MultiCal Documentation
MultiCal is a robust Python Library for Chemometrics and Multivariate Calibration. It provides a comprehensive toolkit for building predictive models from spectroscopic data (NIR, Raman, MIR, etc.), streamlining the workflow from raw spectra preprocessing to variable selection and model deployment.
Note
This project is under active development.
Contents:
Key Features
MultiCal offers a flexible environment for spectral analysis:
Calibration Algorithms: * PLS (Partial Least Squares): The industry standard for quantitative spectral analysis. * PCR (Principal Component Regression): Alternative latent variable method. * SPA (Successive Projections Algorithm): For minimizing collinearity and selecting discrete wavelengths.
Variable Selection: * VIP (Variable Importance in Projection): Identifies the most influential spectral regions. * Evolutionary Algorithms: Particle Swarm Optimization (PSO) and Simulated Annealing (SA) for optimizing feature subsets.
Preprocessing Pipeline: * Comprehensive suite including Savitzky-Golay (smoothing/derivatives), MSC, SNV, and Normalization. * Customizable pipeline to chain multiple pretreatment steps.
Workflow Flexibility: * Script-Based: Optimized for batch processing and reproducible research (run_calibration.py, etc.). * GUI-Based: User-friendly interface for visual inspection and quick model building.
Quick Install
git clone https://github.com/LadabioMPAR/Multical.git
cd Multical
pip install -r requirements.txt
Indices and tables
Support & Contribute
Issues: Report bugs or suggest features on GitHub.
License: Licensed under the GNU General Public License v3.0.