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Most dictionaries removed from Obs.__init__, the dicitonaries are now

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only initialized when the gamma_method is executed
This commit is contained in:
Fabian Joswig 2021-10-26 15:43:30 +01:00
parent 7874bdb1fd
commit caa5fdb348
2 changed files with 22 additions and 37 deletions
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2
pyerrors/input/bdio.py
View file

@ -175,7 +175,7 @@ def write_ADerrors(obs_list, file_path, bdio_path='./libbdio.so', **kwargs):
"""
for obs in obs_list:
if not obs.e_names:
if not hasattr(obs, 'e_names'):
raise Exception('Run the gamma method first for all obs.')
bdio = ctypes.cdll.LoadLibrary(bdio_path)

57
pyerrors/pyerrors.py
View file

@ -87,23 +87,6 @@ class Obs:
self.ddvalue = 0.0
self.reweighted = 0
self.S = {}
self.tau_exp = {}
self.N_sigma = 0
self.e_names = {}
self.e_content = {}
self.e_dvalue = {}
self.e_ddvalue = {}
self.e_tauint = {}
self.e_dtauint = {}
self.e_windowsize = {}
self.e_rho = {}
self.e_drho = {}
self.e_n_tauint = {}
self.e_n_dtauint = {}
self.tag = None
@property
@ -340,19 +323,20 @@ class Obs:
else:
percentage = np.abs(self.dvalue / self.value) * 100
print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue, self.ddvalue, percentage))
if len(self.e_names) > 1:
print(' Ensemble errors:')
for e_name in self.e_names:
if hasattr(self, 'e_names'):
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))
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 level > 1:
print(self.N, 'samples in', len(self.e_names), 'ensembles:')
print(' Ensemble errors:')
for e_name in self.e_names:
print(e_name, ':', self.e_content[e_name])
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))
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 level > 1:
print(self.N, 'samples in', len(self.e_names), 'ensembles:')
for e_name in self.e_names:
print(e_name, ':', self.e_content[e_name])
def is_zero_within_error(self, sigma=1):
"""Checks whether the observable is zero within 'sigma' standard errors.
@ -362,11 +346,12 @@ class Obs:
return self.is_zero() or np.abs(self.value) <= sigma * self.dvalue
def is_zero(self):
"""Checks whether the observable is zero within machine precision."""
return np.isclose(0.0, self.value) and all(np.allclose(0.0, delta) for delta in self.deltas.values())
def plot_tauint(self, save=None):
"""Plot integrated autocorrelation time for each ensemble."""
if not self.e_names:
if not hasattr(self, 'e_names'):
raise Exception('Run the gamma method first.')
fig = plt.figure()
@ -399,7 +384,7 @@ class Obs:
def plot_rho(self):
"""Plot normalized autocorrelation function time for each ensemble."""
if not self.e_names:
if not hasattr(self, 'e_names'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
plt.xlabel('W')
@ -421,7 +406,7 @@ class Obs:
def plot_rep_dist(self):
"""Plot replica distribution for each ensemble with more than one replicum."""
if not self.e_names:
if not hasattr(self, 'e_names'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
if len(self.e_content[e_name]) == 1:
@ -443,7 +428,7 @@ class Obs:
def plot_history(self):
"""Plot derived Monte Carlo history for each ensemble."""
if not self.e_names:
if not hasattr(self, 'e_names'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
@ -465,7 +450,7 @@ class Obs:
def plot_piechart(self):
"""Plot piechart which shows the fractional contribution of each
ensemble to the error and returns a dictionary containing the fractions."""
if not self.e_names:
if not hasattr(self, 'e_names'):
raise Exception('Run the gamma method first.')
if self.dvalue == 0.0:
raise Exception('Error is 0.0')
@ -967,7 +952,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if obs1.e_names == {} or obs2.e_names == {}:
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
@ -1021,7 +1006,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if obs1.e_names == {} or obs2.e_names == {}:
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0
@ -1117,7 +1102,7 @@ def covariance3(obs1, obs2, correlation=False, **kwargs):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if obs1.e_names == {} or obs2.e_names == {}:
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
raise Exception('The gamma method has to be applied to both Obs first.')
tau_exp = []