def test_Simple6ProfileModel_compute_mask(simple6_profile_model,
test_experiment):
experiments = [test_experiment]
# Create the index generator
index_generator = IndexGenerator(
experiments[0].crystal.get_unit_cell(),
experiments[0].crystal.get_space_group().type(),
2.0,
)
# Get an array of miller indices
miller_indices = index_generator.to_array()
reflections = simple6_profile_model.predict_reflections(experiments,
miller_indices,
probability=0.9973)
s2 = reflections["s2"]
s0 = matrix.col(experiments[0].beam.get_s0())
quantile = chisq_quantile(3, 0.9973)
sigma_inv = matrix.sqr(flumpy.from_numpy(
simple6_profile_model.sigma())).inverse()
for s2 in map(matrix.col, reflections["s2"]):
x = s2.normalize() * s0.length() - s2
d = (x.transpose() * sigma_inv * x)[0]
assert d < quantile
simple6_profile_model.compute_bbox(experiments, reflections)
reflections["shoebox"] = flex.shoebox(reflections["panel"],
reflections["bbox"],
allocate=True)
simple6_profile_model.compute_mask(experiments, reflections)
def test_Simple1ProfileModel_predict_reflections(
simple1_profile_model,
test_experiment,
):
# Create the index generator
index_generator = IndexGenerator(
test_experiment.crystal.get_unit_cell(),
test_experiment.crystal.get_space_group().type(),
2.0,
)
# Get an array of miller indices
miller_indices = index_generator.to_array()
reflections = simple1_profile_model.predict_reflections([test_experiment],
miller_indices,
probability=0.9973)
s0 = matrix.col(test_experiment.beam.get_s0())
quantile = chisq_quantile(3, 0.9973)
sigma_inv = matrix.sqr(flumpy.from_numpy(
simple1_profile_model.sigma())).inverse()
for s2 in reflections["s2"]:
s2_ = matrix.col(s2)
x = s2_.normalize() * s0.length() - s2_
d = (x.transpose() * sigma_inv * x)[0]
assert d < quantile
def predict(experiments, d_min=None, prediction_probability=0.9973):
"""Predict the reflections"""
logger.info("\n" + "=" * 80 + "\nPredicting reflections")
# Set a resolution range
if d_min is None:
s0 = experiments[0].beam.get_s0()
d_min = experiments[0].detector.get_max_resolution(s0)
# Create the index generator
index_generator = IndexGenerator(
experiments[0].crystal.get_unit_cell(),
experiments[0].crystal.get_space_group().type(),
d_min,
)
# Get an array of miller indices
miller_indices_to_test = index_generator.to_array()
logger.info("Generated %d miller indices" % len(miller_indices_to_test))
# Get the covariance matrix
profile = experiments[0].crystal.mosaicity
reflection_table = profile.parameterisation.predict_reflections(
experiments, miller_indices_to_test, prediction_probability)
# Do the prediction
reflection_table.compute_d(experiments)
logger.info("Predicted %d reflections" % len(reflection_table))
return reflection_table
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(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(refs)
return refs
def ref_gen_static(experiments):
"""Generate some reflections using the static predictor"""
beam = experiments[0].beam
crystal = experiments[0].crystal
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 sequence range
sequence_range = scan.get_oscillation_range(deg=False)
ray_predictor = ScansRayPredictor(experiments, sequence_range)
refs = ray_predictor(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 = ScansExperimentsPredictor(experiments)
refs["id"] = flex.int(len(refs), 0)
refs = ref_predictor(refs)
return refs
def generate_reflections(self):
# Build a mock scan for a 3 degree sweep
from dxtbx.model import ScanFactory
sf = ScanFactory()
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(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)
def test(dials_regression):
from iotbx.xds import xparm, integrate_hkl
from dials.util import ioutil
from dials.algorithms.spot_prediction import IndexGenerator
import numpy
from rstbx.cftbx.coordinate_frame_converter import \
coordinate_frame_converter
from scitbx import matrix
# The XDS files to read from
integrate_filename = os.path.join(dials_regression, 'data', 'sim_mx',
'INTEGRATE.HKL')
gxparm_filename = os.path.join(dials_regression, 'data', 'sim_mx',
'GXPARM.XDS')
# Read the XDS files
integrate_handle = integrate_hkl.reader()
integrate_handle.read_file(integrate_filename)
gxparm_handle = xparm.reader()
gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
d_min = 1.6
space_group_type = ioutil.get_space_group_type_from_xparm(gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get('real_space_a')
b_vec = cfc.get('real_space_b')
c_vec = cfc.get('real_space_c')
unit_cell = cfc.get_unit_cell()
UB = matrix.sqr(a_vec + b_vec + c_vec).inverse()
ub_matrix = UB
# Generate the indices
index_generator = IndexGenerator(unit_cell, space_group_type, d_min)
miller_indices = index_generator.to_array()
# Get individual generated hkl
gen_h = [hkl[0] for hkl in miller_indices]
gen_k = [hkl[1] for hkl in miller_indices]
gen_l = [hkl[2] for hkl in miller_indices]
# Get individual xds generated hkl
xds_h = [hkl[0] for hkl in integrate_handle.hkl]
xds_k = [hkl[1] for hkl in integrate_handle.hkl]
xds_l = [hkl[2] for hkl in integrate_handle.hkl]
# Get min/max generated hkl
min_gen_h, max_gen_h = numpy.min(gen_h), numpy.max(gen_h)
min_gen_k, max_gen_k = numpy.min(gen_k), numpy.max(gen_k)
min_gen_l, max_gen_l = numpy.min(gen_l), numpy.max(gen_l)
# Get min/max xds generated hkl
min_xds_h, max_xds_h = numpy.min(xds_h), numpy.max(xds_h)
min_xds_k, max_xds_k = numpy.min(xds_k), numpy.max(xds_k)
min_xds_l, max_xds_l = numpy.min(xds_l), numpy.max(xds_l)
# Ensure we have the whole xds range in the generated set
assert min_gen_h <= min_xds_h and max_gen_h >= max_xds_h
assert min_gen_k <= min_xds_k and max_gen_k >= max_xds_k
assert min_gen_l <= min_xds_l and max_gen_l >= max_xds_l
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 test(dials_regression):
import numpy as np
from iotbx.xds import integrate_hkl, xparm
from rstbx.cftbx.coordinate_frame_converter import coordinate_frame_converter
from dials.algorithms.spot_prediction import IndexGenerator
from dials.util import ioutil
# The XDS files to read from
integrate_filename = os.path.join(dials_regression, "data", "sim_mx",
"INTEGRATE.HKL")
gxparm_filename = os.path.join(dials_regression, "data", "sim_mx",
"GXPARM.XDS")
# Read the XDS files
integrate_handle = integrate_hkl.reader()
integrate_handle.read_file(integrate_filename)
gxparm_handle = xparm.reader()
gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
d_min = 1.6
space_group_type = ioutil.get_space_group_type_from_xparm(gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
unit_cell = cfc.get_unit_cell()
# Generate the indices
index_generator = IndexGenerator(unit_cell, space_group_type, d_min)
miller_indices = index_generator.to_array()
# Get individual generated hkl
gen_h = [hkl[0] for hkl in miller_indices]
gen_k = [hkl[1] for hkl in miller_indices]
gen_l = [hkl[2] for hkl in miller_indices]
# Get individual xds generated hkl
xds_h = [hkl[0] for hkl in integrate_handle.hkl]
xds_k = [hkl[1] for hkl in integrate_handle.hkl]
xds_l = [hkl[2] for hkl in integrate_handle.hkl]
# Get min/max generated hkl
min_gen_h, max_gen_h = np.min(gen_h), np.max(gen_h)
min_gen_k, max_gen_k = np.min(gen_k), np.max(gen_k)
min_gen_l, max_gen_l = np.min(gen_l), np.max(gen_l)
# Get min/max xds generated hkl
min_xds_h, max_xds_h = np.min(xds_h), np.max(xds_h)
min_xds_k, max_xds_k = np.min(xds_k), np.max(xds_k)
min_xds_l, max_xds_l = np.min(xds_l), np.max(xds_l)
# Ensure we have the whole xds range in the generated set
assert min_gen_h <= min_xds_h and max_gen_h >= max_xds_h
assert min_gen_k <= min_xds_k and max_gen_k >= max_xds_k
assert min_gen_l <= min_xds_l and max_gen_l >= max_xds_l
def generate_reflections(self):
from cctbx.sgtbx import space_group, space_group_symbols
from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection
sequence_range = self.scan.get_oscillation_range(deg=False)
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()
# Predict rays within the sequence range
ray_predictor = ScansRayPredictor(self.experiments, sequence_range)
obs_refs = ray_predictor(indices)
# Take only those rays that intersect the detector
intersects = ray_intersection(self.detector, obs_refs)
obs_refs = obs_refs.select(intersects)
# Re-predict using the Experiments predictor for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = self.ref_predictor(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 / 180.0
px_size = self.detector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(
var_x, var_y, var_phi)
# set the flex random seed to an 'uninteresting' number
flex.set_random_seed(12407)
# take 10 random reflections for speed
reflections = obs_refs.select(flex.random_selection(len(obs_refs), 10))
# use a BlockCalculator to calculate the blocks per image
from dials.algorithms.refinement.reflection_manager import BlockCalculator
block_calculator = BlockCalculator(self.experiments, reflections)
reflections = block_calculator.per_image()
return reflections
def generate_reflections(experiments):
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansRayPredictor,
ScansExperimentsPredictor,
)
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 sequence range
scan = experiments[0].scan
sequence_range = scan.get_oscillation_range(deg=False)
ray_predictor = ScansRayPredictor(experiments, sequence_range)
obs_refs = ray_predictor(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 = ScansExperimentsPredictor(experiments)
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor(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 / 180.0
px_size = detector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
return obs_refs, ref_predictor
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(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(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
def generate_reflections(self):
sweep_range = self.scan.get_oscillation_range(deg=False)
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()
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(self.experiments, sweep_range)
obs_refs = ray_predictor(indices)
# Take only those rays that intersect the detector
intersects = ray_intersection(self.detector, obs_refs)
obs_refs = obs_refs.select(intersects)
# Re-predict using the Experiments predictor for all these reflections. The
# result is the same, but we gain also the flags and xyzcal.px columns
obs_refs['id'] = flex.int(len(obs_refs), 0)
obs_refs = self.ref_predictor(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 = self.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)
# set the flex random seed to an 'uninteresting' number
flex.set_random_seed(12407)
# take 5 random reflections for speed
reflections = obs_refs.select(flex.random_selection(len(obs_refs), 5))
# use a BlockCalculator to calculate the blocks per image
from dials.algorithms.refinement.reflection_manager import BlockCalculator
block_calculator = BlockCalculator(self.experiments, reflections)
reflections = block_calculator.per_image()
return reflections
def test_Simple1ProfileModel_compute_bbox(simple1_profile_model,
test_experiment):
experiments = [test_experiment]
# Create the index generator
index_generator = IndexGenerator(
experiments[0].crystal.get_unit_cell(),
experiments[0].crystal.get_space_group().type(),
2.0,
)
# Get an array of miller indices
miller_indices = index_generator.to_array()
reflections = simple1_profile_model.predict_reflections(experiments,
miller_indices,
probability=0.9973)
simple1_profile_model.compute_bbox(experiments, reflections)
def generate_reflections(self):
# Build a mock scan for a 3 degree sequence
sf = ScanFactory()
self.scan = sf.make_scan(
image_range=(1, 1),
exposure_times=0.1,
oscillation=(0, 3.0),
epochs=list(range(1)),
deg=True,
)
sequence_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, sequence_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(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)
# Set dummy observed variances to allow statistical weights to be set
self.reflections["xyzobs.mm.variance"] += (1e-3, 1e-3, 1e-6)
def generate_reflections(experiments):
from cctbx.sgtbx import space_group, space_group_symbols
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansExperimentsPredictor,
ScansRayPredictor,
)
from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection
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 sequence range
scan = experiments[0].scan
sequence_range = scan.get_oscillation_range(deg=False)
ray_predictor = ScansRayPredictor(experiments, sequence_range)
obs_refs = ray_predictor(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 = ScansExperimentsPredictor(experiments)
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor(obs_refs)
# Set 'observed' centroids from the predicted ones
obs_refs["xyzobs.mm.value"] = obs_refs["xyzcal.mm"]
return obs_refs
def predict(self):
"""
Predict the reflections
"""
logger.info("")
logger.info("Predicting reflections")
# Set a resolution range
if self.params.prediction.d_min is None:
s0 = self.experiments[0].beam.get_s0()
d_min = self.experiments[0].detector.get_max_resolution(s0)
else:
d_min = self.params.predictions.d_min
# Create the index generator
index_generator = IndexGenerator(
self.experiments[0].crystal.get_unit_cell(),
self.experiments[0].crystal.get_space_group().type(),
d_min,
)
# Get an array of miller indices
miller_indices_to_test = index_generator.to_array()
logger.info("Generated %d miller indices" %
len(miller_indices_to_test))
# Get the covariance matrix
profile = self.experiments[0].crystal.mosaicity
self.reflections = profile.predict_reflections(
self.experiments, miller_indices_to_test,
self.params.prediction.probability)
# Do the prediction
self.reference = self.reference
self.reflections.compute_d(self.experiments)
logger.info("Predicted %d reflections" % len(self.reflections))
# Match with the reference reflections
_, _, unmatched = self.reflections.match_with_reference(self.reference)
#############################
# Generate some reflections #
#############################
#print "Reflections will be generated with the following geometry:"
#print mybeam
#print mydetector
#print crystal1
#print crystal2
# 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
def test():
# 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 import ScanFactory
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 1),
exposure_times=0.1,
oscillation=(0, 3.0),
epochs=list(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(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 = [1.0e-7] * len(p_vals)
fd_grads = []
p_names = pred_param.get_param_names()
for i, delta in enumerate(deltas):
# save parameter value
val = p_vals[i]
# calc reverse state
p_vals[i] -= delta / 2.0
pred_param.set_param_vals(p_vals)
ref_predictor.update()
ref_predictor.predict(reflections)
x, y, _ = reflections["xyzcal.mm"].deep_copy().parts()
s1 = reflections["s1"].deep_copy()
rev_state = s1
# calc forward state
p_vals[i] += delta
pred_param.set_param_vals(p_vals)
ref_predictor.update()
ref_predictor.predict(reflections)
x, y, _ = reflections["xyzcal.mm"].deep_copy().parts()
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 = 1.0 / delta
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
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 a == pytest.approx(b, abs=1e-7)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(mycrystal.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.e5 for e in S.forward_independent_parameters()])
xluc_param.set_param_vals(X)
#############################
# 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()
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(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
def test(args=[]):
# Python and cctbx imports
from math import pi
import random
from scitbx import matrix
from scitbx.array_family import flex
from libtbx.phil import parse
from libtbx.test_utils import approx_equal
# Experimental model builder
from dials.test.algorithms.refinement.setup_geometry import Extract
# We will set up a mock scan and a mock experiment list
from dxtbx.model import ScanFactory
from dxtbx.model.experiment_list import ExperimentList, Experiment
# Model parameterisations
from dials.algorithms.refinement.parameterisation.detector_parameters import (
DetectorParameterisationSinglePanel, )
from dials.algorithms.refinement.parameterisation.beam_parameters import (
BeamParameterisation, )
from dials.algorithms.refinement.parameterisation.crystal_parameters import (
CrystalOrientationParameterisation,
CrystalUnitCellParameterisation,
)
# Reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansRayPredictor,
ScansExperimentsPredictor,
)
from cctbx.sgtbx import space_group, space_group_symbols
# Parameterisation of the prediction equation
from dials.algorithms.refinement.parameterisation.prediction_parameters import (
XYPhiPredictionParameterisation, )
# Imports for the target function
from dials.algorithms.refinement.target import (
LeastSquaresPositionalResidualWithRmsdCutoff, )
from dials.algorithms.refinement.reflection_manager import ReflectionManager
# Local functions
def random_direction_close_to(vector, sd=0.5):
return vector.rotate_around_origin(
matrix.col((random.random(), random.random(),
random.random())).normalize(),
random.gauss(0, sd),
deg=True,
)
#############################
# Setup experimental models #
#############################
# make a small cell to speed up calculations
overrides = """geometry.parameters.crystal.a.length.range = 10 15
geometry.parameters.crystal.b.length.range = 10 15
geometry.parameters.crystal.c.length.range = 10 15"""
master_phil = parse(
"""
include scope dials.test.algorithms.refinement.geometry_phil
""",
process_includes=True,
)
models = Extract(master_phil, overrides, cmdline_args=args)
mydetector = models.detector
mygonio = models.goniometer
mycrystal = models.crystal
mybeam = models.beam
# Build a mock scan for a 180 degree sweep of 0.1 degree images
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 1800),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(1800)),
deg=True,
)
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert sweep_range == (0.0, pi)
assert approx_equal(im_width, 0.1 * pi / 180.0)
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
###########################
# Parameterise the models #
###########################
det_param = DetectorParameterisationSinglePanel(mydetector)
s0_param = BeamParameterisation(mybeam, mygonio)
xlo_param = CrystalOrientationParameterisation(mycrystal)
xluc_param = CrystalUnitCellParameterisation(mycrystal)
########################################################################
# Link model parameterisations together into a parameterisation of the #
# prediction equation #
########################################################################
pred_param = XYPhiPredictionParameterisation(experiments, [det_param],
[s0_param], [xlo_param],
[xluc_param])
################################
# Apply known parameter shifts #
################################
# shift detector by 0.2 mm each translation and 2 mrad each rotation
det_p_vals = det_param.get_param_vals()
p_vals = [
a + b for a, b in zip(det_p_vals, [2.0, 2.0, 2.0, 2.0, 2.0, 2.0])
]
det_param.set_param_vals(p_vals)
# shift beam by 2 mrad in one axis
s0_p_vals = s0_param.get_param_vals()
p_vals = list(s0_p_vals)
p_vals[1] += 2.0
s0_param.set_param_vals(p_vals)
# rotate crystal a bit (=2 mrad each rotation)
xlo_p_vals = xlo_param.get_param_vals()
p_vals = [a + b for a, b in zip(xlo_p_vals, [2.0, 2.0, 2.0])]
xlo_param.set_param_vals(p_vals)
#############################
# 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()
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs = ray_predictor(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 = ScansExperimentsPredictor(experiments)
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor(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 / 180.0
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
###############################
# Undo known parameter shifts #
###############################
s0_param.set_param_vals(s0_p_vals)
det_param.set_param_vals(det_p_vals)
xlo_param.set_param_vals(xlo_p_vals)
#####################################
# Select reflections for refinement #
#####################################
refman = ReflectionManager(obs_refs, experiments)
##############################
# Set up the target function #
##############################
# Redefine the reflection predictor to use the type expected by the Target class
ref_predictor = ScansExperimentsPredictor(experiments)
mytarget = LeastSquaresPositionalResidualWithRmsdCutoff(
experiments,
ref_predictor,
refman,
pred_param,
restraints_parameterisation=None)
# get the functional and gradients
mytarget.predict()
L, dL_dp, curvs = mytarget.compute_functional_gradients_and_curvatures()
####################################
# Do FD calculation for comparison #
####################################
# function for calculating finite difference gradients of the target function
def get_fd_gradients(target, pred_param, deltas):
"""Calculate centered finite difference gradients for each of the
parameters of the target function.
"deltas" must be a sequence of the same length as the parameter list, and
contains the step size for the difference calculations for each parameter.
