Vous êtes un professionnel et vous avez besoin d'une formation ? RAG (Retrieval-Augmented Generation)
et Fine Tuning d'un LLM Voir le programme détaillé

Module « scipy.special »

Fonction besselpoly - module scipy.special

Signature de la fonction besselpoly

def besselpoly(*args, **kwargs) 

Description

help(scipy.special.besselpoly)

besselpoly(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature])


    besselpoly(a, lmb, nu, out=None)

    Weighted integral of the Bessel function of the first kind.

    Computes

    .. math::

       \int_0^1 x^\lambda J_\nu(2 a x) \, dx

    where :math:`J_\nu` is a Bessel function and :math:`\lambda=lmb`,
    :math:`\nu=nu`.

    Parameters
    ----------
    a : array_like
        Scale factor inside the Bessel function.
    lmb : array_like
        Power of `x`
    nu : array_like
        Order of the Bessel function.
    out : ndarray, optional
        Optional output array for the function results.

    Returns
    -------
    scalar or ndarray
        Value of the integral.

    References
    ----------
    .. [1] Cephes Mathematical Functions Library,
           http://www.netlib.org/cephes/

    Examples
    --------
    Evaluate the function for one parameter set.

    >>> from scipy.special import besselpoly
    >>> besselpoly(1, 1, 1)
    0.24449718372863877

    Evaluate the function for different scale factors.

    >>> import numpy as np
    >>> factors = np.array([0., 3., 6.])
    >>> besselpoly(factors, 1, 1)
    array([ 0.        , -0.00549029,  0.00140174])

    Plot the function for varying powers, orders and scales.

    >>> import matplotlib.pyplot as plt
    >>> fig, ax = plt.subplots()
    >>> powers = np.linspace(0, 10, 100)
    >>> orders = [1, 2, 3]
    >>> scales = [1, 2]
    >>> all_combinations = [(order, scale) for order in orders
    ...                     for scale in scales]
    >>> for order, scale in all_combinations:
    ...     ax.plot(powers, besselpoly(scale, powers, order),
    ...             label=rf"$\nu={order}, a={scale}$")
    >>> ax.legend()
    >>> ax.set_xlabel(r"$\lambda$")
    >>> ax.set_ylabel(r"$\int_0^1 x^{\lambda} J_{\nu}(2ax)\,dx$")
    >>> plt.show()
    

Vous êtes un professionnel et vous avez besoin d'une formation ? Programmation Python
Les compléments Voir le programme détaillé