def generate_reflections(self):
# Build a mock scan for a 3 degree sweep
from dxtbx.model.scan import scan_factory
sf = scan_factory()
self.scan = sf.make_scan(image_range = (1,1),
exposure_times = 0.1,
oscillation = (0, 3.0),
epochs = range(1),
deg = True)
sweep_range = self.scan.get_oscillation_range(deg=False)
# Create a scans ExperimentList, only for generating reflections
experiments = ExperimentList()
experiments.append(Experiment(
beam=self.beam, detector=self.detector, goniometer=self.gonio, scan=self.scan,
crystal=self.crystal, imageset=None))
# Create a ScansRayPredictor
ray_predictor = ScansRayPredictor(experiments, sweep_range)
# Generate rays - only to work out which hkls are predicted
resolution = 2.0
index_generator = IndexGenerator(self.crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(),
resolution)
indices = index_generator.to_array()
rays = ray_predictor.predict(indices)
# Make a standard reflection_table and copy in the ray data
self.reflections = flex.reflection_table.empty_standard(len(rays))
self.reflections.update(rays)
return
def ref_gen_static(experiments):
"""Generate some reflections using the static predictor"""
beam = experiments[0].beam
crystal = experiments[0].crystal
goniometer = experiments[0].goniometer
detector = experiments[0].detector
scan = experiments[0].scan
# All indices to the detector max resolution
dmin = detector.get_max_resolution(beam.get_s0())
index_generator = IndexGenerator(crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), dmin)
indices = index_generator.to_array()
# Predict rays within the sweep range
sweep_range = scan.get_oscillation_range(deg=False)
ray_predictor = ScansRayPredictor(experiments, sweep_range)
refs = ray_predictor.predict(indices)
# Take only those rays that intersect the detector
intersects = ray_intersection(detector, refs)
refs = refs.select(intersects)
# Make a reflection predictor and re-predict for these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
ref_predictor = ExperimentsPredictor(experiments)
refs['id'] = flex.int(len(refs), 0)
refs = ref_predictor.predict(refs)
return refs
def generate_reflections(experiments):
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.refinement.prediction import \
ScansRayPredictor, ExperimentsPredictor
from dials.algorithms.spot_prediction import ray_intersection
from cctbx.sgtbx import space_group, space_group_symbols
from scitbx.array_family import flex
detector = experiments[0].detector
crystal = experiments[0].crystal
# All indices in a 2.0 Angstrom sphere
resolution = 2.0
index_generator = IndexGenerator(crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices = index_generator.to_array()
# Predict rays within the sweep range
scan = experiments[0].scan
sweep_range = scan.get_oscillation_range(deg=False)
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs = ray_predictor.predict(indices)
# Take only those rays that intersect the detector
intersects = ray_intersection(detector, obs_refs)
obs_refs = obs_refs.select(intersects)
# Make a reflection predictor and re-predict for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
ref_predictor = ExperimentsPredictor(experiments)
obs_refs['id'] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor.predict(obs_refs)
# Set 'observed' centroids from the predicted ones
obs_refs['xyzobs.mm.value'] = obs_refs['xyzcal.mm']
# Invent some variances for the centroid positions of the simulated data
im_width = 0.1 * pi / 180.
