Numerical Integration Python

I need to reduce the running time for quad() in python (I am integrating some thousands integrals). I found a similar question in here where they suggested to do several integratio

Solution 1:

quadpy (a project of mine) is vectorized and can integrate a function over many domains (e.g., intervals) at once. You do have to choose your own integration method though.

import numpy
import quadpy

a = 0.0
b = 1.0
n = 100
start_points = numpy.linspace(a, b, n, endpoint=False)
h = (b-a) / n
end_points = start_points + h
intervals = numpy.array([start_points, end_points])

scheme = quadpy.line_segment.gauss_kronrod(3)
vals = scheme.integrate(numpy.exp, intervals)
print(vals)

[0.10050167 0.10151173 0.10253194 0.1035624  0.10460322 0.1056545
 0.10671635 0.10778886 0.10887216 0.10996634 0.11107152 0.11218781
 0.11331532 0.11445416 0.11560444 0.11676628 0.1179398  0.11912512
 0.12032235 0.12153161 0.12275302 0.12398671 0.12523279 0.1264914
 0.12776266 0.1290467  0.13034364 0.13165362 0.13297676 0.1343132
 0.13566307 0.1370265  0.13840364 0.13979462 0.14119958 0.14261866
 0.144052   0.14549975 0.14696204 0.14843904 0.14993087 0.15143771
 0.15295968 0.15449695 0.15604967 0.157618   0.15920208 0.16080209
 0.16241818 0.16405051 0.16569924 0.16736455 0.16904659 0.17074554
 0.17246156 0.17419482 0.17594551 0.17771379 0.17949985 0.18130385
 0.18312598 0.18496643 0.18682537 0.188703   0.1905995  0.19251505
 0.19444986 0.19640412 0.19837801 0.20037174 0.20238551 0.20441952
 0.20647397 0.20854907 0.21064502 0.21276204 0.21490033 0.21706012
 0.21924161 0.22144502 0.22367058 0.22591851 0.22818903 0.23048237
 0.23279875 0.23513842 0.2375016  0.23988853 0.24229945 0.2447346
 0.24719422 0.24967857 0.25218788 0.25472241 0.25728241 0.25986814
 0.26247986 0.26511783 0.2677823  0.27047356]