mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-05-14 11:33:42 +02:00
merge with develop
This commit is contained in:
jkuhl-uni
commit
71fe86b8ba
59 changed files with 5367 additions and 1798 deletions
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@ -31,6 +31,13 @@ def test_function_overloading():
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assert np.isclose(con[0].dvalue, t2.dvalue)
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assert np.allclose(con[0].deltas['t'], t2.deltas['t'])
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np.arcsin(corr_a)
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np.arccos(corr_a)
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np.arctan(corr_a)
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np.arcsinh(corr_a)
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np.arccosh(corr_a + 1.1)
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np.arctanh(corr_a)
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def test_modify_correlator():
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corr_content = []
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@ -38,24 +45,58 @@ def test_modify_correlator():
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exponent = np.random.normal(3, 5)
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corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
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corr = pe.correlators.Corr(corr_content)
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corr = pe.Corr(corr_content)
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with pytest.warns(RuntimeWarning):
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corr.symmetric()
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with pytest.warns(RuntimeWarning):
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corr.anti_symmetric()
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corr.roll(np.random.randint(100))
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corr.deriv(symmetric=True)
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corr.deriv(symmetric=False)
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corr.second_deriv()
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for pad in [0, 2]:
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corr = pe.Corr(corr_content, padding=[pad, pad])
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corr.roll(np.random.randint(100))
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corr.deriv(variant="forward")
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corr.deriv(variant="symmetric")
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corr.deriv(variant="improved")
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corr.deriv().deriv()
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corr.second_deriv(variant="symmetric")
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corr.second_deriv(variant="improved")
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corr.second_deriv().second_deriv()
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for i, e in enumerate(corr.content):
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corr.content[i] = None
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for func in [pe.Corr.deriv, pe.Corr.second_deriv]:
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for variant in ["symmetric", "improved", "forward", "gibberish", None]:
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with pytest.raises(Exception):
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func(corr, variant=variant)
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def test_deriv():
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corr_content = []
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for t in range(24):
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exponent = 1.2
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corr_content.append(pe.pseudo_Obs(2 + t ** exponent, 0.2, 't'))
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corr = pe.Corr(corr_content)
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forward = corr.deriv(variant="forward")
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backward = corr.deriv(variant="backward")
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sym = corr.deriv(variant="symmetric")
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assert np.all([o == 0 for o in (0.5 * (forward + backward) - sym)[1:-1]])
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assert np.all([o == 0 for o in (corr.deriv('forward').deriv('backward') - corr.second_deriv())[1:-1]])
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assert np.all([o == 0 for o in (corr.deriv('backward').deriv('forward') - corr.second_deriv())[1:-1]])
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def test_m_eff():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(8, 0.1, 't'), pe.pseudo_Obs(7, 0.05, 't')])
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my_corr.m_eff('log')
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my_corr.m_eff('cosh')
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my_corr.m_eff('sinh')
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my_corr.m_eff('arccosh')
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for padding in [0, 4]:
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(9, 0.1, 't'), pe.pseudo_Obs(10, 0.05, 't')], padding=[padding, padding])
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my_corr.m_eff('log')
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my_corr.m_eff('cosh')
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my_corr.m_eff('arccosh')
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with pytest.warns(RuntimeWarning):
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my_corr.m_eff('sinh')
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def test_reweighting():
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@ -99,6 +140,31 @@ def test_plateau():
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with pytest.raises(Exception):
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my_corr.plateau()
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def test_padded_correlator():
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my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
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my_corr = pe.Corr(my_list, padding=[7, 3])
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my_corr.reweighted
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[o for o in my_corr]
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def test_corr_exceptions():
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obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
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obs_b= pe.Obs([np.random.normal(0.1, 0.1, 99)], ['test'])
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with pytest.raises(Exception):
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pe.Corr([obs_a, obs_b])
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obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
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obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'], idl=[range(1, 200, 2)])
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with pytest.raises(Exception):
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pe.Corr([obs_a, obs_b])
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obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
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obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test2'])
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with pytest.raises(Exception):
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pe.Corr([obs_a, obs_b])
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def test_utility():
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corr_content = []
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for t in range(8):
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@ -110,10 +176,68 @@ def test_utility():
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corr.print([2, 4])
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corr.show()
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corr.dump('test_dump')
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corr.dump('test_dump', datatype="pickle", path='.')
