Exemple #1
0
    def __init__(self,
                 experiments,
                 reflection_predictor,
                 ref_man,
                 prediction_parameterisation,
                 restraints_parameterisation,
                 frac_binsize_cutoff=0.33333,
                 absolute_cutoffs=None,
                 gradient_calculation_blocksize=None):

        Target.__init__(self, experiments, reflection_predictor, ref_man,
                        prediction_parameterisation,
                        gradient_calculation_blocksize)

        # Set up the RMSD achieved criterion. For simplicity, we take models from
        # the first Experiment only. If this is not appropriate for refinement over
        # all experiments then absolute cutoffs should be used instead.
        detector = experiments[0].detector
        if not absolute_cutoffs:
            pixel_sizes = [p.get_pixel_size() for p in detector]
            min_px_size_x = min(e[0] for e in pixel_sizes)
            min_px_size_y = min(e[1] for e in pixel_sizes)
            self._binsize_cutoffs = [
                min_px_size_x * frac_binsize_cutoff,
                min_px_size_y * frac_binsize_cutoff
            ]
        else:
            self._binsize_cutoffs = absolute_cutoffs[:2]

        # predict reflections and finalise reflection manager
        self.predict()

        return
Exemple #2
0
  def __init__(self, experiments, reflection_predictor, ref_man,
               prediction_parameterisation, restraints_parameterisation,
               frac_binsize_cutoff=0.33333,
               absolute_cutoffs=None,
               gradient_calculation_blocksize=None):

    Target.__init__(self, experiments, reflection_predictor, ref_man,
                    prediction_parameterisation, gradient_calculation_blocksize)

    # Set up the RMSD achieved criterion. For simplicity, we take models from
    # the first Experiment only. If this is not appropriate for refinement over
    # all experiments then absolute cutoffs should be used instead.
    detector = experiments[0].detector
    if not absolute_cutoffs:
      pixel_sizes = [p.get_pixel_size() for p in detector]
      min_px_size_x = min(e[0] for e in pixel_sizes)
      min_px_size_y = min(e[1] for e in pixel_sizes)
      self._binsize_cutoffs = [min_px_size_x * frac_binsize_cutoff,
                               min_px_size_y * frac_binsize_cutoff]
    else:
      self._binsize_cutoffs = absolute_cutoffs[:2]

    # predict reflections and finalise reflection manager
    self.predict()

    return
Exemple #3
0
    def __init__(self, experiments, reflection_predictor, ref_man,
                 prediction_parameterisation):
        Target.__init__(self, experiments, reflection_predictor, ref_man,
                        prediction_parameterisation)

        # set the single cutoff for 2theta residual to essentially zero
        self._binsize_cutoffs = [1.e-6]

        # predict reflections and finalise reflection manager
        self.predict()
        self._reflection_manager.finalise()

        return
Exemple #4
0
  def __init__(self, experiments, reflection_predictor, ref_man,
               prediction_parameterisation):
    Target.__init__(self, experiments, reflection_predictor, ref_man,
                    prediction_parameterisation)

    # set the single cutoff for 2theta residual to essentially zero
    self._binsize_cutoffs = [1.e-6]

    # predict reflections and finalise reflection manager
    self.predict()
    self._reflection_manager.finalise()

    return