def test_update_imageset_ids(dials_data):
expts = ExperimentList()
refls = []
for i in [1, 2, 3, 4, 5, 7, 8, 10]:
refls.append(
flex.reflection_table.from_file(
dials_data("multi_crystal_proteinase_k", pathlib=True) /
f"reflections_{i}.pickle"))
expts.extend(
load.experiment_list(
dials_data("multi_crystal_proteinase_k", pathlib=True) /
f"experiments_{i}.json",
check_format=False,
))
# first make sure ids are set up correctly.
experiments, reflections = assign_unique_identifiers(expts, refls)
reflections = update_imageset_ids(experiments, reflections)
joint_reflections = flex.reflection_table()
for refls in reflections:
joint_reflections.extend(refls)
# check that there are 8 unique id and imageset_ids, and that these
# correctly correspond to each experiment
assert len(set(joint_reflections["id"])) == 8
assert len(set(joint_reflections["imageset_id"])) == 8
for id_ in range(8):
sel = joint_reflections["id"] == id_
assert set(joint_reflections["imageset_id"].select(sel)) == {id_}
def _reindex_experiments_reflections(experiments, reflections, space_group, cb_op):
"""Reindex the input data."""
reindexed_experiments = reindex_experiments(
experiments, cb_op, space_group=space_group
)
reindexed_reflections = flex.reflection_table()
reflections = update_imageset_ids(experiments, reflections)
for i in range(len(reindexed_experiments)):
reindexed_refl = copy.deepcopy(reflections[i])
reindexed_refl["miller_index"] = cb_op.apply(reindexed_refl["miller_index"])
reindexed_reflections.extend(reindexed_refl)
return reindexed_experiments, [reindexed_reflections]
def finish(self):
"""Save the experiments json and scaled pickle file."""
# Now create a joint reflection table. Delete all other data before
# joining reflection tables - just need experiments for mtz export
# and a reflection table.
del self.scaler
gc.collect()
# update imageset ids before combining reflection tables.
self.reflections = update_imageset_ids(self.experiments,
self.reflections)
joint_table = flex.reflection_table()
for i in range(len(self.reflections)):
joint_table.extend(self.reflections[i])
# del reflection_table
self.reflections[i] = 0
gc.collect()
# remove reflections with very low scale factors
sel = joint_table["inverse_scale_factor"] <= 0.001
good_sel = ~joint_table.get_flags(joint_table.flags.bad_for_scaling,
all=False)
n_neg = (good_sel & sel).count(True)
if n_neg > 0:
logger.warning(
"""%s non-excluded reflections were assigned scale factors < 0.001 during scaling.
These will be excluded in the output reflection table. It may be best to rerun
scaling from this point for an improved model.""",
n_neg,
)
joint_table.set_flags(sel, joint_table.flags.excluded_for_scaling)
to_del = [
"variance",
"intensity",
"s0",
"s0c",
"s1c",
"prescaling_correction",
"batch",
]
for col in to_del:
try:
del joint_table[col]
except KeyError:
pass
return self.experiments, joint_table
def export(self):
"""Output the datafiles for cosym.
This includes the cosym.json, reflections and experiments files."""
reindexed_reflections = flex.reflection_table()
self._reflections = update_imageset_ids(self._experiments,
self._reflections)
for refl in self._reflections:
reindexed_reflections.extend(refl)
reindexed_reflections.reset_ids()
logger.info("Saving reindexed experiments to %s",
self.params.output.experiments)
self._experiments.as_file(self.params.output.experiments)
logger.info("Saving reindexed reflections to %s",
self.params.output.reflections)
reindexed_reflections.as_file(self.params.output.reflections)