def get_miller_index_i_seqs(i_img, parallel=True):
mt = flex.mersenne_twister(seed=work_params.noise.random_seed + i_img)
crystal_rotation = mt.random_double_r3_rotation_matrix_arvo_1992()
if (work_params.kirian_delta_vs_ewald_proximity):
kirian_delta_vs_ewald_proximity(
unit_cell=i_calc.p1_anom.unit_cell(),
miller_indices=i_calc.p1_anom.indices(),
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1 / work_params.wavelength,
d_min=work_params.d_min,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels)
img = image_simple(
store_miller_index_i_seqs=True, store_signals=True).compute(
unit_cell=i_calc.p1_anom.unit_cell(),
miller_indices=i_calc.p1_anom.indices(),
spot_intensity_factors=None,
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1 / work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=1,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
result = img.miller_index_i_seqs
if (work_params.usable_partiality_threshold is not None):
result = result.select(
img.signals > work_params.usable_partiality_threshold)
if (parallel):
return result.copy_to_byte_str()
return result
def compute(work_params,
use_wavelength_2=False,
store_miller_index_i_seqs=False,
store_spots=False,
store_signals=False,
set_pixels=False):
i_calc = build_i_calc(work_params)
from scitbx.math.euler_angles import xyz_matrix
crystal_rotation_matrix = xyz_matrix(*work_params.euler_angles_xyz)
work_params.crystal_rotation_matrix = crystal_rotation_matrix
if (not use_wavelength_2):
wavelength = work_params.wavelength
else:
wavelength = work_params.wavelength_2
from rstbx.simage import image_simple
return i_calc, image_simple(
store_miller_index_i_seqs=store_miller_index_i_seqs,
store_spots=store_spots,
store_signals=store_signals,
set_pixels=set_pixels).compute(
unit_cell=i_calc.p1_anom.unit_cell(),
miller_indices=i_calc.p1_anom.indices(),
spot_intensity_factors=i_calc.p1_anom.data(),
crystal_rotation_matrix=crystal_rotation_matrix,
ewald_radius=1 / wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=work_params.signal_max,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
def get_miller_index_i_seqs(i_img, parallel=True):
mt = flex.mersenne_twister(seed=work_params.noise.random_seed+i_img)
crystal_rotation = mt.random_double_r3_rotation_matrix_arvo_1992()
if (work_params.kirian_delta_vs_ewald_proximity):
kirian_delta_vs_ewald_proximity(
unit_cell=i_calc.p1_anom.unit_cell(),
miller_indices=i_calc.p1_anom.indices(),
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1/work_params.wavelength,
d_min=work_params.d_min,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels)
img = image_simple(
store_miller_index_i_seqs=True,
store_signals=True).compute(
unit_cell=i_calc.p1_anom.unit_cell(),
miller_indices=i_calc.p1_anom.indices(),
spot_intensity_factors=None,
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1/work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=1,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
result = img.miller_index_i_seqs
if (work_params.usable_partiality_threshold is not None):
result = result.select(
img.signals > work_params.usable_partiality_threshold)
if (parallel):
return result.copy_to_byte_str()
return result
def compute(
work_params,
use_wavelength_2=False,
store_miller_index_i_seqs=False,
store_spots=False,
store_signals=False,
set_pixels=False):
i_calc = build_i_calc(work_params)
from scitbx.math.euler_angles import xyz_matrix
crystal_rotation_matrix = xyz_matrix(*work_params.euler_angles_xyz)
work_params.crystal_rotation_matrix = crystal_rotation_matrix
if (not use_wavelength_2):
wavelength = work_params.wavelength
else:
wavelength = work_params.wavelength_2
from rstbx.simage import image_simple
return i_calc, image_simple(
store_miller_index_i_seqs=store_miller_index_i_seqs,
store_spots=store_spots,
store_signals=store_signals,
set_pixels=set_pixels).compute(
unit_cell=i_calc.p1_anom.unit_cell(),
miller_indices=i_calc.p1_anom.indices(),
spot_intensity_factors=i_calc.p1_anom.data(),
crystal_rotation_matrix=crystal_rotation_matrix,
ewald_radius=1/wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=work_params.signal_max,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
def get_f():
vals = tuple(O.x[:6])
try:
O.unit_cell = uctbx.unit_cell(vals)
except RuntimeError as e:
raise InfeasibleError(str(e))
vals = matrix.col(O.x[6:]) / O.uq_scale
try:
vals = vals.normalize()
except ZeroDivisionError as e:
raise InfeasibleError(str(e))
if (O.average_unit_cell is not None):
O.unit_cell = O.average_unit_cell(O.unit_cell)
O.crystal_rotation = vals.unit_quaternion_as_r3_rotation_matrix()
from rstbx.simage import image_simple
O.predicted_spots = image_simple(
apply_detector_clipping=False,
apply_proximity_filter=False,
store_spots=True).compute(
unit_cell=O.unit_cell,
miller_indices=O.miller_indices,
spot_intensity_factors=None,
crystal_rotation_matrix=O.crystal_rotation,
ewald_radius=1 / O.work_params.wavelength,
ewald_proximity=O.work_params.ewald_proximity,
signal_max=O.work_params.signal_max,
detector_distance=O.work_params.detector.distance,
detector_size=O.work_params.detector.size,
detector_pixels=O.work_params.detector.pixels,
point_spread=O.work_params.point_spread,
gaussian_falloff_scale=O.work_params.gaussian_falloff_scale
).spots
assert O.predicted_spots.size() == O.spots_xy0.size()
return O.spots_xy0.rms_difference(O.predicted_spots)
def build_one_image(i_img):
mt = flex.mersenne_twister(seed=work_params.noise.random_seed+i_img)
scale = int(work_params.signal_max*(0.1+0.9*mt.random_double()))
crystal_rotation = mt.random_double_r3_rotation_matrix_arvo_1992()
i_perm = mt.random_size_t() % len(i_calc_data_perms)
image = image_simple(
store_miller_index_i_seqs=True,
store_spots=True,
store_signals=True,
set_pixels=True).compute(
unit_cell=i_calc.unit_cell(),
miller_indices=i_calc.indices(),
spot_intensity_factors=i_calc_data_perms[i_perm],
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1/work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=scale,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
add_noise(work_params, pixels=image.pixels)
if (not work_params.index_and_integrate):
pixels = None
else:
pixels = image.pixels
miller_index_i_seqs = image.miller_index_i_seqs
if (use_mp):
# to by-pass portable but slower pickling
if (pixels is not None):
assert pixels.is_0_based()
assert not pixels.is_padded()
assert pixels.all() == tuple(work_params.detector.pixels)
pixels = pixels.copy_to_byte_str()
miller_index_i_seqs = miller_index_i_seqs.copy_to_byte_str()
return image_model(
pixels=pixels,
spot_positions=image.spots,
spot_intensities=image.signals,
unit_cell=i_calc.unit_cell(),
crystal_rotation=crystal_rotation,
miller_index_i_seqs=miller_index_i_seqs,
scale=scale,
i_perm=i_perm)
def build_one_image(i_img):
mt = flex.mersenne_twister(seed=work_params.noise.random_seed+i_img)
scale = int(work_params.signal_max*(0.1+0.9*mt.random_double()))
crystal_rotation = mt.random_double_r3_rotation_matrix_arvo_1992()
i_perm = mt.random_size_t() % len(i_calc_data_perms)
image = image_simple(
store_miller_index_i_seqs=True,
store_spots=True,
store_signals=True,
set_pixels=True).compute(
unit_cell=i_calc.unit_cell(),
miller_indices=i_calc.indices(),
spot_intensity_factors=i_calc_data_perms[i_perm],
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1/work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=scale,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
add_noise(work_params, pixels=image.pixels)
if (not work_params.index_and_integrate):
pixels = None
else:
pixels = image.pixels
miller_index_i_seqs = image.miller_index_i_seqs
if (use_mp):
# to by-pass portable but slower pickling
if (pixels is not None):
assert pixels.is_0_based()
assert not pixels.is_padded()
assert pixels.all() == tuple(work_params.detector.pixels)
pixels = pixels.copy_to_byte_str()
miller_index_i_seqs = miller_index_i_seqs.copy_to_byte_str()
return image_model(
pixels=pixels,
spot_positions=image.spots,
spot_intensities=image.signals,
unit_cell=i_calc.unit_cell(),
crystal_rotation=crystal_rotation,
miller_index_i_seqs=miller_index_i_seqs,
scale=scale,
i_perm=i_perm)
def predict_spot_positions(work_params, miller_indices, unit_cell,
crystal_rotation):
from rstbx.simage import image_simple
image = image_simple(
store_spots=True, store_miller_index_i_seqs=True).compute(
unit_cell=unit_cell,
miller_indices=miller_indices,
spot_intensity_factors=None,
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1 / work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=work_params.signal_max,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
