def create_models(self):
# 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)
# keep track of the models
self.detector = models.detector
self.gonio = models.goniometer
self.crystal = models.crystal
self.beam = models.beam
# Create a stills ExperimentList
self.stills_experiments = ExperimentList()
self.stills_experiments.append(
Experiment(beam=self.beam,
detector=self.detector,
crystal=self.crystal,
imageset=None))
# keep track of the parameterisation of the models
self.det_param = DetectorParameterisationSinglePanel(self.detector)
self.s0_param = BeamParameterisation(self.beam, self.gonio)
self.xlo_param = CrystalOrientationParameterisation(self.crystal)
self.xluc_param = CrystalUnitCellParameterisation(self.crystal)
def create_models(self, cmdline_overrides=None):
from dxtbx.model import ScanFactory
from libtbx.phil import parse
from dials.test.algorithms.refinement.setup_geometry import Extract
if cmdline_overrides is None:
cmdline_overrides = []
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.test.algorithms.refinement.geometry_phil
""",
process_includes=True,
)
# Extract models
models = Extract(master_phil,
overrides,
cmdline_args=cmdline_overrides)
self.detector = models.detector
self.goniometer = models.goniometer
self.crystal = models.crystal
self.beam = models.beam
# Make a scan of 1-20 * 0.5 deg images
sf = ScanFactory()
self.scan = sf.make_scan((1, 20), 0.5, (0, 0.5), list(range(20)))
# Generate an ExperimentList
self.experiments = ExperimentList()
self.experiments.append(
Experiment(
beam=self.beam,
detector=self.detector,
goniometer=self.goniometer,
scan=self.scan,
crystal=self.crystal,
imageset=None,
))
# Create a reflection predictor for the experiments
self.ref_predictor = ScansExperimentsPredictor(self.experiments)
# Create scan-varying parameterisations of these models, with 3 samples
self.det_param = ScanVaryingDetectorParameterisationSinglePanel(
self.detector, self.scan.get_array_range(), 3)
self.s0_param = ScanVaryingBeamParameterisation(
self.beam, self.scan.get_array_range(), 3, self.goniometer)
self.xlo_param = ScanVaryingCrystalOrientationParameterisation(
self.crystal, self.scan.get_array_range(), 3)
self.xluc_param = ScanVaryingCrystalUnitCellParameterisation(
self.crystal, self.scan.get_array_range(), 3)
self.gon_param = ScanVaryingGoniometerParameterisation(
self.goniometer, self.scan.get_array_range(), 3, self.beam)
def create_experiments(image_start=1):
# Create models
from libtbx.phil import parse
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.test.algorithms.refinement.geometry_phil
""",
process_includes=True,
)
from dials.test.algorithms.refinement.setup_geometry import Extract
models = Extract(master_phil, overrides)
detector = models.detector
goniometer = models.goniometer
crystal = models.crystal
beam = models.beam
# Build a mock scan for a 72 degree sequence
from dxtbx.model import ScanFactory
sf = ScanFactory()
scan = sf.make_scan(
image_range=(image_start, image_start + 720 - 1),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=list(range(720)),
deg=True,
)
# No matter what image_start is, scan should start at 0.0 and end at 72.0 deg
assert scan.get_oscillation_range(deg=True) == (0.0, 72.0)
# Create an ExperimentList
experiments = ExperimentList()
experiments.append(
Experiment(
beam=beam,
detector=detector,
goniometer=goniometer,
scan=scan,
crystal=crystal,
imageset=None,
)
)
return experiments
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_fd_derivatives():
"""Test derivatives of the prediction equation"""
from libtbx.phil import parse
# Import model builder
from dials.test.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.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 72 degree sequence
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 a parameterisation of the crystal unit cell
from dials.algorithms.refinement.parameterisation.crystal_parameters import (
CrystalUnitCellParameterisation, )
xluc_param = CrystalUnitCellParameterisation(mycrystal)
# Create an ExperimentList
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
# Build a prediction parameterisation for two theta prediction
pred_param = TwoThetaPredictionParameterisation(
experiments,
detector_parameterisations=None,
beam_parameterisations=None,
xl_orientation_parameterisations=None,
xl_unit_cell_parameterisations=[xluc_param],
)
# Generate some reflections
obs_refs, ref_predictor = generate_reflections(experiments)
