def generate_reflections(self):
"""Use reeke_model to generate indices of reflections near to the Ewald
sphere that might be observed on a still image. Build a reflection_table
of these."""
from cctbx.sgtbx import space_group_info
space_group_type = space_group_info("P 1").group().type()
# create a ReekeIndexGenerator
UB = self.crystal.get_A()
axis = self.goniometer.get_rotation_axis()
s0 = self.beam.get_s0()
dmin = 1.5
# use the same UB at the beginning and end - the margin parameter ensures
# we still have indices close to the Ewald sphere generated
from dials.algorithms.spot_prediction import ReekeIndexGenerator
r = ReekeIndexGenerator(UB, UB, space_group_type, axis, s0, dmin=1.5, margin=1)
# generate indices
hkl = r.to_array()
nref = len(hkl)
# create a reflection table
from dials.array_family import flex
table = flex.reflection_table()
table['flags'] = flex.size_t(nref, 0)
table['id'] = flex.int(nref, 0)
table['panel'] = flex.size_t(nref, 0)
table['miller_index'] = flex.miller_index(hkl)
table['entering'] = flex.bool(nref, True)
table['s1'] = flex.vec3_double(nref)
table['xyzcal.mm'] = flex.vec3_double(nref)
table['xyzcal.px'] = flex.vec3_double(nref)
return table
def generate_reflections(self):
"""Use reeke_model to generate indices of reflections near to the Ewald
sphere that might be observed on a still image. Build a reflection_table
of these."""
from cctbx.sgtbx import space_group_info
space_group_type = space_group_info("P 1").group().type()
# create a ReekeIndexGenerator
UB = self.crystal.get_U() * self.crystal.get_B()
axis = self.goniometer.get_rotation_axis()
s0 = self.beam.get_s0()
dmin = 1.5
# use the same UB at the beginning and end - the margin parameter ensures
# we still have indices close to the Ewald sphere generated
from dials.algorithms.spot_prediction import ReekeIndexGenerator
r = ReekeIndexGenerator(UB, UB, space_group_type, axis, s0, dmin=1.5, margin=1)
# generate indices
hkl = r.to_array()
nref = len(hkl)
# create a reflection table
from dials.array_family import flex
table = flex.reflection_table()
table['flags'] = flex.size_t(nref, 0)
table['id'] = flex.int(nref, 0)
table['panel'] = flex.size_t(nref, 0)
table['miller_index'] = flex.miller_index(hkl)
table['entering'] = flex.bool(nref, True)
table['s1'] = flex.vec3_double(nref)
table['xyzcal.mm'] = flex.vec3_double(nref)
table['xyzcal.px'] = flex.vec3_double(nref)
return table
def test_scan_varying(raypredictor):
from dials.algorithms.spot_prediction import ScanVaryingRayPredictor
from dials.algorithms.spot_prediction import ReekeIndexGenerator
from scitbx import matrix
import scitbx.math
s0 = raypredictor.beam.get_s0()
m2 = raypredictor.gonio.get_rotation_axis()
UB = raypredictor.ub_matrix
dphi = raypredictor.scan.get_oscillation_range(deg=False)
# For quick comparison look at reflections on one frame only
frame = 0
angle_beg = raypredictor.scan.get_angle_from_array_index(frame, deg=False)
angle_end = raypredictor.scan.get_angle_from_array_index(frame+1, deg=False)
frame0_refs = raypredictor.reflections.select(
(raypredictor.reflections['phi'] >= angle_beg) & (raypredictor.reflections['phi'] <= angle_end))
# Get UB matrices at beginning and end of frame
r_osc_beg = matrix.sqr(
scitbx.math.r3_rotation_axis_and_angle_as_matrix(
axis = m2, angle = angle_beg, deg=False))
UB_beg = r_osc_beg * raypredictor.ub_matrix
r_osc_end = matrix.sqr(
scitbx.math.r3_rotation_axis_and_angle_as_matrix(
axis = m2, angle = angle_end, deg=False))
UB_end = r_osc_end * raypredictor.ub_matrix
# Get indices
r = ReekeIndexGenerator(UB_beg, UB_end, raypredictor.space_group_type, m2,
s0, raypredictor.d_min, margin=1)
h = r.to_array()
# Fn to loop through hkl applying a prediction function to each and testing
# the results are the same as those from the ScanStaticRayPredictor
def test_each_hkl(hkl_list, predict_fn):
DEG2RAD = math.pi/180.
count = 0
for hkl in hkl_list:
ray = predict_fn(hkl)
if ray is not None:
count += 1
ref = frame0_refs.select(frame0_refs['miller_index']==hkl)[0]
assert ref['entering'] == ray.entering
assert ref['phi'] == pytest.approx(ray.angle * DEG2RAD, abs=1e-6) # ray angle is in degrees (!)
