Commit: a0ba34107497f072988cf7a7c9cf7cd73550b97d - myprojects-hg-reborn (hg)

Commit MetaInfo

• Revisão: a0ba34107497f072988cf7a7c9cf7cd73550b97d (tree)
• Hora: 2008-01-30 07:46:00
• Autor: iselllo
• Commiter: iselllo

Mensagem de Log

I added r_g_time_aver.py which calculates the scaling law for N vs R_g
taking also a time average and selecting only the clusters which appear
at least a certain (to be specified by the user) number of times.

Mudança Sumário

• add: Python-codes/r_g_time_aver.py

Diff

diff -r 662186348ea3 -r a0ba34107497 Python-codes/r_g_time_aver.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Python-codes/r_g_time_aver.py	Tue Jan 29 22:46:00 2008 +0000
@@ -0,0 +1,182 @@
+#! /usr/bin/env python
+
+import scipy as s
+from scipy import stats #I need this module for the linear fit
+import numpy as n
+import pylab as p
+#import rpy as r
+
+
+ini_conf=50  
+fin_conf=1000   #configurations I read initially in read_test.tcl
+
+
+
+
+
+
+
+
+#my_config=19 #here labels the saved configurations but it is not directly the number
+# of the file with the saved configuration
+
+by=50 #I need it to select the elements from time.dat
+
+ini_conf=ini_conf/by
+fin_conf=fin_conf/by
+
+# time=p.load("time.dat")
+# #print "time (initially read) is, ", time
+# print "by is, ", by
+# print "len(time) is, ", len(time)
+# pick_time=s.arange(0,len(time),by)
+# print "pick_time is, ", pick_time
+# time=time[pick_time]
+
+
+
+cluster_long=s.zeros(1)
+r_gyr_long=s.zeros(1)
+
+for my_config in xrange(ini_conf,fin_conf):
+
+
+    my_config=my_config*by
+
+    cluster_name="cluster_dist%05d"%my_config
+
+    n_comp=p.load(cluster_name)
+
+    #print "n_comp is,", n_comp
+
+    my_choice=s.where(n_comp>6.) #I do not calculate the radius of gyration of monomers!
+    n_comp=n_comp[my_choice] # select from dimer on
+
+    #print 'my_choice is, ', my_choice
+
+    cluster_name="R_gyr_dist%05d"%my_config
+
+    cluster_long=s.concatenate((cluster_long,n_comp))
+    r_gyr_dist=p.load(cluster_name)
+
+    r_gyr_dist=r_gyr_dist[my_choice]
+    r_gyr_long=s.concatenate((r_gyr_long,r_gyr_dist))
+    
+
+cluster_long=cluster_long[1:]
+r_gyr_long=r_gyr_long[1:]
+
+lin_fit=stats.linregress(s.log10(cluster_long),s.log10(r_gyr_long))
+
+print "the fractal dimension is, ", 1/lin_fit[0]
+    
+
+p.loglog(cluster_long,r_gyr_long, "ro")
+p.grid(True)
+p.savefig("all_together.pdf")
+p.clf()
+
+
+
+cluster_ord=s.sort(cluster_long)
+r_gyr_ord=r_gyr_long[s.argsort(cluster_long)]
+
+cluster_uni=n.unique1d(cluster_ord)
+r_bound=cluster_ord.searchsorted(cluster_uni,side='right')
+l_bound=cluster_ord.searchsorted(cluster_uni,side='left')
+
+print "the length of r_bound is, ", len(r_bound)
+
+r_gyr_ari_mean=s.zeros(len(cluster_uni))
+r_gyr_ari_mean_log=s.zeros(len(cluster_uni))
+
+for i in xrange(0,len(cluster_uni)):
+    r_gyr_ari_mean[i]=r_gyr_ord[l_bound[i]:r_bound[i]].mean()
+    r_gyr_ari_mean_log[i]=s.exp(s.sum(s.log(r_gyr_ord[l_bound[i]:r_bound[i]]))\
+                                    /len(r_gyr_ord[l_bound[i]:r_bound[i]]))
+
+
+lin_fit2=stats.linregress(s.log10(cluster_uni),s.log10(r_gyr_ari_mean))
+
+my_fit2=lin_fit2[1]+lin_fit2[0]*s.log10(cluster_uni)
+
+print "the fractal dimension [arithmetic mean] is, ", 1/lin_fit2[0]
+
+
+lin_fit3=stats.linregress(s.log10(cluster_uni),s.log10(r_gyr_ari_mean_log))
+
+my_fit3=lin_fit3[1]+lin_fit3[0]*s.log10(cluster_uni)
+
+print "the fractal dimension [log mean] is, ", 1/lin_fit3[0]
+
+
+
+p.plot(s.log10(cluster_uni),s.log10(r_gyr_ari_mean), "bo",\
+       s.log10(cluster_uni), my_fit2,linewidth=2.)
+p.xlabel('N')
+p.ylabel('R(N)')
+#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="Fractal_dimension_from_mean_and_time.pdf"
+p.savefig(cluster_name)
+p.hold(False)
+p.clf()    
+
+
+
+
+
+
+p.plot(s.log10(cluster_uni),s.log10(r_gyr_ari_mean_log), "bo",\
+       s.log10(cluster_uni), my_fit3,linewidth=2.)
+p.xlabel('N')
+p.ylabel('R(N)')
+#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="Fractal_dimension_from_mean_log_and_time.pdf"
+p.savefig(cluster_name)
+p.hold(False)
+p.clf()    
+
+
+
+
+count_number=s.zeros(len(r_bound))
+
+for i in xrange(0,len(r_bound)):
+    count_number[i]=r_bound[i]-l_bound[i]  #Now I counted how many clusters I have
+                                          #for each existing size
+survival=s.where(count_number>10.)
+
+cluster_surv=cluster_uni[survival]
+r_log_surv=r_gyr_ari_mean_log[survival]
+
+
+
+lin_fit4=stats.linregress(s.log10(cluster_surv),s.log10(r_log_surv))
+
+my_fit4=lin_fit4[1]+lin_fit4[0]*s.log10(cluster_surv)
+
+print "the fractal dimension [selective log mean] is, ", 1./lin_fit4[0]
+
+
+p.plot(s.log10(cluster_surv),s.log10(r_log_surv), "bo",\
+       s.log10(cluster_surv), my_fit4,linewidth=2.)
+p.xlabel('N')
+p.ylabel('R(N)')
+#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="Fractal_dimension_from_mean_log_and_time_selection.pdf"
+p.savefig(cluster_name)
+p.hold(False)
+p.clf()    
+
+
+
+print "So far so good"