# Build a ReflectionManager with overloads for handling 2theta residuals
refman = TwoThetaReflectionManager(obs_refs,
experiments,
outlier_detector=None)
# Build a TwoThetaExperimentsPredictor
ref_predictor = TwoThetaExperimentsPredictor(experiments)
# Make a target for the least squares 2theta residual
target = TwoThetaTarget(experiments, ref_predictor, refman, pred_param)
# Keep only 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 in range(len(deltas)):
val = p_vals[i]
p_vals[i] -= deltas[i] / 2.0
pred_param.set_param_vals(p_vals)
target.predict()
reflections = refman.get_matches()
rev_state = reflections["2theta_resid"].deep_copy()
p_vals[i] += deltas[i]
pred_param.set_param_vals(p_vals)
target.predict()
reflections = refman.get_matches()
fwd_state = reflections["2theta_resid"].deep_copy()
p_vals[i] = val
fd = fwd_state - rev_state
fd /= deltas[i]
# compare with analytical calculation
assert approx_equal(fd, an_grads[i]["d2theta_dp"], eps=1.0e-6)
# return to the initial state
pred_param.set_param_vals(p_vals)
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)
def test_single_crystal_restraints_gradients():
"""Simple test with a single triclinic crystal restrained to a target unit cell"""
from dxtbx.model.experiment_list import Experiment, ExperimentList
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.prediction_parameters import (
XYPhiPredictionParameterisation, )
from dials.test.algorithms.refinement.setup_geometry import Extract
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.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 72 degree sequence
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)
# Create an ExperimentList
experiments = ExperimentList()
experiments.append(
Experiment(
beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None,
))
# 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],
)
# Build a restraints parameterisation
rp = RestraintsParameterisation(
detector_parameterisations=[det_param],
beam_parameterisations=[s0_param],
xl_orientation_parameterisations=[xlo_param],
xl_unit_cell_parameterisations=[xluc_param],
)
# make a unit cell target
sigma = 1.0
uc = mycrystal.get_unit_cell().parameters()
target_uc = [random.gauss(e, sigma) for e in uc]
rp.add_restraints_to_target_xl_unit_cell(experiment_id=0,
values=target_uc,
sigma=[sigma] * 6)
# get analytical values and gradients
vals, grads, weights = rp.get_residuals_gradients_and_weights()
assert len(vals) == rp.num_residuals
# get finite difference gradients
p_vals = pred_param.get_param_vals()
deltas = [1.0e-7] * len(p_vals)
fd_grad = []
for i, delta in enumerate(deltas):
val = p_vals[i]
p_vals[i] -= delta / 2.0
pred_param.set_param_vals(p_vals)
rev_state, foo, bar = rp.get_residuals_gradients_and_weights()
rev_state = flex.double(rev_state)
p_vals[i] += delta
pred_param.set_param_vals(p_vals)
fwd_state, foo, bar = rp.get_residuals_gradients_and_weights()
fwd_state = flex.double(fwd_state)
p_vals[i] = val
fd = (fwd_state - rev_state) / delta
fd_grad.append(fd)
# for comparison, fd_grad is a list of flex.doubles, each of which corresponds
# to a column of the sparse matrix grads.
for i, fd in enumerate(fd_grad):
# extract dense column from the sparse matrix
an = grads.col(i).as_dense_vector()
assert an == pytest.approx(fd, abs=1e-5)
#############################
# Setup experimental models #
#############################
args = sys.argv[1:]
# 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=range(1800),
deg=True)
sweep_range = myscan.get_oscillation_range(deg=False)
im_width = myscan.get_oscillation(deg=False)[1]
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%
def test2():
'''Simple test with two triclinic crystals restrained to a target unit cell'''
from math import pi
from random import gauss
from dials.test.algorithms.refinement.setup_geometry import Extract
from dxtbx.model.experiment.experiment_list import ExperimentList, Experiment
#### Import model parameterisations
from dials.algorithms.refinement.parameterisation.prediction_parameters import \
XYPhiPredictionParameterisation
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
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.test.algorithms.refinement.geometry_phil
""",
process_includes=True)
models = Extract(master_phil, overrides)
mydetector = models.detector
mygonio = models.goniometer
mycrystal = models.crystal
# duplicate the crystal
from copy import deepcopy
mycrystal2 = deepcopy(mycrystal)
mybeam = models.beam
# Build a mock scan for a 72 degree sweep
sweep_range = (0., pi / 5.)
