def do_index(reciprocal_space_vectors, verbose=True):
D = dps_core()
D.setMaxcell(algorithm_parameters.max_cell_edge_for_dps_fft)
D.setXyzData(reciprocal_space_vectors)
hemisphere_shortcut(ai = D,
characteristic_sampling = algorithm_parameters.directional_sampling_granularity,
max_cell = algorithm_parameters.max_cell_edge_basis_choice)
M = SelectBasisMetaprocedure(D)
from rstbx.dps_core.lepage import iotbx_converter
L = iotbx_converter(D.getOrientation().unit_cell().minimum_cell(),5.0)
supergroup = L[0]
triclinic = D.getOrientation().unit_cell()
cb_op = supergroup['cb_op_inp_best'].c().as_double_array()[0:9]
orient = D.getOrientation()
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(supergroup['system'])
D.setOrientation(constrain_orient)
if verbose:
for subgroup in L:
print subgroup.short_digest()
print "\ntriclinic cell=%s volume(A^3)=%.3f"%(triclinic,triclinic.volume())
print "\nafter symmetrizing to %s:"%supergroup.reference_lookup_symbol()
M.show_rms()
return D,L
def do_index(reciprocal_space_vectors, verbose=True):
D = dps_core()
D.setMaxcell(algorithm_parameters.max_cell_edge_for_dps_fft)
D.setXyzData(reciprocal_space_vectors)
hemisphere_shortcut(
ai=D,
characteristic_sampling=algorithm_parameters.
directional_sampling_granularity,
max_cell=algorithm_parameters.max_cell_edge_basis_choice)
M = SelectBasisMetaprocedure(D)
from rstbx.dps_core.lepage import iotbx_converter
L = iotbx_converter(D.getOrientation().unit_cell().minimum_cell(), 5.0)
supergroup = L[0]
triclinic = D.getOrientation().unit_cell()
cb_op = supergroup['cb_op_inp_best'].c().as_double_array()[0:9]
orient = D.getOrientation()
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(supergroup['system'])
D.setOrientation(constrain_orient)
if verbose:
for subgroup in L:
print subgroup.short_digest()
print "\ntriclinic cell=%s volume(A^3)=%.3f" % (triclinic,
triclinic.volume())
print "\nafter symmetrizing to %s:" % supergroup.reference_lookup_symbol(
)
M.show_rms()
return D, L
def get_input_symmetry_subgroups(self):
cb_op_input_to_primitive = self.symmetry.change_of_basis_op_to_primitive_setting()
primitive_symmetry = self.symmetry.primitive_setting()
cb_op_input_to_minimum = primitive_symmetry.change_of_basis_op_to_minimum_cell() * cb_op_input_to_primitive
subgroup_list = iotbx_converter(
self.symmetry.change_basis(cb_op_input_to_minimum).unit_cell(),max_delta=3.0,
bravais_types_only=False,
space_group_symbol= str(self.symmetry.change_basis(cb_op_input_to_minimum).space_group_info()),
force_minimum=False,interest_focus="input_symmetry")
return subgroup_list
def get_input_symmetry_subgroups(self):
cb_op_input_to_primitive = self.symmetry.change_of_basis_op_to_primitive_setting()
primitive_symmetry = self.symmetry.primitive_setting()
cb_op_input_to_minimum = primitive_symmetry.change_of_basis_op_to_minimum_cell() * cb_op_input_to_primitive
subgroup_list = iotbx_converter(
self.symmetry.change_basis(cb_op_input_to_minimum).unit_cell(),max_delta=3.0,
bravais_types_only=False,
space_group_symbol= str(self.symmetry.change_basis(cb_op_input_to_minimum).space_group_info()),
force_minimum=False,interest_focus="input_symmetry")
return subgroup_list
def apply_symmetry(self, crystal_model):
if not (self.target_symmetry_primitive
and self.target_symmetry_primitive.space_group()):
return crystal, sgtbx.change_of_basis_op()
target_space_group = self.target_symmetry_primitive.space_group()
A = crystal_model.get_A()
max_delta = self._max_delta
