def METHOD_NAME(signal, delay=1, k=4, show=False, **kwargs):
    """**Phase Entropy (PhasEn)**
    Phase entropy (PhasEn or PhEn) has been developed by quantifying the distribution of the signal
    in accross *k* parts (of a two-dimensional phase space referred to as a second order difference
    plot (SODP). It build on the concept of :func:`Grid Entropy <entropy_grid>`, that uses
    :func:`Poincaré plot <.hrv_nonlinear>` as its basis.
    .. figure:: ../img/rohila2019.png
       :alt: Figure from Rohila et al. (2019).
       :target: https://doi.org/10.1088/1361-6579/ab499e
    Parameters
    ----------
    signal : Union[list, np.array, pd.Series]
        The signal (i.e., a time series) in the form of a vector of values.
    delay : int
        Time delay (often denoted *Tau* :math:`\\tau`, sometimes referred to as *lag*) in samples.
        See :func:`complexity_delay` to estimate the optimal value for this parameter.
    k : int
        The number of sections that the SODP is divided into. It is a coarse graining parameter
        that defines how fine the grid is. It is recommended to use even-numbered (preferably
        multiples of 4) partitions for sake of symmetry.
    show : bool
        Plot the Second Order Difference Plot (SODP).
    **kwargs : optional
        Other keyword arguments, such as the logarithmic ``base`` to use for
        :func:`entropy_shannon`.
    Returns
    -------
    phasen : float
        Phase Entropy
    info : dict
        A dictionary containing additional information regarding the parameters used.
    See Also
    --------
    entropy_shannon
    Examples
    ----------
    .. ipython:: python
      import neurokit2 as nk
      # Simulate a Signal
      signal = nk.signal_simulate(duration=2, sampling_rate=200, frequency=[5, 6], noise=0.5)
      # Compute Phase Entropy
      @savefig p_entropy_phase1.png scale=100%
      phasen, info = nk.entropy_phase(signal, k=4, show=True)
      @suppress
      plt.close()
    .. ipython:: python
      phasen
    .. ipython:: python
      @savefig p_entropy_phase2.png scale=100%
      phasen, info = nk.entropy_phase(signal, k=8, show=True)
      @suppress
      plt.close()
    References
    ----------
    * Rohila, A., & Sharma, A. (2019). Phase entropy: A new complexity measure for heart rate
      variability. Physiological Measurement, 40(10), 105006.
    """
    # Sanity checks
    if isinstance(signal, (np.ndarray, pd.DataFrame)) and signal.ndim > 1:
        raise ValueError(
            "Multidimensional inputs (e.g., matrices or multichannel data) are not supported yet."
        )
    info = {"k": k, "Delay": delay}
    # 1. Compute SODP axes
    y = signal[2 * delay :] - signal[delay:-delay]
    x = signal[delay:-delay] - signal[: -2 * delay]
    # 2. Compute the slope (angle theta) of each scatter point from the origin
    with np.errstate(divide="ignore", invalid="ignore"):
        theta = np.arctan(y / x)
        theta[np.logical_and((y < 0), (x < 0))] += np.pi
        theta[np.logical_and((y < 0), (x > 0))] += 2 * np.pi
        theta[np.logical_and((y > 0), (x < 0))] += np.pi
    # 3. The entire plot is divided into k sections having an angle span of 2pi*k radians each
    angles = np.linspace(0, 2 * np.pi, k + 1)
    # 4. The cumulative slope of each sector is obtained by adding the slope of each scatter point # within that sector
    # 5. The probability distribution of the slopes in each sector is computed
    freq = [
        np.sum(theta[np.logical_and((theta > angles[i]), (theta < angles[i + 1]))])
        for i in range(k)
    ]
    freq = np.array(freq) / np.sum(freq)
    # 6. the Shannon entropy computed from the distribution p(i)
    phasen, _ = entropy_shannon(freq=freq, **kwargs)
    # Normalize
    phasen = phasen / np.log(k)
    if show is True:
        Tx = np.zeros((k, len(theta)))
        for i in range(k):
            Temp = np.logical_and((theta > angles[i]), (theta < angles[i + 1]))
            Tx[i, Temp] = 1
        limx = np.ceil(np.max(np.abs([y, x])))
        Tx = Tx.astype(bool)
        Ys = np.sin(angles) * limx * np.sqrt(2)
        Xs = np.cos(angles) * limx * np.sqrt(2)
        colors = plt.get_cmap("jet")(np.linspace(0, 1, k))
        plt.figure()
        for i in range(k):
            plt.plot(x[Tx[i, :]], y[Tx[i, :]], ".", color=tuple(colors[i, :]))
            plt.plot(
                np.vstack((np.zeros(k + 1), Xs)), np.vstack((np.zeros(k + 1), Ys)), color="red"
            )
        plt.axis([-limx, limx, -limx, limx])
        plt.gca().set_aspect("equal", "box")
        plt.xlabel(r"$X(n +  \tau) - X(n)$"),
        plt.ylabel(r"$X(n + 2 \tau) - X(n + \tau)$")
        plt.xticks([-limx, 0, limx])
        plt.yticks([-limx, 0, limx])
        plt.title("Second Order Difference Plot (SODP)")
    return phasen, info