from dxtbx.model.scan import scan_factory
sf = scan_factory()
myscan = sf.make_scan(image_range=(1, 720),
exposure_times=0.1,
oscillation=(0, 0.1),
epochs=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)
xluc_param2 = CrystalUnitCellParameterisation(mycrystal2,
experiment_ids=[1])
# Create an ExperimentList with the crystal duplicated
experiments = ExperimentList()
experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal,
imageset=None))
experiments.append(
Experiment(beam=mybeam,
detector=mydetector,
goniometer=mygonio,
scan=myscan,
crystal=mycrystal2,
imageset=None))
# 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, xluc_param2])
# Build a restraints parameterisation
rp = RestraintsParameterisation(
detector_parameterisations=[det_param],
beam_parameterisations=[s0_param],
xl_orientation_parameterisations=[xlo_param],
xl_unit_cell_parameterisations=[xluc_param, xluc_param2])
# make a unit cell target
sigma = 1.
uc = mycrystal.get_unit_cell().parameters()
target_uc = [gauss(e, sigma) for e in uc]
rp.add_restraints_to_target_xl_unit_cell(experiment_id=0,
values=target_uc,
sigma=[sigma] * 6)
rp.add_restraints_to_target_xl_unit_cell(experiment_id=1,
values=target_uc,
sigma=[sigma] * 6)
# get analytical values and gradients
vals, grads, weights = rp.get_residuals_gradients_and_weights()
# get finite difference gradients
p_vals = pred_param.get_param_vals()
deltas = [1.e-7] * len(p_vals)
fd_grad = []
for i in range(len(deltas)):
val = p_vals[i]
p_vals[i] -= deltas[i] / 2.
pred_param.set_param_vals(p_vals)
rev_state, foo, bar = rp.get_residuals_gradients_and_weights()
rev_state = flex.double(rev_state)
p_vals[i] += deltas[i]
pred_param.set_param_vals(p_vals)
fwd_state, foo, bar = rp.get_residuals_gradients_and_weights()
fwd_state = flex.double(fwd_state)
p_vals[i] = val
fd = (fwd_state - rev_state) / deltas[i]
fd_grad.append(fd)
# for comparison, fd_grad is a list of flex.doubles, each of which corresponds
# to a column of the sparse matrix grads.
for i, fd in enumerate(fd_grad):
# extract dense column from the sparse matrix
an = grads.col(i).as_dense_vector()
assert approx_equal(an, fd, eps=1e-5)
print "OK"
def test():
from cctbx.sgtbx import space_group, space_group_symbols
from libtbx.phil import parse
from scitbx.array_family import flex
##### Import model builder
from dials.test.algorithms.refinement.setup_geometry import Extract
##### Imports for reflection prediction
from dials.algorithms.spot_prediction import IndexGenerator, ray_intersection
from dxtbx.model.experiment_list import ExperimentList, Experiment
from dials.algorithms.refinement.prediction import ScansRayPredictor, \
ExperimentsPredictor
#### Import model parameterisations
from dials.algorithms.refinement.parameterisation.prediction_parameters import \
XYPhiPredictionParameterisation
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
from dials.algorithms.refinement.parameterisation.goniometer_parameters import \
GoniometerParameterisation
#### 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.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 72 degree sweep
sweep_range = (0., math.pi/5.)
from dxtbx.model import ScanFactory
sf = ScanFactory()
myscan = sf.make_scan(image_range = (1,720),
exposure_times = 0.1,
oscillation = (0, 0.1),
epochs = 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 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 = 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 * math.pi / 180.
px_size = mydetector[0].get_pixel_size()
var_x = flex.double(len(obs_refs), (px_size[0] / 2.)**2)
var_y = flex.double(len(obs_refs), (px_size[1] / 2.)**2)
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)
# 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)
# Redefine the reflection predictor to use the type expected by the Target class
ref_predictor = ExperimentsPredictor(experiments)
# make a target to ensure reflections are predicted and refman is finalised
from dials.algorithms.refinement.target import \
LeastSquaresPositionalResidualWithRmsdCutoff
target = LeastSquaresPositionalResidualWithRmsdCutoff(experiments,
ref_predictor, refman, pred_param, restraints_parameterisation=None)
# 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.e-7] * len(p_vals)
for i, delta in enumerate(deltas):
val = p_vals[i]
p_vals[i] -= delta / 2.
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.e-6)
assert y_grads == pytest.approx(an_grads[i]["dY_dp"], abs=5.e-6)
assert phi_grads == pytest.approx(an_grads[i]["dphi_dp"], abs=5.e-6)
# return to the initial state
pred_param.set_param_vals(p_vals)
# We will set up a mock scan
from dxtbx.model import ScanFactory
args = sys.argv[1:]
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, cmdline_args=args)
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)
