def run_scale_and_filter(self):
"""Run cycles of scaling and filtering."""
start_time = time.time()
results = AnalysisResults()
for counter in range(1,
self.params.filtering.deltacchalf.max_cycles + 1):
self.run_scaling_cycle()
if counter == 1:
results.initial_expids_and_image_ranges = [
(exp.identifier,
exp.scan.get_image_range()) if exp.scan else None
for exp in self.experiments
]
delta_cc_params = deltacc_phil_scope.extract()
delta_cc_params.mode = self.params.filtering.deltacchalf.mode
delta_cc_params.group_size = self.params.filtering.deltacchalf.group_size
delta_cc_params.stdcutoff = self.params.filtering.deltacchalf.stdcutoff
logger.info("\nPerforming a round of filtering.\n")
script = deltaccscript(delta_cc_params, self.experiments,
self.reflections)
script.run()
valid_image_ranges = get_valid_image_ranges(self.experiments)
results.expids_and_image_ranges = [
(exp.identifier, valid_image_ranges[i]) if exp.scan else None
for i, exp in enumerate(self.experiments)
]
self.experiments = script.experiments
self.params.dataset_selection.use_datasets = None
self.params.dataset_selection.exclude_datasets = None
results = log_cycle_results(results, self, script)
logger.info(
"Cycle %s of filtering, n_reflections removed this cycle: %s",
counter,
results.get_last_cycle_results()["n_removed"],
)
# Test termination conditions
latest_results = results.get_last_cycle_results()
if latest_results["n_removed"] == 0:
logger.info(
"Finishing scaling and filtering as no data removed in this cycle."
)
if self.params.scaling_options.full_matrix:
self.reflections = parse_multiple_datasets(
script.reflections)
results = self._run_final_scale_cycle(results)
else:
self.reflections = script.reflections
results.finish(termination_reason="no_more_removed")
break
# Need to split reflections for further processing.
self.reflections = parse_multiple_datasets(script.reflections)
if (latest_results["cumul_percent_removed"] >
self.params.filtering.deltacchalf.max_percent_removed):
logger.info(
"Finishing scale and filtering as have now removed more than the limit."
)
results = self._run_final_scale_cycle(results)
results.finish(termination_reason="max_percent_removed")
break
if self.params.filtering.deltacchalf.min_completeness:
if (latest_results["merging_stats"]["completeness"] <
self.params.filtering.deltacchalf.min_completeness):
logger.info(
"Finishing scaling and filtering as completeness now below cutoff."
)
results = self._run_final_scale_cycle(results)
results.finish(
termination_reason="below_completeness_limit")
break
if counter == self.params.filtering.deltacchalf.max_cycles:
logger.info("Finishing as reached max number of cycles.")
results = self._run_final_scale_cycle(results)
results.finish(termination_reason="max_cycles")
break
# If not finished then need to create new scaler to try again
self._create_model_and_scaler()
register_scaler_observers(self.scaler)
self.filtering_results = results
# Print summary of results
logger.info(results.make_summary())
# All done!
logger.info("\nTotal time taken: {:.4f}s ".format(time.time() -
start_time))
logger.info("%s%s%s", "\n", "=" * 80, "\n")
def _run_final_scale_cycle(self, results):
self._create_model_and_scaler()
register_scaler_observers(self.scaler)
self.run()
results.add_final_stats(self.merging_statistics_result)
return results
def _run_final_scale_cycle(self, results):
self._create_model_and_scaler()
register_scaler_observers(self.scaler)
super(ScaleAndFilterAlgorithm, self).run()
results.add_final_stats(self.merging_statistics_result)
return results