def constrain(self, constraints):
#algorithm 1. Use pre-defined crystal_systems to give hard-coded restraints.
# dps_core.constrainment.s_minimizer uses LBFGS minimizer to adapt
# all 9 components of the orientation matrix. This gives the best-fit
# to the starting matrix (better than algorithm #2), but the disadvantage
# is that it is keyed to the crystal_system descriptors. It is therefore
# not adapted to all small-molecule space groups (monoclinics),
# and will not take into account non-standard settings.
if constraints in [
"triclinic", "monoclinic", 'orthorhombic', 'tetragonal',
"cubic", "rhombohedral", 'hexagonal'
]:
from rstbx.dps_core.constrainment import s_minimizer
S = s_minimizer(self, constraint=constraints)
return S.newOrientation()
#algorithm 2: Tensor_rank_2 symmetrization
# Advantages: constraints are calculated directly from the space
# group, so will account for non-standard settings.
# Disadvantages: drift away from starting orientation is greater than
# for algorithm #1.
from cctbx.sgtbx import space_group
if isinstance(constraints, space_group):
from rstbx.symmetry.constraints import AGconvert
converter = AGconvert()
converter.forward(self)
average_cell = constraints.average_unit_cell(self.unit_cell())
converter.validate_and_setG(
average_cell.reciprocal().metrical_matrix())
return Orientation(converter.back(), basis_type.reciprocal)
def symmetrize(self):
converter = AGconvert()
converter.forward(self.orientation) # allows subsequent back-conversion of symmetrized
# takes member-data orientation; returns symmetrized metrical matrix
uc = self.orientation.unit_cell()
avg = self.space_group.average_unit_cell(uc) # assumes direct-space cell
sym_mm = avg.reciprocal().metrical_matrix()
converter.validate_and_setG(sym_mm)
self.orientation = converter.back_as_orientation()
def symmetrize(self):
converter = AGconvert()
converter.forward(self.orientation) # allows subsequent back-conversion of symmetrized
# takes member-data orientation; returns symmetrized metrical matrix
uc = self.orientation.unit_cell()
avg = self.space_group.average_unit_cell(uc) # assumes direct-space cell
sym_mm = avg.reciprocal().metrical_matrix()
converter.validate_and_setG(sym_mm)
self.orientation = converter.back_as_orientation()
class symmetrize_reduce_enlarge(object):
# symmetrize the metrical matrix &
# reduce the number of parameters to reflect symmetry
# also, provide back transform to increase
# number of parameters back to six
def __init__(self, space_group):
self.space_group = space_group
self.constraints = sgtbx.tensor_rank_2_constraints(
space_group=self.space_group,reciprocal_space=True)
def set_orientation(self, orientation, length_unit=1.E-10):
#provide orientation as either an A matrix (Rossmann) or B matrix (Busing & Levy)
# data type can be either scitbx.matrix.sqr or scitbx::mat3
# in either the reciprocal or direct space setting
# or as a cctbx.crystal_orientation.crystal_orientation
# if space group is not triclinic the orientation matrix should be close to
# symmetrized, but exact symmetrization is done by averaging within the constructor.
# length unit defaults to 1.E-10 meters = 1 Angstrom
if "direct_matrix" in dir(orientation):
self.orientation = orientation # data is already a cctbx orientation
else:
from cctbx.crystal_orientation import crystal_orientation
which_setting = [crystal_orientation(orientation,True),
crystal_orientation(orientation,False)]
#kludgy test for space setting: unit cell volume is never < 100 Angstroms^3
conversion_to_A3 = (length_unit*length_unit*length_unit)/1.E-30
select = [a.unit_cell().volume()*conversion_to_A3 > 100.
for a in which_setting]
self.orientation = which_setting[select.index(True)]
def symmetrize(self):
converter = AGconvert()
converter.forward(self.orientation) # allows subsequent back-conversion of symmetrized
# takes member-data orientation; returns symmetrized metrical matrix
uc = self.orientation.unit_cell()
avg = self.space_group.average_unit_cell(uc) # assumes direct-space cell
sym_mm = avg.reciprocal().metrical_matrix()
converter.validate_and_setG(sym_mm)
self.orientation = converter.back_as_orientation()
# it is assumed that metrical_matrix and independent are in reciprocal setting
def reduce(self,metrical_matrix):
# takes 6-parameter metrical matrix, returns reduced number of independent parameters
return self.constraints.independent_params(all_params=metrical_matrix)
def enlarge(self,independent):
# takes reduced number independent parameters, returns 6-parameter metrical matrix
u_star = self.constraints.all_params(independent_params=tuple(independent))
assert len(u_star) == 6
return u_star
def forward_independent_parameters(self):
# returns the independent parameters given the set_orientation() B matrix
self.Bconverter=AGconvert()
self.Bconverter.forward(self.orientation)
return self.reduce(metrical_matrix = self.Bconverter.G)
def forward_gradients(self):
#Specifically for refinement of the B-matrix parameters.
