Releases: stephenlzc/pythesis-plot
Releases · stephenlzc/pythesis-plot
PyThesisPlot v1.0.4
🎉 PyThesisPlot v1.0.4 - Multi-Platform Support
✨ New Features
- 🚀 Multi-Platform Support - Now works with:
- Claude Code
- OpenCode / OpenClaw
- Kimi CLI
- Trae
- Cursor
📚 Documentation Updates
- Added platform-specific installation instructions
- Updated README with supported platforms badge
- Added installation guides for each AI assistant
🛠️ Installation (One Command)
Claude Code:
git clone https://github.com/stephenlzc/pythesis-plot.git ~/.claude/skills/pythesis-plotOpenCode / OpenClaw:
git clone https://github.com/stephenlzc/pythesis-plot.git ~/.opencode/skills/pythesis-plotKimi CLI:
git clone https://github.com/stephenlzc/pythesis-plot.git ~/.kimi/skills/pythesis-plotTrae:
git clone https://github.com/stephenlzc/pythesis-plot.git ~/.trae/skills/pythesis-plotFull Changelog: https://github.com/stephenlzc/pythesis-plot/commits/main
PyThesisPlot v1.0.3
🐛 PyThesisPlot v1.0.3 - Hotfix
Fixed
- Fixed YAML frontmatter format in SKILL.md (mapping values error)
✨ Previous Changes (v1.0.2)
- Extracted Installation Guide to docs/installation.md
- Simplified README with AI-first approach
- For Humans / For LLM Agents structure
Full Changelog: https://github.com/stephenlzc/pythesis-plot/commits/main
PyThesisPlot v1.0.2
🎉 PyThesisPlot v1.0.2
✨ What's New
- 📦 Extracted Installation Guide to docs/installation.md
- 🎯 Simplified README with AI-first approach
- 🤖 For Humans / For LLM Agents structure
- 📝 Complete installation docs with troubleshooting
🚀 Quick Install
For Humans:
Install PyThesisPlot: https://github.com/stephenlzc/pythesis-plot
For LLM Agents:
git clone https://github.com/stephenlzc/pythesis-plot.git ~/.claude/skills/pythesis-plot
pip install pandas matplotlib seaborn openpyxl numpy scipy📁 Changes
- docs/installation.md - Complete installation guide
- Simplified README installation section
- Added troubleshooting and verification steps
Full Changelog: https://github.com/stephenlzc/pythesis-plot/commits/main
PyThesisPlot v1.0.1
🎉 PyThesisPlot v1.0.1 Release Notes
🚀 What's New in v1.0.1
✨ New Features
- 📦 Added bilingual release notes (English & Chinese)
- 🔗 Updated README with release badges and version links
- 📝 Improved documentation with GitHub repository links
🐛 Bug Fixes
- None
📚 Documentation
- Added
RELEASE_NOTE_EN.md- Comprehensive English release notes - Added
RELEASE_NOTE_CN.md- Detailed Chinese release notes - Updated navigation links in both README files
🎉 PyThesisPlot v1.0.1 发行说明
🚀 v1.0.1 更新内容
✨ 新功能
- 📦 添加双语发行说明（英文和中文）
- 🔗 README 新增版本标签和发布链接
- 📝 完善文档，添加 GitHub 仓库链接
🐛 Bug 修复
- 无
📚 文档更新
- 新增
RELEASE_NOTE_EN.md- 完整英文发行说明 - 新增
RELEASE_NOTE_CN.md- 详细中文发行说明 - 更新双语 README 导航链接
📦 Full Release Information | 完整发布信息
Version | 版本: v1.0.1
Release Date | 发布日期: 2026-03-12
Repository | 仓库: https://github.com/stephenlzc/pythesis-plot
Changes from v1.0.0 | v1.0.0 以来的变更
- Added RELEASE_NOTE files
- Updated README badges
- Improved documentation links
Made with ❤️ for Researchers Worldwide | 用 ❤️ 为全球科研工作者打造
PyThesisPlot v1.0.0 - Professional Scientific Plotting
🎉 PyThesisPlot v1.0.0 Release Notes
Overview
PyThesisPlot is a professional scientific plotting tool designed for academic publications. It provides a complete workflow from data upload to publication-ready figures, following top journal standards (Nature/Science/Lancet).
