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4
examples/01_basic_example.ipynb
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@ -58,7 +58,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"From this we can construct `Obs`, which are the basic object of `pyerrors`. For each sample we give to the obs, we also have to specify an ensemble/replica name. In this example we assume that both datasets originate from the same gauge field ensemble labeled 'ensemble1'." "From this we can construct `Obs`, which are the basic object of `pyerrors`. For each sample we give to the obs, we also have to specify an ensemble/replica name. In this example we assume that both datasets originate from the same gauge field ensemble labeled 'ensemble1'. **For correct error propagation it is crucial that observations from the same Monte Carlo chain are initialized with the same name.**"
] ]
}, },
{ {
@ -233,7 +233,7 @@
], ],
"source": [ "source": [
"c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n", "c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n",
"c_obs3.gamma_method()\n", "c_obs3.gm() # gm is a short alias for gamma_method\n",
"c_obs3.details()" "c_obs3.details()"
] ]
}, },

2
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. `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. 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 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. **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.** 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-ensemblesreplica) for details.**
```python ```python
import pyerrors as pe import pyerrors as pe