def mde(simulated_array, observed_array, replace_nan=None, replace_inf=None,
        remove_neg=False, remove_zero=False):
    """
    Compute the median error (MdE) between the simulated and observed data.

    .. image:: /pictures/MdE.png

    **Range** -inf < MdE < inf, closer to zero is better.

    **Notes** This metric indicates bias. It is similar to the mean error (ME), only it takes the
    median rather than the mean. Median measures reduces the impact of outliers.

    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.

    Examples
    --------

    Note that the last outlier residual in the time series is negated using the median.

    >>> 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, 100])
    >>> he.mde(sim, obs)
    -0.10000000000000009

    Returns
    -------
    float
        The median error value.

    """

    # 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.median(simulated_array - observed_array)
Compute the median error (MdE) between the simulated and observed data.

    .. image:: /pictures/MdE.png

    **Range** -inf < MdE < inf, closer to zero is better.

    **Notes** This metric indicates bias. It is similar to the mean error (ME), only it takes the
    median rather than the mean. Median measures reduces the impact of outliers.

    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.

    Examples
    --------

    Note that the last outlier residual in the time series is negated using the median.

    >>> 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, 100])
    >>> he.mde(sim, obs)
    -0.10000000000000009

    Returns
    -------
    float
        The median error value.