Commit: f957d0aae85b96c511b9429b51a0249b29e0667d - myprojects-hg-reborn (hg)

Commit MetaInfo

• Révision: f957d0aae85b96c511b9429b51a0249b29e0667d (tree)
• l'heure: 2008-08-06 05:08:59
• Auteur: iselllo
• Commiter: iselllo

Message de Log

I modified the code: now I simply give the number of directories as a
parameter and it goes through them and reads them as above.
But much cleaner code and easier to avoid mistakes.
I also introduced a new s.copy to properly copy an array.

Change Summary

• modified: Python-codes/r_gyr_iteration.py

Modification

diff -r 4b3899771ab4 -r f957d0aae85b Python-codes/r_gyr_iteration.py
--- a/Python-codes/r_gyr_iteration.py	Mon Aug 04 21:35:36 2008 +0000
+++ b/Python-codes/r_gyr_iteration.py	Tue Aug 05 20:08:59 2008 +0000
@@ -14,10 +14,10 @@
 #p.rc('text', usetex=True)
 
 ini_conf=0  
-fin_conf=1500   #configurations I read initially in read_test.tcl
-
-
-figure=1  #tells whether many pdf's should be produced or not.
+fin_conf=3000   #configurations I read initially in read_test.tcl
+
+
+figure=0  #tells whether many pdf's should be produced or not.
 
 save_every=50 # in case figure == 1, it plots the results every save_every configurations
 
@@ -45,9 +45,9 @@
 #my_config=19 #here labels the saved configurations but it is not directly the number
 # of the file with the saved configuration
 
-by=1 #I need it to select the elements from time.dat
-
-by_clusters=1  # this tells how often was the cluster number recorded
+by=2 #I need it to select the elements from time.dat
+
+by_clusters=2  # this tells how often was the cluster number recorded
 
 
 min_cluster=5. # minimum amount of particles a cluster has to contain in order to be considered
@@ -231,14 +231,16 @@
 
     
 
+n_dir=8.
+
+
+
+dir_list=s.arange(n_dir)+1
+
+    
+
 
 for my_config in xrange(ini_conf,fin_conf):
-    print "my_config is, ", my_config
-    #cluster_long=s.zeros(1)
-    #r_gyr_long=s.zeros(1)
-
-
-
     r_gyr_dist=s.zeros(1)  # something to start concatenating the arrays
     n_comp=s.zeros(1) #same for the clusters
 
@@ -246,310 +248,75 @@
 
     my_config=my_config*by
 
-    #cluster_name="cluster_dist%05d"%my_config
-
-    temp_clu=p.load("../1/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../1/R_gyr_dist%05d"%my_config)
-
-
-    if (count_config==0):
-        
-        temp_clus=+p.load("../1/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-        print "n_clus[0] is, ", n_clus[0]
-
-        stack_clus=temp_clus
-
-
-    n_dir=n_dir+1
-
-    #n_clus=n_clus+temp_nc
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
- 
-
-
-    temp_clu=p.load("../2/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../2/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
- 
-    #temp_nc=p.load("../2/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-    if (count_config==0):
-        temp_clus=+p.load("../2/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-        stack_clus=s.vstack((stack_clus,temp_clus))
-        print "n_clus[0] now is, ", n_clus[0] 
-        
-
-
-    temp_clu=p.load("../3/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../3/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
-    #temp_nc=p.load("../3/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-
-    if (count_config==0):
-
-        temp_clus=+p.load("../3/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
-
-       
-        print "n_clus[0] now is, ", n_clus[0] 
-
-
-    temp_clu=p.load("../4/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../4/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
- 
-    #temp_nc=p.load("../4/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-    if (count_config==0):
-
-        temp_clus=+p.load("../4/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
-
-
- 
-
-        
-        print "n_clus[0] now is, ", n_clus[0] 
-
-
-
-    temp_clu=p.load("../5/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../5/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
-    #temp_nc=p.load("../5/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-    if (count_config==0):
-
-        temp_clus=+p.load("../5/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
+
+    for m in xrange(len(dir_list)):
+
+        dir_name="../%01d"%(dir_list[m])
+
+        print "my_config is, ", my_config
+        #cluster_long=s.zeros(1)
+        #r_gyr_long=s.zeros(1)
+
+        temp_clu_name=dir_name+"/cluster_dist%05d"%my_config
+        temp_r_gyr_name=dir_name+"/R_gyr_dist%05d"%my_config
+
+  
+        #cluster_name="cluster_dist%05d"%my_config
+
+        temp_clu=p.load(temp_clu_name)
+        temp_r_gyr=p.load(temp_r_gyr_name)
+
+
+        if (count_config==0):
+
+            temp_clus_name=dir_name+"/number_cluster.dat"
+            temp_clus=p.load(temp_clus_name)
+
+#           temp_clus=+p.load("../1/number_cluster.dat") #I load the number of clusters at each time (but I load it only once!)
+            n_clus=n_clus+temp_clus
+            print "n_clus[0] is, ", n_clus[0]
+
+            stack_clus=temp_clus
+
+
+#        n_dir=n_dir+1
+
+        #n_clus=n_clus+temp_nc
+
+        n_comp=s.concatenate([n_comp,temp_clu])
+        r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
 
