def __init__(self, flipping_iterator, f_obs, **kwds):
self.flipping_iterator = flipping_iterator
adopt_optional_init_args(self, kwds)
assert (self.min_delta_guessing_iterations
< self.max_delta_guessing_iterations)
self.attempts = []
self.normalisations = None
self.f_calc_solutions = []
self.had_phase_transition = False
self.max_attempts_exceeded = False
# prepare f_obs
f_obs = f_obs.eliminate_sys_absent()\
.as_non_anomalous_array() \
.merge_equivalents().array()
# setup state machine
self.state = self.starting = self._starting(f_obs)
self.guessing_delta = {
"sigma": self._guessing_delta_with_map_sigma,
"c_tot_over_c_flip": self._guessing_delta_with_c_tot_over_c_flip,
}[self.delta_guessing_method]()
self.solving = self._solving()
self.polishing = self._polishing()
self.evaluating = self._evaluating(f_obs)
self.finished = self._finished()
def __init__(self, f_in_p1, **kwds):
isinstance(f_in_p1, miller.array)
assert f_in_p1.space_group().type().hall_symbol() == ' P 1'
self.f_in_p1 = f_in_p1
adopt_optional_init_args(self, kwds)
self.search_parameters = maptbx.peak_search_parameters(
peak_search_level=3,
interpolate=True,
min_distance_sym_equiv=0.25,
)
self.space_group = sgtbx.space_group('P 1', t_den=sg_t_den)
self.origin = None
self.symmetry_pool = []
self.find_centring_translations()
if self.space_group.order_z() > 1:
f_in_centered = self.f_in_p1.customized_copy(
space_group_info=sgtbx.space_group_info(group=self.space_group)
).eliminate_sys_absent().merge_equivalents().array()
self.cb_op_to_primitive = \
f_in_centered.change_of_basis_op_to_primitive_setting()
self.f_in_p1 = f_in_centered.change_basis(self.cb_op_to_primitive)
self.space_group = self.f_in_p1.space_group()
else:
self.cb_op_to_primitive = sgtbx.change_of_basis_op()
self.f_in_p1 = self.f_in_p1.generate_bijvoet_mates()
self.find_space_group()
self.space_group = sgtbx.space_group(self.space_group.type().hall_symbol(),
t_den=sgtbx.sg_t_den)
self.space_group_info = sgtbx.space_group_info(group=self.space_group)
def __init__(self, f_in_p1, **kwds):
isinstance(f_in_p1, miller.array)
assert f_in_p1.space_group().type().hall_symbol() == ' P 1'
self.f_in_p1 = f_in_p1
adopt_optional_init_args(self, kwds)
self.search_parameters = maptbx.peak_search_parameters(
peak_search_level=3,
interpolate=True,
min_distance_sym_equiv=0.25,
)
self.space_group = sgtbx.space_group('P 1', t_den=sg_t_den)
self.origin = None
self.symmetry_pool = []
self.find_centring_translations()
if self.space_group.order_z() > 1:
f_in_centered = self.f_in_p1.customized_copy(
space_group_info=sgtbx.space_group_info(group=self.space_group)
).eliminate_sys_absent().merge_equivalents().array()
self.cb_op_to_primitive = \
f_in_centered.change_of_basis_op_to_primitive_setting()
self.f_in_p1 = f_in_centered.change_basis(self.cb_op_to_primitive)
self.space_group = self.f_in_p1.space_group()
else:
self.cb_op_to_primitive = sgtbx.change_of_basis_op()
self.f_in_p1 = self.f_in_p1.generate_bijvoet_mates()
self.find_space_group()
self.space_group = sgtbx.space_group(
self.space_group.type().hall_symbol(), t_den=sgtbx.sg_t_den)
self.space_group_info = sgtbx.space_group_info(group=self.space_group)
def __init__(
self,
target,
prediction_parameterisation,
constraints_manager=None,
log=None,
tracking=None,
max_iterations=20,
**kwds
):
AdaptLstbx.__init__(
self,
target,
prediction_parameterisation,
constraints_manager,
log=log,
tracking=tracking,
max_iterations=max_iterations,
)
# add an attribute to the journal
self.history.add_column("reduced_chi_squared") # flex.double()
# adopt any overrides of the defaults above
libtbx.adopt_optional_init_args(self, kwds)
