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- import pytest
- import numpy as np
- from numpy.testing import assert_allclose
- from scipy.stats import _boost
- type_char_to_type_tol = {'f': (np.float32, 32*np.finfo(np.float32).eps),
- 'd': (np.float64, 32*np.finfo(np.float64).eps),
- 'g': (np.longdouble, 32*np.finfo(np.longdouble).eps)}
- # Each item in this list is
- # (func, args, expected_value)
- # All the values can be represented exactly, even with np.float32.
- #
- # This is not an exhaustive test data set of all the functions!
- # It is a spot check of several functions, primarily for
- # checking that the different data types are handled correctly.
- test_data = [
- (_boost._beta_cdf, (0.5, 2, 3), 0.6875),
- (_boost._beta_ppf, (0.6875, 2, 3), 0.5),
- (_boost._beta_pdf, (0.5, 2, 3), 1.5),
- (_boost._beta_sf, (0.5, 2, 1), 0.75),
- (_boost._beta_isf, (0.75, 2, 1), 0.5),
- (_boost._binom_cdf, (1, 3, 0.5), 0.5),
- (_boost._binom_pdf, (1, 4, 0.5), 0.25),
- (_boost._hypergeom_cdf, (2, 3, 5, 6), 0.5),
- (_boost._nbinom_cdf, (1, 4, 0.25), 0.015625),
- (_boost._ncf_mean, (10, 12, 2.5), 1.5),
- ]
- @pytest.mark.filterwarnings('ignore::RuntimeWarning')
- @pytest.mark.parametrize('func, args, expected', test_data)
- def test_stats_boost_ufunc(func, args, expected):
- type_sigs = func.types
- type_chars = [sig.split('->')[-1] for sig in type_sigs]
- for type_char in type_chars:
- typ, rtol = type_char_to_type_tol[type_char]
- args = [typ(arg) for arg in args]
- value = func(*args)
- assert isinstance(value, typ)
- assert_allclose(value, expected, rtol=rtol)
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