items = iotbx_converter(crystal_model.get_unit_cell(),
max_delta=max_delta)
target_sg_ref = target_space_group.info().reference_setting().group()
best_angular_difference = 1e8
best_subgroup = None
for item in items:
if bravais_lattice(group=target_sg_ref) != bravais_lattice(
group=item["ref_subsym"].space_group()):
continue
if item["max_angular_difference"] < best_angular_difference:
best_angular_difference = item["max_angular_difference"]
best_subgroup = item
if best_subgroup is None:
return None, None
cb_op_inp_best = best_subgroup["cb_op_inp_best"]
orient = crystal_orientation(A, True)
orient_best = orient.change_basis(
scitbx.matrix.sqr(
cb_op_inp_best.c().as_double_array()[0:9]).transpose())
constrain_orient = orient_best.constrain(best_subgroup["system"])
best_subsym = best_subgroup["best_subsym"]
cb_op_best_ref = best_subsym.change_of_basis_op_to_reference_setting()
target_sg_best = target_sg_ref.change_basis(cb_op_best_ref.inverse())
ref_subsym = best_subsym.change_basis(cb_op_best_ref)
cb_op_ref_primitive = ref_subsym.change_of_basis_op_to_primitive_setting(
)
cb_op_best_primitive = cb_op_ref_primitive * cb_op_best_ref
cb_op_inp_primitive = cb_op_ref_primitive * cb_op_best_ref * cb_op_inp_best
direct_matrix = constrain_orient.direct_matrix()
a = scitbx.matrix.col(direct_matrix[:3])
b = scitbx.matrix.col(direct_matrix[3:6])
c = scitbx.matrix.col(direct_matrix[6:9])
model = Crystal(a, b, c, space_group=target_sg_best)
assert target_sg_best.is_compatible_unit_cell(model.get_unit_cell())
model = model.change_basis(cb_op_best_primitive)
return model, cb_op_inp_primitive
def get_basis_general(self):
"""
In this function, self requires the following abstract interface:
n_candidates() = number of candidate basis solutions presented
__getitem__(i) = return the ith candidate basis vector of type rstbx_ext.Direction
setOrientation(orientation) where orientation is a cctbx.crystal_orientation object.
must represent the primitive setting.
getOrientation()
niggli() adjusts the stored orientation to the niggli setting
getMosaicity() = mosaicity in degrees, from labelit, will be removed from interface
hklobserved()
combos()
rmsdev()
model_likelihood()
"""
"""side-effect: sets orientation matrix"""
from rstbx.indexing_api.basis_choice import SelectBasisMetaprocedure as SBM
pd = {}
M = SBM(input_index_engine=self,
input_dictionary=pd,
horizon_phil=self.horizon_phil) # extended API
print("Finished SELECT BASIS with solution M", M)
from rstbx.dps_core.lepage import iotbx_converter
L = iotbx_converter(self.getOrientation().unit_cell().minimum_cell(),
5.0) # extended API
supergroup = L[0]
triclinic = self.getOrientation().unit_cell() # extended API
cb_op = supergroup['cb_op_inp_best'].c().as_double_array()[0:9]
orient = self.getOrientation() # extended API
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(supergroup['system'])
self.setOrientation(constrain_orient) # extended API
L[-1]["orient"] = orient
if True:
for subgroup in L:
print(subgroup.short_digest())
print("\ntriclinic cell=%s volume(A^3)=%.3f" %
(triclinic, triclinic.volume()))
print("\nafter symmetrizing to %s:" %
supergroup.reference_lookup_symbol())
#M.show_rms()
return L
def get_basis_general(self):
"""
In this function, self requires the following abstract interface:
n_candidates() = number of candidate basis solutions presented
__getitem__(i) = return the ith candidate basis vector of type rstbx_ext.Direction
setOrientation(orientation) where orientation is a cctbx.crystal_orientation object.
