def run(self):
'''Execute the script.'''
from dials.algorithms.refinement.two_theta_refiner import \
TwoThetaReflectionManager, TwoThetaTarget, \
TwoThetaPredictionParameterisation
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
# set up global experiments and reflections lists
from dials.array_family import flex
reflections = flex.reflection_table()
global_id = 0
from dxtbx.model.experiment.experiment_list import ExperimentList
experiments = ExperimentList()
# loop through the input, building up the global lists
nrefs_per_exp = []
for ref_wrapper, exp_wrapper in zip(params.input.reflections,
params.input.experiments):
refs = ref_wrapper.data
exps = exp_wrapper.data
for i, exp in enumerate(exps):
sel = refs['id'] == i
sub_ref = refs.select(sel)
nrefs_per_exp.append(len(sub_ref))
sub_ref['id'] = flex.int(len(sub_ref), global_id)
reflections.extend(sub_ref)
experiments.append(exp)
global_id += 1
# Try to load the models and data
nexp = len(experiments)
if nexp == 0:
print "No Experiments found in the input"
self.parser.print_help()
return
if len(reflections) == 0:
print "No reflection data found in the input"
self.parser.print_help()
return
self.check_input(reflections)
# Configure the logging
log.config(info=params.output.log, debug=params.output.debug_log)
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not '':
logger.info('The following parameters have been modified:\n')
logger.info(diff_phil)
# Convert to P 1?
if params.refinement.triclinic:
reflections, experiments = self.convert_to_P1(
reflections, experiments)
# Combine crystals?
if params.refinement.combine_crystal_models and len(experiments) > 1:
logger.info('Combining {0} crystal models'.format(
len(experiments)))
experiments = self.combine_crystals(experiments)
# Filter integrated centroids?
if params.refinement.filter_integrated_centroids:
reflections = self.filter_integrated_centroids(reflections)
# Get the refiner
logger.info('Configuring refiner')
refiner = self.create_refiner(params, reflections, experiments)
# Refine the geometry
if nexp == 1:
logger.info('Performing refinement of a single Experiment...')
else:
logger.info(
'Performing refinement of {0} Experiments...'.format(nexp))
# Refine and get the refinement history
history = refiner.run()
# get the refined experiments
experiments = refiner.get_experiments()
crystals = experiments.crystals()
if len(crystals) == 1:
# output the refined model for information
logger.info('')
logger.info('Final refined crystal model:')
logger.info(crystals[0])
logger.info(self.cell_param_table(crystals[0]))
# Save the refined experiments to file
output_experiments_filename = params.output.experiments
logger.info('Saving refined experiments to {0}'.format(
output_experiments_filename))
from dxtbx.model.experiment.experiment_list import ExperimentListDumper
dump = ExperimentListDumper(experiments)
dump.as_json(output_experiments_filename)
# Correlation plot
if params.output.correlation_plot.filename is not None:
from os.path import splitext
root, ext = splitext(params.output.correlation_plot.filename)
if not ext: ext = ".pdf"
steps = params.output.correlation_plot.steps
if steps is None: steps = [history.get_nrows() - 1]
# extract individual column names or indices
col_select = params.output.correlation_plot.col_select
num_plots = 0
for step in steps:
fname_base = root
if len(steps) > 1: fname_base += "_step%02d" % step
plot_fname = fname_base + ext
corrmat, labels = refiner.get_parameter_correlation_matrix(
step, col_select)
if [corrmat, labels].count(None) == 0:
from dials.algorithms.refinement.refinement_helpers import corrgram
plt = corrgram(corrmat, labels)
if plt is not None:
logger.info(
'Saving parameter correlation plot to {}'.format(
plot_fname))
plt.savefig(plot_fname)
num_plots += 1
mat_fname = fname_base + ".pickle"
with open(mat_fname, 'wb') as handle:
py_mat = corrmat.as_scitbx_matrix(
) #convert to pickle-friendly form
logger.info(
'Saving parameter correlation matrix to {0}'.
format(mat_fname))
pickle.dump({
'corrmat': py_mat,
'labels': labels
}, handle)
if num_plots == 0:
msg = "Sorry, no parameter correlation plots were produced. Please set " \
"track_parameter_correlation=True to ensure correlations are " \
"tracked, and make sure correlation_plot.col_select is valid."
logger.info(msg)
if params.output.cif is not None:
self.generate_cif(crystals[0], refiner, file=params.output.cif)
if params.output.p4p is not None:
self.generate_p4p(crystals[0],
experiments[0].beam,
file=params.output.p4p)
if params.output.mmcif is not None:
self.generate_mmcif(crystals[0], refiner, file=params.output.mmcif)
# Log the total time taken
logger.info("\nTotal time taken: {0:.2f}s".format(time() - start_time))
def run(self):
'''Execute the script.'''
