Academic Figures

Publication-quality figure generator. 14 chart types (bar, hbar, stacked_bar, heatmap, scatter, line, dual_axis, box, forest, violin, km, roc, composite, diagram), 6 themes (okabe-ito colorblind-safe, nature, lancet, conservative, default, glm). CJK support, hatching patterns, ratio annotations, multi-panel layouts, flow diagrams. Output PNG/SVG/PDF/TIFF/EPS. matplotlib+numpy only. Triggers: make figure, generate chart, plot data, bar chart, scatter, heatmap, forest plot, violin, Kaplan-Meier, ROC, survival curve, 论文配图, 画图, 柱状图, 热力图, 散点图, 森林图, SCI配图, 学术图表, 科研绘图, 统计图, 数据可视化, 发表级图片, 期刊配图, hatching, 斜纹, composite, 组合图, flow diagram, 流程图.

docsor1212@docsor1212

Install

openclaw skills install @docsor1212/academic-figures

Academic Figures — Publication-Quality Chart Generator

Generate figures from JSON/CSV data. Local execution, no data leaves the machine.

Quick Start

bash

# Bar chart with colorblind-safe palette (recommended for all submissions)
python3 scripts/gen_figure.py -t bar -d data.json -o figure.png --theme okabe-ito \
  --title "图2 主标题 / Subtitle" --ylabel "准确率 Accuracy (%)"

# Forest plot for meta-analysis (PDF output)
python3 scripts/gen_figure.py -t forest -d forest.json -o forest.pdf --theme okabe-ito

# Heatmap with CJK support
python3 scripts/gen_figure.py -t heatmap -d data.json -o heatmap.png --cjk \
  --cmap RdBu_r --vmin -20 --vmax 45

# Scatter with trend line, Nature style
python3 scripts/gen_figure.py -t scatter -d data.csv -o scatter.png \
  --xlabel "Baseline (%" --ylabel "Gain (%)" --theme nature

# Kaplan-Meier survival curve with log-rank test
python3 scripts/gen_figure.py -t km -d survival.json -o km.png --theme okabe-ito \
  --title "图3 Kaplan-Meier生存曲线" --xlabel "时间 (月)" --ylabel "生存概率"

# ROC curve with AUC
python3 scripts/gen_figure.py -t roc -d roc.json -o roc.png --theme okabe-ito \
  --title "图4 ROC曲线" --xlabel "1 - 特异度" --ylabel "敏感度"

# Stacked bar chart (subgroup proportions)
python3 scripts/gen_figure.py -t stacked_bar -d subgroups.json -o stacked.png --theme okabe-ito \
  --title "图5 ANCA相关血管炎器官受累"

# Dual Y-axis chart (clinical score + lab marker)
python3 scripts/gen_figure.py -t dual_axis -d dual.json -o dual.png --theme okabe-ito \
  --title "图6 CRP与DAS28随治疗变化"

# TIFF output for Lancet submission (photo content → 300dpi)
python3 scripts/gen_figure.py -t bar -d data.json -o figure.tiff --dpi 300 --theme lancet

# Horizontal bar chart with ratio annotations + GLM palette (GLM-5.2 blog style)
python3 scripts/gen_figure.py -t hbar -d throughput.json -o perf.png --theme glm \
  --show-ratio --title "Throughput Improvement" --xlabel "Normalized Throughput"

# Bar chart with hatching patterns (print-friendly, colorblind-safe)
python3 scripts/gen_figure.py -t bar -d data.json -o hatch.png --theme okabe-ito \
  --hatch --show-values --title "ACR50 Response Rates"

# Multi-panel composite (Panel A+B+C, journal figure layout)
python3 scripts/gen_figure.py -t composite -d composite.json -o figure4.png --theme okabe-ito

# Architecture/flow diagram (research design, CONSORT-style)
python3 scripts/gen_figure.py -t diagram -d flow.json -o flow.png --theme glm --width 12 --height 6