return image.spots, image.miller_index_i_seqs
def reset_partialities(O, work_params, miller_indices):
from rstbx.simage import image_simple
O.partialities = image_simple(
apply_detector_clipping=False,
apply_proximity_filter=False,
store_signals=True).compute(
unit_cell=O.unit_cell,
miller_indices=miller_indices.select(O.miller_index_i_seqs),
spot_intensity_factors=None,
crystal_rotation_matrix=O.crystal_rotation,
ewald_radius=1/work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=1,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale).signals
assert O.partialities.size() == O.miller_index_i_seqs.size()
def reset_partialities(O, work_params, miller_indices):
from rstbx.simage import image_simple
O.partialities = image_simple(
apply_detector_clipping=False,
apply_proximity_filter=False,
store_signals=True).compute(
unit_cell=O.unit_cell,
miller_indices=miller_indices.select(O.miller_index_i_seqs),
spot_intensity_factors=None,
crystal_rotation_matrix=O.crystal_rotation,
ewald_radius=1/work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=1,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale).signals
assert O.partialities.size() == O.miller_index_i_seqs.size()
def predict_spot_positions(
work_params,
miller_indices,
unit_cell,
crystal_rotation):
from rstbx.simage import image_simple
image = image_simple(
store_spots=True,
store_miller_index_i_seqs=True).compute(
unit_cell=unit_cell,
miller_indices=miller_indices,
spot_intensity_factors=None,
crystal_rotation_matrix=crystal_rotation,
ewald_radius=1/work_params.wavelength,
ewald_proximity=work_params.ewald_proximity,
signal_max=work_params.signal_max,
detector_distance=work_params.detector.distance,
detector_size=work_params.detector.size,
detector_pixels=work_params.detector.pixels,
point_spread=work_params.point_spread,
gaussian_falloff_scale=work_params.gaussian_falloff_scale)
return image.spots, image.miller_index_i_seqs
def run_labelit_index(O, use_original_uc_cr=False):
if (O.spots is None):
O.run_spotfinder()
if (O.spots is None):
return
if (O.spots.size() < 10):
print "Insufficient number of spotfinder spots."
print
return
else:
if (use_original_uc_cr):
print
print "Using original unit cell and crystal rotation" \
" for spot prediction."
print
uc = O.work_params.unit_cell
cr = O.work_params.crystal_rotation_matrix
else:
from rstbx.simage.run_labelit_index import process
try:
ai = process(work_params=O.work_params, spots=O.spots)
except Exception, e:
print "Indexing exception:", e
print
return
print "Spots indexed: %d of %d" % (
ai.hklobserved().size(),
O.spots.size())
co = ai.getOrientation()
uc = co.unit_cell()
cr = co.crystal_rotation_matrix()
print "labelit index unit cell:", uc
from rstbx.simage import refine_uc_cr
refined = refine_uc_cr.refine(
work_params=O.work_params,
spots=O.spots,
good_i_seqs=ai.get_observed_spot_i_seqs_good_only(),
miller_indices=ai.hklobserved(),
unit_cell=uc,
crystal_rotation=cr)
uc = refined.unit_cell
cr = refined.crystal_rotation
print
import cctbx.crystal
crystal_symmetry = cctbx.crystal.symmetry(
unit_cell=uc,
space_group_symbol="P1")
d_min = O.work_params.d_min
if (d_min is None):
d_min = O.work_params.wavelength
import cctbx.miller
miller_set = cctbx.miller.build_set(
crystal_symmetry=crystal_symmetry,
d_min=d_min,
anomalous_flag=True)
from rstbx.simage import image_simple
O.predicted_spots = image_simple(store_spots=True).compute(
unit_cell=miller_set.unit_cell(),
miller_indices=miller_set.indices(),
spot_intensity_factors=None,
crystal_rotation_matrix=cr,
ewald_radius=1/O.work_params.wavelength,
ewald_proximity=O.work_params.ewald_proximity,
signal_max=O.work_params.signal_max,
detector_distance=O.work_params.detector.distance,
detector_size=O.work_params.detector.size,
detector_pixels=O.work_params.detector.pixels,
point_spread=O.work_params.point_spread,
gaussian_falloff_scale=O.work_params.gaussian_falloff_scale).spots
print "Number of predicted spots:", O.predicted_spots.size()
print
def run_labelit_index(O, use_original_uc_cr=False):
if (O.spots is None):
O.run_spotfinder()
if (O.spots is None):
return
if (O.spots.size() < 10):
print("Insufficient number of spotfinder spots.")
print()
return
else:
if (use_original_uc_cr):
print()
print("Using original unit cell and crystal rotation" \
" for spot prediction.")
