Darshan Patil
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kashish shrivastav
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Mohammad Adnan
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mohyware
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Pratham
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The main goal of fitting is to find the best values for unknown parameters (like temperature Tgas, mole fraction, etc.) that make your theoretical (simulated) spectrum match the experimental data as closely as possible.
This feature was not previously available in the app, but it is now (once the PR gets merged).
To use it: Activate Fit Spectrum Mode from the header to open the fitting form.
The last few weeks have been a whirlwind of ideas, coding, and rethinking as we figured out the best way to build my dashboard project.
Our First Idea:
Hey everyone welcome to the second episode of my Google Summer of Code project series, where I’m working on partial decompression for large datasets.
So, what’s the big catch here? Well, the CO₂ dataset I’m working with is about 6 GB in its compressed .bz2form, and when you decompress it, it explodes into 50 GB. Most systems struggle to load that much data into memory or parse it into a DataFrame, either due to storage limits, memory, or swap issues.
And obviously not everyone wants the whole 50 GB anyway. Usually, people need just a 1 GB chunk from somewhere inside. So decompressing the entire thing just to fetch a small part is a massive waste of time and resources.
The past couple of weeks have been really productive. After the initial planning and community bonding period, I’ve finally started working on the actual implementation. The transition from understanding the theory to getting hands-on with the code has been challenging but rewarding.
After writing my last blog post and going through the codebase more thoroughly, I discovered there are several existing classes and methods that can be reused for this project. This has led to a revised approach that builds on what’s already working well in RADIS.
The core approach is the same as originally planned, but now the rovibrational populations are calculated using the existing RovibParFuncCalculator. This lets me reuse code while adding the electronic state functionality we need.
In my continued journey with Stingray.jl During GSoC 2025, this phase focused on a core aspect of high-energy astrophysics: time filtering using GTIs (Good Time Intervals) and BTIs (Bad Time Intervals). After a productive discussion with my mentor @matteobachetti during our meet, I dove into implementing and refining functionality around GTIs—an essential tool in the timing analysis of astrophysical data.
As part of the ongoing development, we’ve now begun focusing on the frontend side of the project, improving the user experience and preparing for more database integrations.
To ensure a clean development workflow, I started by:
A major enhancement was implementing dark mode across the entire frontend, not just the MUI (Material UI) components, but also the plotting graph area.