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]) t1.gamma_method() 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.Corr(corr_content) with pytest.warns(RuntimeWarning): corr.symmetric() with pytest.warns(RuntimeWarning): corr.anti_symmetric() for pad in [0, 2]: corr = pe.Corr(corr_content, padding=[pad, pad]) corr.roll(np.random.randint(100)) corr.deriv(variant="forward") corr.deriv(variant="symmetric") corr.deriv(variant="improved") corr.deriv().deriv() corr.second_deriv(variant="symmetric") corr.second_deriv(variant="improved") corr.second_deriv().second_deriv() for i, e in enumerate(corr.content): corr.content[i] = None for func in [pe.Corr.deriv, pe.Corr.second_deriv]: for variant in ["symmetric", "improved", "forward", "gibberish", None]: with pytest.raises(Exception): func(corr, variant=variant) def test_deriv(): corr_content = [] for t in range(24): exponent = 1.2 corr_content.append(pe.pseudo_Obs(2 + t ** exponent, 0.2, 't')) corr = pe.Corr(corr_content) forward = corr.deriv(variant="forward") backward = corr.deriv(variant="backward") sym = corr.deriv(variant="symmetric") assert np.all([o == 0 for o in (0.5 * (forward + backward) - sym)[1:-1]]) assert np.all([o == 0 for o in (corr.deriv('forward').deriv('backward') - corr.second_deriv())[1:-1]]) assert np.all([o == 0 for o in (corr.deriv('backward').deriv('forward') - corr.second_deriv())[1:-1]]) def test_m_eff(): for padding in [0, 4]: 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]) my_corr.m_eff('log') my_corr.m_eff('cosh') my_corr.m_eff('arccosh') with pytest.warns(RuntimeWarning): my_corr.m_eff('sinh') with pytest.raises(Exception): my_corr.m_eff('unkown_variant') 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] with pytest.raises(Exception): my_corr.fit(f, "from 0 to 3") with pytest.raises(Exception): my_corr.fit(f, [0, 2, 3]) 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.gamma_method() corr.print() corr.print([2, 4]) corr.show() corr.show(comp=corr) corr.dump('test_dump', datatype="pickle", path='.') corr.dump('test_dump', datatype="pickle") new_corr = pe.load_object('test_dump.p') new_corr.gamma_method() 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') new_corr.gamma_method() 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']) def test_prange(): corr_content = [] for t in range(8): corr_content.append(pe.pseudo_Obs(2 + 10 ** (1.1 * t), 0.2, 't')) corr = pe.correlators.Corr(corr_content) corr.set_prange([2, 4]) with pytest.raises(Exception): corr.set_prange([2]) with pytest.raises(Exception): corr.set_prange([2, 2.3]) with pytest.raises(Exception): corr.set_prange([4, 1]) def test_matrix_corr(): corr_aa = _gen_corr(1) corr_ab = 0.5 * corr_aa corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]])) corr_mat.item(0, 0) vec_0 = corr_mat.GEVP(0, 0, sorted_list=None) vec_1 = corr_mat.GEVP(0, 0, state=1, sorted_list=None) corr_0 = corr_mat.projected(vec_0) corr_1 = corr_mat.projected(vec_1) assert np.all([o == 0 for o in corr_0 - corr_aa]) assert np.all([o == 0 for o in corr_1 - corr_aa]) corr_mat.GEVP(0, sorted_list="Eigenvalue") corr_mat.GEVP(0, 0, sorted_list="Eigenvector") corr_mat.matrix_symmetric() with pytest.warns(RuntimeWarning): corr_mat.GEVP(0, 1, sorted_list="Eigenvalue") with pytest.raises(Exception): corr_mat.plottable() with pytest.raises(Exception): corr_mat.show() with pytest.raises(Exception): corr_mat.m_eff() with pytest.raises(Exception): corr_mat.Hankel() with pytest.raises(Exception): corr_mat.plateau() with pytest.raises(Exception): corr_mat.plateau([2, 4]) with pytest.raises(Exception): corr_mat.hankel(3) with pytest.raises(Exception): corr_mat.fit(lambda x: x[0]) with pytest.raises(Exception): corr_0.item(0, 0) with pytest.raises(Exception): corr_0.matrix_symmetric() def test_matrix_symmetric(): corr_aa = _gen_corr(1) corr_ab = _gen_corr(0.3) corr_ba = _gen_corr(0.2) corr_bb = _gen_corr(0.8) corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ba, corr_bb]])) sym_corr_mat = corr_mat.matrix_symmetric() assert np.all([np.all(o == o.T) for o in sym_corr_mat]) def test_hankel(): corr_content = [] for t in range(8): exponent = 1.2 corr_content.append(pe.pseudo_Obs(2 + t ** exponent, 0.2, 't')) corr = pe.Corr(corr_content) corr.Hankel(2) corr.Hankel(6, periodic=True) def test_thin(): c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)]) c *= pe.cov_Obs(1., .1, '#ren') thin = c.thin() thin.gamma_method() thin.fit(lambda a, x: a[0] * x) c.thin(offset=1) c.thin(3, offset=1) def test_corr_matrix_none_entries(): dim = 8 x = np.arange(dim) y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim) yerr = [0.1] * dim oy = [] for i, item in enumerate(x): oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test')) corr = pe.Corr(oy) corr = corr.deriv() pe.Corr(np.array([[corr, corr], [corr, corr]])) def test_corr_vector_operations(): my_corr = _gen_corr(1.0) my_vec = np.arange(1, 17) my_corr + my_vec my_corr - my_vec my_corr * my_vec my_corr / my_vec assert np.all([o == 0 for o in ((my_corr + my_vec) - my_vec) - my_corr]) assert np.all([o == 0 for o in ((my_corr - my_vec) + my_vec) - my_corr]) assert np.all([o == 0 for o in ((my_corr * my_vec) / my_vec) - my_corr]) assert np.all([o == 0 for o in ((my_corr / my_vec) * my_vec) - my_corr]) def test_spaghetti_plot(): corr = _gen_corr(12, 50) corr += pe.pseudo_Obs(0.0, 0.1, 'another_ensemble') corr += pe.cov_Obs(0.0, 0.01 ** 2, 'covobs') corr.spaghetti_plot(True) corr.spaghetti_plot(False) def _gen_corr(val, samples=2000): corr_content = [] for t in range(16): corr_content.append(pe.pseudo_Obs(val, 0.1, 't', samples)) return pe.correlators.Corr(corr_content) def test_prune(): corr_aa = _gen_corr(1) corr_ab = 0.5 * corr_aa corr_ac = 0.25 * corr_aa corr_mat = pe.Corr(np.array([[corr_aa, corr_ab, corr_ac], [corr_ab, corr_aa, corr_ab], [corr_ac, corr_ab, corr_aa]])) p = corr_mat.prune(2) assert(all([o.is_zero() for o in p.item(0, 1)])) a = [(o - 1) for o in p.item(1, 1)] [o.gamma_method() for o in a] assert(all([o.is_zero_within_error() for o in a])) with pytest.raises(Exception): corr_mat.prune(3) corr_mat.prune(4)