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
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