def run(args):
cmd_line = command_line.argument_interpreter(
master_params=master_phil_scope)
working_phil, args = cmd_line.process_and_fetch(
args=args, custom_processor="collect_remaining")
working_phil.show()
params = working_phil.extract()
if params.unit_cell is not None:
unit_cell = params.unit_cell
crystal_symmetry = crystal.symmetry(unit_cell=unit_cell)
else:
crystal_symmetry = None
from iotbx.reflection_file_reader import any_reflection_file
result = any_reflection_file(args[0])
unmerged_intensities = None
batches_all = None
for ma in result.as_miller_arrays(merge_equivalents=False,
crystal_symmetry=crystal_symmetry):
#print ma.info().labels
if ma.info().labels == ['I(+)', 'SIGI(+)', 'I(-)', 'SIGI(-)']:
assert ma.anomalous_flag()
unmerged_intensities = ma
elif ma.info().labels == ['I', 'SIGI']:
assert not ma.anomalous_flag()
unmerged_intensities = ma
elif ma.info().labels == ['BATCH']:
batches_all = ma
assert batches_all is not None
assert unmerged_intensities is not None
id_to_batches = None
if len(params.batch) > 0:
id_to_batches = {}
for b in params.batch:
assert b.id is not None
assert b.range is not None
assert b.id not in id_to_batches, "Duplicate batch id: %s" % b.id
id_to_batches[b.id] = b.range
result = delta_cc_half(unmerged_intensities,
batches_all,
n_bins=params.n_bins,
d_min=params.d_min,
cc_one_half_method=params.cc_one_half_method,
id_to_batches=id_to_batches)
hist_filename = 'delta_cc_hist.png'
print 'Saving histogram to %s' % hist_filename
result.plot_histogram(hist_filename)
print result.get_table()
from xia2.Handlers.Citations import Citations
Citations.cite('delta_cc_half')
for citation in Citations.get_citations_acta():
print citation
def write_citations():
# tell the user which programs were used...
logger.info("XIA2 used... %s" % ", ".join(Citations.get_programs()))
logger.info("Here are the appropriate citations (BIBTeX in xia2-citations.bib.)")
for citation in Citations.get_citations_acta():
logger.info(citation)
# and write the bibtex versions
out = open("xia2-citations.bib", "w")
for citation in Citations.get_citations():
out.write("%s\n" % citation)
out.close()
def write_citations():
# tell the user which programs were used...
Chatter.write('XIA2 used... %s' % \
', '.join(Citations.get_programs()))
Chatter.write(
'Here are the appropriate citations (BIBTeX in xia2-citations.bib.)')
for citation in Citations.get_citations_acta():
Chatter.write(citation)
# and write the bibtex versions
out = open('xia2-citations.bib', 'w')
for citation in Citations.get_citations():
out.write('%s\n' % citation)
out.close()
def run(args=sys.argv[1:]):
# Create the parser
parser = OptionParser(
# usage=usage,
phil=phil_scope,
read_reflections=True,
read_experiments=True,
check_format=False,
# epilog=help_message,
)
# Parse the command line
params, options, args = parser.parse_args(args=args,
show_diff_phil=False,
return_unhandled=True)
# Configure the logging
xia2.Handlers.Streams.setup_logging(logfile=params.output.log,
verbose=options.verbose)
logger.info(dials_version())
# Log the diff phil
diff_phil = parser.diff_phil.as_str()
if diff_phil != "":
logger.info("The following parameters have been modified:\n")
logger.info(diff_phil)
if params.unit_cell is not None:
unit_cell = params.unit_cell
crystal_symmetry = crystal.symmetry(unit_cell=unit_cell)
else:
crystal_symmetry = None
if len(params.input.experiments):
experiments = flatten_experiments(params.input.experiments)
reflections = flatten_reflections(params.input.reflections)
reflections = parse_multiple_datasets(reflections)
if len(experiments) != len(reflections):
raise sys.exit(
"Mismatched number of experiments and reflection tables found: %s & %s."
% (len(experiments), len(reflections)))
experiments, reflections = assign_unique_identifiers(
experiments, reflections)
# transform models into miller arrays
intensities, batches = filtered_arrays_from_experiments_reflections(
experiments,
reflections,
outlier_rejection_after_filter=False,
partiality_threshold=0.99,
return_batches=True,
)
if args and os.path.isfile(args[0]):
result = any_reflection_file(args[0])
unmerged_intensities = None
batches_all = None
for ma in result.as_miller_arrays(merge_equivalents=False,
crystal_symmetry=crystal_symmetry):
if ma.info().labels == ["I(+)", "SIGI(+)", "I(-)", "SIGI(-)"]:
assert ma.anomalous_flag()
unmerged_intensities = ma
elif ma.info().labels == ["I", "SIGI"]:
assert not ma.anomalous_flag()
unmerged_intensities = ma
elif ma.info().labels == ["BATCH"]:
batches_all = ma
assert batches_all is not None
assert unmerged_intensities is not None
sel = unmerged_intensities.sigmas() > 0
unmerged_intensities = unmerged_intensities.select(sel).set_info(
unmerged_intensities.info())
batches_all = batches_all.select(sel)
id_to_batches = None
if len(params.batch) > 0:
id_to_batches = {}
for b in params.batch:
assert b.id is not None
assert b.range is not None
assert b.id not in id_to_batches, "Duplicate batch id: %s" % b.id
id_to_batches[b.id] = b.range
separate = separate_unmerged(unmerged_intensities,
batches_all,
id_to_batches=id_to_batches)
batches = list(separate.batches.values())
intensities = list(separate.intensities.values())
result = DeltaCcHalf(
intensities,
batches,
n_bins=params.n_bins,
d_min=params.d_min,
cc_one_half_method=params.cc_one_half_method,
group_size=params.group_size,
)
logger.info(tabulate(result.get_table(), headers="firstrow"))
hist_filename = "delta_cc_hist.png"
logger.info("Saving histogram to %s" % hist_filename)
result.plot_histogram(hist_filename)
normalised_scores_filename = "normalised_scores.png"
logger.info("Saving normalised scores to %s" % normalised_scores_filename)
result.plot_normalised_scores(normalised_scores_filename)
Citations.cite("delta_cc_half")
for citation in Citations.get_citations_acta():
logger.info(citation)
