NorrisWilliams#

class chemotools.derivative.NorrisWilliams(window_size: int = 5, gap_size: int = 3, derivative_order: int = 1, mode='nearest')[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that calculates the Norris-Williams derivative of the input data.

Parameters:

  • window_size (int, optional, default=5) – The size of the window to use for the derivative calculation. Must be odd. Default is 5.

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

  • derivative_order (int, optional, default=1) – The order of the derivative to calculate. Can be 1 or 2. 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 NorrisWilliams
>>> from chemotools.datasets import load_fermentation_train
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the transformer
>>> transformer = NorrisWilliams(window_size=5, gap_size=3)
NorrisWilliams()
>>> transformer.fit(X)
>>> # Calculate Norris-Williams derivative
>>> X_corrected = transformer.transform(X)
fit(X: ndarray, y=None) → NorrisWilliams[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:

NorrisWilliams

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

Transform the input data by calculating the Norris-Williams 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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