r26856 - in /trunk: specific_analyses/relax_disp/ test_suite/system_tests/ -- November 29, 2014 - 19:23

Author: bugman
Date: Sat Nov 29 19:23:11 2014
New Revision: 26856

URL: http://svn.gna.org/viewcvs/relax?rev=26856&view=rev
Log:
Fixes for the relaxation dispersion analysis for the recent relaxation 
curve-fitting analysis changes.

The Relax_fit_opt target function class requires the model argument to be 
supplied to be correctly
set up.


Modified:
    trunk/specific_analyses/relax_disp/estimate_r2eff.py
    trunk/specific_analyses/relax_disp/optimisation.py
    trunk/test_suite/system_tests/relax_disp.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=26856&r1=26855&r2=26856&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/estimate_r2eff.py        (original)
+++ trunk/specific_analyses/relax_disp/estimate_r2eff.py        Sat Nov 29 
19:23:11 2014
@@ -119,7 +119,7 @@
 
             # Initialise data in C code.
             scaling_list = [1.0, 1.0]
-            model = Relax_fit_opt(num_params=len(param_vector), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
+            model = Relax_fit_opt(model='exp', num_params=len(param_vector), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
 
             # Use the direct Jacobian from function.
             jacobian_matrix_exp = transpose(asarray( 
model.jacobian(param_vector) ) )
@@ -789,7 +789,7 @@
 
         # Initialise the function to minimise.
         scaling_list = [1.0, 1.0]
-        model = Relax_fit_opt(num_params=len(x0), values=E.values, 
errors=E.errors, relax_times=E.times, scaling_matrix=scaling_list)
+        model = Relax_fit_opt(model='exp', num_params=len(x0), 
values=E.values, errors=E.errors, relax_times=E.times, 
scaling_matrix=scaling_list)
 
         # Define function to minimise for minfx.
         t_func = model.func

Modified: trunk/specific_analyses/relax_disp/optimisation.py
URL: 
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/optimisation.py?rev=26856&r1=26855&r2=26856&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/optimisation.py  (original)
+++ trunk/specific_analyses/relax_disp/optimisation.py  Sat Nov 29 19:23:11 
2014
@@ -92,7 +92,7 @@
         scaling_list.append(1.0)
 
     # Initialise the relaxation fit functions.
-    model = Relax_fit_opt(num_params=len(spin.params), values=values, 
errors=errors, relax_times=times, scaling_matrix=scaling_list)
+    model = Relax_fit_opt(model='exp', num_params=len(spin.params), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
 
     # Make a single function call.  This will cause back calculation and the 
data will be stored in the C module.
     model.func(param_vector)
@@ -403,7 +403,7 @@
                     scaling_list.append(scaling_matrix[i, i])
 
             # Initialise the function to minimise.
-            model = Relax_fit_opt(num_params=len(param_vector), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
+            model = Relax_fit_opt(model='exp', num_params=len(param_vector), 
values=values, errors=errors, relax_times=times, scaling_matrix=scaling_list)
 
             # Grid search.
             if search('^[Gg]rid', min_algor):

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=26856&r1=26855&r2=26856&view=diff
==============================================================================
--- trunk/test_suite/system_tests/relax_disp.py (original)
+++ trunk/test_suite/system_tests/relax_disp.py Sat Nov 29 19:23:11 2014
@@ -3591,7 +3591,7 @@
                         I_err = asarray(I_err)
 
                         x0 = [r2eff, i0]
-                        model = Relax_fit_opt(num_params=len(x0), 
values=I_err, errors=errors, relax_times=times, scaling_matrix=scaling_list)
+                        model = Relax_fit_opt(model='exp', 
num_params=len(x0), values=I_err, errors=errors, relax_times=times, 
scaling_matrix=scaling_list)
 
                         params_minfx_sim_j, chi2_minfx_sim_j, iter_count, 
f_count, g_count, h_count, warning = generic_minimise(func=model.func, 
dfunc=model.dfunc, d2func=model.d2func, args=(), x0=x0, min_algor=min_algor, 
min_options=min_options, full_output=True, print_flag=0)
                         R_m_sim_j, I0_m_sim_j = params_minfx_sim_j
@@ -3730,7 +3730,7 @@
         errors = array([  9.48032653,  11.34093541,   9.35149017,  
10.84867928,  12.17590736])
 
         scaling_list = [1.0, 1.0]
-        model = Relax_fit_opt(num_params=2, values=I, errors=errors, 
relax_times=times, scaling_matrix=scaling_list)
+        model = Relax_fit_opt(model='exp', num_params=2, values=I, 
errors=errors, relax_times=times, scaling_matrix=scaling_list)
 
         R = - 500.
         I0 = 1000.