Journal Plot

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English

Overview

journal-plot is a Claude Code skill for generating publication-ready matplotlib figures that meet international journal submission standards.

It helps researchers create Python/matplotlib visualizations compliant with major scientific publishers (Nature, Elsevier, Springer, IEEE, PLOS, etc.).

Features

• Smart variable analysis: Parses your Python file (AST) to detect numpy arrays, shapes, and types
• Proactive chart suggestions: Analyzes your data and suggests 2-4 chart types with reasoning
• Multi-panel recommendation: Automatically suggests multi-panel figures when 3+ related arrays are detected
• Axis label inference: Maps variable names to quantity-unit labels (e.g., velocity → "Velocity (m/s)"); asks when uncertain
• Multi-publisher support: Settings for Nature, Elsevier, Springer, IEEE, PLOS ONE, Cell Press, Science, PNAS
• Publication-quality defaults: Arial font, 1200 dpi vector output, golden-ratio dimensions
• Color-blind friendly: Viridis/cividis palettes, warns about red-green only combinations
• Self-explanatory figures: Quantity-unit axis labels, error bar definitions, statistical annotations
• Data file analysis: Analyzes .npz, .npy, .csv files loaded in your script
• Runtime error auto-fix: Detects common errors and proposes fixes
• File injection mode: Adds plotting code directly to your existing scripts
• Duplicate injection prevention: Detects existing plotting functions before modifying files
• Bilingual support: Full Chinese/English trigger keywords and responses

Quick Start

# Run the demo
python quick_start.py

# Or manually:
python demo_data.py          # generates data (plotting disabled)
# Edit demo_data.py: set PLOT_FIGURE = True
python demo_data.py           # generates figures

Usage

Invoke with /journal-plot, then describe your figure:

• "Create a scatter plot of temperature vs pressure with error bars for Elsevier"
• "Make a two-panel figure for Nature with (a) bar chart and (b) line plot"
• "Generate a time-series plot for PLOS ONE"
• "帮我添加 IEEE 标准的误差棒折线图到 demo_data.py"

File Injection Mode

When you mention an existing file, the skill injects conditional plotting code:

/journal-plot 帮我添加绘图到 my_script.py

The skill will:

1. Read your existing Python file
2. Analyze variables via AST parsing
3. Suggest chart types based on your data
4. Inject PLOT_FIGURE flag and plot_journal_figure() function
5. Set PLOT_FIGURE = False by default

Example Output

# === Optional Journal-Grade Plotting ===
PLOT_FIGURE = False  # Set to True to enable plotting

def plot_journal_figure(x, y, publisher='IEEE'):
    """Generate publication-ready figure"""
    import matplotlib.pyplot as plt

    # IEEE publication settings
    plt.rcParams.update({
        'font.family': 'Times New Roman',
        'font.size': 8,
        'axes.labelsize': 9,
        'savefig.dpi': 300,
        'savefig.format': 'eps',
    })

    fig, ax = plt.subplots(figsize=(3.5, 2.625))
    ax.errorbar(x, y, yerr=0.1, fmt='o-')
    plt.savefig('figure_1.eps')
    plt.show()

if PLOT_FIGURE:
    plot_journal_figure(x, y)

Supported Publishers

Publisher	Line Art DPI	Format	Font
Nature	300 max	JPEG, TIFF	Arial
Elsevier	1000	EPS, TIFF	Arial/Helvetica
IEEE	300	EPS, TIFF	Times New Roman
PLOS ONE	900-1200	TIFF, EPS	Arial
Cell Press	600	PDF, EPS	Arial
Science	300	TIFF, JPEG	Arial
PNAS	300-600	EPS, TIFF	Arial
Default	1200	PDF, EPS	Arial

Requirements

• Python 3.9+
• matplotlib 3.5+
• (optional) seaborn for statistical plots

pip install matplotlib seaborn

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中文

概述

journal-plot 是用于生成符合国际期刊投稿标准的可发表级别 matplotlib 图表的 Claude Code 技能。

帮助研究人员创建符合主流科研出版机构（Nature、Elsevier、Springer、IEEE、PLOS 等）规范的 Python/matplotlib 可视化图表。

功能特点

• 智能变量分析：通过 AST 解析检测 numpy 数组、形状和类型
• 主动图表建议：分析数据后主动建议 2-4 种图表类型并说明原因
• 多面板建议：检测到 3+ 相关数组时自动建议多面板图
• 坐标轴标签推断：将变量名映射到带单位的标签（如 velocity → "Velocity (m/s)"）；不确定时询问用户
• 多出版机构支持：Nature、Elsevier、Springer、IEEE、PLOS ONE、Cell Press、Science、PNAS 的设置
• 发表级默认配置：Arial 字体、1200 dpi 矢量输出、黄金比例尺寸
• 色盲友好：Viridis/cividis 色板，对红绿组合发出警告
• 自解释图表：带量纲的坐标轴标签、误差棒定义、统计注释
• 数据文件分析：分析脚本中加载的 .npz、.npy、.csv 文件
• 运行失败自动修复：检测常见错误并提出修复方案
• 文件注入模式：可将绘图代码直接添加到现有脚本中
• 重复注入防护：修改文件前检测已有绘图函数，防止重复注入
• 中英双语支持：触发词和响应均支持中文/英文

快速开始

# 运行演示
python quick_start.py

# 或手动操作：
python demo_data.py          # 生成数据（默认绘图关闭）
# 编辑 demo_data.py：设置 PLOT_FIGURE = True
python demo_data.py           # 生成图表

使用方法

调用 /journal-plot，然后描述你的图表需求：

• "为 Elsevier 创建一张温度 vs 压强的带误差棒的散点图"
• "为 Nature 创建一张双面板图，包含 (a) 柱状图和 (b) 折线图"
• "为 PLOS ONE 生成一张时间序列图"
• "帮我添加 IEEE 标准的误差棒折线图到 demo_data.py"

文件注入模式

当你提到现有文件名时，技能会将绘图代码直接注入文件中：

/journal-plot 帮我添加绘图到 my_script.py

技能会：

1. 读取你现有的 Python 文件
2. 通过 AST 解析分析变量
3. 根据数据建议图表类型
4. 注入 PLOT_FIGURE 标志和 plot_journal_figure() 函数
5. 默认设置 PLOT_FIGURE = False

示例输出

# === 可选期刊级绘图 ===
PLOT_FIGURE = False  # 设置为 True 启用绘图

def plot_journal_figure(x, y, publisher='IEEE'):
    """生成符合期刊标准的图表"""
    import matplotlib.pyplot as plt

    # IEEE 出版设置
    plt.rcParams.update({
        'font.family': 'Times New Roman',
        'font.size': 8,
        'axes.labelsize': 9,
        'savefig.dpi': 300,
        'savefig.format': 'eps',
    })

    fig, ax = plt.subplots(figsize=(3.5, 2.625))
    ax.errorbar(x, y, yerr=0.1, fmt='o-')
    plt.savefig('figure_1.eps')
    plt.show()

if PLOT_FIGURE:
    plot_journal_figure(x, y)

支持的出版机构

出版机构	线图 DPI	格式	字体
Nature	300 最大	JPEG, TIFF	Arial
Elsevier	1000	EPS, TIFF	Arial/Helvetica
IEEE	300	EPS, TIFF	Times New Roman
PLOS ONE	900-1200	TIFF, EPS	Arial
Cell Press	600	PDF, EPS	Arial
Science	300	TIFF, JPEG	Arial
PNAS	300-600	EPS, TIFF	Arial
默认	1200	PDF, EPS	Arial

依赖要求

• Python 3.9+
• matplotlib 3.5+
• （可选）seaborn 用于统计图表

pip install matplotlib seaborn