must represent the primitive setting.
getOrientation()
niggli() adjusts the stored orientation to the niggli setting
getMosaicity() = mosaicity in degrees, from labelit, will be removed from interface
hklobserved()
combos()
rmsdev()
model_likelihood()
"""
"""side-effect: sets orientation matrix"""
from rstbx.indexing_api.basis_choice import SelectBasisMetaprocedure as SBM
pd = {}
M = SBM(input_index_engine = self,input_dictionary = pd, horizon_phil = self.horizon_phil) # extended API
print "Finished SELECT BASIS with solution M",M
from rstbx.dps_core.lepage import iotbx_converter
L = iotbx_converter(self.getOrientation().unit_cell().minimum_cell(),5.0) # extended API
supergroup = L[0]
triclinic = self.getOrientation().unit_cell() # extended API
cb_op = supergroup['cb_op_inp_best'].c().as_double_array()[0:9]
orient = self.getOrientation() # extended API
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(supergroup['system'])
self.setOrientation(constrain_orient) # extended API
L[-1]["orient"] = orient
if True:
for subgroup in L:
print subgroup.short_digest()
print "\ntriclinic cell=%s volume(A^3)=%.3f"%(triclinic,triclinic.volume())
print "\nafter symmetrizing to %s:"%supergroup.reference_lookup_symbol()
#M.show_rms()
return L
def refined_settings_factory_from_refined_triclinic(params,
experiments,
reflections,
i_setting=None,
lepage_max_delta=5.0,
nproc=1,
refiner_verbosity=0):
assert len(experiments.crystals()) == 1
crystal = experiments.crystals()[0]
used_reflections = copy.deepcopy(reflections)
UC = crystal.get_unit_cell()
from rstbx.dps_core.lepage import iotbx_converter
Lfat = refined_settings_list()
for item in iotbx_converter(UC, lepage_max_delta):
Lfat.append(bravais_setting(item))
supergroup = Lfat.supergroup()
triclinic = Lfat.triclinic()
triclinic_miller = used_reflections['miller_index']
# assert no transformation between indexing and bravais list
assert str(triclinic['cb_op_inp_best']) == "a,b,c"
Nset = len(Lfat)
for j in xrange(Nset):
Lfat[j].setting_number = Nset - j
from cctbx.crystal_orientation import crystal_orientation
from cctbx import sgtbx
from scitbx import matrix
for j in xrange(Nset):
cb_op = Lfat[j]['cb_op_inp_best'].c().as_double_array()[0:9]
orient = crystal_orientation(crystal.get_A(), True)
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(Lfat[j]['system'])
bravais = Lfat[j]["bravais"]
cb_op_best_ref = Lfat[j][
'best_subsym'].change_of_basis_op_to_reference_setting()
space_group = sgtbx.space_group_info(
number=bravais_lattice_to_lowest_symmetry_spacegroup_number[
bravais]).group()
space_group = space_group.change_basis(cb_op_best_ref.inverse())
bravais = str(bravais_types.bravais_lattice(group=space_group))
Lfat[j]["bravais"] = bravais
Lfat[j].unrefined_crystal = dials_crystal_from_orientation(
constrain_orient, space_group)
args = []
for subgroup in Lfat:
args.append((params, subgroup, used_reflections, experiments,
refiner_verbosity))
results = easy_mp.parallel_map(func=refine_subgroup,
iterable=args,
processes=nproc,
method="multiprocessing",
preserve_order=True,
asynchronous=True,
preserve_exception_message=True)
for i, result in enumerate(results):
Lfat[i] = result
identify_likely_solutions(Lfat)
return Lfat
def apply_symmetry(self, crystal_model):
"""Apply symmetry constraints to a crystal model.
Returns the crystal model (with symmetry constraints applied) in the same
setting as provided as input. The cb_op returned by the method is
that necessary to transform that model to the user-provided
target symmetry.