"""
p_vals = pred_param.get_param_vals()
assert len(deltas) == len(p_vals)
fd_grad = []
fd_curvs = []
for i in range(len(deltas)):
val = p_vals[i]
p_vals[i] -= deltas[i] / 2.0
pred_param.set_param_vals(p_vals)
target.predict()
rev_state = target.compute_functional_gradients_and_curvatures()
p_vals[i] += deltas[i]
pred_param.set_param_vals(p_vals)
target.predict()
fwd_state = target.compute_functional_gradients_and_curvatures()
# finite difference estimation of first derivatives
fd_grad.append((fwd_state[0] - rev_state[0]) / deltas[i])
# finite difference estimation of curvatures, using the analytical
# first derivatives
fd_curvs.append((fwd_state[1][i] - rev_state[1][i]) / deltas[i])
# set parameter back to centred value
p_vals[i] = val
# return to the initial state
pred_param.set_param_vals(p_vals)
return fd_grad, fd_curvs
# test normalised differences between FD and analytical calculations
fdgrads = get_fd_gradients(mytarget, pred_param,
[1.0e-7] * len(pred_param))
diffs = [a - b for a, b in zip(dL_dp, fdgrads[0])]
norm_diffs = tuple([a / b for a, b in zip(diffs, fdgrads[0])])
for e in norm_diffs:
assert abs(e) < 0.001 # check differences less than 0.1%
# test normalised differences between FD curvatures and analytical least
# squares approximation. We don't expect this to be especially close
if curvs:
diffs = [a - b for a, b in zip(curvs, fdgrads[1])]
norm_diffs = tuple([a / b for a, b in zip(diffs, fdgrads[1])])
for e in norm_diffs:
assert abs(e) < 0.1 # check differences less than 10%
class RayPredictor:
def __init__(self, dials_regression):
import dxtbx
from iotbx.xds import integrate_hkl, xparm
from rstbx.cftbx.coordinate_frame_converter import coordinate_frame_converter
from dials.algorithms.spot_prediction import (
IndexGenerator,
ScanStaticRayPredictor,
)
from dials.util import ioutil
# The XDS files to read from
integrate_filename = os.path.join(dials_regression, "data", "sim_mx",
"INTEGRATE.HKL")
gxparm_filename = os.path.join(dials_regression, "data", "sim_mx",
"GXPARM.XDS")
# Read the XDS files
self.integrate_handle = integrate_hkl.reader()
self.integrate_handle.read_file(integrate_filename)
self.gxparm_handle = xparm.reader()
self.gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
models = dxtbx.load(gxparm_filename)
self.beam = models.get_beam()
self.gonio = models.get_goniometer()
self.detector = models.get_detector()
self.scan = models.get_scan()
# Get crystal parameters
self.space_group_type = ioutil.get_space_group_type_from_xparm(
self.gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get("real_space_a")
b_vec = cfc.get("real_space_b")
c_vec = cfc.get("real_space_c")
self.unit_cell = cfc.get_unit_cell()
self.ub_matrix = matrix.sqr(a_vec + b_vec + c_vec).inverse()
# Get the minimum resolution in the integrate file
d = [self.unit_cell.d(h) for h in self.integrate_handle.hkl]
self.d_min = min(d)
# extend the resolution shell by epsilon>0
# to account for rounding artifacts on 32-bit platforms
self.d_min = self.d_min - 1e-15
# Get the number of frames from the max z value
xcal, ycal, zcal = zip(*self.integrate_handle.xyzcal)
self.scan.set_image_range((
self.scan.get_image_range()[0],
self.scan.get_image_range()[0] + int(math.ceil(max(zcal))),
))
# Print stuff
# print self.beam
# print self.gonio
# print self.detector
# print self.scan
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type,
self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections = self.predict_rays(self.generate_indices.to_array(),
UB)
def test(args=[]):
# Python and cctbx imports
from math import pi
from scitbx import matrix
from libtbx.phil import parse
from libtbx.test_utils import approx_equal
# Import for surgery on reflection_tables
from dials.array_family import flex
# Get module to build models using PHIL
import dials.test.algorithms.refinement.setup_geometry as setup_geometry
# We will set up a mock scan and a mock experiment list
from dxtbx.model import ScanFactory
from dxtbx.model.experiment_list import ExperimentList, Experiment
# Crystal parameterisations
from dials.algorithms.refinement.parameterisation.crystal_parameters import (
CrystalOrientationParameterisation,
CrystalUnitCellParameterisation,
)
# Symmetry constrained parameterisation for the unit cell
from cctbx.uctbx import unit_cell
from rstbx.symmetry.constraints.parameter_reduction import symmetrize_reduce_enlarge
# Reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansRayPredictor,
StillsExperimentsPredictor,
)
from dials.algorithms.spot_prediction import ray_intersection
from cctbx.sgtbx import space_group, space_group_symbols
#############################
# Setup experimental models #
#############################
master_phil = parse(
"""
include scope dials.test.algorithms.refinement.geometry_phil
include scope dials.test.algorithms.refinement.minimiser_phil
""",
process_includes=True,
)
# build models, with a larger crystal than default in order to get enough
# reflections on the 'still' image
param = """
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"""
models = setup_geometry.Extract(master_phil,
cmdline_args=args,
local_overrides=param)
crystal = models.crystal
mydetector = models.detector
mygonio = models.goniometer
mybeam = models.beam
# Build a mock scan for a 1.5 degree wedge. Only used for generating indices near
# the Ewald sphere
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 1),
exposure_times=0.1,
oscillation=(0, 1.5),
epochs=list(range(1)),
deg=True,
)
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert approx_equal(im_width, 1.5 * pi / 180.0)
# Build experiment lists
stills_experiments = ExperimentList()
stills_experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
crystal=crystal,
imageset=None))
scans_experiments = ExperimentList()
scans_experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
crystal=crystal,
goniometer=mygonio,
scan=myscan,
imageset=None,
))
##########################################################
# Parameterise the models (only for perturbing geometry) #
##########################################################
xlo_param = CrystalOrientationParameterisation(crystal)
xluc_param = CrystalUnitCellParameterisation(crystal)
################################
# Apply known parameter shifts #
################################
# rotate crystal (=5 mrad each rotation)
xlo_p_vals = []
p_vals = xlo_param.get_param_vals()
xlo_p_vals.append(p_vals)
new_p_vals = [a + b for a, b in zip(p_vals, [5.0, 5.0, 5.0])]
xlo_param.set_param_vals(new_p_vals)
# change unit cell (=1.0 Angstrom length upsets, 0.5 degree of
# gamma angle)
xluc_p_vals = []
p_vals = xluc_param.get_param_vals()
xluc_p_vals.append(p_vals)
cell_params = crystal.get_unit_cell().parameters()
cell_params = [
a + b for a, b in zip(cell_params, [1.0, 1.0, -1.0, 0.0, 0.0, 0.5])
]
new_uc = unit_cell(cell_params)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(crystal.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.0e5 for e in S.forward_independent_parameters()])
xluc_param.set_param_vals(X)
# keep track of the target crystal model to compare with refined
from copy import deepcopy
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(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 / 180.0
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
# Re-predict using the stills reflection predictor
stills_ref_predictor = StillsExperimentsPredictor(stills_experiments)
obs_refs_stills = stills_ref_predictor(obs_refs)
# Set 'observed' centroids from the predicted ones
obs_refs_stills["xyzobs.mm.value"] = obs_refs_stills["xyzcal.mm"]
###############################
# Undo known parameter shifts #
###############################
xlo_param.set_param_vals(xlo_p_vals[0])
xluc_param.set_param_vals(xluc_p_vals[0])
# make a refiner
from dials.algorithms.refinement.refiner import phil_scope
params = phil_scope.fetch(source=parse("")).extract()
# Change this to get a plot
do_plot = False
if do_plot:
params.refinement.refinery.journal.track_parameter_correlation = True
from dials.algorithms.refinement.refiner import RefinerFactory
# decrease bin_size_fraction to terminate on RMSD convergence
params.refinement.target.bin_size_fraction = 0.01
params.refinement.parameterisation.beam.fix = "all"
params.refinement.parameterisation.detector.fix = "all"
refiner = RefinerFactory.from_parameters_data_experiments(
params, obs_refs_stills, stills_experiments)
# run refinement
history = refiner.run()
# regression tests
assert len(history["rmsd"]) == 9
refined_crystal = refiner.get_experiments()[0].crystal
uc1 = refined_crystal.get_unit_cell()
uc2 = target_crystal.get_unit_cell()
assert uc1.is_similar_to(uc2)
if do_plot:
plt = refiner.parameter_correlation_plot(
len(history["parameter_correlation"]) - 1)
plt.show()
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
def test2():
"""Test on simulated data"""
# Get models for reflection prediction
import dials.test.algorithms.refinement.setup_geometry as setup_geometry
from libtbx.phil import parse
overrides = """geometry.parameters.crystal.a.length.value = 77
geometry.parameters.crystal.b.length.value = 77
geometry.parameters.crystal.c.length.value = 37"""
master_phil = parse(
"""
include scope dials.test.algorithms.refinement.geometry_phil
""",
process_includes=True,
)
from dxtbx.model import Crystal
models = setup_geometry.Extract(master_phil)
crystal = Crystal(
real_space_a=(2.62783398111729, -63.387215823567125,
-45.751375737456975),
real_space_b=(15.246640559660356, -44.48254330406616,
62.50501032727026),
real_space_c=(-76.67246874451074, -11.01804131886244,
10.861322446352226),
space_group_symbol="I 2 3",
)
detector = models.detector
goniometer = models.goniometer
beam = models.beam
# Build a mock scan for a 180 degree sweep
from dxtbx.model import ScanFactory
sf = ScanFactory()
scan = sf.make_scan(
image_range=(1, 1800),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=range(1800),
deg=True,
)
# Build an experiment list
from dxtbx.model.experiment_list import ExperimentList, Experiment
experiments = ExperimentList()
experiments.append(
Experiment(
beam=beam,
detector=detector,
goniometer=goniometer,
scan=scan,
crystal=crystal,
imageset=None,
))
# Generate all indices in a 1.5 Angstrom sphere
from dials.algorithms.spot_prediction import IndexGenerator
from cctbx.sgtbx import space_group, space_group_symbols
resolution = 1.5
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
from dials.algorithms.refinement.prediction import ScansRayPredictor
sweep_range = scan.get_oscillation_range(deg=False)
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs = ray_predictor(indices)
# Take only those rays that intersect the detector
from dials.algorithms.spot_prediction import ray_intersection
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
from dials.algorithms.refinement.prediction import ExperimentsPredictor
ref_predictor = ExperimentsPredictor(experiments)
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor(obs_refs)
# Copy 'observed' centroids from the predicted ones, applying sinusoidal
# offsets
obs_x, obs_y, obs_z = obs_refs["xyzcal.mm"].parts()
# obs_z is in range (0, pi). Calculate offsets for phi at twice that
# frequency
im_width = scan.get_oscillation(deg=False)[1]
z_off = flex.sin(2 * obs_z) * im_width
obs_z += z_off
# Calculate offsets for x
pixel_size = detector[0].get_pixel_size()
x_off = flex.sin(20 * obs_z) * pixel_size[0]
# Calculate offsets for y with a phase-shifted sine wave
from math import pi
y_off = flex.sin(4 * obs_z + pi / 6) * pixel_size[1]
# Incorporate the offsets into the 'observed' centroids
obs_z += z_off
obs_x += x_off
obs_y += y_off
obs_refs["xyzobs.mm.value"] = flex.vec3_double(obs_x, obs_y, obs_z)
# Now do centroid analysis of the residuals
results = CentroidAnalyser(obs_refs, debug=True)()
# FIXME this test shows that the suggested interval width heuristic is not
# yet robust. This simulation function seems a useful direction to proceed
# in though
raise RuntimeError("test2 failed")
print("OK")
return
def __init__(self):
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from dials.algorithms.spot_prediction import ray_intersection
from iotbx.xds import xparm, integrate_hkl
from dials.util import ioutil
from math import ceil
import dxtbx
from rstbx.cftbx.coordinate_frame_converter import \
coordinate_frame_converter
from scitbx import matrix
# The XDS files to read from
integrate_filename = join(dials_regression,
'data/sim_mx/INTEGRATE.HKL')
gxparm_filename = join(dials_regression, 'data/sim_mx/GXPARM.XDS')
# Read the XDS files
self.integrate_handle = integrate_hkl.reader()
self.integrate_handle.read_file(integrate_filename)
self.gxparm_handle = xparm.reader()
self.gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
models = dxtbx.load(gxparm_filename)
self.beam = models.get_beam()
self.gonio = models.get_goniometer()
self.detector = models.get_detector()
self.scan = models.get_scan()
assert (len(self.detector) == 1)
#print self.detector
# Get crystal parameters
self.space_group_type = ioutil.get_space_group_type_from_xparm(
self.gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get('real_space_a')
b_vec = cfc.get('real_space_b')
c_vec = cfc.get('real_space_c')
self.unit_cell = cfc.get_unit_cell()
self.ub_matrix = matrix.sqr(a_vec + b_vec + c_vec).inverse()
# Get the minimum resolution in the integrate file
self.d_min = self.detector[0].get_max_resolution_at_corners(
self.beam.get_s0())
# Get the number of frames from the max z value
xcal, ycal, zcal = zip(*self.integrate_handle.xyzcal)
self.scan.set_image_range(
(self.scan.get_image_range()[0],
self.scan.get_image_range()[0] + int(ceil(max(zcal)))))
# Create the index generator
generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type, self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections = self.predict_rays(generate_indices.to_array(), UB)
# Calculate the intersection of the detector and reflection frames
success = ray_intersection(self.detector, self.reflections)
self.reflections.select(success)
def test(args=[]):
#############################
# Setup experimental models #
#############################
master_phil = parse(
"""
include scope dials.test.algorithms.refinement.geometry_phil
include scope dials.test.algorithms.refinement.minimiser_phil
""",
process_includes=True,
)
models = setup_geometry.Extract(master_phil, cmdline_args=args)
