| name | paper-plot |
| description | Generate publication-quality matplotlib figures in real academic paper styles. Two modes: from-data (pick a pre-built catalog style, fill in your numbers) and from-image (reproduce an uploaded paper figure as a matplotlib script). Use for "把数据画成论文图", "用某风格画我的数据", "复现这张图", or a named catalog style. Drawing only, no paper reading; route journal-compliance figures (IEEE/Elsevier/Nature specs) to academic-figure.
|
| category | research-learning-knowledge |
| tags | ["matplotlib","plotting","figures","paper","reproduction","visualization"] |
| version | 1.0.0 |
Paper Plot
Produce paper-quality matplotlib figures, either by filling a pre-built style with
your data or by reproducing an uploaded paper figure. All outputs are dpi=300 PNG.
<skill-dir> below is this skill's directory — substitute the absolute path
announced when the skill loads. On Windows, prefix script runs with
PYTHONUTF8=1 when reading/writing UTF-8 (see resources).
Pick a mode
| You have… | Mode | Read |
|---|
| Data + a target style (or a style name) | from-data | references/modes/from-data.md |
| A paper figure image to recreate | from-image | references/modes/from-image.md |
If unsure which style fits the data, from-data explains how to infer it from the
data shape; from-image explains how to match an image to a style or build from scratch.
Style catalog (from-data)
| Style | Type | Script | 适用场景 |
|---|
bar_paired_delta | 柱状图 | scripts/bar_memevolve.py | Baseline vs method 配对对比 + 增益箭头 |
bar_grouped_hatch | 柱状图 | scripts/bar_spice.py | 多方法消融，主方法斜线填充，柱顶数值 |
line_confidence_band | 折线图 | scripts/line_selfdistill.py | 带置信区间的训练曲线 |
line_training_curve | 折线图 | scripts/line_aime.py | 垂直断点线 + 水平参考线 |
line_loss_with_inset | 折线图 | scripts/line_loss_inset.py | L 形 spine + 局部放大 inset |
scatter_tsne_cluster | 散点图 | scripts/scatter_tsne.py | t-SNE 聚类 + 注释框 |
scatter_broken_axis | 散点图 | scripts/scatter_break.py | 折断 X 轴，多 marker 系列 |
radar_dual_series | 雷达图 | scripts/radar_dora.py | 双方法多维对比，正八边形网格 |
Per-style exact parameters (rcParams, colors, font sizes, spines, ticks) live in
references/styles/<style>.md — read the matching file before generating.
Running a script
python <skill-dir>/scripts/<script>.py
python <skill-dir>/scripts/<script>.py my_figure.png
Each script embeds its data near the top (clearly marked) — copy the script, swap
the data block, then run. The output path is argv[1] (defaults to a *_repro.png
name in the working directory). line_selfdistill.py emits two figures
(argv[1], argv[2]).
Dependencies & caveats
- Needs
matplotlib, numpy. scatter_break.py also needs scipy; the
usetex=True styles (bar_grouped_hatch, line_confidence_band,
line_loss_with_inset, scatter_tsne_cluster) require a working LaTeX install —
swap to text.usetex: False if LaTeX is unavailable.
Resources
- Modes:
references/modes/from-data.md, references/modes/from-image.md
- Styles:
references/styles/ — 8 parameter files
- From-scratch analysis:
references/reproduction_guide.md
- Scripts:
scripts/ — 8 style scripts + classwise_iou_table.py (from-image example)
- Gallery:
assets/originals/ — 10 paper figures used to derive the styles (visual reference for from-image matching)
