Current File : //proc/self/root/lib/python3/dist-packages/scipy/special/tests/test_ndtri_exp.py
import pytest
import numpy as np
from numpy.testing import assert_equal
from scipy.special import log_ndtr, ndtri_exp
from scipy.special._testutils import assert_func_equal
def log_ndtr_ndtri_exp(y):
return log_ndtr(ndtri_exp(y))
@pytest.fixture(scope="class")
def uniform_random_points():
random_state = np.random.RandomState(1234)
points = random_state.random_sample(1000)
return points
class TestNdtriExp:
"""Tests that ndtri_exp is sufficiently close to an inverse of log_ndtr.
We have separate tests for the five intervals (-inf, -10),
[-10, -2), [-2, -0.14542), [-0.14542, -1e-6), and [-1e-6, 0).
ndtri_exp(y) is computed in three different ways depending on if y
is in (-inf, -2), [-2, log(1 - exp(-2))], or [log(1 - exp(-2), 0).
Each of these intervals is given its own test with two additional tests
for handling very small values and values very close to zero.
"""
@pytest.mark.parametrize(
"test_input", [-1e1, -1e2, -1e10, -1e20, -np.finfo(float).max]
)
def test_very_small_arg(self, test_input, uniform_random_points):
scale = test_input
points = scale * (0.5 * uniform_random_points + 0.5)
assert_func_equal(
log_ndtr_ndtri_exp,
lambda y: y, points,
rtol=1e-14,
nan_ok=True
)
@pytest.mark.parametrize(
"interval,expected_rtol",
[
((-10, -2), 1e-14),
((-2, -0.14542), 1e-12),
((-0.14542, -1e-6), 1e-10),
((-1e-6, 0), 1e-6),
],
)
def test_in_interval(self, interval, expected_rtol, uniform_random_points):
left, right = interval
points = (right - left) * uniform_random_points + left
assert_func_equal(
log_ndtr_ndtri_exp,
lambda y: y, points,
rtol=expected_rtol,
nan_ok=True
)
def test_extreme(self):
assert_func_equal(
log_ndtr_ndtri_exp,
lambda y: y,
[-np.finfo(float).max, -np.finfo(float).min],
rtol=1e-12,
nan_ok=True
)
def test_asymptotes(self):
assert_equal(ndtri_exp([-np.inf, 0.0]), [-np.inf, np.inf])
def test_outside_domain(self):
assert np.isnan(ndtri_exp(1.0))
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