print()
uc = O.work_params.unit_cell
cr = O.work_params.crystal_rotation_matrix
else:
from rstbx.simage.run_labelit_index import process
try:
ai = process(work_params=O.work_params, spots=O.spots)
except Exception as e:
print("Indexing exception:", e)
print()
return
print("Spots indexed: %d of %d" %
(ai.hklobserved().size(), O.spots.size()))
co = ai.getOrientation()
uc = co.unit_cell()
cr = co.crystal_rotation_matrix()
print("labelit index unit cell:", uc)
from rstbx.simage import refine_uc_cr
refined = refine_uc_cr.refine(
work_params=O.work_params,
spots=O.spots,
good_i_seqs=ai.get_observed_spot_i_seqs_good_only(),
miller_indices=ai.hklobserved(),
unit_cell=uc,
crystal_rotation=cr)
uc = refined.unit_cell
cr = refined.crystal_rotation
print()
import cctbx.crystal
crystal_symmetry = cctbx.crystal.symmetry(unit_cell=uc,
space_group_symbol="P1")
d_min = O.work_params.d_min
if (d_min is None):
d_min = O.work_params.wavelength
import cctbx.miller
miller_set = cctbx.miller.build_set(
crystal_symmetry=crystal_symmetry,
d_min=d_min,
anomalous_flag=True)
from rstbx.simage import image_simple
O.predicted_spots = image_simple(store_spots=True).compute(
unit_cell=miller_set.unit_cell(),
miller_indices=miller_set.indices(),
spot_intensity_factors=None,
crystal_rotation_matrix=cr,
ewald_radius=1 / O.work_params.wavelength,
ewald_proximity=O.work_params.ewald_proximity,
signal_max=O.work_params.signal_max,
detector_distance=O.work_params.detector.distance,
detector_size=O.work_params.detector.size,
detector_pixels=O.work_params.detector.pixels,
point_spread=O.work_params.point_spread,
gaussian_falloff_scale=O.work_params.gaussian_falloff_scale
).spots
print("Number of predicted spots:", O.predicted_spots.size())
print()
O.Refresh()
class refinery(object):
def __init__(O,
work_params,
spots_xy0,
miller_indices,
unit_cell,
crystal_rotation_uq):
assert spots_xy0.size() == miller_indices.size()
O.work_params = work_params
O.spots_xy0 = spots_xy0
O.miller_indices = miller_indices
O.uq_scale = 100 # to balance gradients
O.average_unit_cell = O.work_params.lattice_symmetry.group() \
.average_unit_cell
unit_cell = O.average_unit_cell(unit_cell)
from scitbx.array_family import flex
O.x = flex.double(
unit_cell.parameters()
+ (crystal_rotation_uq * O.uq_scale).elems)
assert O.x.size() == 10
O.initial_functional = None
import scitbx.lbfgs
scitbx.lbfgs.run(target_evaluator=O)
O.final_functional = O.compute_functional_and_gradients(
functional_only=True)
del O.average_unit_cell
def compute_functional_and_gradients(O, functional_only=False):
from cctbx import uctbx
from scitbx import matrix
def get_f():
vals = tuple(O.x[:6])
try:
O.unit_cell = uctbx.unit_cell(vals)
except RuntimeError, e:
raise InfeasibleError(str(e))
vals = matrix.col(O.x[6:]) / O.uq_scale
try:
vals = vals.normalize()
except ZeroDivisionError, e:
raise InfeasibleError(str(e))
if (O.average_unit_cell is not None):
O.unit_cell = O.average_unit_cell(O.unit_cell)
O.crystal_rotation = vals.unit_quaternion_as_r3_rotation_matrix()
from rstbx.simage import image_simple
O.predicted_spots = image_simple(
apply_detector_clipping=False,
apply_proximity_filter=False,
store_spots=True).compute(
unit_cell=O.unit_cell,
miller_indices=O.miller_indices,
spot_intensity_factors=None,
crystal_rotation_matrix=O.crystal_rotation,
ewald_radius=1/O.work_params.wavelength,
ewald_proximity=O.work_params.ewald_proximity,
signal_max=O.work_params.signal_max,
detector_distance=O.work_params.detector.distance,
detector_size=O.work_params.detector.size,
detector_pixels=O.work_params.detector.pixels,
point_spread=O.work_params.point_spread,
gaussian_falloff_scale=O.work_params.gaussian_falloff_scale).spots
assert O.predicted_spots.size() == O.spots_xy0.size()
return O.spots_xy0.rms_difference(O.predicted_spots)
