relax SVN archive

For reference, added small script with symbolic derivation of TSMFK01.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Initial try to convert multifit_covar() to higher dimensional data.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Further extended the function, to call the co-variance function.

There are some issues with dimensionality of arrays.

Maybe an initial looping will be good.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Added target function for the Jacobian of TSMFK01, in target_functions/relax_disp.py.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Added partial derivatives for model TSMFK01 and the Jacobian function.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Started function estimate_par_err, to estimate errors. All data is collected, and target function initialized.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Added initial systemtest Relax_disp.test_task_model_par_est_tsmfk01, to load data, and call back end function for estimating errors for model parameters.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Added the simulated data, which will be used for comparison.

This is for 10.000 simulations, since my computer started having problems with 50.000 suimulations.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Added initial test data set for TSMK01.
A 50.000 Monte-Carlo simulation will be performed.

For this relax state, all simulated numbers will get extracted to a numpy array and saved with numpy.save.
All spin.PAR_sim attributes will get deleted to save disk space, and the relax state saved again.

In this way, access to all data is granted.

task #7824(https://gna.org/task/index.php?7824): Model parameter ERROR estimation from Jacobian and Co-variance matrix of dispersion models.

Updated the minfx version numbers in the release checklist document.

The version is now 1.0.10, which has not been released yet but will contain the implementation of
the log-barrier constraint algorithm gradient and Hessian.