px_size = detector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.)**2)
obs_refs['xyzobs.mm.variance'] = flex.vec3_double(var_x, var_y, var_phi)
return obs_refs, ref_predictor
# All indices in a 2.0 Angstrom sphere for crystal1
resolution = 2.0
index_generator = IndexGenerator(crystal1.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices1 = index_generator.to_array()
# All indices in a 2.0 Angstrom sphere for crystal2
resolution = 2.0
index_generator = IndexGenerator(crystal2.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices2 = index_generator.to_array()
# Predict rays within the sweep range. Set experiment IDs
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs1 = ray_predictor.predict(indices1, experiment_id=0)
obs_refs1['id'] = flex.int(len(obs_refs1), 0)
obs_refs2 = ray_predictor.predict(indices1, experiment_id=1)
obs_refs2['id'] = flex.int(len(obs_refs2), 1)
# Take only those rays that intersect the detector
intersects = ray_intersection(mydetector, obs_refs1)
obs_refs1 = obs_refs1.select(intersects)
intersects = ray_intersection(mydetector, obs_refs2)
obs_refs2 = obs_refs2.select(intersects)
# Make a reflection predictor and re-predict for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
ref_predictor = ExperimentsPredictor(experiments)
obs_refs1 = ref_predictor.predict(obs_refs1)
obs_refs2 = ref_predictor.predict(obs_refs2)
def run(verbose = False):
# Build models, with a larger crystal than default in order to get plenty of
# reflections on the 'still' image
overrides = """
geometry.parameters.crystal.a.length.range=40 50;
geometry.parameters.crystal.b.length.range=40 50;
geometry.parameters.crystal.c.length.range=40 50;
geometry.parameters.random_seed = 42"""
master_phil = parse("""
include scope dials.test.algorithms.refinement.geometry_phil
""", process_includes=True)
models = Extract(master_phil, overrides)
mydetector = models.detector
mygonio = models.goniometer
mycrystal = models.crystal
mybeam = models.beam
# Build a mock scan for a 3 degree sweep
from dxtbx.model.scan import scan_factory
sf = scan_factory()
myscan = sf.make_scan(image_range = (1,1),
exposure_times = 0.1,
oscillation = (0, 3.0),
epochs = range(1),
deg = True)
sweep_range = myscan.get_oscillation_range(deg=False)
# Create parameterisations of these models
det_param = DetectorParameterisationSinglePanel(mydetector)
s0_param = BeamParameterisation(mybeam, mygonio)
xlo_param = CrystalOrientationParameterisation(mycrystal)
xluc_param = CrystalUnitCellParameterisation(mycrystal)
# Create a scans ExperimentList, only for generating reflections
experiments = ExperimentList()
experiments.append(Experiment(
beam=mybeam, detector=mydetector, goniometer=mygonio, scan=myscan,
crystal=mycrystal, imageset=None))
# Create a stills ExperimentList
stills_experiments = ExperimentList()
stills_experiments.append(Experiment(
beam=mybeam, detector=mydetector, crystal=mycrystal, imageset=None))
# Generate rays - only to work out which hkls are predicted
ray_predictor = ScansRayPredictor(experiments, sweep_range)
resolution = 2.0
index_generator = IndexGenerator(mycrystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(),
resolution)
indices = index_generator.to_array()
rays = ray_predictor.predict(indices)
# Make a standard reflection_table and copy in the ray data
reflections = flex.reflection_table.empty_standard(len(rays))
reflections.update(rays)
# Build a standard prediction parameterisation for the stills experiment to do
# FD calculation (not used for its analytical gradients)
pred_param = StillsPredictionParameterisation(stills_experiments,
detector_parameterisations = [det_param],
beam_parameterisations = [s0_param],
xl_orientation_parameterisations = [xlo_param],
xl_unit_cell_parameterisations = [xluc_param])
# Make a managed SphericalRelpStillsReflectionPredictor reflection predictor
# for the first (only) experiment
ref_predictor = Predictor(stills_experiments)
# Predict these reflections in place. Must do this ahead of calculating
# the analytical gradients so quantities like s1 are correct
ref_predictor.update()
ref_predictor.predict(reflections)
# calculate analytical gradients
ag = AnalyticalGradients(stills_experiments,
detector_parameterisation=det_param,
beam_parameterisation=s0_param,
xl_orientation_parameterisation=xlo_param,
xl_unit_cell_parameterisation=xluc_param)
an_grads = ag.get_beam_gradients(reflections)
an_grads.update(ag.get_crystal_orientation_gradients(reflections))
an_grads.update(ag.get_crystal_unit_cell_gradients(reflections))
# get finite difference gradients
p_vals = pred_param.get_param_vals()
deltas = [1.e-7] * len(p_vals)
fd_grads = []
p_names = pred_param.get_param_names()
for i in range(len(deltas)):
# save parameter value
val = p_vals[i]
# calc reverse state
p_vals[i] -= deltas[i] / 2.