✨ Key Features
🎯 Intelligent Workflow
- 5-Stage Process: Data Upload → Analysis → Recommendations → Confirmation → Generation
- Smart Analysis: Automatic data type detection, statistical summaries, and relationship analysis
- AI Recommendations: Intelligent chart type suggestions based on data characteristics
- User Confirmation: Required confirmation before generation to ensure accuracy
🎨 Publication-Ready Output
- 300 DPI High Resolution: Suitable for journal submission
- Academic Style Compliance: Nature/Science/Lancet journal standards
- Statistical Annotations: Automatic significance markers (* / ** / ***)
- Colorblind-Friendly Palettes: Okabe-Ito, Paul Tol, and Nature/Science color schemes
🔬 Multi-Domain Support
| Domain | Application Examples |
|---|---|
| 🧬 Biology & Medicine | qPCR, Western Blot, Cell assays, Histology |
| 🧠 Psychology & Social Sciences | Survey data, RCT studies, Questionnaires |
| 📈 Economics & Business | Time series, Market analysis, Financial data |
| 🧪 Chemistry & Materials | Spectroscopy, Chromatography, Measurements |
📦 What's Included
Core Scripts
| Script | Description |
|---|---|
workflow.py |
Main workflow orchestrator - Complete pipeline from data to figures |
data_analyzer.py |
Data analysis engine - Automatic insights and recommendations |
plot_generator.py |
Chart generation engine - Publication-ready visualization |
Style Themes
- academic.mplstyle - General academic style
- nature.mplstyle - Nature journal style
- presentation.mplstyle - Presentation-optimized style
Documentation
- README.md - English documentation
- README.zh-CN.md - Chinese documentation
- workflow_guide.md - Detailed workflow instructions
- chart_types.md - Chart type selection guide
- style_guide.md - Visual style standards
- examples.md - Code examples and tutorials
🚀 Quick Start
Installation
# Clone the repository
git clone https://github.com/stephenlzc/pythesis-plot.git
# Navigate to project directory
cd pythesis-plot
# Install dependencies
pip install pandas matplotlib seaborn openpyxl numpy scipyBasic Usage
# Complete workflow (Recommended)
python scripts/workflow.py --input your_data.csv
# Analysis only
python scripts/data_analyzer.py --input your_data.csv
# Generate from config
python scripts/plot_generator.py --config plot_config.json💡 Example Use Cases
Example 1: PCOS Study (Biomedical)
Data: Mouse PCOS model with BRAC1 gene expression (108 samples, 3 groups)
Generated Figures:
- Body weight comparison with significance markers
- Ovary weight analysis
- BRAC1 relative expression (log scale, 55× downregulation)
