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Merge branch 'develop' into feature/irregularMC

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Simon Kuberski 2021-10-28 17:46:38 +02:00
commit 9580a3a080
19 changed files with 618 additions and 221 deletions
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pyerrors/pyerrors.py
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@ -34,11 +34,11 @@ class Obs:
ensemble.
N_sigma_global -- Standard value for N_sigma (default 1.0)
"""
# __slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', 'value', 'dvalue',
# 'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma', 'e_names',
# 'e_content', 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
# 'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
# 'tag']
__slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma', 'e_names',
'e_content', 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
'tag', '__dict__']
e_tag_global = 0
S_global = 2.0
@ -111,23 +111,6 @@ 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
@ -392,33 +375,35 @@ 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.
"""Checks whether the observable is zero within 'sigma' standard errors.
Works only properly when the gamma method was run.
"""
return np.abs(self.value) <= sigma * self.dvalue
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()
@ -451,7 +436,7 @@ 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')
@ -473,7 +458,7 @@ 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:
@ -495,7 +480,7 @@ class Obs:
def plot_history(self, expand=True):
"""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):
@ -519,7 +504,7 @@ 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')
@ -737,19 +722,23 @@ class CObs:
return self._imag
def gamma_method(self, **kwargs):
"""Executes the gamma_method for the real and the imaginary part."""
if isinstance(self.real, Obs):
self.real.gamma_method(**kwargs)
if isinstance(self.imag, Obs):
self.imag.gamma_method(**kwargs)
def is_zero(self):
"""Checks whether both real and imaginary part are zero within machine precision."""
return self.real == 0.0 and self.imag == 0.0
def conjugate(self):
return CObs(self.real, -self.imag)
def __add__(self, other):
if hasattr(other, 'real') and hasattr(other, 'imag'):
if isinstance(other, np.ndarray):
return other + self
elif hasattr(other, 'real') and hasattr(other, 'imag'):
return CObs(self.real + other.real,
self.imag + other.imag)
else:
@ -759,7 +748,9 @@ class CObs:
return self + y
def __sub__(self, other):
if hasattr(other, 'real') and hasattr(other, 'imag'):
if isinstance(other, np.ndarray):
return -1 * (other - self)
elif hasattr(other, 'real') and hasattr(other, 'imag'):
return CObs(self.real - other.real, self.imag - other.imag)
else:
return CObs(self.real - other, self.imag)
@ -768,29 +759,43 @@ class CObs:
return -1 * (self - other)
def __mul__(self, other):
if all(isinstance(i, Obs) for i in [self.real, self.imag, other.real, other.imag]):
return CObs(derived_observable(lambda x, **kwargs: x[0] * x[1] - x[2] * x[3],
[self.real, other.real, self.imag, other.imag],
man_grad=[other.real.value, self.real.value, -other.imag.value, -self.imag.value]),
derived_observable(lambda x, **kwargs: x[2] * x[1] + x[0] * x[3],
[self.real, other.real, self.imag, other.imag],
man_grad=[other.imag.value, self.imag.value, other.real.value, self.real.value]))
elif hasattr(other, 'real') and getattr(other, 'imag', 0) != 0:
return CObs(self.real * other.real - self.imag * other.imag,
self.imag * other.real + self.real * other.imag)
if isinstance(other, np.ndarray):
return other * self
elif hasattr(other, 'real') and hasattr(other, 'imag'):
if all(isinstance(i, Obs) for i in [self.real, self.imag, other.real, other.imag]):
return CObs(derived_observable(lambda x, **kwargs: x[0] * x[1] - x[2] * x[3],
[self.real, other.real, self.imag, other.imag],
man_grad=[other.real.value, self.real.value, -other.imag.value, -self.imag.value]),
derived_observable(lambda x, **kwargs: x[2] * x[1] + x[0] * x[3],
[self.real, other.real, self.imag, other.imag],
man_grad=[other.imag.value, self.imag.value, other.real.value, self.real.value]))
elif getattr(other, 'imag', 0) != 0:
return CObs(self.real * other.real - self.imag * other.imag,
self.imag * other.real + self.real * other.imag)
else:
return CObs(self.real * other.real, self.imag * other.real)
else:
return CObs(self.real * np.real(other), self.imag * np.real(other))
return CObs(self.real * other, self.imag * other)
def __rmul__(self, other):
return self * other
def __truediv__(self, other):
if hasattr(other, 'real') and hasattr(other, 'imag'):
if isinstance(other, np.ndarray):
return 1 / (other / self)
elif hasattr(other, 'real') and hasattr(other, 'imag'):
r = other.real ** 2 + other.imag ** 2
return CObs((self.real * other.real + self.imag * other.imag) / r, (self.imag * other.real - self.real * other.imag) / r)
else:
return CObs(self.real / other, self.imag / other)
def __rtruediv__(self, other):
r = self.real ** 2 + self.imag ** 2
if hasattr(other, 'real') and hasattr(other, 'imag'):
return CObs((self.real * other.real + self.imag * other.imag) / r, (self.real * other.imag - self.imag * other.real) / r)
else:
return CObs(self.real * other / r, -self.imag * other / r)
def __abs__(self):
return np.sqrt(self.real**2 + self.imag**2)
@ -1148,7 +1153,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
(1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], merge_idx([obs1.idl[name], obs2.idl[name]])]])))):
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
@ -1232,7 +1237,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
return gamma
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
@ -1322,7 +1327,7 @@ def covariance3(obs1, obs2, correlation=False, **kwargs):
(1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], merge_idx([obs1.idl[name], obs2.idl[name]])]])))):
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 = []