         stack_clus=s.vstack((stack_clus,temp_clus))
 
 
-
-
-    temp_clu=p.load("../6/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../6/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-   
-
-
-    #temp_nc=p.load("../6/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-    if (count_config==0):
-
-        temp_clus=+p.load("../6/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
-
-
-
-
-
-    temp_clu=p.load("../7/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../7/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
-    #temp_nc=p.load("../7/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-    if (count_config==0):
-
-        temp_clus=+p.load("../7/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
-
-
-
-
-    temp_clu=p.load("../8/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../8/R_gyr_dist%05d"%my_config)
-
-    n_dir=n_dir+1
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
-    #temp_nc=p.load("../8/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-    if (count_config==0):
-
-        temp_clus=+p.load("../8/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
-
-
-
-
-    temp_clu=p.load("../9/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../9/R_gyr_dist%05d"%my_config)
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
-    #temp_nc=p.load("../9/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-
-    if (count_config==0):
-
-        temp_clus=+p.load("../9/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
-
-
-
-    n_dir=n_dir+1
-
-
-    temp_clu=p.load("../10/cluster_dist%05d"%my_config)
-    temp_r_gyr=p.load("../10/R_gyr_dist%05d"%my_config)
-
-
-    n_comp=s.concatenate([n_comp,temp_clu])
-    r_gyr_dist=s.concatenate([r_gyr_dist,temp_r_gyr])
-
-    n_dir=n_dir+1
-
-    n_dir_count=n_dir
-
-    n_dir=0
-
-    #cluster_long=s.concatenate([cluster_long,temp_clu])
-    #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
-    cluster_tot=s.concatenate([cluster_tot,temp_clu])
-    r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
-
-
-
-    #temp_nc=p.load("../10/number_cluster.dat") #I load the number of clusters at each time
-
-    #n_clus=n_clus+temp_nc
-
-    if (count_config==0):
-
-        temp_clus=+p.load("../10/number_cluster.dat") #I load the number of clusters at each time
-        n_clus=n_clus+temp_clus
-
-        stack_clus=s.vstack((stack_clus,temp_clus))
+        #cluster_long=s.concatenate([cluster_long,temp_clu])
+        #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
+        cluster_tot=s.concatenate([cluster_tot,temp_clu])
+        r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
+
+
+#     #cluster_long=s.concatenate([cluster_long,temp_clu])
+#     #r_gyr_long=s.concatenate([r_gyr_long,temp_r_gyr])
+#     cluster_tot=s.concatenate([cluster_tot,temp_clu])
+#     r_gyr_tot=s.concatenate([r_gyr_tot,temp_r_gyr])
+
+
+
+#     #temp_nc=p.load("../10/number_cluster.dat") #I load the number of clusters at each time
+
+#     #n_clus=n_clus+temp_nc
+
+#     if (count_config==0):
+
+#         temp_clus=+p.load("../10/number_cluster.dat") #I load the number of clusters at each time
+#         n_clus=n_clus+temp_clus
+
  
 
 
 
 
