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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: 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.') raise Exception('Run the gamma method first for all obs.')
bdio = ctypes.cdll.LoadLibrary(bdio_path) bdio = ctypes.cdll.LoadLibrary(bdio_path)

57
pyerrors/pyerrors.py
View file

@ -87,23 +87,6 @@ class Obs:
self.ddvalue = 0.0 self.ddvalue = 0.0
self.reweighted = 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 self.tag = None
@property @property
@ -340,19 +323,20 @@ class Obs:
else: else:
percentage = np.abs(self.dvalue / self.value) * 100 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)) print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue, self.ddvalue, percentage))
if len(self.e_names) > 1: if hasattr(self, 'e_names'):
print(' Ensemble errors:')
for e_name in self.e_names:
if len(self.e_names) > 1: if len(self.e_names) > 1:
print('', e_name, '\t %3.8e +/- %3.8e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name])) print(' Ensemble errors:')
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: 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): def is_zero_within_error(self, sigma=1):
"""Checks whether the observable is zero within 'sigma' standard errors. """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 return self.is_zero() or np.abs(self.value) <= sigma * self.dvalue
def is_zero(self): 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()) 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): def plot_tauint(self, save=None):
"""Plot integrated autocorrelation time for each ensemble.""" """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.') raise Exception('Run the gamma method first.')
fig = plt.figure() fig = plt.figure()
@ -399,7 +384,7 @@ class Obs:
def plot_rho(self): def plot_rho(self):
"""Plot normalized autocorrelation function time for each ensemble.""" """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.') raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names): for e, e_name in enumerate(self.e_names):
plt.xlabel('W') plt.xlabel('W')
@ -421,7 +406,7 @@ class Obs:
def plot_rep_dist(self): def plot_rep_dist(self):
"""Plot replica distribution for each ensemble with more than one replicum.""" """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.') raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names): for e, e_name in enumerate(self.e_names):
if len(self.e_content[e_name]) == 1: if len(self.e_content[e_name]) == 1:
@ -443,7 +428,7 @@ class Obs:
def plot_history(self): def plot_history(self):
"""Plot derived Monte Carlo history for each ensemble.""" """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.') raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names): for e, e_name in enumerate(self.e_names):
@ -465,7 +450,7 @@ class Obs:
def plot_piechart(self): def plot_piechart(self):
"""Plot piechart which shows the fractional contribution of each """Plot piechart which shows the fractional contribution of each
ensemble to the error and returns a dictionary containing the fractions.""" 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.') raise Exception('Run the gamma method first.')
if self.dvalue == 0.0: if self.dvalue == 0.0:
raise Exception('Error is 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): 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') 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.') raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0 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): 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') 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.') raise Exception('The gamma method has to be applied to both Obs first.')
dvalue = 0 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): 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') 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.') raise Exception('The gamma method has to be applied to both Obs first.')
tau_exp = [] tau_exp = []