论文标题
Pyerrors：用于蒙特卡洛数据错误分析的Python框架
pyerrors: a python framework for error analysis of Monte Carlo data
论文作者
论文摘要
我们介绍了Pyerrors Python软件包，用于蒙特卡洛数据的统计误差分析。在面向对象的框架中使用自动分化的线性误差传播与$γ$ -METHOD相结合，以可靠地估计自相关时间。可以轻松地组合来自不同来源的数据，在整个分析过程中保持完整的误差组件来源的信息。可以将Pyerrors平滑整合到现有的科学Python生态系统中，从而可以进行有效而紧凑的分析。
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.
