A Python library for calculations with uncertainties (error propagation), transparently handling correlations and providing an intuitive interface.
uncertainties
- Python
- Mathematics, Metrology, Quality, Simulation
- Engineering, Error Prevention, Mathematics, Process Improvement, Quality Control, Quality Management, Statistical Analysis, Sustainability Metrics
Features:
- Calculation with numbers with uncertainties, automatic error propagation (linear approximation and exact for some cases), correlation handling, transparent calculations (works with standard math operators), NumPy support for arrays with uncertainties, pretty printing of results, access to derivatives (sensitivity analysis), support for complex numbers with uncertainties
Pricing:
- Free
- Easy to use and intuitive, automates error propagation which reduces manual calculation errors, handles correlations correctly, integrates well with NumPy, good documentation.
- Performance overhead compared to raw float calculations (though usually acceptable for typical use cases), primarily focused on linear error propagation theory for complex functions (though exact for basic ops), may not cover all advanced statistical uncertainty quantification methods.
Best for:
- Scientists, engineers, and researchers who need to perform calculations involving quantities with uncertainties and require automatic error propagation.