Drilling Data Collection App

A mobile-friendly Streamlit web application for collecting drilling data in the field and generating professional Excel reports for lithosphere layer analysis.

🎯 Purpose

This application solves the problem of manually transcribing drilling data from paper forms to Excel spreadsheets. Field workers can now:

• Collect drilling data directly on smartphones/tablets
• Generate professional Excel reports automatically
• Ensure data consistency and validation
• Work offline (planned feature)

🚀 Features

• Mobile-Responsive Interface: Optimized for smartphones and tablets
• Form-Based Data Collection: Intuitive forms for site info and layer data
• Real-Time Validation: Immediate feedback on data quality
• Professional Excel Reports: Multiple report templates with charts
• Data Visualization: Charts and graphs for layer analysis
• Export Options: Multiple Excel formats and templates

📋 Data Collection

Site Information

• Site name and location
• Drilling date and operator
• Project details
• Drilling method and depth
• Weather conditions

Layer Information

• Depth ranges (from/to)
• Rock type classification
• Hardness assessment
• Physical properties (color, porosity)
• Geological features (water, fractures, fossils)
• Weathering degree
• Detailed descriptions

🛠️ Technology Stack

• Framework: Streamlit (Python web framework)
• Data Processing: Pandas for data manipulation
• Excel Generation: OpenPyXL for professional reports
• Validation: Custom validation system
• Charts: Plotly for interactive visualizations
• Mobile Support: Responsive CSS and touch-friendly interface

📁 Project Structure

appli_sondage/
├── app.py                      # Main Streamlit application
├── requirements.txt            # Python dependencies
├── README.md                   # Project documentation
├── config/
│   └── settings.py            # Configuration settings
├── src/
│   ├── components/
│   │   └── form_components.py # Form UI components
│   └── utils/
│       ├── excel_generator.py # Excel report generation
│       └── data_validator.py  # Data validation utilities
├── templates/                 # Excel templates
├── data/
│   ├── reports/              # Generated reports
│   ├── samples/              # Sample data
│   └── temp/                 # Temporary files
└── tests/                    # Unit tests

🔧 Installation

Using Pixi (Recommended)

1. Install Pixi: Follow instructions at pixi.sh

2. Clone and setup:

git clone <repository-url>
cd appli_sondage
pixi install

3. Run the application:

# Local development
pixi run dev

# Network access (for mobile devices)
pixi run dev-network

Using Traditional Python/Pip

1. Clone the repository:

git clone <repository-url>
cd appli_sondage

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

streamlit run app.py

Access the app:

• Local: http://localhost:8501
• Network: Access from mobile devices on same network

📱 Mobile Usage

1. Connect to WiFi: Ensure mobile device is on same network as computer
2. Open Browser: Navigate to the network URL shown in terminal
3. Add to Home Screen: (Optional) Add as PWA for app-like experience
4. Collect Data: Use touch-friendly interface to input drilling data
5. Generate Reports: Create and download Excel reports directly to device

📊 Report Types

Standard Report

• Site information summary
• Layer data table
• Basic statistics and analysis
• Rock type distribution charts

Detailed Analysis

• All standard features plus:
• Advanced geological analysis
• Engineering recommendations
• Additional charts and visualizations

Summary Only

• Condensed report with key information
• Perfect for quick field reports

🔍 Data Validation

The app includes comprehensive validation:

• Required field checking
• Depth sequence validation
• Layer continuity verification
• Rock type classification
• Cross-validation between site and layer data

🎨 Customization

Adding Rock Types

Edit config/settings.py to add new rock types:

ROCK_TYPES = {
    "sedimentary": ["Clay", "Sand", "Your_New_Type"],
    # ...
}

Excel Templates

• Place custom templates in templates/ directory
• Update template list in config/settings.py
• Templates should follow OpenPyXL format structure

🧪 Testing

Run tests using Pixi:

# Run all tests
pixi run test

# Run tests with coverage
pixi run test-coverage

# Lint code
pixi run lint

# Format code
pixi run format

Or with traditional Python:

python -m pytest tests/

🔧 Troubleshooting

Common Issues

App won't start: Check Python version (3.8+ required)

python --version
pip install --upgrade streamlit

Mobile access issues: Ensure devices are on same network

streamlit run app.py --server.address 0.0.0.0

Excel generation errors: Verify OpenPyXL installation

pip install --upgrade openpyxl

🚀 Deployment Options

Local Network (Recommended for field use)

# With Pixi
pixi run dev-network

# With traditional Python
streamlit run app.py --server.address 0.0.0.0 --server.port 8501

Cloud Deployment

• Streamlit Cloud: Deploy directly from GitHub
• Heroku: Use provided Procfile
• Docker: Container-based deployment

PWA (Progressive Web App)

Add to app.py for offline capabilities:

st.set_page_config(
    page_title="Drilling Data",
    page_icon="🏗️",
    layout="wide",
    initial_sidebar_state="expanded",
    menu_items={
        'Get Help': None,
        'Report a bug': None,
        'About': "Drilling Data Collection App v1.0"
    }
)

📈 Future Enhancements

• Offline data collection with sync
• GPS location integration
• Photo attachments for layers
• Database integration for data persistence
• Multi-language support
• Advanced geological analysis algorithms
• User authentication and project management
• Real-time collaboration features

🤝 Contributing

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

📝 License

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

👥 Authors

• Nathan - Initial development and geological expertise

🙏 Acknowledgments

• Streamlit team for the excellent framework
• OpenPyXL contributors for Excel generation capabilities
• Geological survey teams for domain expertise and feedback

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Built with ❤️ for field geologists and drilling professionals