MedianFilter#

class chemotools.smooth.MedianFilter(window_size: int = 3, mode: Literal['reflect', 'constant', 'nearest', 'mirror', 'wrap', 'grid-constant', 'grid-mirror', 'grid-wrap'] = 'nearest')[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A smoothing transformer that calculates the median filter of the input data.

Parameters:

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

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

Variables:

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

Examples

>>> from chemotools.datasets import load_fermentation_train
>>> from chemotools.smooth import MedianFilter
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Initialize MedianFilter
>>> md = MedianFilter()
MedianFilter()
>>> # Fit and transform the data
>>> X_smoothed = md.fit_transform(X)
fit(X: ndarray, y=None) → MedianFilter[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:

MedianFilter

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

Transform the input data by calculating the median filter.

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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