Chart Types

Type	Command	Key Features
Bar	`-t bar`	Grouped bars, error bars, significance brackets, hatching, ratio annotations
Horizontal Bar	`-t hbar`	Horizontal bars, ratio annotations
Stacked Bar	`-t stacked_bar`	Subgroup proportions, percentage labels, total annotations
Heatmap	`-t heatmap`	Cell annotations, custom colormap, colorbar
Scatter	`-t scatter`	Trend line, r value, color grouping, mean points
Line	`-t line`	Multiple series, error bands, markers
Dual Y-Axis	`-t dual_axis`	Two Y-axes, solid+dashed lines, combined legend
Box	`-t box`	Box-and-whisker, jitter points
Forest	`-t forest`	CI whiskers, weight bubbles, overall diamond, I², events/total
Kaplan-Meier	`-t km`	Step function, censor marks, log-rank test, risk table, median survival
ROC	`-t roc`	AUC, 95% CI, optimal cutoff, multi-model comparison
Violin	`-t violin`	Density estimation, inner mean/median
Composite	`-t composite`	Multi-panel (A+B+C), any chart type per panel, journal figure layouts
Diagram	`-t diagram`	Architecture/flow blocks, arrows, groupings, annotations

Color Themes

Theme	Description	Colorblind Safe
`okabe-ito`	Nature Methods gold standard (Wong 2011)	✅ Yes
`nature`	NPG Nature journal palette	❌
`lancet`	Lancet medical palette	❌
`conservative`	Professional muted palette	❌
`default`	Balanced, versatile palette	❌
`glm`	Muted/elegant tech-blog palette (GLM-5.2 pixel-extracted)	✅ Yes

⚠️ Recommendation: Use --theme okabe-ito for all submissions. Nature, Science, Cell, and most major journals now require colorblind-accessible figures. Red-green color schemes are a top rejection reason.

Why Okabe-Ito?

The Okabe-Ito palette (#E69F00, #56B4E9, #009E73, #F0E442, #0072B2, #D55E00, #CC79A7, #000000) is the gold standard for colorblind-safe scientific visualization:

Explicitly recommended by Nature Methods (Wong 2011, Nat Methods 8:441)
Default in Wilke's "Fundamentals of Data Visualization"
All 8 colors distinguishable under protanopia, deuteranopia, and tritanopia
Visually vibrant — no aesthetic compromise vs. traditional palettes

CJK / Chinese Support

Pass --cjk to auto-detect and load system CJK fonts. Zero manual configuration needed.

bash

python3 scripts/gen_figure.py -t bar -d data.json -o fig.png --cjk

Font detection priority: Noto Sans CJK → PingFang → Microsoft YaHei → WQY → AR PL → Droid.

For custom font: --cjk-font /path/to/font.ttf

Output Formats

Format	Extension	DPI	Best For
PNG	`.png`	600 (default)	General use, presentations
SVG	`.svg`	Vector	Web, editable graphics
PDF	`.pdf`	Vector	Journal submissions (preferred)
TIFF	`.tiff`	600 (override: `--dpi 300`)	Nature/Lancet photo requirements
EPS	`.eps`	Vector	Legacy journal requirements

Tip: Nature and Science prefer PDF/EPS vector for line art. Use .pdf or .eps extension.

DPI Standards (2026)

Content Type	Required DPI	How
Line art (graphs, charts)	600-1000+	Default is 600; use `--dpi 1000` for strict journals
Photos / micrographs	300-600	Use `--dpi 300`
Mixed (graphs + photos)	600	Default
Vector (PDF/SVG/EPS)	N/A	Resolution-independent

Data Input

JSON (full features) or CSV (basic). See references/data-formats.md for complete schema per chart type.

