Gsoc 2026: My Approach and understanding of the 1st project and how should i procced further? #5334
Replies: 2 comments 1 reply
-
|
@orbeckst @kurtmckee @jandom @Marcello-Sega Can anyone please Guide me i am really interested in this project as it connects Chemistry with Tech.... |
Beta Was this translation helpful? Give feedback.
-
|
Hello @Adarshsharath , thank you for your interest. As you see in https://www.mdanalysis.org/2026/02/19/gsoc2026/ we had a pre-proposal and video-interview stage for GSCO 2026 and the deadline for prepropsal submission has passed. If you have not been invited to submit a full proposal then we will not be able to consider your GSOC proposal this year. You're still welcome to contribute to MDAnalysis, of course. |
Beta Was this translation helpful? Give feedback.
Uh oh!
There was an error while loading. Please reload this page.
Uh oh!
There was an error while loading. Please reload this page.
-
Hello, I’m Adarsh Prakash, a second-year B.E./B.Tech student with a strong interest in real-time systems and scientific computing.
I’ve been studying Project 1 (Real-Time MD Simulation Dashboard) and tried to break it down into a clear implementation plan. I would really appreciate your feedback on whether my understanding is correct and if I am heading in the right direction.
Proposed Approach to Solve Project 1: Real-Time MD Simulation Dashboard
My approach is to build the system incrementally, starting from a simple real-time pipeline and progressively integrating MDAnalysis and streaming capabilities.
1. Overall Design
The system will follow a streaming pipeline:
Simulation (IMDv3 stream) → imdclient → MDAnalysis → Backend (FastAPI) → WebSocket → Browser Dashboard
The backend will act as the central coordinator, receiving streaming data, performing analysis, and pushing results to the frontend in real time.
2. Phase-wise Implementation Plan
Phase 1: Prototype Real-Time Dashboard (No MDAnalysis)
Goal: Establish a working real-time data pipeline
Phase 2: Integrate MDAnalysis with File-Based Trajectories
Load sample datasets using MDAnalysis (e.g., PSF/DCD)
Implement core operations:
Compute simple observables:
Stream computed values to the frontend via WebSocket
Goal: Replace mock data with real analysis
Phase 3: Transition to Streaming Data (IMDv3)
Goal: Enable real-time processing of live simulations
Phase 4: Interactive Analysis Layer
Add UI controls for:
Dynamically update backend computations based on user input
Goal: Make the dashboard interactive and user-driven
Phase 5: Time-Dependent Analysis
Implement buffering of previous frames
Support advanced analyses:
Goal: Enable analysis beyond single-frame computations
Phase 6: Visualization and Alerts
Add real-time plots (Chart.js or similar)
Implement event detection:
Display warnings in the UI
Goal: Provide actionable insights during simulation
3. Technical Stack
4. Key Challenges & Solutions
Real-time performance:
Use asynchronous processing and efficient frame handling
Streaming integration:
Use the IMDReader abstraction to handle streams like trajectories
Scalability:
Start with a modular single-user system and extend to multi-user support
5. Expected Outcome
6. Development Strategy
I will follow an iterative approach:
This approach ensures steady progress, early validation, and a robust final system.
I would be grateful if you could let me know whether my understanding aligns with the project expectations, and if there are any areas I should improve or explore further.
Beta Was this translation helpful? Give feedback.
All reactions