pyerrors/tests/test_pyerrors.py

import autograd.numpy as np
import os
import random
import string
import copy
import pyerrors as pe
import pytest

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)


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(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(0, 100, 10)
    b = pe.pseudo_Obs(4, 0.8, 'e1')

    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__