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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
1d6f7f65c0
* feat: corr_matrix kwargs as input for least squares fit * feat/tests: inverse covariance matrix and correlation matrix kwargs as input for least squares function * feat/tests/example: reduced new kwargs to 'inv_chol_cov_matrix' and outsourced the inversion & cholesky decomposition of the covariance matrix (function 'invert_corr_cov_cholesky(corr, covdiag)') * tests: added tests for inv_chol_cov_matrix kwarg for the case of combined fits * fix: renamed covdiag to inverrdiag needed for the cholesky decomposition and corrected its documentation * examples: added an example of a correlated combined fit to the least_squares documentation * feat/tests/fix(of typos): added function 'sort_corr()' (and a test of it) to sort correlation matrix according to a list of alphabetically sorted keys * docs: added more elaborate documentation/example of sort_corr(), fixed typos in documentation of invert_corr_cov_cholesky() |
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| 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
- 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
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.