From 5f86aaba4bdb64fced8b0ccdf13f3dcf29d34aa9 Mon Sep 17 00:00:00 2001 From: Fabian Joswig Date: Tue, 15 Feb 2022 13:25:01 +0000 Subject: [PATCH] feat: calls to the gamma_method removed in Corr.__init__ and other method of the Corr class. Test adjusted by adding additional calls to the gamma_method --- pyerrors/correlators.py | 6 ------ tests/correlators_test.py | 5 +++++ 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/pyerrors/correlators.py b/pyerrors/correlators.py index abc4b0a9..7d9e2ae0 100644 --- a/pyerrors/correlators.py +++ b/pyerrors/correlators.py @@ -97,8 +97,6 @@ class Corr: self.T = len(self.content) self.prange = prange - self.gamma_method() - def __getitem__(self, idx): """Return the content of timeslice idx""" if self.content[idx] is None: @@ -139,8 +137,6 @@ class Corr: if self.N == 1: raise Exception("Trying to project a Corr, that already has N=1.") - self.gamma_method() - if vector_l is None: vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.]) elif(vector_r is None): @@ -642,7 +638,6 @@ class Corr: xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None] result = least_squares(xs, ys, function, silent=silent, **kwargs) - result.gamma_method() return result def plateau(self, plateau_range=None, method="fit"): @@ -673,7 +668,6 @@ class Corr: return self.fit(const_func, plateau_range)[0] elif method in ["avg", "average", "mean"]: returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None]) - returnvalue.gamma_method() return returnvalue else: diff --git a/tests/correlators_test.py b/tests/correlators_test.py index 2bbea0b5..550e2a5b 100644 --- a/tests/correlators_test.py +++ b/tests/correlators_test.py @@ -24,6 +24,7 @@ def test_function_overloading(): for i, f in enumerate(fs): t1 = f([corr_a, corr_b]) + t1.gamma_method() for o_a, o_b, con in zip(corr_content_a, corr_content_b, t1.content): t2 = f([o_a, o_b]) t2.gamma_method() @@ -175,6 +176,7 @@ def test_utility(): corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't')) corr = pe.correlators.Corr(corr_content) + corr.gamma_method() corr.print() corr.print([2, 4]) corr.show() @@ -183,6 +185,7 @@ def test_utility(): corr.dump('test_dump', datatype="pickle", path='.') corr.dump('test_dump', datatype="pickle") new_corr = pe.load_object('test_dump.p') + new_corr.gamma_method() os.remove('test_dump.p') for o_a, o_b in zip(corr.content, new_corr.content): assert np.isclose(o_a[0].value, o_b[0].value) @@ -192,6 +195,7 @@ def test_utility(): corr.dump('test_dump', datatype="json.gz", path='.') corr.dump('test_dump', datatype="json.gz") new_corr = pe.input.json.load_json('test_dump') + new_corr.gamma_method() os.remove('test_dump.json.gz') for o_a, o_b in zip(corr.content, new_corr.content): assert np.isclose(o_a[0].value, o_b[0].value) @@ -288,6 +292,7 @@ def test_thin(): c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)]) c *= pe.cov_Obs(1., .1, '#ren') thin = c.thin() + thin.gamma_method() thin.fit(lambda a, x: a[0] * x) c.thin(offset=1) c.thin(3, offset=1)