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corr.dump('test_dump', datatype="pickle")
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new_corr = pe.load_object('test_dump.p')
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os.remove('test_dump.p')
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for o_a, o_b in zip(corr.content, new_corr.content):
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assert np.isclose(o_a[0].value, o_b[0].value)
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assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
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assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
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corr.dump('test_dump', datatype="json.gz", path='.')
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corr.dump('test_dump', datatype="json.gz")
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new_corr = pe.input.json.load_json('test_dump')
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os.remove('test_dump.json.gz')
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for o_a, o_b in zip(corr.content, new_corr.content):
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assert np.isclose(o_a[0].value, o_b[0].value)
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assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
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assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
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def test_matrix_corr():
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def _gen_corr(val):
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corr_content = []
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for t in range(16):
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corr_content.append(pe.pseudo_Obs(val, 0.1, 't', 2000))
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return pe.correlators.Corr(corr_content)
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corr_aa = _gen_corr(1)
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corr_ab = _gen_corr(0.5)
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corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]]))
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corr_mat.smearing(0, 0)
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vec_0 = corr_mat.GEVP(0, 0)
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vec_1 = corr_mat.GEVP(0, 0, state=1)
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corr_0 = corr_mat.projected(vec_0)
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corr_1 = corr_mat.projected(vec_1)
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assert np.all([o == 0 for o in corr_0 - corr_aa])
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assert np.all([o == 0 for o in corr_1 - corr_aa])
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corr_mat.GEVP(0, 0, sorted_list="Eigenvalue")
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corr_mat.GEVP(0, 0, sorted_list="Eigenvector")
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with pytest.raises(Exception):
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corr_mat.plottable()
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with pytest.raises(Exception):
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corr_mat.show()
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with pytest.raises(Exception):
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corr_mat.m_eff()
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with pytest.raises(Exception):
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corr_mat.Hankel()
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with pytest.raises(Exception):
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corr_mat.plateau()
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with pytest.raises(Exception):
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corr_mat.plateau([2, 4])
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with pytest.raises(Exception):
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corr_o.smearing(0, 0)
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@ -32,3 +32,32 @@ def test_grid_dirac():
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pe.dirac.Grid_gamma(gamma)
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with pytest.raises(Exception):
