, optional
    If bool, determines whether to extrapolate to out-of-bounds points 
    based on first and last intervals, or to return NaNs. If None, 
    ``extrapolate`` is set to False.
    
Methods
-------
__call__
derivative
antiderivative
roots

See Also
--------
PchipInterpolator : PCHIP 1-D monotonic cubic interpolator.
CubicSpline : Cubic spline data interpolator.
PPoly : Piecewise polynomial in terms of coefficients and breakpoints

Notes
-----
.. versionadded:: 0.14

Use only for precise data, as the fitted curve passes through the given
points exactly. This routine is useful for plotting a pleasingly smooth
curve through a few given points for purposes of plotting.

Let :math:`\delta_i = (y_{i+1} - y_i) / (x_{i+1} - x_i)` be the slopes of
the interval :math:`\left[x_i, x_{i+1}\right)`. Akima's derivative at
:math:`x_i` is defined as:

.. math::

    d_i = \frac{w_1}{w_1 + w_2}\delta_{i-1} + \frac{w_2}{w_1 + w_2}\delta_i

In the Akima interpolation [1]_ (``method="akima"``), the weights are:

.. math::

    \begin{aligned}
    w_1 &= |\delta_{i+1} - \delta_i| \\
    w_2 &= |\delta_{i-1} - \delta_{i-2}|
    \end{aligned}

In the modified Akima interpolation [2]_ (``method="makima"``),
to eliminate overshoot and avoid edge cases of both numerator and
denominator being equal to 0, the weights are modified as follows:

.. math::

    \begin{align*}
    w_1 &= |\delta_{i+1} - \delta_i| + |\delta_{i+1} + \delta_i| / 2 \\
    w_2 &= |\delta_{i-1} - \delta_{i-2}| + |\delta_{i-1} + \delta_{i-2}| / 2
    \end{align*}

Examples
--------
Comparison of ``method="akima"`` and ``method="makima"``:

>>> import numpy as np
>>> from scipy.interpolate import Akima1DInterpolator
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(1, 7, 7)
>>> y = np.array([-1, -1, -1, 0, 1, 1, 1])
>>> xs = np.linspace(min(x), max(x), num=100)
>>> y_akima = Akima1DInterpolator(x, y, method="akima")(xs)
>>> y_makima = Akima1DInterpolator(x, y, method="makima")(xs)

>>> fig, ax = plt.subplots()
>>> ax.plot(x, y, "o", label="data")
>>> ax.plot(xs, y_akima, label="akima")
>>> ax.plot(xs, y_makima, label="makima")
>>> ax.legend()
>>> fig.show()

The overshoot that occurred in ``"akima"`` has been avoided in ``"makima"``.

References
----------
.. [1] A new method of interpolation and smooth curve fitting based
       on local procedures. Hiroshi Akima, J. ACM, October 1970, 17(4),
       589-602. :doi:`10.1145/321607.321609`
.. [2] Makima Piecewise Cubic Interpolation. Cleve Moler and Cosmin Ionita, 2019.
       https://blogs.mathworks.com/cleve/2019/04/29/makima-piecewise-cubic-interpolation/

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