pred_param.set_param_vals(p_vals)
ref_predictor.update()
ref_predictor.predict(reflections)
x, y, _ = reflections['xyzcal.mm'].deep_copy().parts()
delpsi = reflections['delpsical.rad'].deep_copy()
s1 = reflections['s1'].deep_copy()
rev_state = s1
# calc forward state
p_vals[i] += deltas[i]
pred_param.set_param_vals(p_vals)
ref_predictor.update()
ref_predictor.predict(reflections)
x, y, _ = reflections['xyzcal.mm'].deep_copy().parts()
delpsi = reflections['delpsical.rad'].deep_copy()
s1 = reflections['s1'].deep_copy()
fwd_state = s1
# reset parameter to saved value
p_vals[i] = val
# finite difference - currently for s1 only
fd = (fwd_state - rev_state)
inv_delta = 1. / deltas[i]
s1_grads = fd * inv_delta
# store gradients
fd_grads.append({'name':p_names[i], 'ds1':s1_grads})
# return to the initial state
pred_param.set_param_vals(p_vals)
for i, fd_grad in enumerate(fd_grads):
## compare FD with analytical calculations
if verbose: print "\n\nParameter {0}: {1}". format(i, fd_grad['name'])
print "d[s1]/dp for the first reflection"
print 'finite diff', fd_grad['ds1'][0]
try:
an_grad = an_grads[fd_grad['name']]
except KeyError:
continue
print 'checking analytical vs finite difference gradients for s1'
for a, b in zip(fd_grad['ds1'], an_grad['ds1']):
assert approx_equal(a, b)
print 'OK'
#############################
# All indices in a 2.0 Angstrom sphere
resolution = 2.0
index_generator = IndexGenerator(mycrystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices = index_generator.to_array()
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert sweep_range == (0., pi)
assert approx_equal(im_width, 0.1 * pi / 180.)
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs = ray_predictor.predict(indices)
# Take only those rays that intersect the detector
intersects = ray_intersection(mydetector, obs_refs)
obs_refs = obs_refs.select(intersects)
# Make a reflection predictor and re-predict for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
ref_predictor = ExperimentsPredictor(experiments)
obs_refs['id'] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor.predict(obs_refs)
# Set 'observed' centroids from the predicted ones
obs_refs['xyzobs.mm.value'] = obs_refs['xyzcal.mm']
# Invent some variances for the centroid positions of the simulated data
def generate_reflections(experiments, xyzvar=(0.0, 0.0, 0.0)):
"""Generate synthetic reflection centroids using the supplied experiments,
with normally-distributed errors applied the variances in xyzvar"""
# check input
if [e >= 0.0 for e in xyzvar].count(False) > 0:
msg = "negative variance requested in " + str(xyzvar) + "!"
raise RuntimeError(msg)
refs = []
for iexp, exp in enumerate(experiments):
info("Generating reflections for experiment {0}".format(iexp))
# All indices in a 1.5 Angstrom sphere
resolution = 1.5
index_generator = IndexGenerator(
exp.crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(),
resolution,
)
indices = index_generator.to_array()
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(
experiments, exp.scan.get_oscillation_range(deg=False)
)
obs_refs = ray_predictor.predict(indices, experiment_id=iexp)
info("Total number of reflections excited: {0}".format(len(obs_refs)))
# Take only those rays that intersect the detector
intersects = ray_intersection(exp.detector, obs_refs)
obs_refs = obs_refs.select(intersects)
obs_refs["id"] = flex.size_t(len(obs_refs), iexp)
refs.append(obs_refs)
info("Total number of impacts: {0}".format(len(obs_refs)))
# Concatenate reflections
obs_refs = reduce(lambda x, y: x.extend(y), refs)
# Make a reflection predictor and re-predict for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
ref_predictor = ExperimentsPredictor(experiments)
obs_refs = ref_predictor.predict(obs_refs)
# calculate (uncorrelated) errors to offset the centroids
# this is safe as elts of xyzvar are already tested to be > 0
sigX, sigY, sigZ = [sqrt(e) for e in xyzvar]
shift = [
(random.gauss(0, sigX), random.gauss(0, sigY), random.gauss(0, sigZ))
for _ in xrange(len(obs_refs))
]
shift = flex.vec3_double(shift)
# Set 'observed' centroids from the predicted ones
obs_refs["xyzobs.mm.value"] = obs_refs["xyzcal.mm"] + shift
# Store variances for the centroid positions of the simulated data. If errors
# are zero, invent some variances
if tuple(xyzvar) == (0.0, 0.0, 0.0):
im_width = exp.scan.get_oscillation()[1] * pi / 180.0
px_size = exp.detector[0].get_pixel_size()
xyzvar = (
(px_size[0] / 2.0) ** 2,
(px_size[1] / 2.0) ** 2,
(im_width / 2.0) ** 2,
)
var_x = flex.double(len(obs_refs), xyzvar[0])
var_y = flex.double(len(obs_refs), xyzvar[1])
var_phi = flex.double(len(obs_refs), xyzvar[2])
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
info("Total number of observations made: {0}".format(len(obs_refs)))
return obs_refs
target_crystal = deepcopy(crystal)
#############################
# Generate some reflections #
#############################
# All indices in a 2.0 Angstrom sphere for crystal
resolution = 2.0
index_generator = IndexGenerator(crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices = index_generator.to_array()
# Build a ray predictor and predict rays close to the Ewald sphere by using
# the narrow rotation scan
ref_predictor = ScansRayPredictor(scans_experiments, sweep_range)
obs_refs = ref_predictor.predict(indices, experiment_id=0)
# Take only those rays that intersect the detector
intersects = ray_intersection(mydetector, obs_refs)
obs_refs = obs_refs.select(intersects)
# Add in flags and ID columns by copying into standard reflection table
tmp = flex.reflection_table.empty_standard(len(obs_refs))
tmp.update(obs_refs)
obs_refs = tmp
# Invent some variances for the centroid positions of the simulated data
im_width = 0.1 * pi / 180.
