def test_determine_space_group(space_group):
sgi = sgtbx.space_group_info(symbol=space_group)
sg = sgi.group()
cs = sgi.any_compatible_crystal_symmetry(volume=10000)
cs = cs.best_cell()
cs = cs.minimum_cell()
intensities = (
generate_intensities(cs, d_min=1.0)
.generate_bijvoet_mates()
.set_observation_type_xray_intensity()
)
intensities = intensities.expand_to_p1()
# needed to give vaguely sensible E_cc_true values
intensities = intensities.customized_copy(sigmas=intensities.data() / 50)
intensities.set_info(miller.array_info(source="fake", source_type="mtz"))
result = LaueGroupAnalysis([intensities], normalisation=None)
print(result)
assert (
result.best_solution.subgroup["best_subsym"].space_group()
== sg.build_derived_patterson_group()
)
assert result.best_solution.likelihood > 0.8
for score in result.subgroup_scores[1:]:
assert score.likelihood < 0.1
def test_determine_space_group(space_group):
sgi = sgtbx.space_group_info(symbol=space_group)
sg = sgi.group()
cs = sgi.any_compatible_crystal_symmetry(volume=10000)
cs = cs.best_cell()
cs = cs.minimum_cell()
intensities = generate_fake_intensities(cs)
result = LaueGroupAnalysis([intensities], normalisation=None)
print(result)
assert (result.best_solution.subgroup["best_subsym"].space_group() ==
sg.build_derived_patterson_group())
assert result.best_solution.likelihood > 0.8
for score in result.subgroup_scores[1:]:
assert score.likelihood < 0.1
def test_determine_space_group_best_monoclinic_beta():
cs = crystal.symmetry(
unit_cell=(44.66208171, 53.12629403, 62.53397661, 64.86329707,
78.27343894, 90),
space_group_symbol="C 1 2/m 1 (z,x+y,-2*x)",
)
cs = cs.best_cell().primitive_setting()
intensities = generate_fake_intensities(cs)
result = LaueGroupAnalysis([intensities], normalisation=None)
print(result)
assert result.best_solution.subgroup["best_subsym"].is_similar_symmetry(
crystal.symmetry(
unit_cell=(44.66208171, 53.12629403, 111.9989451, 90, 99.89337396,
90),
space_group_symbol="I 1 2/m 1",
))
d = result.as_dict()
assert cs.change_basis(
sgtbx.change_of_basis_op(
d["subgroup_scores"][0]["cb_op"])).is_similar_symmetry(
result.best_solution.subgroup["best_subsym"])
result = LaueGroupAnalysis([intensities],
best_monoclinic_beta=False,
normalisation=None)
print(result)
assert result.best_solution.subgroup["best_subsym"].is_similar_symmetry(
crystal.symmetry(
unit_cell=(113.2236274, 53.12629403, 44.66208171, 90, 102.9736126,
90),
space_group_symbol="C 1 2/m 1",
))
d = result.as_dict()
assert cs.change_basis(
sgtbx.change_of_basis_op(
d["subgroup_scores"][0]["cb_op"])).is_similar_symmetry(
result.best_solution.subgroup["best_subsym"])
def symmetry(experiments, reflection_tables, params=None):
"""
Run symmetry analysis
Args:
experiments: An experiment list.
reflection_tables: A list of reflection tables.
params: The dials.symmetry phil scope.
"""
result = None
if params is None:
params = phil_scope.extract()
refls_for_sym = []
if params.laue_group is Auto:
logger.info("=" * 80)
logger.info("")
logger.info("Performing Laue group analysis")
logger.info("")
# Transform models into miller arrays
n_datasets = len(experiments)
# Map experiments and reflections to minimum cell
# Eliminate reflections that are systematically absent due to centring
# of the lattice, otherwise they would lead to non-integer miller indices
# when reindexing to a primitive setting
cb_ops = change_of_basis_ops_to_minimum_cell(
experiments,
params.lattice_symmetry_max_delta,
params.relative_length_tolerance,
params.absolute_angle_tolerance,
)
reflection_tables = eliminate_sys_absent(experiments,
reflection_tables)
experiments, reflection_tables = apply_change_of_basis_ops(
experiments, reflection_tables, cb_ops)
refls_for_sym = get_subset_for_symmetry(experiments, reflection_tables,
params.exclude_images)
datasets = filtered_arrays_from_experiments_reflections(
experiments,
refls_for_sym,
outlier_rejection_after_filter=True,
partiality_threshold=params.partiality_threshold,
)
if len(datasets) != n_datasets:
raise ValueError(
"""Some datasets have no reflection after prefiltering, please check
input data and filtering settings e.g partiality_threshold""")
datasets = [
ma.as_anomalous_array().merge_equivalents().array()
for ma in datasets
]
result = LaueGroupAnalysis(
datasets,
normalisation=params.normalisation,
d_min=params.d_min,
min_i_mean_over_sigma_mean=params.min_i_mean_over_sigma_mean,
lattice_symmetry_max_delta=params.lattice_symmetry_max_delta,
relative_length_tolerance=params.relative_length_tolerance,
absolute_angle_tolerance=params.absolute_angle_tolerance,
best_monoclinic_beta=params.best_monoclinic_beta,
)
logger.info("")
logger.info(result)
if params.output.json is not None:
d = result.as_dict()
d["cb_op_inp_min"] = [str(cb_op) for cb_op in cb_ops]
# Copy the "input_symmetry" to "min_cell_symmetry" as it isn't technically
# the input symmetry to dials.symmetry
d["min_cell_symmetry"] = d["input_symmetry"]
del d["input_symmetry"]
json_str = json.dumps(d, indent=2)
with open(params.output.json, "w") as f:
f.write(json_str)
# Change of basis operator from input unit cell to best unit cell
cb_op_inp_best = result.best_solution.subgroup["cb_op_inp_best"]