Args:
crystal_model (dxtbx.model.Crystal): The input crystal model to which to
apply symmetry constraints.
Returns: (dxtbx.model.Crystal, cctbx.sgtbx.change_of_basis_op):
The crystal model with symmetry constraints applied, and the change_of_basis_op
that transforms the returned model to the user-specified target symmetry.
"""
if not (
self.target_symmetry_primitive
and self.target_symmetry_primitive.space_group()
):
return crystal, sgtbx.change_of_basis_op()
target_space_group = self.target_symmetry_primitive.space_group()
A = crystal_model.get_A()
max_delta = self._max_delta
items = iotbx_converter(crystal_model.get_unit_cell(), max_delta=max_delta)
target_sg_ref = target_space_group.info().reference_setting().group()
best_angular_difference = 1e8
best_subgroup = None
for item in items:
if bravais_lattice(group=target_sg_ref) != item["bravais"]:
continue
if item["max_angular_difference"] < best_angular_difference:
best_angular_difference = item["max_angular_difference"]
best_subgroup = item
if best_subgroup is None:
return None, None
cb_op_inp_best = best_subgroup["cb_op_inp_best"]
best_subsym = best_subgroup["best_subsym"]
ref_subsym = best_subgroup["ref_subsym"]
cb_op_ref_best = ref_subsym.change_of_basis_op_to_best_cell()
cb_op_best_ref = cb_op_ref_best.inverse()
cb_op_inp_ref = cb_op_best_ref * cb_op_inp_best
cb_op_ref_inp = cb_op_inp_ref.inverse()
orient = crystal_orientation(A, True)
orient_ref = orient.change_basis(
scitbx.matrix.sqr((cb_op_inp_ref).c().as_double_array()[0:9]).transpose()
)
constrain_orient = orient_ref.constrain(best_subgroup["system"])
direct_matrix = constrain_orient.direct_matrix()
a = scitbx.matrix.col(direct_matrix[:3])
b = scitbx.matrix.col(direct_matrix[3:6])
c = scitbx.matrix.col(direct_matrix[6:9])
model = Crystal(a, b, c, space_group=target_sg_ref)
assert target_sg_ref.is_compatible_unit_cell(model.get_unit_cell())
model = model.change_basis(cb_op_ref_inp)
if self.cb_op_inp_best is not None:
# Then the unit cell has been provided: this is the cb_op to map to the
# user-provided input unit cell
return model, self.cb_op_inp_best.inverse() * cb_op_inp_best
if not self.cb_op_ref_inp.is_identity_op():
if self.target_symmetry_inp.space_group() == best_subsym.space_group():
# Handle where e.g. the user has requested I2 instead of the reference C2
return model, cb_op_inp_best
# The user has specified a setting that is not the reference setting
return model, self.cb_op_ref_inp * cb_op_inp_ref
# Default to reference setting
# This change of basis op will ensure that we get the best beta angle without
# changing the centring (e.g. from C2 to I2)
cb_op_ref_best = ref_subsym.change_of_basis_op_to_best_cell(
best_monoclinic_beta=False
)
return model, cb_op_ref_best * cb_op_inp_ref
def refined_settings_from_refined_triclinic(experiments, reflections, params):
"""Generate a RefinedSettingsList from a triclinic model.
Args:
experiments: The experiments refined with a triclinic model
reflections: A reflection table containing observed centroids
params: The working PHIL parameters.
Returns:
RefinedSettingsList: A list of the refined settings. The highest symmetry
setting will be first item in the list, and the triclinic setting will be last.
