About PEcAN :

The Predictive Ecosystem Analyzer (PEcAn) is a scientific workflow system to manage the immense amounts of publicly available environmental data and a Bayesian data assimilation system to synthesize this information within state-of-the-art ecosystems models.

Project Summary

Organization: PecAn Project

Mentors: Christopher Black (#infotroph), Shashank Singh(#moki1202)

Contributor: Abhinav Pandey (#Sweetdevil144)

Project Duration : 350 hours

Project Title : Optimize PEcAn for freestanding use of single
packages

Thanks a lot, Chris, for carefully listening to my approaches and refining them in much better ways !!

A special thanks to other mentors too :David LeBauer, Shashank Singh and Michael Dietze

About the Project :

The objective of this project was to enhance the PEcAn Project by optimizing its modules for standalone use. Despite PEcAn's robust framework and interconnected modules, there was a growing need to make these modules independently operable. This shift was essential to simplify module usage, testing, and development, making the system more accessible and efficient for users and contributors. The focus was on optimizing the modules for standalone use, enhancing their individual operability within PEcAn's interconnected framework. Our top priority was to "re-loosen these couplings" by revisiting the design and interface of PEcAn packages.

Overview

This summer, I had the privilege of participating in Google Summer of Code, 2024 with the PEcAn Project. Among the many talented candidates selected for this year's program, I was among one of them selected to contribute to a real-world Open-Source Software that has a significant global impact. As the program draws to a close, I'd like to reflect on what I've learned over the past three months with PEcAn.

My journey with the PEcAn Project began well before the official GSoC period, with my first PR being merged as early as December 2023—five months ahead of the GSoC timeline. This early involvement gave me valuable experience navigating PEcAn's intricate architecture and complex codebase design. It also allowed me to familiarise myself with the organisation's work, particularly in the R programming language. From those early days, I was eager to dive deeper into the project and make meaningful contributions.

The most valuable lesson GSoC has taught me is this: We learn by doing, and we pave the path forward even when the way ahead seems uncertain. My experience during this GSoC journey has shown me that what I initially planned to accomplish was just a fraction of what I ultimately achieved.

With 15+ pull requests, 6+ issues resolved, and countless hours spent in meetings with my mentors, I found myself progressively aligned with the goals of the PEcAn Project.

My Contributions to PEcAn

(All of my work which I will be discussing is linked at the bottom of this page.)

The GSoC period was structured into three key phases:

1. Phase 1: Community Bonding Period : During this phase, I familiarised myself with the project and built strong connections with my mentors. This time allowed me to gain a solid understanding of the project's goals and intricacies. I began by making minor changes to the codebase while grasping the key aspects of PEcAn. My efforts focused on gathering data that would later become crucial in tackling the project challenges.

2. Phase 2: Decoupling PEcAn's Packages : My main task in this phase was to begin decoupling PEcAn's packages, with my starting point being the data.land package. This involved carefully examining the packages which required improvement in modularity and flexibility within the project. During this phase, I removed dependency of data.land from data.atmosphere package by figuring out minute instances of dependencies and redirecting them back to the DB calls instead which resulted in reduction in overall dependencies. In addition to this, I also did some minor changes to Add test suites for met2Cf.csv.R and to Remove db.site.lat.lon function and replace all usages with query.site . I also combined multiple DB calls which further helped in reducing database calls being made in our system and further reducing latency on the DB. I also created a custom python script to pinpoint Orphaned functions which weren't being utilised in the codebase anymore and performed cleanups of such instances.

3. Phase 3: Enhancing the convert_input Function : This phase proved to be the most challenging of all due to the complexity of the convert_input function in PEcAn. I devoted significant time to finding a good approach to tackle the various issues we had been facing. However, this phase was also the most productive, thanks to the much deeper understanding of the codebase I had gained by this point.

During this phase, I proposed a new function to optionally retrieve site.info by #3324, enhancing flexibility. Additionally, I helped refactor and remove the now-discontinued BrownDog package, ensuring a clean and modern codebase via #3348. I successfully removed all instances of BrownDog while actively discussing these changes with my mentors.

To further improve the modularity of the convert_input function, I decided to break it down into smaller helper functions in #3338. This restructuring simplifies navigation and understanding of the codebase, making it easier for future developers to work with.

Throughout the GSoC period, I had regular meetings(every Wednesday) with my mentors to discuss our weekly progress, future plans and strategy to move forward in the project. This regular assistance from my mentors really helped me focus my attention on the project.

I am deeply grateful to the entire PEcAn team for providing me with this incredible opportunity to grow, learn, and collaborate with others. What truly makes GSoC unique is the joy of the journey itself. I not only gained proficiency in a new programming language, but I also grew as a person, stepping out of my comfort zone through weekly meetings and interactions with my mentors. This experience has been transformative, both technically and personally.

Moving on, I plan to continue working with PEcAN on a long term and improve PEcAN's capabilities in any capacity I can !! That's a wrap for now!! 🚀🚀

List of My PRs in PEcAn Project 💜