feat: added log-derivatives and symmetric effective mass

This commit is contained in:
Simon Kuberski 2022-10-19 16:08:31 +02:00
parent 46c54a3f95
commit 9813f741f9
2 changed files with 65 additions and 2 deletions

View file

@ -523,7 +523,7 @@ class Corr:
----------
variant : str
decides which definition of the finite differences derivative is used.
Available choice: symmetric, forward, backward, improved, default: symmetric
Available choice: symmetric, forward, backward, improved, log, default: symmetric
"""
if self.N != 1:
raise Exception("deriv only implemented for one-dimensional correlators.")
@ -567,6 +567,17 @@ class Corr:
if (all([x is None for x in newcontent])):
raise Exception('Derivative is undefined at all timeslices')
return Corr(newcontent, padding=[2, 2])
elif variant == 'log':
newcontent = []
for t in range(self.T):
if (self.content[t] is None) or (self.content[t] <= 0):
newcontent.append(None)
else:
newcontent.append(np.log(self.content[t]))
if (all([x is None for x in newcontent])):
raise Exception("Log is undefined at all timeslices")
logcorr = Corr(newcontent)
return self * logcorr.deriv('symmetric')
else:
raise Exception("Unknown variant.")
@ -577,7 +588,7 @@ class Corr:
----------
variant : str
decides which definition of the finite differences derivative is used.
Available choice: symmetric, improved, default: symmetric
Available choice: symmetric, improved, log, default: symmetric
"""
if self.N != 1:
raise Exception("second_deriv only implemented for one-dimensional correlators.")
@ -601,6 +612,17 @@ class Corr:
if (all([x is None for x in newcontent])):
raise Exception("Derivative is undefined at all timeslices")
return Corr(newcontent, padding=[2, 2])
elif variant == 'log':
newcontent = []
for t in range(self.T):
if (self.content[t] is None) or (self.content[t] <= 0):
newcontent.append(None)
else:
newcontent.append(np.log(self.content[t]))
if (all([x is None for x in newcontent])):
raise Exception("Log is undefined at all timeslices")
logcorr = Corr(newcontent)
return self * (logcorr.second_deriv('symmetric') + (logcorr.deriv('symmetric'))**2)
else:
raise Exception("Unknown variant.")
@ -615,6 +637,7 @@ class Corr:
sinh : Use anti-periodicitiy of the correlator by solving C(t) / C(t+1) = sinh(m * (t - T/2)) / sinh(m * (t + 1 - T/2)) for m.
See, e.g., arXiv:1205.5380
arccosh : Uses the explicit form of the symmetrized correlator (not recommended)
logsym: uses the symmetric effective mass log(C(t-1) / C(t+1))/2
guess : float
guess for the root finder, only relevant for the root variant
"""
@ -634,6 +657,20 @@ class Corr:
return np.log(Corr(newcontent, padding=[0, 1]))
elif variant == 'logsym':
newcontent = []
for t in range(1, self.T - 1):
if ((self.content[t - 1] is None) or (self.content[t + 1] is None)) or (self.content[t + 1][0].value == 0):
newcontent.append(None)
elif self.content[t - 1][0].value / self.content[t + 1][0].value < 0:
newcontent.append(None)
else:
newcontent.append(self.content[t - 1] / self.content[t + 1])
if (all([x is None for x in newcontent])):
raise Exception('m_eff is undefined at all timeslices')
return np.log(Corr(newcontent, padding=[1, 1])) / 2
elif variant in ['periodic', 'cosh', 'sinh']:
if variant in ['periodic', 'cosh']:
func = anp.cosh

View file

@ -90,14 +90,38 @@ def test_deriv():
assert np.all([o == 0 for o in (corr.deriv('forward').deriv('backward') - corr.second_deriv())[1:-1]])
assert np.all([o == 0 for o in (corr.deriv('backward').deriv('forward') - corr.second_deriv())[1:-1]])
corr_content = []
exponent = -0.05
for t in range(24):
corr_content.append(pe.pseudo_Obs(np.exp(t * exponent), np.exp(t * exponent) * 0.02, 't'))
corr = pe.Corr(corr_content)
for o in [(corr.deriv('log') / corr / exponent - 1)[10], (corr.second_deriv('log') / corr / exponent**2 - 1)[12]]:
o.gamma_method()
assert (o.is_zero_within_error() and np.isclose(0.0, o.value, 1e-12, 1e-12))
def test_m_eff():
for padding in [0, 4]:
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(9, 0.1, 't'), pe.pseudo_Obs(10, 0.05, 't')], padding=[padding, padding])
my_corr.m_eff('log')
my_corr.m_eff('logsym')
my_corr.m_eff('cosh')
my_corr.m_eff('arccosh')
corr_content = []
exponent = -2.2
for t in range(24):
corr_content.append(pe.pseudo_Obs(np.exp(t * exponent), np.exp(t * exponent) * 0.02, 't'))
corr = pe.Corr(corr_content)
for variant in ['log', 'logsym']:
o = (corr.m_eff(variant) / exponent + 1)[7]
o.gamma_method()
assert (o.is_zero_within_error() and np.isclose(0.0, o.value, 1e-12, 1e-12))
with pytest.warns(RuntimeWarning):
my_corr.m_eff('sinh')
@ -112,6 +136,8 @@ def test_m_eff_negative_values():
assert m_eff_log[padding + 1] is None
m_eff_cosh = my_corr.m_eff('cosh')
assert m_eff_cosh[padding + 1] is None
with pytest.raises(Exception):
my_corr.m_eff('logsym')
def test_reweighting():