FM / tests

/opt/imh-python/lib/python3.9/site-packages/numpy/random/tests/

/opt/imh-python/lib/python3.9/site-packages/numpy/random/tests

Name	Size	Mode	Actions
data/	-	0755	rm
__pycache__/	-	0755	rm
test_direct.py	17779	0644	edit dl rm
test_extending.py	4038	0644	edit dl rm
test_generator_mt19937.py	115477	0644	edit dl rm
test_generator_mt19937_regressions.py	6387	0644	edit dl rm
test_random.py	69953	0644	edit dl rm
test_randomstate.py	84877	0644	edit dl rm
test_randomstate_regression.py	7917	0644	edit dl rm
test_regression.py	5439	0644	edit dl rm
test_seed_sequence.py	3311	0644	edit dl rm
test_smoke.py	28183	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /opt/imh-python/lib/python3.9/site-packages/numpy/random/tests/test_smoke.py (28183B)

import pickle
from functools import partial

import numpy as np
import pytest
from numpy.testing import assert_equal, assert_, assert_array_equal
from numpy.random import (Generator, MT19937, PCG64, PCG64DXSM, Philox, SFC64)

@pytest.fixture(scope='module',
                params=(np.bool_, np.int8, np.int16, np.int32, np.int64,
                        np.uint8, np.uint16, np.uint32, np.uint64))
def dtype(request):
    return request.param

def params_0(f):
    val = f()
    assert_(np.isscalar(val))
    val = f(10)
    assert_(val.shape == (10,))
    val = f((10, 10))
    assert_(val.shape == (10, 10))
    val = f((10, 10, 10))
    assert_(val.shape == (10, 10, 10))
    val = f(size=(5, 5))
    assert_(val.shape == (5, 5))

def params_1(f, bounded=False):
    a = 5.0
    b = np.arange(2.0, 12.0)
    c = np.arange(2.0, 102.0).reshape((10, 10))
    d = np.arange(2.0, 1002.0).reshape((10, 10, 10))
    e = np.array([2.0, 3.0])
    g = np.arange(2.0, 12.0).reshape((1, 10, 1))
    if bounded:
        a = 0.5
        b = b / (1.5 * b.max())
        c = c / (1.5 * c.max())
        d = d / (1.5 * d.max())
        e = e / (1.5 * e.max())
        g = g / (1.5 * g.max())

# Scalar
    f(a)
    # Scalar - size
    f(a, size=(10, 10))
    # 1d
    f(b)
    # 2d
    f(c)
    # 3d
    f(d)
    # 1d size
    f(b, size=10)
    # 2d - size - broadcast
    f(e, size=(10, 2))
    # 3d - size
    f(g, size=(10, 10, 10))

def comp_state(state1, state2):
    identical = True
    if isinstance(state1, dict):
        for key in state1:
            identical &= comp_state(state1[key], state2[key])
    elif type(state1) != type(state2):
        identical &= type(state1) == type(state2)
    else:
        if (isinstance(state1, (list, tuple, np.ndarray)) and isinstance(
                state2, (list, tuple, np.ndarray))):
            for s1, s2 in zip(state1, state2):
                identical &= comp_state(s1, s2)
        else:
            identical &= state1 == state2
    return identical

def warmup(rg, n=None):
    if n is None:
        n = 11 + np.random.randint(0, 20)
    rg.standard_normal(n)
    rg.standard_normal(n)
    rg.standard_normal(n, dtype=np.float32)
    rg.standard_normal(n, dtype=np.float32)
    rg.integers(0, 2 ** 24, n, dtype=np.uint64)
    rg.integers(0, 2 ** 48, n, dtype=np.uint64)
    rg.standard_gamma(11.0, n)
    rg.standard_gamma(11.0, n, dtype=np.float32)
    rg.random(n, dtype=np.float64)
    rg.random(n, dtype=np.float32)

class RNG:
    @classmethod
    def setup_class(cls):
        # Overridden in test classes. Place holder to silence IDE noise
        cls.bit_generator = PCG64
        cls.advance = None
        cls.seed = [12345]
        cls.rg = Generator(cls.bit_generator(*cls.seed))
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 64
        cls._extra_setup()

@classmethod
    def _extra_setup(cls):
        cls.vec_1d = np.arange(2.0, 102.0)
        cls.vec_2d = np.arange(2.0, 102.0)[None, :]
        cls.mat = np.arange(2.0, 102.0, 0.01).reshape((100, 100))
        cls.seed_error = TypeError

def _reset_state(self):
        self.rg.bit_generator.state = self.initial_state

def test_init(self):
        rg = Generator(self.bit_generator())
        state = rg.bit_generator.state
        rg.standard_normal(1)
        rg.standard_normal(1)
        rg.bit_generator.state = state
        new_state = rg.bit_generator.state
        assert_(comp_state(state, new_state))

def test_advance(self):
        state = self.rg.bit_generator.state
        if hasattr(self.rg.bit_generator, 'advance'):
            self.rg.bit_generator.advance(self.advance)
            assert_(not comp_state(state, self.rg.bit_generator.state))
        else:
            bitgen_name = self.rg.bit_generator.__class__.__name__
            pytest.skip(f'Advance is not supported by {bitgen_name}')

def test_jump(self):
        state = self.rg.bit_generator.state
        if hasattr(self.rg.bit_generator, 'jumped'):
            bit_gen2 = self.rg.bit_generator.jumped()
            jumped_state = bit_gen2.state
            assert_(not comp_state(state, jumped_state))
            self.rg.random(2 * 3 * 5 * 7 * 11 * 13 * 17)
            self.rg.bit_generator.state = state
            bit_gen3 = self.rg.bit_generator.jumped()
            rejumped_state = bit_gen3.state
            assert_(comp_state(jumped_state, rejumped_state))
        else:
            bitgen_name = self.rg.bit_generator.__class__.__name__
            if bitgen_name not in ('SFC64',):
                raise AttributeError(f'no "jumped" in {bitgen_name}')
            pytest.skip(f'Jump is not supported by {bitgen_name}')

def test_uniform(self):
        r = self.rg.uniform(-1.0, 0.0, size=10)
        assert_(len(r) == 10)
        assert_((r > -1).all())
        assert_((r <= 0).all())

def test_uniform_array(self):
        r = self.rg.uniform(np.array([-1.0] * 10), 0.0, size=10)
        assert_(len(r) == 10)
        assert_((r > -1).all())
        assert_((r <= 0).all())
        r = self.rg.uniform(np.array([-1.0] * 10),
                            np.array([0.0] * 10), size=10)
        assert_(len(r) == 10)
        assert_((r > -1).all())
        assert_((r <= 0).all())
        r = self.rg.uniform(-1.0, np.array([0.0] * 10), size=10)
        assert_(len(r) == 10)
        assert_((r > -1).all())
        assert_((r <= 0).all())

def test_random(self):
        assert_(len(self.rg.random(10)) == 10)
        params_0(self.rg.random)

def test_standard_normal_zig(self):
        assert_(len(self.rg.standard_normal(10)) == 10)

def test_standard_normal(self):
        assert_(len(self.rg.standard_normal(10)) == 10)
        params_0(self.rg.standard_normal)

def test_standard_gamma(self):
        assert_(len(self.rg.standard_gamma(10, 10)) == 10)
        assert_(len(self.rg.standard_gamma(np.array([10] * 10), 10)) == 10)
        params_1(self.rg.standard_gamma)

def test_standard_exponential(self):
        assert_(len(self.rg.standard_exponential(10)) == 10)
        params_0(self.rg.standard_exponential)

def test_standard_exponential_float(self):
        randoms = self.rg.standard_exponential(10, dtype='float32')
        assert_(len(randoms) == 10)
        assert randoms.dtype == np.float32
        params_0(partial(self.rg.standard_exponential, dtype='float32'))

def test_standard_exponential_float_log(self):
        randoms = self.rg.standard_exponential(10, dtype='float32',
                                               method='inv')
        assert_(len(randoms) == 10)
        assert randoms.dtype == np.float32
        params_0(partial(self.rg.standard_exponential, dtype='float32',
                         method='inv'))

def test_standard_cauchy(self):
        assert_(len(self.rg.standard_cauchy(10)) == 10)
        params_0(self.rg.standard_cauchy)

def test_standard_t(self):
        assert_(len(self.rg.standard_t(10, 10)) == 10)
        params_1(self.rg.standard_t)

def test_binomial(self):
        assert_(self.rg.binomial(10, .5) >= 0)
        assert_(self.rg.binomial(1000, .5) >= 0)

