Porchlight

Porchlight is a preprocessing aid for spectroscopists and for students to learn preprocessing.

Features

Porchlight allows you to open a variety of spectral files and immediately visualize the preprocessing you perform on your spectra. This way, you can get a better intuition for the impact various preprocessing methods have. The software is also designed for the classroom as well, with important features to support learning such as dynamic plot axis labelling.

Installation

To install the most up-to-date version from GitHub, you can use the following:

pip install git+https://github.com/Jakub-Konkol/Porchlight.git

Use

This section describes the use of Porchlight, either through the GUI or as a class you can use in your scripts.

GUI

To invoke the GUI, call the following from the terminal

python -m porchlight

Within scripts

The data handling backend code be called in scripts using

from porchlight.spectralData import SpectralData

From here, one can instantiate the class by providing a list of file directories containing spectral data, either in CSV, TXT, or Thermo-Gram SPC (if optional spc-spectra is installed).

data = SpectralData(['myFile.csv'])

All preprocessing techniques are methods of this class, which are performed in-place. To perform SNV, one can use

data.reset()
data.SNV()

We recommend performing a reset at the start, to make sure you don't accidentally process processed data.

Class Variables

wav - numpy array containing the abscissa axis

war - pandas DataFrame containing labels for each spectrum (not currently used)

tf_history - a list that describes the preprocessing steps performed to the spectra. Each list element is a list where the first element is the preprocessing step and the second element is a dictionary with the values given for the method.

file_source - a list of directories that describe the source file for each spectrum. If one file provides multiple spectra, then it is repeated multiple times.

spc - a pandas DataFrame containing the spectral data in a tidy format. Each row is one spectrum, each column is a corresponding abscissa value. The column names are equivalent to the variable wav.

_baselines - a pandas DataFrame with calculated baselines, if one was calculated.

Acknowledgements

This material is based upon work supported in part by Rutgers, The State University of New Jersey, and the National Science Foundation Award 1751683.

This software has been developed by Jakub Konkol of the Dr. Tsilomelekis group at Rutgers, the State University of New Jersey. Come see our research!

©️ 2021-2024 Rutgers, the State University of New Jersey