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→Features: Updated the feature list to match the CHANGES file.
== Features ==
* Huge speed ups for all of the relaxation dispersion models ranging from 1.366x 452x to 164163.180x 004x times faster. The speed ups for the clustered spin analysis are far greater than for the single spin analysis.
* Implementation of a zooming grid search algorithm for optimisation in all analyses. This includes the addition of the minimise.grid_zoom user function to set the zoom level. The grid width will be divided by 2**zoom_level and centred at the current parameter values. If the new grid is outside of the bounds of the original grid, the entire grid will be translated so that it lies entirely within the original.
* Increased the amount of user feedback for the minimise.grid_search user function. Now a comment for each parameter is included in the printed grid search setup table. This includes if the lower or upper bounds, or both, have been supplied and if a preset value has been used instead.
* Improved Expanded support for R1rho 2D graph plotting in the relax_disp.plot_disp_curves user function as the X-axis can now be the nu1 value, the effective field omega_eff, or the rotating frame title angle. And the plots are interpolation over the spin-lock offset.
* Ability to optimise the R1 relaxation rate parameter in the off-resonance relaxation dispersion models.
* Creation of the relax_disp.r1_fit user function for activating and deactivating R1 fitting in the dispersion analysis.
* Expanded model nesting capabilities in the relaxation dispersion auto-analysis to speed up the protocol.
* The spin-lock offset is now included in the spectra list GUI element for the relaxation dispersion analysis.
* Creation of the relax_disp.r2eff_estimate user function for the fast estimation of R2eff/R1rho values and errors when full exponential curves have been collected. This experiment experimental feature uses linearisation to estimate the R2eff and I0 parameters and the covariance matrix to estimate parameter errors.
* Gradients and Hessians are implemented for the exponential curve-fitting, hence all optimisation algorithms and constraint algorithms are now available for this analysis type. Using Newton optimisation instead of Nelder-Mead Simplex can save over an order of magnitude in computation time. This is also available in the relaxation dispersion analysis.
* The minimisation statistics are now being reset for all analysis types. The minimise.calculate, minimise.grid_search, and minimise.execute user functions now all reset the minimisation statistics for either the model or the Monte Carlo simulations prior to performing any optimisation. This is required for both parallelised grid searches and repetitive optimisation schemes to allow the result to overwrite an old result in all situations, as sometimes the original chi-squared value is lower and the new result hence is rejected.
