def run_scaling(params, experiments, reflections):
"""Run scaling algorithms; stats only, cross validation or standard."""
if params.stats_only:
Script.stats_only(reflections, experiments, params)
sys.exit()
if params.export_mtz_only:
Script.export_mtz_only(reflections, experiments, params)
sys.exit()
if params.output.delete_integration_shoeboxes:
for r in reflections:
del r["shoebox"]
if params.cross_validation.cross_validation_mode:
from dials.algorithms.scaling.cross_validation.cross_validate import (
cross_validate,
)
from dials.algorithms.scaling.cross_validation.crossvalidator import (
DialsScaleCrossValidator,
)
cross_validator = DialsScaleCrossValidator(experiments, reflections)
try:
cross_validate(params, cross_validator)
except ValueError as e:
raise Sorry(e)
logger.info(
"Cross validation analysis does not produce scaling output files, rather\n"
"it gives insight into the dataset. Choose an appropriate parameterisation\n"
"and rerun scaling without cross_validation_mode.\n"
)
else:
script = Script(params, experiments, reflections)
# Register the observers at the highest level
if params.output.html:
register_default_scaling_observers(script)
else:
register_merging_stats_observers(script)
if params.filtering.method:
if script.scaler.id_ != "multi":
raise Sorry(
"""
Scaling and filtering can only be performed in multi-dataset scaling mode
(not single dataset or scaling against a reference)"""
)
register_scale_and_filter_observers(script)
script.run_scale_and_filter()
with open(params.filtering.output.scale_and_filter_results, "w") as f:
json.dump(script.filtering_results.to_dict(), f, indent=2)
else:
script.run()
script.export()
def run_script(self, params, config_no):
"""Run the scaling script with the params, get the free/work set results
and add to the results dict"""
from dials.command_line.scale import Script
params.scaling_options.__setattr__("use_free_set", True)
script = Script(
params,
experiments=deepcopy(self.experiments),
reflections=deepcopy(self.reflections),
)
register_merging_stats_observers(script)
script.run()
results = self.get_results_from_script(script)
self.add_results_to_results_dict(config_no, results)
def run_scaling(params, experiments, reflections):
"""Run scaling algorithms; cross validation, scaling + filtering or standard.
Returns:
experiments: an experiment list with scaled data (if created)
joint_table: a single reflection table containing scaled data (if created).
"""
if params.output.delete_integration_shoeboxes:
for r in reflections:
del r["shoebox"]
if params.cross_validation.cross_validation_mode:
from dials.algorithms.scaling.cross_validation.cross_validate import (
cross_validate, )
from dials.algorithms.scaling.cross_validation.crossvalidator import (
DialsScaleCrossValidator, )
cross_validator = DialsScaleCrossValidator(experiments, reflections)
cross_validate(params, cross_validator)
logger.info(
"Cross validation analysis does not produce scaling output files, rather\n"
"it gives insight into the dataset. Choose an appropriate parameterisation\n"
"and rerun scaling without cross_validation_mode.\n")
return (None, None)
else:
# Register the observers at the highest level
if params.filtering.method:
algorithm = ScaleAndFilterAlgorithm(params, experiments,
reflections)
register_scale_and_filter_observers(algorithm)
else:
algorithm = ScalingAlgorithm(params, experiments, reflections)
if params.output.html:
register_default_scaling_observers(algorithm)
else:
register_merging_stats_observers(algorithm)
algorithm.run()
experiments, joint_table = algorithm.finish()
return experiments, joint_table