def run(self):
'''Execute the script.'''
from dials.util.options import flatten_reflections, flatten_experiments, \
flatten_datablocks
import cPickle as pickle
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=True)
reflections = flatten_reflections(params.input.reflections)
experiments = flatten_experiments(params.input.experiments)
datablocks = flatten_datablocks(params.input.datablock)
# Try to load the models and data
slice_exps = len(experiments) > 0
slice_refs = len(reflections) > 0
slice_dbs = len(datablocks) > 0
# Catch case of nothing to do
if not any([slice_exps, slice_refs, slice_dbs]):
print "No suitable input provided"
self.parser.print_help()
return
if reflections:
if len(reflections) > 1:
raise Sorry(
"Only one reflections list can be imported at present")
reflections = reflections[0]
# calculate frame numbers if needed
if experiments:
reflections = calculate_frame_numbers(reflections, experiments)
# if we still don't have the right column give up
if 'xyzobs.px.value' not in reflections:
raise Sorry(
"These reflections do not have frame numbers set, and "
"there are no experiments provided to calculate these.")
# set trivial case where no scan range is provided at all
if not params.scan_range:
params.scan_range = [None]
# check if slicing into blocks
if params.block_size is not None:
# in this case for simplicity, ensure that there is either an
# an experiment list or datablocks, but not both. Ensure there is only
# a single scan contained within.
if [slice_exps, slice_dbs].count(True) != 1:
raise Sorry(
"For slicing into blocks please provide either datablocks"
" or experiments, but not both.")
if slice_exps:
if len(experiments) > 1:
raise Sorry(
"For slicing into blocks please provide a single "
"scan only")
scan = experiments[0].scan
if slice_dbs:
scans = datablocks[0].unique_scans()
if len(scans) > 1 or len(datablocks) > 1:
raise Sorry(
"For slicing into blocks please provide a single "
"scan only")
scan = scans[0]
# Having extracted the scan, calculate the blocks
params.scan_range = calculate_block_ranges(scan, params.block_size)
# Do the slicing then recombine
if slice_exps:
sliced = [slice_experiments(experiments, [sr])[0] \
for sr in params.scan_range]
sliced_experiments = ExperimentList()
for exp in sliced:
sliced_experiments.append(exp)
if slice_dbs:
sliced = [slice_datablocks(datablocks, [sr])[0] \
for sr in params.scan_range]
imagesets = [db.extract_imagesets()[0] for db in sliced]
sliced_datablocks = DataBlock(imagesets)
# slice reflections if present
if slice_refs:
sliced = [slice_reflections(reflections, [sr]) \
for sr in params.scan_range]
sliced_reflections = sliced[0]
for i, rt in enumerate(sliced[1:]):
rt['id'] += (i + 1) # set id
sliced_reflections.extend(rt)
else:
# slice each dataset into the requested subset
if slice_exps:
sliced_experiments = slice_experiments(experiments,
params.scan_range)
if slice_refs:
sliced_reflections = slice_reflections(reflections,
params.scan_range)
if slice_dbs:
sliced_datablocks = slice_datablocks(datablocks,
params.scan_range)
# Save sliced experiments
if slice_exps:
output_experiments_filename = params.output.experiments_filename
if output_experiments_filename is None:
# take first filename as template
bname = basename(params.input.experiments[0].filename)
bname = splitext(bname)[0]
if not bname: bname = "experiments"
if len(params.scan_range
) == 1 and params.scan_range[0] is not None:
ext = "_{0}_{1}.json".format(*params.scan_range[0])
else:
ext = "_sliced.json"
output_experiments_filename = bname + ext
print 'Saving sliced experiments to {0}'.format(
output_experiments_filename)
from dxtbx.model.experiment.experiment_list import ExperimentListDumper
dump = ExperimentListDumper(sliced_experiments)
dump.as_json(output_experiments_filename)
# Save sliced reflections
if slice_refs:
output_reflections_filename = params.output.reflections_filename
if output_reflections_filename is None:
# take first filename as template
bname = basename(params.input.reflections[0].filename)
bname = splitext(bname)[0]
if not bname: bname = "reflections"
if len(params.scan_range
) == 1 and params.scan_range[0] is not None:
ext = "_{0}_{1}.pickle".format(*params.scan_range[0])
else:
ext = "_sliced.pickle"
output_reflections_filename = bname + ext
print 'Saving sliced reflections to {0}'.format(
output_reflections_filename)
sliced_reflections.as_pickle(output_reflections_filename)
# Save sliced datablocks
if slice_dbs:
output_datablocks_filename = params.output.datablocks_filename
if output_datablocks_filename is None:
# take first filename as template
bname = basename(params.input.datablock[0].filename)
bname = splitext(bname)[0]
if not bname: bname = "datablock"
if len(params.scan_range
) == 1 and params.scan_range[0] is not None:
ext = "_{0}_{1}.json".format(*params.scan_range[0])
else:
ext = "_sliced.json"
output_datablocks_filename = bname + ext
print 'Saving sliced datablocks to {0}'.format(
output_datablocks_filename)
from dxtbx.datablock import DataBlockDumper
dump = DataBlockDumper(sliced_datablocks)
dump.as_file(output_datablocks_filename)
return
if exp.detector: print exp.detector
else: print "no detector!"
if exp.goniometer: print exp.goniometer
else: print "no goniometer!"
if exp.scan: print exp.scan
else: print "no scan!"
if exp.crystal: print exp.crystal
else: print "no crystal!"
# Write the experiment list to a JSON or pickle file
if params.output.filename is None:
params.output.filename = 'experiments.json'
print "-" * 80
print 'Writing experiments to %s' % params.output.filename
dump = ExperimentListDumper(experiments)
dump.as_file(params.output.filename)
# Optionally save as a data block
if params.output.xds_datablock:
print "-" * 80
print "Writing data block to %s" % params.output.xds_datablock
dump = DataBlockDumper(experiments.to_datablocks())
dump.as_file(params.output.xds_datablock)
@staticmethod
def find_best_xds_file(xds_dir):
''' Find the best available file.'''
from os.path import exists, join
# The possible files to check
paths = [join(xds_dir, 'XDS_ASCII.HKL'),
def experiment_list(obj, outfile): ''' Dump an experiment list. ''' from dxtbx.model.experiment.experiment_list import ExperimentListDumper dumper = ExperimentListDumper(obj) dumper.as_file(outfile)
