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
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
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
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
