile_model.modeller import EwaldSphereSampler
from dials.algorithms.profile_model.gaussian_rs.transform import TransformSpec
from math import ceil
# Assume the detector and scan are the same in each case
detector = experiments[0].detector
scan = experiments[0].scan
# Get the number of scan points
scan_range = scan.get_oscillation_range(deg=True)
scan_range = abs(scan_range[1] - scan_range[0])
num_scan_points = int(ceil(scan_range / scan_step))
# If multi panel then set to single
if grid_method in ["regular_grid", "circular_grid"
] and len(detector) > 1:
grid_method = "single"
# Create the sampler
if grid_method == "single":
sampler = SingleSampler(scan.get_array_range(), num_scan_points)
elif grid_method == "regular_grid":
sampler = GridSampler(
detector[0].get_image_size(),
scan.get_array_range(),
(3, 3, num_scan_points),
)
elif grid_method == "circular_grid":
sampler = CircleSampler(detector[0].get_image_size(),
scan.get_array_range(), num_scan_points)
elif grid_method == "spherical_grid":
sampler = EwaldSphereGridSampler(
experiments[0].beam,
experiments[0].detector,
experiments[0].goniometer,
experiments[0].scan,
num_scan_points,
)
else:
raise RuntimeError("Unknown grid type")
# Create the spec list
spec_list = []
for experiment in experiments:
spec = TransformSpec(
experiment.beam,
experiment.detector,
experiment.goniometer,
experiment.scan,
experiment.profile.sigma_b(deg=False),
experiment.profile.sigma_m(deg=False),
experiment.profile.n_sigma() * 1.5,
grid_size,
)
spec_list.append(spec)
# Return the intensity algorithm
return GaussianRSReferenceCalculator(sampler, spec_list)
def tst_weights(self):
from dials.algorithms.profile_model.modeller import SingleSampler
from scitbx import matrix
from math import log, exp
width = 1000
height = 1000
scan_range = (2, 12)
depth = scan_range[1] - scan_range[0]
nz = 2
sampler = SingleSampler(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)
# Ensure we get the expected weight at the next grid point at half way
# between grid points
expected = exp(-4.0 * log(2.0))
for k in range(nz):
coord1 = matrix.col(sampler.coord(k))
if k > 0:
coord = matrix.col(sampler.coord(k - 1))
weight = sampler.weight(k, 0, coord)
assert (abs(weight - expected) < eps)
weight = sampler.weight(k, 0, (coord + coord1) / 2.0)
assert (abs(weight - 0.5) < eps)
if k < nz - 1:
coord = matrix.col(sampler.coord(k + 1))
weight = sampler.weight(k, 0, coord)
assert (abs(weight - expected) < eps)
weight = sampler.weight(k, 0, (coord + coord1) / 2.0)
assert (abs(weight - 0.5) < eps)
print 'OK'
def test_weights():
from scitbx import matrix
from dials.algorithms.profile_model.modeller import SingleSampler
scan_range = (2, 12)
nz = 2
sampler = SingleSampler(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
# Ensure we get the expected weight at the next grid point at half way
# between grid points
expected = math.exp(-4.0 * math.log(2.0))
for k in range(nz):
coord1 = matrix.col(sampler.coord(k))
if k > 0:
coord = matrix.col(sampler.coord(k - 1))
weight = sampler.weight(k, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(k, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
if k < nz - 1:
coord = matrix.col(sampler.coord(k + 1))
weight = sampler.weight(k, 0, coord)
assert abs(weight - expected) < eps
weight = sampler.weight(k, 0, (coord + coord1) / 2.0)
assert abs(weight - 0.5) < eps
