Re: r27219 - /trunk/specific_analyses/relax_disp/api.py -- January 19, 2015 - 10:37

Hi,

That works, just use RelaxImplementError as in the base API.  That
would be the safest.  An implementation for the R2eff model is
unnecessary.

Regards,

Edward



On 19 January 2015 at 10:31, Troels Emtekær Linnet
<tlinnet@xxxxxxxxxxxxx> wrote:
> Hi Edward.
>
> I was wondering to throw an error, if the R2eff model is used with this.
>
> Best
> Troels
>
> 2015-01-19 10:17 GMT+01:00 Edward d'Auvergne <edward@xxxxxxxxxxxxx>:
> > Hi Troels,
> >
> > This still has the same problems with the R2eff model as mentioned at
> > http://thread.gmane.org/gmane.science.nmr.relax.scm/24963/focus=7490.
> >
> > Regards,
> >
> > Edward
> >
> >
> >
> > On 17 January 2015 at 17:25,  <tlinnet@xxxxxxxxxxxxx> wrote:
> > > Author: tlinnet
> > > Date: Sat Jan 17 17:25:20 2015
> > > New Revision: 27219
> > >
> > > URL: http://svn.gna.org/viewcvs/relax?rev=27219&view=rev
> > > Log:
> > > Added API function in relaxation dispersion to return error structure
> > > from the reduced chi2 distribution.
> > >
> > > Task #7882 (https://gna.org/task/?7882): Implement Monte-Carlo
> > > simulation, where errors are generated with width of standard deviation 
> > > or
> > > residuals.): Implement Monte-Carlo simulation, where errors are generated
> > > with width of standard deviation or residuals.
> > >
> > > Modified:
> > >     trunk/specific_analyses/relax_disp/api.py
> > >
> > > Modified: trunk/specific_analyses/relax_disp/api.py
> > > URL:
> > > http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/api.py?rev=27219&r1=27218&r2=27219&view=diff
> > >
> > > ==============================================================================
> > > --- trunk/specific_analyses/relax_disp/api.py   (original)
> > > +++ trunk/specific_analyses/relax_disp/api.py   Sat Jan 17 17:25:20 2015
> > > @@ -27,7 +27,7 @@
> > >  # Python module imports.
> > >  import bmrblib
> > >  from copy import deepcopy
> > > -from numpy import int32, zeros
> > > +from numpy import int32, sqrt, zeros
> > >  from re import match, search
> > >  import string
> > >  import sys
> > > @@ -1035,6 +1035,44 @@
> > >
> > >          # Return the error list.
> > >          return errors
> > > +
> > > +
> > > +    def return_error_red_chi2(self, data_id=None):
> > > +        """Return the standard deviation data structure, where standard
> > > deviation is from the overall gauss distribution described by the 
> > > STD_fit of
> > > the goodness of fit, where STD_fit = sqrt(chi2/(N-p))
> > > +
> > > +        @param data_id: The tuple of the spin container and the
> > > exponential curve identifying key, as yielded by the base_data_loop()
> > > generator method.
> > > +        @type data_id:  SpinContainer instance and float
> > > +        @return:        The standard deviation data structure.
> > > +        @rtype:         list of float
> > > +        """
> > > +
> > > +        # Get the errors structure as above.
> > > +        errors = self.return_error(data_id=data_id)
> > > +
> > > +        # Unpack the data.
> > > +        spin, spin_id = data_id
> > > +
> > > +        # Loop over the spin groupings for the model.
> > > +        for spin_ids in self.model_loop():
> > > +            # If the spin of interest is in the returned spin cluster.
> > > +            if spin_id in spin_ids:
> > > +                # Get the statistics
> > > +                k, n, chi2 = self.model_statistics(model_info=spin_ids)
> > > +
> > > +                # Calculate degrees of freedom.
> > > +                dof = n - k
> > > +
> > > +                # Calculate reduced chi2, or named as the variance of
> > > the squared residuals.
> > > +                red_chi2 = chi2 / float(dof)
> > > +
> > > +                # Calculated the standard deviation.
> > > +                std_red_chi2 = sqrt(red_chi2)
> > > +
> > > +                # Replace values with the stored value.
> > > +                for id in errors:
> > > +                    errors[id] = std_red_chi2
> > > +
> > > +                return errors
> > >
> > >
> > >      def return_value(self, spin, param, sim=None, bc=False):
> > >
> > >
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