single_panel_detector = models.detector
mygonio = models.goniometer
mycrystal = models.crystal
mybeam = models.beam
# Make a 3x3 multi panel detector filling the same space as the existing
# single panel detector. Each panel of the multi-panel detector has pixels with
# 1/3 the length dimensions of the single panel.
multi_panel_detector = Detector()
for x in range(3):
for y in range(3):
new_panel = make_panel_in_array((x, y), single_panel_detector[0])
multi_panel_detector.add_panel(new_panel)
# Build a mock scan for a 180 degree sweep
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 1800),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(1800)),
deg=True,
)
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert sweep_range == (0.0, pi)
assert approx_equal(im_width, 0.1 * pi / 180.0)
# Build ExperimentLists
experiments_single_panel = ExperimentList()
experiments_multi_panel = ExperimentList()
experiments_single_panel.append(
Experiment(
beam=mybeam,
detector=single_panel_detector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
experiments_multi_panel.append(
Experiment(
beam=mybeam,
detector=multi_panel_detector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
###########################
# Parameterise the models #
###########################
det_param = DetectorParameterisationSinglePanel(single_panel_detector)
s0_param = BeamParameterisation(mybeam, mygonio)
xlo_param = CrystalOrientationParameterisation(mycrystal)
xluc_param = CrystalUnitCellParameterisation(mycrystal)
multi_det_param = DetectorParameterisationMultiPanel(
multi_panel_detector, mybeam)
# Fix beam to the X-Z plane (imgCIF geometry), fix wavelength
s0_param.set_fixed([True, False, True])
# Fix crystal parameters
# xluc_param.set_fixed([True, True, True, True, True, True])
########################################################################
# Link model parameterisations together into a parameterisation of the #
# prediction equation #
########################################################################
pred_param = XYPhiPredictionParameterisation(experiments_single_panel,
[det_param], [s0_param],
[xlo_param], [xluc_param])
pred_param2 = XYPhiPredictionParameterisation(
experiments_multi_panel,
[multi_det_param],
[s0_param],
[xlo_param],
[xluc_param],
)
################################
# Apply known parameter shifts #
################################
# shift detectors by 1.0 mm each translation and 2 mrad each rotation
det_p_vals = det_param.get_param_vals()
p_vals = [
a + b for a, b in zip(det_p_vals, [1.0, 1.0, 1.0, 2.0, 2.0, 2.0])
]
det_param.set_param_vals(p_vals)
multi_det_p_vals = multi_det_param.get_param_vals()
p_vals = [
a + b for a, b in zip(multi_det_p_vals, [1.0, 1.0, 1.0, 2.0, 2.0, 2.0])
]
multi_det_param.set_param_vals(p_vals)
# shift beam by 2 mrad in free axis
s0_p_vals = s0_param.get_param_vals()
p_vals = list(s0_p_vals)
p_vals[0] += 2.0
s0_param.set_param_vals(p_vals)
# rotate crystal a bit (=2 mrad each rotation)
xlo_p_vals = xlo_param.get_param_vals()
p_vals = [a + b for a, b in zip(xlo_p_vals, [2.0, 2.0, 2.0])]
xlo_param.set_param_vals(p_vals)
# change unit cell a bit (=0.1 Angstrom length upsets, 0.1 degree of
# gamma angle)
xluc_p_vals = xluc_param.get_param_vals()
cell_params = mycrystal.get_unit_cell().parameters()
cell_params = [
a + b for a, b in zip(cell_params, [0.1, 0.1, 0.1, 0.0, 0.0, 0.1])
]
new_uc = unit_cell(cell_params)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(mycrystal.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.0e5 for e in S.forward_independent_parameters()])
xluc_param.set_param_vals(X)
#############################
# 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(indices)
obs_refs2 = ref_predictor(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"]
# Invent some variances for the centroid positions of the simulated data
im_width = 0.1 * pi / 180.0
px_size = single_panel_detector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
# set the variances and frame numbers
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
obs_refs2["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
# Add in flags and ID columns by copying into standard reflection tables
tmp = flex.reflection_table.empty_standard(len(obs_refs))
tmp.update(obs_refs)
obs_refs = tmp
tmp = flex.reflection_table.empty_standard(len(obs_refs2))
tmp.update(obs_refs2)
obs_refs2 = tmp
###############################
# Undo known parameter shifts #
###############################
s0_param.set_param_vals(s0_p_vals)
det_param.set_param_vals(det_p_vals)
multi_det_param.set_param_vals(det_p_vals)
xlo_param.set_param_vals(xlo_p_vals)
xluc_param.set_param_vals(xluc_p_vals)
#####################################
# Select reflections for refinement #
#####################################
refman = ReflectionManager(obs_refs, experiments_single_panel)
refman2 = ReflectionManager(obs_refs, experiments_multi_panel)
###############################
# Set up the target functions #
###############################
mytarget = LeastSquaresPositionalResidualWithRmsdCutoff(
experiments_single_panel,
ScansExperimentsPredictor(experiments_single_panel),
refman,
pred_param,
restraints_parameterisation=None,
)
mytarget2 = LeastSquaresPositionalResidualWithRmsdCutoff(
experiments_multi_panel,
ScansExperimentsPredictor(experiments_multi_panel),
refman2,
pred_param2,
restraints_parameterisation=None,
)
#################################
# Set up the refinement engines #
#################################
refiner = setup_minimiser.Extract(master_phil,
mytarget,
pred_param,
cmdline_args=args).refiner
refiner2 = setup_minimiser.Extract(master_phil,
mytarget2,
pred_param2,
cmdline_args=args).refiner
refiner.run()
# reset parameters and run refinement with the multi panel detector
s0_param.set_param_vals(s0_p_vals)
multi_det_param.set_param_vals(det_p_vals)
xlo_param.set_param_vals(xlo_p_vals)
xluc_param.set_param_vals(xluc_p_vals)
refiner2.run()
# same number of steps
assert refiner.get_num_steps() == refiner2.get_num_steps()
# same rmsds
for rmsd, rmsd2 in zip(refiner.history["rmsd"], refiner2.history["rmsd"]):
assert approx_equal(rmsd, rmsd2)
# same parameter values each step
for params, params2 in zip(refiner.history["parameter_vector"],
refiner.history["parameter_vector"]):
assert approx_equal(params, params2)
def test(args=[]):
# Python and cctbx imports
from math import pi
from scitbx import matrix
from scitbx.array_family import flex
from libtbx.phil import parse
from libtbx.test_utils import approx_equal
# Get module to build models using PHIL
import dials.test.algorithms.refinement.setup_geometry as setup_geometry
# We will set up a mock scan and a mock experiment list
from dxtbx.model import ScanFactory
from dxtbx.model.experiment_list import ExperimentList, Experiment
# Model parameterisations
from dials.algorithms.refinement.parameterisation.detector_parameters import \
DetectorParameterisationSinglePanel
from dials.algorithms.refinement.parameterisation.beam_parameters import \
BeamParameterisation
from dials.algorithms.refinement.parameterisation.crystal_parameters import \
CrystalOrientationParameterisation, CrystalUnitCellParameterisation
# Symmetry constrained parameterisation for the unit cell
from cctbx.uctbx import unit_cell
from rstbx.symmetry.constraints.parameter_reduction import \
symmetrize_reduce_enlarge
# Reflection prediction
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
# Parameterisation of the prediction equation
from dials.algorithms.refinement.parameterisation.prediction_parameters import \
XYPhiPredictionParameterisation # implicit import
# Imports for the target function
from dials.algorithms.refinement.target import \
LeastSquaresPositionalResidualWithRmsdCutoff # implicit import
#############################
# Setup experimental models #
#############################
master_phil = parse("""
include scope dials.test.algorithms.refinement.geometry_phil
include scope dials.test.algorithms.refinement.minimiser_phil
""",
process_includes=True)
models = setup_geometry.Extract(
master_phil,
cmdline_args=args,
local_overrides="geometry.parameters.random_seed = 1")
crystal1 = models.crystal
models = setup_geometry.Extract(
master_phil,
cmdline_args=args,
local_overrides="geometry.parameters.random_seed = 2")
mydetector = models.detector
mygonio = models.goniometer
crystal2 = models.crystal
mybeam = models.beam
# Build a mock scan for a 180 degree sweep
sf = ScanFactory()
myscan = sf.make_scan(image_range=(1, 1800),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=range(1800),
deg=True)
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.)
# Build an experiment list
experiments = ExperimentList()
experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=crystal1,
imageset=None))
experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=crystal2,
imageset=None))
assert len(experiments.detectors()) == 1
##########################################################
# Parameterise the models (only for perturbing geometry) #
##########################################################
det_param = DetectorParameterisationSinglePanel(mydetector)
s0_param = BeamParameterisation(mybeam, mygonio)
xl1o_param = CrystalOrientationParameterisation(crystal1)
xl1uc_param = CrystalUnitCellParameterisation(crystal1)
xl2o_param = CrystalOrientationParameterisation(crystal2)
xl2uc_param = CrystalUnitCellParameterisation(crystal2)
# Fix beam to the X-Z plane (imgCIF geometry), fix wavelength
s0_param.set_fixed([True, False, True])
# Fix crystal parameters
#xluc_param.set_fixed([True, True, True, True, True, True])
########################################################################
# Link model parameterisations together into a parameterisation of the #
# prediction equation #
########################################################################
#pred_param = XYPhiPredictionParameterisation(experiments,
# [det_param], [s0_param], [xlo_param], [xluc_param])
################################
# Apply known parameter shifts #
################################
# shift detector by 1.0 mm each translation and 2 mrad each rotation
det_p_vals = det_param.get_param_vals()
p_vals = [a + b for a, b in zip(det_p_vals, [1.0, 1.0, 1.0, 2., 2., 2.])]
det_param.set_param_vals(p_vals)
# shift beam by 2 mrad in free axis
s0_p_vals = s0_param.get_param_vals()
p_vals = list(s0_p_vals)
p_vals[0] += 2.
s0_param.set_param_vals(p_vals)
# rotate crystal a bit (=2 mrad each rotation)
xlo_p_vals = []
for xlo in (xl1o_param, xl2o_param):
p_vals = xlo.get_param_vals()
xlo_p_vals.append(p_vals)
new_p_vals = [a + b for a, b in zip(p_vals, [2., 2., 2.])]
