relax SVN archive

Inserted checks for C module is available in module for estimateing R2eff error.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Moved code around, and made function multifit_covar() independent of Class object.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Cleaned up code in R2eff error module. Also removed a non working Hessian matrix.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Added several tests to: test_estimate_r2eff_error, to compare different output from algorithms.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Fix to systemtest test_estimate_r2eff_error(), to first delete the old error estimations.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Added back-end to estimate R2eff errors.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Modified systemtest Relax_disp.test_estimate_r2eff() to first do a Grid Search, then minimise and then estimate the errors for R2eff and i0.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Added method to automatically perform error analysis on peak heights.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Added front-end to the new user function relax_disp.r2eff_err_estimate(), which will estimate the R2eff errors
from a pipe and spins with optimised values of R2eff and i0.

The co-variance matrix can be calculated from the optimised parameters, and the Jacobian.

Big care should be taken not to directly trust these results, since the errors are quite different compared to the Monte-Carlo simulations.

This implementation, will reach the exact same error estimation as scipy.optimize.leastsq.

But with much better control over the data, and insight into the calculations.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.

Removed the user function to estimate the R2eff values and errors with scipy.optimize.leastsq.

With the newly implemented Jacobian and Hessian of the exponential decay function, the front-end to
scipy.optimize.leastsq does not serve a purpose.

This is because minfx is now as fast as scipy.optimize.leastsq, and can estimate the errors from the
Jacobian to the exact same numbers as scipy.optimize.leastsq.

In addition to that, the covariance can be calculated by QR decomposition.
This adds additional feature for checking for a singular matrix.

The back-end wilÃl still be kept in place for the coming tim, but could be removed later.

task #7822(https://gna.org/task/index.php?7822): Implement user function to estimate R2eff and associated errors for exponential curve fitting.