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- from __future__ import division, print_function, absolute_import
- import numpy as np
- from numpy import cos, sin, pi
- from numpy.testing import assert_equal, \
- assert_almost_equal, assert_allclose, assert_
- from scipy._lib._numpy_compat import suppress_warnings
- from scipy.integrate import (quadrature, romberg, romb, newton_cotes,
- cumtrapz, quad, simps, fixed_quad)
- from scipy.integrate.quadrature import AccuracyWarning
- class TestFixedQuad(object):
- def test_scalar(self):
- n = 4
- func = lambda x: x**(2*n - 1)
- expected = 1/(2*n)
- got, _ = fixed_quad(func, 0, 1, n=n)
- # quadrature exact for this input
- assert_allclose(got, expected, rtol=1e-12)
- def test_vector(self):
- n = 4
- p = np.arange(1, 2*n)
- func = lambda x: x**p[:,None]
- expected = 1/(p + 1)
- got, _ = fixed_quad(func, 0, 1, n=n)
- assert_allclose(got, expected, rtol=1e-12)
- class TestQuadrature(object):
- def quad(self, x, a, b, args):
- raise NotImplementedError
- def test_quadrature(self):
- # Typical function with two extra arguments:
- def myfunc(x, n, z): # Bessel function integrand
- return cos(n*x-z*sin(x))/pi
- val, err = quadrature(myfunc, 0, pi, (2, 1.8))
- table_val = 0.30614353532540296487
- assert_almost_equal(val, table_val, decimal=7)
- def test_quadrature_rtol(self):
- def myfunc(x, n, z): # Bessel function integrand
- return 1e90 * cos(n*x-z*sin(x))/pi
- val, err = quadrature(myfunc, 0, pi, (2, 1.8), rtol=1e-10)
- table_val = 1e90 * 0.30614353532540296487
- assert_allclose(val, table_val, rtol=1e-10)
- def test_quadrature_miniter(self):
- # Typical function with two extra arguments:
- def myfunc(x, n, z): # Bessel function integrand
- return cos(n*x-z*sin(x))/pi
- table_val = 0.30614353532540296487
- for miniter in [5, 52]:
- val, err = quadrature(myfunc, 0, pi, (2, 1.8), miniter=miniter)
- assert_almost_equal(val, table_val, decimal=7)
- assert_(err < 1.0)
- def test_quadrature_single_args(self):
- def myfunc(x, n):
- return 1e90 * cos(n*x-1.8*sin(x))/pi
- val, err = quadrature(myfunc, 0, pi, args=2, rtol=1e-10)
- table_val = 1e90 * 0.30614353532540296487
- assert_allclose(val, table_val, rtol=1e-10)
- def test_romberg(self):
- # Typical function with two extra arguments:
- def myfunc(x, n, z): # Bessel function integrand
- return cos(n*x-z*sin(x))/pi
- val = romberg(myfunc, 0, pi, args=(2, 1.8))
- table_val = 0.30614353532540296487
- assert_almost_equal(val, table_val, decimal=7)
- def test_romberg_rtol(self):
- # Typical function with two extra arguments:
- def myfunc(x, n, z): # Bessel function integrand
- return 1e19*cos(n*x-z*sin(x))/pi
- val = romberg(myfunc, 0, pi, args=(2, 1.8), rtol=1e-10)
- table_val = 1e19*0.30614353532540296487
- assert_allclose(val, table_val, rtol=1e-10)
- def test_romb(self):
- assert_equal(romb(np.arange(17)), 128)
- def test_romb_gh_3731(self):
- # Check that romb makes maximal use of data points
- x = np.arange(2**4+1)
- y = np.cos(0.2*x)
- val = romb(y)
- val2, err = quad(lambda x: np.cos(0.2*x), x.min(), x.max())
- assert_allclose(val, val2, rtol=1e-8, atol=0)
- # should be equal to romb with 2**k+1 samples
- with suppress_warnings() as sup:
- sup.filter(AccuracyWarning, "divmax .4. exceeded")
- val3 = romberg(lambda x: np.cos(0.2*x), x.min(), x.max(), divmax=4)
- assert_allclose(val, val3, rtol=1e-12, atol=0)
- def test_non_dtype(self):
- # Check that we work fine with functions returning float
- import math
- valmath = romberg(math.sin, 0, 1)
- expected_val = 0.45969769413185085
- assert_almost_equal(valmath, expected_val, decimal=7)
- def test_newton_cotes(self):
- """Test the first few degrees, for evenly spaced points."""
