SavitzkyGolay#

class chemotools.derivative.SavitzkyGolay(window_size: int = 3, polynomial_order: int = 1, derivate_order: int = 1, mode: Literal['mirror', 'constant', 'nearest', 'wrap', 'interp'] = 'nearest')[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that calculates the Savitzky-Golay derivative of the input data.

Parameters:

  • window_size (int, optional, default=3) – The size of the window to use for the derivative calculation. Must be odd. Default is 3.

  • polynomial_order (int, optional, default=1) – The order of the polynomial to use for the derivative calculation. Must be less than window_size. Default is 1.

  • derivative_order (int, optional, default=1) – The order of the derivative to calculate. Default is 1.

  • mode (str, optional, default="nearest") – The mode to use for the derivative calculation. Can be “nearest”, “constant”, “reflect”, “wrap”, “mirror” or “interp”. Default is “nearest”.

Variables:

n_features_in (int) – The number of features in the input data.

References

[1] Åsmund Rinnan, Frans van den Berg, Søren Balling Engelsen,

“Review of the most common pre-processing techniques for near-infrared spectra,” TrAC Trends in Analytical Chemistry 28 (10) 1201-1222 (2009).

Examples

>>> from chemotools.derivative import SavitzkyGolay
>>> from chemotools.datasets import load_fermentation_train
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the transformer
>>> transformer = SavitzkyGolay(window_size=3, polynomial_order=1)
SavitzkyGolay()
>>> transformer.fit(X)
>>> # Calculate Savitzky-Golay derivative
>>> X_corrected = transformer.transform(X)
fit(X: ndarray, y=None) → SavitzkyGolay[source]

Fit the transformer to the input data.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to fit the transformer to.

  • y (None) – Ignored to align with API.

Returns:

self – The fitted transformer.

Return type:

SavitzkyGolay

transform(X: ndarray, y=None) → ndarray[source]

Transform the input data by calculating the Savitzky-Golay derivative.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to transform.

  • y (None) – Ignored to align with API.

Returns:

X_transformed – The transformed data.

Return type:

np.ndarray of shape (n_samples, n_features)

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