def __init__(self, flipping_iterator, f_obs, **kwds):
self.flipping_iterator = flipping_iterator
adopt_optional_init_args(self, kwds)
assert (self.min_delta_guessing_iterations <
self.max_delta_guessing_iterations)
self.attempts = []
self.normalisations = None
self.f_calc_solutions = []
self.had_phase_transition = False
self.max_attempts_exceeded = False
# prepare f_obs
f_obs = f_obs.eliminate_sys_absent()\
.as_non_anomalous_array() \
.merge_equivalents().array()
# setup state machine
self.state = self.starting = self._starting(f_obs)
self.guessing_delta = {
"sigma": self._guessing_delta_with_map_sigma,
"c_tot_over_c_flip": self._guessing_delta_with_c_tot_over_c_flip,
}[self.delta_guessing_method]()
self.solving = self._solving()
self.polishing = self._polishing()
self.evaluating = self._evaluating(f_obs)
self.finished = self._finished()
def __init__(self, structure, **kwds):
from cctbx.eltbx import covalent_radii
self.structure = structure
libtbx.adopt_optional_init_args(self, kwds)
max_r = 0
for st in structure.scattering_type_registry(
).type_index_pairs_as_dict():
r = 0
if self.radii:
r = self.radii.get(st, 0)
if r == 0:
r = covalent_radii.table(st).radius()
if r > max_r: max_r = r
self.structure = structure
self.buffer_thickness = 2 * max_r + self.covalent_bond_tolerance
asu_mappings = structure.asu_mappings(
buffer_thickness=self.buffer_thickness)
self._pair_asu_table = crystal.pair_asu_table(asu_mappings)
self._pair_asu_table_needs_updating = False
if self.radii is None:
self.radii = {}
self._pair_asu_table.add_covalent_pairs(
structure.scattering_types(),
conformer_indices=self.conformer_indices,
sym_excl_indices=self.sym_excl_indices,
tolerance=self.covalent_bond_tolerance,
radii=self.radii)
self.pair_sym_table = self.pair_asu_table.extract_pair_sym_table()
def __init__(self,
target,
prediction_parameterisation,
log=None,
verbosity=0,
track_step=False,
track_gradient=False,
track_parameter_correlation=False,
track_out_of_sample_rmsd=False,
max_iterations=20,
**kwds):
AdaptLstbx.__init__(
self,
target,
prediction_parameterisation,
log=log,
verbosity=verbosity,
track_step=track_step,
track_gradient=track_gradient,
track_parameter_correlation=track_parameter_correlation,
track_out_of_sample_rmsd=track_out_of_sample_rmsd,
max_iterations=max_iterations)
# add an attribute to the journal
self.history.add_column("reduced_chi_squared") #flex.double()
# adopt any overrides of the defaults above
libtbx.adopt_optional_init_args(self, kwds)
def __init__(self,
structure,
**kwds):
from cctbx.eltbx import covalent_radii
self.structure = structure
libtbx.adopt_optional_init_args(self, kwds)
max_r = 0
for st in structure.scattering_type_registry().type_index_pairs_as_dict():
r = 0
if self.radii:
r = self.radii.get(st, 0)
if r == 0:
r = covalent_radii.table(st).radius()
if r > max_r: max_r = r
self.structure = structure
self.buffer_thickness = 2*max_r + self.covalent_bond_tolerance
asu_mappings = structure.asu_mappings(
buffer_thickness=self.buffer_thickness)
self._pair_asu_table = crystal.pair_asu_table(asu_mappings)
self._pair_asu_table_needs_updating = False
if self.radii is None:
self.radii = {}
self._pair_asu_table.add_covalent_pairs(
structure.scattering_types(),
conformer_indices=self.conformer_indices,
sym_excl_indices=self.sym_excl_indices,
tolerance=self.covalent_bond_tolerance,
radii=self.radii
)
self.pair_sym_table = self.pair_asu_table.extract_pair_sym_table()
def __init__(
self,
target,
prediction_parameterisation,
log=None,
verbosity=0,
track_step=False,
track_gradient=False,
track_parameter_correlation=False,
track_out_of_sample_rmsd=False,
max_iterations=20,
**kwds
):
AdaptLstbx.__init__(
