def sc(simulated_array, observed_array, replace_nan=None, replace_inf=None,
       remove_neg=False, remove_zero=False):
    """Compute the Spectral Correlation (SC).

    .. image:: /pictures/SC.png

    **Range:** -π/2 ≤ SA < π/2, closer to 0 is better.

    **Notes:** The spectral correlation metric measures the angle between the two vectors in
    hyperspace. It indicates how well the shape of the two series match – not magnitude.

    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 Spectral Correlation 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.sc(sim, obs)
    0.27991341383646606

    References
    ----------
    - Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
      data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
      pp. 163-166.
    """

    # 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 = np.dot(observed_array - np.mean(observed_array), simulated_array - np.mean(simulated_array))
    b = np.linalg.norm(observed_array - np.mean(observed_array))
    c = np.linalg.norm(simulated_array - np.mean(simulated_array))
    e = b * c
    return np.arccos(a / e)
Compute the Spectral Correlation (SC).

    .. image:: /pictures/SC.png

    **Range:** -π/2 ≤ SA < π/2, closer to 0 is better.

    **Notes:** The spectral correlation metric measures the angle between the two vectors in
    hyperspace. It indicates how well the shape of the two series match – not magnitude.

    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 Spectral Correlation 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.sc(sim, obs)
    0.27991341383646606

    References
    ----------
    - Robila, S.A., Gershman, A., 2005. Spectral matching accuracy in processing hyperspectral
      data, Signals, Circuits and Systems, 2005. ISSCS 2005. International Symposium on. IEEE,
      pp. 163-166.