xlo.set_param_vals(new_p_vals)
# change unit cell a bit (=0.1 Angstrom length upsets, 0.1 degree of
# gamma angle)
xluc_p_vals = []
for xluc, xl in ((xl1uc_param, crystal1), ((xl2uc_param, crystal2))):
p_vals = xluc.get_param_vals()
xluc_p_vals.append(p_vals)
cell_params = xl.get_unit_cell().parameters()
cell_params = [
a + b for a, b in zip(cell_params, [0.1, 0.1, 0.1, 0.0, 0.0, 0.1])
]
new_uc = unit_cell(cell_params)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(xl.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.e5 for e in S.forward_independent_parameters()])
xluc.set_param_vals(X)
#############################
# Generate some reflections #
#############################
#print "Reflections will be generated with the following geometry:"
#print mybeam
#print mydetector
#print crystal1
#print crystal2
# 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(indices1, experiment_id=0)
obs_refs1['id'] = flex.int(len(obs_refs1), 0)
obs_refs2 = ray_predictor(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(obs_refs1)
obs_refs2 = ref_predictor(obs_refs2)
# Set 'observed' centroids from the predicted ones
obs_refs1['xyzobs.mm.value'] = obs_refs1['xyzcal.mm']
obs_refs2['xyzobs.mm.value'] = obs_refs2['xyzcal.mm']
# 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_refs1), (px_size[0] / 2.)**2)
var_y = flex.double(len(obs_refs1), (px_size[1] / 2.)**2)
var_phi = flex.double(len(obs_refs1), (im_width / 2.)**2)
obs_refs1['xyzobs.mm.variance'] = flex.vec3_double(var_x, var_y, var_phi)
var_x = flex.double(len(obs_refs2), (px_size[0] / 2.)**2)
var_y = flex.double(len(obs_refs2), (px_size[1] / 2.)**2)
var_phi = flex.double(len(obs_refs2), (im_width / 2.)**2)
obs_refs2['xyzobs.mm.variance'] = flex.vec3_double(var_x, var_y, var_phi)
#print "Total number of reflections excited for crystal1", len(obs_refs1)
#print "Total number of reflections excited for crystal2", len(obs_refs2)
# concatenate reflection lists
obs_refs1.extend(obs_refs2)
obs_refs = obs_refs1
###############################
# Undo known parameter shifts #
###############################
s0_param.set_param_vals(s0_p_vals)
det_param.set_param_vals(det_p_vals)
xl1o_param.set_param_vals(xlo_p_vals[0])
xl2o_param.set_param_vals(xlo_p_vals[1])
xl1uc_param.set_param_vals(xluc_p_vals[0])
xl2uc_param.set_param_vals(xluc_p_vals[1])
#print "Initial values of parameters are"
#msg = "Parameters: " + "%.5f " * len(pred_param)
#print msg % tuple(pred_param.get_param_vals())
#print
# make a refiner
from dials.algorithms.refinement.refiner import phil_scope
params = phil_scope.fetch(source=parse('')).extract()
# in case we want a plot
params.refinement.refinery.journal.track_parameter_correlation = True
# scan static first
from dials.algorithms.refinement.refiner import RefinerFactory
refiner = RefinerFactory.from_parameters_data_experiments(params,
obs_refs,
experiments,
verbosity=0)
history = refiner.run()
# scan varying
params.refinement.parameterisation.scan_varying = True
refiner = RefinerFactory.from_parameters_data_experiments(params,
obs_refs,
experiments,
verbosity=0)
history = refiner.run()
def test():
# Python and cctbx imports
from math import pi
from scitbx import matrix
from scitbx.array_family import flex
from libtbx.phil import parse
from libtbx.test_utils import approx_equal
# Get modules to build models and minimiser using PHIL
import dials.test.algorithms.refinement.setup_geometry as setup_geometry
import dials.test.algorithms.refinement.setup_minimiser as setup_minimiser
# We will set up a mock scan and a mock experiment list
from dxtbx.model import ScanFactory
from dxtbx.model.experiment_list import ExperimentList, Experiment
# Model parameterisations
from dials.algorithms.refinement.parameterisation.detector_parameters import (
DetectorParameterisationSinglePanel, )
from dials.algorithms.refinement.parameterisation.beam_parameters import (
BeamParameterisation, )
from dials.algorithms.refinement.parameterisation.crystal_parameters import (
CrystalOrientationParameterisation,
CrystalUnitCellParameterisation,
)
# Symmetry constrained parameterisation for the unit cell
from cctbx.uctbx import unit_cell
from rstbx.symmetry.constraints.parameter_reduction import symmetrize_reduce_enlarge
# Reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansRayPredictor,
ScansExperimentsPredictor,
)
from cctbx.sgtbx import space_group, space_group_symbols
# Parameterisation of the prediction equation
from dials.algorithms.refinement.parameterisation.prediction_parameters import (
XYPhiPredictionParameterisation, )
# Imports for the target function
from dials.algorithms.refinement.target import (
LeastSquaresPositionalResidualWithRmsdCutoff, )
from dials.algorithms.refinement.reflection_manager import ReflectionManager
#############################
# Setup experimental models #
#############################
override = """geometry.parameters
{
beam.wavelength.random=False
beam.wavelength.value=1.0
beam.direction.inclination.random=False
crystal.a.length.random=False
crystal.a.length.value=12.0
crystal.a.direction.method=exactly
crystal.a.direction.exactly.direction=1.0 0.002 -0.004
crystal.b.length.random=False
crystal.b.length.value=14.0
crystal.b.direction.method=exactly
crystal.b.direction.exactly.direction=-0.002 1.0 0.002
crystal.c.length.random=False
crystal.c.length.value=13.0
crystal.c.direction.method=exactly
crystal.c.direction.exactly.direction=0.002 -0.004 1.0
detector.directions.method=exactly
detector.directions.exactly.dir1=0.99 0.002 -0.004
detector.directions.exactly.norm=0.002 -0.001 0.99
detector.centre.method=exactly
detector.centre.exactly.value=1.0 -0.5 199.0
}"""
master_phil = parse(
"""
include scope dials.test.algorithms.refinement.geometry_phil
include scope dials.test.algorithms.refinement.minimiser_phil
""",
process_includes=True,
)
models = setup_geometry.Extract(master_phil,
local_overrides=override,
verbose=False)
mydetector = models.detector
mygonio = models.goniometer
mycrystal = models.crystal
mybeam = models.beam
###########################
# Parameterise the models #
###########################
det_param = DetectorParameterisationSinglePanel(mydetector)
s0_param = BeamParameterisation(mybeam, mygonio)
xlo_param = CrystalOrientationParameterisation(mycrystal)
xluc_param = CrystalUnitCellParameterisation(mycrystal)
# Fix beam to the X-Z plane (imgCIF geometry), fix wavelength
s0_param.set_fixed([True, False, True])
########################################################################
# Link model parameterisations together into a parameterisation of the #
# prediction equation #
########################################################################
# Build a mock scan for a 180 degree sweep
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 1800),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(1800)),
deg=True,
)
# Build an ExperimentList
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
# Create the PredictionParameterisation
pred_param = XYPhiPredictionParameterisation(experiments, [det_param],
[s0_param], [xlo_param],
[xluc_param])
################################
# Apply known parameter shifts #
################################
# shift detector by 1.0 mm each translation and 4 mrad each rotation
det_p_vals = det_param.get_param_vals()
p_vals = [
a + b for a, b in zip(det_p_vals, [1.0, 1.0, 1.0, 4.0, 4.0, 4.0])
]
det_param.set_param_vals(p_vals)
# shift beam by 4 mrad in free axis
s0_p_vals = s0_param.get_param_vals()
p_vals = list(s0_p_vals)
p_vals[0] += 4.0
s0_param.set_param_vals(p_vals)
# rotate crystal a bit (=3 mrad each rotation)
xlo_p_vals = xlo_param.get_param_vals()
p_vals = [a + b for a, b in zip(xlo_p_vals, [3.0, 3.0, 3.0])]
xlo_param.set_param_vals(p_vals)
# change unit cell a bit (=0.1 Angstrom length upsets, 0.1 degree of
# alpha and beta angles)
xluc_p_vals = xluc_param.get_param_vals()
cell_params = mycrystal.get_unit_cell().parameters()
cell_params = [
a + b for a, b in zip(cell_params, [0.1, -0.1, 0.1, 0.1, -0.1, 0.0])
]
new_uc = unit_cell(cell_params)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(mycrystal.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.0e5 for e in S.forward_independent_parameters()])
xluc_param.set_param_vals(X)
#############################
# 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()
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert sweep_range == (0.0, pi)
assert approx_equal(im_width, 0.1 * pi / 180.0)
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs = ray_predictor(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 = ScansExperimentsPredictor(experiments)
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor(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 / 180.0
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
# The total number of observations should be 1128
assert len(obs_refs) == 1128
###############################
# Undo known parameter shifts #
###############################
s0_param.set_param_vals(s0_p_vals)
det_param.set_param_vals(det_p_vals)
xlo_param.set_param_vals(xlo_p_vals)
xluc_param.set_param_vals(xluc_p_vals)
#####################################
# Select reflections for refinement #
#####################################
refman = ReflectionManager(obs_refs,
experiments,
outlier_detector=None,
close_to_spindle_cutoff=0.1)
##############################
# Set up the target function #
##############################
