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tests: tests for GEVP fixed, docstring for Corr.Eigenvalue added, typos
fixed
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2 changed files with 20 additions and 3 deletions
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@ -255,9 +255,9 @@ class Corr:
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The state one is interested in ordered by energy. The lowest state is zero.
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sorted_list : string
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if this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.
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"Eigenvalue" - The eigenvector is chosen according to which einvenvalue it belongs individually on every timeslice.
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"Eigenvalue" - The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
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"Eigenvector" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.
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The referense state is identified by its eigenvalue at t=ts
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The reference state is identified by its eigenvalue at t=ts
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"""
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if sorted_list is None:
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if (ts is None):
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@ -302,6 +302,23 @@ class Corr:
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return all_vecs
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def Eigenvalue(self, t0, ts=None, state=0, sorted_list=None):
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"""Determines the eigenvalue of the GEVP by solving and projecting the correlator
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Parameters
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----------
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t0 : int
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The time t0 for G(t)v= lambda G(t_0)v
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ts : int
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fixed time G(t_s)v= lambda G(t_0)v if return_list=False
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If return_list=True and sorting=Eigenvector it gives a reference point for the sorting method.
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state : int
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The state one is interested in ordered by energy. The lowest state is zero.
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sorted_list : string
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if this argument is set, a list of vectors (len=self.T) is returned. If it is left as None, only one vector is returned.
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"Eigenvalue" - The eigenvector is chosen according to which eigenvalue it belongs individually on every timeslice.
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"Eigenvector" - Use the method described in arXiv:2004.10472 [hep-lat] to find the set of v(t) belonging to the state.
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The reference state is identified by its eigenvalue at t=ts
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"""
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vec = self.GEVP(t0, ts=ts, state=state, sorted_list=sorted_list)
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return self.projected(vec)
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@ -220,7 +220,7 @@ def test_prange():
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def test_matrix_corr():
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corr_aa = _gen_corr(1)
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corr_ab = _gen_corr(0.5)
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corr_ab = 0.5 * corr_aa
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corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]]))
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corr_mat.item(0, 0)
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