HilbertTransformer

class HilbertTransformer(output_type='envelope', N=None, unwrap_phase=True, fs=1.0)[source]

Extract instantaneous features from a time series via Hilbert transform.

Computes the analytic signal using the Hilbert transform and returns one of several derived representations: the amplitude envelope, instantaneous phase, instantaneous frequency, the quadrature component, or all three main features as a multi-column DataFrame.

Wraps scipy.signal.hilbert.

Parameters:

output_typestr, default=”envelope”

Which feature to extract. One of:

"envelope" : instantaneous amplitude, |z(t)|
"phase" : unwrapped instantaneous phase in radians
"frequency" : instantaneous frequency in cycles per sample
"quadrature" : imaginary part of the analytic signal
"all" : envelope, phase and frequency as separate columns

Nint or None, default=None

Number of Fourier components (FFT length). If None, defaults to the length of the input.

unwrap_phasebool, default=True

Whether to unwrap the phase to remove 2-pi discontinuities.

fsfloat, default=1.0

Sampling frequency, used to scale instantaneous frequency into physical units (Hz) when output_type is "frequency" or "all".

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.transformations.hilbert import HilbertTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> t = HilbertTransformer(output_type="envelope")
>>> y_envelope = t.fit_transform(y)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.