matplotlib/matplotlib
Feb 2026 – Feb 2026
1 Month active
Languages Used
Python
Technical Skills
Code MaintenancePythonPython programmingSoftware Developmentbackend developmentcode quality
Archil Jain
PROFILE
Archil Jain
Worked on the matplotlib/matplotlib repository to overhaul axis autoscaling and improve scatter plot rendering reliability. Introduced a per-artist autoscale participation flag and transitioned autoscaling logic to be artist-driven, removing outdated update limit behavior and updating documentation to clarify calculations. Addressed a bug in scatter relim logic, ensuring axis limits accurately reflect data changes for PathCollection objects, and added regression tests to prevent future issues. Enhanced code quality by refactoring internal APIs and improving CI and test hygiene, including formatting and whitespace fixes. Demonstrated expertise in Python, backend development, and software testing while collaborating on code maintenance and documentation.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
2
Lines of code
141
Activity Months1
Your Network
157 peopleShared Repositories
157
Work History
February 2026
10 Commits • 2 Features
Feb 1, 2026February 2026 highlights for matplotlib/matplotlib focusing on reliable axis autoscaling and scatter rendering, coupled with code quality improvements and CI hygiene. Key features delivered: - Autoscaling overhaul and per-artist control: Introduced per-artist autoscale participation flag; switched autoscaling to artist-driven behavior; removed legacy update limit logic where applicable; updated artist handling and documented autoscaling calculations. Major bugs fixed: - Scatter relim improvements: Fixed relim() to update axis limits for scatter PathCollection objects and ensure y-limits reflect data changes; added a regression test and refactored scatter handling to use _update_collection_limits; fixed a linter issue in related tests. Code quality and test hygiene: - CI/test hygiene improvements including whitespace and formatting fixes in test files; addressed CI failures in tests. Overall impact and accomplishments: - Increased reliability of axis scaling for scatter-heavy plots, reducing misrepresented data and manual intervention; regression tests guard against future regressions; CI stability supports faster iteration and releases. Technologies/skills demonstrated: - Python, Matplotlib internals, and regression testing; refactoring to internal APIs (_update_collection_limits); cross-functional collaboration (co-authored commits) and test hygiene."
10 Commits • 2 Features
Feb 1, 2026February 2026 highlights for matplotlib/matplotlib focusing on reliable axis autoscaling and scatter rendering, coupled with code quality improvements and CI hygiene. Key features delivered: - Autoscaling overhaul and per-artist control: Introduced per-artist autoscale participation flag; switched autoscaling to artist-driven behavior; removed legacy update limit logic where applicable; updated artist handling and documented autoscaling calculations. Major bugs fixed: - Scatter relim improvements: Fixed relim() to update axis limits for scatter PathCollection objects and ensure y-limits reflect data changes; added a regression test and refactored scatter handling to use _update_collection_limits; fixed a linter issue in related tests. Code quality and test hygiene: - CI/test hygiene improvements including whitespace and formatting fixes in test files; addressed CI failures in tests. Overall impact and accomplishments: - Increased reliability of axis scaling for scatter-heavy plots, reducing misrepresented data and manual intervention; regression tests guard against future regressions; CI stability supports faster iteration and releases. Technologies/skills demonstrated: - Python, Matplotlib internals, and regression testing; refactoring to internal APIs (_update_collection_limits); cross-functional collaboration (co-authored commits) and test hygiene."
February 2026
Activity
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Quality Metrics
Correctness96.0%
Maintainability94.0%
Architecture94.0%
Performance94.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Code MaintenancePythonPython programmingSoftware Developmentbackend developmentcode qualitycontinuous integrationdata visualizationdocumentationlibrary developmentobject-oriented programmingsoftware testingtesting
Repositories Contributed To
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