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
    --------
    >>> import HydroErr as he
    >>> import numpy as np

    >>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
    >>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
    >>> he.ned(sim, obs)
    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)
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
    --------
    >>> import HydroErr as he
    >>> import numpy as np

    >>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
    >>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
    >>> he.ned(sim, obs)
    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.