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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 mae(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False):
 """Compute the mean absolute error of the simulated and observed data. .. image:: /pictures/MAE.png **Range:** 0 ≤ MAE < inf, data units, smaller is better. **Notes:** The ME measures the absolute difference between the simulated data and the observed data. For the mean abolute error, a smaller number indicates a better fit to the original data. Also note that random errors do not cancel. Also referred to as an L1-norm. 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. 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 error value. References - Willmott, Cort J., and Kenji Matsuura. “Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance.” Climate Research 30, no. 1 (2005):
 79–82. - Willmott, Cort J., and Kenji Matsuura. “On the Use of Dimensioned Measures of Error to Evaluate the Performance of Spatial Interpolators.” International Journal of Geographical Information Science 20, no. 1 (2006):
 89–102. Examples -------- 0.5666666666666665 """


    # 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
    )

    return np.mean(np.absolute(simulated_array - observed_array))