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pe.dirac.Grid_gamma('Not a gamma matrix')
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def test_epsilon_tensor():
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check = {(1, 2, 3) : 1.0,
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(3, 1, 2) : 1.0,
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(2, 3, 1) : 1.0,
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(1, 1, 1) : 0.0,
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(3, 2, 1) : -1.0,
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(1, 3, 2) : -1.0,
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(1, 1, 3) : 0.0}
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for key, value in check.items():
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assert pe.dirac.epsilon_tensor(*key) == value
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with pytest.raises(Exception):
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pe.dirac.epsilon_tensor(0, 1, 3)
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def test_epsilon_tensor_rank4():
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check = {(1, 4, 3, 2) : -1.0,
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(1, 2, 3, 4) : 1.0,
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(2, 1, 3, 4) : -1.0,
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(4, 3, 2, 1) : 1.0,
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(3, 2, 4, 3) : 0.0,
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(0, 1, 2, 3) : 1.0,
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(1, 1, 1, 1) : 0.0,
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(1, 2, 3, 1) : 0.0}
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for key, value in check.items():
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assert pe.dirac.epsilon_tensor_rank4(*key) == value
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with pytest.raises(Exception):
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pe.dirac.epsilon_tensor_rank4(0, 1, 3, 4)
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@ -84,6 +84,33 @@ def test_least_squares():
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assert math.isclose(pe.covariance(betac[0], betac[1]), pcov[0, 1], abs_tol=1e-3)
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def test_alternative_solvers():
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dim = 192
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x = np.arange(dim)
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y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
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yerr = 0.1 + 0.1 * np.random.rand(dim)
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oy = []
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for i, item in enumerate(x):
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oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test'))
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def func(a, x):
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y = a[0] * np.exp(-a[1] * x)
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return y
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chisquare_values = []
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out = pe.least_squares(x, oy, func, method='migrad')
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chisquare_values.append(out.chisquare)
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out = pe.least_squares(x, oy, func, method='Powell')
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chisquare_values.append(out.chisquare)
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out = pe.least_squares(x, oy, func, method='Nelder-Mead')
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chisquare_values.append(out.chisquare)
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out = pe.least_squares(x, oy, func, method='Levenberg-Marquardt')
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chisquare_values.append(out.chisquare)
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chisquare_values = np.array(chisquare_values)
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assert np.all(np.isclose(chisquare_values, chisquare_values[0]))
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def test_correlated_fit():
