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    def normalize(self, norm = "highest", shift_to_zero = True, inplace = True):
        '''
        Normalize spectrum by linearly changing values of intensity and eventually shifting spectrum to zero.

        ARGUMENTS:

        by - way of normalizing the spectrum. If \"highest\", then the spectrum is scaled to 1, so that its greatest value is 1. 
        If \"intensity\", then spectrum is scaled, so that its integral is equal to 1.
        
        shift_to_zero - if \"True\", then spectrum is shifted by X axis, by simply np.rolling it.
        '''
        import numpy as np

        if norm not in ["sup", "L1", "L2"]:
            raise Exception("\"norm\" parameter must be either \"sup\", \"L1\" or \"L2\".")

        X_safe = self.X.copy()
        Y_safe = self.Y.copy()
        safe_spectrum = spectrum(X_safe, Y_safe, self.x_type, self.y_type)

        if norm == "sup":
            max = np.max(np.abs(Y_safe))
            max_idx = np.argmax(np.abs(Y_safe))
            Y_safe /= max
            if shift_to_zero:
                zero_idx = np.searchsorted(X_safe, 0)
                shift_idx = max_idx - zero_idx
                X_safe = np.roll(X_safe, shift_idx)

        if norm == "L1":
            integral = np.sum(np.abs(Y_safe))/(X_safe[-1]-X_safe[0])
            median = safe_spectrum.quantile(1/2)
            max_idx = np.searchsorted(np.abs(Y_safe), median)
            Y_safe /= integral
            if shift_to_zero:
                zero_idx = np.searchsorted(X_safe, 0)
                shift_idx = max_idx - zero_idx
                X_safe = np.roll(X_safe, shift_idx)

        if norm == "L2":
            integral = np.sum(Y_safe**2)/(X_safe[-1]-X_safe[0])
            median = safe_spectrum.quantile(1/2)
            max_idx = np.searchsorted(np.abs(Y_safe), median)
            Y_safe /= np.sqrt(integral)
            if shift_to_zero:
                zero_idx = np.searchsorted(X_safe, 0)
                shift_idx = max_idx - zero_idx
                X_safe = np.roll(X_safe, shift_idx)

        if inplace == True:
            self.X = X_safe
            self.Y = Y_safe
        
        if inplace == False:
            return spectrum(X_safe, Y_safe, self.x_type, self.y_type)