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Automatic analysis for blackbox modelfree results.
The dauvergne_protocol autoanalysis is designed for those who appreciate blackboxes or those who appreciate complex code. Importantly, data at multiple magnetic field strengths is essential for this analysis. If you would like to change how modelfree analysis is performed, the code in the file auto_analyses/dauvergne_protocol.py in the base relax directory can be copied and modified as needed and used with the relax script interface. This file is simply a complex relax script. For a description of objectoriented coding in python using classes, functions/methods, self, etc., please see the python tutorial.
The modelfree optimisation methodology herein is that of:
Other references for features of this dauvergne_protocol autoanalysis include modelfree model selection using Akaike's Information Criterion:
The elimination of failed modelfree models and Monte Carlo simulations:
Significant modelfree optimisation improvements:
Rather than searching for the lowest chisquared value, this autoanalysis searches for the model with the lowest AIC criterion. This complex multiuniverse, multidimensional problem is formulated, using set theory, as the universal solution:
The basic three references for the original and extended modelfree theories are:
The five diffusion models used in this autoanalysis are:
If using the scriptbased user interface (UI), changing the value of the variable diff_model will determine the behaviour of this autoanalysis. Model I must be optimised prior to any of the other diffusion models, while the Models II to V can be optimised in any order. To select the various models, set the variable diff_model to the following strings:
This approach has the advantage of eliminating the need for an initial estimate of a global diffusion tensor and removing all the problems associated with the initial estimate.
It is important that the number of parameters in a model does not exceed the number of relaxation data sets for that spin. If this is the case, the list of models in the mf_models and local_tm_models variables will need to be trimmed.
This will optimise the diffusion model whereby all spin of the molecule have a local tm value, i.e. there is no global diffusion tensor. This model needs to be optimised prior to optimising any of the other diffusion models. Each spin is fitted to the multiple modelfree models separately, where the parameter tm is included in each model.
AIC model selection is used to select the models for each spin.
This will optimise the isotropic diffusion model. Multiple steps are required, an initial optimisation of the diffusion tensor, followed by a repetitive optimisation until convergence of the diffusion tensor. In the relax script UI each of these steps requires this script to be rerun, unless the conv_loop flag is True. In the GUI (graphical user interface), the procedure is repeated automatically until convergence. For the initial optimisation, which will be placed in the directory './sphere/init/', the following steps are used:
The modelfree models and parameter values for each spin are set to those of diffusion model MI.
The local tm parameter is removed from the models.
The modelfree parameters are fixed and a global spherical diffusion tensor is minimised.
For the repetitive optimisation, each minimisation is named from 'round_1' onwards. The initial 'round_1' optimisation will extract the diffusion tensor from the results file in './sphere/init/', and the results will be placed in the directory './sphere/round_1/'. Each successive round will take the diffusion tensor from the previous round. The following steps are used:
The global diffusion tensor is fixed and the multiple modelfree models are fitted to each spin.
AIC model selection is used to select the models for each spin.
All modelfree and diffusion parameters are allowed to vary and a global optimisation of all parameters is carried out.
The methods used are identical to those of diffusion model MII, except that an axially symmetric diffusion tensor with Da >= 0 is used. The base directory containing all the results is './prolate/'.
The methods used are identical to those of diffusion model MII, except that an axially symmetric diffusion tensor with Da <= 0 is used. The base directory containing all the results is './oblate/'.
The methods used are identical to those of diffusion model MII, except that a fully anisotropic diffusion tensor is used (also known as rhombic or asymmetric diffusion). The base directory is './ellipsoid/'.
Once all the diffusion models have converged, the final run can be executed. This is done by setting the variable diff_model to 'final'. This consists of two steps, diffusion tensor model selection, and Monte Carlo simulations. Firstly AIC model selection is used to select between the diffusion tensor models. Monte Carlo simulations are then run solely on this selected diffusion model. Minimisation of the model is bypassed as it is assumed that the model is already fully optimised (if this is not the case the final run is not yet appropriate).
The final blackbox modelfree results will be placed in the file 'final/results'.


dAuvergne_protocol The modelfree autoanalysis. 

Container Empty container for data storage. 


info = None


status = Status()


doc =


__doc__ = to_docstring(doc)


__package__ =

Imports: F_OK, access, getcwd, listdir, sep, search, lower, sleep, LIST, PARAGRAPH, SECTION, SUBSECTION, TITLE, to_docstring, floatAsByteArray, Info_box, exists_mol_res_spin_data, generate_spin_id, spin_index_loop, spin_loop, cdp_name, get_pipe, has_pipe, pipe_names, switch, selection, Interpreter, RelaxError, RelaxNoSequenceError, RelaxNoValueError, DummyFileObject, Status

doc

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