exp(v\pi\imath/2) I_v(-\imath z)\qquad (\Im z > 0)

        J_v(z) = \exp(-v\pi\imath/2) I_v(\imath z)\qquad (\Im z < 0)

    For negative `v` values the formula,

    .. math:: J_{-v}(z) = J_v(z) \cos(\pi v) - Y_v(z) \sin(\pi v)

    is used, where :math:`Y_v(z)` is the Bessel function of the second
    kind, computed using the AMOS routine `zbesy`.  Note that the second
    term is exactly zero for integer `v`; to improve accuracy the second
    term is explicitly omitted for `v` values such that `v = floor(v)`.

    Not to be confused with the spherical Bessel functions (see `spherical_jn`).

    References
    ----------
    .. [1] Donald E. Amos, "AMOS, A Portable Package for Bessel Functions
           of a Complex Argument and Nonnegative Order",
           http://netlib.org/amos/

    Examples
    --------
    Evaluate the function of order 0 at one point.

    >>> from scipy.special import jv
    >>> jv(0, 1.)
    0.7651976865579666

    Evaluate the function at one point for different orders.

    >>> jv(0, 1.), jv(1, 1.), jv(1.5, 1.)
    (0.7651976865579666, 0.44005058574493355, 0.24029783912342725)

    The evaluation for different orders can be carried out in one call by
    providing a list or NumPy array as argument for the `v` parameter:

    >>> jv([0, 1, 1.5], 1.)
    array([0.76519769, 0.44005059, 0.24029784])

    Evaluate the function at several points for order 0 by providing an
    array for `z`.

    >>> import numpy as np
    >>> points = np.array([-2., 0., 3.])
    >>> jv(0, points)
    array([ 0.22389078,  1.        , -0.26005195])

    If `z` is an array, the order parameter `v` must be broadcastable to
    the correct shape if different orders shall be computed in one call.
    To calculate the orders 0 and 1 for an 1D array:

    >>> orders = np.array([[0], [1]])
    >>> orders.shape
    (2, 1)

    >>> jv(orders, points)
    array([[ 0.22389078,  1.        , -0.26005195],
           [-0.57672481,  0.        ,  0.33905896]])

    Plot the functions of order 0 to 3 from -10 to 10.

    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots()
    >>> x = np.linspace(-10., 10., 1000)
    >>> for i in range(4):
    ...     ax.plot(x, jv(i, x), label=f'$J_{i!r}$')
    >>> ax.legend()
    >>> plt.show()

    Ú