def test_cosym_analyse_datasets(space_group, tmpdir):
tmpdir.chdir()
import matplotlib
matplotlib.use('Agg')
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group())
expected_space_group = sgtbx.space_group_info(symbol=space_group).group()
params = phil_scope.extract()
params.cluster.agglomerative.n_clusters = len(expected_reindexing_ops)
result = analyse_datasets(datasets, params)
space_groups = {}
reindexing_ops = {}
for dataset_id in result.reindexing_ops.iterkeys():
if 0 in result.reindexing_ops[dataset_id]:
cb_op = result.reindexing_ops[dataset_id][0]
reindexing_ops.setdefault(cb_op, set())
reindexing_ops[cb_op].add(dataset_id)
if dataset_id in result.space_groups:
space_groups.setdefault(result.space_groups[dataset_id], set())
space_groups[result.space_groups[dataset_id]].add(dataset_id)
assert len(reindexing_ops) == len(expected_reindexing_ops)
assert sorted(reindexing_ops.keys()) == sorted(
expected_reindexing_ops.keys())
for ridx_set in reindexing_ops.values():
for expected_set in expected_reindexing_ops.values():
assert ((len(ridx_set.symmetric_difference(expected_set)) == 0)
or (len(ridx_set.intersection(expected_set)) == 0))
def run_cosym(self):
from dials.algorithms.symmetry.cosym import phil_scope
params = phil_scope.extract()
from dials.algorithms.symmetry.cosym import analyse_datasets
datasets = self.individual_merged_intensities
datasets = [d.primitive_setting() for d in datasets]
params.lattice_group = datasets[0].space_group_info()
params.cluster.method = "dbscan"
params.plot_prefix = self._prefix
results = analyse_datasets(self.individual_merged_intensities, params)
results.plot()
self.cosym = results
def run_cosym(self):
from dials.algorithms.symmetry.cosym import phil_scope
params = phil_scope.extract()
from dials.algorithms.symmetry.cosym import CosymAnalysis
datasets = self.individual_merged_intensities
datasets = [
d.eliminate_sys_absent(integral_only=True).primitive_setting()
for d in datasets
]
params.lattice_group = datasets[0].space_group_info()
params.space_group = datasets[0].space_group_info()
params.cluster.method = "dbscan"
self.cosym = CosymAnalysis(datasets, params)
self.cosym.run()
def test_cosym(
space_group,
unit_cell,
dimensions,
sample_size,
use_known_space_group,
use_known_lattice_group,
best_monoclinic_beta,
run_in_tmpdir,
):
import matplotlib
matplotlib.use("Agg")
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group(),
unit_cell=unit_cell,
unit_cell_volume=10000,
d_min=1.5,
map_to_p1=True,
sample_size=sample_size,
seed=1,
)
expected_space_group = sgtbx.space_group_info(symbol=space_group).group()
params = phil_scope.extract()
params.dimensions = dimensions
params.best_monoclinic_beta = best_monoclinic_beta
if use_known_space_group:
params.space_group = expected_space_group.info()
if use_known_lattice_group:
params.lattice_group = expected_space_group.info()
params.normalisation = None
cosym = CosymAnalysis(datasets, params)
cosym.run()
d = cosym.as_dict()
if not use_known_space_group:
assert d["subgroup_scores"][0]["likelihood"] > 0.89
assert (sgtbx.space_group(d["subgroup_scores"][0]["patterson_group"])
== sgtbx.space_group_info(
space_group).group().build_derived_patterson_group())
expected_sg = (sgtbx.space_group_info(
space_group).group().build_derived_patterson_group())
else:
expected_sg = sgtbx.space_group_info(space_group).group()
assert cosym.best_subgroup["best_subsym"].space_group() == expected_sg
assert len(cosym.reindexing_ops) == len(expected_reindexing_ops)
space_group_info = cosym.best_subgroup["subsym"].space_group_info()
reference = None
for d_id, cb_op in enumerate(cosym.reindexing_ops):
reindexed = (datasets[d_id].change_basis(
sgtbx.change_of_basis_op(cb_op)).customized_copy(
space_group_info=space_group_info.change_basis(
cosym.cb_op_inp_min.inverse())))
assert reindexed.is_compatible_unit_cell(), str(
reindexed.crystal_symmetry())
if reference:
assert (reindexed.correlation(
reference, assert_is_similar_symmetry=False).coefficient() >
0.99)
else:
reference = reindexed
def test_cosym(
space_group,
unit_cell,
dimensions,
sample_size,
use_known_space_group,
use_known_lattice_group,
best_monoclinic_beta,
run_in_tmpdir,
):
import matplotlib
matplotlib.use("Agg")
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group(),
unit_cell=unit_cell,
unit_cell_volume=10000,
d_min=1.5,
map_to_p1=True,
sample_size=sample_size,
seed=1,
)
expected_space_group = sgtbx.space_group_info(symbol=space_group).group()
params = phil_scope.extract()
params.cluster.n_clusters = len(expected_reindexing_ops)
params.dimensions = dimensions
