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doc on irregular chains extended

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Fabian Joswig 2021-11-08 15:03:52 +00:00
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pyerrors/__init__.py
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@ -30,12 +30,10 @@ my_obs = pe.Obs([samples], ['ensemble_name'])
## Multiple ensembles/replica
Error propagation for multiple ensemblesi (Markov chains with different simulation parameters) are automatically handled.
Error propagation for multiple ensembles (Markov chains with different simulation parameters) is handeled automatically. Ensembles are uniquely identified by their `name`.
**Example:**
Example:
```python
import pyerrors as pe
obs1 = pe.Obs([samples1], ['ensemble1'])
obs2 = pe.Obs([samples1], ['ensemble2'])
@ -50,10 +48,8 @@ my_sum.details()
`pyerrors` identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar `|` in the name of the dataset.
**Example:**
Example:
```python
import pyerrors as pe
obs1 = pe.Obs([samples1], ['ensemble1|r01'])
obs2 = pe.Obs([samples1], ['ensemble1|r02'])
@ -65,14 +61,21 @@ my_sum.details()
```
## Irregular Monte Carlo chains
Irregular Monte Carlo chains can be initilized with the parameter `idl`.
Example:
```python
import pyerrors as pe
obs1 = pe.Obs([samples1], ['ensemble1|r01'])
obs2 = pe.Obs([samples1], ['ensemble1|r02'])
my_sum = obs1 + obs2
# Observable defined on configurations 20 to 519
obs1 = pe.Obs([samples1], ['ensemble1'], idl=[range(20, 520)])
# Observable defined on every second configuration between 5 and 1003
obs2 = pe.Obs([samples2], ['ensemble1'], idl=[range(5, 1005, 2)])
# Observable defined on configurations 2, 9, 28, 29 and 501
obs3 = pe.Obs([samples3], ['ensemble1'], idl=[[2, 9, 28, 29, 501]])
```
**Warning:** Irregular Monte Carlo chains can result in odd patterns in the autocorrelation functions.
Make sure to check the with e.g. `pyerrors.obs.Obs.plot_rho` or `pyerrors.obs.Obs.plot_tauint`.
# Error propagation
Automatic differentiation, cite Alberto,