import autograd.numpy as np import os import random import string import copy import pyerrors as pe import pytest np.random.seed(0) 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') new_obs = pe.load_object('test_dump.p') os.remove('test_dump.p') assert test_obs.deltas['t'].all() == new_obs.deltas['t'].all() def test_comparison(): value1 = np.random.normal(0, 100) test_obs1 = pe.pseudo_Obs(value1, 0.1, 't') value2 = np.random.normal(0, 100) test_obs2 = pe.pseudo_Obs(value2, 0.1, 't') assert (value1 > value2) == (test_obs1 > test_obs2) assert (value1 < value2) == (test_obs1 < test_obs2) assert (value1 >= value2) == (test_obs1 >= test_obs2) assert (value1 <= value2) == (test_obs1 <= test_obs2) assert test_obs1 >= test_obs1 assert test_obs2 <= test_obs2 assert test_obs1 == test_obs1 assert test_obs2 == test_obs2 assert test_obs1 != test_obs2 assert test_obs2 != test_obs1 def test_function_overloading(): a = pe.pseudo_Obs(17, 2.9, 'e1') b = pe.pseudo_Obs(4, 0.8, 'e1') 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]), 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([a, b]) t2 = pe.derived_observable(f, [a, b]) c = t2 - t1 assert c.value == 0.0, str(i) assert np.all(np.abs(c.deltas['e1']) < 1e-14), str(i) def test_overloading_vectorization(): a = np.random.randint(1, 100, 10) b = pe.pseudo_Obs(4, 0.8, 't') assert [o.value for o in a * b] == [o.value for o in b * a] assert [o.value for o in a + b] == [o.value for o in b + a] assert [o.value for o in a - b] == [-1 * o.value for o in b - a] assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]] assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]] a = np.random.normal(0.0, 1e10, 10) b = pe.pseudo_Obs(4, 0.8, 't') assert [o.value for o in a * b] == [o.value for o in b * a] assert [o.value for o in a + b] == [o.value for o in b + a] assert [o.value for o in a - b] == [-1 * o.value for o in b - a] assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]] assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]] def test_covariance_is_variance(): value = np.random.normal(5, 10) dvalue = np.abs(np.random.normal(0, 1)) test_obs = pe.pseudo_Obs(value, dvalue, 't') test_obs.gamma_method() assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200) test_obs.gamma_method(e_tag=0) assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps def test_fft(): value = np.random.normal(5, 100) dvalue = np.abs(np.random.normal(0, 5)) test_obs1 = pe.pseudo_Obs(value, dvalue, 't', int(500 + 1000 * np.random.rand())) test_obs2 = copy.deepcopy(test_obs1) test_obs1.gamma_method() test_obs2.gamma_method(fft=False) assert max(np.abs(test_obs1.e_rho[''] - test_obs2.e_rho[''])) <= 10 * np.finfo(np.float64).eps assert np.abs(test_obs1.dvalue - test_obs2.dvalue) <= 10 * max(test_obs1.dvalue, test_obs2.dvalue) * np.finfo(np.float64).eps def test_covariance_symmetry(): value1 = np.random.normal(5, 10) dvalue1 = np.abs(np.random.normal(0, 1)) test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't') test_obs1.gamma_method() value2 = np.random.normal(5, 10) dvalue2 = np.abs(np.random.normal(0, 1)) test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't') test_obs2.gamma_method() cov_ab = pe.covariance(test_obs1, test_obs2) cov_ba = pe.covariance(test_obs2, 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) def test_gamma_method(): # Construct pseudo Obs with random shape value = np.random.normal(5, 10) dvalue = np.abs(np.random.normal(0, 1)) test_obs = pe.pseudo_Obs(value, dvalue, 't', int(1000 * (1 + np.random.rand()))) # Test if the error is processed correctly test_obs.gamma_method(e_tag=1) assert np.abs(test_obs.value - value) < 1e-12 assert abs(test_obs.dvalue - dvalue) < 1e-10 * dvalue def test_derived_observables(): # Construct pseudo Obs with random shape test_obs = pe.pseudo_Obs(2, 0.1 * (1 + np.random.rand()), 't', int(1000 * (1 + np.random.rand()))) # Check if autograd and numgrad give the same result d_Obs_ad = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs]) d_Obs_ad.gamma_method() 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 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(e_tag=1) assert i_am_one.value == 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 def test_multi_ens_system(): names = [] for i in range(100 + int(np.random.rand() * 50)): tmp_string = '' for _ in range(int(2 + np.random.rand() * 4)): tmp_string += random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits) names.append(tmp_string) names = list(set(names)) samples = [np.random.rand(5)] * len(names) new_obs = pe.Obs(samples, names) for e_tag_length in range(1, 6): new_obs.gamma_method(e_tag=e_tag_length) e_names = sorted(set([n[:e_tag_length] for n in names])) assert e_names == new_obs.e_names assert sorted(x for y in sorted(new_obs.e_content.values()) for x in y) == sorted(new_obs.names) def test_overloaded_functions(): funcs = [np.exp, np.log, np.sin, np.cos, np.tan, np.sinh, np.cosh, np.arcsinh, np.arccosh] deriv = [np.exp, lambda x: 1 / x, np.cos, lambda x: -np.sin(x), lambda x: 1 / np.cos(x) ** 2, np.cosh, np.sinh, lambda x: 1 / np.sqrt(x ** 2 + 1), lambda x: 1 / np.sqrt(x ** 2 - 1)] val = 3 + 0.5 * np.random.rand() dval = 0.3 + 0.4 * np.random.rand() test_obs = pe.pseudo_Obs(val, dval, 't', int(1000 * (1 + np.random.rand()))) for i, item in enumerate(funcs): ad_obs = item(test_obs) fd_obs = pe.derived_observable(lambda x, **kwargs: item(x[0]), [test_obs], num_grad=True) ad_obs.gamma_method(S=0.01, e_tag=1) assert np.max((ad_obs.deltas['t'] - fd_obs.deltas['t']) / ad_obs.deltas['t']) < 1e-8, item.__name__ assert np.abs((ad_obs.value - item(val)) / ad_obs.value) < 1e-10, item.__name__ assert np.abs(ad_obs.dvalue - dval * np.abs(deriv[i](val))) < 1e-6, item.__name__ def test_utils(): my_obs = pe.pseudo_Obs(1.0, 0.5, 't') my_obs.print(0) my_obs.print(1) my_obs.print(2) assert not my_obs.is_zero_within_error() my_obs.plot_tauint() my_obs.plot_rho() my_obs.plot_rep_dist() my_obs.plot_history() my_obs.plot_piechart() assert my_obs > (my_obs - 1) assert my_obs < (my_obs + 1) def test_cobs(): obs1 = pe.pseudo_Obs(1.0, 0.1, 't') obs2 = pe.pseudo_Obs(-0.2, 0.03, 't') my_cobs = pe.CObs(obs1, obs2) np.abs(my_cobs) 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]]] for other in [1, 1.1, (1.1-0.2j), pe.CObs(obs1), pe.CObs(obs1, obs2)]: for funcs in fs: ta = funcs[0]([my_cobs, other]) tb = funcs[1]([my_cobs, other]) diff = ta - tb assert np.isclose(0.0, float(diff.real)) assert np.isclose(0.0, float(diff.imag)) assert np.allclose(0.0, diff.real.deltas['t']) assert np.allclose(0.0, diff.imag.deltas['t']) ta = my_cobs - other tb = other - my_cobs diff = ta + tb assert np.isclose(0.0, float(diff.real)) assert np.isclose(0.0, float(diff.imag)) assert np.allclose(0.0, diff.real.deltas['t']) assert np.allclose(0.0, diff.imag.deltas['t']) div = my_cobs / other