A Python package to simplify and accelerate analysis of spectroscopy data.

• GitHub repo: https://github.com/spectrapepper/spectrapepper
• Documentation: https://spectrapepper.github.io/spectrapepper
• PyPI: https://pypi.python.org/pypi/spectrapepper
• Conda-forge: https://anaconda.org/conda-forge/spectrapepper
• Free software: MIT license

Introduction

spectrapepper is a Python package that makes advanced analysis of spectroscopic data easy and accessible through straightforward, simple, and intuitive code. This library contains functions for every stage of spectroscopic methodologies, including data acquisition, pre-processing, processing, and analysis. In particular, advanced and high statistic methods are intended to facilitate, namely combinatorial analysis and machine learning, allowing also fast and automated traditional methods.

Features

The following is a short list of some main procedures that spectrapepper package enables.

• Baseline removal functions.
• Normalization methods.
• Noise filters, trimming tools, and despiking methods.
• Chemometric algorithms to find peaks, fit curves, and deconvolution of spectra.
• Combinatorial analysis tools, such as Spearman, Pearson, and n-dimensional correlation coefficients.
• Tools for Machine Learning applications, such as data merging, randomization, and decision boundaries.
• Sample data and examples.

Quickstart

1. Install this library using pip:

    pip install spectrapepper
   

2. Install this library using conda-forge:

    conda install -c conda-forge spectrapepper
   

3. Test it by plotting some data!:

    import spectrapepper as spep
    import matplotlib.pyplot as plt
   
    data = spep.load_spectras()
    for i in data[1:]:
        plt.plot(data[0], i)
    plt.xlabel('Raman shift ($cm^{-1}$)')
    plt.ylabel('Intensity (a.u.)')
    plt.show()
   

Credits

This package was created with Cookiecutter and the giswqs/pypackage project template.