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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 ned(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False):
 """ Compute the normalized Euclidian distance between the simulated and observed data in vector space. .. image:: /pictures/NED.png **Range** 0 ≤ NED < inf, smaller is better. **Notes** Also sometimes referred to as the squared L2-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 normalized euclidean distance value. Examples -------- 0.2872053604165771 References - Kennard, M. J., Mackay, S. J., Pusey, B. J., Olden, J. D., & Marsh, N. (2010). Quantifying uncertainty in estimation of hydrologic metrics for ecohydrological studies. River Research and Applications, 26(2), 137-156. """


    # 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
    )

    a = observed_array / np.mean(observed_array)
    b = simulated_array / np.mean(simulated_array)
    return np.linalg.norm(a - b)