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Source code for dials.algorithms.indexing.basis_vector_search.optimise
from __future__ import absolute_import, division, print_function
import math
from scitbx.array_family import flex
from scitbx import lbfgs
[docs]def optimise_basis_vectors(reciprocal_lattice_points, vectors):
optimised = flex.vec3_double()
for vector in vectors:
minimised = BasisVectorMinimiser(reciprocal_lattice_points, vector)
optimised.append(tuple(minimised.x))
functionals = flex.double(minimised.target.compute_functional(v) for v in vectors)
perm = flex.sort_permutation(functionals)
optimised = optimised.select(perm)
return optimised
# Optimise the initial basis vectors as per equation 11.4.3.4 of
# Otwinowski et al, International Tables Vol. F, chapter 11.4 pp. 282-295
[docs]class BasisVectorTarget(object):
def __init__(self, reciprocal_lattice_points):
self.reciprocal_lattice_points = reciprocal_lattice_points
self._xyz_parts = self.reciprocal_lattice_points.parts()
[docs] def compute_functional(self, vector):
two_pi_S_dot_v = 2 * math.pi * self.reciprocal_lattice_points.dot(vector)
return -flex.sum(flex.cos(two_pi_S_dot_v))
[docs] def compute_functional_and_gradients(self, vector):
assert len(vector) == 3
two_pi_S_dot_v = 2 * math.pi * self.reciprocal_lattice_points.dot(vector)
f = -flex.sum(flex.cos(two_pi_S_dot_v))
sin_part = flex.sin(two_pi_S_dot_v)
g = flex.double(
[flex.sum(2 * math.pi * self._xyz_parts[i] * sin_part) for i in range(3)]
)
return f, g
[docs]class BasisVectorMinimiser(object):
def __init__(
self,
reciprocal_lattice_points,
vector,
lbfgs_termination_params=None,
lbfgs_core_params=lbfgs.core_parameters(m=20),
):
self.reciprocal_lattice_points = reciprocal_lattice_points
if not isinstance(vector, flex.double):
self.x = flex.double(vector)
else:
self.x = vector.deep_copy()
self.n = len(self.x)
assert self.n == 3
self.target = BasisVectorTarget(self.reciprocal_lattice_points)
self.minimizer = lbfgs.run(
target_evaluator=self,
termination_params=lbfgs_termination_params,
core_params=lbfgs_core_params,
)
[docs] def compute_functional_and_gradients(self):
f, g = self.target.compute_functional_and_gradients(tuple(self.x))
return f, g
