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docs: gm alias added to basic example, hyperlink fixed in documentation.
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@ -233,7 +233,7 @@
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],
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],
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"source": [
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"source": [
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"c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n",
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"c_obs3 = np.sin(c_obs1 / c_obs2 - 1)\n",
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"c_obs3.gamma_method()\n",
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"c_obs3.gm() # gm is a short alias for gamma_method\n",
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"c_obs3.details()"
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"c_obs3.details()"
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]
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]
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},
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},
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@ -41,7 +41,7 @@ print(my_new_obs) # Print the result to stdout
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`pyerrors` introduces a new datatype, `Obs`, which simplifies error propagation and estimation for auto- and cross-correlated data.
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`pyerrors` introduces a new datatype, `Obs`, which simplifies error propagation and estimation for auto- and cross-correlated data.
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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.
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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.
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The samples can either be provided as python list or as numpy array.
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The samples can either be provided as python list or as numpy array.
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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.**
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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.**
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```python
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```python
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import pyerrors as pe
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import pyerrors as pe
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