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 Γ-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 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 Γ-method facilitates a controlled estimate of integrated autocorrelation times at any stage of the analysis and provides reliable error estimates without numerical overhead.