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Python 3.8+ Matplotlib License Stars

📊 PyThesisPlot

Professional Scientific Plotting for Academic Publications

From Data to Publication-Ready Figures in Minutes

🚀 Quick Start • 📖 Documentation • 💡 Examples • 🌐 中文

✨ Features

🎯 Workflow-Driven

  • Data Upload → Analysis → Recommendations → Confirmation → Generation
  • Smart data analysis with automatic chart recommendations
  • User confirmation required before generation

📁 Organized Output

output/
└── 20250312-143052-data/
    ├── 20250312-143052-data.csv
    ├── analysis_report.md
    ├── plot_config.json
    ├── 20250312-143052_plot.py
    └── *.png (300 DPI)

🎨 Publication-Ready

  • 300 DPI high-resolution PNG output
  • Nature/Science/Lancet style compliance
  • Automatic statistical annotations (* / ** / ***)
  • Professional color palettes (colorblind-friendly)

🔬 Multi-Domain Support

  • 🧬 Biology & Medicine (qPCR, Western Blot, Cell assays)
  • 📈 Psychology & Social Sciences (Survey data, RCT studies)
  • 📊 Economics & Business (Time series, Comparisons)
  • 🧪 Chemistry & Materials (Spectroscopy, Measurements)

🚀 Quick Start

Installation

# Clone the skill
git clone https://github.com/stephenlzc/pythesis-plot.git
cd pythesis-plot

# Install dependencies
pip install pandas matplotlib seaborn openpyxl numpy scipy

Basic Usage

Option 1: Complete Workflow (Recommended)

python scripts/workflow.py --input your_data.csv

This will:

  1. 📁 Create organized output directory
  2. 🔍 Analyze your data automatically
  3. 💡 Recommend chart schemes
  4. ⏳ Wait for your confirmation
  5. 🎨 Generate publication-ready figures

Option 2: Analysis Only

python scripts/data_analyzer.py --input your_data.csv

Option 3: Generate from Config

python scripts/plot_generator.py --config plot_config.json

📖 Documentation

📋 Table of Contents

🎨 Supported Chart Types

Chart Type Best For Example
📈 Line Plot Time series, Trends Gene expression over time
📊 Bar Chart Group comparisons Treatment vs Control
🎯 Box Plot Distribution, Outliers qPCR Ct values
⚡ Scatter + Regression Correlations Dose-response relationships
🔥 Heatmap Correlation matrices Multi-gene expression
📋 Dashboard Multi-panel figures Complete study overview

💡 Examples

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)
  • qPCR Ct value distributions
  • Comprehensive 2×2 dashboard

Key Finding: BRAC1 expression downregulated 55× in PCOS model (p<0.001)

python scripts/workflow.py --input Mouse_PCOS_BRAC1_RawData_108.xlsx

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

python scripts/workflow.py --input Adolescent_Mental_Health_Intervention_1200.xlsx

🏗️ Architecture

pythesis-plot/
├── 📄 SKILL.md                      # Skill definition
├── 📁 scripts/
│   ├── 🔄 workflow.py               # Main workflow orchestrator
│   ├── 🔍 data_analyzer.py          # Data analysis engine
│   └── 🎨 plot_generator.py         # Chart generation engine
├── 📁 references/
│   ├── 📖 workflow_guide.md         # Workflow documentation
│   ├── 📊 chart_types.md            # Chart type guide
│   ├── 🎨 style_guide.md            # Visual style standards
│   └── 💻 examples.md               # Code examples
├── 📁 assets/themes/
│   ├── 🎓 academic.mplstyle         # Academic style theme
│   ├── 🔬 nature.mplstyle           # Nature journal style
│   └── 📊 presentation.mplstyle     # Presentation style
└── 📁 output/                       # Generated outputs

🎯 Workflow Stages

graph TD
    A[📤 User Uploads Data] --> B[📁 Auto-save to Output]
    B --> C[🔍 Data Analysis]
    C --> D[💡 Chart Recommendations]
    D --> E{⏳ User Confirmation}
    E -->|Confirm| F[🎨 Generate Charts]
    E -->|Modify| D
    F --> G[✅ Delivery]
    
    style A fill:#e1f5fe
    style E fill:#fff3e0
    style G fill:#e8f5e9
Loading

Stage 1: Data Reception 📤

  • Automatic file renaming with timestamp
  • Organized directory creation
  • Support for CSV, Excel, TXT, Markdown

Stage 2: Data Analysis 🔍

  • Automatic dimension detection
  • Column type identification (numeric/categorical/datetime)
  • Statistical summary generation
  • Relationship analysis

Stage 3: Chart Recommendations 💡

  • AI-powered chart type suggestions
  • Layout recommendations
  • Statistical test suggestions

Stage 4: User Confirmation ⏳

  • CRITICAL: Must wait for explicit confirmation
  • Interactive modification support
  • Preview recommendations

Stage 5: Generation & Delivery ✅

  • High-resolution PNG generation (300 DPI)
  • Reproducible Python code output
  • Organized file management

📦 Dependencies

[dependencies]
python = ">=3.8"
pandas = ">=1.3.0"
matplotlib = ">=3.5.0"
seaborn = ">=0.11.0"
openpyxl = ">=3.0.0"  # Excel support
numpy = ">=1.20.0"
scipy = ">=1.7.0"

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • 🎨 Color palettes inspired by Nature and Science style guides
  • 📊 Statistical visualization best practices from Seaborn
  • 🎓 Academic plotting standards from Matplotlib

Made with ❤️ for Researchers

⬆ Back to Top

🌐 Languages: English | 中文