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

autocorrelation autograd automatic-differentiation condensed-matter correlation data-analysis error-propagation lattice-field-theory lattice-qcd markov-chain monte-carlo particle-physics physics python qcd statistical-analysis statistical-mechanics

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Justus Kuhlmann 52b91d83d8 add typehints for other util functions		2025-01-09 09:25:57 +00:00
.github	[CI] Add E252 to flake8 exceptions	2025-01-03 19:06:26 +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	add typehints for other util functions	2025-01-09 09:25:57 +00:00
tests	Fix/sfcf ensname (#253 )	2025-01-06 10:46:49 +01:00
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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	[Fix] Fix type hints in misc.py and remove strict zips for python 3.9	2025-01-03 18:39:34 +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

README.md

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

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.