Error propagation and statistical analysis for Markov chain Monte Carlo simulations in lattice QCD and statistical mechanics using autograd
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Pia Leonie Jones Petrak 1d6f7f65c0
Feature/corr matrix and inverse cov matrix as input in least squares function for correlated fits (#223)
* 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()
2024-09-13 08:35:10 +02:00
.github [Build] Remove python3.8 and add support for numpy 2 (#239) 2024-08-22 21:59:07 +02:00
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 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
tests 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
.gitignore build: .hypothesis added to gitignore. 2023-03-17 17:56:40 +00:00
CHANGELOG.md [Release] Prepare v2.12.0 (#240) 2024-08-22 22:04:54 +02:00
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 [Build] Remove python3.8 and add support for numpy 2 (#239) 2024-08-22 21:59:07 +02:00
setup.py [Build] Remove python3.8 and add support for numpy 2 (#239) 2024-08-22 21:59:07 +02:00

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.