def transform(self, X, y=None):
        X_tot = None
        for freqs in self.freqs_pairs:
            if len(freqs) == 1:
                b, a = butter(5, freqs[0] / 250.0, btype='lowpass')
            else:
                if freqs[1] - freqs[0] < 3:
                    b, a = butter(3, np.array(freqs) / 250.0, btype='bandpass')
                else:
                    b, a = butter(5, np.array(freqs) / 250.0, btype='bandpass')
            X_filtered = lfilter(b, a, X, axis=0)
            X_tot = X_filtered if X_tot is None else np.c_[X_tot, X_filtered]

        return X_tot
Iterable of chart attribute specifications, associated with columns.
dataset/ETHPy150Open alexandrebarachant/Grasp-and-lift-EEG-challenge/preprocessing/filterBank.py/FilterBank.transform--crossover with--dataset/ETHPy150Open bokeh/bokeh/bokeh/charts/data_source.py/ChartDataSource.groupby