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test_correlators added, tests extended
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4 changed files with 64 additions and 5 deletions
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@ -242,7 +242,9 @@ class Corr:
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Parameters
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----------
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variant -- log: uses the standard effective mass log(C(t) / C(t+1))
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periodic : Solves C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m. See, e.g., arXiv:1205.5380
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cosh : Use periodicitiy of the correlator by solving C(t) / C(t+1) = cosh(m * (t - T/2)) / cosh(m * (t + 1 - T/2)) for m.
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sinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.
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See, e.g., arXiv:1205.5380
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guess -- guess for the root finder, only relevant for the root variant
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"""
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if self.N != 1:
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48
tests/test_correlators.py
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48
tests/test_correlators.py
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@ -0,0 +1,48 @@
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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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@ -35,7 +35,7 @@ def test_function_overloading():
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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(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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@ -47,8 +47,17 @@ def test_function_overloading():
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def test_overloading_vectorization():
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a = np.random.randint(0, 100, 10)
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b = pe.pseudo_Obs(4, 0.8, 'e1')
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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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@ -16,4 +16,4 @@ def test_root_linear():
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assert np.isclose(my_root.value, value)
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difference = my_obs - my_root
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assert all(np.isclose(0.0, difference.deltas['t']))
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assert np.allclose(0.0, difference.deltas['t'])
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