<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def mase(simulated_array, observed_array, m=1, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False):
 """Compute the mean absolute scaled error between the simulated and observed data. .. image:: /pictures/MASE.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. m: int If given, indicates the seasonal period m. If not given, the default is 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 scaled error. Examples -------- 0.17341040462427745 References - Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy. International Journal of Forecasting 22(4) 679-688. """


    # Checking and cleaning the 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
    )

    start = m
    end = simulated_array.size - m
    a = np.mean(np.abs(simulated_array - observed_array))
    b = np.abs(observed_array[start:observed_array.size] - observed_array[:end])
    return a / (np.sum(b) / end)