- n = 1
- wts, errcoff = newton_cotes(n, 1)
- assert_equal(wts, n*np.array([0.5, 0.5]))
- assert_almost_equal(errcoff, -n**3/12.0)
- n = 2
- wts, errcoff = newton_cotes(n, 1)
- assert_almost_equal(wts, n*np.array([1.0, 4.0, 1.0])/6.0)
- assert_almost_equal(errcoff, -n**5/2880.0)
- n = 3
- wts, errcoff = newton_cotes(n, 1)
- assert_almost_equal(wts, n*np.array([1.0, 3.0, 3.0, 1.0])/8.0)
- assert_almost_equal(errcoff, -n**5/6480.0)
- n = 4
- wts, errcoff = newton_cotes(n, 1)
- assert_almost_equal(wts, n*np.array([7.0, 32.0, 12.0, 32.0, 7.0])/90.0)
- assert_almost_equal(errcoff, -n**7/1935360.0)
- def test_newton_cotes2(self):
- """Test newton_cotes with points that are not evenly spaced."""
- x = np.array([0.0, 1.5, 2.0])
- y = x**2
- wts, errcoff = newton_cotes(x)
- exact_integral = 8.0/3
- numeric_integral = np.dot(wts, y)
- assert_almost_equal(numeric_integral, exact_integral)
- x = np.array([0.0, 1.4, 2.1, 3.0])
- y = x**2
- wts, errcoff = newton_cotes(x)
- exact_integral = 9.0
- numeric_integral = np.dot(wts, y)
- assert_almost_equal(numeric_integral, exact_integral)
- def test_simps(self):
- y = np.arange(17)
- assert_equal(simps(y), 128)
- assert_equal(simps(y, dx=0.5), 64)
- assert_equal(simps(y, x=np.linspace(0, 4, 17)), 32)
- y = np.arange(4)
- x = 2**y
- assert_equal(simps(y, x=x, even='avg'), 13.875)
- assert_equal(simps(y, x=x, even='first'), 13.75)
- assert_equal(simps(y, x=x, even='last'), 14)
- class TestCumtrapz(object):
- def test_1d(self):
- x = np.linspace(-2, 2, num=5)
- y = x
- y_int = cumtrapz(y, x, initial=0)
- y_expected = [0., -1.5, -2., -1.5, 0.]
- assert_allclose(y_int, y_expected)
- y_int = cumtrapz(y, x, initial=None)
- assert_allclose(y_int, y_expected[1:])
- def test_y_nd_x_nd(self):
- x = np.arange(3 * 2 * 4).reshape(3, 2, 4)
- y = x
- y_int = cumtrapz(y, x, initial=0)
- y_expected = np.array([[[0., 0.5, 2., 4.5],
- [0., 4.5, 10., 16.5]],
- [[0., 8.5, 18., 28.5],
- [0., 12.5, 26., 40.5]],
- [[0., 16.5, 34., 52.5],
- [0., 20.5, 42., 64.5]]])
- assert_allclose(y_int, y_expected)
- # Try with all axes
- shapes = [(2, 2, 4), (3, 1, 4), (3, 2, 3)]
- for axis, shape in zip([0, 1, 2], shapes):
- y_int = cumtrapz(y, x, initial=3.45, axis=axis)
- assert_equal(y_int.shape, (3, 2, 4))
- y_int = cumtrapz(y, x, initial=None, axis=axis)
- assert_equal(y_int.shape, shape)
- def test_y_nd_x_1d(self):
- y = np.arange(3 * 2 * 4).reshape(3, 2, 4)
- x = np.arange(4)**2
- # Try with all axes
- ys_expected = (
- np.array([[[4., 5., 6., 7.],
- [8., 9., 10., 11.]],
- [[40., 44., 48., 52.],
- [56., 60., 64., 68.]]]),
- np.array([[[2., 3., 4., 5.]],
- [[10., 11., 12., 13.]],
- [[18., 19., 20., 21.]]]),
- np.array([[[0.5, 5., 17.5],
- [4.5, 21., 53.5]],
- [[8.5, 37., 89.5],
- [12.5, 53., 125.5]],
- [[16.5, 69., 161.5],
- [20.5, 85., 197.5]]]))
- for axis, y_expected in zip([0, 1, 2], ys_expected):
- y_int = cumtrapz(y, x=x[:y.shape[axis]], axis=axis, initial=None)
- assert_allclose(y_int, y_expected)
- def test_x_none(self):
- y = np.linspace(-2, 2, num=5)
- y_int = cumtrapz(y)
- y_expected = [-1.5, -2., -1.5, 0.]
- assert_allclose(y_int, y_expected)
- y_int = cumtrapz(y, initial=1.23)
- y_expected = [1.23, -1.5, -2., -1.5, 0.]
- assert_allclose(y_int, y_expected)
- y_int = cumtrapz(y, dx=3)
- y_expected = [-4.5, -6., -4.5, 0.]
- assert_allclose(y_int, y_expected)
- y_int = cumtrapz(y, dx=3, initial=1.23)
- y_expected = [1.23, -4.5, -6., -4.5, 0.]
- assert_allclose(y_int, y_expected)
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