- qPCR Ct value distributions
- Comprehensive 2×2 dashboard
Key Finding: BRAC1 expression significantly downregulated in PCOS model (p<0.001)
Example 2: Mental Health RCT (Psychology)
Data: Adolescent mental health intervention (1200 participants, 4 groups)
Generated Figures:
- CONSORT-style study overview
- SDQ pre/post comparison
- Responder analysis (0.3% → 61.3%)
- Dose-response relationship
- 6-panel comprehensive dashboard
Key Finding: Combined CBT+Mindfulness intervention achieved 61.3% response rate
📊 Supported Chart Types
| Chart Type | Best For | Output Format |
|---|---|---|
| 📈 Line Plot | Time series, Trends | PNG (300 DPI) |
| 📊 Bar Chart | Group comparisons | PNG (300 DPI) |
| 🎯 Box Plot | Distribution, Outliers | PNG (300 DPI) |
| ⚡ Scatter + Regression | Correlations | PNG (300 DPI) |
| 🔥 Heatmap | Correlation matrices | PNG (300 DPI) |
| 📋 Dashboard | Multi-panel figures | PNG (300 DPI) |
🏗️ Project Structure
pythesis-plot/
├── 📄 README.md # English documentation
├── 📄 README.zh-CN.md # Chinese documentation
├── 📄 SKILL.md # Skill definition
├── 📁 scripts/
│ ├── 🔄 workflow.py # Main workflow
│ ├── 🔍 data_analyzer.py # Data analysis
│ └── 🎨 plot_generator.py # Chart generation
├── 📁 references/ # Documentation
├── 📁 assets/themes/ # Style themes
└── 📁 output/ # Generated outputs
📋 Output Organization
All outputs are organized in timestamped directories:
output/
└── 20250312-143052-your-data/
├── 20250312-143052-your-data.csv # Original data
├── analysis_report.md # Analysis report
├── plot_config.json # Chart configuration
├── 20250312-143052_plot.py # Reproducible Python code
└── *.png # 300 DPI figures
🔧 Dependencies
[dependencies]
python = ">=3.8"
pandas = ">=1.3.0"
matplotlib = ">=3.5.0"
seaborn = ">=0.11.0"
openpyxl = ">=3.0.0"
numpy = ">=1.20.0"
scipy = ">=1.7.0"🌐 Languages
- English: README.md
- 中文: README.zh-CN.md
📄 License
This project is licensed under the MIT License.
🙏 Acknowledgments
- 🎨 Color palettes inspired by Nature and Science style guides
- 📊 Statistical visualization best practices from Seaborn
- 🎓 Academic plotting standards from Matplotlib
📮 Feedback & Contributions
- 💡 Feature Requests: Open an Issue
- 🐛 Bug Reports: Open an Issue
- 🤝 Contributions: Submit a Pull Request
Made with ❤️ for Researchers Worldwide
🎉 PyThesisPlot v1.0.0 发行说明
概述
PyThesisPlot 是一款专为学术发表设计的专业科研作图工具。它提供从数据上传到顶刊级图表的完整工作流，符合 Nature/Science/Lancet 等顶级期刊标准。
✨ 核心功能
🎯 智能工作流
- 5阶段流程: 数据上传 → 分析 → 推荐 → 确认 → 生成
- 智能分析: 自动数据类型检测、统计摘要、关系分析
- AI 推荐: 基于数据特征智能推荐图表类型
- 用户确认: 生成前必须经用户确认，确保准确性
🎨 顶刊品质输出
- 300 DPI 高分辨率: 适合期刊投稿
