msmbuilder.featurizer.AtomPairsFeaturizer¶

class msmbuilder.featurizer.AtomPairsFeaturizer(pair_indices, periodic=False, exponent=1.0)¶

Featurizer based on distances between specified pairs of atoms.

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 of the distances between the specified pairs of atoms.

Parameters:	pair_indices : np.ndarray, shape=(n_pairs, 2), dtype=int Each row gives the indices of two atoms involved in the interaction. periodic : bool, default=False If periodic is True and the trajectory contains unitcell information, we will compute distances under the minimum image convention. exponent : float Modify the distances by raising them to this exponent.

Methods

`describe_features`(traj)	Return a list of dictionaries describing the atom pair 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 pairwise atom-atom distances
`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__(pair_indices, periodic=False, exponent=1.0)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(pair_indices[, periodic, exponent])	Initialize self.
`describe_features`(traj)	Return a list of dictionaries describing the atom pair 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 pairwise atom-atom distances
`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)¶

Return a list of dictionaries describing the atom pair features.

Parameters:

traj : mdtraj.Trajectory: The trajectory to describe

Returns:

feature_descs : list of dict

Dictionary describing each feature with the following information about the atoms participating in each dihedral

resnames: unique names of residues

atominds: the two atom inds

resseqs: unique residue sequence ids (not necessarily 0-indexed)

resids: unique residue ids (0-indexed)

featurizer: AtomPairsFeaturizer

featuregroup: Distance.

other info : Value of the exponent

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 pairwise atom-atom distances

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.