msmbuilder.featurizer.SuperposeFeaturizer¶

class msmbuilder.featurizer.SuperposeFeaturizer(atom_indices, reference_traj, superpose_atom_indices=None)¶

Featurizer based on euclidian atom distances to reference structure.

This featurizer transforms a dataset containing MD trajectories into a vector dataset by representing each frame in each of the MD trajectories by a vector containing the distances from a specified set of atoms to the ‘reference position’ of those atoms, in reference_traj.

Parameters:

atom_indices : np.ndarray, shape=(n_atoms,), dtype=int: The indices of the atoms to superpose and compute the distances with
reference_traj : md.Trajectory: The reference conformation to superpose each frame with respect to (only the first frame in reference_traj is used)
superpose_atom_indices : np.ndarray, shape=(n_atoms,), dtype=int: If not None, these atom_indices are used for the superposition

Methods

`describe_features`(traj)	Generic method for describing features.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`partial_transform`(traj)	Featurize an MD trajectory into a vector space via distance after superposition
`set_params`(**params)	Set the parameters of this estimator.
`summarize`()	Return some diagnostic summary statistics about this Markov model
`transform`(traj_list[, y])	Featurize a several trajectories.

featurize
fit

__init__(atom_indices, reference_traj, superpose_atom_indices=None)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(atom_indices, reference_traj[, …])	Initialize self.
`describe_features`(traj)	Generic method for describing features.
`featurize`(traj)
`fit`(traj_list[, y])
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_params`([deep])	Get parameters for this estimator.
`partial_transform`(traj)	Featurize an MD trajectory into a vector space via distance after superposition
`set_params`(**params)	Set the parameters of this estimator.
`summarize`()	Return some diagnostic summary statistics about this Markov model
`transform`(traj_list[, y])	Featurize a several trajectories.

describe_features(traj)¶

Generic method for describing features.

Parameters:	traj : mdtraj.Trajectory Trajectory to use
Returns:	feature_descs : list of dict Dictionary describing each feature with the following information about the atoms participating in each feature resnames: unique names of residues atominds: the four atom indicies resseqs: unique residue sequence ids (not necessarily 0-indexed) resids: unique residue ids (0-indexed) featurizer: Featurizer name featuregroup: Other information

Notes

Method resorts to returning N/A for everything if describe_features in not implemented in the sub_class

fit_transform(X, y=None, **fit_params)¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

partial_transform(traj)¶

Featurize an MD trajectory into a vector space via distance after superposition

Parameters:	traj : mdtraj.Trajectory A molecular dynamics trajectory to featurize.
Returns:	features : np.ndarray, dtype=float, shape=(n_samples, n_features) A featurized trajectory is a 2D array of shape (length_of_trajectory x n_features) where each features[i] vector is computed by applying the featurization function to the `i`th snapshot of the input trajectory.