Commit: 8880adc2f05d8e03cbcee07be81f106a11dfaa8a - myprojects-hg-reborn (hg)

Commit MetaInfo

• Revision: 8880adc2f05d8e03cbcee07be81f106a11dfaa8a (tree)
• Zeit: 2008-08-03 07:55:30
• Autor: iselllo
• Commiter: iselllo

Log Message

I can now choose freely a second time span along which I can calculate
the kernel element.

Ändern Zusammenfassung

• modified: Python-codes/plot_kernel_elements.py

Diff

diff -r c9d06e7e1e13 -r 8880adc2f05d Python-codes/plot_kernel_elements.py
--- a/Python-codes/plot_kernel_elements.py	Fri Aug 01 09:23:57 2008 +0000
+++ b/Python-codes/plot_kernel_elements.py	Sat Aug 02 22:55:30 2008 +0000
@@ -41,7 +41,7 @@
 
 sel_ker=s.arange(2)
 sel_ker[0]=2
-sel_ker[1]=2
+sel_ker[1]=3
 
 
 sel_ker=sel_ker*1.  #to get a floating point array
@@ -329,6 +329,7 @@
  
 p.clf()
 
+p.save("residuals.dat",fitted_kernel[1])
 
 
 fig = p.figure()
@@ -347,5 +348,58 @@
  
 p.clf()
 
+#Now I do as above but I specify a time-span
+
+time_inf=0.
+time_sup=100.
+
+time_span=s.where((time_coll>time_inf) & (time_coll<time_sup))
+
+
+fitted_kernel=residuals_eval(coll_dens[time_span], n_in_j[time_span], k_ij_inf, k_ij_sup, n_steps)
+
+print "fitted_kernel is, ", fitted_kernel[0]
+
+
+
+
+fig = p.figure()
+axes = fig.gca()
+
+
+axes.plot(time_coll[time_span],coll_dens[time_span], "bo", label="n_ij")
+
+axes.plot(time_coll[time_span],fitted_kernel[0]*n_in_j[time_span], "k^", label="k_{ij}n_in_j")
+
+
+p.xlabel('Time')
+p.ylabel('i-mers and j-mers collisions per unit volume and time')
+#p.title("Evolution Mean-free path")
+p.grid(True)
+cluster_name="i-mers_and_j-mers_collisions_and_fit_vs_time_time_span.pdf"
+axes.legend()
+p.savefig(cluster_name)
+ 
+p.clf()
+
+
+
+fig = p.figure()
+axes = fig.gca()
+
+
+axes.plot(s.linspace(k_ij_inf,k_ij_sup, n_steps),fitted_kernel[1], "bo", label="residuals")
+
+p.xlabel('Value of k_{ij}')
+p.ylabel('Squared sum of the residuals')
+#p.title("Evolution Mean-free path")
+p.grid(True)
+cluster_name="residuals_vs_k_ij_time_span.pdf"
+axes.legend()
+p.savefig(cluster_name)
+ 
+p.clf()
+
+p.save("residuals_span.dat", fitted_kernel[1])
 
 print "So far so good"