Re: r25429 - /trunk/specific_analyses/relax_disp/estimate_r2eff.py -- August 29, 2014 - 21:22

Cheers.

2014-08-29 17:15 GMT+02:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:
> Hi,
>
> I've now reintroduced the old C module
> target_function.relax_fit.jacobian() function.  See r25429
> (http://article.gmane.org/gmane.science.nmr.relax.scm/23188).  I also
> renamed target_function.relax_fit.jacobian() to
> target_function.relax_fit.jacobian_chi2().
>
> Regards,
>
> Edward
>
>
> On 29 August 2014 12:40,  <tlinnet@xxxxxxxxxxxxx> wrote:
> > 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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> >
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