text: 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)
  