# The current 'achieved' criterion compares RMSD against 1/3 the pixel size and
# 1/3 the image width in radians. For the simulated data, these are just made up
mytarget = LeastSquaresPositionalResidualWithRmsdCutoff(
experiments,
ref_predictor,
refman,
pred_param,
restraints_parameterisation=None)
######################################
# Set up the LSTBX refinement engine #
######################################
overrides = """minimiser.parameters.engine=GaussNewton
minimiser.parameters.logfile=None"""
refiner = setup_minimiser.Extract(master_phil,
mytarget,
pred_param,
local_overrides=overrides).refiner
refiner.run()
assert mytarget.achieved()
assert refiner.get_num_steps() == 1
assert approx_equal(
mytarget.rmsds(),
(0.00508252354876, 0.00420954552156, 8.97303428289e-05))
###############################
# Undo known parameter shifts #
###############################
s0_param.set_param_vals(s0_p_vals)
det_param.set_param_vals(det_p_vals)
xlo_param.set_param_vals(xlo_p_vals)
xluc_param.set_param_vals(xluc_p_vals)
######################################################
# Set up the LBFGS with curvatures refinement engine #
######################################################
overrides = """minimiser.parameters.engine=LBFGScurvs
minimiser.parameters.logfile=None"""
refiner = setup_minimiser.Extract(master_phil,
mytarget,
pred_param,
local_overrides=overrides).refiner
refiner.run()
assert mytarget.achieved()
assert refiner.get_num_steps() == 9
assert approx_equal(mytarget.rmsds(),
(0.0558857700305, 0.0333446685335, 0.000347402754278))
def test(args=[]):
#############################
# Setup experimental models #
#############################
master_phil = parse(
"""
include scope dials.tests.algorithms.refinement.geometry_phil
include scope dials.tests.algorithms.refinement.minimiser_phil
""",
process_includes=True,
)
models = setup_geometry.Extract(
master_phil,
cmdline_args=args,
local_overrides="geometry.parameters.random_seed = 1",
)
crystal1 = models.crystal
models = setup_geometry.Extract(
master_phil,
cmdline_args=args,
local_overrides="geometry.parameters.random_seed = 2",
)
mydetector = models.detector
mygonio = models.goniometer
crystal2 = models.crystal
mybeam = models.beam
# Build a mock scan for an 18 degree sequence
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 180),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(180)),
deg=True,
)
sequence_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
assert sequence_range == (0.0, pi / 10)
assert approx_equal(im_width, 0.1 * pi / 180.0)
# Build an experiment list
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=crystal1,
imageset=None,
))
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=crystal2,
imageset=None,
))
assert len(experiments.detectors()) == 1
##########################################################
# Parameterise the models (only for perturbing geometry) #
##########################################################
det_param = DetectorParameterisationSinglePanel(mydetector)
s0_param = BeamParameterisation(mybeam, mygonio)
xl1o_param = CrystalOrientationParameterisation(crystal1)
xl1uc_param = CrystalUnitCellParameterisation(crystal1)
xl2o_param = CrystalOrientationParameterisation(crystal2)
xl2uc_param = CrystalUnitCellParameterisation(crystal2)
# Fix beam to the X-Z plane (imgCIF geometry), fix wavelength
s0_param.set_fixed([True, False, True])
################################
# Apply known parameter shifts #
################################
# shift detector by 1.0 mm each translation and 2 mrad each rotation
det_p_vals = det_param.get_param_vals()
p_vals = [
a + b for a, b in zip(det_p_vals, [1.0, 1.0, 1.0, 2.0, 2.0, 2.0])
]
det_param.set_param_vals(p_vals)
# shift beam by 2 mrad in free axis
s0_p_vals = s0_param.get_param_vals()
p_vals = list(s0_p_vals)
p_vals[0] += 2.0
s0_param.set_param_vals(p_vals)
# rotate crystal a bit (=2 mrad each rotation)
xlo_p_vals = []
for xlo in (xl1o_param, xl2o_param):
p_vals = xlo.get_param_vals()
xlo_p_vals.append(p_vals)
new_p_vals = [a + b for a, b in zip(p_vals, [2.0, 2.0, 2.0])]
xlo.set_param_vals(new_p_vals)
# change unit cell a bit (=0.1 Angstrom length upsets, 0.1 degree of
# gamma angle)
xluc_p_vals = []
for xluc, xl in ((xl1uc_param, crystal1), ((xl2uc_param, crystal2))):
p_vals = xluc.get_param_vals()
xluc_p_vals.append(p_vals)
cell_params = xl.get_unit_cell().parameters()
cell_params = [
a + b for a, b in zip(cell_params, [0.1, 0.1, 0.1, 0.0, 0.0, 0.1])
]
new_uc = unit_cell(cell_params)
newB = matrix.sqr(new_uc.fractionalization_matrix()).transpose()
S = symmetrize_reduce_enlarge(xl.get_space_group())
S.set_orientation(orientation=newB)
X = tuple([e * 1.0e5 for e in S.forward_independent_parameters()])
xluc.set_param_vals(X)
#############################
# Generate some reflections #
#############################
# All indices in a 2.5 Angstrom sphere for crystal1
resolution = 2.5
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 2.5 Angstrom sphere for crystal2
resolution = 2.5
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 sequence range. Set experiment IDs
ray_predictor = ScansRayPredictor(experiments, sequence_range)
obs_refs1 = ray_predictor(indices1, experiment_id=0)
obs_refs1["id"] = flex.int(len(obs_refs1), 0)
obs_refs2 = ray_predictor(indices2, 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 = ScansExperimentsPredictor(experiments)
obs_refs1 = ref_predictor(obs_refs1)
obs_refs2 = ref_predictor(obs_refs2)
# Set 'observed' centroids from the predicted ones
obs_refs1["xyzobs.mm.value"] = obs_refs1["xyzcal.mm"]
obs_refs2["xyzobs.mm.value"] = obs_refs2["xyzcal.mm"]
# Invent some variances for the centroid positions of the simulated data
im_width = 0.1 * pi / 18.0
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs1), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs1), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs1), (im_width / 2.0)**2)
obs_refs1["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
var_x = flex.double(len(obs_refs2), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs2), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs2), (im_width / 2.0)**2)
obs_refs2["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
# concatenate reflection lists
obs_refs1.extend(obs_refs2)
obs_refs = obs_refs1
###############################
# Undo known parameter shifts #
###############################
s0_param.set_param_vals(s0_p_vals)
det_param.set_param_vals(det_p_vals)
xl1o_param.set_param_vals(xlo_p_vals[0])
xl2o_param.set_param_vals(xlo_p_vals[1])
xl1uc_param.set_param_vals(xluc_p_vals[0])
xl2uc_param.set_param_vals(xluc_p_vals[1])
# scan static first
params = phil_scope.fetch(source=parse("")).extract()
refiner = RefinerFactory.from_parameters_data_experiments(
params, obs_refs, experiments)
refiner.run()
# scan varying
params.refinement.parameterisation.scan_varying = True
refiner = RefinerFactory.from_parameters_data_experiments(
params, obs_refs, experiments)
refiner.run()
# Ensure all models have scan-varying state set
# (https://github.com/dials/dials/issues/798)
refined_experiments = refiner.get_experiments()
sp = [xl.get_num_scan_points() for xl in refined_experiments.crystals()]
assert sp.count(181) == 2
def test():
from cctbx.sgtbx import space_group, space_group_symbols
# We will set up a mock scan
from dxtbx.model import ScanFactory
from dxtbx.model.experiment_list import Experiment, ExperimentList
from libtbx.phil import parse
from scitbx import matrix
from scitbx.array_family import flex
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansExperimentsPredictor,
ScansRayPredictor,
)
# Reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator
# Building experimental models
from dials.test.algorithms.refinement.setup_geometry import Extract
master_phil = parse(
"""
include scope dials.test.algorithms.refinement.geometry_phil
include scope dials.test.algorithms.refinement.minimiser_phil
""",
process_includes=True,
)
overrides = """geometry.parameters.crystal.a.length.range = 10 50
geometry.parameters.crystal.b.length.range = 10 50
geometry.parameters.crystal.c.length.range = 10 50"""
models = Extract(master_phil, local_overrides=overrides)
mydetector = models.detector
mygonio = models.goniometer
mycrystal = models.crystal
mybeam = models.beam
#############################
# 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()
# Build a mock scan for a 30 degree sequence
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 300),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(300)),
deg=True,
)
sequence_range = myscan.get_oscillation_range(deg=False)
assert sequence_range == pytest.approx((0.0, math.pi / 6.0))
im_width = myscan.get_oscillation(deg=False)[1]
assert im_width == pytest.approx(0.1 * math.pi / 180.0)
# Create an ExperimentList for ScansRayPredictor
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
# Select those that are excited in a 30 degree sequence and get angles
ray_predictor = ScansRayPredictor(experiments, sequence_range)
obs_refs = ray_predictor(indices)
# Set the experiment number
obs_refs["id"] = flex.int(len(obs_refs), 0)
# Calculate intersections
ref_predictor = ScansExperimentsPredictor(experiments)
obs_refs = ref_predictor(obs_refs)
print("Total number of observations made", len(obs_refs))
s0 = matrix.col(mybeam.get_s0())
spindle = matrix.col(mygonio.get_rotation_axis())
for ref in obs_refs.rows():
# get the s1 vector of this reflection
s1 = matrix.col(ref["s1"])
r = s1 - s0
r_orig = r.rotate_around_origin(spindle, -1.0, deg=True)
