pynir 0.4

NIR calibration toolbox in python

Commom Calibration methods for multivariate calibration

This is a Python library for dealing with multivariate calibration, e.g., Near infrared spectra regression and classification tasks.

Installation

Use the package manager pip to install toolkit in requirements.txt.

pip install -r requirements.txt

Usage

Simulata NIR spectra (spc) and reference values (conc) or load your own data

from pynir.utils import simulateNIR

spc, conc = simulateNIR()

Demon

Feature selection demostration of corn sample near infrared (NIR) spectra by Monte Carlo-uninformative variable elimination (MC-UVE), randomization test(RT), Variable selection via Combination (VC), and multi-step VC（MSVC）.

python FeatureSelectionDemo_mcuve.py
python FeatureSelectionDemo_RT.py
python FeatureSelectionDemo_VC.py
python FeatureSelectionDemo_MSVC.py

Ref

1. Cai, W. S.; Li, Y. K.; Shao, X. G., A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemom. Intell. Lab. Syst. 2008, 90 (2), 188-194.

2. Xu, H.; Liu, Z. C.; Cai, W. S.; Shao, X. G., A wavelength selection method based on randomization test for near-infrared spectral analysis. Chemom. Intell. Lab. Syst. 2009, 97 (2), 189-193.

3. Zhang, J.; Cui, X. Y.; Cai, W. S.; Shao, X. G., A variable importance criterion for variable selection in near-infrared spectral analysis. Sci. China Chem. 2018, 62, 271–279.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT