#!/usr/bin/env python
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import sys
from numpy.testing import (TestCase, assert_,
                           assert_array_equal, assert_raises)
import mkl_random as rnd
import numpy as np

import pytest
import gc


def test_VonMises_range():
    # Make sure generated random variables are in [-pi, pi].
    # Regression test for ticket #986.
    for mu in np.linspace(-7., 7., 5):
        r = rnd.vonmises(mu, 1, 50)
        assert_(np.all(r > -np.pi) and np.all(r <= np.pi))


def test_hypergeometric_range():
    # Test for ticket #921
    assert_(np.all(rnd.hypergeometric(3, 18, 11, size=10) < 4))
    assert_(np.all(rnd.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
        (2 ** 30 - 1, 2 ** 30 - 2, 2 ** 30 - 1)
    ]
    for arg in args:
        assert_(rnd.hypergeometric(*arg) > 0)


def test_logseries_convergence():
    # Test for ticket #923
    N = 1000
    rnd.seed(0, brng='MT19937')
    rvsn = rnd.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 = "Frequency was %f, should be > 0.45" % freq
    assert_(freq > 0.45, msg)
    # theoretical large N result is 0.19882718
    freq = np.sum(rvsn == 2) / float(N)
    msg = "Frequency was %f, should be < 0.23" % freq
    assert_(freq < 0.23, msg)


def test_permutation_longs():
    rnd.seed(1234, brng='MT19937')
    a = rnd.permutation(12)
    rnd.seed(1234, brng='MT19937')
    dt_long = np.dtype("long")
    twelve_long = dt_long.type(12)
    b = rnd.permutation(twelve_long)
    assert_array_equal(a, b)


def test_randint_range():
    # Test for ticket #1690
    lmax = np.iinfo('l').max
    lmin = np.iinfo('l').min
    try:
        rnd.randint(lmin, lmax)
    except:
        raise AssertionError


def test_shuffle_mixed_dimension():
    # 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]]:
        rnd.seed(12345, brng='MT2203')
        shuffled = np.array(list(t), dtype=object)
        rnd.shuffle(shuffled)
        expected = np.array([t[0], t[2], t[1], t[3]], dtype=object)
        assert_array_equal(shuffled, expected)


def test_call_within_randomstate():
    # Check that custom RandomState does not call into global state
    m = rnd.RandomState()
    res = np.array([5, 7, 5, 4, 5, 5, 6, 9, 6, 1])
    for i in range(3):
        rnd.seed(i)
        m.seed(4321, brng='SFMT19937')
        # 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():
    # Test for multivariate_normal issue with 'size' argument.
    # Check that the multivariate_normal size argument can be a
    # numpy integer.
    rnd.multivariate_normal([0], [[0]], size=1)
    rnd.multivariate_normal([0], [[0]], size=np.int_(1))
    rnd.multivariate_normal([0], [[0]], size=np.int64(1))


def test_beta_small_parameters():
    # Test that beta with small a and b parameters does not produce
    # NaNs due to roundoff errors causing 0 / 0, gh-5851
    rnd.seed(1234567890, brng='MT19937')
    x = rnd.beta(0.0001, 0.0001, size=100)
    assert_(not np.any(np.isnan(x)), 'Nans in rnd.beta')


def test_choice_sum_of_probs_tolerance():
    # 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.
    rnd.seed(1234, brng='MT19937')
    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 = rnd.choice(a, p=probs)
        assert_(c in a)
        assert_raises(ValueError, rnd.choice, a, p=probs*0.9)


def test_shuffle_of_array_of_different_length_strings():
    # Test that permuting an array of different length strings
    # will not cause a segfault on garbage collection
    # Tests gh-7710
    rnd.seed(1234, brng='MT19937')

    a = np.array(['a', 'a' * 1000])

    for _ in range(100):
        rnd.shuffle(a)

    # Force Garbage Collection - should not segfault.
    gc.collect()


def test_shuffle_of_array_of_objects():
    # Test that permuting an array of objects will not cause
    # a segfault on garbage collection.
    # See gh-7719
    rnd.seed(1234, brng='MT19937')
    a = np.array([np.arange(4), np.arange(4)])

    for _ in range(1000):
        rnd.shuffle(a)

    # Force Garbage Collection - should not segfault.
    gc.collect()


def test_non_central_chi_squared_df_one():
    a = rnd.noncentral_chisquare(df = 1.0, nonc=2.3, size=10**4)
    assert(a.min() > 0.0)