Abstract
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.
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Data and code availability
All data used in this study and the source code of pyMPSLib are available on GitHub at https://github.com/GS-3DMG/pyMPSLib.
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Acknowledgements
The authors would like to thank the reviewers and the editor for the constructive feedback and the insightful comments on the manuscript which greatly improved the work.
Funding
This work is supported by the National Natural Science Foundation of China (42172333, 41902304, U1711267) and the Knowledge Innovation Program of Wuhan-Shuguang Project (2022010801020206).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Qiyu Chen, Ruihong Zhou, Cui Liu, Qianhong Huang. The first draft of the manuscript was written by Qiyu Chen. All authors read and approved the final manuscript.
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Communicated by: Xiang Que
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Chen, Q., Zhou, R., Liu, C. et al. pyMPSLib: A robust and scalable open-source Python library for mutiple-point statistical simulation. Earth Sci Inform 16, 3179–3190 (2023). https://doi.org/10.1007/s12145-023-01086-5
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DOI: https://doi.org/10.1007/s12145-023-01086-5