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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
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2 changed files with 5 additions and 6 deletions
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@ -97,8 +97,6 @@ class Corr:
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self.T = len(self.content)
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self.prange = prange
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self.gamma_method()
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def __getitem__(self, idx):
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"""Return the content of timeslice idx"""
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if self.content[idx] is None:
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@ -139,8 +137,6 @@ class Corr:
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if self.N == 1:
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raise Exception("Trying to project a Corr, that already has N=1.")
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self.gamma_method()
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if vector_l is None:
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vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
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elif(vector_r is None):
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@ -642,7 +638,6 @@ class Corr:
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xs = [x for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
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ys = [self.content[x][0] for x in range(fitrange[0], fitrange[1] + 1) if not self.content[x] is None]
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result = least_squares(xs, ys, function, silent=silent, **kwargs)
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result.gamma_method()
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return result
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def plateau(self, plateau_range=None, method="fit"):
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@ -673,7 +668,6 @@ class Corr:
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return self.fit(const_func, plateau_range)[0]
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elif method in ["avg", "average", "mean"]:
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returnvalue = np.mean([item[0] for item in self.content[plateau_range[0]:plateau_range[1] + 1] if item is not None])
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returnvalue.gamma_method()
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return returnvalue
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else:
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@ -24,6 +24,7 @@ def test_function_overloading():
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for i, f in enumerate(fs):
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t1 = f([corr_a, corr_b])
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t1.gamma_method()
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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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@ -175,6 +176,7 @@ def test_utility():
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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.gamma_method()
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corr.print()
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corr.print([2, 4])
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corr.show()
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@ -183,6 +185,7 @@ def test_utility():
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corr.dump('test_dump', datatype="pickle", path='.')
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corr.dump('test_dump', datatype="pickle")
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new_corr = pe.load_object('test_dump.p')
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new_corr.gamma_method()
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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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@ -192,6 +195,7 @@ def test_utility():
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corr.dump('test_dump', datatype="json.gz", path='.')
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corr.dump('test_dump', datatype="json.gz")
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new_corr = pe.input.json.load_json('test_dump')
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new_corr.gamma_method()
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os.remove('test_dump.json.gz')
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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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@ -288,6 +292,7 @@ def test_thin():
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c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
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c *= pe.cov_Obs(1., .1, '#ren')
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thin = c.thin()
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thin.gamma_method()
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thin.fit(lambda a, x: a[0] * x)
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c.thin(offset=1)
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c.thin(3, offset=1)
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