.. image:: logo.png :alt: MultiCal Logo :align: center :width: 300px 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. .. toctree:: :maxdepth: 2 :caption: Contents: user_guide modules 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 ------------- .. code-block:: bash git clone https://github.com/LadabioMPAR/Multical.git cd Multical pip install -r requirements.txt Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` Support & Contribute -------------------- * **Issues**: Report bugs or suggest features on `GitHub `_. * **License**: Licensed under the GNU General Public License v3.0.