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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 nrmse_mean(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False):
 """Compute the mean normalized root mean square error between the simulated and observed data. .. image:: /pictures/NRMSE_Mean.png **Range:** 0 ≤ NRMSE < inf. **Notes:** This metric is the RMSE normalized by the mean of the observed time series (x). Normalizing allows comparison between data sets with different scales. 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 normalized root mean square error. Examples -------- 0.11725109740212526 References - Pontius, R.G., Thontteh, O., Chen, H., 2008. Components of information for multiple resolution comparison between maps that share a real variable. Environmental and Ecological Statistics 15(2) 111-142. """


    # 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
    )

    rmse_value = np.sqrt(np.mean((simulated_array - observed_array) ** 2))
    obs_mean = np.mean(observed_array)
    return rmse_value / obs_mean