ordpy：一个Python包，用于使用置换熵和有序网络方法进行数据分析
ordpy: A Python package for data analysis with permutation entropy and ordinal network methods
摘要：
自从Bandt和Pompe的开创性工作以来，排列熵已经被用于几个应用中，现在是时间序列分析的重要工具。除了成为一种流行和成功的技术之外，排列熵还启发了将时间序列映射到符号序列的框架，引发了许多其他工具的发展，包括从时间序列创建网络的方法，称为有序网络。尽管越来越受欢迎，这些方法的计算开发是分散的，仍然没有努力集中在创建一个统一的软件包。在这里，我们介绍了ordpy，一个简单的开源Python模块，它实现了排列熵以及与Bandt和Pompe的框架相关的几种主要方法，用于分析时间序列和二维数据。特别是，ordpy实现了排列熵，Tsallis和Rényi排列熵，复杂性熵平面，复杂性熵曲线，丢失顺序模式，顺序网络，一维（时间序列）和二维（图像）数据的丢失顺序转换以及它们的多尺度推广。我们回顾了这些工具的一些理论方面，并通过复制几个文献结果来说明ordpy的使用。
英文摘要：
Since Bandt and Pompe's seminal work, permutation entropy has been used in several applications and is now an essential tool for time series analysis. Beyond becoming a popular and successful technique, permutation entropy inspired a framework for mapping time series into symbolic sequences that triggered the development of many other tools, including an approach for creating networks from time series known as ordinal networks. Despite the increasing popularity, the computational development of these methods is fragmented, and there were still no efforts focusing on creating a unified software package. Here we present ordpy, a simple and open-source Python module that implements permutation entropy and several of the principal methods related to Bandt and Pompe's framework to analyze time series and two-dimensional data. In particular, ordpy implements permutation entropy, Tsallis and Rényi permutation entropies, complexity-entropy plane, complexity-entropy curves, missing ordinal patterns, ordinal networks, and missing ordinal transitions for one-dimensional (time series) and two-dimensional (images) data as well as their multiscale generalizations. We review some theoretical aspects of these tools and illustrate the use of ordpy by replicating several literature results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
