ConstantBaselineCorrection#

class chemotools.baseline.ConstantBaselineCorrection(start: int = 0, end: int = 1, wavenumbers: ndarray | None = None)[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that corrects a baseline by subtracting a constant value. The constant value is taken by the mean of the features between the start and end indices. This is a common preprocessing technique for UV-Vis spectra.

Parameters:

  • start (int, optional, default=0) – The index of the first feature to use for the baseline correction.

  • end (int, optional, default=1) – The index of the last feature to use for the baseline correction.

  • wavenumbers (np.ndarray, optional, default=None) – The wavenumbers corresponding to each feature in the input data.

Variables:

  • start_index (int) – The index of the start of the range. It is 0 if the wavenumbers are not provided.

  • end_index (int) – The index of the end of the range. It is 1 if the wavenumbers are not provided.

Examples

>>> from chemotools.baseline import ConstantBaselineCorrection
>>> from chemotools.datasets import load_fermentation_train
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the transformer
>>> transformer = ConstantBaselineCorrection(start=0, end=1)
>>> transformer.fit(X)
>>> # Generate baseline-corrected data
>>> X_corrected = transformer.transform(X)
fit(X: ndarray, y=None) → ConstantBaselineCorrection[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:

ConstantBaselineCorrection

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

Transform the input data by subtracting the constant baseline value.

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 input data.

Return type:

np.ndarray of shape (n_samples, n_features)

This Page