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Bug in kwarg parsing fixed, N_sigma_dict added

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Fabian Joswig 2021-11-12 14:06:06 +00:00
parent 3d4aee703e
commit 56e1425835
1 changed files with 11 additions and 12 deletions
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23
pyerrors/obs.py
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@ -28,11 +28,14 @@ class Obs:
exists this overwrites the standard value for that ensemble.
tau_exp_global : float
Standard value for tau_exp (default 0.0)
tau_exp_dict :dict
tau_exp_dict : dict
Dictionary for tau_exp values. If an entry for a given ensemble exists
this overwrites the standard value for that ensemble.
N_sigma_global : float
Standard value for N_sigma (default 1.0)
N_sigma_dict : dict
Dictionary for N_sigma values. If an entry for a given ensemble exists
this overwrites the standard value for that ensemble.
"""
__slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
@ -45,6 +48,7 @@ class Obs:
tau_exp_global = 0.0
tau_exp_dict = {}
N_sigma_global = 1.0
N_sigma_dict = {}
filter_eps = 1e-10
def __init__(self, samples, names, idl=None, means=None, **kwargs):
@ -183,6 +187,7 @@ class Obs:
self.S = {}
self.tau_exp = {}
self.N_sigma = {}
if kwargs.get('fft') is False:
fft = False
@ -193,8 +198,8 @@ class Obs:
if kwarg_name in kwargs:
tmp = kwargs.get(kwarg_name)
if isinstance(tmp, (int, float)):
if tmp <= 0:
raise Exception(kwarg_name + ' has to be larger than 0.')
if tmp < 0:
raise Exception(kwarg_name + ' has to be larger or equal to 0.')
for e, e_name in enumerate(self.e_names):
getattr(self, kwarg_name)[e_name] = tmp
else:
@ -208,13 +213,7 @@ class Obs:
_parse_kwarg('S')
_parse_kwarg('tau_exp')
if 'N_sigma' in kwargs:
self.N_sigma = kwargs.get('N_sigma')
if not isinstance(self.N_sigma, (int, float)):
raise TypeError('N_sigma is not a number.')
else:
self.N_sigma = Obs.N_sigma_global
_parse_kwarg('N_sigma')
for e, e_name in enumerate(self.e_names):
@ -267,7 +266,7 @@ class Obs:
# Critical slowing down analysis
for n in range(1, w_max // 2):
_compute_drho(n + 1)
if (self.e_rho[e_name][n] - self.N_sigma * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
if (self.e_rho[e_name][n] - self.N_sigma[e_name] * self.e_drho[e_name][n]) < 0 or n >= w_max // 2 - 2:
# Bias correction hep-lat/0306017 eq. (49) included
self.e_tauint[e_name] = self.e_n_tauint[e_name][n] * (1 + (2 * n + 1) / e_N) / (1 + 1 / e_N) + texp * np.abs(self.e_rho[e_name][n + 1]) # The absolute makes sure, that the tail contribution is always positive
self.e_dtauint[e_name] = np.sqrt(self.e_n_dtauint[e_name][n] ** 2 + texp ** 2 * self.e_drho[e_name][n + 1] ** 2)
@ -358,7 +357,7 @@ class Obs:
if len(self.e_names) > 1:
print('', e_name, '\t %3.8e +/- %3.8e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name]))
if self.tau_exp[e_name] > 0:
print(' t_int\t %3.8e +/- %3.8e tau_exp = %3.2f, N_sigma = %1.0i' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.tau_exp[e_name], self.N_sigma))
print(' t_int\t %3.8e +/- %3.8e tau_exp = %3.2f, N_sigma = %1.0i' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.tau_exp[e_name], self.N_sigma[e_name]))
else:
print(' t_int\t %3.8e +/- %3.8e S = %3.2f' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.S[e_name]))
if self.tag is not None: