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https://github.com/fjosw/pyerrors.git
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248 lines
9.9 KiB
Python
248 lines
9.9 KiB
Python
import autograd.numpy as np
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import os
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import random
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import string
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import copy
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import pyerrors as pe
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import pytest
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np.random.seed(0)
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def test_dump():
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value = np.random.normal(5, 10)
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dvalue = np.abs(np.random.normal(0, 1))
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test_obs = pe.pseudo_Obs(value, dvalue, 't')
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test_obs.dump('test_dump')
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new_obs = pe.load_object('test_dump.p')
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os.remove('test_dump.p')
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assert test_obs.deltas['t'].all() == new_obs.deltas['t'].all()
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def test_comparison():
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value1 = np.random.normal(0, 100)
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test_obs1 = pe.pseudo_Obs(value1, 0.1, 't')
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value2 = np.random.normal(0, 100)
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test_obs2 = pe.pseudo_Obs(value2, 0.1, 't')
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assert (value1 > value2) == (test_obs1 > test_obs2)
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assert (value1 < value2) == (test_obs1 < test_obs2)
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assert (value1 >= value2) == (test_obs1 >= test_obs2)
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assert (value1 <= value2) == (test_obs1 <= test_obs2)
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assert test_obs1 >= test_obs1
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assert test_obs2 <= test_obs2
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assert test_obs1 == test_obs1
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assert test_obs2 == test_obs2
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assert test_obs1 - test_obs1 == 0.0
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assert test_obs1 / test_obs1 == 1.0
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assert test_obs1 != value1
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assert test_obs2 != value2
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assert test_obs1 != test_obs2
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assert test_obs2 != test_obs1
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def test_function_overloading():
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a = pe.pseudo_Obs(17, 2.9, 'e1')
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b = pe.pseudo_Obs(4, 0.8, 'e1')
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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],
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lambda x: x[0] * x[1], lambda x: x[1] * x[0], lambda x: x[0] / x[1], lambda x: x[1] / x[0],
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lambda x: np.exp(x[0]), lambda x: np.sin(x[0]), lambda x: np.cos(x[0]), lambda x: np.tan(x[0]),
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lambda x: np.log(x[0]), lambda x: np.sqrt(np.abs(x[0])),
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lambda x: np.sinh(x[0]), lambda x: np.cosh(x[0]), lambda x: np.tanh(x[0])]
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for i, f in enumerate(fs):
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t1 = f([a, b])
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t2 = pe.derived_observable(f, [a, b])
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c = t2 - t1
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assert c.value == 0.0, str(i)
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assert np.all(np.abs(c.deltas['e1']) < 1e-14), str(i)
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def test_overloading_vectorization():
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a = np.random.randint(1, 100, 10)
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b = pe.pseudo_Obs(4, 0.8, 't')
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assert [o.value for o in a * b] == [o.value for o in b * a]
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assert [o.value for o in a + b] == [o.value for o in b + a]
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assert [o.value for o in a - b] == [-1 * o.value for o in b - a]
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assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]]
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assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
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a = np.random.normal(0.0, 1e10, 10)
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b = pe.pseudo_Obs(4, 0.8, 't')
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assert [o.value for o in a * b] == [o.value for o in b * a]
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assert [o.value for o in a + b] == [o.value for o in b + a]
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assert [o.value for o in a - b] == [-1 * o.value for o in b - a]
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assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]]
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assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
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def test_gamma_method():
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for data in [np.tile([1, -1], 1000),
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np.random.rand(100001),
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np.zeros(1195),
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np.sin(np.sqrt(2) * np.pi * np.arange(1812))]:
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test_obs = pe.Obs([data], ['t'])
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test_obs.gamma_method()
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assert test_obs.dvalue - test_obs.ddvalue <= np.std(data, ddof=1) / np.sqrt(len(data))
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assert test_obs.e_tauint['t'] - 0.5 <= test_obs.e_dtauint['t']
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test_obs.gamma_method(tau_exp=10)
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assert test_obs.e_tauint['t'] - 10.5 <= test_obs.e_dtauint['t']
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def test_covariance_is_variance():
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value = np.random.normal(5, 10)
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dvalue = np.abs(np.random.normal(0, 1))
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test_obs = pe.pseudo_Obs(value, dvalue, 't')
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test_obs.gamma_method()
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assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
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test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
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test_obs.gamma_method(e_tag=0)
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assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
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def test_fft():
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value = np.random.normal(5, 100)
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dvalue = np.abs(np.random.normal(0, 5))
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test_obs1 = pe.pseudo_Obs(value, dvalue, 't', int(500 + 1000 * np.random.rand()))
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test_obs2 = copy.deepcopy(test_obs1)
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test_obs1.gamma_method()
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test_obs2.gamma_method(fft=False)
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assert max(np.abs(test_obs1.e_rho[''] - test_obs2.e_rho[''])) <= 10 * np.finfo(np.float64).eps
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assert np.abs(test_obs1.dvalue - test_obs2.dvalue) <= 10 * max(test_obs1.dvalue, test_obs2.dvalue) * np.finfo(np.float64).eps
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def test_covariance_symmetry():
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value1 = np.random.normal(5, 10)
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dvalue1 = np.abs(np.random.normal(0, 1))
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test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
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test_obs1.gamma_method()
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value2 = np.random.normal(5, 10)
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dvalue2 = np.abs(np.random.normal(0, 1))
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test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
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test_obs2.gamma_method()
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cov_ab = pe.covariance(test_obs1, test_obs2)
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cov_ba = pe.covariance(test_obs2, test_obs1)
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assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
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assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
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def test_gamma_method():
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# Construct pseudo Obs with random shape
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value = np.random.normal(5, 10)
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dvalue = np.abs(np.random.normal(0, 1))
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test_obs = pe.pseudo_Obs(value, dvalue, 't', int(1000 * (1 + np.random.rand())))
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# Test if the error is processed correctly
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test_obs.gamma_method(e_tag=1)
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assert np.abs(test_obs.value - value) < 1e-12
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assert abs(test_obs.dvalue - dvalue) < 1e-10 * dvalue
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def test_derived_observables():
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# Construct pseudo Obs with random shape
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test_obs = pe.pseudo_Obs(2, 0.1 * (1 + np.random.rand()), 't', int(1000 * (1 + np.random.rand())))
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# Check if autograd and numgrad give the same result
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d_Obs_ad = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs])
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d_Obs_ad.gamma_method()
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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)
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d_Obs_fd.gamma_method()
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assert d_Obs_ad.value == d_Obs_fd.value
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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)
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assert np.abs(d_Obs_ad.dvalue-d_Obs_fd.dvalue) < 1000 * np.finfo(np.float64).eps * d_Obs_ad.dvalue
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i_am_one = pe.derived_observable(lambda x, **kwargs: x[0] / x[1], [d_Obs_ad, d_Obs_ad])
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i_am_one.gamma_method(e_tag=1)
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assert i_am_one.value == 1.0
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assert i_am_one.dvalue < 2 * np.finfo(np.float64).eps
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assert i_am_one.e_dvalue['t'] <= 2 * np.finfo(np.float64).eps
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assert i_am_one.e_ddvalue['t'] <= 2 * np.finfo(np.float64).eps
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def test_multi_ens_system():
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names = []
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for i in range(100 + int(np.random.rand() * 50)):
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tmp_string = ''
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for _ in range(int(2 + np.random.rand() * 4)):
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tmp_string += random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits)
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names.append(tmp_string)
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names = list(set(names))
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samples = [np.random.rand(5)] * len(names)
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new_obs = pe.Obs(samples, names)
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for e_tag_length in range(1, 6):
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new_obs.gamma_method(e_tag=e_tag_length)
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e_names = sorted(set([n[:e_tag_length] for n in names]))
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assert e_names == new_obs.e_names
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assert sorted(x for y in sorted(new_obs.e_content.values()) for x in y) == sorted(new_obs.names)
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def test_overloaded_functions():
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funcs = [np.exp, np.log, np.sin, np.cos, np.tan, np.sinh, np.cosh, np.arcsinh, np.arccosh]
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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)]
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val = 3 + 0.5 * np.random.rand()
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dval = 0.3 + 0.4 * np.random.rand()
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test_obs = pe.pseudo_Obs(val, dval, 't', int(1000 * (1 + np.random.rand())))
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for i, item in enumerate(funcs):
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ad_obs = item(test_obs)
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fd_obs = pe.derived_observable(lambda x, **kwargs: item(x[0]), [test_obs], num_grad=True)
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ad_obs.gamma_method(S=0.01, e_tag=1)
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assert np.max((ad_obs.deltas['t'] - fd_obs.deltas['t']) / ad_obs.deltas['t']) < 1e-8, item.__name__
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assert np.abs((ad_obs.value - item(val)) / ad_obs.value) < 1e-10, item.__name__
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assert np.abs(ad_obs.dvalue - dval * np.abs(deriv[i](val))) < 1e-6, item.__name__
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def test_utils():
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my_obs = pe.pseudo_Obs(1.0, 0.5, 't')
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my_obs.print(0)
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my_obs.print(1)
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my_obs.print(2)
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assert not my_obs.is_zero_within_error()
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my_obs.plot_tauint()
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my_obs.plot_rho()
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my_obs.plot_rep_dist()
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my_obs.plot_history()
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my_obs.plot_piechart()
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assert my_obs > (my_obs - 1)
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assert my_obs < (my_obs + 1)
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def test_cobs():
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obs1 = pe.pseudo_Obs(1.0, 0.1, 't')
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obs2 = pe.pseudo_Obs(-0.2, 0.03, 't')
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my_cobs = pe.CObs(obs1, obs2)
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assert not (my_cobs + my_cobs.conjugate()).real.is_zero()
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assert (my_cobs + my_cobs.conjugate()).imag.is_zero()
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assert (my_cobs - my_cobs.conjugate()).real.is_zero()
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assert not (my_cobs - my_cobs.conjugate()).imag.is_zero()
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np.abs(my_cobs)
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assert (my_cobs * my_cobs / my_cobs - my_cobs).is_zero()
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assert (my_cobs + my_cobs - 2 * my_cobs).is_zero()
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fs = [[lambda x: x[0] + x[1], lambda x: x[1] + x[0]],
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[lambda x: x[0] * x[1], lambda x: x[1] * x[0]]]
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for other in [1, 1.1, (1.1-0.2j), pe.CObs(obs1), pe.CObs(obs1, obs2)]:
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for funcs in fs:
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ta = funcs[0]([my_cobs, other])
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tb = funcs[1]([my_cobs, other])
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diff = ta - tb
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assert np.isclose(0.0, float(diff.real))
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assert np.isclose(0.0, float(diff.imag))
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assert np.allclose(0.0, diff.real.deltas['t'])
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assert np.allclose(0.0, diff.imag.deltas['t'])
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ta = my_cobs - other
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tb = other - my_cobs
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diff = ta + tb
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assert np.isclose(0.0, float(diff.real))
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assert np.isclose(0.0, float(diff.imag))
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assert np.allclose(0.0, diff.real.deltas['t'])
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assert np.allclose(0.0, diff.imag.deltas['t'])
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div = my_cobs / other
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