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.algorithms.scaling.algorithm import ScalingAlgorithm
params.scaling_options.__setattr__("use_free_set", True)
algorithm = ScalingAlgorithm(
params,
experiments=deepcopy(self.experiments),
reflections=deepcopy(self.reflections),
)
algorithm.run()
results = self.get_results_from_script(algorithm)
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
def test_scale_script_prepare_input():
"""Test prepare_input method of scaling script."""
# test the components of the scaling script directly with a test reflection
# table, experiments list and params.
params, exp, reflections = generate_test_input()
# try to pass in unequal number of reflections and experiments
reflections.append(generate_test_reflections())
with pytest.raises(ValueError):
_ = ScalingAlgorithm(params, exp, reflections)
params, exp, reflections = generate_test_input()
# Try to use use_datasets when not identifiers set
params.dataset_selection.use_datasets = [0]
with pytest.raises(ValueError):
_ = ScalingAlgorithm(params, exp, reflections)
# Try to use use_datasets when not identifiers set
params.dataset_selection.use_datasets = None
params.dataset_selection.exclude_datasets = [0]
with pytest.raises(ValueError):
_ = ScalingAlgorithm(params, exp, reflections)
# Now make two experiments with identifiers and select on them
params, exp, reflections = generate_test_input(n=2)
exp[0].identifier = "0"
reflections[0].experiment_identifiers()[0] = "0"
exp[1].identifier = "1"
reflections[1].experiment_identifiers()[0] = "1"
list1 = ExperimentList().append(exp[0])
list2 = ExperimentList().append(exp[1])
reflections[0].assert_experiment_identifiers_are_consistent(list1)
reflections[1].assert_experiment_identifiers_are_consistent(list2)
params.dataset_selection.use_datasets = [0]
params, exp, script_reflections = prepare_input(params, exp, reflections)
assert len(script_reflections) == 1
# Try again, this time excluding
params, exp, reflections = generate_test_input(n=2)
exp[0].identifier = "0"
reflections[0].experiment_identifiers()[0] = "0"
exp[1].identifier = "1"
reflections[1].experiment_identifiers()[1] = "1"
params.dataset_selection.exclude_datasets = [0]
params, exp, script_reflections = prepare_input(params, exp, reflections)
assert len(script_reflections) == 1
assert script_reflections[0] is reflections[1]
# Try having two unequal space groups
params, exp, reflections = generate_test_input(n=2)
exp_dict = {
"__id__": "crystal",
"real_space_a": [1.0, 0.0, 0.0],
"real_space_b": [0.0, 1.0, 0.0],
"real_space_c": [0.0, 0.0, 2.0],
"space_group_hall_symbol": " P 1",
}
crystal = Crystal.from_dict(exp_dict)
exp[0].crystal = crystal
with pytest.raises(ValueError):
_ = prepare_input(params, exp, reflections)
# Test cutting data
params, exp, reflections = generate_test_input(n=1)
params.cut_data.d_min = 1.5
params, _, script_reflections = prepare_input(params, exp, reflections)
r = script_reflections[0]
assert list(r.get_flags(r.flags.user_excluded_in_scaling)) == [
False,
False,
True,
True,
]
# Ensure that the user_excluded_in_scaling flags are reset before applying any new
# cutoffs by re-passing script_reflections to prepare_input
params.cut_data.d_min = None
params, _, script_reflections = prepare_input(params, exp,
script_reflections)
r = script_reflections[0]
assert list(r.get_flags(r.flags.user_excluded_in_scaling)) == [
False,
False,
False,
False,
]
params.cut_data.d_max = 1.25
params, _, script_reflections = prepare_input(params, exp, reflections)
r = script_reflections[0]
assert list(r.get_flags(r.flags.user_excluded_in_scaling)) == [
True,
True,
False,
False,
]
params, exp, reflections = generate_test_input(n=1)
reflections[0]["partiality"] = flex.double([0.5, 0.8, 1.0, 1.0])
params.cut_data.partiality_cutoff = 0.75
_, __, script_reflections = prepare_input(params, exp, reflections)
r = script_reflections[0]
assert list(r.get_flags(r.flags.user_excluded_in_scaling)) == [
True,
False,
False,
False,
]