# Get the best space group.
best_subsym = result.best_solution.subgroup["best_subsym"]
best_space_group = best_subsym.space_group(
).build_derived_acentric_group()
logger.info(
tabulate(
[[
str(best_subsym.space_group_info()),
str(best_space_group.info())
]],
["Patterson group", "Corresponding MX group"],
))
# Reindex the input data
experiments, reflection_tables = _reindex_experiments_reflections(
experiments, reflection_tables, best_space_group, cb_op_inp_best)
elif params.laue_group is not None:
if params.change_of_basis_op is not None:
cb_op = sgtbx.change_of_basis_op(params.change_of_basis_op)
else:
cb_op = sgtbx.change_of_basis_op()
# Reindex the input data
experiments, reflection_tables = _reindex_experiments_reflections(
experiments, reflection_tables, params.laue_group.group(), cb_op)
if params.systematic_absences.check:
logger.info("=" * 80)
logger.info("")
logger.info("Analysing systematic absences")
logger.info("")
# Get the laue class from the current space group.
space_group = experiments[0].crystal.get_space_group()
laue_group = str(space_group.build_derived_patterson_group().info())
logger.info("Laue group: %s", laue_group)
if laue_group in ("I m -3", "I m m m"):
if laue_group == "I m -3":
logger.info(
"""Space groups I 2 3 & I 21 3 cannot be distinguished with systematic absence
analysis, due to lattice centering.
Using space group I 2 3, space group I 21 3 is equally likely.\n""")
if laue_group == "I m m m":
logger.info(
"""Space groups I 2 2 2 & I 21 21 21 cannot be distinguished with systematic absence
analysis, due to lattice centering.
Using space group I 2 2 2, space group I 21 21 21 is equally likely.\n""")
elif laue_group not in laue_groups_for_absence_analysis:
logger.info("No absences to check for this laue group\n")
else:
if not refls_for_sym:
refls_for_sym = get_subset_for_symmetry(
experiments, reflection_tables, params.exclude_images)
if (params.d_min is Auto) and (result is not None):
d_min = result.intensities.resolution_range()[1]
elif params.d_min is Auto:
d_min = resolution_filter_from_reflections_experiments(
refls_for_sym,
experiments,
params.min_i_mean_over_sigma_mean,
params.min_cc_half,
)
else:
d_min = params.d_min
# combine before sys abs test - only triggers if laue_group=None and
# multiple input files.
if len(reflection_tables) > 1:
joint_reflections = flex.reflection_table()
for table in refls_for_sym:
joint_reflections.extend(table)
else:
joint_reflections = refls_for_sym[0]
merged_reflections = prepare_merged_reflection_table(
experiments, joint_reflections, d_min)
run_systematic_absences_checks(
experiments,
merged_reflections,
float(params.systematic_absences.significance_level),
)
logger.info(
"Saving reindexed experiments to %s in space group %s",
params.output.experiments,
str(experiments[0].crystal.get_space_group().info()),
)
experiments.as_file(params.output.experiments)
if params.output.reflections is not None:
if len(reflection_tables) > 1:
joint_reflections = flex.reflection_table()
for table in reflection_tables:
joint_reflections.extend(table)
else:
joint_reflections = reflection_tables[0]
logger.info(
"Saving %s reindexed reflections to %s",
len(joint_reflections),
params.output.reflections,
)
joint_reflections.as_file(params.output.reflections)
if params.output.html and params.systematic_absences.check:
ScrewAxisObserver().generate_html_report(params.output.html)
