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import numpy as np from scipy import integrate from scipy.special import erf from scipy.special import j0 import time import quadpy q = np.linspace(0.03, 1.0, 10000) def f(t): return t * 0.5 * (erf((t - 40) / 3) - 1) * j0(np.multiply.outer(q, t)) start = time.time() y, _ = integrate.quad_vec(f, 0, 50) end = time.time() print(end - start) start = time.time() y1, _ = quadpy.quad(f, 0, 50) end = time.time() print(end - start)
0.0819394588470459 0.4957756996154785
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The speed in quadpy very much depends on the chosen Kronrod degree. The higher the degree, the faster the above example.
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The text was updated successfully, but these errors were encountered: