version: "1.0.0" name: plot-from-image description: | 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.

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):

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=0.95-0.97 before 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_axes widths 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 with alpha=0.28
• Confidence bands: fill_between with alpha=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