diff --git a/onepass.py b/onepass.py
index cbee7b4..aa9a9b5 100644
--- a/onepass.py
+++ b/onepass.py
@@ -1,186 +1,184 @@
 import numpy as np 
 import timeit
 import gurobipy as gp
 from gurobipy import GRB
 
 def addvariables(Z):
     upperbounds=[]
     I=[]
     J=[] 
     for M in database_indices:
         m=len(data['database_ncharges'][M])
-        #adjM=connectivity_data['frag_adj_matrices'][M]
-        I=I+[(i,j,M) for i in range(m) for j in range(n)]
-        #J=J+[(i,j,M) for i in range(m) for j in range(i+1,m) if adjM[i,j]] # for later
-        J=J+[(i,j,M) for i in range(m) for j in range(i+1,m)]
+        CM=data['database_ncharges'][M]
+        adjM=connectivity_data['frag_adj_matrices'][M]
+        I=I+[(i,j,M) for i in range(m) for j in range(n) if CM[i] == CT[j]] # if condition excludes j; i always takes all m values
+        J=J+[(i,j,M) for i in range(m) for j in range(i+1,m) if adjM[i,j]]
         upperbounds.append(int(n/m))
 
     x=Z.addVars(I, vtype=GRB.BINARY)
     e=Z.addVars(J, vtype=GRB.BINARY)
     # dummy variables y associated to number of times molecule M is picked 
     # temporarily set binary
-    y=Z.addVars(database_indices, vtype="B", lb=0, ub=upperbounds)
+    y=Z.addVars(database_indices, vtype="I", lb=0, ub=upperbounds)
     
     print("Variables added.")
-    return x,y,e
+    return x,y,e,I,J
 
-def addconstraints(Z,x,y,e):
+def addconstraints(Z,x,y,e,I,J):
     # bijection into [n]
     Z.addConstrs(x.sum('*',j,'*') == 1 for j in range(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 mol_percent of its size
         Z.addConstr(x.sum('*','*',M) >= mol_percent*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))
         
-        #Z.addConstr(t.sum('*',M) == y[M])        
-        #Z.addConstrs(t[i,M] <= x.sum(i,'*',M) for i in range(m))
- 
-        # ignore incompatible charges
-        for i in range(m):
-            for j in range(n):
-                if(CM[i] != CT[j]):
-                    Z.addConstr(x[i,j,M]==0)
         # forces  #edges >= #nodes - 1, which must be true for any connected graph 
-        adjM=connectivity_data['frag_adj_matrices'][M]
         expr=gp.LinExpr()
-        for i in range(m):
-            for j in range(i+1,m):
-                if adjM[i,j]:
-                    Z.addConstr(e[i,j,M] <= x.sum(i,'*',M))
-                    Z.addConstr(e[i,j,M] <= x.sum(j,'*',M))
-                    Z.addConstr(e[i,j,M] >= x.sum(i,'*',M)+x.sum(j,'*',M) - 1)
-                    #Z.addGenConstrAnd(e[i,j,M], [x.sum(i,'*',M), x.sum(j,'*',M)]) #this doesn't work?
-                    expr += e[i,j,M]
-            for k in range(n):
-                expr -= x[i,k,M]
-        Z.addConstr(expr+1 >= 0)        
-
+        for (i,j) in [u[:2] for u in J if u[2]==M]:
+            Z.addConstr(e[i,j,M] <= x.sum(i,'*',M))
+            Z.addConstr(e[i,j,M] <= x.sum(j,'*',M))
+            #Z.addConstr(e[i,j,M] >= x.sum(i,'*',M)+x.sum(j,'*',M) - 1)
+            d=Z.addVar(vtype="B") # decision variable for minimum
+            Z.addConstr(e[i,j,M] >= x.sum(i,'*',M)-d*4)
+            Z.addConstr(e[i,j,M] >= x.sum(i,'*',M)-(1-d)*4)
+            expr += e[i,j,M]
+        
+        for (i,j) in [v[:2] for v in I if v[2]==M]:
+            expr -= x[i,j,M]
+        
+        Z.addConstr(expr+y[M] >= 0)        
+ 
     print("Constraints added.")
     return 0
 
-def setobjective(Z,x,y):
+def setobjective(Z,x,y,I):
     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(-1, y[M])
         adjM=connectivity_data['frag_adj_matrices'][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)
-                        Z.addConstr(x[i,k,M]+x[j,l,M]-1 <= (adjM[i,j] == adjT[k,l]))
+        for (i,k) in [v[:2] for v in I if v[2]==M]:
+            for (j,l) in [v[:2] for v in I if v[2]==M and v[1]>k]:
+                expr.add(x[i,k,M] * x[j,l,M], np.abs(T[k,l]-Mol[i,j])**2)
+                Z.addConstr(x[i,k,M]+x[j,l,M]-1 <= (adjM[i,j] == adjT[k,l]))
+        
         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):
+def print_sols(Z, x, y, I):
     SolCount=Z.SolCount
+    print("Target has size", n)
     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))
             if amount_picked != 0:
                 m=len(data['database_ncharges'][M])
+                label=data['database_labels'][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==1 and sum(U[:,k]!=0) < mol_percent*m:
+                    for j in [v[1] for v in I if v[0]==i and v[2]==M]:
+                        if np.rint(x[i,j,M].Xn)==1 and sum(U[:,k]!=0) < mol_percent*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("Molecule", label, "has been picked", amount_picked, "time(s) ( size", len(data['database_ncharges'][M]), ", used", sum([x[v].Xn for v in I if v[2]==M]), ")")
                         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.
+# actually the hydrogens are always at the end so new index = old index
 def oldindex(i, M):
+    """
     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
+    """
+    return i
 
 def main():
     # construction of the model
     start=timeit.default_timer() 
     Z = gp.Model()
     Z.setParam('OutputFlag',1)
-    x,y,e=addvariables(Z)
-    addconstraints(Z,x,y,e)
-    setobjective(Z,x,y)
+    x,y,e,I,J=addvariables(Z)
+    addconstraints(Z,x,y,e,I,J)
+    setobjective(Z,x,y,I)
     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", 600) 
+    Z.setParam("TimeLimit", 500) 
     Z.setParam("MIPGap", 0.33) # 1.5-approx. algorithm (not really)
-    Z.setParam("PoolSolutions", 10)
+    Z.setParam("PoolSolutions", 5)
 
     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)
+    print_sols(Z,x,y,I)
     return 0
 
 # global settings modifiable
 target_index=1
-mol_percent=0.5 # portion of each molecule to use if taken
-database_indices=range(2000) # consider only first 500 molecules
+mol_percent=0.55 # portion of each molecule to use if taken
+database_indices=range(1550,1750) # consider only first 500 molecules
 
 # global constants
 connectivity_data = np.load("connectivity_data.npz", allow_pickle=True)
 data=np.load("datafilter.npz", allow_pickle=True)
 olddata=np.load("data.npz", allow_pickle=True)
 CT=data['target_ncharges'][target_index]
 T=data['target_CMs'][target_index]
 n=len(data['target_ncharges'][target_index])
 size_database=len(database_indices)
 adjT=connectivity_data['target_adj_matrices'][target_index]
 
 main()
 #print(connectivity_data['frag_adj_matrices'][0][0,1])