Does it give the same small result? Try turning off parameter scaling. Regards,
Edward On 27 August 2014 11:29, <[email protected]> wrote: > Author: tlinnet > Date: Wed Aug 27 11:29:24 2014 > New Revision: 25330 > > URL: http://svn.gna.org/viewcvs/relax?rev=25330&view=rev > Log: > Tried to implement the Jacobian from C-code. > > This though also report errors which are to small. > > Maybe some scaling is wrong. > > 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=25330&r1=25329&r2=25330&view=diff > ============================================================================== > --- trunk/specific_analyses/relax_disp/estimate_r2eff.py (original) > +++ trunk/specific_analyses/relax_disp/estimate_r2eff.py Wed Aug 27 > 11:29:24 2014 > @@ -42,7 +42,7 @@ > from specific_analyses.relax_disp.variables import MODEL_R2EFF > from specific_analyses.relax_fit.optimisation import func_wrapper, > dfunc_wrapper, d2func_wrapper > from target_functions.chi2 import chi2_rankN > -from target_functions.relax_fit import setup > +from target_functions.relax_fit import jacobian, setup > > > # Scipy installed. > @@ -734,7 +734,7 @@ > E.set_settings_minfx(min_algor=min_algor) > > # Do C code > - do_C = False > + do_C = True > > if do_C: > # Initialise the function to minimise. > @@ -766,19 +766,27 @@ > param_vector, chi2, iter_count, f_count, g_count, h_count, warning = > results_minfx > > # Get the Jacobian. > - # First make a call to the Jacobian function, which store it in the > class. > - E.func_exp_grad(params=param_vector) > - jacobian_matrix = deepcopy(E.jacobian_matrix) > - > + if do_C: > + # First make a call to the Jacobian function, which store it in the > class. > + jacobian_matrix = transpose(asarray( jacobian(param_vector) ) ) > + > + # Compare with python code. > + #E.func_exp_grad(params=param_vector) > + #jacobian_matrix2 = deepcopy(E.jacobian_matrix) > + #print jacobian_matrix > + #print " " > + #print jacobian_matrix2 > + else: > + jacobian_matrix = deepcopy(E.jacobian_matrix) > + > + # Get the co-variance > + pcov = E.multifit_covar(J=jacobian_matrix) > + > + # To compute one standard deviation errors on the parameters, take the > square root of the diagonal covariance. > + param_vector_error = sqrt(diag(pcov)) > # Set error to inf. > #param_vector_error = [inf, inf] > > - # Get the co-variance > - pcov = E.multifit_covar(J=jacobian_matrix) > - > - # To compute one standard deviation errors on the parameters, take the > square root of the diagonal covariance. > - param_vector_error = sqrt(diag(pcov)) > - > # Pack to list. > results = [param_vector, param_vector_error, chi2, iter_count, f_count, > g_count, h_count, warning] > > > > _______________________________________________ > relax (http://www.nmr-relax.com) > > This is the relax-commits mailing list > [email protected] > > To unsubscribe from this list, get a password > reminder, or change your subscription options, > visit the list information page at > https://mail.gna.org/listinfo/relax-commits _______________________________________________ relax (http://www.nmr-relax.com) This is the relax-devel mailing list [email protected] To unsubscribe from this list, get a password reminder, or change your subscription options, visit the list information page at https://mail.gna.org/listinfo/relax-devel