def test_reset_state(self):
        state = self.rg.bit_generator.state
        int_1 = self.rg.integers(2**31)
        self.rg.bit_generator.state = state
        int_2 = self.rg.integers(2**31)
        assert_(int_1 == int_2)

def test_entropy_init(self):
        rg = Generator(self.bit_generator())
        rg2 = Generator(self.bit_generator())
        assert_(not comp_state(rg.bit_generator.state,
                               rg2.bit_generator.state))

def test_seed(self):
        rg = Generator(self.bit_generator(*self.seed))
        rg2 = Generator(self.bit_generator(*self.seed))
        rg.random()
        rg2.random()
        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))

def test_reset_state_gauss(self):
        rg = Generator(self.bit_generator(*self.seed))
        rg.standard_normal()
        state = rg.bit_generator.state
        n1 = rg.standard_normal(size=10)
        rg2 = Generator(self.bit_generator())
        rg2.bit_generator.state = state
        n2 = rg2.standard_normal(size=10)
        assert_array_equal(n1, n2)

def test_reset_state_uint32(self):
        rg = Generator(self.bit_generator(*self.seed))
        rg.integers(0, 2 ** 24, 120, dtype=np.uint32)
        state = rg.bit_generator.state
        n1 = rg.integers(0, 2 ** 24, 10, dtype=np.uint32)
        rg2 = Generator(self.bit_generator())
        rg2.bit_generator.state = state
        n2 = rg2.integers(0, 2 ** 24, 10, dtype=np.uint32)
        assert_array_equal(n1, n2)

def test_reset_state_float(self):
        rg = Generator(self.bit_generator(*self.seed))
        rg.random(dtype='float32')
        state = rg.bit_generator.state
        n1 = rg.random(size=10, dtype='float32')
        rg2 = Generator(self.bit_generator())
        rg2.bit_generator.state = state
        n2 = rg2.random(size=10, dtype='float32')
        assert_((n1 == n2).all())

def test_shuffle(self):
        original = np.arange(200, 0, -1)
        permuted = self.rg.permutation(original)
        assert_((original != permuted).any())

def test_permutation(self):
        original = np.arange(200, 0, -1)
        permuted = self.rg.permutation(original)
        assert_((original != permuted).any())

def test_beta(self):
        vals = self.rg.beta(2.0, 2.0, 10)
        assert_(len(vals) == 10)
        vals = self.rg.beta(np.array([2.0] * 10), 2.0)
        assert_(len(vals) == 10)
        vals = self.rg.beta(2.0, np.array([2.0] * 10))
        assert_(len(vals) == 10)
        vals = self.rg.beta(np.array([2.0] * 10), np.array([2.0] * 10))
        assert_(len(vals) == 10)
        vals = self.rg.beta(np.array([2.0] * 10), np.array([[2.0]] * 10))
        assert_(vals.shape == (10, 10))

def test_bytes(self):
        vals = self.rg.bytes(10)
        assert_(len(vals) == 10)

def test_chisquare(self):
        vals = self.rg.chisquare(2.0, 10)
        assert_(len(vals) == 10)
        params_1(self.rg.chisquare)

def test_exponential(self):
        vals = self.rg.exponential(2.0, 10)
        assert_(len(vals) == 10)
        params_1(self.rg.exponential)

def test_f(self):
        vals = self.rg.f(3, 1000, 10)
        assert_(len(vals) == 10)

def test_gamma(self):
        vals = self.rg.gamma(3, 2, 10)
        assert_(len(vals) == 10)

def test_geometric(self):
        vals = self.rg.geometric(0.5, 10)
        assert_(len(vals) == 10)
        params_1(self.rg.exponential, bounded=True)

def test_gumbel(self):
        vals = self.rg.gumbel(2.0, 2.0, 10)
        assert_(len(vals) == 10)

def test_laplace(self):
        vals = self.rg.laplace(2.0, 2.0, 10)
        assert_(len(vals) == 10)

def test_logitic(self):
        vals = self.rg.logistic(2.0, 2.0, 10)
        assert_(len(vals) == 10)

def test_logseries(self):
        vals = self.rg.logseries(0.5, 10)
        assert_(len(vals) == 10)

def test_negative_binomial(self):
        vals = self.rg.negative_binomial(10, 0.2, 10)
        assert_(len(vals) == 10)

def test_noncentral_chisquare(self):
        vals = self.rg.noncentral_chisquare(10, 2, 10)
        assert_(len(vals) == 10)

def test_noncentral_f(self):
        vals = self.rg.noncentral_f(3, 1000, 2, 10)
        assert_(len(vals) == 10)
        vals = self.rg.noncentral_f(np.array([3] * 10), 1000, 2)
        assert_(len(vals) == 10)
        vals = self.rg.noncentral_f(3, np.array([1000] * 10), 2)
        assert_(len(vals) == 10)
        vals = self.rg.noncentral_f(3, 1000, np.array([2] * 10))
        assert_(len(vals) == 10)

def test_normal(self):
        vals = self.rg.normal(10, 0.2, 10)
        assert_(len(vals) == 10)

def test_pareto(self):
        vals = self.rg.pareto(3.0, 10)
        assert_(len(vals) == 10)

def test_poisson(self):
        vals = self.rg.poisson(10, 10)
        assert_(len(vals) == 10)
        vals = self.rg.poisson(np.array([10] * 10))
        assert_(len(vals) == 10)
        params_1(self.rg.poisson)

def test_power(self):
        vals = self.rg.power(0.2, 10)
        assert_(len(vals) == 10)

def test_integers(self):
        vals = self.rg.integers(10, 20, 10)
        assert_(len(vals) == 10)

def test_rayleigh(self):
        vals = self.rg.rayleigh(0.2, 10)
        assert_(len(vals) == 10)
        params_1(self.rg.rayleigh, bounded=True)

def test_vonmises(self):
        vals = self.rg.vonmises(10, 0.2, 10)
        assert_(len(vals) == 10)

def test_wald(self):
        vals = self.rg.wald(1.0, 1.0, 10)
        assert_(len(vals) == 10)

def test_weibull(self):
        vals = self.rg.weibull(1.0, 10)
        assert_(len(vals) == 10)

def test_zipf(self):
        vals = self.rg.zipf(10, 10)
        assert_(len(vals) == 10)
        vals = self.rg.zipf(self.vec_1d)
        assert_(len(vals) == 100)
        vals = self.rg.zipf(self.vec_2d)
        assert_(vals.shape == (1, 100))
        vals = self.rg.zipf(self.mat)
        assert_(vals.shape == (100, 100))

def test_hypergeometric(self):
        vals = self.rg.hypergeometric(25, 25, 20)
        assert_(np.isscalar(vals))
        vals = self.rg.hypergeometric(np.array([25] * 10), 25, 20)
        assert_(vals.shape == (10,))

def test_triangular(self):
        vals = self.rg.triangular(-5, 0, 5)
        assert_(np.isscalar(vals))
        vals = self.rg.triangular(-5, np.array([0] * 10), 5)
        assert_(vals.shape == (10,))

def test_multivariate_normal(self):
        mean = [0, 0]
        cov = [[1, 0], [0, 100]]  # diagonal covariance
        x = self.rg.multivariate_normal(mean, cov, 5000)
        assert_(x.shape == (5000, 2))
        x_zig = self.rg.multivariate_normal(mean, cov, 5000)
        assert_(x.shape == (5000, 2))
        x_inv = self.rg.multivariate_normal(mean, cov, 5000)
        assert_(x.shape == (5000, 2))
        assert_((x_zig != x_inv).any())

def test_multinomial(self):
        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3])
        assert_(vals.shape == (2,))
        vals = self.rg.multinomial(100, [1.0 / 3, 2.0 / 3], size=10)
        assert_(vals.shape == (10, 2))

def test_dirichlet(self):
        s = self.rg.dirichlet((10, 5, 3), 20)
        assert_(s.shape == (20, 3))

def test_pickle(self):
        pick = pickle.dumps(self.rg)
        unpick = pickle.loads(pick)
        assert_((type(self.rg) == type(unpick)))
        assert_(comp_state(self.rg.bit_generator.state,
                           unpick.bit_generator.state))

pick = pickle.dumps(self.rg)
        unpick = pickle.loads(pick)
        assert_((type(self.rg) == type(unpick)))
        assert_(comp_state(self.rg.bit_generator.state,
                           unpick.bit_generator.state))

def test_seed_array(self):
        if self.seed_vector_bits is None:
            bitgen_name = self.bit_generator.__name__
            pytest.skip(f'Vector seeding is not supported by {bitgen_name}')

if self.seed_vector_bits == 32:
            dtype = np.uint32
        else:
            dtype = np.uint64
        seed = np.array([1], dtype=dtype)
        bg = self.bit_generator(seed)
        state1 = bg.state
        bg = self.bit_generator(1)
        state2 = bg.state
        assert_(comp_state(state1, state2))

seed = np.arange(4, dtype=dtype)
        bg = self.bit_generator(seed)
        state1 = bg.state
        bg = self.bit_generator(seed[0])
        state2 = bg.state
        assert_(not comp_state(state1, state2))

seed = np.arange(1500, dtype=dtype)
        bg = self.bit_generator(seed)
        state1 = bg.state
        bg = self.bit_generator(seed[0])
        state2 = bg.state
        assert_(not comp_state(state1, state2))

seed = 2 ** np.mod(np.arange(1500, dtype=dtype),
                           self.seed_vector_bits - 1) + 1
        bg = self.bit_generator(seed)
        state1 = bg.state
        bg  = self.bit_generator(seed[0])
        state2 = bg.state
        assert_(not comp_state(state1, state2))

def test_uniform_float(self):
        rg = Generator(self.bit_generator(12345))
        warmup(rg)
        state = rg.bit_generator.state
        r1 = rg.random(11, dtype=np.float32)
        rg2 = Generator(self.bit_generator())
        warmup(rg2)
        rg2.bit_generator.state = state
        r2 = rg2.random(11, dtype=np.float32)
        assert_array_equal(r1, r2)
        assert_equal(r1.dtype, np.float32)
        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))

def test_gamma_floats(self):
        rg = Generator(self.bit_generator())
        warmup(rg)
        state = rg.bit_generator.state
        r1 = rg.standard_gamma(4.0, 11, dtype=np.float32)
        rg2 = Generator(self.bit_generator())
        warmup(rg2)
        rg2.bit_generator.state = state
        r2 = rg2.standard_gamma(4.0, 11, dtype=np.float32)
        assert_array_equal(r1, r2)
        assert_equal(r1.dtype, np.float32)
        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))

def test_normal_floats(self):
        rg = Generator(self.bit_generator())
        warmup(rg)
        state = rg.bit_generator.state
        r1 = rg.standard_normal(11, dtype=np.float32)
        rg2 = Generator(self.bit_generator())
        warmup(rg2)
        rg2.bit_generator.state = state
        r2 = rg2.standard_normal(11, dtype=np.float32)
        assert_array_equal(r1, r2)
        assert_equal(r1.dtype, np.float32)
        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))

def test_normal_zig_floats(self):
        rg = Generator(self.bit_generator())
        warmup(rg)
        state = rg.bit_generator.state
        r1 = rg.standard_normal(11, dtype=np.float32)
        rg2 = Generator(self.bit_generator())
        warmup(rg2)
        rg2.bit_generator.state = state
        r2 = rg2.standard_normal(11, dtype=np.float32)
        assert_array_equal(r1, r2)
        assert_equal(r1.dtype, np.float32)
        assert_(comp_state(rg.bit_generator.state, rg2.bit_generator.state))

def test_output_fill(self):
        rg = self.rg
        state = rg.bit_generator.state
        size = (31, 7, 97)
        existing = np.empty(size)
        rg.bit_generator.state = state
        rg.standard_normal(out=existing)
        rg.bit_generator.state = state
        direct = rg.standard_normal(size=size)
        assert_equal(direct, existing)

sized = np.empty(size)
        rg.bit_generator.state = state
        rg.standard_normal(out=sized, size=sized.shape)

existing = np.empty(size, dtype=np.float32)
        rg.bit_generator.state = state
        rg.standard_normal(out=existing, dtype=np.float32)
        rg.bit_generator.state = state
        direct = rg.standard_normal(size=size, dtype=np.float32)
        assert_equal(direct, existing)

def test_output_filling_uniform(self):
        rg = self.rg
        state = rg.bit_generator.state
        size = (31, 7, 97)
        existing = np.empty(size)
        rg.bit_generator.state = state
        rg.random(out=existing)
        rg.bit_generator.state = state
        direct = rg.random(size=size)
        assert_equal(direct, existing)

existing = np.empty(size, dtype=np.float32)
        rg.bit_generator.state = state
        rg.random(out=existing, dtype=np.float32)
        rg.bit_generator.state = state
        direct = rg.random(size=size, dtype=np.float32)
        assert_equal(direct, existing)

def test_output_filling_exponential(self):
        rg = self.rg
        state = rg.bit_generator.state
        size = (31, 7, 97)
        existing = np.empty(size)
        rg.bit_generator.state = state
        rg.standard_exponential(out=existing)
        rg.bit_generator.state = state
        direct = rg.standard_exponential(size=size)
        assert_equal(direct, existing)

existing = np.empty(size, dtype=np.float32)
        rg.bit_generator.state = state
        rg.standard_exponential(out=existing, dtype=np.float32)
        rg.bit_generator.state = state
        direct = rg.standard_exponential(size=size, dtype=np.float32)
        assert_equal(direct, existing)

def test_output_filling_gamma(self):
        rg = self.rg
        state = rg.bit_generator.state
        size = (31, 7, 97)
        existing = np.zeros(size)
        rg.bit_generator.state = state
        rg.standard_gamma(1.0, out=existing)
        rg.bit_generator.state = state
        direct = rg.standard_gamma(1.0, size=size)
        assert_equal(direct, existing)

existing = np.zeros(size, dtype=np.float32)
        rg.bit_generator.state = state
        rg.standard_gamma(1.0, out=existing, dtype=np.float32)
        rg.bit_generator.state = state
        direct = rg.standard_gamma(1.0, size=size, dtype=np.float32)
        assert_equal(direct, existing)

def test_output_filling_gamma_broadcast(self):
        rg = self.rg
        state = rg.bit_generator.state
        size = (31, 7, 97)
        mu = np.arange(97.0) + 1.0
        existing = np.zeros(size)
        rg.bit_generator.state = state
        rg.standard_gamma(mu, out=existing)
        rg.bit_generator.state = state
        direct = rg.standard_gamma(mu, size=size)
        assert_equal(direct, existing)

existing = np.zeros(size, dtype=np.float32)
        rg.bit_generator.state = state
        rg.standard_gamma(mu, out=existing, dtype=np.float32)
        rg.bit_generator.state = state
        direct = rg.standard_gamma(mu, size=size, dtype=np.float32)
        assert_equal(direct, existing)

def test_output_fill_error(self):
        rg = self.rg
        size = (31, 7, 97)
        existing = np.empty(size)
        with pytest.raises(TypeError):
            rg.standard_normal(out=existing, dtype=np.float32)
        with pytest.raises(ValueError):
            rg.standard_normal(out=existing[::3])
        existing = np.empty(size, dtype=np.float32)
        with pytest.raises(TypeError):
            rg.standard_normal(out=existing, dtype=np.float64)

existing = np.zeros(size, dtype=np.float32)
        with pytest.raises(TypeError):
            rg.standard_gamma(1.0, out=existing, dtype=np.float64)
        with pytest.raises(ValueError):
            rg.standard_gamma(1.0, out=existing[::3], dtype=np.float32)
        existing = np.zeros(size, dtype=np.float64)
        with pytest.raises(TypeError):
            rg.standard_gamma(1.0, out=existing, dtype=np.float32)
        with pytest.raises(ValueError):
            rg.standard_gamma(1.0, out=existing[::3])

def test_integers_broadcast(self, dtype):
        if dtype == np.bool_:
            upper = 2
            lower = 0
        else:
            info = np.iinfo(dtype)
            upper = int(info.max) + 1
            lower = info.min
        self._reset_state()
        a = self.rg.integers(lower, [upper] * 10, dtype=dtype)
        self._reset_state()
        b = self.rg.integers([lower] * 10, upper, dtype=dtype)
        assert_equal(a, b)
        self._reset_state()
        c = self.rg.integers(lower, upper, size=10, dtype=dtype)
        assert_equal(a, c)
        self._reset_state()
        d = self.rg.integers(np.array(
            [lower] * 10), np.array([upper], dtype=object), size=10,
            dtype=dtype)
        assert_equal(a, d)
        self._reset_state()
        e = self.rg.integers(
            np.array([lower] * 10), np.array([upper] * 10), size=10,
            dtype=dtype)
        assert_equal(a, e)