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num_samples = 400
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N = 10
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@ -93,10 +120,9 @@ def test_correlated_fit():
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r = np.zeros((N, N))
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for i in range(N):
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for j in range(N):
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r[i, j] = np.exp(-0.1 * np.fabs(i - j))
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r[i, j] = np.exp(-0.8 * np.fabs(i - j))
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errl = np.sqrt([3.4, 2.5, 3.6, 2.8, 4.2, 4.7, 4.9, 5.1, 3.2, 4.2])
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errl *= 4
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for i in range(N):
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for j in range(N):
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r[i, j] *= errl[i] * errl[j]
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@ -127,7 +153,7 @@ def test_correlated_fit():
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for i in range(2):
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diff = fitp[i] - fitpc[i]
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diff.gamma_method()
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assert(diff.is_zero_within_error(sigma=1.5))
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assert(diff.is_zero_within_error(sigma=5))
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def test_total_least_squares():
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@ -310,6 +336,25 @@ def test_error_band():
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pe.fits.error_band(x, f, fitp.fit_parameters)
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def test_ks_test():
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def f(a, x):
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y = a[0] + a[1] * x
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return y
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fit_res = []
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for i in range(20):
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data = []
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for j in range(10):
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data.append(pe.pseudo_Obs(j + np.random.normal(0.0, 0.25), 0.25, 'test'))
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my_corr = pe.Corr(data)
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fit_res.append(my_corr.fit(f, silent=True))
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pe.fits.ks_test()
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pe.fits.ks_test(fit_res)
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|
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def fit_general(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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|
|
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|
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@ -1,136 +0,0 @@
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{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This file is used for testing some of the input methods."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os,sys,inspect\n",
|
||||
"current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
|
||||
"parent_dir = os.path.dirname(current_dir)\n",
|
||||
"sys.path.insert(0, parent_dir) \n",
|
||||
"\n",
|
||||
"import pyerrors as pe\n",
|
||||
"import pyerrors.input.openQCD as qcdin\n",
|
||||
"import pyerrors.input.sfcf as sfin\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First, we will have a look at the input method for the topological charge $Q_{top}$, which is measured by the program ms from the openQCD package. For now, this part still in the making and depends on an actual file. Later, this should be changed to a more efficient way of making a proper input file.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['T29L20k0.13719r2.ms.dat', 'T29L20k0.13719r3.ms.dat', 'T29L20k0.13719r1.ms.dat', 'T29L20k0.13719r4.ms.dat']\n",
|
||||
"dn: 10\n",
|
||||
"nn: 60\n",
|
||||
"tmax: 30\n",
|
||||
"eps: 0.02\n",
|
||||
"max_t: 12.0\n",
|
||||
"t_aim: 6.125\n",
|
||||
