def apply( self, pose, steps, run_tag=None, mer=9 ): # this is silly to put the mer here, as a indicator to name a null_frag_id )
''' '''
assert isinstance( pose, Pose )
assert pose._initialized
self._scorefxn.update_pose( pose ) # make sure the pose in the score function to score with is the current one
for step in range( steps ):
pos = random.sample( pose._dict.keys(), 1 )[0]
boltzmann = Boltzmann( self._temperature )
candidate_frags = self._scorefxn._density_score_dict[ pos ].keys() # this has included a "null_frag_id" when initialize density_score_dict
''' After picking a position to optimize, calculate compatibility scores for all the candidate placements at that residue '''
for frag_id in candidate_frags:
container = self._scorefxn.score_evaluator( frag_id )
boltzmann.probability( container )
#### end of calculating all the candidate_frags at the residue to the selected ones ####
# normalize it
boltzmann.normalization()
# pick one frag
selected_frag_id = boltzmann.pick_frag()
#print "step %s: frag_id: %s %s got picked!!" %( step, selected_frag_id, boltzmann._norm_dict[ selected_frag_id ] )
# update pose
SelectedContainer = self._scorefxn.score_evaluator( selected_frag_id ) # bc this only return scores
SelectedContainer.update_boltz( boltzmann )
pose.update_pose_dict_by_rsd( SelectedContainer )
self._scorefxn.update_pose( pose )
def apply( self, pose, steps ):
'''
refactor it to take indices - avoid any lookup during the run
'''
assert isFragIdxPose( pose )
self.__scorefxn.update_pose( pose ) # make sure the pose in the score function to score with is the current one
# for each step, randomly select one pos to optimize
for step in range( steps ):
pos = random.sample( pose.positions(), 1 )[0]
boltzmann = Boltzmann( self._temperature )
''' After picking a position to optimize, calculate compatibility scores for all the candidate placements at that residue '''
for frag_idx in self.__scorefxn.get_candidate_frags( pos ):
score = self.__scorefxn( frag_idx ) # evaluate the score here
#print score, frag_idx
boltzmann.cal_probability( frag_idx, score ) # the residue is designed for storing scores information in residue for future debugging
boltzmann.normalization()
#### end of calculating all the candidate_frags at the residue to the selected ones ####
# pick one frag, return an Residue object with updated boltzmann which stores probability of all the candidate frags at that residue
selected_frag_idx = boltzmann.pick_frag() # return frag_idx
pose.update_residue( pos, selected_frag_idx ) # shall update pos and the selected frag_idx
self.__scorefxn.update_pose( pose )
