def test_numpy_random_random(self):
        cfunc = self._compile_array_dist("random", 1)
        size = (30, 30)
        res = cfunc(size)
        self.assertIsInstance(res, np.ndarray)
        self.assertEqual(res.shape, size)
        self.assertEqual(res.dtype, np.dtype('float64'))
        # Results are within expected bounds
        self.assertTrue(np.all(res >= 0.0))
        self.assertTrue(np.all(res < 1.0))
        # Crude statistical tests
        self.assertTrue(np.any(res <= 0.1))
        self.assertTrue(np.any(res >= 0.9))
        mean = res.mean()
        self.assertGreaterEqual(mean, 0.45)
        self.assertLessEqual(mean, 0.55)

    # Sanity-check various distributions.  For convenience, we only check
    # those distributions that produce the exact same values as Numpy's.
dataset/ETHPy150Open numba/numba/numba/tests/test_random.py/TestRandomArrays.test_numpy_random_random/original