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tests: additional test added.
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@ -674,6 +674,23 @@ def test_covariance_factorizing():
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assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
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assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
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def test_covariance_alternation():
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length = 12
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t_fac = 2.5
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tt1 = pe.misc.gen_correlated_data(np.zeros(length), -0.00001 * np.ones((length, length)) + 0.002 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=88)
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tt2 = pe.misc.gen_correlated_data(np.zeros(length), 0.9999 * np.ones((length, length)) + 0.0001 * np.diag(np.ones(length)), 'another_test|r0', tau=0.7 + t_fac * np.random.rand(length), samples=73)
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tt3 = pe.misc.gen_correlated_data(np.zeros(length), 0.9999 * np.ones((length, length)) + 0.0001 * np.diag(np.ones(length)), 'another_test|r1', tau=0.7 + t_fac * np.random.rand(length), samples=91)
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tt = np.array(tt1) + (np.array(tt2) + np.array(tt3))
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tt *= np.resize([1, -1], length)
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[o.gamma_method() for o in tt]
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cov = pe.covariance(tt, True)
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assert np.all(np.linalg.eigh(cov)[0] > -1e-15)
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def test_covariance_correlation():
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def test_covariance_correlation():
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test_obs = pe.pseudo_Obs(-4, 0.8, 'test', samples=784)
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test_obs = pe.pseudo_Obs(-4, 0.8, 'test', samples=784)
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assert np.allclose(pe.covariance([test_obs, test_obs, test_obs], correlation=True), np.ones((3, 3)))
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assert np.allclose(pe.covariance([test_obs, test_obs, test_obs], correlation=True), np.ones((3, 3)))
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