Click here to go to the corresponding page for the latest version of DIALS
Source code for dials.algorithms.indexing.basis_vector_search.fft1d
from __future__ import absolute_import, division, print_function
from libtbx import phil
from scitbx.array_family import flex
from scitbx import matrix
from . import Strategy
fft1d_phil_str = """\
characteristic_grid = None
.help = Sampling frequency in radians. See Steller 1997. If None, \
determine a grid sampling automatically using the input \
reflections, using at most 0.029 radians.
.type = float(value_min=0)
"""
[docs]class FFT1D(Strategy):
"""Basis vector search using a 1D FFT.
See:
Steller, I., Bolotovsky, R. & Rossmann, M. G. (1997). J. Appl. Cryst. 30, 1036-1040.
Sauter, N. K., Grosse-Kunstleve, R. W. & Adams, P. D. (2004). J. Appl. Cryst. 37, 399-409.
"""
phil_scope = phil.parse(fft1d_phil_str)
def __init__(self, max_cell, params=None, *args, **kwargs):
"""Construct an FFT1D object.
Args:
max_cell (float): An estimate of the maximum cell dimension of the primitive
cell.
characteristic_grid (float): Sampling frequency in radians. See Steller 1997.
If None, determine a grid sampling automatically using the input
reflections, using at most 0.029 radians.
"""
super(FFT1D, self).__init__(max_cell, params=params, *args, **kwargs)
[docs] def find_basis_vectors(self, reciprocal_lattice_vectors):
"""Find a list of likely basis vectors.
Args:
reciprocal_lattice_vectors (scitbx.array_family.flex.vec3_double):
The list of reciprocal lattice vectors to search for periodicity.
Returns:
A tuple containing the list of basis vectors and a flex.bool array
identifying which reflections were used in indexing.
"""
from rstbx.phil.phil_preferences import indexing_api_defs
import iotbx.phil
used_in_indexing = flex.bool(reciprocal_lattice_vectors.size(), True)
hardcoded_phil = iotbx.phil.parse(input_string=indexing_api_defs).extract()
# Spot_positions: Centroid positions for spotfinder spots, in pixels
# Return value: Corrected for parallax, converted to mm
# derive a max_cell from mm spots
# derive a grid sampling from spots
from rstbx.indexing_api.lattice import DPS_primitive_lattice
# max_cell: max possible cell in Angstroms; set to None, determine from data
# recommended_grid_sampling_rad: grid sampling in radians; guess for now
DPS = DPS_primitive_lattice(
max_cell=self._max_cell,
recommended_grid_sampling_rad=self._params.characteristic_grid,
horizon_phil=hardcoded_phil,
)
# transform input into what Nick needs
# i.e., construct a flex.vec3 double consisting of mm spots, phi in degrees
DPS.index(reciprocal_space_vectors=reciprocal_lattice_vectors)
solutions = DPS.getSolutions()
candidate_basis_vectors = [matrix.col(s.bvec()) for s in solutions]
return candidate_basis_vectors, used_in_indexing
