DataManip

A powerful, user-friendly data manipulation application for experimental sciences. Perform calculations with formulas, units, uncertainty propagation, derivatives, and visualization—all in one place.

📖 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 (pint)
• Automatic Uncertainty Propagation: Symbolic differentiation with SymPy
• High Performance: 3x speedup with lazy evaluation, caching, and parallel execution
• Plotting with Error Bars: Multiple series, X/Y uncertainties, statistics (histogram/box plot)
• Workspace Management: Save/load complete workspaces with all studies
• Undo/Redo: 50-step history for column operations
• Constants System: Numeric, calculated, and function constants (workspace-level)

📋 View v0.2.0 Release Notes →

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📦 Installation

Option 1: Install from PyPI (Recommended)

# Using pip
pip install datamanip

# Using uv (faster)
uv pip install datamanip

# Run directly with uvx (no installation)
uvx datamanip

# Launch the application
python -m datamanip
# or simply:
datamanip

Option 2: Development Setup

# 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
• 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 8 complete demonstrations
examples/
├── 01_basic_introduction.dmw        # Getting started
├── 02_constants_and_formulas.dmw    # Formula engine basics
├── 03_ranges_and_derivatives.dmw    # Numerical differentiation
├── 04_uncertainty_propagation.dmw   # Error analysis
├── 05_custom_functions.dmw          # User-defined functions
├── 06_calculated_constants.dmw      # Dynamic constants
├── 07_advanced_kinematics.dmw       # Physics with units
└── 08_photoelectric_effect.dmw      # Complete experiment

# Open an example
datamanip examples/01_basic_introduction.dmw

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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