"""
if params.nproc is libtbx.Auto:
params.nproc = number_of_processors()
if params.refinement.reflections.outlier.algorithm in ("auto",
libtbx.Auto):
if experiments[0].goniometer is None:
params.refinement.reflections.outlier.algorithm = "sauter_poon"
else:
# different default to dials.refine
# tukey is faster and more appropriate at the indexing step
params.refinement.reflections.outlier.algorithm = "tukey"
assert len(experiments.crystals()) == 1
crystal = experiments.crystals()[0]
used_reflections = copy.deepcopy(reflections)
UC = crystal.get_unit_cell()
refined_settings = RefinedSettingsList()
for item in iotbx_converter(
UC,
params.lepage_max_delta,
best_monoclinic_beta=params.best_monoclinic_beta):
refined_settings.append(BravaisSetting(item))
triclinic = refined_settings.triclinic()
# assert no transformation between indexing and bravais list
assert str(triclinic["cb_op_inp_best"]) == "a,b,c"
Nset = len(refined_settings)
for j in range(Nset):
refined_settings[j].setting_number = Nset - j
for subgroup in refined_settings:
bravais_lattice = str(
bravais_types.bravais_lattice(
group=subgroup["best_subsym"].space_group()))
space_group = lowest_symmetry_space_group_for_bravais_lattice(
bravais_lattice)
orient = crystal_orientation(crystal.get_A(), True).change_basis(
scitbx.matrix.sqr(subgroup["cb_op_inp_best"].c().as_double_array()
[0:9]).transpose())
constrain_orient = orient.constrain(subgroup["system"])
subgroup["bravais"] = bravais_lattice
subgroup.unrefined_crystal = dxtbx_crystal_from_orientation(
constrain_orient, space_group)
with concurrent.futures.ProcessPoolExecutor(
max_workers=params.nproc) as pool:
for i, result in enumerate(
pool.map(
refine_subgroup,
((params, subgroup, used_reflections, experiments)
for subgroup in refined_settings),
)):
refined_settings[i] = result
identify_likely_solutions(refined_settings)
return refined_settings
def refined_settings_factory_from_refined_triclinic(
params, experiments, reflections, i_setting=None,
lepage_max_delta=5.0, nproc=1, refiner_verbosity=0):
assert len(experiments.crystals()) == 1
crystal = experiments.crystals()[0]
used_reflections = copy.deepcopy(reflections)
UC = crystal.get_unit_cell()
from rstbx.dps_core.lepage import iotbx_converter
Lfat = refined_settings_list()
for item in iotbx_converter(UC, lepage_max_delta):
Lfat.append(bravais_setting(item))
supergroup = Lfat.supergroup()
triclinic = Lfat.triclinic()
triclinic_miller = used_reflections['miller_index']
# assert no transformation between indexing and bravais list
assert str(triclinic['cb_op_inp_best'])=="a,b,c"
Nset = len(Lfat)
for j in xrange(Nset): Lfat[j].setting_number = Nset-j
from cctbx.crystal_orientation import crystal_orientation
from cctbx import sgtbx
from scitbx import matrix
for j in xrange(Nset):
cb_op = Lfat[j]['cb_op_inp_best'].c().as_double_array()[0:9]
orient = crystal_orientation(crystal.get_A(),True)
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(Lfat[j]['system'])
bravais = Lfat[j]["bravais"]
cb_op_best_ref = Lfat[j]['best_subsym'].change_of_basis_op_to_reference_setting()
space_group = sgtbx.space_group_info(
number=bravais_lattice_to_lowest_symmetry_spacegroup_number[bravais]).group()
space_group = space_group.change_basis(cb_op_best_ref.inverse())
bravais = str(bravais_types.bravais_lattice(group=space_group))
Lfat[j]["bravais"] = bravais
Lfat[j].unrefined_crystal = dials_crystal_from_orientation(
constrain_orient, space_group)
args = []
for subgroup in Lfat:
args.append((
params, subgroup, used_reflections, experiments, refiner_verbosity))
results = easy_mp.parallel_map(
func=refine_subgroup,
iterable=args,
processes=nproc,
method="multiprocessing",
preserve_order=True,
asynchronous=True,
preserve_exception_message=True)
for i, result in enumerate(results):
Lfat[i] = result
return Lfat
