Implements a collection of scipy.special functions with support for complex arguments during the Numba nopython mode.

Functions

Function Name	scipy
gamma	scipy.special.gamma

Performance

Scipy is already optimized well, so using Numba likely will not improve the speed of the mathematical function in itself and these implementations might even be less optimized. The advantage of these function comes from allowing much larger blocks of nopython mode, which can profit much better from parallelization.

Consider the following example:

from numba import njit, prange


@njit(parallel=True)
def prange_test(A):
    s = 0
    for i in prange(A.shape[0]):
        s += my_func(A[i])
    return s

The function prange_test contains a prange loop that may be parallelized with Numba. However, if it contains functions that are not supported by Numba, this is no longer possible. As a result, having a Numba compatible my_func function can be benefitial, even if it may be slower than an equivalent function from an existing library.