from rstbx.symmetry.constraints.g_gradients import g_gradients
gradient_engine = g_gradients(agadaptor = self.Bconverter, symred = self)
return gradient_engine.dB_dp()
def backward_orientation(self,independent):
# given new values of the independent parameters, back-calculate and
# set the new orientation matrix
new_mm = self.enlarge(independent)
self.Bconverter.validate_and_setG(new_mm)
self.orientation = self.Bconverter.back_as_orientation()
return self.orientation
def parameter_based_model_one_frame_detail(self,frame_id,iframe,all_model):
PIXEL_SZ = 0.11 # mm/pixel
SIGN = -1.
if iframe < self.n_refined_frames:
detector_origin = col((-self.FRAMES["beam_x"][iframe]
+ SIGN * PIXEL_SZ * self.frame_translations.x[2*iframe],
-self.FRAMES["beam_y"][iframe]
+ SIGN * PIXEL_SZ * self.frame_translations.x[1+2*iframe],
0.))
self.OUTPUT["beam_x"][iframe] = -detector_origin[0]
self.OUTPUT["beam_y"][iframe] = -detector_origin[1]
else:
detector_origin = col((-self.FRAMES["beam_x"][iframe],-self.FRAMES["beam_y"][iframe],0.))
if not self.bandpass_models.has_key(frame_id):
reserve_orientation = self.FRAMES["orientation"][iframe]
effective_orientation = reserve_orientation
#Not necessary to apply the 3 offset rotations; they have apparently
# been applied already.\
# .rotate_thru((1,0,0),self.FRAMES["rotation100_rad"][iframe]
# ).rotate_thru((0,1,0),self.FRAMES["rotation010_rad"][iframe]
# ).rotate_thru((0,0,1),self.FRAMES["rotation001_rad"][iframe])
crystal = symmetry(unit_cell=effective_orientation.unit_cell(),space_group = "P1")
indices = all_model.frame_indices(frame_id)
parameters = parameters_bp3(
indices=indices, orientation=effective_orientation,
incident_beam=col(correction_vectors.INCIDENT_BEAM),
packed_tophat=col((1.,1.,0.)),
detector_normal=col(correction_vectors.DETECTOR_NORMAL),
detector_fast=col((0.,1.,0.)),detector_slow=col((1.,0.,0.)),
pixel_size=col((PIXEL_SZ,PIXEL_SZ,0)),
pixel_offset=col((0.,0.,0.0)),
distance=self.FRAMES["distance"][iframe],
detector_origin=detector_origin
)
#print "PARAMETER check ", effective_orientation
#print "PARAMETER distance", self.FRAMES['distance'][iframe]
#print "PARAMETER origin ", detector_origin
ucbp3 = bandpass_gaussian(parameters=parameters)
ucbp3.set_active_areas( self.tiles ) #self.params.effective_tile_boundaries
integration_signal_penetration=0.0 # easier to calculate distance derivatives
ucbp3.set_sensor_model( thickness_mm = 0.5, mu_rho = 8.36644, # CS_PAD detector at 1.3 Angstrom
signal_penetration = integration_signal_penetration)
#ucbp3.set_subpixel( flex.double(tp038_trans_values) ) #back off this; let minimizer figure it out.
half_mosaicity_rad = self.FRAMES["half_mosaicity_deg"][iframe] * pi/180.
