Treffer: pyMPSLib: A robust and scalable open-source Python library for mutiple-point statistical simulation.

Python has become an essential programming language for scientific computing and data analysis and processing. Various multiple-point statistics (MPS) algorithms are used to characterize complex heterogeneous structures and phenomena in earth sciences. However, there is currently no Python library that integrates mainstream MPS methods for simulation and computation in geosciences. Aiming to establish a stable MPS tool, we developed an open-source Python library of commonly used MPS methods, named pyMPSLib. pyMPSLib consists of ENESIM, SNESIM, and DS algorithms and provides a flexible and convenient API interface. To ensure the maintainability of pyMPSLib, the Python objects and toolkits of MPS algorithms are defined and implemented. To improve the compatibility and extensibility of the presented library, uniform coding standard is adopted in pyMPSLib. We performed the parameter sensitivity analysis under multiple configurations to validate the performance of the library. This open-source library also provides optional tools to quantitatively evaluate the realizations of the integrated MPS methods. [ABSTRACT FROM AUTHOR]

Copyright of Earth Science Informatics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)