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110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
import autograd.numpy as np
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import pyerrors as pe
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import pytest
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np.random.seed(0)
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def test_covobs():
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val = 1.123124
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cov = .243423
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name = 'Covariance'
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co = pe.cov_Obs(val, cov, name)
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co.gamma_method()
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co.details()
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assert (co.dvalue == np.sqrt(cov))
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assert (co.value == val)
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do = 2 * co
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assert (do.covobs[name].grad[0] == 2)
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do = co * co
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assert (do.covobs[name].grad[0] == 2 * val)
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assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
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pi = [16.7457, -19.0475]
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cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
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cl = pe.cov_Obs(pi, cov, 'rAP')
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pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
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def rAP(p, g0sq):
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return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
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for g0sq in [1, 1.5, 1.8]:
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oc = rAP(cl, g0sq)
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oc.gamma_method()
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op = rAP(pl, g0sq)
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op.gamma_method()
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assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
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[o.gamma_method() for o in cl]
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assert(np.isclose(pe.covariance([cl[0], cl[1]])[0, 1], cov[0][1]))
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assert(np.isclose(pe.covariance([cl[0], cl[1]])[0, 1], cov[1][0]))
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do = cl[0] * cl[1]
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assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
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def test_covobs_overloading():
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covobs = pe.cov_Obs([0.5, 0.5], np.array([[0.02, 0.02], [0.02, 0.02]]), 'test')
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assert (covobs[0] / covobs[1]) == 1
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assert (covobs[0] - covobs[1]) == 0
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my_obs = pe.pseudo_Obs(2.3, 0.2, 'obs')
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assert (my_obs * covobs[0] / covobs[1]) == my_obs
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covobs = pe.cov_Obs(0.0, 0.3, 'test')
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assert not covobs.is_zero()
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def test_covobs_name_collision():
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covobs = pe.cov_Obs(0.5, 0.002, 'test')
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my_obs = pe.pseudo_Obs(2.3, 0.2, 'test')
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with pytest.raises(Exception):
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summed_obs = my_obs + covobs
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covobs2 = pe.cov_Obs(0.3, 0.001, 'test')
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with pytest.raises(Exception):
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summed_obs = covobs + covobs2
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def test_covobs_replica_separator():
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with pytest.raises(Exception):
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covobs = pe.cov_Obs(0.5, 0.002, 'test|r2')
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def test_covobs_init():
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covobs = pe.cov_Obs(0.5, 0.002, 'test')
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covobs = pe.cov_Obs([1, 2], [0.1, 0.2], 'test')
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covobs = pe.cov_Obs([1, 2], np.array([0.1, 0.2]), 'test')
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covobs = pe.cov_Obs([1, 2], [[0.21, 0.2], [0.2, 0.21]], 'test')
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covobs = pe.cov_Obs([1, 2], np.array([[0.21, 0.2], [0.2, 0.21]]), 'test')
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def test_covobs_covariance():
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a = pe.cov_Obs(2.47, 0.03 ** 2, "Cov_obs 1")
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b = pe.cov_Obs(-4.3, 0.335 ** 2, "Cov_obs 2")
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x = [a + b, a - b]
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[o.gamma_method() for o in x]
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covariance = pe.covariance(x)
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assert np.isclose(covariance[0, 0], covariance[1, 1])
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assert np.isclose(covariance[0, 1], a.dvalue ** 2 - b.dvalue ** 2)
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def test_covobs_exceptions():
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with pytest.raises(Exception):
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covobs = pe.cov_Obs(0.1, [[0.1, 0.2], [0.1, 0.2]], 'test')
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with pytest.raises(Exception):
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covobs = pe.cov_Obs(0.1, np.array([[0.1, 0.2], [0.1, 0.2]]), 'test')
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with pytest.raises(Exception):
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covobs = pe.cov_Obs([0.5, 0.1], np.array([[2, 1, 3], [1, 2, 3]]), 'test')
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with pytest.raises(Exception):
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covobs = pe.cov_Obs([0.5, 0.1], np.random.random((2, 2, 2)), 'test')
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with pytest.raises(Exception):
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covobs = pe.cov_Obs([1.5, 0.1], [[1., .2,], [.3, .5]] , 'test')
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with pytest.raises(Exception):
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covobs = pe.cov_Obs([1.5, 0.1], [[8, 4,], [4, -2]] , 'test')
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