# is it outside the Ewald sphere (i.e. entering)?
test = (s0 + r_orig).length() > s0.length()
assert ref["entering"] == test
def test():
from cctbx.sgtbx import space_group, space_group_symbols
from dxtbx.model.experiment_list import Experiment, ExperimentList
from libtbx.phil import parse
from scitbx.array_family import flex
from dials.algorithms.refinement.parameterisation.beam_parameters import (
BeamParameterisation, )
from dials.algorithms.refinement.parameterisation.crystal_parameters import (
CrystalOrientationParameterisation,
CrystalUnitCellParameterisation,
)
from dials.algorithms.refinement.parameterisation.detector_parameters import (
DetectorParameterisationSinglePanel, )
from dials.algorithms.refinement.parameterisation.goniometer_parameters import (
GoniometerParameterisation, )
#### Import model parameterisations
from dials.algorithms.refinement.parameterisation.prediction_parameters import (
XYPhiPredictionParameterisation, )
from dials.algorithms.refinement.prediction.managed_predictors import (
ScansExperimentsPredictor,
ScansRayPredictor,
)
##### Imports for reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection
##### Import model builder
from dials.tests.algorithms.refinement.setup_geometry import Extract
#### Create models
overrides = """geometry.parameters.crystal.a.length.range = 10 50
geometry.parameters.crystal.b.length.range = 10 50
geometry.parameters.crystal.c.length.range = 10 50"""
master_phil = parse(
"""
include scope dials.tests.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 72 degree sequence
sequence_range = (0.0, math.pi / 5.0)
from dxtbx.model import ScanFactory
sf = ScanFactory()
myscan = sf.make_scan(
image_range=(1, 720),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(720)),
deg=True,
)
#### Create parameterisations of these models
det_param = DetectorParameterisationSinglePanel(mydetector)
s0_param = BeamParameterisation(mybeam, mygonio)
xlo_param = CrystalOrientationParameterisation(mycrystal)
xluc_param = CrystalUnitCellParameterisation(mycrystal)
gon_param = GoniometerParameterisation(mygonio, mybeam)
# Create an ExperimentList
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
#### Unit tests
# Build a prediction parameterisation
pred_param = XYPhiPredictionParameterisation(
experiments,
detector_parameterisations=[det_param],
beam_parameterisations=[s0_param],
xl_orientation_parameterisations=[xlo_param],
xl_unit_cell_parameterisations=[xluc_param],
goniometer_parameterisations=[gon_param],
)
# Generate reflections
resolution = 2.0
index_generator = IndexGenerator(
mycrystal.get_unit_cell(),
space_group(space_group_symbols(1).hall()).type(),
resolution,
)
indices = index_generator.to_array()
# Predict rays within the sequence range
ray_predictor = ScansRayPredictor(experiments, sequence_range)
obs_refs = ray_predictor(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 = ScansExperimentsPredictor(experiments)
obs_refs["id"] = flex.int(len(obs_refs), 0)
obs_refs = ref_predictor(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 * math.pi / 180.0
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.0)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.0)**2)
var_phi = flex.double(len(obs_refs), (im_width / 2.0)**2)
obs_refs["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
# use a ReflectionManager to exclude reflections too close to the spindle
from dials.algorithms.refinement.reflection_manager import ReflectionManager
refman = ReflectionManager(obs_refs, experiments, outlier_detector=None)
refman.finalise()
# Redefine the reflection predictor to use the type expected by the Target class
ref_predictor = ScansExperimentsPredictor(experiments)
# keep only those reflections that pass inclusion criteria and have predictions
reflections = refman.get_matches()
# get analytical gradients
an_grads = pred_param.get_gradients(reflections)
# get finite difference gradients
p_vals = pred_param.get_param_vals()
deltas = [1.0e-7] * len(p_vals)
for i, delta in enumerate(deltas):
val = p_vals[i]
p_vals[i] -= delta / 2.0
pred_param.set_param_vals(p_vals)
ref_predictor(reflections)
rev_state = reflections["xyzcal.mm"].deep_copy()
p_vals[i] += delta
pred_param.set_param_vals(p_vals)
ref_predictor(reflections)
fwd_state = reflections["xyzcal.mm"].deep_copy()
p_vals[i] = val
fd = fwd_state - rev_state
x_grads, y_grads, phi_grads = fd.parts()
x_grads /= delta
y_grads /= delta
phi_grads /= delta
# compare with analytical calculation
assert x_grads == pytest.approx(an_grads[i]["dX_dp"], abs=5.0e-6)
assert y_grads == pytest.approx(an_grads[i]["dY_dp"], abs=5.5e-6)
assert phi_grads == pytest.approx(an_grads[i]["dphi_dp"], abs=5.0e-6)
# return to the initial state
pred_param.set_param_vals(p_vals)
def __init__(self):
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from dials.algorithms.spot_prediction import ray_intersection
from iotbx.xds import xparm, integrate_hkl
from dials.util import ioutil
from math import ceil
import dxtbx
from rstbx.cftbx.coordinate_frame_converter import \
coordinate_frame_converter
from scitbx import matrix
# The XDS files to read from
integrate_filename = join(dials_regression, 'data/sim_mx/INTEGRATE.HKL')
gxparm_filename = join(dials_regression, 'data/sim_mx/GXPARM.XDS')
# Read the XDS files
self.integrate_handle = integrate_hkl.reader()
self.integrate_handle.read_file(integrate_filename)
self.gxparm_handle = xparm.reader()
self.gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
models = dxtbx.load(gxparm_filename)
self.beam = models.get_beam()
self.gonio = models.get_goniometer()
self.detector = models.get_detector()
self.scan = models.get_scan()
assert(len(self.detector) == 1)
#print self.detector
# Get crystal parameters
self.space_group_type = ioutil.get_space_group_type_from_xparm(
self.gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get('real_space_a')
b_vec = cfc.get('real_space_b')
c_vec = cfc.get('real_space_c')
self.unit_cell = cfc.get_unit_cell()
self.ub_matrix = matrix.sqr(a_vec + b_vec + c_vec).inverse()
# Get the minimum resolution in the integrate file
self.d_min = self.detector[0].get_max_resolution_at_corners(
self.beam.get_s0())
# Get the number of frames from the max z value
xcal, ycal, zcal = zip(*self.integrate_handle.xyzcal)
self.scan.set_image_range((self.scan.get_image_range()[0],
self.scan.get_image_range()[0] +
int(ceil(max(zcal)))))
# Create the index generator
generate_indices = IndexGenerator(self.unit_cell, self.space_group_type,
self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections = self.predict_rays(
generate_indices.to_array(), UB)
# Calculate the intersection of the detector and reflection frames
success = ray_intersection(self.detector, self.reflections)
self.reflections.select(success)
class TestRayPredictor:
def __init__(self):
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from iotbx.xds import xparm, integrate_hkl
from dials.util import ioutil
from math import ceil
import dxtbx
from rstbx.cftbx.coordinate_frame_converter import \
coordinate_frame_converter
from scitbx import matrix
# The XDS files to read from
integrate_filename = join(dials_regression, 'data/sim_mx/INTEGRATE.HKL')
gxparm_filename = join(dials_regression, 'data/sim_mx/GXPARM.XDS')
# Read the XDS files
self.integrate_handle = integrate_hkl.reader()
self.integrate_handle.read_file(integrate_filename)
self.gxparm_handle = xparm.reader()
self.gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
models = dxtbx.load(gxparm_filename)
self.beam = models.get_beam()
self.gonio = models.get_goniometer()
self.detector = models.get_detector()
self.scan = models.get_scan()
# Get crystal parameters
self.space_group_type = ioutil.get_space_group_type_from_xparm(
self.gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get('real_space_a')
b_vec = cfc.get('real_space_b')
c_vec = cfc.get('real_space_c')
self.unit_cell = cfc.get_unit_cell()
self.ub_matrix = matrix.sqr(a_vec + b_vec + c_vec).inverse()
# Get the minimum resolution in the integrate file
d = [self.unit_cell.d(h) for h in self.integrate_handle.hkl]
self.d_min = min(d)
# extend the resolution shell by epsilon>0
# to account for rounding artifacts on 32-bit platforms
self.d_min = self.d_min - 1e-15
# Get the number of frames from the max z value
xcal, ycal, zcal = zip(*self.integrate_handle.xyzcal)
self.scan.set_image_range((self.scan.get_image_range()[0],
self.scan.get_image_range()[0] +
int(ceil(max(zcal)))))
# Print stuff
# print self.beam
# print self.gonio
# print self.detector
# print self.scan
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type, self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections = self.predict_rays(
self.generate_indices.to_array(), UB)
def test_miller_index_set(self):
"""Ensure we have the whole set of miller indices"""
gen_hkl = { r['miller_index'] for r in self.reflections }
missing = []
for hkl in self.integrate_handle.hkl:
if hkl not in gen_hkl:
missing.append(hkl)
assert len(missing) == 0, "%d out of %d reflections not in set, including %s" % (len(missing), len(self.integrate_handle.hkl), str(missing[0]))
def test_rotation_angles(self):
"""Ensure the rotation angles agree with XDS"""
# Create a dict of lists of xy for each hkl
gen_phi = {}
for r in self.reflections:
hkl = r['miller_index']
phi = r['phi']
try:
a = gen_phi[hkl]
a.append(phi)
gen_phi[hkl] = a
except KeyError:
gen_phi[hkl] = [phi]
# For each hkl in the xds file
for hkl, xyz in zip(self.integrate_handle.hkl,
self.integrate_handle.xyzcal):
# Calculate the XDS phi value
xds_phi = self.scan.get_oscillation(deg=False)[0] + \
xyz[2]*self.scan.get_oscillation(deg=False)[1]
# Select the nearest xy to use if there are 2
my_phi = gen_phi[hkl]
if len(my_phi) == 2:
my_phi0 = my_phi[0]
my_phi1 = my_phi[1]
diff0 = abs(xds_phi - my_phi0)
diff1 = abs(xds_phi - my_phi1)
if diff0 < diff1:
my_phi = my_phi0
else:
my_phi = my_phi1
else:
my_phi = my_phi[0]
# Check the Phi values are the same
assert(abs(xds_phi - my_phi) < 0.1)
print "OK"
def test_beam_vectors(self):
"""Ensure |s1| == |s0|"""
from scitbx import matrix
s0_length = matrix.col(self.beam.get_s0()).length()
for r in self.reflections:
s1 = r['s1']
s1_length = matrix.col(s1).length()
assert(abs(s0_length - s1_length) < 1e-7)
print "OK"
def test_new(self):
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from dials.algorithms.spot_prediction import IndexGenerator
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type, self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections2 = []
for h in self.generate_indices.to_array():
rays = self.predict_rays(h, UB)
for ray in rays:
if ray.angle >= dphi[0] and ray.angle <= dphi[1]:
self.reflections2.append(ray)
eps = 1e-7
assert(len(self.reflections) == len(self.reflections2))
for r1, r2 in zip(self.reflections, self.reflections2):
assert(all(abs(a - b) < eps for a, b in zip(r1['s1'], r2.s1)))
assert(abs(r1['phi'] - r2.angle) < eps)
assert(r1['entering'] == r2.entering)
print 'OK'
def test_new_from_array(self):
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from dials.algorithms.spot_prediction import IndexGenerator
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type, self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
h = self.generate_indices.to_array()
reflections = self.predict_rays(h, UB)
self.reflections3 = []
for r in reflections:
if r['phi'] >= dphi[0] and r['phi'] <= dphi[1]:
self.reflections3.append(r)
eps = 1e-7
assert(len(self.reflections) == len(self.reflections3))
for r1, r2 in zip(self.reflections, self.reflections3):
assert(all(abs(a - b) < eps for a, b in zip(r1['s1'], r2['s1'])))
assert(abs(r1['phi'] - r2['phi']) < eps)
assert(r1['entering'] == r2['entering'])
print 'OK'
def run(self):
self.test_miller_index_set()
self.test_rotation_angles()
self.test_beam_vectors()
self.test_new()
self.test_new_from_array()
def init_test():
models = setup_geometry.Extract(master_phil)
