Abstract
GSLIB: Geostatistical Library by Clayton V. Deutsch and Andre G. Journel is the original spatial data analytics, geostatistics, open-source library built in FORTRAN with a function-based implementation to maximize workflow construction ease and flexibility. From simple toy problems for education to complicated subsurface model workflows, GSLIB is up to the task. Yet, it is difficult to teach the next generation with FORTRAN executables and PostScript visualizations. While there are a variety of efforts to add geostatistical methods to Python, I failed to find a package to meet my pedagogical needs in the modern Python language. I was compelled to reimplement GSLIB, function-by-function, often the nights before the associated lectures were given, to support my students. For reliability, I committed to rely only on the most common Python packages, such as NumPy, Pandas, SciPy and Numba. Yes, I took shortcuts, the methods are generally only available for 2D and there are missed opportunities to leverage existing code and to further accelerate for faster run times. The good news, it’s an open-source project, so if you see an opportunity to contribute you are most welcome. Participating in this project further expanded my respect for the vision and contributions of the original authors, Professors Clayton V. Deutsch and Andre G. Journel.