Plot From Image
Documentsplot-from-imagePlot From Image is an Agent Skill for documents published in the Trae1ounG/paper-plot-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
Reproduce any academic paper figure from an uploaded image using accumulated style experience. Use when: user uploads/attaches a paper figure and asks to reproduce or recreate it; user says "复现这个图", "reproduce this plot", "match this figure", "照着这个图画"; or user provides a paper figure PNG/screenshot and wants Python matplotlib code that generates it. Includes analysis workflow for font detection, color extraction, proportion matching, and mapping to 8 pre-built styles or creating a new style from scratch.
How to use this skill
Bring this guide into your coding agent with a prompt tailored to the tool you use.
- Open your project in Codex.
- Copy and paste the prompt below.
- Review files and risks before approving any action.
Inspect the Agent Skill at https://github.com/Trae1ounG/paper-plot-skills/blob/HEAD/plot-from-image/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/plot-from-image, but wait for my approval before writing files or running scripts.
Copying this prompt does not install or run the skill. Review third-party files before use.
Plot From Image
Reproduce a paper figure by analyzing the image and leveraging accumulated style knowledge.
Workflow
1. Measure Proportions
python3 -c "from PIL import Image; img=Image.open('fig.png'); print(img.size, f'AR={img.size[0]/img.size[1]:.2f}')"
Set figsize=(FW, FH) so FW/FH matches the original AR exactly.
2. Match to Existing Style
Check if the figure matches a pre-built style (fastest path):
| Figure type | Check for |
|---|---|
| Grouped/paired bar | bar_paired_delta or bar_grouped_hatch |
| Line with shaded bands | line_confidence_band |
| Line with cut lines or reference | line_training_curve |
| Loss curve + zoom panel | line_loss_with_inset |
| Scattered clusters | scatter_tsne_cluster |
| Broken x-axis | scatter_broken_axis |
| Polygon web chart | radar_dual_series |
If matched → read ../plot-from-data/references/<name>.md for exact parameters → adapt ../plot-from-data/scripts/<script>.py.
3. If No Match → Analyze From Scratch
Read references/reproduction_guide.md for the full analysis checklist covering:
- Font family detection (serif vs sans-serif, LaTeX vs not)
- Spine & tick style (L-shape, 4-sided, arrows, in/out direction)
- Color identification (tab10 vs custom)
- Grid style (dashed, dotted, none)
- Special elements (insets, broken axes, radar grids, annotation boxes)
4. Build & Iterate
Write script → python3 <script>.py → visually compare → fix proportions/colors → re-run
Key iteration checklist:
- AR matches original (measure with PIL)
- Font family correct (serif for LaTeX papers, sans-serif for system fonts)
- Colors within ±10 RGB of original
- Spine style matches (L vs 4-sided)
- Tick direction matches (in vs out)
- Grid style matches
- Legend placement matches
- Annotations/labels position matches
Accumulated Experience
From 9 reproduced figures across 7 papers, key lessons:
- Smooth training curves: use EMA with
alpha=0.95-0.97before plotting, not raw noisy data - Radar labels:
label_r = 1.10-1.15(NOT 1.2+, which creates excess whitespace) - Inset figures: measure left/right panel pixel ratio from original → set
add_axeswidths accordingly - Broken axis: use two subplots with
wspace=0.05, break symbol only at bottom spine - t-SNE annotation boxes: unified dark edge color
#2C3E50, cluster-color facecolor withalpha=0.28 - Confidence bands:
fill_betweenwithalpha=0.18-0.22, same color as line
Resources
- Analysis guide:
references/reproduction_guide.md— step-by-step checklist for new images - Style library:
../plot-from-data/references/— 8 pre-built style parameter files - Script templates:
../plot-from-data/scripts/— 8 working reproduction scripts - Originals:
../originals/— paper figures used in development