PointScaler#

class chemotools.scale.PointScaler(point: int = 0, wavenumbers: ndarray | None = None)[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that scales the input data by the intensity value at a given point. The point can be specified by an index or by a wavenumber.

Parameters:

  • point (int, optional, default=0) – The point to scale the data by. It can be an index or a wavenumber.

  • wavenumber (array-like, optional, default=None) – The wavenumbers of the input data. If not provided, the indices will be used instead. Default is None. If provided, the wavenumbers must be provided in ascending order.

Variables:

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

  • point_index (int) – The index of the point to scale the data by. It is 0 if the wavenumbers are not provided.

Examples

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.scale import PointScaler
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize PointScaler with point index
>>> scaler = PointScaler(point=10)
PointScaler(point=10, wavenumbers=None)
>>> # Fit and transform the data
>>> X_scaled = scaler.fit_transform(X)
fit(X: ndarray, y=None) → PointScaler[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:

PointScaler

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

Transform the input data by scaling by the value at a given Point.

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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