# scipy.special.yve#

scipy.special.yve(v, z, out=None) = <ufunc 'yve'>#

Exponentially scaled Bessel function of the second kind of real order.

Returns the exponentially scaled Bessel function of the second kind of real order v at complex z:

yve(v, z) = yv(v, z) * exp(-abs(z.imag))

Parameters:
varray_like

Order (float).

zarray_like

Argument (float or complex).

outndarray, optional

Optional output array for the function results

Returns:
Yscalar or ndarray

Value of the exponentially scaled Bessel function.

yv

Unscaled Bessel function of the second kind of real order.

Notes

For positive v values, the computation is carried out using the AMOS [1] zbesy routine, which exploits the connection to the Hankel Bessel functions $$H_v^{(1)}$$ and $$H_v^{(2)}$$,

$Y_v(z) = \frac{1}{2\imath} (H_v^{(1)} - H_v^{(2)}).$

For negative v values the formula,

$Y_{-v}(z) = Y_v(z) \cos(\pi v) + J_v(z) \sin(\pi v)$

is used, where $$J_v(z)$$ is the Bessel function of the first kind, computed using the AMOS routine zbesj. 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).

Exponentially scaled Bessel functions are useful for large z: for these, the unscaled Bessel functions can easily under-or overflow.

References

[1]

Donald E. Amos, “AMOS, A Portable Package for Bessel Functions of a Complex Argument and Nonnegative Order”, http://netlib.org/amos/

Examples

Compare the output of yv and yve for large complex arguments for z by computing their values for order v=1 at z=1000j. We see that yv returns nan but yve returns a finite number:

>>> import numpy as np
>>> from scipy.special import yv, yve
>>> v = 1
>>> z = 1000j
>>> yv(v, z), yve(v, z)
((nan+nanj), (-0.012610930256928629+7.721967686709076e-19j))


For real arguments for z, yve returns the same as yv up to floating point errors.

>>> v, z = 1, 1000
>>> yv(v, z), yve(v, z)
(-0.02478433129235178, -0.02478433129235179)


The function can be evaluated for several orders at the same time by providing a list or NumPy array for v:

>>> yve([1, 2, 3], 1j)
array([-0.20791042+0.14096627j,  0.38053618-0.04993878j,
0.00815531-1.66311097j])


In the same way, the function can be evaluated at several points in one call by providing a list or NumPy array for z:

>>> yve(1, np.array([1j, 2j, 3j]))
array([-0.20791042+0.14096627j, -0.21526929+0.01205044j,
-0.19682671+0.00127278j])


It is also possible to evaluate several orders at several points at the same time by providing arrays for v and z with broadcasting compatible shapes. Compute yve for two different orders v and three points z resulting in a 2x3 array.

>>> v = np.array([[1], [2]])
>>> z = np.array([3j, 4j, 5j])
>>> v.shape, z.shape
((2, 1), (3,))

>>> yve(v, z)
array([[-1.96826713e-01+1.27277544e-03j, -1.78750840e-01+1.45558819e-04j,
-1.63972267e-01+1.73494110e-05j],
[1.94960056e-03-1.11782545e-01j,  2.02902325e-04-1.17626501e-01j,
2.27727687e-05-1.17951906e-01j]])