datamanip 0.2.0

An open-source data manipulation software for experimental sciences

DataManip

An open-source data manipulation software for experimental sciences

📖 Release Notes | 🗺️ Roadmap | 🤝 Contributing

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✨ Key Features

• 5 Column Types: Data, Calculated, Derivative, Range, Uncertainty
• Formula Engine: NumPy functions, custom functions, unit-aware calculations
• Plotting with Error Bars: Multiple series, X/Y uncertainties, statistics (histogram/box plot), image export
• Workspace Management: Save/load complete workspaces, atomic file operations
• Undo/Redo: 50-step history for column operations
• Constants System: Numeric, calculated, and function constants
• Preferences: Customizable settings with theme support
• Performance: 3x faster with lazy evaluation and parallel execution
• 308 Tests: 99%+ passing unit test coverage (8 new error bar tests)

📋 View Complete Feature List →

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Getting Started

Prerequisites

• uv package manager

Clone and Run

# Clone the repository
git clone https://github.com/brian-sinquin/DataManip.git
cd DataManip

# Install dependencies and run with uv
uv sync
uv run datamanip

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📚 Documentation

• RELEASE_NOTES.md - Complete feature list and v0.2.0 details
• ROADMAP.md - Development roadmap from v0.3.0 to v1.0.0
• TODO.md - Current sprint priorities and task tracking
• CONFIG_MIGRATION.md - Configuration changes guide
• CONTRIBUTING.md - How to contribute to DataManip

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🧮 Uncertainty Propagation

DataManip automatically calculates propagated uncertainties using the standard error propagation formula:

$$\delta f = \sqrt{\sum_i \left(\frac{\partial f}{\partial x_i} \cdot \delta x_i\right)^2}$$

Where:

• $\delta f$ = combined uncertainty of result
• $\frac{\partial f}{\partial x_i}$ = partial derivatives (calculated symbolically using SymPy)
• $\delta x_i$ = uncertainties of input variables

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🚀 Quick Examples

# See examples/ directory for complete demonstrations
examples/
├── basic_usage.py              # Simple data manipulation
├── projectile_motion.py        # Physics with units
├── uncertainty_demo.py         # Error propagation
├── derivative_example.py       # Numerical differentiation
├── error_bars_example.py       # Plotting with uncertainties ⭐ NEW
└── performance_benchmark.py    # Performance testing

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🧪 Testing

# Run all tests
uv run pytest tests/unit/ -v

# Generate coverage report
uv run pytest --cov=src --cov-report=html

# Test by layer
uv run pytest tests/unit/core/ -v
uv run pytest tests/unit/studies/ -v
uv run pytest tests/unit/ui/ -v

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📄 License

MIT License - See LICENSE for details

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🙏 Acknowledgments

Built with ❤️ for experimental scientists worldwide.

Contributors: See assets/contributors.json

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Made with PySide6 • pandas • NumPy • matplotlib • sympy • pint