ucbp3.set_mosaicity(half_mosaicity_rad)
ucbp3.set_bandpass(self.FRAMES["wave_HE_ang"][iframe],self.FRAMES["wave_LE_ang"][iframe])
ucbp3.set_orientation(effective_orientation)
ucbp3.set_domain_size(self.FRAMES["domain_size_ang"][iframe])
ucbp3.set_vector_output_pointers(self.vector_data,
frame_id,iframe<self.n_refined_frames)
if not self.bandpass_models.has_key("best_index"):
from labelit.dptbx import lepage
M = lepage.character(effective_orientation)
s = len(M.best())
for index in M.best():
index['counter'] = s
s-=1
if index["max_angular_difference"]==0.0:
best_index = index
break
self.bandpass_models["best_index"] = best_index
self.bandpass_models["constraints"] = tensor_rank_2_constraints(space_group=best_index['reduced_group'],reciprocal_space=True)
self.bandpass_models["n_independent"] = self.bandpass_models["constraints"].n_independent_params()
self.bandpass_models[frame_id]=ucbp3
if iframe < self.n_refined_frames:
self.bandpass_models[frame_id].set_detector_origin(detector_origin)
self.bandpass_models[frame_id].set_distance(
self.FRAMES["distance"][iframe] + self.frame_distances.x[iframe])
self.OUTPUT["distance"][iframe] = self.FRAMES["distance"][iframe] + self.frame_distances.x[iframe]
#half_mosaicity_rad = self.FRAMES["half_mosaicity_deg"][iframe] * pi/180. + \
# self.half_mosaicity_rad.x[iframe]
#self.bandpass_models[frame_id].set_mosaicity(half_mosaicity_rad)
reserve_orientation = self.FRAMES["orientation"][iframe]
effective_orientation = reserve_orientation.rotate_thru((0,0,1),self.frame_rotz.x[iframe])
effective_orientation = effective_orientation.rotate_thru((0,1,0),self.frame_roty.x[iframe])
effective_orientation = effective_orientation.rotate_thru((1,0,0),self.frame_rotx.x[iframe])
convert = AGconvert()
convert.forward(effective_orientation)
u_independent = list(self.bandpass_models["constraints"].independent_params(all_params=convert.G))
for x in xrange(self.bandpass_models["n_independent"]):
u_independent[x] *= self.g_factor.x[x+6*iframe]
u_star = self.bandpass_models["constraints"].all_params(independent_params=tuple(u_independent))
convert.validate_and_setG(u_star)
effective_orientation = convert.back_as_orientation()
self.OUTPUT["orientation"][iframe]=effective_orientation
self.bandpass_models[frame_id].set_orientation(effective_orientation)
mean_wave = (self.FRAMES["wave_HE_ang"][iframe] + self.FRAMES["wave_LE_ang"][iframe])/2.
#mean_wave *= self.mean_energy_factor.x[iframe]
bandpassHW =(self.FRAMES["wave_LE_ang"][iframe] - self.FRAMES["wave_HE_ang"][iframe])/2.
self.bandpass_models[frame_id].set_bandpass(mean_wave - bandpassHW, mean_wave + bandpassHW)
return detector_origin
class symmetrize_reduce_enlarge(object):
# symmetrize the metrical matrix &
# reduce the number of parameters to reflect symmetry
# also, provide back transform to increase
# number of parameters back to six
def __init__(self, space_group):
self.space_group = space_group
self.constraints = sgtbx.tensor_rank_2_constraints(
space_group=self.space_group,reciprocal_space=True)
def set_orientation(self, orientation, length_unit=1.E-10):
#provide orientation as either an A matrix (Rossmann) or B matrix (Busing & Levy)
# data type can be either scitbx.matrix.sqr or scitbx::mat3
# in either the reciprocal or direct space setting
# or as a cctbx.crystal_orientation.crystal_orientation
# if space group is not triclinic the orientation matrix should be close to
# symmetrized, but exact symmetrization is done by averaging within the constructor.
# length unit defaults to 1.E-10 meters = 1 Angstrom
if "direct_matrix" in dir(orientation):
self.orientation = orientation # data is already a cctbx orientation
else:
from cctbx.crystal_orientation import crystal_orientation
which_setting = [crystal_orientation(orientation,True),
crystal_orientation(orientation,False)]
#kludgy test for space setting: unit cell volume is never < 70 Angstroms^3
conversion_to_A3 = (length_unit*length_unit*length_unit)/1.E-30
select = [a.unit_cell().volume()*conversion_to_A3 > 70.
