relax SVN archive

Added a script and log file for calculating the numerical Jacobian for an exponential curve.

This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and
calculates the Jacobian using the numdifftools.Jacobian object construct and obtain the matrix, both
at the minimum and at a point away from the minimum. The values will be used to construct a unit
test to check the C module implementation.

The target_functions.relax_fit C module Python function jacobian_chi2() is now exposed.

This was previously not visible from within Python.

Comparison of 10,000 Monte Carlo simulations to a different covariance matrix error estimate.

The covariance_matrix.py script has been duplicated and the chi2_jacobian argument of the
relax_disp.r2eff_err_estimate user function has been changed from True to False. As can be seen in
the 2D Grace plots, this error estimate is incredibly different.

The R2eff errors are overestimated by a factor of 1.9555, which indicates that the Jacobian or
covariance matrix formula are not yet correct.

Modified systemtest test_bug_negative_intensities_cpmg, to prepare for testing number of R2eff points.

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

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.