old_contents: """File dedicated to test ``dipy.core.rng`` module."""
  
  from scipy.stats import chisquare
  from dipy.core import rng
  import numpy.testing as npt
  
  
  def test_wichmann_hill2006():
      rng.ix, rng.iy, rng.iz, rng.it = 100001, 200002, 300003, 400004
      n_generated = [rng.WichmannHill2006() for i in range(10000)]
      # The chi-squared test statistic as result and The p-value of the test
      chisq, pvalue = chisquare(n_generated)
      # P-values equal 1 show evidence of the null hypothesis which indicates
      # that it is uniformly distributed. This is what we want to check here
      npt.assert_almost_equal(pvalue, 1.0)
  
  
  def test_wichmann_hill1982():
      rng.ix, rng.iy, rng.iz, rng.it = 100001, 200002, 300003, 400004
      n_generated = [rng.WichmannHill1982() for i in range(10000)]
      chisq, pvalue = chisquare(n_generated)
      # P-values equal 1 show evidence of the null hypothesis which indicates
      # that it is uniformly distributed. This is what we want to check here
      npt.assert_almost_equal(pvalue, 1.0)
  
  
  def test_LEcuyer():
      rng.s1, rng.s2 = 100001, 200002
      n_generated = [rng.LEcuyer() for i in range(10000)]
      chisq, pvalue = chisquare(n_generated)
      # P-values equal 1 show evidence of the null hypothesis which indicates
      # that it is uniformly distributed. This is what we want to check here
      npt.assert_almost_equal(pvalue, 1.0)
  
  
  if __name__ == "__main__":
      test_LEcuyer()
      test_wichmann_hill2006()
      test_wichmann_hill1982()
  
new_contents: """File dedicated to test ``dipy.core.rng`` module."""
  
  from scipy.stats import chisquare
  from dipy.core import rng
  import numpy.testing as npt
  
  
  def test_wichmann_hill2006():
      n_generated = [rng.WichmannHill2006() for i in range(10000)]
      # The chi-squared test statistic as result and The p-value of the test
      chisq, pvalue = chisquare(n_generated)
      # P-values equal 1 show evidence of the null hypothesis which indicates
      # that it is uniformly distributed. This is what we want to check here
      npt.assert_almost_equal(pvalue, 1.0)
      npt.assert_raises(ValueError, rng.WichmannHill2006, ix=0)
  
  
  def test_wichmann_hill1982():
      n_generated = [rng.WichmannHill1982() for i in range(10000)]
      chisq, pvalue = chisquare(n_generated)
      # P-values equal 1 show evidence of the null hypothesis which indicates
      # that it is uniformly distributed. This is what we want to check here
      npt.assert_almost_equal(pvalue, 1.0)
      npt.assert_raises(ValueError, rng.WichmannHill1982, iz=0)
  
  
  def test_LEcuyer():
      n_generated = [rng.LEcuyer() for i in range(10000)]
      chisq, pvalue = chisquare(n_generated)
      # P-values equal 1 show evidence of the null hypothesis which indicates
      # that it is uniformly distributed. This is what we want to check here
      npt.assert_almost_equal(pvalue, 1.0)
      npt.assert_raises(ValueError, rng.LEcuyer, s2=0)
  
  
  if __name__ == "__main__":
      test_LEcuyer()
      test_wichmann_hill2006()
      test_wichmann_hill1982()
  
subject: update tests