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177 lines
5.3 KiB
Python
177 lines
5.3 KiB
Python
import pytest
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import numpy as np
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from numpy.testing import assert_allclose
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from scipy.integrate import quad_vec
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quadrature_params = pytest.mark.parametrize(
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'quadrature', [None, "gk15", "gk21", "trapezoid"])
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@quadrature_params
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def test_quad_vec_simple(quadrature):
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n = np.arange(10)
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f = lambda x: x**n
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for epsabs in [0.1, 1e-3, 1e-6]:
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if quadrature == 'trapezoid' and epsabs < 1e-4:
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# slow: skip
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continue
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kwargs = dict(epsabs=epsabs, quadrature=quadrature)
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exact = 2**(n+1)/(n + 1)
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res, err = quad_vec(f, 0, 2, norm='max', **kwargs)
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assert_allclose(res, exact, rtol=0, atol=epsabs)
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res, err = quad_vec(f, 0, 2, norm='2', **kwargs)
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assert np.linalg.norm(res - exact) < epsabs
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res, err = quad_vec(f, 0, 2, norm='max', points=(0.5, 1.0), **kwargs)
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assert_allclose(res, exact, rtol=0, atol=epsabs)
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res, err, *rest = quad_vec(f, 0, 2, norm='max',
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epsrel=1e-8,
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full_output=True,
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limit=10000,
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**kwargs)
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assert_allclose(res, exact, rtol=0, atol=epsabs)
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@quadrature_params
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def test_quad_vec_simple_inf(quadrature):
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f = lambda x: 1 / (1 + np.float64(x)**2)
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for epsabs in [0.1, 1e-3, 1e-6]:
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if quadrature == 'trapezoid' and epsabs < 1e-4:
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# slow: skip
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continue
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kwargs = dict(norm='max', epsabs=epsabs, quadrature=quadrature)
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res, err = quad_vec(f, 0, np.inf, **kwargs)
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assert_allclose(res, np.pi/2, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, 0, -np.inf, **kwargs)
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assert_allclose(res, -np.pi/2, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, -np.inf, 0, **kwargs)
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assert_allclose(res, np.pi/2, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, np.inf, 0, **kwargs)
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assert_allclose(res, -np.pi/2, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, -np.inf, np.inf, **kwargs)
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assert_allclose(res, np.pi, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, np.inf, -np.inf, **kwargs)
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assert_allclose(res, -np.pi, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, np.inf, np.inf, **kwargs)
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assert_allclose(res, 0, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, -np.inf, -np.inf, **kwargs)
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assert_allclose(res, 0, rtol=0, atol=max(epsabs, err))
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res, err = quad_vec(f, 0, np.inf, points=(1.0, 2.0), **kwargs)
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assert_allclose(res, np.pi/2, rtol=0, atol=max(epsabs, err))
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f = lambda x: np.sin(x + 2) / (1 + x**2)
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exact = np.pi / np.e * np.sin(2)
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epsabs = 1e-5
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res, err, info = quad_vec(f, -np.inf, np.inf, limit=1000, norm='max', epsabs=epsabs,
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quadrature=quadrature, full_output=True)
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assert info.status == 1
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assert_allclose(res, exact, rtol=0, atol=max(epsabs, 1.5 * err))
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def _lorenzian(x):
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return 1 / (1 + x**2)
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def test_quad_vec_pool():
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from multiprocessing.dummy import Pool
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f = _lorenzian
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res, err = quad_vec(f, -np.inf, np.inf, norm='max', epsabs=1e-4, workers=4)
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assert_allclose(res, np.pi, rtol=0, atol=1e-4)
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with Pool(10) as pool:
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f = lambda x: 1 / (1 + x**2)
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res, err = quad_vec(f, -np.inf, np.inf, norm='max', epsabs=1e-4, workers=pool.map)
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assert_allclose(res, np.pi, rtol=0, atol=1e-4)
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@quadrature_params
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def test_num_eval(quadrature):
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def f(x):
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count[0] += 1
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return x**5
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count = [0]
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res = quad_vec(f, 0, 1, norm='max', full_output=True, quadrature=quadrature)
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assert res[2].neval == count[0]
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def test_info():
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def f(x):
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return np.ones((3, 2, 1))
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res, err, info = quad_vec(f, 0, 1, norm='max', full_output=True)
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assert info.success == True
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assert info.status == 0
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assert info.message == 'Target precision reached.'
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assert info.neval > 0
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assert info.intervals.shape[1] == 2
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assert info.integrals.shape == (info.intervals.shape[0], 3, 2, 1)
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assert info.errors.shape == (info.intervals.shape[0],)
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def test_nan_inf():
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def f_nan(x):
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return np.nan
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def f_inf(x):
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return np.inf if x < 0.1 else 1/x
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res, err, info = quad_vec(f_nan, 0, 1, full_output=True)
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assert info.status == 3
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res, err, info = quad_vec(f_inf, 0, 1, full_output=True)
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assert info.status == 3
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@pytest.mark.parametrize('a,b', [(0, 1), (0, np.inf), (np.inf, 0),
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(-np.inf, np.inf), (np.inf, -np.inf)])
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def test_points(a, b):
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# Check that initial interval splitting is done according to
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# `points`, by checking that consecutive sets of 15 point (for
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# gk15) function evaluations lie between `points`
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points = (0, 0.25, 0.5, 0.75, 1.0)
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points += tuple(-x for x in points)
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quadrature_points = 15
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interval_sets = []
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count = 0
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def f(x):
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nonlocal count
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if count % quadrature_points == 0:
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interval_sets.append(set())
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count += 1
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interval_sets[-1].add(float(x))
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return 0.0
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quad_vec(f, a, b, points=points, quadrature='gk15', limit=0)
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# Check that all point sets lie in a single `points` interval
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for p in interval_sets:
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j = np.searchsorted(sorted(points), tuple(p))
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assert np.all(j == j[0])
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