PCAfold is an open-source Python library for generating, analyzing and improving low-dimensional manifolds. It incorporates a variety of data preprocessing tools (including data clustering and sampling), implements several dimensionality reduction strategies and utilizes novel approaches to assess the quality of the obtained low-dimensional manifolds. The latest software version introduces algorithms to optimize projection topologies based on quantities of interest (QoIs) and novel tools to reconstruct QoIs from the low-dimensional data representations using partition of unity networks (POUnets).

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:

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.

Installation

Dependencies

PCAfold requires Python>=3.8 and the latest versions of the following packages:

pip install Cython
pip install matplotlib
pip install numpy
pip install scipy
pip install termcolor
pip install pandas
pip install scikit-learn
pip install tensorflow
pip install keras
pip install tqdm

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 installation from setup.py:

python setup.py build_ext --inplace
python setup.py install

Note, that this will be deprecated in the future, but should still work.

Alternatively, run installation using pip:

python -m pip install .

You are ready to import PCAfold! In Python, you can now import all modules:

from PCAfold import preprocess
from PCAfold import reduction
from PCAfold import analysis
from PCAfold import reconstruction
from PCAfold import utilities

Testing

To run regression tests from the base repo directory run:

python -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

• Kamila Zdybał, Université Libre de Bruxelles, Kamila.Zdybal@ulb.ac.be
• Elizabeth Armstrong, The University of Utah, Elizabeth.Armstrong@chemeng.utah.edu
• Alessandro Parente, Université Libre de Bruxelles, Alessandro.Parente@ulb.ac.be
• James C. Sutherland, The University of Utah, James.Sutherland@utah.edu