relax SVN archive

Added more print out information, when log(I / I_ref) is negative, and raising errors.

This can help the user track back information to the error more easily.

Modified systemtest test_estimate_r2eff_err_methods, to check all Jacobian methods are correctly implemented.

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

Modified module to estimate R2eff errors, to use the C-code Jacobian.

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

Added a script and log for calculating the exponential curve parameter errors via bootstrapping.

This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and
calculates the parameter errors via bootstrapping. As the parameters at the minimum are the exact
parameter values, bootstrapping and Monte Carlo simulation converge and hence this is a true error
estimate. 200,000 simulations where used, so the parameter errors are extremely accurate.

Added a script and log for calculating the numerical covariance matrix for an exponential curve.

This uses the data at http://thread.gmane.org/gmane.science.nmr.relax.devel/6807/focus=6840 and
calculates the covariance matrix via the Jacobian calculated using the numdifftools.Jacobian object
construct and obtain the matrix, both at the minimum and at a point away from the minimum. The
covariance is calculated as inv(J^T.W.J).

Created two unit tests showing the target_functions.relax_fit.jacobian_chi2() function is correct.

This compares the calculated chi-squared Jacobian to the numerically integrated values from the
test_suite/shared_data/curve_fitting/numeric_topology/jacobian_chi2.py script.

Fix for the chi-squared Jacobian numerical approximation script.

The function was modified to that a list of chi-squared elements are returned, i.e. the sum part of
the chi-squared equation has been removed.

Added a script and log for calculating the numerical chi-squared 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 chi-squared 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.

Renamed the test_data/shared_data/curve_fitting/numeric_gradient/ directory to numeric_topology.

This is to better reflect that it contains numeric approximations to the gradient, Hessian, and
Jacobian.

Created two unit tests showing the target_functions.relax_fit.jacobian() function is correct.

This compares the calculated Jacobian to the numerically integrated values from the
test_suite/shared_data/curve_fitting/numeric_gradient/jacobian.py script.