bundles / scipy latest / scipy / special / _basic / jvp
function
scipy.special._basic:jvp
source: /scipy/special/_basic.py :817
Signature
def jvp ( v , z , n = 1 ) Summary
Compute derivatives of Bessel functions of the first kind.
Extended Summary
Compute the nth derivative of the Bessel function Jv with respect to z.
Parameters
v: array_like or floatOrder of Bessel function
z: complexArgument at which to evaluate the derivative; can be real or complex.
n: int, default 1Order of derivative. For 0 returns the Bessel function jv itself.
Returns
: scalar or ndarrayValues of the derivative of the Bessel function.
Notes
The derivative is computed using the relation DLFM 10.6.7 [2].
Examples
Compute the Bessel function of the first kind of order 0 and its first two derivatives at 1.from scipy.special import jvp
✓jvp(0, 1, 0), jvp(0, 1, 1), jvp(0, 1, 2)
✗jvp([0, 1, 2], 1, 1)
✗import numpy as np points = np.array([0., 1.5, 3.])✓
jvp(0, points, 1)
✗import matplotlib.pyplot as plt x = np.linspace(-10, 10, 1000) fig, ax = plt.subplots()✓
ax.plot(x, jvp(1, x, 0), label=r"$J_1$") ax.plot(x, jvp(1, x, 1), label=r"$J_1'$") ax.plot(x, jvp(1, x, 2), label=r"$J_1''$") ax.plot(x, jvp(1, x, 3), label=r"$J_1'''$") plt.legend()✗
plt.show()
✓
Aliases
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scipy.special.jvp