def savitzky_golay(y, window_size, order, deriv=0):
    # code from from scipy cookbook
    """Smooth (and optionally differentiate) data with a Savitzky-Golay
    filter.  The Savitzky-Golay filter removes high frequency noise
    from data.  It has the advantage of preserving the original shape
    and features of the signal better than other types of filtering
    approaches, such as moving averages techhniques.
    
    Parameters
    ----------
    y : array_like, shape (N,)
        the values of the time history of the signal.
    window_size : int
                  the length of the window. Must be an odd integer
                  number.
    order : int
            the order of the polynomial used in the filtering. Must be
            less then `window_size` - 1.
    deriv: int
           the order of the derivative to compute (default = 0 means
           only smoothing)

    Returns
    -------
    ys : ndarray, shape (N)
         the smoothed signal (or it's n-th derivative).

    Notes
    -----
    The Savitzky-Golay is a type of low-pass filter, particularly
    suited for smoothing noisy data. The main idea behind this
    approach is to make for each point a least-square fit with a
    polynomial of high order over a odd-sized window centered at the
    point.

    Examples
    --------
    t = np.linspace(-4, 4, 500)
    y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
    ysg = savitzky_golay(y, window_size=31, order=4)
    import matplotlib.pyplot as plt
    plt.plot(t, y, label='Noisy signal')
    plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
    plt.plot(t, ysg, 'r', label='Filtered signal')
    plt.legend()
    plt.show()
    
    References
    ----------
    .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation
           of Data by Simplified Least Squares Procedures. Analytical
           Chemistry, 1964, 36 (8), pp 1627-1639.

    .. [2] Numerical Recipes 3rd Edition: The Art of Scientific
           Computing W.H. Press, S.A. Teukolsky, W.T. Vetterling,
           B.P. Flannery Cambridge University Press ISBN-13:
           9780521880688
    """
    try:
        window_size = abs(int(window_size))
        order = abs(int(order))
    except __HOLE__(msg):
        raise ValueError("window_size and order have to be of type int")
    if window_size % 2 != 1 or window_size < 1:
        raise TypeError("window_size size must be a positive odd number")
    if window_size < order + 2:
        raise TypeError("window_size is too small for the polynomials order")
    order_range = range(order+1)
    half_window = (window_size -1) // 2
    # precompute coefficients
    b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
    m = np.linalg.pinv(b).A[deriv]
    # pad the signal at the extremes with
    # values taken from the signal itself
    firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
    lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
    y = np.concatenate((firstvals, y, lastvals))
    return np.convolve(m, y, mode='valid')

### MAIN CLASS
dataset/ETHPy150Open xraypy/xraylarch/plugins/io/specfiledata.py/savitzky_golay