msmbuilder.featurizer.DihedralFeaturizer¶

class msmbuilder.featurizer.DihedralFeaturizer(types=['phi', 'psi'], sincos=True)¶

Featurizer based on dihedral angles.

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 one or more of the backbone or side-chain dihedral angles, or the sin and cosine of these angles.

Parameters:	types : list One or more of [‘phi’, ‘psi’, ‘omega’, ‘chi1’, ‘chi2’, ‘chi3’, ‘chi4’] sincos : bool Instead of outputting the angle, return the sine and cosine of the angle as separate features.

Methods

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 calculation
save(filename)
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.

partial_transform(traj)¶

Featurize an MD trajectory into a vector space via calculation of dihedral (torsion) angles

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

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.

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)