WIP: Prototype for lightweight overlay API (canvas-level-rendering)#31382
Draft
joddeepesh-cloud wants to merge 4 commits intomatplotlib:mainfrom
Draft
WIP: Prototype for lightweight overlay API (canvas-level-rendering)#31382joddeepesh-cloud wants to merge 4 commits intomatplotlib:mainfrom
joddeepesh-cloud wants to merge 4 commits intomatplotlib:mainfrom
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Contributor
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I'm going to mark this as draft since it's a prototype. Some explanation of the API choices and the resulting capability will help greatly with the discussion |
Contributor
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Thanks for taking a look and marking this as draft. I agree that the API design needs more explanation. I will update the PR description to clarify the design decisions, intended use cases, and how this integrates with the backend rendering pipeline. This is an early prototype as part of my GSoC proposal, and I’d really appreciate feedback on the overall direction. |
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Status: Draft prototype for discussion
This PR introduces an initial prototype for a lightweight overlay API in Matplotlib.
The goal of this work is to enable temporary graphical primitives (e.g., lines, rectangles) to be rendered directly on the canvas without requiring full figure redraws.
Current implementation includes:
Design Overview
The overlay API is designed to allow temporary graphical primitives to be drawn independently of the main figure rendering pipeline.
Key design decisions:
This approach aims to provide a lightweight, backend-consistent mechanism for interactive drawing, avoiding repeated full redraws.
Motivation
Interactive tools in Matplotlib (e.g., crosshair cursors, selection boxes, measurement guides) often require temporary graphics that are frequently updated. Currently, these workflows rely on full redraws or workarounds, which can impact performance and increase implementation complexity.
This prototype explores a cleaner approach by introducing a dedicated overlay layer.
Notes
This is an early prototype intended for discussion and feedback. The design and API are subject to change.
This work is part of my GSoC 2026 proposal under NumFOCUS, and I would greatly appreciate feedback on the design direction and integration approach.