Merge branch 'develop' into documentation

This commit is contained in:
fjosw 2022-02-15 13:53:12 +00:00
commit 375c4f6b91
5 changed files with 14 additions and 11 deletions

View file

@ -68,7 +68,8 @@
"metadata": {},
"outputs": [],
"source": [
"my_correlator = pe.Corr(correlator_data)"
"my_correlator = pe.Corr(correlator_data)\n",
"my_correlator.gamma_method()"
]
},
{

View file

@ -46,7 +46,8 @@
}
],
"source": [
"fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding=[1, 1])"
"fP = pe.Corr(pe.input.json.load_json(\"./data/f_P\"), padding=[1, 1])\n",
"fP.gamma_method()"
]
},
{
@ -116,6 +117,7 @@
"stop_fit = 18\n",
"\n",
"fit_result = fP.fit(func_exp, [start_fit, stop_fit], resplot=True)\n",
"fit_result.gamma_method()\n",
"print(\"\\n\", fit_result)"
]
},
@ -197,6 +199,7 @@
}
],
"source": [
"m_eff_fP.gamma_method()\n",
"m_eff_plateau = m_eff_fP.plateau([start_fit, stop_fit])\n",
"m_eff_plateau.gamma_method()\n",
"print()\n",
@ -320,7 +323,7 @@
}
],
"source": [
"beta = pe.fits.odr_fit(ox, oy, func)\n",
"beta = pe.fits.total_least_squares(ox, oy, func)\n",
"\n",
"for i, item in enumerate(beta):\n",
" item.gamma_method()\n",

View file

@ -130,7 +130,7 @@
"metadata": {},
"outputs": [],
"source": [
"single_smearing = matrix_V1V1.index(0,0)"
"single_smearing = matrix_V1V1.item(0,0)"
]
},
{
@ -284,7 +284,7 @@
"m_eff_Jpsi = corr_ground.m_eff(variant=\"cosh\")\n",
"\n",
"# We can now pick a plateau range and get a single value for the mass. \n",
"\n",
"m_eff_Jpsi.gamma_method()\n",
"m_Jpsi = m_eff_Jpsi.plateau([5, 24])\n",
"\n",
"# We can now visually compare our plateau value to the data\n",

View file

@ -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:

View file

@ -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)