# Define a generic gaussian function
def gauss_lin(x, *params):
    """
    This defines a generic gaussiann function plus a linear background, with
    an arbitrary number of peaks.  Pass the X values and a pointer to your
    initial guesses.
    Make sure that guesses are in the order of slope, intercept, amplitude,
    mean, and sigma, and that you have a multiple of 2+3n guesses to pass in.

    Parameters
    ----------
    x : independent variable
    params : parameters for the Gaussian function
        - slope: slope of the linear background
        - intercept: y-intercept of the linear background
        - amplitude: height of the peak
        - mean: position of the center of the peak
        - sigma: width of the peak
        - (repeat for multiple peaks)

    Returns
    -------
    y : dependent variable
    """
    y = np.zeros_like(x)
    slope = params[0]
    intercept = params[1]
    y += slope * x + intercept
    for i in range(2, len(params), 3):
        amplitude = params[i]
        mean = params[i + 1]
        sigma = params[i + 2]
        y += amplitude * np.exp(-(( x - mean) ** 2) / (2 * sigma ** 2))
    return y
# End of gauss_lin