def predict_spots_from_rayonix_crystal_model(self, experiments, observed):
""" Reads in the indexed rayonix model, predict spots using the crystal model on the jungfrau detector"""
pass
# Make sure experimental model for rayonix is supplied. Also the experimental geometry of the jungfrau is supplied
assert self.params.LS49.path_to_rayonix_crystal_models is not None, 'Rayonix crystal model path is empty. Needs to be specified'
assert self.params.LS49.path_to_jungfrau_detector_model is not None, 'Jungfrau_detector model path is empty. Needs to be specified'
ts = self.tag.split(
'_'
)[-1] # Assuming jungfrau cbfs are names as 'jungfrauhit_20180501133315870'
# Load rayonix experimental model
rayonix_fname = os.path.join(
self.params.LS49.path_to_rayonix_crystal_models,
'idx-%s_integrated_experiments.json' % ts)
rayonix_expt = ExperimentListFactory.from_json_file(rayonix_fname,
check_format=False)
jungfrau_det = ExperimentListFactory.from_json_file(
self.params.LS49.path_to_jungfrau_detector_model,
check_format=False)
# Reset stuff here
# Should have
# a. Jungfrau detector geometry
# b. Rayonix indexed crystal model
from dials.algorithms.refinement.prediction.managed_predictors import ExperimentsPredictorFactory
from dials.algorithms.indexing import index_reflections
experiments[0].detector = jungfrau_det[0].detector
experiments[0].crystal = rayonix_expt[0].crystal
if False:
observed['id'] = flex.int(len(observed), -1)
observed['imageset_id'] = flex.int(len(observed), 0)
observed.centroid_px_to_mm(experiments[0].detector,
experiments[0].scan)
observed.map_centroids_to_reciprocal_space(
experiments[0].detector, experiments[0].beam,
experiments[0].goniometer)
index_reflections(observed, experiments)
ref_predictor = ExperimentsPredictorFactory.from_experiments(
experiments)
ref_predictor(observed)
observed['id'] = flex.int(len(observed), 0)
from libtbx.easy_pickle import dump
dump('my_observed_prediction_%s.pickle' % self.tag, observed)
dumper = ExperimentListDumper(experiments)
dumper.as_json('my_observed_prediction_%s.json' % self.tag)
predictor = StillsReflectionPredictor(experiments[0])
ubx = predictor.for_ub(experiments[0].crystal.get_A())
ubx['id'] = flex.int(len(ubx), 0)
n_predictions = len(ubx)
n_observed = len(observed)
if len(observed) > 3 and len(ubx) >= len(observed):
from libtbx.easy_pickle import dump
dump('my_prediction_%s.pickle' % self.tag, ubx)
dumper = ExperimentListDumper(experiments)
dumper.as_json('my_prediction_%s.json' % self.tag)
#from IPython import embed; embed(); exit()
exit()
