relax SVN archive

Fixes for all of the exponential functions in target_functions/exponential.c.

The condition whereby Rx is zero is now setting the value correctly - the exponential will be 1, not
zero, hence the intensity and gradient values should not be zero.

Fixes for the target function chi-squared gradient C function.

Clean up of the end of the target_functions.relax_fit.dfunc() function.

The exponential curve-fitting gradient is now scaled by the scaling matrix.

Implementation of the specific_analyses.relax_fit.optimisation.dfunc_wrapper() function.

This interfaces with the target_functions.relax_fit C module and converts the gradient from a Python
list to a numpy array.

Implemented the target_functions.relax_fit.dfunc() gradient function.

This is using the Python/C interface to provide a Python function for calculating and returned the
chi-squared gradient for the exponential curves.

Implemented the chi-squared gradient as a C module for the target functions.

This simply translates the Python code into C to allow any target function C modules to build its
own gradient function.

Tried to implement with scipy.optimize.fmin_ncg and scipy.optimize.fmin_cg, but cannot get it to work.

The matrices are not aligned well.

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

Cleaned up target function for leastsq, since arguments to function can be extracted from class.

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

Intermediate step in estimate R2eff module.

It seems that minfx is minimising in a quadratic space because of the power of chi2, while
the general input to scipy.optimize does not do this.

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