Commit: 9c25f3895466837d55cf2f13aefe813063fd1df7 - myprojects-hg-reborn (hg)

Commit MetaInfo

• Revisión: 9c25f3895466837d55cf2f13aefe813063fd1df7 (tree)
• Tiempo: 2008-01-29 19:22:18
• Autor: iselllo
• Commiter: iselllo

Log Message

I corrected a bug in plot_statistics.py: y_pos2 and z_pos2 were defined
in terms of x_arr instead of y_arr and z_arr respectively!

Cambiar Resumen

• modified: Python-codes/plot_statistics.py

Diferencia incremental

diff -r 143021913bb3 -r 9c25f3895466 Python-codes/plot_statistics.py
--- a/Python-codes/plot_statistics.py	Tue Jan 22 14:22:55 2008 +0000
+++ b/Python-codes/plot_statistics.py	Tue Jan 29 10:22:18 2008 +0000
@@ -107,10 +107,6 @@
 	   
 	print "the length of tot_config is, ", len(tot_config)
 	tot_config=s.reshape(tot_config,(n_config,3*n_part))
-# 	#I swap two columns as a test:
-# 	tot_config[:,[0,3]]=tot_config[:,[3,0]]
-# 	tot_config[:,[1,4]]=tot_config[:,[4,1]]
-# 	tot_config[:,[2,5]]=tot_config[:,[5,2]]
 
 # 	print "tot_config at line 10 is, ", tot_config[10,:]
 
@@ -771,56 +767,6 @@
             radius=s.sqrt(var_x_arr+var_y_arr+var_z_arr)
             return radius
 
-#         def calc_radius2(x_arr,y_arr,z_arr,Len):
-#             #here x_arr is one-dimensional corresponding to a single configuration
-#             r_0j=s.zeros((len(x_arr)-1))
-#             for j in xrange(1,len(x_arr)): #so, particle zero is now the reference particle
-#                 r_0j[j-1]=x_arr[0]-x_arr[j]
-#                 r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
-#                 #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
-#                 #print 'i and j are, ', (i+1), (j+1)
-
-
-#             #Now I re-define the x_arr in order to be able to take tha variance correctly
-#             x_arr[0]=0.
-#             x_arr[1:n_part]=r_0j
-
-#             #var_x_arr[:]=s.var(r_0j, axis=1)
-#             var_x_arr=s.var(x_arr)
-
-#             for j in xrange(1,len(y_arr)): #so, particle zero is now the reference particle
-#                 r_0j[j-1]=y_arr[0]-y_arr[j]
-#                 r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
-#                 #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
-#                 #print 'i and j are, ', (i+1), (j+1)
-
-
-#             #Now I re-define the x_arr in order to be able to take tha variance correctly
-#             y_arr[0]=0.
-#             y_arr[1:n_part]=r_0j
-
-#             #var_x_arr[:]=s.var(r_0j, axis=1)
-#             var_y_arr=s.var(y_arr)
-
-
-
-#             for j in xrange(1,len(z_arr)): #so, particle zero is now the reference particle
-#                 r_0j[j-1]=z_arr[0]-z_arr[j]
-#                 r_0j[j-1]=-(r_0j[j-1]-Len*n.round(r_0j[j-1]/Len))
-#                 #r_ij[count_int]=-r_ij[count_int] #I have better reverse the signs now.
-#                 #print 'i and j are, ', (i+1), (j+1)
-
-
-#             #Now I re-define the x_arr in order to be able to take tha variance correctly
-#             z_arr[0]=0.
-#             z_arr[1:n_part]=r_0j
-
-#             #var_x_arr[:]=s.var(r_0j, axis=1)
-#             var_z_arr=s.var(z_arr)
-
-#             my_dist=s.sqrt(x_arr**2.+y_arr**2.+z_arr**2.)
-#             return my_dist
-
 
 
 
@@ -838,7 +784,6 @@
 	for i in xrange(0,n_config):
 		test_arr=tot_config[i,:]
 		test_arr=s.reshape(test_arr,(n_part,3))
-		
 # 		if (i==14):
 # 			p.save("test_14.dat",test_arr)
 		#dist_mat=r.distance(test_arr)
@@ -846,11 +791,10 @@
 		y_pos=y_arr[i,:]
 		z_pos=z_arr[i,:]
 		dist_mat=d_calc.distance_calc(x_pos,y_pos,z_pos, box_size)
-# 		if (i==10):
-# 			p.save("distance_matrix_10.dat",dist_mat)
+# 		if (i==71):
+# 			p.save("distance_matrix_71.dat",dist_mat)
 # 			p.save("x_pos_71.dat",x_pos)
-# 			dist_save=calc_radius2(x_pos,y_pos,z_pos,box_size)
-# 			p.save("distances_from_reference_10.dat",dist_save)
+
 
 		
                 clust_struc= (r.mycluster2(dist_mat,threshold)) #a cumbersome
@@ -876,7 +820,6 @@
                 cluster_name="hist_number_cluster%05d"%my_selection[i]
                 p.savefig(cluster_name)
                 p.hold(False)
-		p.clf()
 
                 #Now I re-organize the particles in my configuration
                 #by putting together those which are in the same
@@ -886,16 +829,13 @@
                 r_gyr_dist=s.zeros(len(my_cluster)) #this will contain the distribution of
                 #the calculated radia of gyration
 
-                #dist_save=s.zeros(len(my_cluster))
-
-
                 my_sum=s.cumsum(my_cluster)
                 f=s.arange(1) #simply 0 but as an array!
                 my_lim=s.concatenate((f,my_sum))
                 for m in xrange(0,len(my_cluster)):
                     x_pos2=x_arr[i,part_in_clust[my_lim[m]:my_lim[m+1]]]
-                    y_pos2=x_arr[i,part_in_clust[my_lim[m]:my_lim[m+1]]]
-                    z_pos2=x_arr[i,part_in_clust[my_lim[m]:my_lim[m+1]]]
+                    y_pos2=y_arr[i,part_in_clust[my_lim[m]:my_lim[m+1]]]
+                    z_pos2=z_arr[i,part_in_clust[my_lim[m]:my_lim[m+1]]]
                     r_gyr_dist[m]=calc_radius(x_pos2,y_pos2,z_pos2,box_size)
 
 
@@ -906,7 +846,7 @@
                 cluster_name="hist_radius_gyration%05d"%my_selection[i]
                 p.savefig(cluster_name)
                 p.hold(False)
-                p.clf()
+                
 # 	print 'the evolution of the number of clusters is, ', n_clusters
 
 	p.save("number_cluster.dat",n_clusters)
@@ -919,7 +859,7 @@
 	p.grid(True)
 	p.savefig('number_clusters_vs_time.pdf')
 	p.hold(False)
-	p.clf()
+	
 
 # 	p.plot(time, min_dist,linewidth=2.)
 # 	p.xlabel('time')