SCM_initialisation.py

Meow

Organisms.GA.SCM_Scripts.SCM_initialisation.get_SCM_methods(SCM_Scheme)

This method will provide the methods needed to obtain the CNA_profile and similarity_profile, using either the T-SCM or the A-SCM.

Parameters:

SCM_Scheme (str.) – This determines if the SCM scheme being used is the “T-SCM” or “A-SCM”. Must be one of those two schemes.

Organisms.GA.SCM_Scripts.SCM_initialisation.get_rCut_values(rCut_low, rCut_high, rCut_resolution)

This method will give a lost of rCut values that you want to perform the CNA across, in Angstroms

Parm rCut_low:

This is the lowest rCut value you want to obtain the CNA for.

Parm rCut_high:

This is the highest rCut value you want to obtain the CNA for.

Parm rCut_resolution:

This is the resolution of the rCut values you are performing all the CNA’s with. The resolution can be given as one of two forms.

• If given as a float, rCut_resolution is the difference between rCut values that will be sampled. Note that your input for rCut_resolution is not divisible by (rCut_high-rCut_low), then rCut_high will not be included in rCuts.

• If given as a int, rCut_resolution is the number of rCut values in the list rCuts, evenly distributed between (and including) rCut_low and rCut_high.

Returns:

A list of all the rCut values you want the CNA to sample.

Return type:

list of floats

Organisms.GA.SCM_Scripts.SCM_initialisation.get_rCuts(self, Predation_Information)

Obtain the values for values of rCut the user wishes to investigate.

Parameters:

Predation_Information (dict.) – This contains all the information needed by the Predation Operator you want to use to run.

Returns:

A list of all the rCut values you want the CNA to sample.

Return type:

list of floats