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84 lines
3.0 KiB
84 lines
3.0 KiB
import pytest
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import numpy as np
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from numpy.testing import assert_allclose
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import scipy.special._ufuncs as scu
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from scipy.integrate import tanhsinh
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type_char_to_type_tol = {'f': (np.float32, 32*np.finfo(np.float32).eps),
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'd': (np.float64, 32*np.finfo(np.float64).eps)}
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# Each item in this list is
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# (func, args, expected_value)
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# All the values can be represented exactly, even with np.float32.
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#
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# This is not an exhaustive test data set of all the functions!
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# It is a spot check of several functions, primarily for
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# checking that the different data types are handled correctly.
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test_data = [
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(scu._beta_pdf, (0.5, 2, 3), 1.5),
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(scu._beta_pdf, (0, 1, 5), 5.0),
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(scu._beta_pdf, (1, 5, 1), 5.0),
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(scu._beta_ppf, (0.5, 5., 5.), 0.5), # gh-21303
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(scu._binom_cdf, (1, 3, 0.5), 0.5),
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(scu._binom_pmf, (1, 4, 0.5), 0.25),
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(scu._hypergeom_cdf, (2, 3, 5, 6), 0.5),
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(scu._nbinom_cdf, (1, 4, 0.25), 0.015625),
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(scu._ncf_mean, (10, 12, 2.5), 1.5),
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]
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@pytest.mark.parametrize('func, args, expected', test_data)
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def test_stats_boost_ufunc(func, args, expected):
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type_sigs = func.types
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type_chars = [sig.split('->')[-1] for sig in type_sigs]
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for type_char in type_chars:
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typ, rtol = type_char_to_type_tol[type_char]
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args = [typ(arg) for arg in args]
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# Harmless overflow warnings are a "feature" of some wrappers on some
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# platforms. This test is about dtype and accuracy, so let's avoid false
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# test failures cause by these warnings. See gh-17432.
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with np.errstate(over='ignore'):
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value = func(*args)
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assert isinstance(value, typ)
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assert_allclose(value, expected, rtol=rtol)
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def test_landau():
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# Test that Landau distribution ufuncs are wrapped as expected;
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# accuracy is tested by Boost.
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x = np.linspace(-3, 10, 10)
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args = (0, 1)
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res = tanhsinh(lambda x: scu._landau_pdf(x, *args), -np.inf, x)
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cdf = scu._landau_cdf(x, *args)
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assert_allclose(res.integral, cdf)
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sf = scu._landau_sf(x, *args)
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assert_allclose(sf, 1-cdf)
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ppf = scu._landau_ppf(cdf, *args)
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assert_allclose(ppf, x)
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isf = scu._landau_isf(sf, *args)
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assert_allclose(isf, x, rtol=1e-6)
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def test_gh22956():
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_ = scu._ncx2_pdf(30, 1e307, 16)
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@pytest.mark.parametrize("func", [scu._binom_cdf, scu._binom_sf])
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@pytest.mark.parametrize("dtype", [np.float32, np.float64])
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def test_extreme_inputs_for_binomial_probabilities(func, dtype):
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# certain inputs caused C++ exceptions in boost
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# resulting in Python interpreter crashes
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k = 3e18
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n = 10e18
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p = 0.3
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func(dtype(k), dtype(n), dtype(p))
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@pytest.mark.parametrize("func", [scu._binom_ppf, scu._binom_isf])
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@pytest.mark.parametrize("dtype", [np.float32, np.float64])
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def test_extreme_inputs_for_binomial_quantiles(func, dtype):
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# certain inputs caused C++ exceptions in boost
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# resulting in Python interpreter crashes
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n = 10e18
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p = 0.5
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func(dtype(p), dtype(n), dtype(p))
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