diff --git a/examples/01_basic_example.ipynb b/examples/01_basic_example.ipynb index 703e13b5..af868589 100644 --- a/examples/01_basic_example.ipynb +++ b/examples/01_basic_example.ipynb @@ -233,7 +233,7 @@ ], "source": [ "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()" ] }, diff --git a/pyerrors/__init__.py b/pyerrors/__init__.py index 981e0fd7..a65c9a80 100644 --- a/pyerrors/__init__.py +++ b/pyerrors/__init__.py @@ -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. **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 import pyerrors as pe