packman.gnm.gnm module

The ‘GNM’ object host file.

This is file information, not the class information. This information is only for the API developers. Please read the ‘GNM’ object documentation for details.

Example::
from packman.GNM import GNM help( GNM )

Notes

  • Tutorial:
Authors:
class packman.gnm.gnm.GNM(atoms, gamma=1.0, dr=7.3, power=0)

Bases: object

This class contains the functions essential to carry out the Gaussian Network Model.

Notes: * Tutorial:

Parameters:
  • coords ([float]) – Two dimentional array of three dimentional points in the space.
  • gamma (float, optional) – Spring Constant Value. Defaults to 1.0.
  • dr (float, optional) – Distance Cutoff. Defaults to 7.3 (Yang et. al., Protein elastic network models and the ranges of cooperativity. (2009))
  • power (int, optional) – Power of distance (mainly useful in non-parametric mode). Defaults to 0.
calculate_crosscorrelation()

Calculate the cross-correlation. (Read the paper for more details) :returns: True if successful; None otherwise.

calculate_decomposition()

Decompose the Hessian Matrix of the ANM model.

Note

Eigen values and Eigen Vectors are calculated. use ANM().get_eigenvalues() and ANM().get_eigenvectors() to obtain them.

calculate_fluctuations(endmode=None)

Calculate the Fluctuations of the ANM model.

The fluctualtions/ theoretical b-factors are calculated using this method.

Note

  • Fluctuations are calculated. use ANM().get_fluctuations() to obtain the fluctuations.
  • Endmode needs to be put in the code if and when required.
calculate_kirchhoff(gamma=1.0)

Calculate the Gaussian Network Model (GNM) kirchhoff Matrix.

The matrix is stored in the self.GNM_MAT variable.

Returns:True if successful; None otherwise.
get_crosscorrelation()

Get the crosscorrelations.

Notes

  • Make sure that the GNM().calculate_kirchhoff(), GNM().calculate_decomposition() and GNM().calculate_fluctuations() is called before calling this function. (will return None otherwise)
Returns:2D array of crosscorrelations
Return type:numpy.ndarray
get_eigenvalues()

Get the Eigenvalues obtained by decomposing the Hessian Matrix of the ANM model.

Notes

  • Make sure that the ANM().calculate_hessian() and ANM().calculate_decomposition() is called before calling this function. (will return None otherwise)
Returns:Eigenvalues if successful; None otherwise
Return type:numpy.ndarray
get_eigenvectors()

Get the Eigenvectors obtained by decomposing the Hessian Matrix of the ANM model.

Notes

  • Make sure that the ANM().calculate_hessian() and ANM().calculate_decomposition() is called before calling this function. (will return None otherwise)
Returns:Eigenvectors if successful; None otherwise
Return type:numpy.ndarray
get_fluctuations()

Get the Fluctuations obtained from Eigenvectors and Eigenvalues

Notes

  • Make sure that the GNM().calculate_kirchhoff(), GNM().calculate_decomposition() and GNM().calculate_fluctuations() is called before calling this function. (will return None otherwise)
Returns:Eigenvectors if successful; None otherwise
Return type:numpy.ndarray
get_kirchhoff()

Get the Hessian Matrix of the ANM model.

Notes

  • Make sure that the GNM().calculate_kirchhoff() is called before calling this function. (will return None otherwise)
Returns:Hessian matrix if successful; None otherwise
Return type:numpy.ndarray
get_pseudoinverse()

Get the pseudoinverse of the Kirchhoff’s matrix.

Notes

  • Make sure that the GNM().calculate_kirchhoff(), GNM().calculate_decomposition() and GNM().calculate_fluctuations() is called before calling this function. (will return None otherwise)
Returns:2D array of pseudoinverse
Return type:numpy.ndarray