"index_aim: 31\n",
|
||||
"T29L20k0.13719r2\n",
|
||||
"dn: 10\n",
|
||||
"nn: 60\n",
|
||||
"tmax: 30\n",
|
||||
"eps: 0.02\n",
|
||||
"max_t: 12.0\n",
|
||||
"t_aim: 6.125\n",
|
||||
"index_aim: 31\n",
|
||||
"T29L20k0.13719r3\n",
|
||||
"dn: 10\n",
|
||||
"nn: 60\n",
|
||||
"tmax: 30\n",
|
||||
"eps: 0.02\n",
|
||||
"max_t: 12.0\n",
|
||||
"t_aim: 6.125\n",
|
||||
"index_aim: 31\n",
|
||||
"T29L20k0.13719r1\n",
|
||||
"dn: 10\n",
|
||||
"nn: 60\n",
|
||||
"tmax: 30\n",
|
||||
"eps: 0.02\n",
|
||||
"max_t: 12.0\n",
|
||||
"t_aim: 6.125\n",
|
||||
"index_aim: 31\n",
|
||||
"T29L20k0.13719r4\n"
|
||||
]
|
||||
}
|
||||
],
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||||
"source": [
|
||||
"r_qtop = qcdin.read_qtop(\"../../test_data\", prefix = \"T29L20k0.13719\",full = True, r_stop = [500,440,447,410])#, files = [\"T29L20k0.13719r1.ms.dat\"], )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'T29L20k0.13719|r1': 500, 'T29L20k0.13719|r2': 440, 'T29L20k0.13719|r3': 447, 'T29L20k0.13719|r4': 410}\n",
|
||||
"0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -2 -2 -2 -2 -3 -3 -3 -3 -2 -2 -2 -2 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 -1 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 -1 -1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(r_qtop.shape)\n",
|
||||
"#print(r_qtop.deltas['T29L20k0.13719|r1'])\n",
|
||||
"for i in r_qtop.deltas['T29L20k0.13719|r2']:\n",
|
||||
" print(round(r_qtop.value + i), end =\" \")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.9.7 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.7"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
153
tests/io_test.py
153
tests/io_test.py
|
|
@ -3,6 +3,7 @@ import gzip
|
|||
import numpy as np
|
||||
import pyerrors as pe
|
||||
import pyerrors.input.json as jsonio
|
||||
import pytest
|
||||
|
||||
|
||||
def test_jsonio():
|
||||
|
|
@ -89,3 +90,155 @@ def test_json_string_reconstruction():
|
|||
|
||||
assert reconstructed_string == json_string
|
||||
assert my_obs == reconstructed_obs2
|
||||
|
||||
|
||||
def test_json_corr_io():
|
||||
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
|
||||
rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
|
||||
|
||||
for obs_list in [my_list, rw_list]:
|
||||
for tag in [None, "test"]:
|
||||
obs_list[3].tag = tag
|
||||
for pad in [0, 2]:
|
||||
for corr_tag in [None, 'my_Corr_tag']:
|
||||
for prange in [None, [3, 6]]:
|
||||
for gap in [False, True]:
|
||||
my_corr = pe.Corr(obs_list, padding=[pad, pad], prange=prange)
|
||||
my_corr.tag = corr_tag
|
||||
if gap:
|
||||
my_corr.content[4] = None
|
||||
pe.input.json.dump_to_json(my_corr, 'corr')
|
||||
recover = pe.input.json.load_json('corr')
|
||||
os.remove('corr.json.gz')
|
||||
assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
|
||||
for index, entry in enumerate(my_corr):
|
||||
if entry is None:
|
||||
assert recover[index] is None
|
||||
assert my_corr.tag == recover.tag
|
||||
assert my_corr.prange == recover.prange
|
||||
assert my_corr.reweighted == recover.reweighted
|
||||
|
||||
|
||||
def test_json_corr_2d_io():
|
||||
obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(4)]
|
||||
|
||||
for tag in [None, "test"]:
|
||||
obs_list[3][0, 1].tag = tag
|
||||
for padding in [0, 1]:
|
||||
for prange in [None, [3, 6]]:
|
||||
my_corr = pe.Corr(obs_list, padding=[padding, padding], prange=prange)
|
||||
my_corr.tag = tag
|
||||
pe.input.json.dump_to_json(my_corr, 'corr')
|
||||
recover = pe.input.json.load_json('corr')
|
||||
os.remove('corr.json.gz')
|
||||
assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
|
||||
for index, entry in enumerate(my_corr):
|
||||
if entry is None:
|
||||
assert recover[index] is None
|
||||
assert my_corr.tag == recover.tag
|
||||
assert my_corr.prange == recover.prange
|
||||
|
||||
|
||||
def test_json_dict_io():
|
||||
def check_dict_equality(d1, d2):
|
||||
def dict_check_obs(d1, d2):
|
||||
for k, v in d1.items():
|
||||
if isinstance(v, dict):
|
||||
v = dict_check_obs(v, d2[k])
|
||||
elif isinstance(v, list) and all([isinstance(o, pe.Obs) for o in v]):
|
||||
for i in range(len(v)):
|
||||
assert((v[i] - d2[k][i]).is_zero())
|
||||
elif isinstance(v, list):
|
||||
v = list_check_obs(v, d2[k])
|
||||
elif isinstance(v, pe.Obs):
|
||||
assert((v - d2[k]).is_zero())
|
||||
elif isinstance(v, pe.Corr):
|
||||
for i in range(v.T):
|
||||
assert((v[i] - d2[k][i]).is_zero())
