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pyerrors/__init__.py
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@ -30,8 +30,49 @@ my_obs = pe.Obs([samples], ['ensemble_name'])
## Multiple ensembles/replica ## Multiple ensembles/replica
Error propagation for multiple ensemblesi (Markov chains with different simulation parameters) are automatically handled.
**Example:**
```python
import pyerrors as pe
obs1 = pe.Obs([samples1], ['ensemble1'])
obs2 = pe.Obs([samples1], ['ensemble2'])
my_sum = obs1 + obs2
my_sum.details()
> Result 2.00596631e+00 +/- 0.00000000e+00 +/- 0.00000000e+00 (0.000%)
> 1500 samples in 2 ensembles:
> ensemble1: ['ensemble1']
> ensemble2: ['ensemble2']
```
`pyerrors` identifies multiple replica (independent Markov chains with identical simulation parameters) by the vertical bar `|` in the name of the dataset.
**Example:**
```python
import pyerrors as pe
obs1 = pe.Obs([samples1], ['ensemble1|r01'])
obs2 = pe.Obs([samples1], ['ensemble1|r02'])
my_sum = obs1 + obs2
my_sum.details()
> Result 2.00596631e+00 +/- 0.00000000e+00 +/- 0.00000000e+00 (0.000%)
> 1500 samples in 1 ensemble:
> ensemble1: ['ensemble1|r01', 'ensemble1|r02']
```
## Irregular Monte Carlo chains ## Irregular Monte Carlo chains
```python
import pyerrors as pe
obs1 = pe.Obs([samples1], ['ensemble1|r01'])
obs2 = pe.Obs([samples1], ['ensemble1|r02'])
my_sum = obs1 + obs2
```
# Error propagation # Error propagation
Automatic differentiation, cite Alberto, Automatic differentiation, cite Alberto,