google-deepmind/torax
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
API DevelopmentData VisualizationPlotlyPythonPython programmingdata visualization
Shrinu Kushagra
PROFILE
Shrinu Kushagra
Shrinukushagra developed an interactive plotting upgrade for the google-deepmind/torax repository, focusing on enhancing data visualization workflows. Using Python and Plotly, they migrated the existing plotting system from matplotlib to enable interactive visualizations with improved slider navigation. Their work allowed users to generate plots directly from in-memory data trees, eliminating the need for intermediate .nc files and streamlining data preparation. By exposing plot_run in the public API, Shrinukushagra simplified direct plotting for analysts and developers. The upgrade also introduced consistent color mapping across datasets, supporting clearer cross-dataset comparisons and improving the overall user experience for scientific computing and analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
865
Activity Months1
Your Network
4405 peopleSame Organization
@google.com4362
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Aadish GoelMember
Aahil MehtaMember
Shared Repositories
43
Work History
March 2026
4 Commits • 1 Features
Mar 1, 2026March 2026 performance summary for google-deepmind/torax: Delivered a major Interactive Plotting Upgrade using Plotly, enabling interactive visualizations, enhanced slider navigation, and in-memory plotting from data trees without requiring .nc files. Exposed plot_run in the public API to simplify direct plotting and ensured consistent color mapping across datasets for clearer cross-dataset comparisons. The work reduces data preparation steps, speeds up data exploration, and improves user experience for analysts and developers.
4 Commits • 1 Features
Mar 1, 2026March 2026 performance summary for google-deepmind/torax: Delivered a major Interactive Plotting Upgrade using Plotly, enabling interactive visualizations, enhanced slider navigation, and in-memory plotting from data trees without requiring .nc files. Exposed plot_run in the public API to simplify direct plotting and ensured consistent color mapping across datasets for clearer cross-dataset comparisons. The work reduces data preparation steps, speeds up data exploration, and improves user experience for analysts and developers.
March 2026
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture100.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API DevelopmentData VisualizationPlotlyPythonPython programmingdata visualizationmatplotlibplotting librariesscientific computingsoftware development
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