RamanSPy

RamanSPy is an open-source Python package for integrative Raman spectroscopy data analysis.

Key features

• Common data format
• Data loaders
• Preprocessing methods
• Preprocessing pipelining
• Preprocessing protocols
• Analysis methods
• Visualisation tools
• AI integration
• Datasets
• Metrics
• Synthetic data generator (new in versions >= 0.2.0)

Installation

RamanSPy has been published on PyPI and can be installed via pip:

pip install ramanspy

Code example

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()

Documentation

For more information about the functionalities of the package, refer to the documentation.

Credits

If you use this package for your research, please cite our paper:

Georgiev, D., Pedersen, S., Xie, R., Fernández-Galiana, Á., Stevens, M., & Barahona, M. (2023). RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis. ChemRxiv. doi:10.26434/chemrxiv-2023-m3xlm

@article{ramanspy_2023,
    title={RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis},
    author={Georgiev, Dimitar and Pedersen, Simon Vilms and Xie, Ruoxiao and Fernández-Galiana, Álvaro and Stevens, Molly M. and Barahona, Mauricio},
    journal={ChemRxiv},
    publisher={Cambridge Open Engage},
    year={2023},
    DOI={10.26434/chemrxiv-2023-m3xlm}
}

Also, if you find RamanSPy useful, please consider leaving a star on GitHub.