pyerrors: a python framework for error analysis of Monte Carlo data
High Energy Physics - Lattice
2023-05-03 v2 Computational Physics
Data Analysis, Statistics and Probability
Abstract
We present the pyerrors python package for statistical error analysis of Monte Carlo data. Linear error propagation using automatic differentiation in an object oriented framework is combined with the -method for a reliable estimation of autocorrelation times. Data from different sources can easily be combined, keeping the information on the origin of error components intact throughout the analysis. pyerrors can be smoothly integrated into the existing scientific python ecosystem which allows for efficient and compact analyses.
Cite
@article{arxiv.2209.14371,
title = {pyerrors: a python framework for error analysis of Monte Carlo data},
author = {Fabian Joswig and Simon Kuberski and Justus T. Kuhlmann and Jan Neuendorf},
journal= {arXiv preprint arXiv:2209.14371},
year = {2023}
}
Comments
22 pages, 2 figures, version accepted for publication in Computer Physics Communications