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.)**2)
#############################
# Generate some reflections #
#############################
# All indices in a 2.0 Angstrom sphere
resolution = 2.0
index_generator = IndexGenerator(mycrystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices = index_generator.to_array()
# for the reflection predictor, it doesn't matter which experiment list is
# passed, as the detector is not used
ref_predictor = ScansRayPredictor(experiments_single_panel, sweep_range)
# get two sets of identical reflections
obs_refs = ref_predictor.predict(indices)
obs_refs2 = ref_predictor.predict(indices)
for r1, r2 in zip(obs_refs, obs_refs2):
assert r1['s1'] == r2['s1']
# get the panel intersections
sel = ray_intersection(single_panel_detector, obs_refs)
obs_refs = obs_refs.select(sel)
sel = ray_intersection(multi_panel_detector, obs_refs2)
obs_refs2 = obs_refs2.select(sel)
assert len(obs_refs) == len(obs_refs2)
# Set 'observed' centroids from the predicted ones
obs_refs['xyzobs.mm.value'] = obs_refs['xyzcal.mm']
obs_refs2['xyzobs.mm.value'] = obs_refs2['xyzcal.mm']
def generate_reflections(experiments, xyzvar=(0., 0., 0.)):
'''Generate synthetic reflection centroids using the supplied experiments,
with normally-distributed errors applied the variances in xyzvar'''
# check input
if [e >= 0. for e in xyzvar].count(False) > 0:
msg = "negative variance requested in " + str(xyzvar) + "!"
raise RuntimeError(msg)
refs = []
for iexp, exp in enumerate(experiments):
info("Generating reflections for experiment {0}".format(iexp))
# All indices in a 1.5 Angstrom sphere
resolution = 1.5
index_generator = IndexGenerator(exp.crystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(), resolution)
indices = index_generator.to_array()
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(experiments,
exp.scan.get_oscillation_range(deg=False))
obs_refs = ray_predictor.predict(indices, experiment_id=iexp)
info("Total number of reflections excited: {0}".format(len(obs_refs)))
# Take only those rays that intersect the detector
intersects = ray_intersection(exp.detector, obs_refs)
obs_refs = obs_refs.select(intersects)
obs_refs['id'] = flex.size_t(len(obs_refs), iexp)
refs.append(obs_refs)
info("Total number of impacts: {0}".format(len(obs_refs)))
# Concatenate reflections
obs_refs = reduce(lambda x, y: x.extend(y), refs)
# Make a reflection predictor and re-predict for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
ref_predictor = ExperimentsPredictor(experiments)
obs_refs = ref_predictor.predict(obs_refs)
# calculate (uncorrelated) errors to offset the centroids
# this is safe as elts of xyzvar are already tested to be > 0
sigX, sigY, sigZ = [sqrt(e) for e in xyzvar]
shift = [(random.gauss(0, sigX),
random.gauss(0, sigY),
random.gauss(0, sigZ)) for _ in xrange(len(obs_refs))]
shift = flex.vec3_double(shift)
# Set 'observed' centroids from the predicted ones
obs_refs['xyzobs.mm.value'] = obs_refs['xyzcal.mm'] + shift
# Store variances for the centroid positions of the simulated data. If errors
# are zero, invent some variances
if tuple(xyzvar) == (0., 0., 0.):
im_width = exp.scan.get_oscillation()[1] * pi / 180.
px_size = exp.detector[0].get_pixel_size()
xyzvar = ((px_size[0] / 2.)**2, (px_size[1] / 2.)**2, (im_width / 2.)**2)
var_x = flex.double(len(obs_refs), xyzvar[0])
var_y = flex.double(len(obs_refs), xyzvar[1])
var_phi = flex.double(len(obs_refs), xyzvar[2])
obs_refs['xyzobs.mm.variance'] = flex.vec3_double(var_x, var_y, var_phi)
info("Total number of observations made: {0}".format(len(obs_refs)))
return obs_refs
