pos = random.sample(Pose.dict.keys(), 1)[0] # pick a residue number to start with
null_frag_id = (args.mer, pos, 0, 0)
Boltzmann = PerRsdBoltzmann(working_temp)
""" After picking a position to optimize, calculate compatibility scores for all the candidate placements at that residue """
for candidate_frag_id in Scorefxn.density_score_dict[
pos
].keys(): # loop over all candidate frags at the pos
# skip null fragment
if (candidate_frag_id[2], candidate_frag_id[3]) == (0, 0):
Boltzmann.probability(null_frag_id, args.null_frag_score)
continue
Container = Scorefxn.score_evaluator(candidate_frag_id)
Boltzmann.probability(candidate_frag_id, Container.score_)
#### 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 "frag_id: %s %s got picked!!\n" % (selected_frag_id, Boltzmann.dict[selected_frag_id])
# update pose
SelectedContainer = Scorefxn.score_evaluator(selected_frag_id)
SelectedContainer.boltzmann_prob_ = Boltzmann.dict[selected_frag_id]
SelectedContainer.per_rsd_boltz_dict = Boltzmann.dict
pkl = open( args.starts_with , "rb" )
scorefxn.selected_frags_dict = pickle.load( pkl )
else:
scorefxn.initialize( args.initialization )
for each_step in range( args.steps ):
print "round: %s cycle: %s" %( each_round, each_step )
pos = pos_list[ random.randrange( 0, len( pos_list ) ) ] # pick a residue number to start with
boltzmann_prob_Dict = {}
''' After picking a position to optimize, calculate compatibility scores for all the candidate placements at that residue'''
for each_candidate_frag_id in density_score_Dict[ pos ].keys(): # loop over all candidate frags at the pos
if ( each_candidate_frag_id[2], each_candidate_frag_id[3] ) == ( 0, 0 ): continue # skip null fragment
container = scorefxn.score_evaluator( each_candidate_frag_id )
boltzmann_prob_Dict[ each_candidate_frag_id ] = ( boltzmann_prob( container.score_, args.temperature_ ), container.score, container.rmsd )
if args.verbose:
print "each_candidate_frag_id %3s %3s %3s %3s rmsd: %5.4f score: %5.4f density: %5.4f overlap: %5.4f nonoverlap: %5.4f boltzmann: %s" %( each_candidate_frag_id[0], each_candidate_frag_id[1], each_candidate_frag_id[2], each_candidate_frag_id[3], container.rmsd, container.score, container.density_score, container.all_overlap_score, container.all_nonoverlap_score, boltzmann_prob_Dict[ each_candidate_frag_id ] )
#### end of calculating all the candidate_frags at the residue to the selected ones ####
# assign a null frag for each position
null_frag_id = ( args.mer, pos, 0, 0 )
boltzmann_prob_Dict[ null_frag_id ] = ( boltzmann_prob( args.null_frag_score, args.temperature ), args.null_frag_score, 0.0 )
# normalize it - watch out this step
normalized_boltzmann_prob_Dict = normalization( boltzmann_prob_Dict )
# pick one frag
selected_frag_id = pick_frags( normalized_boltzmann_prob_Dict )
mer = mer_list[ random.randrange( 0, len(mer_list) ) ]
# pick a residue number to start with
if args.designated_position: pos = args.designated_position
else: pos = pos_list[ random.randrange( 0, len( pos_list ) ) ]
boltzmann_prob_Dict = {}
''' After picking a position to optimize,
calculate compatibility scores for all the candidate placements at that residue'''
for each_candidate_frag_id in density_score_Dict[ pos ].keys(): # loop over all candidate frags at the pos
if ( each_candidate_frag_id[2], each_candidate_frag_id[3] ) == ( 0, 0 ): continue # skip null fragment
total_score, density_score, all_overlap_score, all_nonoverlap_score, rmsd = ( 0.0, )*5
#each_candidate_frag_id = ( key[0], pos, key[1], key[2] ) # frag_id = ( mer, pos, picker_rank, SHD_rank )
# calculate score
total_score, density_score, all_overlap_score, all_nonoverlap_score, rmsd = scorefxn.score_evaluator( each_candidate_frag_id )
boltzmann_prob_Dict[ each_candidate_frag_id ] = ( boltzmann_prob( total_score, args.temperature ), total_score, rmsd )
if args.verbose:
print "each_candidate_frag_id %3s %3s %3s %3s rmsd: %5.4f score: %5.4f density: %5.4f overlap: %5.4f nonoverlap: %5.4f boltzmann: %s" %(
each_candidate_frag_id[0], each_candidate_frag_id[1], each_candidate_frag_id[2], each_candidate_frag_id[3],
rmsd, total_score,
density_score, all_overlap_score, all_nonoverlap_score,
boltzmann_prob_Dict[ each_candidate_frag_id ] )
#### end of calculating all the candidate_frags at the residue to the selected ones ####
# assign a null frag for each position
null_frag_id = ( mer, pos, 0, 0 )
boltzmann_prob_Dict[ null_frag_id ] = ( boltzmann_prob( args.null_frag_score, args.temperature ), args.null_frag_score, 0.0 )
