def config_parameterisation(params, experiments, refman, do_stills=False):
from dials.algorithms.refinement.parameterisation import (
build_prediction_parameterisation, )
pred_param = build_prediction_parameterisation(params, experiments,
refman, do_stills)
# Parameter reporting
from dials.algorithms.refinement.parameterisation.parameter_report import (
ParameterReporter, )
param_reporter = ParameterReporter(
pred_param.get_detector_parameterisations(),
pred_param.get_beam_parameterisations(),
pred_param.get_crystal_orientation_parameterisations(),
pred_param.get_crystal_unit_cell_parameterisations(),
pred_param.get_goniometer_parameterisations(),
)
return pred_param, param_reporter
def _build_components(cls, params, reflections, experiments):
"""low level build"""
# Currently a refinement job can only have one parameterisation of the
# prediction equation. This can either be of the XYDelPsi (stills) type, the
# XYPhi (scans) type or the scan-varying XYPhi type with a varying crystal
# model
single_as_still = params.refinement.parameterisation.treat_single_image_as_still
exps_are_stills = []
for exp in experiments:
if exp.scan is None:
exps_are_stills.append(True)
elif exp.scan.get_num_images() == 1:
if single_as_still:
exps_are_stills.append(True)
elif exp.scan.is_still():
exps_are_stills.append(True)
else:
exps_are_stills.append(False)
else:
if exp.scan.get_oscillation()[1] <= 0.0:
raise DialsRefineConfigError(
"Cannot refine a zero-width scan")
exps_are_stills.append(False)
# check experiment types are consistent
if not all(exps_are_stills[0] == e for e in exps_are_stills):
raise DialsRefineConfigError(
"Cannot refine a mixture of stills and scans")
do_stills = exps_are_stills[0]
# If experiments are stills, ensure scan-varying refinement won't be attempted
if do_stills:
params.refinement.parameterisation.scan_varying = False
# Refiner does not accept scan_varying=Auto. This is a special case for
# doing macrocycles of refinement in dials.refine.
if params.refinement.parameterisation.scan_varying is libtbx.Auto:
params.refinement.parameterisation.scan_varying = False
# Trim scans and calculate reflection block_width if required for scan-varying refinement
if (params.refinement.parameterisation.scan_varying and
params.refinement.parameterisation.trim_scan_to_observations):
experiments = _trim_scans_to_observations(experiments, reflections)
from dials.algorithms.refinement.reflection_manager import BlockCalculator
block_calculator = BlockCalculator(experiments, reflections)
if params.refinement.parameterisation.compose_model_per == "block":
reflections = block_calculator.per_width(
params.refinement.parameterisation.block_width, deg=True)
elif params.refinement.parameterisation.compose_model_per == "image":
reflections = block_calculator.per_image()
logger.debug("\nBuilding reflection manager")
logger.debug("Input reflection list size = %d observations",
len(reflections))
# create reflection manager
refman = ReflectionManagerFactory.from_parameters_reflections_experiments(
params.refinement.reflections, reflections, experiments, do_stills)
logger.debug(
"Number of observations that pass initial inclusion criteria = %d",
refman.get_accepted_refs_size(),
)
sample_size = refman.get_sample_size()
if sample_size > 0:
logger.debug("Working set size = %d observations", sample_size)
logger.debug("Reflection manager built\n")
# configure use of sparse data types
params = cls.config_sparse(params, experiments)
do_sparse = params.refinement.parameterisation.sparse
# create managed reflection predictor
ref_predictor = ExperimentsPredictorFactory.from_experiments(
experiments,
force_stills=do_stills,
spherical_relp=params.refinement.parameterisation.
spherical_relp_model,
)
# Predict for the managed observations, set columns for residuals and set
# the used_in_refinement flag to the predictions
obs = refman.get_obs()
ref_predictor(obs)
x_obs, y_obs, phi_obs = obs["xyzobs.mm.value"].parts()
x_calc, y_calc, phi_calc = obs["xyzcal.mm"].parts()
obs["x_resid"] = x_calc - x_obs
obs["y_resid"] = y_calc - y_obs
obs["phi_resid"] = phi_calc - phi_obs
# determine whether to do basic centroid analysis to automatically
# determine outlier rejection block
if params.refinement.reflections.outlier.block_width is libtbx.Auto:
ca = refman.get_centroid_analyser()
analysis = ca(calc_average_residuals=False,
calc_periodograms=False)
else:
analysis = None
# Now predictions and centroid analysis are available, so we can finalise
# the reflection manager
refman.finalise(analysis)
# Create model parameterisations
logger.debug("Building prediction equation parameterisation")
pred_param = build_prediction_parameterisation(
params.refinement.parameterisation, experiments, refman, do_stills)
# Build a constraints manager, if requested
cmf = ConstraintManagerFactory(params, pred_param)
constraints_manager = cmf()
# Test for parameters that have too little data to refine and act accordingly
autoreduce = AutoReduce(
params.refinement.parameterisation.auto_reduction,
pred_param,
refman,
constraints_manager,
cmf,
)
autoreduce()
# if reduction was done, constraints_manager will have changed
constraints_manager = autoreduce.constraints_manager
# Build a restraints parameterisation (if requested).
# Only unit cell restraints are supported at the moment.
restraints_parameterisation = cls.config_restraints(
params.refinement.parameterisation, pred_param)
# Parameter reporting
logger.debug("Prediction equation parameterisation built")
logger.debug("Parameter order : name mapping")
for i, e in enumerate(pred_param.get_param_names()):
logger.debug("Parameter %03d : %s", i + 1, e)
param_reporter = ParameterReporter(
pred_param.get_detector_parameterisations(),
pred_param.get_beam_parameterisations(),
pred_param.get_crystal_orientation_parameterisations(),
pred_param.get_crystal_unit_cell_parameterisations(),
pred_param.get_goniometer_parameterisations(),
)
# Create target function
logger.debug("Building target function")
target = cls.config_target(
params.refinement.target,
experiments,
refman,
ref_predictor,
pred_param,
restraints_parameterisation,
do_stills,
do_sparse,
)
logger.debug("Target function built")
# create refinery
logger.debug("Building refinement engine")
refinery = cls.config_refinery(params, target, pred_param,
constraints_manager)
logger.debug("Refinement engine built")
nparam = len(pred_param)
ndim = target.dim
nref = len(refman.get_matches())
logger.info(
"There are %s parameters to refine against %s reflections in %s dimensions",
nparam,
nref,
ndim,
)
if not params.refinement.parameterisation.sparse and isinstance(
refinery, AdaptLstbx):
dense_jacobian_gigabytes = (nparam * nref * ndim *
flex.double.element_size()) / 1e9
avail_memory_gigabytes = psutil.virtual_memory().available / 1e9
# Report if the Jacobian requires a large amount of storage
if (dense_jacobian_gigabytes > 0.2 * avail_memory_gigabytes
or dense_jacobian_gigabytes > 0.5):
logger.info(
"Storage of the Jacobian matrix requires %.1f GB",
dense_jacobian_gigabytes,
)
# build refiner interface and return
if params.refinement.parameterisation.scan_varying:
refiner = ScanVaryingRefiner
else:
refiner = Refiner
return refiner(experiments, pred_param, param_reporter, refman, target,
refinery)