Class usr_param

Instance Methods

__init__(self)
Class containing parameters specified by the user

input(self)
Specify the input data.

model_selection(self)
Model selection method.

palmer_method_param(self)

palmer_run_param(self)
Run file parameters

palmer_mfin_param(self)
mfin file parameters

palmer_mfpar_param(self)
mfpar file parameters

palmer_mfmodel_param(self)
mfmodel file parameters

relax_params(self)
Parameters used by the relaxation curve fitting.

Method Details

input(self)

Specify the input data.

The structure of self.input_info is as follows:  The fields of the first dimension correspond
to each relaxation data set and is flexible in size, ie len(self.input_info) = number of data sets.
The second dimension have the following fixed fields:
        0 - Data type (R1, R2, or NOE)
        1 - NMR frequancy label
        2 - NMR proton frequancy in MHz
        3 - The name of the file containing the relaxation data

The structure of self.nmr_frq is as follows:  The length of the first dimension is equal to the number
of field strengths.  The fields of the second are:
        0 - NMR frequancy label
        1 - NMR proton frequancy in MHz
        2 - R1 flag (0 or 1 depending if data is present).
        3 - R2 flag (0 or 1 depending if data is present).
        4 - NOE flag (0 or 1 depending if data is present).

model_selection(self)

Model selection method.  self.method can be set to the following:

AIC:            Method of model-free analysis based on model selection using the Akaike Information Criteria.

AICc:           Method of model-free analysis based on model selection using the Akaike Information Criteria corrected
                for finit sample size.

BIC:            Method of model-free analysis based on model selection using the Schwartz Information Criteria.

Bootstrap:      Modelfree analysis based on model selection using bootstrap methods to estimate the overall discrepancy.

CV:             Modelfree analysis based on model selection using cross-validation methods to estimate the overall discrepancy.

Expect:         Calculate the expected overall discrepancy (real model-free parameters must be known).

Farrow:         The method given by Farrow et al., 1994.

Palmer:         The method given by Mandel et al., 1995.

Overall:        Calculate the realised overall discrepancy (real model-free parameters must be known).