Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd
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Fabian Joswig 7eabd68c5f
[CI] Speed up test workflow install phase by using uv (#254)
* [CI] Speed up install phase by using uv

* [CI] Use uv in examples workflow

* [CI] Fix yml syntax

* [CI] Install uv into system env

* [CI] Add system install for examples workflow
2025-01-10 09:36:05 +01:00
.github [CI] Speed up test workflow install phase by using uv (#254) 2025-01-10 09:36:05 +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 Fix/sfcf ensname (#253) 2025-01-06 10:46:49 +01:00
tests Fix/sfcf ensname (#253) 2025-01-06 10:46:49 +01:00
.gitignore build: .hypothesis added to gitignore. 2023-03-17 17:56:40 +00:00
CHANGELOG.md [Release] Updated changelog and bumped version 2024-11-03 17:03:06 +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.