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feat: Obs.details does not output zero error anymore in case the

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gamma_method had not been applied. Obs.plot* function now correctly
throw an exception in case the gamma_method had not been run. docs
adjusted accordingly.
This commit is contained in:
Fabian Joswig 2021-11-16 11:58:27 +00:00
parent 04a66439c2
commit e62a957d3c
2 changed files with 27 additions and 12 deletions
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17
pyerrors/__init__.py
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@ -125,7 +125,7 @@ obs2 = pe.Obs([samples2], ['ensemble2'])
my_sum = obs1 + obs2
my_sum.details()
> Result 2.00697958e+00 +/- 0.00000000e+00 +/- 0.00000000e+00 (0.000%)
> Result 2.00697958e+00
> 1500 samples in 2 ensembles:
> · Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)
> · Ensemble 'ensemble2' : 500 configurations (from 1 to 500)
@ -140,7 +140,7 @@ obs2 = pe.Obs([samples2], ['ensemble1|r02'])
> my_sum = obs1 + obs2
> my_sum.details()
> Result 2.00697958e+00 +/- 0.00000000e+00 +/- 0.00000000e+00 (0.000%)
> Result 2.00697958e+00
> 1500 samples in 1 ensemble:
> · Ensemble 'ensemble1'
> · Replicum 'r01' : 1000 configurations (from 1 to 1000)
@ -170,12 +170,25 @@ Example:
```python
# Observable defined on configurations 20 to 519
obs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])
obs1.details()
> Result 9.98319881e-01
> 500 samples in 1 ensemble:
> · Ensemble 'ensemble1' : 500 configurations (from 20 to 519)
# Observable defined on every second configuration between 5 and 1003
obs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])
obs2.details()
> Result 9.99100712e-01
> 500 samples in 1 ensemble:
> · Ensemble 'ensemble1' : 500 configurations (from 5 to 1003 in steps of 2)
# Observable defined on configurations 2, 9, 28, 29 and 501
obs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])
obs3.details()
> Result 1.01718064e+00
> 5 samples in 1 ensemble:
> · Ensemble 'ensemble1' : 5 configurations (irregular range)
```
**Warning:** Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.

22
pyerrors/obs.py
View file

@ -348,12 +348,14 @@ class Obs:
"""
if self.tag is not None:
print("Description:", self.tag)
if self.value == 0.0:
percentage = np.nan
if not hasattr(self, 'e_dvalue'):
print('Result\t %3.8e' % (self.value))
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 hasattr(self, 'e_dvalue'):
if self.value == 0.0:
percentage = np.nan
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:
@ -420,7 +422,7 @@ class Obs:
save : str
saves the figure to a file named 'save' if.
"""
if not hasattr(self, 'e_names'):
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
fig = plt.figure()
@ -453,7 +455,7 @@ class Obs:
def plot_rho(self):
"""Plot normalized autocorrelation function time for each ensemble."""
if not hasattr(self, 'e_names'):
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
plt.xlabel('W')
@ -475,7 +477,7 @@ class Obs:
def plot_rep_dist(self):
"""Plot replica distribution for each ensemble with more than one replicum."""
if not hasattr(self, 'e_names'):
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
if len(self.e_content[e_name]) == 1:
@ -503,7 +505,7 @@ class Obs:
expand : bool
show expanded history for irregular Monte Carlo chains (default: True).
"""
if not hasattr(self, 'e_names'):
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
for e, e_name in enumerate(self.e_names):
@ -527,7 +529,7 @@ 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 hasattr(self, 'e_names'):
if not hasattr(self, 'e_dvalue'):
raise Exception('Run the gamma method first.')
if self.dvalue == 0.0:
raise Exception('Error is 0.0')