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docs: Explanation about ensemble names made more explicit in

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Fabian Joswig 2023-01-24 12:59:15 +00:00
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pyerrors/__init__.py
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@ -41,7 +41,7 @@ print(my_new_obs) # Print the result to stdout
`pyerrors` introduces a new datatype, `Obs`, which simplifies error propagation and estimation for auto- and cross-correlated data.
An `Obs` object can be initialized with two arguments, the first is a list containing the samples for an observable from a Monte Carlo chain.
The samples can either be provided as python list or as numpy array.
The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble.
The second argument is a list containing the names of the respective Monte Carlo chains as strings. These strings uniquely identify a Monte Carlo chain/ensemble. **It is crucial for the correct error propagation that observations from the same Monte Carlo history are labeled with the same name. See [Multiple ensembles/replica](#Multiple-ensembles/replica) for details.**
```python
import pyerrors as pe
@ -142,6 +142,8 @@ my_sum.details()
> · Ensemble 'ensemble1' : 1000 configurations (from 1 to 1000)
> · Ensemble 'ensemble2' : 500 configurations (from 1 to 500)
```
Observables from the **same Monte Carlo chain** have to be initialized with the **same name** for correct error propagation. If different names were used in this case the data would be treated as statistically independent resulting in loss of relevant information and a potential over or under estimate of the statistical error.
`pyerrors` identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar `|` in the name of the data set.