PCAfold is an open-source Python library for generating, analyzing and improving low-dimensional manifolds obtained via Principal Component Analysis (PCA).

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Data AnalysisPCAfold
Overview

License: MIT Documentation Status GitLab Binder

PCAfold is an open-source Python library for generating, analyzing and improving low-dimensional manifolds obtained via Principal Component Analysis (PCA). It incorporates a variety of data preprocessing tools (including data clustering and sampling), uses PCA as a dimensionality reduction technique and utilizes a novel approach to assess the quality of the obtained low-dimensional manifolds.

Citing PCAfold

PCAfold is published in the SoftwareX journal. If you use PCAfold in a scientific publication, you can cite the software as:

Zdybał, K., Armstrong, E., Parente, A. and Sutherland, J.C., 2020. PCAfold: Python software to generate, analyze and improve PCA-derived low-dimensional manifolds. SoftwareX, 12, p.100630.

or using BibTeX:

@article{pcafold2020,
title = "PCAfold: Python software to generate, analyze and improve PCA-derived low-dimensional manifolds",
journal = "SoftwareX",
volume = "12",
pages = "100630",
year = "2020",
issn = "2352-7110",
doi = "https://doi.org/10.1016/j.softx.2020.100630",
url = "http://www.sciencedirect.com/science/article/pii/S2352711020303435",
author = "Kamila Zdybał and Elizabeth Armstrong and Alessandro Parente and James C. Sutherland"
}

PCAfold documentation

PCAfold documentation contains a thorough user guide including equations, references and example code snippets. Numerous illustrative tutorials and demos are presented as well. The corresponding Jupyter notebooks can be found in the docs/tutorials directory.

Software architecture

A general overview for using PCAfold modules is presented in the diagram below:

Screenshot

Each module's functionalities can also be used as a standalone tool for performing a specific task and can easily combine with techniques outside of this software, such as K-Means algorithm or Artificial Neural Networks.

Installation

Dependencies

PCAfold requires python3.7 and the following packages:

  • Cython
  • matplotlib
  • numpy
  • scipy
  • termcolor

Build from source

Clone the PCAfold repository and move into the PCAfold directory created:

git clone http://gitlab.multiscale.utah.edu/common/PCAfold.git
cd PCAfold

Run the setup.py script as below to complete the installation:

python3.7 setup.py build_ext --inplace
python3.7 setup.py install

You are ready to import PCAfold!

Testing

To run regression tests from the base repo directory run:

python3.7 -m unittest discover

To switch verbose on, use the -v flag.

All tests should be passing. If any of the tests is failing and you can’t sort out why, please open an issue on GitLab.

Authors and contacts

Owner
Burn Research
Burn Research
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