self._reset_state()
        a = self.rg.integers(0, upper, size=10, dtype=dtype)
        self._reset_state()
        b = self.rg.integers([upper] * 10, dtype=dtype)
        assert_equal(a, b)

def test_integers_numpy(self, dtype):
        high = np.array([1])
        low = np.array([0])

out = self.rg.integers(low, high, dtype=dtype)
        assert out.shape == (1,)

out = self.rg.integers(low[0], high, dtype=dtype)
        assert out.shape == (1,)

out = self.rg.integers(low, high[0], dtype=dtype)
        assert out.shape == (1,)

def test_integers_broadcast_errors(self, dtype):
        if dtype == np.bool_:
            upper = 2
            lower = 0
        else:
            info = np.iinfo(dtype)
            upper = int(info.max) + 1
            lower = info.min
        with pytest.raises(ValueError):
            self.rg.integers(lower, [upper + 1] * 10, dtype=dtype)
        with pytest.raises(ValueError):
            self.rg.integers(lower - 1, [upper] * 10, dtype=dtype)
        with pytest.raises(ValueError):
            self.rg.integers([lower - 1], [upper] * 10, dtype=dtype)
        with pytest.raises(ValueError):
            self.rg.integers([0], [0], dtype=dtype)

class TestMT19937(RNG):
    @classmethod
    def setup_class(cls):
        cls.bit_generator = MT19937
        cls.advance = None
        cls.seed = [2 ** 21 + 2 ** 16 + 2 ** 5 + 1]
        cls.rg = Generator(cls.bit_generator(*cls.seed))
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 32
        cls._extra_setup()
        cls.seed_error = ValueError

def test_numpy_state(self):
        nprg = np.random.RandomState()
        nprg.standard_normal(99)
        state = nprg.get_state()
        self.rg.bit_generator.state = state
        state2 = self.rg.bit_generator.state
        assert_((state[1] == state2['state']['key']).all())
        assert_((state[2] == state2['state']['pos']))

class TestPhilox(RNG):
    @classmethod
    def setup_class(cls):
        cls.bit_generator = Philox
        cls.advance = 2**63 + 2**31 + 2**15 + 1
        cls.seed = [12345]
        cls.rg = Generator(cls.bit_generator(*cls.seed))
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 64
        cls._extra_setup()

class TestSFC64(RNG):
    @classmethod
    def setup_class(cls):
        cls.bit_generator = SFC64
        cls.advance = None
        cls.seed = [12345]
        cls.rg = Generator(cls.bit_generator(*cls.seed))
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 192
        cls._extra_setup()

class TestPCG64(RNG):
    @classmethod
    def setup_class(cls):
        cls.bit_generator = PCG64
        cls.advance = 2**63 + 2**31 + 2**15 + 1
        cls.seed = [12345]
        cls.rg = Generator(cls.bit_generator(*cls.seed))
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 64
        cls._extra_setup()

class TestPCG64DXSM(RNG):
    @classmethod
    def setup_class(cls):
        cls.bit_generator = PCG64DXSM
        cls.advance = 2**63 + 2**31 + 2**15 + 1
        cls.seed = [12345]
        cls.rg = Generator(cls.bit_generator(*cls.seed))
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 64
        cls._extra_setup()

class TestDefaultRNG(RNG):
    @classmethod
    def setup_class(cls):
        # This will duplicate some tests that directly instantiate a fresh
        # Generator(), but that's okay.
        cls.bit_generator = PCG64
        cls.advance = 2**63 + 2**31 + 2**15 + 1
        cls.seed = [12345]
        cls.rg = np.random.default_rng(*cls.seed)
        cls.initial_state = cls.rg.bit_generator.state
        cls.seed_vector_bits = 64
        cls._extra_setup()

def test_default_is_pcg64(self):
        # In order to change the default BitGenerator, we'll go through
        # a deprecation cycle to move to a different function.
        assert_(isinstance(self.rg.bit_generator, PCG64))

def test_seed(self):
        np.random.default_rng()
        np.random.default_rng(None)
        np.random.default_rng(12345)
        np.random.default_rng(0)
        np.random.default_rng(43660444402423911716352051725018508569)
        np.random.default_rng([43660444402423911716352051725018508569,
                               279705150948142787361475340226491943209])
        with pytest.raises(ValueError):
            np.random.default_rng(-1)
        with pytest.raises(ValueError):
            np.random.default_rng([12345, -1])

cmd: