def test_indexing_multiple_images_with_mosflm(regression_test, ccp4,
dials_data, run_in_tmpdir):
templ, directory = get_template_and_directory(dials_data)
# now exercise indexing off multiple images and test more settings
indexer = MosflmIndex()
indexer.set_images((5, 15, 25, 35, 45))
indexer.set_directory(directory)
indexer.set_template(templ)
indexer.set_unit_cell((78, 78, 78, 90, 90, 90))
indexer.set_distance(159)
indexer.set_space_group_number(197)
indexer.run()
print("".join(indexer.get_all_output()))
assert indexer.get_refined_beam_centre() == pytest.approx((94.33, 94.57),
abs=0.3)
assert approx_equal(indexer.get_refined_unit_cell(),
(78.2082, 78.2082, 78.2082, 90.0, 90.0, 90.0))
assert approx_equal(indexer.get_refined_distance(), 159.0)
assert approx_equal(indexer.get_resolution_estimate(), 2.12)
assert approx_equal(indexer.get_separation(), [0.48, 0.57])
assert approx_equal(indexer.get_raster(), [15, 17, 9, 5, 5])
assert approx_equal(indexer.get_mosaic_spreads(),
[0.5, 0.35, 0.45, 0.65, 0.4],
eps=1e-1)
assert indexer.get_lattice() == "cI"
def test_index_two_images_with_mosflm(dials_regression, tmpdir):
tmpdir.chdir()
templ, directory = get_template_and_directory(dials_regression)
# exercise basic indexing from two images
indexer = MosflmIndex()
indexer.set_images((1,45))
indexer.set_directory(directory)
indexer.set_template(templ)
indexer.run()
print(''.join(indexer.get_all_output()))
assert approx_equal(indexer.get_refined_beam_centre(),
two_images_indexing['beam_centre'])
assert approx_equal(indexer.get_refined_unit_cell(),
(78.655, 78.655, 78.655, 90.0, 90.0, 90.0), 1e-3)
assert approx_equal(indexer.get_refined_distance(), 160.0)
assert approx_equal(indexer.get_resolution_estimate(), 2.12)
assert approx_equal(indexer.get_separation(), [0.57, 0.57])
assert approx_equal(indexer.get_raster(), [17, 17, 10, 5, 5])
assert approx_equal(indexer.get_mosaic_spreads(), [two_images_indexing['mosaicity']]*2)
assert indexer.get_lattice() == 'cI'
def test_index_two_images_with_mosflm(ccp4, dials_data, run_in_tmpdir):
templ, directory = get_template_and_directory(dials_data)
# exercise basic indexing from two images
indexer = MosflmIndex()
indexer.set_images((1, 45))
indexer.set_directory(directory)
indexer.set_template(templ)
indexer.run()
print("".join(indexer.get_all_output()))
assert approx_equal(indexer.get_refined_beam_centre(),
two_images_indexing["beam_centre"])
assert approx_equal(
indexer.get_refined_unit_cell(),
(78.655, 78.655, 78.655, 90.0, 90.0, 90.0),
1e-2,
)
assert approx_equal(indexer.get_refined_distance(), 160.0)
assert approx_equal(indexer.get_resolution_estimate(), 2.12)
assert approx_equal(indexer.get_separation(), [0.57, 0.57])
assert approx_equal(indexer.get_raster(), [17, 17, 10, 5, 5])
assert indexer.get_mosaic_spreads() == pytest.approx(
[two_images_indexing["mosaicity"]] * 2, abs=0.05)
assert indexer.get_lattice() == "cI"
def _index(self):
'''Implement the indexer interface.'''
Citations.cite('mosflm')
indexer = MosflmIndex()
indexer.set_working_directory(self.get_working_directory())
auto_logfiler(indexer)
from xia2.lib.bits import unique_elements
_images = unique_elements(self._indxr_images)
indexer.set_images(_images)
images_str = ', '.join(map(str, _images))
cell_str = None
if self._indxr_input_cell:
cell_str = '%.2f %.2f %.2f %.2f %.2f %.2f' % \
self._indxr_input_cell
if self._indxr_sweep_name:
#if len(self._fp_directory) <= 50:
#dirname = self._fp_directory
#else:
#dirname = '...%s' % self._fp_directory[-46:]
dirname = os.path.dirname(self.get_imageset().get_template())
Journal.block(
'autoindexing', self._indxr_sweep_name, 'mosflm', {
'images': images_str,
'target cell': self._indxr_input_cell,
'target lattice': self._indxr_input_lattice,
'template': self.get_imageset().get_template(),
'directory': dirname
})
#task = 'Autoindex from images:'
#for i in _images:
#task += ' %s' % self.get_image_name(i)
#self.set_task(task)
indexer.set_template(os.path.basename(self.get_template()))
indexer.set_directory(self.get_directory())
xsweep = self.get_indexer_sweep()
if xsweep is not None:
if xsweep.get_distance() is not None:
indexer.set_distance(xsweep.get_distance())
#if self.get_wavelength_prov() == 'user':
#index.set_wavelength(self.get_wavelength())
if xsweep.get_beam_centre() is not None:
indexer.set_beam_centre(xsweep.get_beam_centre())
if self._indxr_input_cell:
indexer.set_unit_cell(self._indxr_input_cell)
if self._indxr_input_lattice is not None:
spacegroup_number = lattice_to_spacegroup(
self._indxr_input_lattice)
indexer.set_space_group_number(spacegroup_number)
if not self._mosflm_autoindex_thresh:
try:
min_peaks = 200
Debug.write('Aiming for at least %d spots...' % min_peaks)
thresholds = []
for i in _images:
p = Printpeaks()
p.set_working_directory(self.get_working_directory())
auto_logfiler(p)
p.set_image(self.get_image_name(i))
thresh = p.threshold(min_peaks)
Debug.write('Autoindex threshold for image %d: %d' % \
(i, thresh))
thresholds.append(thresh)
thresh = min(thresholds)
self._mosflm_autoindex_thresh = thresh
except Exception as e:
print str(e) #XXX this should disappear!
Debug.write('Error computing threshold: %s' % str(e))
Debug.write('Using default of 20.0')
thresh = 20.0
else:
thresh = self._mosflm_autoindex_thresh
Debug.write('Using autoindex threshold: %d' % thresh)
if self._mosflm_autoindex_sol:
indexer.set_solution_number(self._mosflm_autoindex_sol)
indexer.set_threshold(thresh)
# now forget this to prevent weird things happening later on
if self._mosflm_autoindex_sol:
self._mosflm_autoindex_sol = 0
indexer.run()
indxr_cell = indexer.get_refined_unit_cell()
self._indxr_lattice = indexer.get_lattice()
space_group_number = indexer.get_indexed_space_group_number()
detector_distance = indexer.get_refined_distance()
beam_centre = indexer.get_refined_beam_centre()
mosaic_spreads = indexer.get_mosaic_spreads()
if min(list(indxr_cell)) < 10.0 and \
indxr_cell[2] / indxr_cell[0] > 6:
Debug.write('Unrealistic autoindexing solution: ' +
'%.2f %.2f %.2f %.2f %.2f %.2f' % indxr_cell)
# tweak some parameters and try again...
self._mosflm_autoindex_thresh *= 1.5
self.set_indexer_done(False)
return
intgr_params = {}
# look up other possible indexing solutions (not well - in
# standard settings only!) This is moved earlier as it could
# result in returning if Mosflm has selected the wrong
# solution!
try:
self._indxr_other_lattice_cell = indexer.get_solutions()
# Change 27/FEB/08 to support user assigned spacegroups
if self._indxr_user_input_lattice:
lattice_to_spacegroup_dict = {
'aP': 1,
'mP': 3,
'mC': 5,
'oP': 16,
'oC': 20,
'oF': 22,
'oI': 23,
'tP': 75,
'tI': 79,
'hP': 143,
'hR': 146,
'cP': 195,
'cF': 196,
'cI': 197
}
for k in self._indxr_other_lattice_cell.keys():
if lattice_to_spacegroup_dict[k] > \
lattice_to_spacegroup_dict[
self._indxr_input_lattice]:
del (self._indxr_other_lattice_cell[k])
# check that the selected unit cell matches - and if
# not raise a "horrible" exception
if self._indxr_input_cell:
assert indxr_cell is not None
for j in range(6):
if math.fabs(self._indxr_input_cell[j] -
indxr_cell[j]) > 2.0:
Chatter.write('Mosflm autoindexing did not select ' +
'correct (target) unit cell')
raise RuntimeError(
'something horrible happened in indexing')
except RuntimeError as e:
# check if mosflm rejected a solution we have it
if 'horribl' in str(e):
# ok it did - time to break out the big guns...
if not self._indxr_input_cell:
raise RuntimeError(
'error in solution selection when not preset')
# XXX FIXME
self._mosflm_autoindex_sol = _get_indexing_solution_number(
indexer.get_all_output(), self._indxr_input_cell,
self._indxr_input_lattice)
# set the fact that we are not done...
self.set_indexer_done(False)
# and return - hopefully this will restart everything
return
else:
raise e
if len(mosaic_spreads) == 0:
# then consider setting it do a default value...
# equal to the oscillation width (a good guess)
phi_width = self.get_phi_width()
Chatter.write(
'Mosaic estimation failed, so guessing at %4.2f' % \
phi_width)
# only consider this if we have thus far no idea on the
# mosaic spread...
mosaic_spreads.append(phi_width)
intgr_params['raster'] = indexer.get_raster()
intgr_params['separation'] = indexer.get_separation()
self._indxr_resolution_estimate = indexer.get_resolution_estimate()
# compute mosaic as mean(mosaic_spreads)
self._indxr_mosaic = sum(mosaic_spreads) / len(mosaic_spreads)
self._indxr_payload['mosflm_integration_parameters'] = intgr_params
self._indxr_payload['mosflm_orientation_matrix'] = open(
os.path.join(self.get_working_directory(), 'xiaindex.mat'),
'r').readlines()
import copy
from dxtbx.model.detector_helpers import set_mosflm_beam_centre
from xia2.Wrappers.Mosflm.AutoindexHelpers import set_distance
from xia2.Wrappers.Mosflm.AutoindexHelpers import crystal_model_from_mosflm_mat
from cctbx import sgtbx, uctbx
# update the beam centre (i.e. shift the origin of the detector)
detector = copy.deepcopy(self.get_detector())
beam = copy.deepcopy(self.get_beam())
set_mosflm_beam_centre(detector, beam, beam_centre)
if detector_distance is not None:
set_distance(detector, detector_distance)
# make a dxtbx crystal_model object from the mosflm matrix
space_group = sgtbx.space_group_info(number=space_group_number).group()
crystal_model = crystal_model_from_mosflm_mat(
self._indxr_payload['mosflm_orientation_matrix'],
unit_cell=uctbx.unit_cell(tuple(indxr_cell)),
space_group=space_group)
# construct an experiment_list
from dxtbx.model import Experiment, ExperimentList
experiment = Experiment(beam=beam,
detector=detector,
goniometer=self.get_goniometer(),
scan=self.get_scan(),
crystal=crystal_model)
experiment_list = ExperimentList([experiment])
self.set_indexer_experiment_list(experiment_list)
def exercise_mosflm_index():
if not have_dials_regression:
print "Skipping exercise_mosflm_index(): dials_regression not configured"
return
xia2_demo_data = os.path.join(dials_regression, "xia2_demo_data")
template = os.path.join(xia2_demo_data, "insulin_1_%03i.img")
from xia2.Wrappers.Mosflm.MosflmIndex import MosflmIndex
# exercise basic indexing from two images
cwd = os.path.abspath(os.curdir)
tmp_dir = open_tmp_directory()
os.chdir(tmp_dir)
indexer = MosflmIndex()
indexer.set_images((1,45))
from xia2.Experts.FindImages import image2template_directory
templ, directory = image2template_directory(template %1)
indexer.set_directory(directory)
indexer.set_template(templ)
indexer.run()
output = ''.join(indexer.get_all_output())
print output
assert approx_equal(indexer.get_refined_beam_centre(), (94.33, 94.58))
assert approx_equal(indexer.get_refined_unit_cell(),
(78.655, 78.655, 78.655, 90.0, 90.0, 90.0), 1e-3)
assert approx_equal(indexer.get_refined_distance(), 160.0)
assert approx_equal(indexer.get_resolution_estimate(), 2.12)
assert approx_equal(indexer.get_separation(), [0.57, 0.57])
assert approx_equal(indexer.get_raster(), [17, 17, 10, 5, 5])
assert approx_equal(indexer.get_mosaic_spreads(), [0.4, 0.4])
assert indexer.get_lattice() == 'cI'
# now exercise indexing off multiple images and test more settings
os.chdir(cwd)
tmp_dir = open_tmp_directory()
os.chdir(tmp_dir)
indexer = MosflmIndex()
indexer.set_images((5,15,25,35,45))
indexer.set_directory(directory)
indexer.set_template(templ)
indexer.set_unit_cell((78,78,78,90,90,90))
indexer.set_distance(159)
indexer.set_space_group_number(197)
indexer.run()
output = ''.join(indexer.get_all_output())
print output
assert approx_equal(indexer.get_refined_beam_centre(), (94.33, 94.57))
assert approx_equal(indexer.get_refined_unit_cell(),
(78.2082, 78.2082, 78.2082, 90.0, 90.0, 90.0))
assert approx_equal(indexer.get_refined_distance(), 159.0)
assert approx_equal(indexer.get_resolution_estimate(), 2.12)
assert approx_equal(indexer.get_separation(), [0.48, 0.57])
assert approx_equal(indexer.get_raster(), [15, 17, 9, 5, 5])
assert approx_equal(
indexer.get_mosaic_spreads(), [0.5, 0.35, 0.45, 0.65, 0.4], eps=1e-1)
assert indexer.get_lattice() == 'cI'