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- from __future__ import division, print_function, absolute_import
- import numpy as np
- from numpy.testing import assert_equal, assert_allclose
- import scipy.special as sc
- def test_symmetries():
- np.random.seed(1234)
- a, h = np.random.rand(100), np.random.rand(100)
- assert_equal(sc.owens_t(h, a), sc.owens_t(-h, a))
- assert_equal(sc.owens_t(h, a), -sc.owens_t(h, -a))
- def test_special_cases():
- assert_equal(sc.owens_t(5, 0), 0)
- assert_allclose(sc.owens_t(0, 5), 0.5*np.arctan(5)/np.pi,
- rtol=5e-14)
- # Target value is 0.5*Phi(5)*(1 - Phi(5)) for Phi the CDF of the
- # standard normal distribution
- assert_allclose(sc.owens_t(5, 1), 1.4332574485503512543e-07,
- rtol=5e-14)
- def test_nans():
- assert_equal(sc.owens_t(20, np.nan), np.nan)
- assert_equal(sc.owens_t(np.nan, 20), np.nan)
- assert_equal(sc.owens_t(np.nan, np.nan), np.nan)
- def test_infs():
- h = 1
- res = 0.5*sc.erfc(h/np.sqrt(2))
- assert_allclose(sc.owens_t(h, np.inf), res, rtol=5e-14)
- assert_allclose(sc.owens_t(h, -np.inf), -res, rtol=5e-14)
- assert_equal(sc.owens_t(np.inf, 1), 0)
- assert_equal(sc.owens_t(-np.inf, 1), 0)
- assert_equal(sc.owens_t(np.inf, np.inf), 0)
- assert_equal(sc.owens_t(-np.inf, np.inf), 0)
- assert_equal(sc.owens_t(np.inf, -np.inf), -0.0)
- assert_equal(sc.owens_t(-np.inf, -np.inf), -0.0)
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