for a in which_setting]
self.orientation = which_setting[select.index(True)]
def symmetrize(self):
converter = AGconvert()
converter.forward(self.orientation) # allows subsequent back-conversion of symmetrized
# takes member-data orientation; returns symmetrized metrical matrix
uc = self.orientation.unit_cell()
avg = self.space_group.average_unit_cell(uc) # assumes direct-space cell
sym_mm = avg.reciprocal().metrical_matrix()
converter.validate_and_setG(sym_mm)
self.orientation = converter.back_as_orientation()
# it is assumed that metrical_matrix and independent are in reciprocal setting
def reduce(self,metrical_matrix):
# takes 6-parameter metrical matrix, returns reduced number of independent parameters
return self.constraints.independent_params(all_params=metrical_matrix)
def enlarge(self,independent):
# takes reduced number independent parameters, returns 6-parameter metrical matrix
u_star = self.constraints.all_params(independent_params=tuple(independent))
assert len(u_star) == 6
return u_star
def forward_independent_parameters(self):
# returns the independent parameters given the set_orientation() B matrix
self.Bconverter=AGconvert()
self.Bconverter.forward(self.orientation)
return self.reduce(metrical_matrix = self.Bconverter.G)
def forward_gradients(self):
#Specifically for refinement of the B-matrix parameters.
from rstbx.symmetry.constraints.g_gradients import g_gradients
gradient_engine = g_gradients(agadaptor = self.Bconverter, symred = self)
return gradient_engine.dB_dp()
def backward_orientation(self,independent):
# given new values of the independent parameters, back-calculate and
# set the new orientation matrix
new_mm = self.enlarge(independent)
self.Bconverter.validate_and_setG(new_mm)
self.orientation = self.Bconverter.back_as_orientation()
return self.orientation
def finite_difference_test(orient):
from rstbx.symmetry.constraints import AGconvert as AG
from labelit.symmetry.metricsym.a_g_conversion import pp
from libtbx.tst_utils import approx_equal
adaptor = AG()
adaptor.forward(orient)
grad = g_gradients(adaptor, symred=None)
epsilon = 1.E-10
dAij_dphi = grad.get_all_da()[0]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi + x * epsilon, adaptor.psi, adaptor.theta)
AGback.G = adaptor.G
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dphi: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dphi))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dphi, diff_mat, 1.E-7)): raise Exception(rule)
dAij_dpsi = grad.get_all_da()[1]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi + x * epsilon, adaptor.theta)
AGback.G = adaptor.G
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dpsi: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dpsi))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dpsi, diff_mat, 1.E-7)): raise Exception(rule)
dAij_dtheta = grad.get_all_da()[2]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta + x * epsilon)
AGback.G = adaptor.G
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dtheta: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dtheta))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dtheta, diff_mat, 1.E-7)): raise Exception(rule)
g0, g1, g2, g3, g4, g5 = adaptor.G
dAij_dg0 = grad.get_all_da()[3]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta)
AGback.G = (g0 + x * epsilon, g1, g2, g3, g4, g5)
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dg0: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dg0))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dg0, diff_mat, 1.E-7)): raise Exception(rule)
dAij_dg1 = grad.get_all_da()[4]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta)
AGback.G = (g0, g1 + x * epsilon, g2, g3, g4, g5)
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dg1: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dg1))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dg1, diff_mat, 1.E-7)): raise Exception(rule)
dAij_dg2 = grad.get_all_da()[5]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta)
AGback.G = (g0, g1, g2 + x * epsilon, g3, g4, g5)
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dg2: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dg2))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dg2, diff_mat, 1.E-6)): raise Exception(rule)
dAij_dg3 = grad.get_all_da()[6]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta)
AGback.G = (g0, g1, g2, g3 + x * epsilon, g4, g5)
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dg3: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dg3))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dg3, diff_mat, 1.E-7)): raise Exception(rule)
dAij_dg4 = grad.get_all_da()[7]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta)
AGback.G = (g0, g1, g2, g3, g4 + x * epsilon, g5)
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dg4: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dg4))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dg4, diff_mat, 1.E-7)): raise Exception(rule)
dAij_dg5 = grad.get_all_da()[8]
AGback = AG()
F = []
for x in [-1., 1.]:
AGback.setAngles(adaptor.phi, adaptor.psi, adaptor.theta)
AGback.G = (g0, g1, g2, g3, g4, g5 + x * epsilon)
F.append(flex.double(AGback.back()))
diff_mat = (F[1] - F[0]) / (2. * epsilon)
rule = "dAij_dg5: Analytical gradient vs. finite difference gradient\n"+\
pp(list(dAij_dg5))+"\n"+\
pp(diff_mat)
if not (approx_equal(dAij_dg5, diff_mat, 1.E-6)): raise Exception(rule)
