import os import numpy as np import pyerrors as pe import pytest np.random.seed(0) def test_function_overloading(): corr_content_a = [] corr_content_b = [] for t in range(24): corr_content_a.append(pe.pseudo_Obs(np.random.normal(1e-10, 1e-8), 1e-4, 't')) corr_content_b.append(pe.pseudo_Obs(np.random.normal(1e8, 1e10), 1e7, 't')) corr_a = pe.correlators.Corr(corr_content_a) corr_b = pe.correlators.Corr(corr_content_b) fs = [lambda x: x[0] + x[1], lambda x: x[1] + x[0], lambda x: x[0] - x[1], lambda x: x[1] - x[0], lambda x: x[0] * x[1], lambda x: x[1] * x[0], lambda x: x[0] / x[1], lambda x: x[1] / x[0], lambda x: np.exp(x[0]), lambda x: np.sin(x[0]), lambda x: np.cos(x[0]), lambda x: np.tan(x[0]), lambda x: np.log(x[0] + 0.1), lambda x: np.sqrt(np.abs(x[0])), lambda x: np.sinh(x[0]), lambda x: np.cosh(x[0]), lambda x: np.tanh(x[0])] for i, f in enumerate(fs): t1 = f([corr_a, corr_b]) for o_a, o_b, con in zip(corr_content_a, corr_content_b, t1.content): t2 = f([o_a, o_b]) t2.gamma_method() assert np.isclose(con[0].value, t2.value) assert np.isclose(con[0].dvalue, t2.dvalue) assert np.allclose(con[0].deltas['t'], t2.deltas['t']) np.arcsin(corr_a) np.arccos(corr_a) np.arctan(corr_a) np.arcsinh(corr_a) np.arccosh(corr_a + 1.1) np.arctanh(corr_a) def test_modify_correlator(): corr_content = [] for t in range(24): exponent = np.random.normal(3, 5) corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't')) corr = pe.correlators.Corr(corr_content) with pytest.warns(RuntimeWarning): corr.symmetric() with pytest.warns(RuntimeWarning): corr.anti_symmetric() corr.roll(np.random.randint(100)) corr.deriv(symmetric=True) corr.deriv(symmetric=False) corr.deriv().deriv() corr.second_deriv() corr.second_deriv().second_deriv() def test_m_eff(): 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')]) my_corr.m_eff('log') my_corr.m_eff('cosh') my_corr.m_eff('arccosh') with pytest.warns(RuntimeWarning): my_corr.m_eff('sinh') def test_reweighting(): my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')]) assert my_corr.reweighted is False r_my_corr = my_corr.reweight(pe.pseudo_Obs(1, 0.1, 't')) assert r_my_corr.reweighted is True def test_correlate(): my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')]) corr1 = my_corr.correlate(my_corr) corr2 = my_corr.correlate(my_corr[0]) with pytest.raises(Exception): corr3 = my_corr.correlate(7.3) def test_T_symmetry(): my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')]) with pytest.warns(RuntimeWarning): T_symmetric = my_corr.T_symmetry(my_corr) def test_fit_correlator(): my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(2.042345, 0.0004, 't')]) def f(a, x): y = a[0] + a[1] * x return y fit_res = my_corr.fit(f) assert fit_res[0] == my_corr[0] assert fit_res[1] == my_corr[1] - my_corr[0] def test_plateau(): my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(1.042345, 0.008, 't')]) my_corr.plateau([0, 1], method="fit") my_corr.plateau([0, 1], method="mean") with pytest.raises(Exception): my_corr.plateau() def test_padded_correlator(): my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)] my_corr = pe.Corr(my_list, padding=[7, 3]) my_corr.reweighted [o for o in my_corr] def test_corr_exceptions(): obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test']) obs_b= pe.Obs([np.random.normal(0.1, 0.1, 99)], ['test']) with pytest.raises(Exception): pe.Corr([obs_a, obs_b]) obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test']) obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'], idl=[range(1, 200, 2)]) with pytest.raises(Exception): pe.Corr([obs_a, obs_b]) obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test']) obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test2']) with pytest.raises(Exception): pe.Corr([obs_a, obs_b]) def test_utility(): corr_content = [] for t in range(8): exponent = np.random.normal(3, 5) corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't')) corr = pe.correlators.Corr(corr_content) corr.print() corr.print([2, 4]) corr.show() corr.dump('test_dump', datatype="pickle", path='.') corr.dump('test_dump', datatype="pickle") new_corr = pe.load_object('test_dump.p') os.remove('test_dump.p') for o_a, o_b in zip(corr.content, new_corr.content): assert np.isclose(o_a[0].value, o_b[0].value) assert np.isclose(o_a[0].dvalue, o_b[0].dvalue) assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t']) corr.dump('test_dump', datatype="json.gz", path='.') corr.dump('test_dump', datatype="json.gz") new_corr = pe.input.json.load_json('test_dump') os.remove('test_dump.json.gz') for o_a, o_b in zip(corr.content, new_corr.content): assert np.isclose(o_a[0].value, o_b[0].value) assert np.isclose(o_a[0].dvalue, o_b[0].dvalue) assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])