self,
target,
prediction_parameterisation,
log=log,
verbosity=verbosity,
track_step=track_step,
track_gradient=track_gradient,
track_parameter_correlation=track_parameter_correlation,
track_out_of_sample_rmsd=track_out_of_sample_rmsd,
max_iterations=max_iterations,
)
# add an attribute to the journal
self.history.add_column("reduced_chi_squared") # flex.double()
# adopt any overrides of the defaults above
libtbx.adopt_optional_init_args(self, kwds)
def __init__(self, non_linear_ls, **kwds):
"""
"""
libtbx.adopt_optional_init_args(self, kwds)
if self.track_all: self.track_step = self.track_gradient = True
self.non_linear_ls = journaled_non_linear_ls(non_linear_ls, self,
self.track_gradient,
self.track_step)
self.do()
def __init__(self, non_linear_ls, **kwds):
"""
"""
libtbx.adopt_optional_init_args(self, kwds)
if self.track_all: self.track_step = self.track_gradient = True
self.non_linear_ls = journaled_non_linear_ls(non_linear_ls, self,
self.track_gradient,
self.track_step)
self.do()
def __init__(self, unit_cell, light_position, **kwds):
super(widget, self).__init__(QGLFormat(QGL.SampleBuffers),)
adopt_optional_init_args(self, kwds)
self.unit_cell = unit_cell
self.light_position = light_position
self.set_extent(self.from_here, self.to_there)
self.orthogonaliser = gltbx.util.matrix(
unit_cell.orthogonalization_matrix())
self.orbiting = None
self.dolly = None
self.mouse_position = None
def __init__(self, unit_cell, light_position, **kwds):
super(widget, self).__init__(QGLFormat(QGL.SampleBuffers),)
adopt_optional_init_args(self, kwds)
self.unit_cell = unit_cell
self.light_position = light_position
self.set_extent(self.from_here, self.to_there)
self.orthogonaliser = gltbx.util.matrix(
unit_cell.orthogonalization_matrix())
self.orbiting = None
self.dolly = None
self.mouse_position = None
def __init__(self, normalised, **kwds):
super(polynomial_fit, self).__init__(n_parameters=3,
normalised=normalised)
libtbx.adopt_optional_init_args(self, kwds)
self.t = t = flex.double_range(self.n_data)/self.n_data
noise = self.noise*flex.double([ (-1)**i for i in xrange(self.n_data) ])
self.yo = 2.*t**2.*(1-t) + noise
self.one = flex.double(self.n_data, 1)
self.t2 = t**2
self.t3 = t**3
self.restart()
def __init__(self, normalised, **kwds):
super(klass, self).__init__(n_parameters=3, normalised=normalised)
libtbx.adopt_optional_init_args(self, kwds)
self.t = t = flex.double_range(self.n_data) / self.n_data
noise = self.noise * flex.double([(-1)**i
for i in xrange(self.n_data)])
self.yo = 2. * t**2. * (1 - t) + noise
self.one = flex.double(self.n_data, 1)
self.t2 = t**2
self.t3 = t**3
self.restart()
def __init__(self, observations, reparametrisation, **kwds):
super(klass, self).__init__(reparametrisation.n_independents)
self.observations = observations
self.reparametrisation = reparametrisation
adopt_optional_init_args(self, kwds)
if self.f_mask is not None:
assert self.f_mask.size() == observations.fo_sq.size()
self.one_h_linearisation = direct.f_calc_modulus_squared(
self.xray_structure)
if self.weighting_scheme == "default":
self.weighting_scheme = self.default_weighting_scheme()
self.origin_fixing_restraint = self.origin_fixing_restraints_type(
self.xray_structure.space_group())
self.taken_step = None
self.restraints_normalisation_factor = None
def __init__(self, observations, reparametrisation,
**kwds):
super(crystallographic_ls, self).__init__(reparametrisation.n_independents)
self.observations = observations
self.reparametrisation = reparametrisation
adopt_optional_init_args(self, kwds)
if self.f_mask is not None:
assert self.f_mask.size() == observations.fo_sq.size()
