Merge branch 'develop' into documentation

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fjosw 2021-11-15 10:11:44 +00:00
commit 47b32b2ec7

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@ -61,9 +61,41 @@ my_m_eff = np.log(my_obs1 / my_obs2)
The error propagation is based on the gamma method introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). The error propagation is based on the gamma method introduced in [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017).
After having arrived at
Example:
```python
my_sum.gamma_method()
my_sum.details()
```
The standard value for the automatic windowing procedure is $S=2$. Other values for $S$ can be passed to the `gamma_method` as parameter.
Example:
```python
my_sum.gamma_method(S=3.0)
my_sum.details()
```
The integrated autocorrelation time $\tau_\mathrm{int}$ and the autocorrelation function $\rho(W)$ can be monitored via the methods ´pyerrors.obs.Obs.plot_tauint` and ´pyerrors.obs.Obs.plot_tauint`.
Example:
```python
my_sum.plot_tauint()
my_sum.plot_rho()
```
### Exponential tails
Slow modes in the Monte Carlo history can be accounted for by attaching and exponntial tail to the autocorrelation function $\rho$ as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228). The longest autocorrelation time in the history, $\tau_\mathrm{exp}$, can be passed to the `gamma_method` as parameter. In this case the automatic windowing procedure is vacated and the parameter $S$ does not affect the error estimate.
Example:
```python
my_sum.gamma_method(tau_exp=4.2)
my_sum.details()
```
For the full API see `pyerrors.obs.Obs.gamma_method` For the full API see `pyerrors.obs.Obs.gamma_method`
### Exponential tails
## Multiple ensembles/replica ## Multiple ensembles/replica
@ -97,6 +129,18 @@ obs2 = pe.Obs([samples2], ['ensemble1|r02'])
> · Replicum 'r01' : 1000 configurations (from 1 to 1000) > · Replicum 'r01' : 1000 configurations (from 1 to 1000)
> · Replicum 'r02' : 500 configurations (from 1 to 500) > · Replicum 'r02' : 500 configurations (from 1 to 500)
``` ```
### Error estimation for multiple ensembles
In order to keep track of different error analyis parameters for different ensembles one can make use of global dictionaries as detailed in the following example.
Example:
```python
pe.Obs.S_dict['ensemble1'] = 2.5
pe.Obs.tau_exp_dict['ensemble2'] = 8.0
pe.Obs.tau_exp_dict['ensemble3'] = 2.0
```
## Irregular Monte Carlo chains ## Irregular Monte Carlo chains
Irregular Monte Carlo chains can be initilized with the parameter `idl`. Irregular Monte Carlo chains can be initilized with the parameter `idl`.