Skills

Plot From Data

Generate publication-quality matplotlib figures by selecting a pre-built paper style...

Install

npx skills add Trae1ounG/paper-plot-skills --skill plot-from-data -g -a claude-code -y
Category
Engineering
Updated
7mo ago
License
See repository
Context cost
Lightabout 436 tokens
Runs scripts
Yes, review before use

What it does

Generate publication-quality matplotlib figures by selecting a pre-built paper style and substituting user data. Use when: user provides data (numbers, arrays, or CSV) and wants a chart in a specific academic style; user asks to "plot this data", "make a bar chart", "draw a radar chart", "用这个风格画图", "把我的数据画出来"; or user selects a style name from the style catalog (bar_paired_delta, bar_grouped_hatch, line_confidence_band, line_training_curve, line_loss_with_inset, scatter_tsne_cluster, scatter_broken_axis, radar_dual_series).

Plot From Data

Generate a paper-quality figure by picking a style template and filling it with user data. All outputs are dpi=300 PNG.

Available Styles

StyleTypeScript适用场景
bar_paired_delta柱状图scripts/bar_memevolve.pyBaseline 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.pyL 形 spine + 局部放大 inset
scatter_tsne_cluster散点图scripts/scatter_tsne.pyt-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

Workflow

1. 确认用户的图类型和数据
2. 选择对应 style(如不确定,询问用户或根据数据形状推断)
3. 读取对应 references/<style_name>.md 获取精确参数
4. 复制对应 scripts/<script>.py,替换数据区(脚本顶部有清晰注释标注数据区)
5. 运行:python3 scripts/<script>.py
6. 检查输出,必要时微调颜色/标签/字号

Data Substitution Tips

每个 repro 脚本的数据区在文件顶部,通常是 np.array(...) 或字典。替换规则:

  • 保持数组维度和类型不变
  • 若类别数变化(如从 4 组改为 6 组),同步调整颜色列表和宽度计算
  • x 轴标签、图例标签直接修改对应字符串列表

Detailed Style Parameters

Read the corresponding file in references/ for exact rcParams, colors, font sizes, spine settings, and tick directions before generating:

  • Bar: references/bar_paired_delta.md, references/bar_grouped_hatch.md
  • Line: references/line_confidence_band.md, references/line_training_curve.md, references/line_loss_with_inset.md
  • Scatter: references/scatter_tsne_cluster.md, references/scatter_broken_axis.md
  • Radar: references/radar_dual_series.md

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