GSoC 2025 – Improve Raw Data Browsing with PyQtGraph
Dear MNE-Python Team
My name is Muhammad Fodio Sani, and I’m applying for GSoC 2025 under MNE-Python. I am particularly interested in the project “Improve Raw Data Browsing with PyQtGraph.” I have experience in Python, statistics, and data visualization using PyQtGraph, NumPy, and SciPy.
I have reviewed the project details and the mne-qt-browser module, and I would love to contribute. Could you provide guidance on the key priorities for this project? Also, are there any existing issues or discussions I should follow?
Thank you for your time. I look forward to your advice!
Hi Muhammad, thanks for your interest in MNE-Python!
I think the points listed on our GSoC ideas page are the key priorities. Stuff listed in the linked project board (or our general list of open issues) can be considered secondary priorities that might or might not make sense to tackle in parallel.
I don’t think so… this project idea has been sitting idle for a couple years so the info on the ideas page is probably as current as it gets.
Thank you for your response and clarification! I’ll focus on the priorities outlined on the GSoC ideas page and refer to the project board for any relevant secondary tasks.
Since the project has been idle for a couple of years, are there any specific challenges or limitations that I should be aware of when working on it? Additionally, would you recommend any particular past discussions or PRs that might provide useful context?
I spent a few minutes trying to find relevant past discussions, but didn’t turn up anything. I think the most important thing to know is that the qt-browser hasn’t been getting nearly as much developer/contributor attention as the rest of MNE-Python (this is probably clear from the issues list — there are a lot of smallish bugs that nobody has fixed yet). This means that maintainers / mentors may need a bit of extra time to dig into the code to remember/figure out how things work before they’re able to help you, so “quick questions” may not be as quick to answer as you might hope. Therefore, structuring your project to start out with small, isolated bugfixes in the early weeks will probably be a good idea; that way, by the time you get to the harder parts, both you and the mentors will have eased (back) into the codebase already.
My name is Himasri Pithani, and I am currently pursuing a B.Tech in Computer Science and Engineering with a specialization in Artificial Intelligence and Data Science. I am very enthusiastic about participating in Google Summer of Code 2025 and contributing to MNE-Python.
I came across the project idea titled “Improving Raw Data Browsing in MNE-Python”, and I found it extremely interesting. I would be grateful for any guidance or resources you could share on how to get started, as well as information on the expected deliverables or contribution process.
I have prior experience with Python, data preprocessing, and visualization, and I am eager to learn more and contribute meaningfully to your project.
Thank you for your time and consideration. I look forward to hearing from you.
My name is Aditya, and I am currently pursuing a B.Tech in Computer Science and Engineering with a focus on Artificial Intelligence and Machine Learning. I am excited to participate in Google Summer of Code 2025, and I am particularly interested in contributing to the enhancement of visualization tools within MNE-Python.
I came across this issue regarding the addition of a colorbar parameter to mne.viz.plot_topomap(), and I believe this would be a valuable feature to improve usability and align the API with other similar functions. I would love to work on this enhancement and contribute to the project.
Please let me know if this issue is still available to work on. I am happy to begin exploring the codebase and preparing a pull request accordingly.
My name is PRASAD, and I am currently pursuing a B.Tech in Computer Science and Engineering with a focus on Artificial Intelligence and Machine Learning. I am excited to participate in Google Summer of Code 2025 and am particularly interested in contributing to educational and documentation enhancements within MNE-Python.
I came across the issue proposing a tutorial for creating MNE core data structures (Info, Raw, Epochs, Evoked) from scratch using simulated data, and I believe this would be a highly valuable resource for new users, educators, and developers. I would love to work on this tutorial and contribute to making the learning experience with MNE more accessible.
Please let me know if the issue is still available. I would be happy to begin exploring the codebase and preparing a pull request accordingly.