-    n_comp_mon=n_comp
+    n_comp_mon=s.copy(n_comp)
 
     n_comp_mon=n_comp_mon[s.where(n_comp_mon>0.)] #I need this line since n_comp_mon comes from n_comp
     #which in turn is defined first as [0] (array with an only entry equal to zero)
@@ -906,7 +673,7 @@
 
     #d=s.concatenate((a,b)).reshape(2,4).transpose()
     N_k_vs_k=s.zeros((len(n_comp_mon_uni),2))
-    N_k_arr=N_k_arr/(n_dir_count*1.)
+    N_k_arr=N_k_arr/(n_dir*1.)
     #print "n_dir is, ", n_dir
     N_k_vs_k[:,0]=N_k_arr
     N_k_vs_k[:,1]=n_comp_mon_uni*1. #n_comp_mon_uni is the k array
@@ -1066,15 +833,15 @@
 
 #print "now n_dir is, ", n_dir
 
-n_clus=n_clus/(n_dir_count)
-
-n_clus_small=n_clus_small/(n_dir_count)
-n_clus_large=n_clus_large/(n_dir_count)
-n_clus_test=n_clus_test/(n_dir_count)
+n_clus=n_clus/(n_dir)
+
+n_clus_small=n_clus_small/(n_dir)
+n_clus_large=n_clus_large/(n_dir)
+n_clus_test=n_clus_test/(n_dir)
 
 mean_clus_separation=(n_clus/box_vol)**(-1./3)
 
-print "n_dir_count is, ", n_dir_count
+print "n_dir is, ", n_dir
 
 #print "the total volume is, ", k_aver*n_clus
 
@@ -1250,42 +1017,42 @@
 
 
 
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.errorbar(time,my_fractal_dim,yerr=delta_df )
-p.ylim(1.4,3.)
-p.xlabel('Time')
-p.ylabel('Fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-#p.title('Evolution Fractal Dimension from R_g')
-#p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-#cluster_name="std_err_D_f.pdf"
-p.savefig("time_evolution_D_f_with_error_bars.pdf")
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.errorbar(time,my_fractal_dim,yerr=delta_df )
+# p.ylim(1.4,3.)
+# p.xlabel('Time')
+# p.ylabel('Fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# #p.title('Evolution Fractal Dimension from R_g')
+# #p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# #cluster_name="std_err_D_f.pdf"
+# p.savefig("time_evolution_D_f_with_error_bars.pdf")
  
-p.clf()    
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.errorbar(time,my_fractal_dim,yerr=delta_df )
-p.ylim(1.4,3.)
-p.xlabel('Time')
-p.ylabel('Fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-#p.title('Evolution Fractal Dimension from R_g')
-#p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-#cluster_name="std_err_D_f.pdf"
-p.savefig("time_evolution_D_f_with_error_bars_large_cluster.pdf")
+# p.clf()    
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.errorbar(time,my_fractal_dim,yerr=delta_df )
+# p.ylim(1.4,3.)
+# p.xlabel('Time')
+# p.ylabel('Fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# #p.title('Evolution Fractal Dimension from R_g')
+# #p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# #cluster_name="std_err_D_f.pdf"
+# p.savefig("time_evolution_D_f_with_error_bars_large_cluster.pdf")
  
-p.clf()    
+# p.clf()    
 
 
 
@@ -1306,6 +1073,12 @@
 
 my_tau_coll=1./(n_clus_test/box_vol*mean_velocity*s.pi*R_gyr_mean**2.)
 
+print "R_gyr_mean is, ", R_gyr_mean
+
+print "mean_velocity is, ", mean_velocity
+
+print "n_clus_test is, ", n_clus_test
+
 p.save("collision_time.dat", my_tau_coll)
 
 
@@ -1313,140 +1086,140 @@
 
 
 
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.plot(time,my_tau_coll, "bo",label="collision_time")
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('collision time')
-p.title("Evolution collision tim")
-p.grid(True)
-cluster_name="collision_time.pdf"
-axes.legend()
-p.savefig(cluster_name)
- 
-p.clf()
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.loglog(time,my_tau_coll, "bo",label="collision_time")
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('collision time')
-p.title("Evolution collision tim")
-p.grid(True)
-cluster_name="collision_time_log_log.pdf"
-axes.legend()
-p.savefig(cluster_name)
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.plot(time,my_tau_coll, "bo",label="collision_time")
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('collision time')
+# p.title("Evolution collision tim")
+# p.grid(True)
+# cluster_name="collision_time.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
  