assert ref['s1'] == pytest.approx(ray.s1, abs=1e-6)
# ensure all reflections were matched
assert count == len(frame0_refs)
# Create the ray predictor
sv_predict_rays = ScanVaryingRayPredictor(s0, m2,
raypredictor.scan.get_array_range()[0], raypredictor.scan.get_oscillation(), raypredictor.d_min)
# Test with the method that allows only differing UB matrices
test_each_hkl(h, lambda x: sv_predict_rays(x, UB_beg, UB_end, frame))
# Now repeat prediction using the overload that allows for different s0
# at the beginning and end of the frame. Here, pass in the same s0 each time
# and the result should be the same as before
test_each_hkl(h, lambda x: sv_predict_rays(x, UB_beg, UB_end, s0, s0, frame))
def generate_cpp(self, frame):
from dials.algorithms.spot_prediction import ReekeIndexGenerator
from cctbx.sgtbx import space_group_info
space_group_type = space_group_info("P 1").group().type()
ub_beg, ub_end = self.get_ub(frame)
r = ReekeIndexGenerator(ub_beg, ub_end, space_group_type, self.axis, self.s0, self.dmin, self.margin)
hkl = r.to_array()
return sorted(list(hkl))
def test_varying_s0(self):
from dials.algorithms.spot_prediction import ReekeIndexGenerator
from cctbx.sgtbx import space_group_info
space_group_type = space_group_info("P 1").group().type()
ub_beg, ub_end = self._get_ub(0)
hkl_sets = []
# loop over random beam changes and ensure we can generate indices
us0 = self.s0.normalize()
for i in range(100):
# find a random axis orthogonal to the beam about which to rotate it
axis = (us0.ortho()).rotate_around_origin(axis=us0,
angle=random.uniform(
0, 2 * math.pi))
# apply small angle of rotation (up to ~1mrad) to perturb the beam direction
s0_2 = self.s0.rotate_around_origin(axis=axis,
angle=random.uniform(0, 0.057),
deg=True)
# alter the wavelength by up to about 0.1%
s0_2 = s0_2 * random.uniform(0.999, 1.001)
# now try to generate indices
r = ReekeIndexGenerator(
ub_beg,
ub_end,
space_group_type,
self.axis,
self.s0,
s0_2,
self.dmin,
self.margin,
)
hkl = r.to_array()
hkl_sets.append(set(list(hkl)))
# count common reflections in every set. For this example let's say we are
# satisfied if 98% of the smallest set of generated indices are common
# to every set. It is unclear how optimal this requirement is, but it at
# least shows that beam changes across one image that are much larger than
# we'd expect in normal processing do not hugely alter the generated list
# of HKLs.
min_set_len = min(len(e) for e in hkl_sets)
common = set.intersection(*hkl_sets)
# print "{0:.3f}% common".format(len(common) / min_set_len)
assert len(common) >= 0.98 * min_set_len
def test_versus_brute_force():
"""Perform a regression test by comparing to indices generated by the brute
force method"""
# cubic, 50A cell, 1A radiation, 1 deg osciillation, everything ideal
a = 50.0
ub_beg = matrix.sqr(
(1.0 / a, 0.0, 0.0, 0.0, 1.0 / a, 0.0, 0.0, 0.0, 1.0 / a))
axis = matrix.col((0, 1, 0))
r_osc = matrix.sqr(
r3_rotation_axis_and_angle_as_matrix(axis=axis, angle=1.0, deg=True))
ub_end = r_osc * ub_beg
uc = unit_cell((a, a, a, 90, 90, 90))
sg = space_group(space_group_symbols("P23").hall())
s0 = matrix.col((0, 0, 1))
wavelength = 1.0
dmin = 1.5
# get the full set of indices
indices = full_sphere_indices(unit_cell=uc,
resolution_limit=dmin,
space_group=sg)
# find the observed indices
ra = rotation_angles(dmin, ub_beg, wavelength, axis)
obs_indices, obs_angles = ra.observed_indices_and_angles_from_angle_range(
phi_start_rad=0.0 * math.pi / 180.0,
phi_end_rad=1.0 * math.pi / 180.0,
indices=indices,
)
# r = reeke_model(ub_beg, ub_end, axis, s0, dmin, 1.0)
# reeke_indices = r.generate_indices()
# now try the Reeke method to generate indices
r = ReekeIndexGenerator(ub_beg,
ub_end,
sg.type(),
axis,
s0,
dmin,
margin=1)
reeke_indices = r.to_array()
for oi in obs_indices:
assert tuple(map(int, oi)) in reeke_indices
def _search_on_image(self, t):
from cctbx.sgtbx import space_group_info
space_group_type = space_group_info("P 1").group().type()
self._predictor.prepare(t)
A1 = self._predictor.get_A1()
A2 = self._predictor.get_A2()
index_generator = ReekeIndexGenerator(A1, A2, space_group_type,
self._axis, self._s0,
self._dmin, margin = 1)
indices = index_generator.to_array()
reflections = []
for hkl in indices:
r = self._predictor.predict(hkl)
if r: reflections.append(r)
return reflections
def _search_on_image(self, t):
from cctbx.sgtbx import space_group_info
space_group_type = space_group_info("P 1").group().type()
self._predictor.prepare(t)
A1 = self._predictor.get_A1()
A2 = self._predictor.get_A2()
index_generator = ReekeIndexGenerator(
A1, A2, space_group_type, self._axis, self._s0, self._dmin, margin=1
)
indices = index_generator.to_array()
reflections = []
for hkl in indices:
r = self._predictor.predict(hkl)
if r:
reflections.append(r)
return reflections
