d'Auvergne protocol script mode - analysis variables

At the start of the script you will notice a number of Analysis variables. Unless you know what you are doing, you should only change the DIFF_MODEL variable to the following:

`local_tm':
This is the first diffusion model which must be optimised prior to optimising any of the other diffusion models. It consists of the local τm models (equations 7.23.0 to 7.23.9 on page [*]).
`sphere':
This second diffusion model is that of isotropic Brownian diffusion.
`prolate':
This third diffusion model is that of the prolate axially-symmetric rotor.
`oblate':
This fourth diffusion model is that of the oblate axially-symmetric rotor.
`ellipsoid':
This fifth diffusion model is that of fully rhombic Brownian diffusion (see Perrin (1934,1936) for the original theory).
`final':
This is a special value which will finalise the analysis by selecting the best diffusion model to describe your system and to perform Monte Carlo simulations for error propagation.

The MF_MODELS and LOCAL_TM_MODELS variables specify which model-free models will be used in the analysis. But, as the full protocol behind this script which is designed to find the solution of the universal set $ \mathfrak{U}$ (see section 7.7.2 on page [*]) expects that all these models are present, you should not change these variables. If you do remove some model-free models, you should fully expect to see artificial motions which you will not be able to distinguish from the real molecular motions.

The next variables GRID_INC and MIN_ALGOR are related to the optimisation of the model-free models. These should also not be touched unless you fully understand the consequences (and have read d'Auvergne and Gooley (2008b)). The variable MC_NUM specifies the number of Monte Carlo simulations. This number can be increased but, for realistic parameter errors in your publication, it should not set lower than 500 simulations.

Finally the CONV_LOOP variable is designed to make your life easier. If left at the value of True, the script will iterate until convergence (see Figure 2 in d'Auvergne and Gooley (2008c) to understand this concept). If changed to False, then you will need to run the script manually for the 15 or so iterations of each diffusion model, and then repeat this for all diffusion models II to V.

The relax user manual (PDF), created 2016-10-28.