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.
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