pyerrors: A python framework for error analysis of Monte Carlo data

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 Gamma-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. Program summary Program Title: pyerrors CPC Library link to program files: https://doi.org/10.17632/7ncw242ymh.1 Developer's repository link: https://github.com/fjosw/pyerrors Licensing provisions: MIT Programming language: python Nature of problem: Data obtained from Markov chain Monte Carlo simulations exhibits autocorrelations. These become particularly severe when approaching the continuum limit of lattice discretized quantum field theories which becomes more and more relevant in modern day large scale simulations. In order to obtain reliable error estimates these autocorrelations have to be taken into account in complex data analysis workflows. Solution method: Linear error propagation in combination with automatic differentiation is implemented in a new python data type which keeps track of statistical errors across multiple sources of uncertainty. Operator overloading allows for a seamless integration into the scientific python ecosystem and into existing workflows. The Gamma-method facilitates a controlled estimate of integrated autocorrelation times at any stage of the analysis and provides reliable error estimates without numerical overhead. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

We introduce pyerrors, a Python package for statistical error analysis of Monte Carlo data. It combines linear error propagation using automatic differentiation with the Gamma-method for reliable estimation of autocorrelation times. pyerrors allows for easy combination of data from different sources while preserving the information on the origin of error components. It can be seamlessly integrated into the existing scientific python ecosystem for efficient and compact analyses.