relax SVN archive

Implementation of the target_functions.relax_fit.jacobian() function.

This follows from the discussions at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807.

The function will calculate the Jacobian matrix for the exponential curve-fitting module. The
Jacobian can be used to directly calculate the covariance matrix, for example as described at
https://www.gnu.org/software/gsl/manual/html_node/Computing-the-covariance-matrix-of-best-fit-parameters.html.
The Jacobian is calculated using the help of the new exponential_dI() and exponential_dR() functions
in the target_functions/exponential.c file. These calculate the partial derivatives of the
exponential curve with respect to each model parameter separately.

The implementation still needs testing and debugging.

Removal of unnecessary imports in specific_analyses.relax_disp.optimisation().

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

Fix for missing imports.

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

Implemented first try to minimise with minfx in estimate_r2eff() function.

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

Split function to minimise with scipy.optimize.leastsq out in estimate_r2eff module.

This is to prepare for implementing with minfx.

Isolated all code related to user function:

relax_disp.r2eff_estimate() into independent module file.

All has been isolated to: specific_analyses.relax_disp.estimate_r2eff.

Moved target function for curve fitting with scipy into specific_analyses.relax_disp.estimate_r2eff.

This will later include the backend specific_analyses.relax_disp.optimisation.estimate_r2eff()
function and the code in the target_functions package.

The code in target_functions.relax_disp_curve_fit is a lot more than just a target function, so it doesn't really belong in this package.

This is also to isolate this experimental feature.

Re-used the dependency check "scipy_module", since leastsq() has been part of Scipy since 2003.

Lowering precision in systemtest Relax_disp.test_r1rho_kjaergaard_missing_r1.

This is R1 estimation with: MODEL_NS_R1RHO_2SITE.

The lowering of precision is due different system precision.

Add dependency check for scipy.optimize.leastsq.

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