def maape(simulated_array, observed_array, replace_nan=None, replace_inf=None,
          remove_neg=False, remove_zero=False):
    """Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).

    .. image:: /pictures/MAAPE.png

    **Range:** 0 ≤ MAAPE < π/2, does not indicate bias, smaller is better.

    **Notes:** Represents the mean absolute error as a percentage of the observed values. Handles
    0s in the observed data. This metric is not as biased as MAPE by under-over predictions.

    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 arctangent absolute percentage error.

    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.mape(sim, obs)
    11.639226612630866

    References
    ----------
    - Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand
      forecasts. International Journal of Forecasting 32(3) 669-679.
    """

    # Treats 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 = simulated_array - observed_array
    b = np.abs(a / observed_array)
    return np.mean(np.arctan(b))
Compute the the Mean Arctangent Absolute Percentage Error (MAAPE).

    .. image:: /pictures/MAAPE.png

    **Range:** 0 ≤ MAAPE < π/2, does not indicate bias, smaller is better.

    **Notes:** Represents the mean absolute error as a percentage of the observed values. Handles
    0s in the observed data. This metric is not as biased as MAPE by under-over predictions.

    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 arctangent absolute percentage error.

    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.mape(sim, obs)
    11.639226612630866

    References
    ----------
    - Kim, S., Kim, H., 2016. A new metric of absolute percentage error for intermittent demand
      forecasts. International Journal of Forecasting 32(3) 669-679.