The Procrustes library provides a set of functions for transforming a matrix to make it as similar as possible to a target matrix. For more information, visit Procrustes Documentation.
Please use the following citation in any publication using Procrustes library:
@article{Meng2022procrustes,
title = {Procrustes: A python library to find transformations that maximize the similarity between matrices},
author = {Fanwang Meng and Michael Richer and Alireza Tehrani and Jonathan La and Taewon David Kim and Paul W. Ayers and Farnaz Heidar-Zadeh},
journal = {Computer Physics Communications},
volume = {276},
number = {108334},
pages = {1--37},
year = {2022},
issn = {0010-4655},
doi = {https://doi.org/10.1016/j.cpc.2022.108334},
url = {https://www.sciencedirect.com/science/article/pii/S0010465522000522},
keywords = {Procrustes analysis, Orthogonal, Symmetric, Rotational, Permutation, Softassign},
}The following dependencies are required to run Procrustes properly,
- Python >= 3.6: http://www.python.org/
- NumPy >= 1.21.5: http://www.numpy.org/
- SciPy >= 1.5.0: http://www.scipy.org/
- PyTest >= 5.3.4: https://docs.pytest.org/
- PyTest-Cov >= 2.8.0: https://pypi.org/project/pytest-cov/
To install Procrustes using the conda package management system, install miniconda or anaconda first, and then:
# Create and activate myenv conda environment (optional, but recommended)
conda create -n myenv python=3.6
conda activate myenv
# Install the stable release.
conda install -c theochem qc-procrustesTo install Procrustes with pip, you may want to create a virtual environment, and then:
# Install the stable release.
pip install qc-procrustesSee https://procrustes.qcdevs.org/usr_doc_installization.html for full details.