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).
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 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.
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.
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
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 utilitiesTo 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.
- 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