'''selected_frags_Dict = { residue_number : ( ( mer, pos, picker_rank, SHD_rank ),
( boltzmann_prob, total_score ),
( density_score, rmsd ) ) '''
''' after the first round, every position has a fragment assigned,
now we can start do Monte Carlo sampling '''
#selected_frags_Dict = {}
# three object are being created
etable = ScoreTable( density_score_Dict, overlap_score_Dict, nonoverlap_score_Dict )
wts = Weights( args.density_score_wt, args.overlap_score_wt, args.closab_score_wt, args.clash_score_wt )
scorefxn = ScoreFunction( etable, wts, args.null_frag_score )
for each_round in range( 1, args.round+1 ):
scorefxn.clean_selected_frags_dict()
#mypose = Pose()
if args.starts_with:
assert not args.dump_results == args.starts_with
print "Initialize with %s" % args.starts_with
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 = {}
