plot-from-image

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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.

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Source SKILL.md: https://github.com/Trae1ounG/paper-plot-skills/blob/HEAD/plot-from-image/SKILL.md

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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=0.95-0.97 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