diff --git a/code/Simulation/sim_model.py b/code/Simulation/sim_model.py
index 9304991..11e42a5 100644
--- a/code/Simulation/sim_model.py
+++ b/code/Simulation/sim_model.py
@@ -20,7 +20,7 @@
 import time
 import sys 
 
-def brownian(d,v,sigma,t_bin=0.01):
+def brownian(d,v,sigma,t_bin=0.001):
     
     # simulate brownian motion
     
@@ -59,7 +59,7 @@ def eq1(t,T):
     s_arr[s-2] = 1
     return s_arr
         
-def sim(N,d,v,sigma,T,tau, no_sim=100000):
+def sim(N,d,v,sigma,T,tau, no_sim=500000):
     
     # simulate N particle model
     
@@ -67,13 +67,13 @@ def sim(N,d,v,sigma,T,tau, no_sim=100000):
     for i in range(1,no_sim):
         
         tauT = np.zeros((N,),dtype=float)
-        tauT[0] = 0;
+        tauT[0] = 0
         for i in range(1,len(tauT)):
-            tauT[i] =tauT[i-1]+ np.random.exponential(1/tau);
+            tauT[i] =tauT[i-1]+ np.random.exponential(tau)
             
         t = np.zeros((N,),dtype=float)
         for i in range(0,len(t)):
-            t[i] =tauT[i]+ brownian(d,v,sigma);
+            t[i] =tauT[i]+ brownian(d,v,sigma)
         
         t=t-t[0] # making relatiove to 1st particle
             
@@ -110,7 +110,7 @@ def main(N):
     
     # run for different parameter combination
     
-    d=1; v=1; sigma=1;
+    d=1; v=1; sigma=1
     t_range = [0.5,1,1.5,2,2.5,3,3.5,4]
     tau_range = [0.5,1,1.5,2,2.5,3,3.5,4]
     count=0; Data = np.zeros((len(t_range)*len(tau_range),5+N-1),dtype=float)
@@ -137,12 +137,13 @@ def test():
 #            for sigma in [1,0.5,0.25]:
 #                isIG(d,v,sigma)
                 
-    N=4; d=1; v=1; sigma=1; T=1; tau=4;
+    N=4; d=1; v=1; sigma=1; T=1; tau=1
     start = time.time()
     res = sim(N,d,v,sigma,T,tau)
     print("result : ", res, " Time: ", time.time()-start)
         
-main(int(sys.argv[1]))
+test()
+# main(int(sys.argv[1]))
 
     
     
diff --git a/code/Simulation/sim_model_prl.py b/code/Simulation/sim_model_prl.py
new file mode 100644
index 0000000..f4a5069
--- /dev/null
+++ b/code/Simulation/sim_model_prl.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun 12 13:02:14 2019
+
+@author: ranap
+
+V = l/mu
+D = 2 * sigma^2 = (2*l^2) / lambda 
+
+Args:
+    N: number of particle
+Return:
+    dataframe with probabilty 
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+plt.switch_backend('agg')
+from scipy.stats import invgauss
+import math
+import time
+import sys
+from joblib import Parallel, delayed 
+
+def brownian(d,v,sigma,t_bin=0.001):
+    
+    # simulate brownian motion  
+    pos= 0
+    t= 1
+    v_t = v*t_bin
+    while pos < d:
+        pos = pos + v_t + (sigma * np.random.randn() * math.sqrt(t_bin))
+        t+=1
+    return t * t_bin
+
+def eq1(t,T):
+    s = 2
+    arr =np.sort(t, axis=None)
+    
+    t_bind=1
+    t_next=2
+    while t_next < len(arr):
+        if(arr[t_next] -arr[t_bind] > T):
+            s+=1; t_bind = t_next; t_next+=1
+        else:
+            t_next+=1
+    s_arr = np.zeros((len(arr)-1,), dtype=int)
+    s_arr[s-2] = 1
+    return s_arr
+        
+def sim(N,d,v,sigma,T,tau, no_sim=100000):
+    
+    # simulate N particle model
+    
+    s_count = np.zeros(N-1)
+    for i in range(1,no_sim):
+        
+        tauT = np.zeros((N,),dtype=float)
+        tauT[0] = 0
+        for i in range(1,len(tauT)):
+            tauT[i] =tauT[i-1]+ np.random.exponential(tau)
+            
+        t = np.zeros((N,),dtype=float)
+        for i in range(0,len(t)):
+            t[i] =tauT[i]+ brownian(d,v,sigma)
+        
+        t=t-t[0] # making relatiove to 1st particle
+            
+        s = eq1(t,T)
+        
+        s_count +=s
+            
+    s_prob = s_count/no_sim
+    res= np.zeros((5+N-1,), dtype=float)
+    res[0]=d; res[1]=v; res[2]=sigma; res[3]=T; res[4]=tau;
+    res[5:]=s_prob
+    return(res)
+
+def test():
+    
+    N=4; d=1; v=1; sigma=1; T=1; tau=1
+    start = time.time()
+    res = sim(N,d,v,sigma,T,tau)
+    print("result : ", res, " Time: ", time.time()-start)
+
+def main_prl(N):
+    
+    # run for different parameter combination
+    
+    d=1; v=1; sigma=1
+    t_range = np.arange(0.1,4,0.1)
+    tau_range = np.arange(0.5,4,0.1)
+    Data = np.zeros((len(t_range)*len(tau_range),5+N-1),dtype=float)
+    res = Parallel(n_jobs=16)(delayed(sim)(N,d,v,sigma,i,j) for j in tau_range for i in t_range)    
+    res = np.array(res)
+    col_name= ['d', 'v', 'sigma', 'T', 'tau'] + ['S'+ str(i) for i in range(2,N+1)]
+    Data = pd.DataFrame(res, columns=col_name)
+    Data.to_csv('dataSimulation'+str(N)+'.csv', index =False)
+  
+main_prl(int(sys.argv[1]))
+
+    
+    
+