|
||||
elif isinstance(v, np.ndarray):
|
||||
a1 = np.ravel(v)
|
||||
a2 = np.ravel(d2[k])
|
||||
for i in range(len(a1)):
|
||||
assert((a1[i] - a2[i]).is_zero())
|
||||
|
||||
def list_check_obs(l1, l2):
|
||||
for ei in range(len(l1)):
|
||||
e = l1[ei]
|
||||
if isinstance(e, list):
|
||||
e = list_check_obs(e, l2[ei])
|
||||
elif isinstance(e, list) and all([isinstance(o, pe.Obs) for o in e]):
|
||||
for i in range(len(e)):
|
||||
assert((e[i] - l2[ei][i]).is_zero())
|
||||
elif isinstance(e, dict):
|
||||
e = dict_check_obs(e, l2[ei])
|
||||
elif isinstance(e, pe.Obs):
|
||||
assert((e - l2[ei]).is_zero())
|
||||
elif isinstance(e, pe.Corr):
|
||||
for i in range(e.T):
|
||||
assert((e[i] - l2[ei][i]).is_zero())
|
||||
elif isinstance(e, np.ndarray):
|
||||
a1 = np.ravel(e)
|
||||
a2 = np.ravel(l2[ei])
|
||||
for i in range(len(a1)):
|
||||
assert((a1[i] - a2[i]).is_zero())
|
||||
dict_check_obs(d1, d2)
|
||||
return True
|
||||
|
||||
od = {
|
||||
'l':
|
||||
{
|
||||
'a': pe.pseudo_Obs(1, .2, 'testa', samples=10),
|
||||
'b': [pe.pseudo_Obs(1.1, .1, 'test', samples=10), pe.pseudo_Obs(1.2, .1, 'test', samples=10), pe.pseudo_Obs(1.3, .1, 'test', samples=10)],
|
||||
'c': {
|
||||
'd': 1,
|
||||
'e': pe.pseudo_Obs(.2, .01, 'teste', samples=10),
|
||||
'f': pe.Corr([pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10)]),
|
||||
'g': np.reshape(np.asarray([pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10)]), (2, 2)),
|
||||
}
|
||||
},
|
||||
's':
|
||||
{
|
||||
'a': 'Infor123',
|
||||
'b': ['Some', 'list'],
|
||||
'd': pe.pseudo_Obs(.01, .001, 'testd', samples=10) * pe.cov_Obs(1, .01, 'cov1'),
|
||||
'se': None,
|
||||
'sf': 1.2,
|
||||
}
|
||||
}
|
||||
|
||||
fname = 'test_rw'
|
||||
|
||||
desc = 'This is a random description'
|
||||
|
||||
with pytest.raises(Exception):
|
||||
jsonio.dump_dict_to_json(od, fname, description=desc, reps='|Test')
|
||||
|
||||
jsonio.dump_dict_to_json(od, fname, description=desc, reps='TEST')
|
||||
nd = jsonio.load_json_dict(fname, full_output=True, reps='TEST')
|
||||
|
||||
with pytest.raises(Exception):
|
||||
nd = jsonio.load_json_dict(fname, full_output=True)
|
||||
|
||||
jsonio.dump_dict_to_json(od, fname, description=desc)
|
||||
nd = jsonio.load_json_dict(fname, full_output=True)
|
||||
assert (desc == nd['description'])
|
||||
|
||||
assert(check_dict_equality(od, nd['obsdata']))
|
||||
nd = jsonio.load_json_dict(fname, full_output=False)
|
||||
assert(check_dict_equality(od, nd))
|
||||
|
||||
nl = jsonio.load_json(fname, full_output=True)
|
||||
nl = jsonio.load_json(fname, full_output=False)
|
||||
|
||||
with pytest.raises(Exception):
|
||||
jsonio.dump_dict_to_json(nl, fname, description=desc)
|
||||
|
||||
od['k'] = 'DICTOBS2'
|
||||
with pytest.raises(Exception):
|
||||
jsonio.dump_dict_to_json(od, fname, description=desc)
|
||||
|
||||
od['k'] = ['DICTOBS2']
|
||||
with pytest.raises(Exception):
|
||||
jsonio.dump_dict_to_json(od, fname, description=desc)
|
||||
|
||||
os.remove(fname + '.json.gz')
|
||||
|
|
|
|||
|
|
@ -314,6 +314,8 @@ def test_matrix_functions():
|
|||
# Check determinant
|
||||
assert pe.linalg.det(np.diag(np.diag(matrix))) == np.prod(np.diag(matrix))
|
||||
|
||||
pe.linalg.pinv(matrix[:,:3])
|
||||
|
||||
|
||||
def test_complex_matrix_operations():
|
||||
dimension = 4
|
||||
|
|
|
|||
14
tests/mpm_test.py
Normal file
14
tests/mpm_test.py
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
import numpy as np
|
||||
import pyerrors as pe
|
||||
import pytest
|
||||
|
||||
np.random.seed(0)
|
||||
|
||||
|
||||
def test_mpm():
|
||||
corr_content = []
|
||||
for t in range(8):
|
||||
f = 0.8 * np.exp(-0.4 * t)
|
||||
corr_content.append(pe.pseudo_Obs(np.random.normal(f, 1e-2 * f), 1e-2 * f, 't'))
|
||||
|
||||
res = pe.mpm.matrix_pencil_method(corr_content)
|
||||
|
|
@ -57,10 +57,16 @@ def test_dump():
|
|||
value = np.random.normal(5, 10)
|
||||
dvalue = np.abs(np.random.normal(0, 1))
|
||||
test_obs = pe.pseudo_Obs(value, dvalue, 't')
|
||||
test_obs.dump('test_dump')
|
||||
test_obs.dump('test_dump', datatype="pickle", path=".")
|
||||
test_obs.dump('test_dump', datatype="pickle")
|
||||
new_obs = pe.load_object('test_dump.p')
|
||||
os.remove('test_dump.p')
|
||||
assert test_obs.deltas['t'].all() == new_obs.deltas['t'].all()
|
||||
assert test_obs == new_obs
|
||||
test_obs.dump('test_dump', dataype="json.gz", path=".")
