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Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd
autocorrelationautogradautomatic-differentiationcondensed-mattercorrelationdata-analysiserror-propagationlattice-field-theorylattice-qcdmarkov-chainmonte-carloparticle-physicsphysicspythonqcdstatistical-analysisstatistical-mechanics
525c61ed20
* tests: failing test for compute_drho edge case added. * tests: example file for failing compute_drho added. * tests: assertion that dvalue stays the same added to compute drho test. * fix: another edge case in computation of drho fixed. |
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| .github | ||
| examples | ||
| pyerrors | ||
| tests | ||
| .gitignore | ||
| CHANGELOG.md | ||
| CITATION.cff | ||
| conftest.py | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| pyproject.toml | ||
| README.md | ||
| setup.py | ||
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
- Bug reports: https://github.com/fjosw/pyerrors/issues
Installation
Install the most recent release using pip and pypi:
pip install pyerrors # Fresh install
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.