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: |
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__init__
(types=['phi', 'psi'], sincos=True)¶Methods
__init__ ([types, sincos]) |
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describe_features (traj) |
Return a list of dictionaries describing the dihderal features. |
featurize (traj) |
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fit (traj_list[, y]) |
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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 |
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 dihderal features.
Parameters: | traj (mdtraj.Trajectory) – The trajectory to describe |
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Returns: | feature_descs – Dictionary describing each feature with the following information
about the atoms participating in each dihedral
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Return type: | list of dict |
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: |
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Returns: | X_new – Transformed array. |
Return type: | numpy array of shape [n_samples, n_features_new] |
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. |
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Returns: | params – Parameter names mapped to their values. |
Return type: | mapping of string to any |
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. |
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Returns: | 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. |
Return type: | np.ndarray, dtype=float, shape=(n_samples, n_features) |
See also
transform()
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 latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each
component of a nested object.
Returns: | |
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Return type: | 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. |
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Returns: | features – The featurized trajectories. features[i] is the featurized version of traj_list[i] and has shape (n_samples_i, n_features) |
Return type: | list(np.ndarray), length = len(traj_list) |