Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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86 lines
2.5 KiB
86 lines
2.5 KiB
4 years ago
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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 as sc
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from scipy.special._testutils import FuncData
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class TestVoigtProfile(object):
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@pytest.mark.parametrize('x, sigma, gamma', [
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(np.nan, 1, 1),
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(0, np.nan, 1),
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(0, 1, np.nan),
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(1, np.nan, 0),
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(np.nan, 1, 0),
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(1, 0, np.nan),
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(np.nan, 0, 1),
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(np.nan, 0, 0)
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])
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def test_nan(self, x, sigma, gamma):
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assert np.isnan(sc.voigt_profile(x, sigma, gamma))
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@pytest.mark.parametrize('x, desired', [
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(-np.inf, 0),
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(np.inf, 0)
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])
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def test_inf(self, x, desired):
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assert sc.voigt_profile(x, 1, 1) == desired
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def test_against_mathematica(self):
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# Results obtained from Mathematica by computing
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#
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# PDF[VoigtDistribution[gamma, sigma], x]
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#
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points = np.array([
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[-7.89, 45.06, 6.66, 0.0077921073660388806401],
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[-0.05, 7.98, 24.13, 0.012068223646769913478],
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[-13.98, 16.83, 42.37, 0.0062442236362132357833],
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[-12.66, 0.21, 6.32, 0.010052516161087379402],
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[11.34, 4.25, 21.96, 0.0113698923627278917805],
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[-11.56, 20.40, 30.53, 0.0076332760432097464987],
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[-9.17, 25.61, 8.32, 0.011646345779083005429],
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[16.59, 18.05, 2.50, 0.013637768837526809181],
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[9.11, 2.12, 39.33, 0.0076644040807277677585],
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[-43.33, 0.30, 45.68, 0.0036680463875330150996]
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])
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FuncData(
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sc.voigt_profile,
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points,
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(0, 1, 2),
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3,
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atol=0,
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rtol=1e-15
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).check()
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def test_symmetry(self):
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x = np.linspace(0, 10, 20)
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assert_allclose(
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sc.voigt_profile(x, 1, 1),
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sc.voigt_profile(-x, 1, 1),
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rtol=1e-15,
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atol=0
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)
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@pytest.mark.parametrize('x, sigma, gamma, desired', [
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(0, 0, 0, np.inf),
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(1, 0, 0, 0)
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])
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def test_corner_cases(self, x, sigma, gamma, desired):
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assert sc.voigt_profile(x, sigma, gamma) == desired
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@pytest.mark.parametrize('sigma1, gamma1, sigma2, gamma2', [
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(0, 1, 1e-16, 1),
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(1, 0, 1, 1e-16),
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(0, 0, 1e-16, 1e-16)
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])
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def test_continuity(self, sigma1, gamma1, sigma2, gamma2):
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x = np.linspace(1, 10, 20)
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assert_allclose(
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sc.voigt_profile(x, sigma1, gamma1),
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sc.voigt_profile(x, sigma2, gamma2),
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rtol=1e-16,
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atol=1e-16
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)
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