Skip to main content

A Python package to simplify and accelerate analysis of spectroscopy data

Project description

image Anaconda-Server Badge image CodeQL image codecov Downloads image image DOI

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

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
    
     x, y = spep.load_spectras()
     for i in y:
         plt.plot(x, i)
     plt.xlabel('Raman shift ($cm^{-1}$)')
     plt.ylabel('Intensity (a.u.)')
     plt.show()
    
  4. If you find this library useful, please consider a reference or citation as:

     Grau-Luque et al., (2021). spectrapepper: A Python toolbox for advanced analysis
     of spectroscopic data for materials and devices. Journal of Open Source Software,
     6(67), 3781, https://doi.org/10.21105/joss.03781
    
  5. Stay up-to-date by updating the library using:

    conda update spectrapepper
    pip install --update spectrapepper
    
  6. If you encounter problems when updating, try uninstalling and then re-installing::

     pip uninstall spectrapepper
     conda remove spectrapepper
    

Credits

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

spectrapepper-0.1.10.tar.gz (757.3 kB view details)

Uploaded Source

Built Distribution

spectrapepper-0.1.10-py2.py3-none-any.whl (755.1 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file spectrapepper-0.1.10.tar.gz.

File metadata

  • Download URL: spectrapepper-0.1.10.tar.gz
  • Upload date:
  • Size: 757.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.12.2

File hashes

Hashes for spectrapepper-0.1.10.tar.gz
Algorithm Hash digest
SHA256 947b985a90b14484bcb09094eb7e58019fbbd6537ef4f67d3ab2f620c470f504
MD5 7edd1579e5c791e387b000caea092a57
BLAKE2b-256 5a8a38caac662591263a467271530cb344b3749475e9aad9054e0cfcb4557429

See more details on using hashes here.

File details

Details for the file spectrapepper-0.1.10-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for spectrapepper-0.1.10-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 fc1759d937952971f50643e529c8f3b037ae266cce9e43fa12569947969e5492
MD5 2aebd9b2e0dba4671b5cc506fe6c4e28
BLAKE2b-256 ee56eac8b023107b069e243bfb8b3709ad76519f3c5a47586b8671185e30c8a7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page