from dials.algorithms.refinement.two_theta_refiner import \
TwoThetaReflectionManager, TwoThetaTarget, \
TwoThetaPredictionParameterisation
start_time = time()
# Parse the command line
params, options = self.parser.parse_args(show_diff_phil=False)
# set up global experiments and reflections lists
from dials.array_family import flex
reflections = flex.reflection_table()
global_id = 0
from dxtbx.model.experiment.experiment_list import ExperimentList
experiments=ExperimentList()
# loop through the input, building up the global lists
nrefs_per_exp = []
for ref_wrapper, exp_wrapper in zip(params.input.reflections,
params.input.experiments):
refs = ref_wrapper.data
exps = exp_wrapper.data
for i, exp in enumerate(exps):
sel = refs['id'] == i
sub_ref = refs.select(sel)
nrefs_per_exp.append(len(sub_ref))
sub_ref['id'] = flex.int(len(sub_ref), global_id)
reflections.extend(sub_ref)
experiments.append(exp)
global_id += 1
# Try to load the models and data
nexp = len(experiments)
if nexp == 0:
print "No Experiments found in the input"
self.parser.print_help()
return
if len(reflections) == 0:
print "No reflection data found in the input"
self.parser.print_help()
return
self.check_input(reflections)
# Configure the logging
log.config(info=params.output.log,
debug=params.output.debug_log)
logger.info(dials_version())
# Log the diff phil
diff_phil = self.parser.diff_phil.as_str()
if diff_phil is not '':
logger.info('The following parameters have been modified:\n')
logger.info(diff_phil)
# Convert to P 1?
if params.refinement.triclinic:
reflections, experiments = self.convert_to_P1(reflections, experiments)
# Combine crystals?
if params.refinement.combine_crystal_models and len(experiments) > 1:
logger.info('Combining {0} crystal models'.format(len(experiments)))
experiments = self.combine_crystals(experiments)
# Filter integrated centroids?
if params.refinement.filter_integrated_centroids:
reflections = self.filter_integrated_centroids(reflections)
# Get the refiner
logger.info('Configuring refiner')
refiner = self.create_refiner(params, reflections, experiments)
# Refine the geometry
if nexp == 1:
logger.info('Performing refinement of a single Experiment...')
else:
logger.info('Performing refinement of {0} Experiments...'.format(nexp))
# Refine and get the refinement history
history = refiner.run()
# get the refined experiments
experiments = refiner.get_experiments()
crystals = experiments.crystals()
if len(crystals) == 1:
# output the refined model for information
logger.info('')
logger.info('Final refined crystal model:')
logger.info(crystals[0])
logger.info(self.cell_param_table(crystals[0]))
# Save the refined experiments to file
output_experiments_filename = params.output.experiments
logger.info('Saving refined experiments to {0}'.format(output_experiments_filename))
from dxtbx.model.experiment.experiment_list import ExperimentListDumper
dump = ExperimentListDumper(experiments)
dump.as_json(output_experiments_filename)
# Correlation plot
if params.output.correlation_plot.filename is not None:
from os.path import splitext
root, ext = splitext(params.output.correlation_plot.filename)
if not ext: ext = ".pdf"
steps = params.output.correlation_plot.steps
if steps is None: steps = [history.get_nrows()-1]
# extract individual column names or indices
col_select = params.output.correlation_plot.col_select
num_plots = 0
for step in steps:
fname_base = root
if len(steps) > 1: fname_base += "_step%02d" % step
plot_fname = fname_base + ext
corrmat, labels = refiner.get_parameter_correlation_matrix(step, col_select)
if [corrmat, labels].count(None) == 0:
from dials.algorithms.refinement.refinement_helpers import corrgram
plt = corrgram(corrmat, labels)
if plt is not None:
logger.info('Saving parameter correlation plot to {}'.format(plot_fname))
plt.savefig(plot_fname)
num_plots += 1
mat_fname = fname_base + ".pickle"
with open(mat_fname, 'wb') as handle:
py_mat = corrmat.as_scitbx_matrix() #convert to pickle-friendly form
logger.info('Saving parameter correlation matrix to {0}'.format(mat_fname))
pickle.dump({'corrmat':py_mat, 'labels':labels}, handle)
if num_plots == 0:
msg = "Sorry, no parameter correlation plots were produced. Please set " \
"track_parameter_correlation=True to ensure correlations are " \
"tracked, and make sure correlation_plot.col_select is valid."
logger.info(msg)
if params.output.cif is not None:
self.generate_cif(crystals[0], refiner, file=params.output.cif)
if params.output.p4p is not None:
self.generate_p4p(crystals[0], experiments[0].beam,
file=params.output.p4p)
if params.output.mmcif is not None:
self.generate_mmcif(crystals[0], refiner, file=params.output.mmcif)
# Log the total time taken
logger.info("\nTotal time taken: {0:.2f}s".format(time() - start_time))
