r25849 - in /trunk: auto_analyses/relax_disp_repeat_cpmg.py test_suite/system_tests/relax_disp.py -- September 15, 2014 - 23:04

Author: tlinnet
Date: Mon Sep 15 23:04:25 2014
New Revision: 25849

URL: http://svn.gna.org/viewcvs/relax?rev=25849&view=rev
Log:
Adding another statistic method to plot for multi-data sets.

Task #7826 (https://gna.org/task/index.php?7826): Write an python class for 
the repeated analysis of dispersion data.

Modified:
    trunk/auto_analyses/relax_disp_repeat_cpmg.py
    trunk/test_suite/system_tests/relax_disp.py

Modified: trunk/auto_analyses/relax_disp_repeat_cpmg.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/auto_analyses/relax_disp_repeat_cpmg.py?rev=25849&r1=25848&r2=25849&view=diff
==============================================================================
--- trunk/auto_analyses/relax_disp_repeat_cpmg.py       (original)
+++ trunk/auto_analyses/relax_disp_repeat_cpmg.py       Mon Sep 15 23:04:25 
2014
@@ -1183,7 +1183,13 @@
 
                 r = sum( (x - x_m)*(y - y_m)  ) / sqrt( sum((x - x_m)**2) * 
sum((y - y_m)**2) )
 
-                #print method_ref, method_cur, glob_ini, 
pearsons_correlation_coefficient, r
+
+                # Solve by linear least squares.
+                n = len(y)
+                b = (sum(x*y) - 1./n * sum(x) * sum(y) ) / ( sum(x**2) - 
1./n * (sum(x))**2 )
+                a = 1./n * sum(y) - b * 1./n * sum(x)
+
+                print(method_ref, method_cur, glob_ini, 
pearsons_correlation_coefficient, r, b, a)
 
                 # Get the normalised array.
                 r2eff_norm_arr = r2eff_arr/r2eff_arr_ref

Modified: trunk/test_suite/system_tests/relax_disp.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/test_suite/system_tests/relax_disp.py?rev=25849&r1=25848&r2=25849&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Mon Sep 15 23:04:25 2014
@@ -6048,7 +6048,7 @@
 
 
         # Try plot some R2eff statistics.
-        if False:
+        if True:
             # Collect r2eff values.
             selection = ':2,3'
             r2eff_ft_sel = RDR.col_r2eff(method='FT', list_glob_ini=[128, 
126], selection=selection)