-p.clf()
-
-
-
-
-
-
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.plot(time,mean_free_path, "bo",label="Mean-free path")
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Particle mean free path')
-p.title("Evolution Mean-free path")
-p.grid(True)
-cluster_name="mean_free_path.pdf"
-axes.legend()
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.loglog(time,my_tau_coll, "bo",label="collision_time")
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('collision time')
+# p.title("Evolution collision tim")
+# p.grid(True)
+# cluster_name="collision_time_log_log.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
  
-p.clf()
-
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.loglog(time,mean_free_path, "bo",label="Mean-free path")
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Particle mean free path')
-p.title("Evolution Mean-free path")
-p.grid(True)
-cluster_name="mean_free_path_loglog.pdf"
-axes.legend()
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+
+
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.plot(time,mean_free_path, "bo",label="Mean-free path")
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Particle mean free path')
+# p.title("Evolution Mean-free path")
+# p.grid(True)
+# cluster_name="mean_free_path.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
  
-p.clf()
-
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.plot(time,R_gyr_mean, "bo",label="Mean radius of gyration")
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Mean radius of gyration')
-p.title("Evolution Mean Radius of gyration")
-p.grid(True)
-cluster_name="mean_radius_gyration.pdf"
-axes.legend()
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.loglog(time,mean_free_path, "bo",label="Mean-free path")
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Particle mean free path')
+# p.title("Evolution Mean-free path")
+# p.grid(True)
+# cluster_name="mean_free_path_loglog.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
  
-p.clf()
-
-
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.loglog(time,R_gyr_mean, "bo",label="Mean radius of gyration")
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Mean radius of gyration')
-p.title("Evolution Mean Radius of gyration")
-p.grid(True)
-cluster_name="mean_radius_gyration_loglog.pdf"
-axes.legend()
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.plot(time,R_gyr_mean, "bo",label="Mean radius of gyration")
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Mean radius of gyration')
+# p.title("Evolution Mean Radius of gyration")
+# p.grid(True)
+# cluster_name="mean_radius_gyration.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
  
-p.clf()
+# p.clf()
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.loglog(time,R_gyr_mean, "bo",label="Mean radius of gyration")
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Mean radius of gyration')
+# p.title("Evolution Mean Radius of gyration")
+# p.grid(True)
+# cluster_name="mean_radius_gyration_loglog.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
+ 
+# p.clf()
 
 
 #Now a fit of the behaviour of the mean radius of gyration vs time
@@ -1464,26 +1237,26 @@
 
 p.save("mean_radius_gyration.dat",R_gyr_mean)
 
-fig = p.figure()
-axes = fig.gca()
-
-#axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
-axes.loglog(time,R_gyr_mean, "bo",label="Mean radius of gyration")
-axes.loglog(time[choice_time_R],10.**(R_gyr_mean_fit), "r", linewidth=2.,label="Mean radius of gyration (fit)")
-
-# axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
-# axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
-# axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
-#             label=r"Fit of $N_\infty $",linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Mean radius of gyration')
-p.title("Evolution Mean Radius of gyration")
-p.grid(True)
-cluster_name="mean_radius_gyration_loglog_fitting.pdf"
-axes.legend()
-p.savefig(cluster_name)
+# fig = p.figure()
+# axes = fig.gca()
+
+# #axes.plot(s.log10(n_comp_large),s.log10(r_gyr_ari_large), "bo")
+# axes.loglog(time,R_gyr_mean, "bo",label="Mean radius of gyration")
+# axes.loglog(time[choice_time_R],10.**(R_gyr_mean_fit), "r", linewidth=2.,label="Mean radius of gyration (fit)")
+
+# # axes.loglog(time_clu,n_clus_small, "k+",label="$N_<$")
+# # axes.loglog(time_clu,n_clus_large, "rx",label="$N_>$")
+# # axes.loglog(time_clu[choice_time_clu],(10.**my_conc),"k",\
+# #             label=r"Fit of $N_\infty $",linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Mean radius of gyration')
+# p.title("Evolution Mean Radius of gyration")
+# p.grid(True)
+# cluster_name="mean_radius_gyration_loglog_fitting.pdf"
+# axes.legend()
+# p.savefig(cluster_name)
  
