RamanSPy has been published on PyPI and can be installed via pip:
pip install ramanspy
Below is a simple example of how RamanSPy can be used to load, preprocess and analyse Raman spectroscopic data. Here, we load a data file from a commercial Raman instrument; apply a preprocessing pipeline consisting of spectral cropping, cosmic ray removal, denoising, baseline correction and normalisation; perform spectral unmixing; and visualise the results.
import ramanspy as rp
# load data
image_data = rp.load.witec("<PATH>")
# apply a preprocessing pipeline
pipeline = rp.preprocessing.Pipeline([
rp.preprocessing.misc.Cropper(region=(700, 1800)),
rp.preprocessing.despike.WhitakerHayes(),
rp.preprocessing.denoise.SavGol(window_length=9, polyorder=3),
rp.preprocessing.baseline.ASPLS(),
rp.preprocessing.normalise.MinMax()
])
data = pipeline.apply(image_data)
# perform spectral unmixing
nfindr = rp.analysis.unmix.NFINDR(n_endmembers=5)
amaps, endmembers = nfindr.apply(data)
# plot results
rp.plot.spectra(endmembers)
rp.plot.image(amaps)
rp.plot.show()
For more information about the functionalities of the package, refer to the online documentation.
If you use RamanSPy for your research, please cite our paper:
@article{georgiev2024ramanspy,
title={RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis},
author={Georgiev, Dimitar and Pedersen, Simon Vilms and Xie, Ruoxiao and Fern{\'a}ndez-Galiana, Alvaro and Stevens, Molly M and Barahona, Mauricio},
journal={Analytical Chemistry},
volume={96},
number={21},
pages={8492-8500},
year={2024},
doi={10.1021/acs.analchem.4c00383}
}
Also, if you find RamanSPy useful, please consider leaving a star on GitHub.