Primary INTERA Participants: Jeremy White (lead developer), Ahmad Askar, Ed DeSousa, Mohamed Hayek, Rui Hugman, Katie Markovich

Challenge: To address the challenges associated with pre- and post-processing for running PEST(++).  While PEST (++) is an extremely powerful set of software tools for undertaking model-independent parameter estimation, uncertainty analysis, and optimization, the pre- and post-processing for running PEST(++) can be cumbersome, especially when there is a need for reproducibility. pyEMU is a set of python modules that addresses this challenge by enabling the programmatic creation, manipulation, and processing of PEST(++) inputs and outputs. Additional functionality that pyEMU provides for PEST(++) practitioners include observation reweighting tools, geostatistics, regularization utilities, helper functions for parallel runs, and diagnostic plotting functions, to name a few.

Method: The pyEMU codebase is maintained in a public GitHub repository employing a rapid, iterative development and testing approach. That is, all participants listed above, as well as collaborators from federal agencies, industry, and academic, contribute their own improvements to the codebase as well as provide feedback from testing in their respective applications. In addition, INTERA provides internal funding to continue adding and improving upon functionality in pyEMU.

Primary Impact: Developing and maintaining an open-source community tool that enables rapid, reproducible, and robust PEST(++) workflows leads to better environmental decision support outcomes.