-p.clf()
+# p.clf()
 
 
 
@@ -1796,25 +1569,25 @@
 
 
 
-fig = p.figure()
-axes = fig.gca()
-
-axes.plot(time,prefactor_R,"k^",time,prefactor_small_R,"ro"\
-       ,time,prefactor_large_R,"bx")
-p.xlabel('Dimensionless Time',fontsize=20)
-labels = p.getp(p.gca(), 'xticklabels')
-p.setp(labels, color='k', fontsize=15)
-p.ylabel('Fractal Dimension',fontsize=20)
-labels2 = p.getp(p.gca(), 'yticklabels')
-p.setp(labels2, color='k', fontsize=15)
-p.legend(('overall prefactor','prefactor small clusters', 'prefactor large clusters'))
-#p.title('Evolution Fractal Dimension')
-p.grid(False)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="Evolution_prefactors.pdf"
-p.savefig(cluster_name)
+# fig = p.figure()
+# axes = fig.gca()
+
+# axes.plot(time,prefactor_R,"k^",time,prefactor_small_R,"ro"\
+#        ,time,prefactor_large_R,"bx")
+# p.xlabel('Dimensionless Time',fontsize=20)
+# labels = p.getp(p.gca(), 'xticklabels')
+# p.setp(labels, color='k', fontsize=15)
+# p.ylabel('Fractal Dimension',fontsize=20)
+# labels2 = p.getp(p.gca(), 'yticklabels')
+# p.setp(labels2, color='k', fontsize=15)
+# p.legend(('overall prefactor','prefactor small clusters', 'prefactor large clusters'))
+# #p.title('Evolution Fractal Dimension')
+# p.grid(False)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="Evolution_prefactors.pdf"
+# p.savefig(cluster_name)
  
-p.clf()
+# p.clf()
 
 
 
@@ -1833,203 +1606,203 @@
 #print "time[ini_conf:fin_conf], ", time[ini_conf:fin_conf]
 
 
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.plot(time,my_fractal_dim,"k^")
-p.xlabel('Dimensionless Time',fontsize=20)
-labels = p.getp(p.gca(), 'xticklabels')
-p.setp(labels, color='k', fontsize=15)
-p.ylabel('Fractal Dimension',fontsize=20)
-labels2 = p.getp(p.gca(), 'yticklabels')
-p.setp(labels2, color='k', fontsize=15)
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-#p.title('Evolution Fractal Dimension')
-p.grid(False)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="Evolution_fractal_dimension-no-mean.pdf"
-p.savefig(cluster_name)
- 
-p.clf()
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-axes.plot(time,my_fra_low,"k^")
-p.xlabel('Dimensionless Time',fontsize=20)
-labels = p.getp(p.gca(), 'xticklabels')
-p.setp(labels, color='k', fontsize=15)
-p.ylabel('Fractal Dimension',fontsize=20)
-labels2 = p.getp(p.gca(), 'yticklabels')
-p.setp(labels2, color='k', fontsize=15)
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('Evolution Fractal Dimension for small clusters')
-p.grid(False)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="Evolution_fractal_dimension-no-mean-small-clusters.pdf"
-p.savefig(cluster_name)
- 
-p.clf()
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-axes.plot(time,my_fra_high,"k^")
-p.xlabel('Dimensionless Time',fontsize=20)
-labels = p.getp(p.gca(), 'xticklabels')
-p.setp(labels, color='k', fontsize=15)
-p.ylabel('Fractal Dimension',fontsize=20)
-labels2 = p.getp(p.gca(), 'yticklabels')
-p.setp(labels2, color='k', fontsize=15)
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('Evolution Fractal Dimension for large clusters')
-p.grid(False)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="Evolution_fractal_dimension-no-mean-large-clusters.pdf"
-p.savefig(cluster_name)
- 
-p.clf()
-
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-
-axes.plot(time,my_fractal_dim2, "bo",\
-       time,my_fractal_dim2,linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Fitted D_f')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('Evolution Fractal Dimension from R_g')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="Evolution_fractal_dimension_from_mean.pdf"
-p.savefig(cluster_name)
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.plot(time,my_fractal_dim,"k^")
+# p.xlabel('Dimensionless Time',fontsize=20)
+# labels = p.getp(p.gca(), 'xticklabels')
+# p.setp(labels, color='k', fontsize=15)
+# p.ylabel('Fractal Dimension',fontsize=20)
+# labels2 = p.getp(p.gca(), 'yticklabels')
+# p.setp(labels2, color='k', fontsize=15)
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# #p.title('Evolution Fractal Dimension')
+# p.grid(False)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="Evolution_fractal_dimension-no-mean.pdf"
+# p.savefig(cluster_name)
  
