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Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd
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README.md

pytest License: MIT arXiv DOI

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.

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.