encodermap.moldata package#

Submodules#

encodermap.moldata.moldata module#

New MolData class. Uses PyEMMA to calculate many trajectories in Parallel.

Even when the set of trajectories or even collective variables is too large to keep in memory.

Allows creation of tfrecord files to pass large datasets to tensorflow that normally won’t fit into memory.

Is Backwards-compatible to the old MolData class.

class NewMolData(trajs, cache_path='', top=None, write_traj=False, fmt='.nc', start=None, stop=None, step=None)[source]#

Bases: object

MolData version 2. Extracts and holds conformational information of trajectories.

In version 2. You can either use MDAnalysis or the out-of memory option using EncoderMap’s new TrajEnsemble and SingleTraj classes.

Collective Variables is a term used for data of some dimension matching the dimension of your trajectory.

Collective variables of dimensionality 1 assign a single (float) value to every frame of a simulation or simulation ensemble. This could the the membership to a cluster, the distance between the termini of a protein or the distance between two spin labels. Collective variables of dimensionality 2 assign a list of floats to every simulation frame. The backbone torsions are such a collective variable. A flattened array of pairwise distances between CA atoms would also fall into this category. CVs of dimensionality 3 ascribe a value to every atom in every frame. This could be the xyz-coordinates of the atom or the beta-factor or the charge.

Encodermap in its Angle-Dihedral-Cartesioan mode uses the following collective variables:

  • cartesians: The xyz-coordinates of every atom in every frame in every trajectory.

  • central_cartesians: The xyz-coordinates of the backbone C, CA, N atoms.

  • dihedrals: The omega-phi-psi angles of the backbone.

  • angles: The angles between the central_cartesian atoms.

  • lengths: The distances between the central_cartesian atoms.

  • sidedihedrals: The dihedrals of the sidechains in order residue1-chi1-chi5 residue2-ch1-chi5.

write_tfrecords(path=None)[source]#

Todo

Module contents#