self.one_h_linearisation = direct.f_calc_modulus_squared(
self.xray_structure)
if self.weighting_scheme == "default":
self.weighting_scheme = self.default_weighting_scheme()
self.origin_fixing_restraint = self.origin_fixing_restraints_type(
self.xray_structure.space_group())
self.taken_step = None
self.restraints_normalisation_factor = None
def __init__(self, space_group_info, **kwds):
libtbx.adopt_optional_init_args(self, kwds)
self.space_group_info = space_group_info
self.structure = random_structure.xray_structure(
space_group_info,
elements=self.elements,
volume_per_atom=20.,
min_distance=1.5,
general_positions_only=True,
use_u_aniso=False,
u_iso=adptbx.b_as_u(10),
)
self.structure.set_inelastic_form_factors(1.54, "sasaki")
self.scale_factor = 0.05 + 10 * flex.random_double()
fc = self.structure.structure_factors(anomalous_flag=True,
d_min=self.d_min,
algorithm="direct").f_calc()
fo = fc.as_amplitude_array()
fo.set_observation_type_xray_amplitude()
if self.use_students_t_errors:
nu = random.uniform(1, 10)
normal_g = variate(normal_distribution())
gamma_g = variate(gamma_distribution(0.5 * nu, 2))
errors = normal_g(fc.size()) / flex.sqrt(2 * gamma_g(fc.size()))
else:
# use gaussian errors
g = variate(normal_distribution())
errors = g(fc.size())
fo2 = fo.as_intensity_array()
self.fo2 = fo2.customized_copy(
data=(fo2.data() + errors) * self.scale_factor,
sigmas=flex.double(fc.size(), 1),
)
self.fc = fc
xs_i = self.structure.inverse_hand()
self.fc_i = xs_i.structure_factors(anomalous_flag=True,
d_min=self.d_min,
algorithm="direct").f_calc()
fo2_twin = self.fc.customized_copy(
data=self.fc.data() + self.fc_i.data()).as_intensity_array()
self.fo2_twin = fo2_twin.customized_copy(
data=(errors + fo2_twin.data()) * self.scale_factor,
sigmas=self.fo2.sigmas())
def __init__(self, space_group_info, **kwds):
libtbx.adopt_optional_init_args(self, kwds)
self.space_group_info = space_group_info
self.structure = random_structure.xray_structure(
space_group_info,
elements=self.elements,
volume_per_atom=20.,
min_distance=1.5,
general_positions_only=True,
use_u_aniso=False,
u_iso=adptbx.b_as_u(10),
)
self.structure.set_inelastic_form_factors(1.54, "sasaki")
self.scale_factor = 0.05 + 10 * flex.random_double()
fc = self.structure.structure_factors(
anomalous_flag=True, d_min=self.d_min, algorithm="direct").f_calc()
fo = fc.as_amplitude_array()
fo.set_observation_type_xray_amplitude()
if self.use_students_t_errors:
nu = random.uniform(1, 10)
normal_g = variate(normal_distribution())
gamma_g = variate(gamma_distribution(0.5*nu, 2))
errors = normal_g(fc.size())/flex.sqrt(2*gamma_g(fc.size()))
else:
# use gaussian errors
g = variate(normal_distribution())
errors = g(fc.size())
fo2 = fo.as_intensity_array()
self.fo2 = fo2.customized_copy(
data=(fo2.data()+errors)*self.scale_factor,
sigmas=flex.double(fc.size(), 1),
)
self.fc = fc
xs_i = self.structure.inverse_hand()
self.fc_i = xs_i.structure_factors(
anomalous_flag=True, d_min=self.d_min, algorithm="direct").f_calc()
fo2_twin = self.fc.customized_copy(
data=self.fc.data()+self.fc_i.data()).as_intensity_array()
self.fo2_twin = fo2_twin.customized_copy(
data=(errors + fo2_twin.data()) * self.scale_factor,
sigmas=self.fo2.sigmas())
def __init__(self, **kwds):
adopt_optional_init_args(self, kwds)
self.values = flex.double()
self.raw_values = flex.double()
self.differences = flex.double()
def __init__(self, **kwds): adopt_optional_init_args(self, kwds)
def __init__(self, **kwds):
adopt_optional_init_args(self, kwds)
def __init__(self, **kwds): adopt_optional_init_args(self, kwds) self.values = flex.double() self.raw_values = flex.double() self.differences = flex.double()
