text: from numpy.testing import (assert_, assert_array_equal)
  import numpy as np
  import pytest
  from numpy.random import Generator, MT19937
  
  mt19937 = Generator(MT19937())
  
  
  class TestRegression:
  
      def test_VonMises_range(self):
          # Make sure generated random variables are in [-pi, pi].
          # Regression test for ticket #986.
          for mu in np.linspace(-7., 7., 5):
              r = mt19937.vonmises(mu, 1, 50)
              assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
  
      def test_hypergeometric_range(self):
          # Test for ticket #921
          assert_(np.all(mt19937.hypergeometric(3, 18, 11, size=10) < 4))
          assert_(np.all(mt19937.hypergeometric(18, 3, 11, size=10) > 0))
  
          # Test for ticket #5623
          args = (2**20 - 2, 2**20 - 2, 2**20 - 2)  # Check for 32-bit systems
          assert_(mt19937.hypergeometric(*args) > 0)
  
      def test_logseries_convergence(self):
          # Test for ticket #923
          N = 1000
          mt19937 = Generator(MT19937(0))
          rvsn = mt19937.logseries(0.8, size=N)
          # these two frequency counts should be close to theoretical
          # numbers with this large sample
          # theoretical large N result is 0.49706795
          freq = np.sum(rvsn == 1) / float(N)
          msg = f'Frequency was {freq:f}, should be > 0.45'
          assert_(freq > 0.45, msg)
          # theoretical large N result is 0.19882718
          freq = np.sum(rvsn == 2) / float(N)
          msg = f'Frequency was {freq:f}, should be < 0.23'
          assert_(freq < 0.23, msg)
  
      def test_shuffle_mixed_dimension(self):
          # Test for trac ticket #2074
          for t in [[1, 2, 3, None],
                    [(1, 1), (2, 2), (3, 3), None],
                    [1, (2, 2), (3, 3), None],
                    [(1, 1), 2, 3, None]]:
              mt19937 = Generator(MT19937(12345))
              shuffled = np.array(t, dtype=object)
              mt19937.shuffle(shuffled)
              expected = np.array([t[2], t[0], t[3], t[1]], dtype=object)
              assert_array_equal(np.array(shuffled, dtype=object), expected)
  
      def test_call_within_randomstate(self):
          # Check that custom BitGenerator does not call into global state
          res = np.array([1, 8, 0, 1, 5, 3, 3, 8, 1, 4])
          for i in range(3):
              mt19937 = Generator(MT19937(i))
              m = Generator(MT19937(4321))
              # If m.state is not honored, the result will change
              assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
  
      def test_multivariate_normal_size_types(self):
          # Test for multivariate_normal issue with 'size' argument.
          # Check that the multivariate_normal size argument can be a
          # numpy integer.
          mt19937.multivariate_normal([0], [[0]], size=1)
          mt19937.multivariate_normal([0], [[0]], size=np.int_(1))
          mt19937.multivariate_normal([0], [[0]], size=np.int64(1))
  
      def test_beta_small_parameters(self):
          # Test that beta with small a and b parameters does not produce
          # NaNs due to roundoff errors causing 0 / 0, gh-5851
          mt19937 = Generator(MT19937(1234567890))
          x = mt19937.beta(0.0001, 0.0001, size=100)
          assert_(not np.any(np.isnan(x)), 'Nans in mt19937.beta')
  
      def test_choice_sum_of_probs_tolerance(self):
          # The sum of probs should be 1.0 with some tolerance.
          # For low precision dtypes the tolerance was too tight.
          # See numpy github issue 6123.
          mt19937 = Generator(MT19937(1234))
          a = [1, 2, 3]
          counts = [4, 4, 2]
          for dt in np.float16, np.float32, np.float64:
              probs = np.array(counts, dtype=dt) / sum(counts)
              c = mt19937.choice(a, p=probs)
              assert_(c in a)
              with pytest.raises(ValueError):
                  mt19937.choice(a, p=probs*0.9)
  
      def test_shuffle_of_array_of_different_length_strings(self):
          # Test that permuting an array of different length strings
          # will not cause a segfault on garbage collection
          # Tests gh-7710
          mt19937 = Generator(MT19937(1234))
  
          a = np.array(['a', 'a' * 1000])
  
          for _ in range(100):
              mt19937.shuffle(a)
  
          # Force Garbage Collection - should not segfault.
          import gc
          gc.collect()
  
      def test_shuffle_of_array_of_objects(self):
          # Test that permuting an array of objects will not cause
          # a segfault on garbage collection.
          # See gh-7719
          mt19937 = Generator(MT19937(1234))
          a = np.array([np.arange(1), np.arange(4)], dtype=object)
  
          for _ in range(1000):
              mt19937.shuffle(a)
  
          # Force Garbage Collection - should not segfault.
          import gc
          gc.collect()
  
      def test_permutation_subclass(self):
          class N(np.ndarray):
              pass
  
          mt19937 = Generator(MT19937(1))
          orig = np.arange(3).view(N)
          perm = mt19937.permutation(orig)
          assert_array_equal(perm, np.array([2, 0, 1]))
          assert_array_equal(orig, np.arange(3).view(N))
  
          class M:
              a = np.arange(5)
  
              def __array__(self):
                  return self.a
  
          mt19937 = Generator(MT19937(1))
          m = M()
          perm = mt19937.permutation(m)
          assert_array_equal(perm, np.array([4, 1, 3, 0, 2]))
          assert_array_equal(m.__array__(), np.arange(5))
  
      def test_gamma_0(self):
          assert mt19937.standard_gamma(0.0) == 0.0
          assert_array_equal(mt19937.standard_gamma([0.0]), 0.0)
  
          actual = mt19937.standard_gamma([0.0], dtype='float')
          expected = np.array([0.], dtype=np.float32)
          assert_array_equal(actual, expected)
  