- 学术风格合规: Nature/Science/Lancet 期刊标准
- 统计显著性标注: 自动添加 * / ** / *** 标记
- 色盲友好配色: Okabe-Ito、Paul Tol、Nature/Science 配色方案
🔬 多领域支持
| 领域 | 应用示例 |
|---|---|
| 🧬 生物医学 | qPCR、Western Blot、细胞实验、组织学 |
| 🧠 心理与社会科学 | 问卷调查、RCT研究、量表数据 |
| 📈 经济与商科 | 时间序列、市场分析、金融数据 |
| 🧪 化学与材料 | 光谱分析、色谱、测量数据 |
📦 包含内容
核心脚本
| 脚本 | 说明 |
|---|---|
workflow.py |
主工作流编排器 - 从数据到图表的完整流水线 |
data_analyzer.py |
数据分析引擎 - 自动洞察与推荐 |
plot_generator.py |
图表生成引擎 - 顶刊级可视化 |
样式主题
- academic.mplstyle - 通用学术风格
- nature.mplstyle - Nature 期刊风格
- presentation.mplstyle - 演示优化风格
文档
- README.md - 英文文档
- README.zh-CN.md - 中文文档
- workflow_guide.md - 详细工作流说明
- chart_types.md - 图表类型选择指南
- style_guide.md - 视觉样式标准
- examples.md - 代码示例与教程
🚀 快速开始
安装
# 克隆仓库
git clone https://github.com/stephenlzc/pythesis-plot.git
# 进入项目目录
cd pythesis-plot
# 安装依赖
pip install pandas matplotlib seaborn openpyxl numpy scipy基本用法
# 完整工作流（推荐）
python scripts/workflow.py --input your_data.csv
# 仅数据分析
python scripts/data_analyzer.py --input your_data.csv
# 根据配置生成
python scripts/plot_generator.py --config plot_config.json💡 应用示例
示例 1：PCOS 研究（生物医学）
数据: 小鼠PCOS模型，BRAC1基因表达（108样本，3组）
生成图表:
- 体重对比（含显著性标记）
- 卵巢重量分析
- BRAC1相对表达量（对数刻度，下调55倍）
- qPCR Ct值分布
- 2×2 综合仪表盘
核心发现: PCOS模型组BRAC1表达显著下调 (p<0.001)
示例 2：心理健康 RCT（心理学）
数据: 青少年心理健康干预（1200参与者，4组）
生成图表:
- CONSORT风格研究概况
- SDQ干预前后对比
- 响应者分析（0.3% → 61.3%）
- 剂量-效应关系
- 6图综合仪表盘
核心发现: CBT+正念联合干预响应率达61.3%
📊 支持的图表类型
| 图表类型 | 适用场景 | 输出格式 |
|---|---|---|
| 📈 折线图 | 时间序列、趋势 | PNG (300 DPI) |
| 📊 柱状图 | 分组对比 | PNG (300 DPI) |
| 🎯 箱线图 | 分布、异常值 | PNG (300 DPI) |
| ⚡ 散点+回归 | 相关性 | PNG (300 DPI) |
| 🔥 热力图 | 相关性矩阵 | PNG (300 DPI) |
| 📋 仪表盘 | 多子图组合 | PNG (300 DPI) |
🏗️ 项目结构
pythesis-plot/
├── 📄 README.md # 英文文档
├── 📄 README.zh-CN.md # 中文文档
├── 📄 SKILL.md # Skill定义
├── 📁 scripts/
│ ├── 🔄 workflow.py # 主工作流
│ ├── 🔍 data_analyzer.py # 数据分析
│ └── 🎨 plot_generator.py # 图表生成
├── 📁 references/ # 文档
├── 📁 assets/themes/ # 样式主题
└── 📁 output/ # 输出目录
📋 输出组织
所有输出按时间戳目录组织：
output/
└── 20250312-143052-your-data/
├── 20250312-143052-your-data.csv # 原始数据
├── analysis_report.md # 分析报告
├── plot_config.json # 图表配置
├── 20250312-143052_plot.py # 可复现Python代码
└── *.png # 300 DPI 图表
🔧 依赖要求
[dependencies]
python = ">=3.8"
pandas = ">=1.3.0"
matplotlib = ">=3.5.0"
seaborn = ">=0.11.0"
openpyxl = ">=3.0.0"
numpy = ">=1.20.0"
scipy = ">=1.7.0"🌐 多语言支持
- English: README.md
- 中文: README.zh-CN.md
📄 开源协议
本项目采用 MIT 协议。
🙏 致谢
- 🎨 配色方案参考 Nature 和 Science 风格指南
- 📊 统计可视化最佳实践来自 Seaborn
- 🎓 学术作图标准参考 Matplotlib
📮 反馈与贡献
- 💡 功能建议: 提交 Issue
- 🐛 Bug 报告: 提交 Issue
- 🤝 代码贡献: 提交 Pull Request
用 ❤️ 为全球科研工作者打造
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