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fix: covobs names can no longer contain replica separator '|'. Tests
added.
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parent
ed47d50286
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3 changed files with 71 additions and 59 deletions
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@ -32,6 +32,8 @@ class Covobs:
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raise Exception('Covariance matrix has to be a square matrix!')
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else:
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raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
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if '|' in name:
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raise Exception("Covobs name must not contain replica separator '|'.")
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self.name = name
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if grad is None:
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if pos is None:
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69
tests/covobs_test.py
Normal file
69
tests/covobs_test.py
Normal file
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@ -0,0 +1,69 @@
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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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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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assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
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assert(pe.covariance2(cl[0], cl[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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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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@ -542,62 +542,3 @@ def test_import_jackknife():
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my_jacks = my_obs.export_jackknife()
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reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
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assert my_obs == reconstructed_obs
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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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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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assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
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assert(pe.covariance2(cl[0], cl[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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