r25429 - /trunk/specific_analyses/relax_disp/estimate_r2eff.py -- August 29, 2014 - 12:40

Author: tlinnet
Date: Fri Aug 29 12:40:07 2014
New Revision: 25429

URL: http://svn.gna.org/viewcvs/relax?rev=25429&view=rev
Log:
Swithced in estimate_r2eff_err() to use the chi2 Jacobian from C code, and 
Jacobian from python code.

task #7822(https://gna.org/task/index.php?7822): Implement user function to 
estimate R2eff and associated errors for exponential curve fitting.

Modified:
    trunk/specific_analyses/relax_disp/estimate_r2eff.py

Modified: trunk/specific_analyses/relax_disp/estimate_r2eff.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/estimate_r2eff.py?rev=25429&r1=25428&r2=25429&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Fri Aug 29 
12:40:07 2014
@@ -175,7 +175,7 @@
             i0 = getattr(cur_spin, 'i0')[param_key]
 
             # Pack data
-            params = [r2eff, i0]
+            param_vector = [r2eff, i0]
 
             # The peak intensities, errors and times.
             values = []
@@ -193,15 +193,18 @@
 
             # Initialise data in C code.
             scaling_list = [1.0, 1.0]
-            setup(num_params=len(params), num_times=len(times), 
values=values, sd=errors, relax_times=times, scaling_matrix=scaling_list)
-
+            setup(num_params=len(param_vector), num_times=len(times), 
values=values, sd=errors, relax_times=times, scaling_matrix=scaling_list)
+
+            # Determine Jacobian and weights.
             if chi2_jacobian:
-                jacobian_matrix_exp = func_exp_chi2_grad(params=params, 
times=times, values=values, errors=errors)
+                # Calculate the direct exponential Jacobian matrix from C 
code.
+                jacobian_matrix_exp = transpose(asarray( 
jacobian(param_vector) ) )
+
+                # The Jacobian in the C-code is from chi2 function, and is 
already weighted.
                 weights = ones(errors.shape)
             else:
-                # Calculate the direct exponential Jacobian matrix from C 
code.
-                #jacobian_matrix_exp = transpose(asarray( jacobian(params) ) 
)
-                jacobian_matrix_exp = func_exp_grad(params=params, 
times=times, values=values, errors=errors)
+                # Use the direct Jacobian from python Code
+                jacobian_matrix_exp = func_exp_grad(params=param_vector, 
times=times, values=values, errors=errors)
                 weights = 1. / errors**2
 
             # Get the co-variance