relax SVN archive

Moved intensity negative value from reference to CPMG point.

If math domain errors are found when calculating the the two point R2eff values, the point is being skipped.

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

Epydoc docstring fixes for many methods in the relaxation dispersion auto-analysis module.

Epydoc fixes for the pipe_control.mol_res_spin.format_info_full() function.

Reintroduced the original target_functions.relax_fit.jacobian() function.

The new function for the Jacobian of the chi-squared function has been renamed to
target_functions.relax_fit.jacobian_chi2() so that both Python functions are accessible within the C
module.

An attempt at documenting the Monte Carlo simulation verses covariance matrix error estimates.

This is for the R2eff and I0 parameters of the exponential curves. For the Monte Carlo errors,
10000 simulations were preformed. This means that these errors can perform as a gold standard by
which to judge the covariance matrix technique.

Currently it can be seen that the relax_disp.r2eff_err_estimate user function with the chi2_jacobian
flag set to True performs extremely poorly.

Implemented systemtest: test_bug_negative_intensities_cpmg, to show lack of error message to user.

Maybe these spins should be de-selected, or at least show a better warning.

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

Moved unnessary function in R2eff error estimate module into experimental class.

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

Replaced the way to calculate the chi2 Jacobian, for exponential fit in minfx.

This is only for the test class, but reuses library code.

This should make it much easier in the future to implement chi2 gradient functions to minfx, since it
is only necessary to implement the direct gradient of the function, and then pass the direct gradient to chi2 library, which turn it into
the chi2 gradient function which minfx use.

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

Tried implementing getting the chi2 gradient, using target_function.chi2.dchi2().

The output seem equal.

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