|
||||
test_obs.dump('test_dump', dataype="json.gz")
|
||||
new_obs = pe.input.json.load_json("test_dump")
|
||||
os.remove('test_dump.json.gz')
|
||||
assert test_obs == new_obs
|
||||
|
||||
|
||||
def test_comparison():
|
||||
|
|
@ -105,6 +111,12 @@ def test_function_overloading():
|
|||
assert np.sqrt(b ** 2) == b
|
||||
assert np.sqrt(b) ** 2 == b
|
||||
|
||||
np.arcsin(1 / b)
|
||||
np.arccos(1 / b)
|
||||
np.arctan(1 / b)
|
||||
np.arctanh(1 / b)
|
||||
np.sinc(1 / b)
|
||||
|
||||
|
||||
def test_overloading_vectorization():
|
||||
a = np.random.randint(1, 100, 10)
|
||||
|
|
@ -292,14 +304,14 @@ def test_derived_observables():
|
|||
d_Obs_fd = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs], num_grad=True)
|
||||
d_Obs_fd.gamma_method()
|
||||
|
||||
assert d_Obs_ad.value == d_Obs_fd.value
|
||||
assert d_Obs_ad == d_Obs_fd
|
||||
assert np.abs(4.0 * np.sin(4.0) - d_Obs_ad.value) < 1000 * np.finfo(np.float64).eps * np.abs(d_Obs_ad.value)
|
||||
assert np.abs(d_Obs_ad.dvalue-d_Obs_fd.dvalue) < 1000 * np.finfo(np.float64).eps * d_Obs_ad.dvalue
|
||||
|
||||
i_am_one = pe.derived_observable(lambda x, **kwargs: x[0] / x[1], [d_Obs_ad, d_Obs_ad])
|
||||
i_am_one.gamma_method()
|
||||
|
||||
assert i_am_one.value == 1.0
|
||||
assert i_am_one == 1.0
|
||||
assert i_am_one.dvalue < 2 * np.finfo(np.float64).eps
|
||||
assert i_am_one.e_dvalue['t'] <= 2 * np.finfo(np.float64).eps
|
||||
assert i_am_one.e_ddvalue['t'] <= 2 * np.finfo(np.float64).eps
|
||||
|
|
@ -428,6 +440,14 @@ def test_reweighting():
|
|||
assert r_obs[0].reweighted
|
||||
r_obs2 = r_obs[0] * my_obs
|
||||
assert r_obs2.reweighted
|
||||
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
||||
with pytest.raises(Exception):
|
||||
pe.reweight(my_obs, [my_covobs])
|
||||
my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
|
||||
with pytest.raises(Exception):
|
||||
pe.reweight(my_obs, [my_obs + my_obs2])
|
||||
with pytest.raises(Exception):
|
||||
pe.reweight(my_irregular_obs, [my_obs])
|
||||
|
||||
|
||||
def test_merge_obs():
|
||||
|
|
@ -436,6 +456,12 @@ def test_merge_obs():
|
|||
merged = pe.merge_obs([my_obs1, my_obs2])
|
||||
diff = merged - my_obs2 - my_obs1
|
||||
assert diff == -(my_obs1.value + my_obs2.value) / 2
|
||||
with pytest.raises(Exception):
|
||||
pe.merge_obs([my_obs1, my_obs1])
|
||||
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
||||
with pytest.raises(Exception):
|
||||
pe.merge_obs([my_obs1, my_covobs])
|
||||
|
||||
|
||||
|
||||
def test_merge_obs_r_values():
|
||||
|
|
@ -468,6 +494,17 @@ def test_correlate():
|
|||
corr3 = pe.correlate(my_obs5, my_obs6)
|
||||
assert my_obs5.idl == corr3.idl
|
||||
|
||||
my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
|
||||
with pytest.raises(Exception):
|
||||
pe.correlate(my_obs1, my_new_obs)
|
||||
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
||||
with pytest.raises(Exception):
|
||||
pe.correlate(my_covobs, my_covobs)
|
||||
r_obs = pe.reweight(my_obs1, [my_obs1])[0]
|
||||
with pytest.warns(RuntimeWarning):
|
||||
pe.correlate(r_obs, r_obs)
|
||||
|
||||
|
||||
|
||||
def test_irregular_error_propagation():
|
||||
obs_list = [pe.Obs([np.random.rand(100)], ['t']),
|
||||
|
|
@ -583,7 +620,7 @@ def test_covariance_symmetry():
|
|||
cov_ab = pe.covariance(test_obs1, a)
|
||||
cov_ba = pe.covariance(a, test_obs1)
|
||||
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
|
||||
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
|
||||
assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
|
||||
|
||||
|
||||
def test_empty_obs():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue