msmbuilder.featurizer.RawPositionsFeaturizer¶

class msmbuilder.featurizer.RawPositionsFeaturizer(atom_indices=None, ref_traj=None)¶

Featurize an MD trajectory into a vector space with the raw cartesian coordinates

Parameters:

atom_indices : None or array-like, dtype=int, shape=(n_atoms) If specified, only return the coordinates for the atoms given by atom_indices. Otherwise return all atoms ref_traj : None or md.Trajectory If specified, superpose each trajectory to the first frame of ref_traj before getting positions. If atom_indices is also specified, only superpose based on those atoms. The superposition will modify each transformed trajectory in place.

Methods

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 with the raw cartesian coordinates.
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.

__init__(atom_indices=None, ref_traj=None)¶

Methods

__init__([atom_indices, ref_traj])
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 with the raw cartesian coordinates.
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 with the raw cartesian coordinates.

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.

See also

transform

simultaneously featurize a collection of MD trajectories

Notes

If you requested superposition (gave ref_traj in __init__) the input trajectory will be modified.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:	self

summarize()¶

Return some diagnostic summary statistics about this Markov model

transform(traj_list, y=None)¶

Featurize a several trajectories.

Parameters:	traj_list : list(mdtraj.Trajectory) Trajectories to be featurized.
Returns:	features : list(np.ndarray), length = len(traj_list) The featurized trajectories. features[i] is the featurized version of traj_list[i] and has shape (n_samples_i, n_features)