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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 mle(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False):
 """ Compute the mean log error of the simulated and observed data. .. image:: /pictures/MLE.png **Range:** -inf < MLE < inf, data units, closer to zero is better. **Notes** Same as the mean erro (ME) only use log ratios as the error term. Limits the impact of outliers, more evenly weights high and low data values. 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 log error value. Examples -------- Note that the value is very small because it is in log space. 0.002961767058151136 References - Törnqvist, Leo, Pentti Vartia, and Yrjö O. Vartia. “How Should Relative Changes Be Measured?” The American Statistician 39, no. 1 (1985):
 43–46. """


    # 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
    )

    sim_log = np.log1p(simulated_array)
    obs_log = np.log1p(observed_array)
    return np.mean(sim_log - obs_log)