params.best_monoclinic_beta = best_monoclinic_beta
if use_known_space_group:
params.space_group = expected_space_group.info()
if use_known_lattice_group:
params.lattice_group = expected_space_group.info()
params.normalisation = None
cosym = CosymAnalysis(datasets, params)
cosym.run()
d = cosym.as_dict()
if not use_known_space_group:
assert d["subgroup_scores"][0]["likelihood"] > 0.89
assert (sgtbx.space_group(d["subgroup_scores"][0]["patterson_group"])
== sgtbx.space_group_info(
space_group).group().build_derived_patterson_group())
reindexing_ops = {}
for dataset_id in cosym.reindexing_ops.keys():
if 0 in cosym.reindexing_ops[dataset_id]:
cb_op = cosym.reindexing_ops[dataset_id][0]
reindexing_ops.setdefault(cb_op, set())
reindexing_ops[cb_op].add(dataset_id)
assert len(reindexing_ops) == len(expected_reindexing_ops)
if use_known_space_group:
expected_sg = sgtbx.space_group_info(space_group).group()
else:
expected_sg = (sgtbx.space_group_info(
space_group).group().build_derived_patterson_group())
assert cosym.best_subgroup["best_subsym"].space_group() == expected_sg
space_group_info = cosym.best_subgroup["subsym"].space_group_info()
for cb_op, ridx_set in reindexing_ops.items():
for expected_set in expected_reindexing_ops.values():
assert (len(ridx_set.symmetric_difference(expected_set))
== 0) or (len(ridx_set.intersection(expected_set)) == 0)
for d_id in ridx_set:
reindexed = (datasets[d_id].change_basis(
sgtbx.change_of_basis_op(cb_op)).customized_copy(
space_group_info=space_group_info.change_basis(
cosym.cb_op_inp_min.inverse())))
assert reindexed.is_compatible_unit_cell(), str(
reindexed.crystal_symmetry())
def test_cosym(
space_group,
unit_cell,
dimensions,
sample_size,
use_known_space_group,
use_known_lattice_group,
run_in_tmpdir,
):
import matplotlib
matplotlib.use("Agg")
datasets, expected_reindexing_ops = generate_test_data(
space_group=sgtbx.space_group_info(symbol=space_group).group(),
unit_cell=unit_cell,
unit_cell_volume=10000,
d_min=1.5,
map_to_p1=True,
sample_size=sample_size,
)
expected_space_group = sgtbx.space_group_info(symbol=space_group).group()
# Workaround fact that the minimum cell reduction can occassionally be unstable
# The input *should* be already the minimum cell, but for some combinations of unit
# cell parameters the change_of_basis_op_to_minimum_cell is never the identity.
# Therefore apply this cb_op to the expected_reindexing_ops prior to the comparison.
cb_op_inp_min = datasets[0].crystal_symmetry(
).change_of_basis_op_to_minimum_cell()
expected_reindexing_ops = {
(cb_op_inp_min.inverse() * sgtbx.change_of_basis_op(cb_op) *
cb_op_inp_min).as_xyz(): dataset_ids
for cb_op, dataset_ids in expected_reindexing_ops.items()
}
params = phil_scope.extract()
params.cluster.n_clusters = len(expected_reindexing_ops)
params.dimensions = dimensions
if use_known_space_group:
params.space_group = expected_space_group.info()
if use_known_lattice_group:
params.lattice_group = expected_space_group.info()
cosym = CosymAnalysis(datasets, params)
cosym.run()
d = cosym.as_dict()
if not use_known_space_group:
assert d["subgroup_scores"][0]["likelihood"] > 0.89
assert (sgtbx.space_group(d["subgroup_scores"][0]["patterson_group"])
== sgtbx.space_group_info(
space_group).group().build_derived_patterson_group())
space_groups = {}
reindexing_ops = {}
for dataset_id in cosym.reindexing_ops.keys():
if 0 in cosym.reindexing_ops[dataset_id]:
cb_op = cosym.reindexing_ops[dataset_id][0]
reindexing_ops.setdefault(cb_op, set())
reindexing_ops[cb_op].add(dataset_id)
if dataset_id in cosym.space_groups:
space_groups.setdefault(cosym.space_groups[dataset_id], set())
space_groups[cosym.space_groups[dataset_id]].add(dataset_id)
assert len(reindexing_ops) == len(expected_reindexing_ops)
assert sorted(reindexing_ops.keys()) == sorted(
expected_reindexing_ops.keys())
assert len(space_groups) == 1
if use_known_space_group:
expected_sg = sgtbx.space_group_info(space_group).group()
else:
expected_sg = (sgtbx.space_group_info(
space_group).group().build_derived_patterson_group())
assert cosym.best_subgroup["best_subsym"].space_group() == expected_sg
for cb_op, ridx_set in reindexing_ops.items():
for expected_set in expected_reindexing_ops.values():
assert (len(ridx_set.symmetric_difference(expected_set))
== 0) or (len(ridx_set.intersection(expected_set)) == 0)
for d_id in ridx_set:
reindexed = (datasets[d_id].change_basis(cb_op).customized_copy(
space_group_info=space_groups.keys()[0].info()))
assert reindexed.is_compatible_unit_cell(), str(
reindexed.crystal_symmetry())