single_panel_detector = models.detector
gonio = models.goniometer
crystal = models.crystal
beam = models.beam
# Make a 3x3 multi panel detector filling the same space as the existing
# single panel detector. Each panel of the multi-panel detector has pixels
# with 1/3 the length dimensions of the single panel.
multi_panel_detector = Detector()
for x in range(3):
for y in range(3):
new_panel = make_panel_in_array((x, y), single_panel_detector[0])
multi_panel_detector.add_panel(new_panel)
# Build a mock scan for a 180 degree sequence
sf = ScanFactory()
scan = sf.make_scan(
image_range=(1, 1800),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(1800)),
deg=True,
)
sequence_range = scan.get_oscillation_range(deg=False)
im_width = scan.get_oscillation(deg=False)[1]
assert sequence_range == (0.0, pi)
assert approx_equal(im_width, 0.1 * pi / 180.0)
# Build ExperimentLists
experiments_single_panel = ExperimentList()
experiments_multi_panel = ExperimentList()
experiments_single_panel.append(
Experiment(
beam=beam,
detector=single_panel_detector,
goniometer=gonio,
scan=scan,
crystal=crystal,
imageset=None,
)
)
experiments_multi_panel.append(
Experiment(
beam=beam,
detector=multi_panel_detector,
goniometer=gonio,
scan=scan,
crystal=crystal,
imageset=None,
)
)
# Generate some reflections
# 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()
# 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, scan.get_oscillation_range(deg=False)
)
# get two sets of identical reflections
obs_refs_single = ref_predictor(indices)
obs_refs_multi = ref_predictor(indices)
for r1, r2 in zip(obs_refs_single.rows(), obs_refs_multi.rows()):
assert r1["s1"] == r2["s1"]
# get the panel intersections
sel = ray_intersection(single_panel_detector, obs_refs_single)
obs_refs_single = obs_refs_single.select(sel)
sel = ray_intersection(multi_panel_detector, obs_refs_multi)
obs_refs_multi = obs_refs_multi.select(sel)
assert len(obs_refs_single) == len(obs_refs_multi)
# Set 'observed' centroids from the predicted ones
obs_refs_single["xyzobs.mm.value"] = obs_refs_single["xyzcal.mm"]
obs_refs_multi["xyzobs.mm.value"] = obs_refs_multi["xyzcal.mm"]
# Invent some variances for the centroid positions of the simulated data
im_width = 0.1 * pi / 180.0
px_size = single_panel_detector[0].get_pixel_size()
var_x = flex.double(len(obs_refs_single), (px_size[0] / 2.0) ** 2)
var_y = flex.double(len(obs_refs_single), (px_size[1] / 2.0) ** 2)
var_phi = flex.double(len(obs_refs_single), (im_width / 2.0) ** 2)
# set the variances and frame numbers
obs_refs_single["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
obs_refs_multi["xyzobs.mm.variance"] = flex.vec3_double(var_x, var_y, var_phi)
# Add in flags and ID columns by copying into standard reflection tables
tmp = flex.reflection_table.empty_standard(len(obs_refs_single))
tmp.update(obs_refs_single)
obs_refs_single = tmp
tmp = flex.reflection_table.empty_standard(len(obs_refs_multi))
tmp.update(obs_refs_multi)
obs_refs_multi = tmp
test_data = namedtuple(
"test_data",
[
"experiments_single_panel",
"experiments_multi_panel",
"observations_single_panel",
"observations_multi_panel",
],
)
return test_data(
experiments_single_panel,
experiments_multi_panel,
obs_refs_single,
obs_refs_multi,
)
#############################
print "Reflections will be generated with the following geometry:"
print mybeam
print mydetector
print mycrystal
print "Target values of parameters are"
msg = "Parameters: " + "%.5f " * len(pred_param)
print msg % tuple(pred_param.get_param_vals())
print
# 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()
# Predict rays within the sweep range
ray_predictor = ScansRayPredictor(experiments, sweep_range)
obs_refs = ray_predictor(indices)
print "Total number of reflections excited", len(obs_refs)
# 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.size_t(len(obs_refs), 0)
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'
#############################
# Generate some reflections #
#############################
#print "Reflections will be generated with the following geometry:"
#print mybeam
#print mydetector
#print crystal1
#print crystal2
# 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(indices1, experiment_id=0)
obs_refs1['id'] = flex.int(len(obs_refs1), 0)
obs_refs2 = ray_predictor(indices1, experiment_id=1)
obs_refs2['id'] = flex.int(len(obs_refs2), 1)
class TestRayPredictor:
def __init__(self):
from dials.algorithms.spot_prediction import IndexGenerator
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from iotbx.xds import xparm, integrate_hkl
from dials.util import ioutil
from math import ceil
import dxtbx
from rstbx.cftbx.coordinate_frame_converter import \
coordinate_frame_converter
from scitbx import matrix
# The XDS files to read from
integrate_filename = join(dials_regression,
'data/sim_mx/INTEGRATE.HKL')
gxparm_filename = join(dials_regression, 'data/sim_mx/GXPARM.XDS')
# Read the XDS files
self.integrate_handle = integrate_hkl.reader()
self.integrate_handle.read_file(integrate_filename)
self.gxparm_handle = xparm.reader()
self.gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
models = dxtbx.load(gxparm_filename)
self.beam = models.get_beam()
self.gonio = models.get_goniometer()
self.detector = models.get_detector()
self.scan = models.get_scan()
# Get crystal parameters
self.space_group_type = ioutil.get_space_group_type_from_xparm(
self.gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get('real_space_a')
b_vec = cfc.get('real_space_b')
c_vec = cfc.get('real_space_c')
self.unit_cell = cfc.get_unit_cell()
self.ub_matrix = matrix.sqr(a_vec + b_vec + c_vec).inverse()
# Get the minimum resolution in the integrate file
d = [self.unit_cell.d(h) for h in self.integrate_handle.hkl]
self.d_min = min(d)
# extend the resolution shell by epsilon>0
# to account for rounding artifacts on 32-bit platforms
self.d_min = self.d_min - 1e-15
# Get the number of frames from the max z value
xcal, ycal, zcal = zip(*self.integrate_handle.xyzcal)
self.scan.set_image_range(
(self.scan.get_image_range()[0],
self.scan.get_image_range()[0] + int(ceil(max(zcal)))))
# Print stuff
# print self.beam
# print self.gonio
# print self.detector
# print self.scan
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type,
self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections = self.predict_rays(self.generate_indices.to_array(),
UB)
def test_miller_index_set(self):
"""Ensure we have the whole set of miller indices"""
gen_hkl = {r['miller_index'] for r in self.reflections}
missing = []
for hkl in self.integrate_handle.hkl:
if hkl not in gen_hkl:
missing.append(hkl)
assert len(
missing
) == 0, "%d out of %d reflections not in set, including %s" % (
len(missing), len(self.integrate_handle.hkl), str(missing[0]))
def test_rotation_angles(self):
"""Ensure the rotation angles agree with XDS"""
# Create a dict of lists of xy for each hkl
gen_phi = {}
for r in self.reflections:
hkl = r['miller_index']
phi = r['phi']
try:
a = gen_phi[hkl]
a.append(phi)
gen_phi[hkl] = a
except KeyError:
gen_phi[hkl] = [phi]
# For each hkl in the xds file
for hkl, xyz in zip(self.integrate_handle.hkl,
self.integrate_handle.xyzcal):
# Calculate the XDS phi value
xds_phi = self.scan.get_oscillation(deg=False)[0] + \
xyz[2]*self.scan.get_oscillation(deg=False)[1]
# Select the nearest xy to use if there are 2
my_phi = gen_phi[hkl]
if len(my_phi) == 2:
my_phi0 = my_phi[0]
my_phi1 = my_phi[1]
diff0 = abs(xds_phi - my_phi0)
diff1 = abs(xds_phi - my_phi1)
if diff0 < diff1:
my_phi = my_phi0
else:
my_phi = my_phi1
else:
my_phi = my_phi[0]
# Check the Phi values are the same
assert (abs(xds_phi - my_phi) < 0.1)
print "OK"
def test_beam_vectors(self):
"""Ensure |s1| == |s0|"""
from scitbx import matrix
s0_length = matrix.col(self.beam.get_s0()).length()
for r in self.reflections:
s1 = r['s1']
s1_length = matrix.col(s1).length()
assert (abs(s0_length - s1_length) < 1e-7)
print "OK"
def test_new(self):
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from dials.algorithms.spot_prediction import IndexGenerator
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type,
self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
self.reflections2 = []
for h in self.generate_indices.to_array():
rays = self.predict_rays(h, UB)
for ray in rays:
if ray.angle >= dphi[0] and ray.angle <= dphi[1]:
self.reflections2.append(ray)
eps = 1e-7
assert (len(self.reflections) == len(self.reflections2))
for r1, r2 in zip(self.reflections, self.reflections2):
assert (all(abs(a - b) < eps for a, b in zip(r1['s1'], r2.s1)))
assert (abs(r1['phi'] - r2.angle) < eps)
assert (r1['entering'] == r2.entering)
print 'OK'
def test_new_from_array(self):
from dials.algorithms.spot_prediction import ScanStaticRayPredictor
from dials.algorithms.spot_prediction import IndexGenerator
# Create the index generator
self.generate_indices = IndexGenerator(self.unit_cell,
self.space_group_type,
self.d_min)
s0 = self.beam.get_s0()
m2 = self.gonio.get_rotation_axis()
fixed_rotation = self.gonio.get_fixed_rotation()
setting_rotation = self.gonio.get_setting_rotation()
UB = self.ub_matrix
dphi = self.scan.get_oscillation_range(deg=False)
# Create the ray predictor
self.predict_rays = ScanStaticRayPredictor(s0, m2, fixed_rotation,
setting_rotation, dphi)
# Predict the spot locations
h = self.generate_indices.to_array()
reflections = self.predict_rays(h, UB)
self.reflections3 = []
for r in reflections:
if r['phi'] >= dphi[0] and r['phi'] <= dphi[1]:
self.reflections3.append(r)
eps = 1e-7
assert (len(self.reflections) == len(self.reflections3))
for r1, r2 in zip(self.reflections, self.reflections3):
assert (all(abs(a - b) < eps for a, b in zip(r1['s1'], r2['s1'])))
assert (abs(r1['phi'] - r2['phi']) < eps)
assert (r1['entering'] == r2['entering'])
print 'OK'
def run(self):
self.test_miller_index_set()
self.test_rotation_angles()
self.test_beam_vectors()
self.test_new()
self.test_new_from_array()
def run():
from iotbx.xds import xparm, integrate_hkl
from dials.util import ioutil
from dials.algorithms.spot_prediction import IndexGenerator
from os.path import realpath, dirname, join
import numpy
from rstbx.cftbx.coordinate_frame_converter import \
coordinate_frame_converter
from scitbx import matrix
# The XDS files to read from
test_path = dirname(dirname(dirname(realpath(__file__))))
integrate_filename = join(test_path, 'data/sim_mx/INTEGRATE.HKL')
gxparm_filename = join(test_path, 'data/sim_mx/GXPARM.XDS')
# Read the XDS files
integrate_handle = integrate_hkl.reader()
integrate_handle.read_file(integrate_filename)
gxparm_handle = xparm.reader()
gxparm_handle.read_file(gxparm_filename)
# Get the parameters we need from the GXPARM file
d_min = 1.6
space_group_type = ioutil.get_space_group_type_from_xparm(gxparm_handle)
cfc = coordinate_frame_converter(gxparm_filename)
a_vec = cfc.get('real_space_a')
b_vec = cfc.get('real_space_b')
c_vec = cfc.get('real_space_c')
unit_cell = cfc.get_unit_cell()
UB = matrix.sqr(a_vec + b_vec + c_vec).inverse()
ub_matrix = UB
# Generate the indices
index_generator = IndexGenerator(unit_cell, space_group_type, d_min)
miller_indices = index_generator.to_array()
# Get individual generated hkl
gen_h = [hkl[0] for hkl in miller_indices]
gen_k = [hkl[1] for hkl in miller_indices]
gen_l = [hkl[2] for hkl in miller_indices]
# Get individual xds generated hkl
xds_h = [hkl[0] for hkl in integrate_handle.hkl]
xds_k = [hkl[0] for hkl in integrate_handle.hkl]
xds_l = [hkl[0] for hkl in integrate_handle.hkl]
# Get min/max generated hkl
min_gen_h, max_gen_h = numpy.min(gen_h), numpy.max(gen_h)
min_gen_k, max_gen_k = numpy.min(gen_k), numpy.max(gen_k)
min_gen_l, max_gen_l = numpy.min(gen_l), numpy.max(gen_l)
# Get min/max xds generated hkl
min_xds_h, max_xds_h = numpy.min(xds_h), numpy.max(xds_h)
min_xds_k, max_xds_k = numpy.min(xds_k), numpy.max(xds_k)
min_xds_l, max_xds_l = numpy.min(xds_l), numpy.max(xds_l)
# Ensure we have the whole xds range in the generated set
assert(min_gen_h <= min_xds_h and max_gen_h >= max_xds_h)
assert(min_gen_k <= min_xds_k and max_gen_k >= max_xds_k)
assert(min_gen_l <= min_xds_l and max_gen_l >= max_xds_l)
# Test Passed
print "OK"
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