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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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Fabian Joswig b62a18643e
Bootstrap export/import (#198) 
* feat: export_bootstrap method added.

* feat: import_bootstrap function added.

* tests: first test for import/export bootstrap added.

* feat: bootstrap feature cleaned up.

* docs: boostrap docstrings improved.
2023-07-14 13:12:11 +01:00
.github ci: mac-os python version fixed. 2023-06-02 16:17:28 +01:00
examples docs: version guard added to combined fit example. 2023-03-09 13:38:51 +00:00
pyerrors Bootstrap export/import (#198) 2023-07-14 13:12:11 +01:00
tests Bootstrap export/import (#198) 2023-07-14 13:12:11 +01:00
.gitignore build: .hypothesis added to gitignore. 2023-03-17 17:56:40 +00:00
CHANGELOG.md build: version bumped to 2.8.2, CHANGELOG updated. 2023-06-02 15:06:39 +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 build: pyproject.toml added. 2022-08-01 16:43:19 +01:00
README.md docs: link to Changelog added to README. 2023-07-10 16:13:32 +01:00
setup.py build: flake8 added to test build option in setup.py. 2023-07-10 16:06:55 +01:00

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.