pyerrors 2.13.0

Error propagation and statistical analysis for Monte Carlo simulations

pyerrors

pyerrors is a python framework for error computation and propagation of Markov chain Monte Carlo data from lattice field theory and statistical mechanics simulations.

• Documentation: https://fjosw.github.io/pyerrors/pyerrors.html
• Examples: https://github.com/fjosw/pyerrors/tree/develop/examples
• Ask a question: https://github.com/fjosw/pyerrors/discussions/new?category=q-a
• Changelog: https://github.com/fjosw/pyerrors/blob/develop/CHANGELOG.md
• Bug reports: https://github.com/fjosw/pyerrors/issues

Installation

Install the most recent release using pip and pypi:

python -m pip install pyerrors     # Fresh install
python -m pip install -U pyerrors  # Update

Install the most recent release using conda and conda-forge:

conda install -c conda-forge pyerrors  # Fresh install
conda update -c conda-forge pyerrors   # Update

Contributing

We appreciate all contributions to the code, the documentation and the examples. If you want to get involved please have a look at our contribution guideline.

Citing pyerrors

If you use pyerrors for research that leads to a publication we suggest citing the following papers:

• Fabian Joswig, Simon Kuberski, Justus T. Kuhlmann, Jan Neuendorf, pyerrors: a python framework for error analysis of Monte Carlo data. Comput.Phys.Commun. 288 (2023) 108750.
• Ulli Wolff, Monte Carlo errors with less errors. Comput.Phys.Commun. 156 (2004) 143-153, Comput.Phys.Commun. 176 (2007) 383 (erratum).
• Alberto Ramos, Automatic differentiation for error analysis of Monte Carlo data. Comput.Phys.Commun. 238 (2019) 19-35.
• Stefan Schaefer, Rainer Sommer, Francesco Virotta, Critical slowing down and error analysis in lattice QCD simulations. Nucl.Phys.B 845 (2011) 93-119.