-p.clf()    
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.plot(time,my_fractal_dim3, "bo",\
-       time,my_fractal_dim3,linewidth=2.)
-p.xlabel('Time')
-p.ylabel('Fitted D_f')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('Evolution Fractal Dimension from R_g')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="Evolution_fractal_dimension_from_mean_log.pdf"
-p.savefig(cluster_name)
- 
-p.clf()    
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-
-
-axes.plot(time,r_stat, "bo")
-p.xlabel('Time')
-p.ylabel('R of the fitted fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('R calculation')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="r_statistics.pdf"
-p.savefig(cluster_name)
- 
-p.clf()    
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-
-axes.plot(time,r_stat_low, "bo")
-p.xlabel('Time')
-p.ylabel('R of the fitted fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('R calculation for small clusters')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="r_statistics_small_clusters.pdf"
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# axes.plot(time,my_fra_low,"k^")
+# p.xlabel('Dimensionless Time',fontsize=20)
+# labels = p.getp(p.gca(), 'xticklabels')
+# p.setp(labels, color='k', fontsize=15)
+# p.ylabel('Fractal Dimension',fontsize=20)
+# labels2 = p.getp(p.gca(), 'yticklabels')
+# p.setp(labels2, color='k', fontsize=15)
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('Evolution Fractal Dimension for small clusters')
+# p.grid(False)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="Evolution_fractal_dimension-no-mean-small-clusters.pdf"
+# p.savefig(cluster_name)
  
-p.clf()    
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.plot(time,r_stat_high, "bo")
-p.xlabel('Time')
-p.ylabel('R of the fitted fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('R calculation for large clusters')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="r_statistics_large_clusters.pdf"
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+# axes.plot(time,my_fra_high,"k^")
+# p.xlabel('Dimensionless Time',fontsize=20)
+# labels = p.getp(p.gca(), 'xticklabels')
+# p.setp(labels, color='k', fontsize=15)
+# p.ylabel('Fractal Dimension',fontsize=20)
+# labels2 = p.getp(p.gca(), 'yticklabels')
+# p.setp(labels2, color='k', fontsize=15)
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('Evolution Fractal Dimension for large clusters')
+# p.grid(False)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="Evolution_fractal_dimension-no-mean-large-clusters.pdf"
+# p.savefig(cluster_name)
  
-p.clf()    
-
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.plot(time,delta_df, "bo")
-p.xlabel('Time')
-p.ylabel('Uncertainty on the fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('Evolution Fractal Dimension from R_g')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="delta_df.pdf"
-p.savefig(cluster_name)
+# p.clf()
+
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+
+# axes.plot(time,my_fractal_dim2, "bo",\
+#        time,my_fractal_dim2,linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Fitted D_f')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('Evolution Fractal Dimension from R_g')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="Evolution_fractal_dimension_from_mean.pdf"
+# p.savefig(cluster_name)
  
-p.clf()    
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-choice_inf=s.where(s.isinf(std_err == 0))
-
-axes.plot(time[choice_inf],std_err[choice_inf], "bo")
-p.xlabel('Time')
-p.ylabel('Std error on 1/fractal dimension')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-p.title('Standard error on 1/fractal dimension')
-p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-cluster_name="std_err_1_over_D_f.pdf"
-p.savefig(cluster_name)
+# p.clf()    
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.plot(time,my_fractal_dim3, "bo",\
+#        time,my_fractal_dim3,linewidth=2.)
+# p.xlabel('Time')
+# p.ylabel('Fitted D_f')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('Evolution Fractal Dimension from R_g')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="Evolution_fractal_dimension_from_mean_log.pdf"
+# p.savefig(cluster_name)
  
