Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd
Find a file
2025-03-09 12:37:42 +01:00
.github [CI] Add ARM runner and bump macos runner python version to 3.12 (#260) 2025-02-19 18:23:56 +01:00
examples Feature/corr matrix and inverse cov matrix as input in least squares function for correlated fits (#223) 2024-09-13 08:35:10 +02:00
pyerrors [Version] Bump version to 2.15.0-dev 2025-03-09 12:37:42 +01:00
tests [Fix] Removed the possibility to create an Obs from data on several replica (#258) 2025-02-25 16:58:44 +01:00
.gitignore build: .hypothesis added to gitignore. 2023-03-17 17:56:40 +00:00
CHANGELOG.md [Release] Bump version to 2.14.0 and update CHANGELOG 2025-03-09 12:35:29 +01:00
CITATION.cff docs: citation file corrected. 2023-04-29 10:59:45 +01:00
conftest.py tests: conftest.py added 2022-01-20 13:56:56 +00:00
CONTRIBUTING.md docs: Contributing guidelines clarified. 2023-07-10 16:11:25 +01:00
LICENSE Initial public release 2020-10-13 16:53:00 +02:00
pyproject.toml [ci] Add ruff workflow (#250) 2024-12-24 17:52:08 +01:00
README.md [docs] Simplify README 2024-12-18 13:00:06 +01:00
setup.py [ci] Add python 3.13 to pytest workflow. (#242) 2024-10-14 23:27:24 +02:00

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

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.