diff --git a/onepass.py b/onepass.py
index 2303f0e..71f9e5b 100644
--- a/onepass.py
+++ b/onepass.py
@@ -1,143 +1,163 @@
 import numpy as np 
 import timeit
 import gurobipy as gp
 from gurobipy import GRB
 
 def addvariables(Z):
     upperbounds=[]
     I=[]
     for M in database_indices:
         m=len(data['database_ncharges'][M])
         I=I+[(i,j,M) for i in range(m) for j in range(n)]
         upperbounds.append(int(n/m))
 
     x=Z.addVars(I, vtype=GRB.BINARY)
     # dummy variables y associated to number of times molecule M is picked 
     y=Z.addVars(database_indices, vtype="I", lb=0, ub=upperbounds)
     print("Variables added.")
     return x,y,I
 
 def addconstraints(Z,x,y):
     # injection into [n]
     Z.addConstrs(x.sum('*',j,'*') <= 1 for j in range(n))
     # use at least 80% of target
     Z.addConstr(x.sum() >= 0.8*n)
 
     # additional constraints: take whole molecule or leave it out, and matching charges
     for M in database_indices:
         CM=data['database_ncharges'][M]
         m=len(CM)
         # the number of indices of M used (counting multiple use) is at least 55% of its size
         Z.addConstr(x.sum('*','*',M) >= 0.55*m*y[M])
         # each index in M is used at most y[M] times
         Z.addConstrs(x.sum(i,'*',M) <= y[M] for i in range(m))
         
         # ignore incompatible charges and atoms of charge 1
         for i in range(m):
             for j in range(n):
                 if(CM[i] != CT[j]):
                 #if(CM[i] != CT[j] or CM[i]==1):
                     Z.addConstr(x[i,j,M]==0)
         
 
     print("Constraints added.")
     return 0
 
 def setobjective(Z,x,y):
     expr=gp.QuadExpr()
     print("Constructing objective function... ")
     key=0
     for M in database_indices:
         key=key+1
         Mol=data['database_CMs'][M]
         m=len(Mol)
         expr.addTerms(-m, y[M])
         for i in range(m):
             for j in range(m):
                 for k in range(n):
                     for l in range(k,n):
                         expr.add(x[i,k,M] * x[j,l,M], np.abs(T[k,l]-Mol[i,j])**2) 
         print(key, "  /  ", size_database)
 
     Z.setObjective(expr, GRB.MINIMIZE)
     print("Objective function set.")
     return 0
 
+# prints mappings of positions (indices+1) of each molecule (before preprocess) to positions inside target (before preprocess, but the hydrogens are at the end anyway)
 def print_sols(Z, x, y):
-    print("Target molecule has size", len(CT))
     SolCount=Z.SolCount
     for solnb in range(SolCount):
         print()
         print("--------------------------------")
         Z.setParam("SolutionNumber",solnb)
         print("Solution number", solnb+1, ", objective value", Z.PoolObjVal)
         
         for M in database_indices:
             amount_picked=int(np.rint(y[M].Xn))
-            m=len(data['database_ncharges'][M])
-            U=np.zeros((m,n))
-            for k in range(amount_picked):
-                if k==0:
-                    print("Molecule", data['database_labels'][M], "has been picked", amount_picked, "time(s) ( size", len(data['database_ncharges'][M]), ", used", sum([x[i,j,M].Xn for i in range(m) for j in range(n)]), ")")
-                print(k+1, end=': ')
-                s=0
+            if amount_picked != 0:
+                m=len(data['database_ncharges'][M])
+                U=np.zeros((m,amount_picked))
+                
+                # constructing U   
                 for i in range(m):
+                    k=0
                     for j in range(n):
-                        if x[i,j,M].Xn - U[i,j] != 0 and s<0.55*m:
-                            s=s+1
-                            U[i,j]=U[i,j]+1
-                            print(i, "->", j, end=', ')
-                print("used", s) 
-        
+                        if x[i,j,M].Xn==1 and sum(U[:,k]!=0) < 0.55*m:
+                            U[i,k]=j+1
+                            k=k+1
+                
+                # reading U
+                for k in range(amount_picked):
+                    if np.any(U[:,k] != 0):
+                        if k==0:
+                            print("Molecule", data['database_labels'][M], "has been picked", amount_picked, "time(s) ( size", len(data['database_ncharges'][M]), ", used", sum([x[i,j,M].Xn for i in range(m) for j in range(n)]), ")")
+                        print(k+1, end=": ")
+                        for i in range(m):
+                            if U[i,k]!=0:
+                                print(oldindex(i,M)+1, "->", U[i,k], end=", ")
+                        print("used", sum(U[:,k]!=0)) 
+
+# converts new index (with hydrogens removed) to old index.
+def oldindex(i, M, target):
+    k=0
+    notones=0
+    CM=olddata['database_ncharges'][M]
+    while notones<i or CM[k]==1:
+        if CM[k]!=1:
+            notones=notones+1
+        k=k+1
+    return k
+
 def main():
     # construction of the model
     start=timeit.default_timer() 
     Z = gp.Model()
     Z.setParam('OutputFlag',1)
     x,y,I=addvariables(Z)
     addconstraints(Z,x,y)
     setobjective(Z,x,y)
     stop=timeit.default_timer()
     print("Model setup: ", stop-start, "s")
     
     # optimization
     # trying parameters from Z.tune() and others.
     Z.setParam("DegenMoves", 2)
     Z.setParam("ZeroHalfCuts", 2)
     Z.setParam("BQPCuts", 2)
     Z.setParam("RLTCuts", 2)
     
     # time (seconds) and gap (%) limit, (max) number of solutions kept in memory
     Z.setParam("TimeLimit", 180) 
     Z.setParam("MIPGap", 0.33) # 1.5-approx. algorithm (not really)
     Z.setParam("PoolSolutions", 10)
 
     print("------------")
     print("Optimization")
     print("------------")
     Z.optimize()
 
     print("Optimization runtime: ", Z.RunTime, "s")
     
     if(Z.status == 3):
         print("Model was proven to be infeasible.")
         return 1
     
     print_sols(Z,x,y)
     return 0
 
 # global constants 
 
 data=np.load("datafilter.npz", allow_pickle=True)
+olddata=np.load("data.npz", allow_pickle=True)
 target_index=1
 print("Target", data['target_labels'][target_index])
 CT=data['target_ncharges'][target_index]
 T=data['target_CMs'][target_index]
 n=len(data['target_ncharges'][target_index])
 
 #database_indices=np.loadtxt("filtered"+str(target_index), dtype=int)
 #database_indices=database_indices[:100]
-database_indices=range(1000)
+database_indices=range(500)
 size_database=len(database_indices)
 
 main()