Source code for spectoprep.preprocessing.smoothing

"""
Smoothing methods for spectroscopic data.
"""

from scipy.signal import savgol_filter
from sklearn.base import BaseEstimator, TransformerMixin




class SavitzkyGolay(BaseEstimator, TransformerMixin):
    """
    Savitzky-Golay filter for smoothing and differentiation of data.

    Parameters
    ----------
    filter_win : int, default=11
        Length of the filter window (must be positive odd integer).
    poly_order : int, default=2
        Order of the polynomial used to fit the samples.
        Must be less than filter_win.
    deriv_order : int, default=0
        Order of the derivative to compute. 0 means smoothing only.

    Notes
    -----
    The Savitzky-Golay filter is a digital smoothing polynomial filter
    that can preserve the high-frequency components of the signal
    better than standard averaging techniques.
    """

    def __init__(self, filter_win=11, poly_order=2, deriv_order=0):
        self.filter_win = filter_win
        self.poly_order = poly_order
        self.deriv_order = deriv_order



    def fit(self, X, y=None):
        """
        Validate parameters.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The input spectra.
        y : None
            Ignored.

        Returns
        -------
        self : object
            Returns self.
        """
        if self.filter_win % 2 == 0 or self.filter_win < 1:
            raise ValueError("filter_win must be a positive odd number.")
        if self.poly_order >= self.filter_win:
            raise ValueError("poly_order must be less than filter_win")
        return self




    def transform(self, X):
        """
        Apply Savitzky-Golay filter to the data.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features)
            The input spectra.

        Returns
        -------
        X_transformed : ndarray of shape (n_samples, n_features)
            Filtered spectra.
        """
        if self.filter_win % 2 == 0 or self.filter_win < 1:
            raise ValueError("filter_win must be a positive odd number.")
        return savgol_filter(X, self.filter_win, self.poly_order, deriv=self.deriv_order)


        # return np.array([
        #     savgol_filter(
        #         row,
        #         self.filter_win,
        #         self.poly_order,
        #         deriv=self.deriv_order
        #     ) for row in X
        # ])