def get_indexing_offset_correlation_coefficients(
reflections,
crystal,
grid,
d_min=None,
d_max=None,
map_to_asu=False,
grid_h=0,
grid_k=0,
grid_l=0,
reference=None,
):
if grid:
if grid_h == 0:
grid_h = grid
if grid_k == 0:
grid_k = grid
if grid_l == 0:
grid_l = grid
return origin.get_hkl_offset_correlation_coefficients(
reflections,
crystal,
map_to_asu=map_to_asu,
grid_h=grid_h,
grid_k=grid_k,
grid_l=grid_l,
reference=reference,
)
def get_indexing_offset_correlation_coefficients(reflections,
crystal,
grid,
d_min=None,
d_max=None,
map_to_asu=False,
grid_h=0,
grid_k=0,
grid_l=0,
reference=None):
from dials.algorithms.symmetry import origin
if grid:
if grid_h == 0: grid_h = grid
if grid_k == 0: grid_k = grid
if grid_l == 0: grid_l = grid
if True:
return origin.get_hkl_offset_correlation_coefficients(
reflections,
crystal,
map_to_asu=map_to_asu,
grid_h=grid_h,
grid_k=grid_k,
grid_l=grid_l,
reference=reference)
from copy import deepcopy
from dials.array_family import flex
space_group = crystal.get_space_group()
unit_cell = crystal.get_unit_cell()
from cctbx.crystal import symmetry as crystal_symmetry
cs = crystal_symmetry(unit_cell, space_group.type().lookup_symbol())
from cctbx.miller import set as miller_set
data = reflections['intensity.sum.value'] / \
flex.sqrt(reflections['intensity.sum.variance'])
if reference:
reference = reference.select(reference['intensity.sum.variance'] > 0)
reference_data = reference['intensity.sum.value'] / \
flex.sqrt(reference['intensity.sum.variance'])
reference_ms = miller_set(
cs, reference['miller_index']).array(reference_data)
else:
reference_ms = None
ccs = flex.double()
offsets = flex.vec3_int()
nref = flex.size_t()
original_miller_indices = reflections['miller_index']
ms = miller_set(cs, original_miller_indices).array(data)
if d_min is not None or d_max is not None:
ms = ms.resolution_filter(d_min=d_min, d_max=d_max)
gh = gk = gl = grid
if grid_h: gh = grid_h
if grid_k: gk = grid_k
if grid_l: gl = grid_l
# essentially just inversion operation - this *should* have good CC - unless
# we are working on a reference set where we don't reindex
if reference:
cb_op = sgtbx.change_of_basis_op('x,y,z')
else:
cb_op = sgtbx.change_of_basis_op('-x,-y,-z')
for h in range(-gh, gh + 1):
for k in range(-gk, gk + 1):
for l in range(-gl, gl + 1):
miller_indices = offset_miller_indices(ms.indices(), (h, k, l))
reindexed_miller_indices = cb_op.apply(miller_indices)
rms = miller_set(cs, reindexed_miller_indices).array(data)
if reference_ms:
_ms = reference_ms
else:
_ms = miller_set(cs, miller_indices).array(data)
if map_to_asu:
rms = rms.map_to_asu()
_ms = _ms.map_to_asu()
intensity, intensity_rdx = rms.common_sets(_ms)
cc = intensity.correlation(intensity_rdx).coefficient()
ccs.append(cc)
offsets.append((h, k, l))
nref.append(intensity.size())
return offsets, ccs, nref
def get_indexing_offset_correlation_coefficients(
reflections, crystal, grid, d_min=None, d_max=None,
map_to_asu=False, grid_h=0, grid_k=0, grid_l=0, reference=None):
from dials.algorithms.symmetry import origin
if grid:
if grid_h == 0: grid_h = grid
if grid_k == 0: grid_k = grid
if grid_l == 0: grid_l = grid
if True:
return origin.get_hkl_offset_correlation_coefficients(
reflections, crystal, map_to_asu=map_to_asu,
grid_h=grid_h, grid_k=grid_k, grid_l=grid_l, reference=reference)
from copy import deepcopy
from dials.array_family import flex
space_group = crystal.get_space_group()
unit_cell = crystal.get_unit_cell()
from cctbx.crystal import symmetry as crystal_symmetry
cs = crystal_symmetry(unit_cell, space_group.type().lookup_symbol())
from cctbx.miller import set as miller_set
data = reflections['intensity.sum.value'] / \
flex.sqrt(reflections['intensity.sum.variance'])
if reference:
reference = reference.select(reference['intensity.sum.variance'] > 0)
reference_data = reference['intensity.sum.value'] / \
flex.sqrt(reference['intensity.sum.variance'])
reference_ms = miller_set(cs, reference['miller_index']).array(
reference_data)
else:
reference_ms = None
ccs = flex.double()
offsets = flex.vec3_int()
nref = flex.size_t()
original_miller_indices = reflections['miller_index']
ms = miller_set(cs, original_miller_indices).array(data)
if d_min is not None or d_max is not None:
ms = ms.resolution_filter(d_min=d_min, d_max=d_max)
gh = gk = gl = grid
if grid_h: gh = grid_h
if grid_k: gk = grid_k
if grid_l: gl = grid_l
# essentially just inversion operation - this *should* have good CC - unless
# we are working on a reference set where we don't reindex
if reference:
cb_op = sgtbx.change_of_basis_op('x,y,z')
else:
cb_op = sgtbx.change_of_basis_op('-x,-y,-z')
for h in range(-gh, gh + 1):
for k in range(-gk, gk + 1):
for l in range(-gl, gl + 1):
miller_indices = offset_miller_indices(ms.indices(), (h, k, l))
reindexed_miller_indices = cb_op.apply(miller_indices)
rms = miller_set(cs, reindexed_miller_indices).array(data)
if reference_ms:
_ms = reference_ms
else:
_ms = miller_set(cs, miller_indices).array(data)
if map_to_asu:
rms = rms.map_to_asu()
_ms = _ms.map_to_asu()
intensity, intensity_rdx = rms.common_sets(_ms)
cc = intensity.correlation(intensity_rdx).coefficient()
ccs.append(cc)
offsets.append((h, k, l))
nref.append(intensity.size())
return offsets, ccs, nref