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

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 $\Gamma$-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.

22 pages, 2 figures, version accepted for publication in Computer Physics Communications

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• Quantum Entanglement is Quantum: ZZ Production at the LHC
• Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects
• S-wave flavor-singlet meson mixing in QCD with light and charm quarks
• The determination of potential scales in 2+1 flavor QCD
• Casimir effect in critical $\mathrm{O}(N)$ models from non-equilibrium Monte Carlo simulations
• Hadronic decay of vector charmonium from the lattice
• SU(2) gauge theory with one and two adjoint fermions towards the continuum limit