-p.clf()    
+# p.clf()    
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+
+
+# axes.plot(time,r_stat, "bo")
+# p.xlabel('Time')
+# p.ylabel('R of the fitted fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('R calculation')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="r_statistics.pdf"
+# p.savefig(cluster_name)
+ 
+# p.clf()    
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+
+# axes.plot(time,r_stat_low, "bo")
+# p.xlabel('Time')
+# p.ylabel('R of the fitted fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('R calculation for small clusters')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="r_statistics_small_clusters.pdf"
+# p.savefig(cluster_name)
+ 
+# p.clf()    
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.plot(time,r_stat_high, "bo")
+# p.xlabel('Time')
+# p.ylabel('R of the fitted fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('R calculation for large clusters')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="r_statistics_large_clusters.pdf"
+# p.savefig(cluster_name)
+ 
+# p.clf()    
+
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.plot(time,delta_df, "bo")
+# p.xlabel('Time')
+# p.ylabel('Uncertainty on the fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('Evolution Fractal Dimension from R_g')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="delta_df.pdf"
+# p.savefig(cluster_name)
+ 
+# p.clf()    
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# choice_inf=s.where(s.isinf(std_err == 0))
+
+# axes.plot(time[choice_inf],std_err[choice_inf], "bo")
+# p.xlabel('Time')
+# p.ylabel('Std error on 1/fractal dimension')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# p.title('Standard error on 1/fractal dimension')
+# p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# cluster_name="std_err_1_over_D_f.pdf"
+# p.savefig(cluster_name)
+ 
+# p.clf()    
 
 
 
@@ -2611,24 +2384,24 @@
 df_time_aver_vec[:]=1./lin_fit2[0]
 
 
-fig = p.figure()
-axes = fig.gca()
-
-axes.errorbar(time,my_fractal_dim,yerr=delta_df, linewidth=2. )
-
-axes.errorbar(time,my_fractal_dim_small,yerr=delta_df_small,\
-          label="Fractal dimension for small clusters",linewidth=2.)
-
-axes.errorbar(time,my_fractal_dim_large,yerr=delta_df_large,\
-          label="Fractal dimension for small clusters",linewidth=2.)
-
-#p.ylim(1.4,2.6)
-#axes.legend()
-p.xlabel('Time')
-p.ylabel('Fractal dimension')
-p.savefig("time_evolution_D_f_with_error_bars_all.pdf")
+# fig = p.figure()
+# axes = fig.gca()
+
+# axes.errorbar(time,my_fractal_dim,yerr=delta_df, linewidth=2. )
+
+# axes.errorbar(time,my_fractal_dim_small,yerr=delta_df_small,\
+#           label="Fractal dimension for small clusters",linewidth=2.)
+
+# axes.errorbar(time,my_fractal_dim_large,yerr=delta_df_large,\
+#           label="Fractal dimension for small clusters",linewidth=2.)
+
+# #p.ylim(1.4,2.6)
+# #axes.legend()
+# p.xlabel('Time')
+# p.ylabel('Fractal dimension')
+# p.savefig("time_evolution_D_f_with_error_bars_all.pdf")
  
-p.clf()    
+# p.clf()    
 
 
 
@@ -3187,57 +2960,57 @@
 
 
 
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.errorbar(time,prefactor_small_R_raw,yerr=delta_prefactor_small_R_raw)
-axes.errorbar(time,prefactor_large_R_raw,yerr=delta_prefactor_large_R_raw)
-axes.errorbar(time,prefactor_R_raw,yerr=delta_prefactor_R_raw)
-
-# axes.errorbar(time[ini_conf:fin_conf],prefactor_small_R_raw,yerr=delta_prefactor_small_R_raw,"ko",\
-#               time[ini_conf:fin_conf],prefactor_large_R_raw,yerr=delta_prefactor_large_R_raw, "r+",\
-#               time[ini_conf:fin_conf],prefactor_R_raw,yerr=delta_prefactor_R_raw, "bx")
-
-
-#p.ylim(1.4,3.)
-p.xlabel('Time')
-p.ylabel('Raw prefactor and error bars')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-#p.title('Evolution Fractal Dimension from R_g')
-#p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-#cluster_name="std_err_D_f.pdf"
-p.savefig("time_evolution_raw_prefactor_with_error_bars.pdf")
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.errorbar(time,prefactor_small_R_raw,yerr=delta_prefactor_small_R_raw)
+# axes.errorbar(time,prefactor_large_R_raw,yerr=delta_prefactor_large_R_raw)
+# axes.errorbar(time,prefactor_R_raw,yerr=delta_prefactor_R_raw)
+
+# # axes.errorbar(time[ini_conf:fin_conf],prefactor_small_R_raw,yerr=delta_prefactor_small_R_raw,"ko",\
+# #               time[ini_conf:fin_conf],prefactor_large_R_raw,yerr=delta_prefactor_large_R_raw, "r+",\
+# #               time[ini_conf:fin_conf],prefactor_R_raw,yerr=delta_prefactor_R_raw, "bx")
+
+
+# #p.ylim(1.4,3.)
+# p.xlabel('Time')
+# p.ylabel('Raw prefactor and error bars')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# #p.title('Evolution Fractal Dimension from R_g')
+# #p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# #cluster_name="std_err_D_f.pdf"
+# p.savefig("time_evolution_raw_prefactor_with_error_bars.pdf")
  
-p.clf()    
-
-
-
-fig = p.figure()
-axes = fig.gca()
-
-
-axes.errorbar(time,prefactor_small_R,yerr=delta_prefactor_small_R)
-axes.errorbar(time,prefactor_large_R,yerr=delta_prefactor_large_R)
-axes.errorbar(time,prefactor_R,yerr=delta_prefactor_R)
-
-# axes.errorbar(time[ini_conf:fin_conf],prefactor_small_R_raw,yerr=delta_prefactor_small_R_raw,"ko",\
-#               time[ini_conf:fin_conf],prefactor_large_R_raw,yerr=delta_prefactor_large_R_raw, "r+",\
-#               time[ini_conf:fin_conf],prefactor_R_raw,yerr=delta_prefactor_R_raw, "bx")
-
-
-#p.ylim(1.4,3.)
-p.xlabel('Time')
-p.ylabel('Prefactor and error bars')
-#p.legend(('from power-law fitting','from linear fitting on log-log'))
-#p.title('Evolution Fractal Dimension from R_g')
-#p.grid(True)
-    #cluster_name="number_cluster_vs_time2%05d"%my_config
-#cluster_name="std_err_D_f.pdf"
-p.savefig("time_evolution_prefactor_with_error_bars.pdf")
+# p.clf()    
+
+
+
+# fig = p.figure()
+# axes = fig.gca()
+
+
+# axes.errorbar(time,prefactor_small_R,yerr=delta_prefactor_small_R)
+# axes.errorbar(time,prefactor_large_R,yerr=delta_prefactor_large_R)
+# axes.errorbar(time,prefactor_R,yerr=delta_prefactor_R)
+
+# # axes.errorbar(time[ini_conf:fin_conf],prefactor_small_R_raw,yerr=delta_prefactor_small_R_raw,"ko",\
+# #               time[ini_conf:fin_conf],prefactor_large_R_raw,yerr=delta_prefactor_large_R_raw, "r+",\
+# #               time[ini_conf:fin_conf],prefactor_R_raw,yerr=delta_prefactor_R_raw, "bx")
+
+
+# #p.ylim(1.4,3.)
+# p.xlabel('Time')
+# p.ylabel('Prefactor and error bars')
+# #p.legend(('from power-law fitting','from linear fitting on log-log'))
+# #p.title('Evolution Fractal Dimension from R_g')
+# #p.grid(True)
+#     #cluster_name="number_cluster_vs_time2%05d"%my_config
+# #cluster_name="std_err_D_f.pdf"
+# p.savefig("time_evolution_prefactor_with_error_bars.pdf")
  
-p.clf()    
+# p.clf()