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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def h6_mahe(simulated_array, observed_array, k=1, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False):
 """Compute the H6 mean absolute error. .. image:: /pictures/H6.png .. image:: /pictures/AHE.png **Range:** **Notes:** Parameters simulated_array: one dimensional ndarray An array of simulated data from the time series. observed_array: one dimensional ndarray An array of observed data from the time series. k: int or float If given, sets the value of k. If None, k=1. replace_nan: float, optional If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation. replace_inf: float, optional If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation. remove_neg: boolean, optional If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation. remove_zero: boolean, optional If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation. Returns ------- float The mean absolute H6 error. Examples -------- 0.11743831388794852 References - Tornquist, L., Vartia, P., Vartia, Y.O., 1985. How Should Relative Changes be Measured? The American Statistician 43-46. """


    # Treats data
    simulated_array, observed_array = treat_values(
        simulated_array,
        observed_array,
        replace_nan=replace_nan,
        replace_inf=replace_inf,
        remove_neg=remove_neg,
        remove_zero=remove_zero
    )

    top = (simulated_array / observed_array - 1)
    bot = np.power(0.5 * (1 + np.power(simulated_array / observed_array, k)), 1 / k)
    h = top / bot
    return np.mean(np.abs(h))