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feat: calls to the gamma_method removed in Corr.__init__ and other

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method of the Corr class. Test adjusted by adding additional calls to
the gamma_method
This commit is contained in:
Fabian Joswig 2022-02-15 13:25:01 +00:00
parent a0753fa984
commit 5f86aaba4b
2 changed files with 5 additions and 6 deletions
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6
pyerrors/correlators.py
View file

@ -97,8 +97,6 @@ class Corr:
self.T = len(self.content) self.T = len(self.content)
self.prange = prange self.prange = prange
self.gamma_method()
def __getitem__(self, idx): def __getitem__(self, idx):
"""Return the content of timeslice idx""" """Return the content of timeslice idx"""
if self.content[idx] is None: if self.content[idx] is None:
@ -139,8 +137,6 @@ class Corr:
if self.N == 1: if self.N == 1:
raise Exception("Trying to project a Corr, that already has N=1.") raise Exception("Trying to project a Corr, that already has N=1.")
self.gamma_method()
if vector_l is None: if vector_l is None:
vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.]) vector_l, vector_r = np.asarray([1.] + (self.N - 1) * [0.]), np.asarray([1.] + (self.N - 1) * [0.])
elif(vector_r is None): 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] 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] 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 = least_squares(xs, ys, function, silent=silent, **kwargs)
result.gamma_method()
return result return result
def plateau(self, plateau_range=None, method="fit"): def plateau(self, plateau_range=None, method="fit"):
@ -673,7 +668,6 @@ class Corr:
return self.fit(const_func, plateau_range)[0] return self.fit(const_func, plateau_range)[0]
elif method in ["avg", "average", "mean"]: 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 = 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 return returnvalue
else: else:

5
tests/correlators_test.py
View file

@ -24,6 +24,7 @@ def test_function_overloading():
for i, f in enumerate(fs): for i, f in enumerate(fs):
t1 = f([corr_a, corr_b]) 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): for o_a, o_b, con in zip(corr_content_a, corr_content_b, t1.content):
t2 = f([o_a, o_b]) t2 = f([o_a, o_b])
t2.gamma_method() t2.gamma_method()
@ -175,6 +176,7 @@ def test_utility():
corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't')) corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
corr = pe.correlators.Corr(corr_content) corr = pe.correlators.Corr(corr_content)
corr.gamma_method()
corr.print() corr.print()
corr.print([2, 4]) corr.print([2, 4])
corr.show() corr.show()
@ -183,6 +185,7 @@ def test_utility():
corr.dump('test_dump', datatype="pickle", path='.') corr.dump('test_dump', datatype="pickle", path='.')
corr.dump('test_dump', datatype="pickle") corr.dump('test_dump', datatype="pickle")
new_corr = pe.load_object('test_dump.p') new_corr = pe.load_object('test_dump.p')
new_corr.gamma_method()
os.remove('test_dump.p') os.remove('test_dump.p')
for o_a, o_b in zip(corr.content, new_corr.content): for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value) 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", path='.')
corr.dump('test_dump', datatype="json.gz") corr.dump('test_dump', datatype="json.gz")
new_corr = pe.input.json.load_json('test_dump') new_corr = pe.input.json.load_json('test_dump')
new_corr.gamma_method()
os.remove('test_dump.json.gz') os.remove('test_dump.json.gz')
for o_a, o_b in zip(corr.content, new_corr.content): for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value) 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.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
c *= pe.cov_Obs(1., .1, '#ren') c *= pe.cov_Obs(1., .1, '#ren')
thin = c.thin() thin = c.thin()
thin.gamma_method()
thin.fit(lambda a, x: a[0] * x) thin.fit(lambda a, x: a[0] * x)
c.thin(offset=1) c.thin(offset=1)
c.thin(3, offset=1) c.thin(3, offset=1)