Resampler

class gtda.time_series.Resampler(period=2)[source]

Time series resampling at regular intervals.

Parameters

period (int, default: 2) – The sampling period, i.e. one point every period will be kept.

Examples

>>> import numpy as np
>>> from gtda.time_series import Resampler
>>> # Create a noisy signal
>>> signal = np.asarray([np.sin(x /40) + np.random.random()
...                      for x in range(0, 300)])
>>> # Set up the Resampler
>>> period = 10
>>> periodic_sampler = Resampler(period=period)
>>> # Fit and transform the signal
>>> signal_resampled = periodic_sampler.fit_transform(signal)
>>> print(signal_resampled.shape)
(30,)
__init__(period=2)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)[source]

Do nothing and return the estimator unchanged.

This method is here to implement the usual scikit-learn API and hence work in pipelines.

Parameters
  • X (ndarray of shape (n_samples,) or (n_samples, ..)) – Input data.

  • y (None) – Ignored.

Returns

self

Return type

object

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 (ndarray of shape (n_samples,) or (n_samples, ..)) – Input data.

  • y (None) – Ignored.

Returns

Xt – Resampled array. n_samples_new = n_samples // period.

Return type

ndarray of shape (n_samples_new, ..)

fit_transform_resample(X, y, **fit_params)

Fit to data, then transform the input and resample the target. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X ans a resampled version of y.

Parameters
  • X (ndarray of shape (n_samples, ..)) – Input data.

  • y (ndarray of shape (n_samples,)) – Target data.

Returns

  • Xt (ndarray of shape (n_samples, …)) – Transformed input.

  • yr (ndarray of shape (n_samples, …)) – Resampled target.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

resample(y, X=None)[source]

Resample y.

Parameters
  • y (ndarray of shape (n_samples,)) – Target.

  • X (None) – There is no need for input data, yet the pipeline API requires this parameter.

Returns

yr – Resampled target. n_samples_new = n_samples // period.

Return type

ndarray of shape (n_samples_new,)

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.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

object

transform(X, y=None)[source]

Resample X.

Parameters
  • X (ndarray of shape (n_samples,) or (n_samples, ..)) – Input data.

  • y (None) – There is no need for a target, yet the pipeline API requires this parameter.

Returns

Xt – Resampled array. n_samples_new = n_samples // period.

Return type

ndarray of shape (n_samples_new, ..)

transform_resample(X, y)

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 (ndarray of shape (n_samples, ..)) – Input data.

  • y (ndarray of shape (n_samples,)) – Target data.

Returns

  • Xt (ndarray of shape (n_samples, …)) – Transformed input.

  • yr (ndarray of shape (n_samples, …)) – Resampled target.