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119 lines
4 KiB
Python
119 lines
4 KiB
Python
import os
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import numpy as np
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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_function_overloading():
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corr_content_a = []
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corr_content_b = []
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for t in range(24):
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corr_content_a.append(pe.pseudo_Obs(np.random.normal(1e-10, 1e-8), 1e-4, 't'))
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corr_content_b.append(pe.pseudo_Obs(np.random.normal(1e8, 1e10), 1e7, 't'))
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corr_a = pe.correlators.Corr(corr_content_a)
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corr_b = pe.correlators.Corr(corr_content_b)
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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] + 0.1), 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([corr_a, corr_b])
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for o_a, o_b, con in zip(corr_content_a, corr_content_b, t1.content):
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t2 = f([o_a, o_b])
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t2.gamma_method()
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assert np.isclose(con[0].value, t2.value)
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assert np.isclose(con[0].dvalue, t2.dvalue)
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assert np.allclose(con[0].deltas['t'], t2.deltas['t'])
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def test_modify_correlator():
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corr_content = []
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for t in range(24):
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exponent = np.random.normal(3, 5)
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corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
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corr = pe.correlators.Corr(corr_content)
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with pytest.warns(RuntimeWarning):
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corr.symmetric()
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with pytest.warns(RuntimeWarning):
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corr.anti_symmetric()
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corr.roll(np.random.randint(100))
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corr.deriv(symmetric=True)
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corr.deriv(symmetric=False)
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corr.second_deriv()
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def test_m_eff():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(8, 0.1, 't'), pe.pseudo_Obs(7, 0.05, 't')])
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my_corr.m_eff('log')
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my_corr.m_eff('cosh')
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my_corr.m_eff('sinh')
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my_corr.m_eff('arccosh')
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def test_reweighting():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
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assert my_corr.reweighted is False
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r_my_corr = my_corr.reweight(pe.pseudo_Obs(1, 0.1, 't'))
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assert r_my_corr.reweighted is True
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def test_correlate():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
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corr1 = my_corr.correlate(my_corr)
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corr2 = my_corr.correlate(my_corr[0])
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with pytest.raises(Exception):
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corr3 = my_corr.correlate(7.3)
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def test_T_symmetry():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
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with pytest.warns(RuntimeWarning):
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T_symmetric = my_corr.T_symmetry(my_corr)
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def test_fit_correlator():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(2.042345, 0.0004, 't')])
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def f(a, x):
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y = a[0] + a[1] * x
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return y
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fit_res = my_corr.fit(f)
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assert fit_res[0] == my_corr[0]
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assert fit_res[1] == my_corr[1] - my_corr[0]
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def test_plateau():
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my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(1.042345, 0.008, 't')])
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my_corr.plateau([0, 1], method="fit")
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my_corr.plateau([0, 1], method="mean")
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with pytest.raises(Exception):
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my_corr.plateau()
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def test_utility():
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corr_content = []
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for t in range(8):
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exponent = np.random.normal(3, 5)
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corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
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corr = pe.correlators.Corr(corr_content)
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corr.print()
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corr.print([2, 4])
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corr.show()
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corr.dump('test_dump')
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new_corr = pe.load_object('test_dump.p')
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os.remove('test_dump.p')
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for o_a, o_b in zip(corr.content, new_corr.content):
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assert np.isclose(o_a[0].value, o_b[0].value)
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assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
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assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
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