JSON bar chart example:

json

{
  "labels": ["Group A", "Group B"],
  "series": {"Treatment": [75, 82], "Control": [68, 70]},
  "errors": {"Treatment": [3, 2], "Control": [2, 1]},
  "significance": {"Treatment:0": "***", "Control:1": "NS"}
}

Key Flags

Flag	Description
`--title "text"`	Figure title. Supports `\n` for newline
`--xlabel`, `--ylabel`	Axis labels
`--width N`, `--height N`	Figure size in inches
`--format F`	Force output format: png, svg, pdf, tiff, eps
`--dpi N`	Override DPI for raster output
`--show-values`	Show numeric labels on bars
`--no-trend`	Hide trend line (scatter)
`--no-legend`	Hide legend
`--cmap NAME`	Colormap (heatmap)
`--vmin`, `--vmax`	Value range (heatmap)
`--horizontal`	Horizontal bar chart (alias: `-t hbar`)
`--hatch`	Add hatching patterns to bars (print-friendly, 10 patterns cycle)
`--show-ratio`	Show ratio annotations (e.g., "4.96x") on grouped bars
`--ratio-base N`	Base series index for ratio calculation (default: 0)

Accessibility & Alt Text

When submitting to journals, provide alt text for each figure describing what the figure shows. Example:

"Bar chart showing Treatment group (mean 75, SD 3) vs Control group (mean 68, SD 2). Error bars represent standard deviation. Asterisks indicate statistical significance (p < 0.001)."

Springer Nature, NSF, and most major publishers require alt text for accessibility compliance.

When Agent Generates Figures (Not CLI)

If creating a figure via Python script rather than CLI:

Always call detect_cjk_font() first if any label may contain CJK
Use fontproperties=font_prop on all text-setting calls with CJK content
Set plt.rcParams['axes.unicode_minus'] = False (prevents minus sign boxes)
Use Okabe-Ito colors for any multi-category plot
Verify output: file size > 20KB for multi-label charts indicates font loaded
Preferred output: PDF for submissions, PNG at 600 DPI for previews
Hatching preference: User prefers hatched/striped bar patterns (--hatch) for print-friendliness and visual distinction between series. Hatch lines use _darken_color() (darkened fill color) for guaranteed contrast on any fill. Do NOT manually override edgecolor when hatching is active — the gen_bar function handles it.
White background mandatory: This is a publication-quality figure generator for journal submissions. ALL output must use white background (facecolor='white'). Dark/black backgrounds are NEVER acceptable. Do not add dark themes or dark background options.

Design Principles

White background is non-negotiable. This skill exists for journal submissions (Nature, Lancet, Science). save_kwargs["facecolor"] is hardcoded to 'white'. Never add dark theme support.
Hatching = darker lines on colored fill. When --hatch is active, hatch lines are computed via _darken_color(fc, 0.45) — a darkened version of each bar's fill color. This guarantees visibility on any fill color without needing dark backgrounds.
Okabe-Ito as default recommendation. Always recommend --theme okabe-ito for colorblind safety. The glm theme is colorblind-safe too and suitable for tech blogs/presentations that still use white background.

Negative Triggers (DO NOT trigger this skill for)

SVG medical diagrams (→ medical-svg)
Terminal/CLI charts (→ data-viz)
Spectrogram/time-frequency (→ pywayne-plot)
HTML slide presentations (→ html-presentation-restyler)
Pure data analysis without visualization (→ data-analysis)

File Structure

text

academic-figures/
├── SKILL.md                 ← English documentation (this file)
├── SKILL_ZH.md              ← Chinese documentation
├── scripts/
│   ├── gen_figure.py        ← Main generator (matplotlib+numpy)
│   └── detect_cjk_font.py   ← CJK font auto-detector
├── references/
│   ├── data-formats.md      ← JSON/CSV schema per chart type
│   ├── pitfalls.md          ← Common errors and white-bg rule
│   └── reverse-engineering-colors.md  ← Extract exact colors from reference images