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Source code for dials.algorithms.spot_prediction.reflection_predictor

from __future__ import absolute_import, division, print_function
import logging

logger = logging.getLogger(__name__)

from libtbx.phil import parse
from dials.util import Sorry

# The phil parameters
phil_scope = parse(
    """

  prediction {

    d_min = None
      .type = float
      .help = "The maximum resolution limit"

    d_max = None
      .type = float
      .help = "The minimum resolution limit"

    margin = 1
      .type = int
      .help = "The margin to use to scan varying prediction"

    force_static = False
      .type = bool
      .help = "For scan-varying prediction force scan-static prediction"

    padding = 1.0
      .type = float(value_min=0)
      .help = "The padding in degrees"
  }
"""
)


[docs]class ReflectionPredictor(object): """ A reflection predictor that takes a number of experiments and does the proper prediction for each type of experiment. """ def __init__( self, experiment, dmin=None, dmax=None, margin=1, force_static=False, padding=0 ): """ Initialise a predictor for each experiment. :param experiment: The experiment to predict for :param dmin: The maximum resolution :param dmax: The minimum resolution :param margin: The margin of hkl to predict :param force_static: force scan varying prediction to be static """ from dials.algorithms.spot_prediction import ScanStaticReflectionPredictor from dials.algorithms.spot_prediction import ScanVaryingReflectionPredictor from dials.algorithms.spot_prediction import StillsReflectionPredictor from dxtbx.imageset import ImageSequence from dials.array_family import flex class Predictor(object): def __init__(self, name, func): self.name = name self.func = func def __call__(self): result = self.func() if dmax is not None: assert dmax > 0 result.compute_d_single(experiment) mask = result["d"] > dmax result.del_selected(mask) return result # Check prediction to maximum resolution is possible wl = experiment.beam.get_wavelength() if dmin is not None and dmin < 0.5 * wl: raise Sorry( "Prediction at d_min of {0} is not possible " "with wavelength {1}".format(dmin, wl) ) # Select the predictor class if isinstance(experiment.imageset, ImageSequence): xl_nsp = experiment.crystal.num_scan_points bm_nsp = experiment.beam.num_scan_points gn_nsp = experiment.goniometer.num_scan_points nim = experiment.scan.get_num_images() sv_compatible = (xl_nsp == nim + 1) or (bm_nsp == nim + 1) if not force_static and sv_compatible: predictor = ScanVaryingReflectionPredictor( experiment, dmin=dmin, margin=margin, padding=padding ) if bm_nsp == 0 and gn_nsp == 0: # Only varying crystal A = [ experiment.crystal.get_A_at_scan_point(i) for i in range(experiment.crystal.num_scan_points) ] predict = Predictor( "scan varying crystal prediction", lambda: predictor.for_ub(flex.mat3_double(A)), ) else: # Any allowed model may vary if xl_nsp == nim + 1: A = [ experiment.crystal.get_A_at_scan_point(i) for i in range(experiment.crystal.num_scan_points) ] else: A = [experiment.crystal.get_A() for i in range(nim + 1)] if bm_nsp == nim + 1: s0 = [ experiment.beam.get_s0_at_scan_point(i) for i in range(experiment.beam.num_scan_points) ] else: s0 = [experiment.beam.get_s0() for i in range(nim + 1)] if gn_nsp == nim + 1: S = [ experiment.goniometer.get_setting_rotation_at_scan_point(i) for i in range(experiment.goniometer.num_scan_points) ] else: S = [ experiment.goniometer.get_setting_rotation() for i in range(nim + 1) ] predict = Predictor( "scan varying model prediction", lambda: predictor.for_varying_models( flex.mat3_double(A), flex.vec3_double(s0), flex.mat3_double(S), ), ) else: predictor = ScanStaticReflectionPredictor( experiment, dmin=dmin, padding=padding ) # Choose index generation method based on number of images # https://github.com/dials/dials/issues/585 if experiment.scan.get_num_images() > 50: predict_method = predictor.for_ub_old_index_generator else: predict_method = predictor.for_ub predict = Predictor( "scan static prediction", lambda: predict_method(experiment.crystal.get_A()), ) else: predictor = StillsReflectionPredictor(experiment, dmin=dmin) predict = Predictor( "stills prediction", lambda: predictor.for_ub(experiment.crystal.get_A()), ) # Create and add the predictor class self._predict = predict def __call__(self): """ Predict all the observable reflections. :return: A reflection table """ logger.info("Prediction type: %s" % self._predict.name) table = self._predict() logger.info("Predicted %d reflections" % len(table)) return table
[docs] def predictor(self, index): """ Get the predictor for the given experiment index. :param index: The experiment index :return: The predictor """ return self._predict[index]