Paper Plot

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.

> ` 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](#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/.md — read the matching file before generating.

Running a script

# default output name in the current directory:
python /scripts/.py
# or choose the output path:
python /scripts/.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)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bahayonghang
• Source: bahayonghang/my-ai-cli-toolkit
• License: MIT

Install and usage instructions live in the source repository linked above.