%0 Computer Program
%A Ghasemi Khourinia, Alireza
%A Kok, Jim
%D 2022
%T PARAMOUNT: parallel modal analysis of large datasets
%U https://data.4tu.nl/articles/software/PARAMOUNT_parallel_modal_analysis_of_large_datasets/20089760/1
%R 10.4121/20089760.v1
%K proper orthogonal decomposition (POD)
%K singular value decomposition (SVD)
%K Spectral Analysis
%K Parallel Processing
%K Unsupervised Machine Learning
%X <p><strong>PARAMOUNT: parallel modal analysis of large datasets</strong><br>
<br>
PARAMOUNT is a python package developed at University of Twente to perform modal analysis of large numerical and experimental datasets. Brief video introduction into the theory and methodology is presented  <sub><a href="https://youtu.be/uz0q_TKrC84" target="_blank">here.</a></sub><br>
<br>
<strong>Features</strong><br>
 </p>
<p>- Distributed processing of data on local machines or clusters using Dask Distributed<br>
- Reading CSV files in glob format from specified folders<br>
- Extracting relevant columns from CSV files and writing Parquet database for each specified variable<br>
- Distributed computation of Proper Orthogonal Decomposition (POD)<br>
- Writing U, S and V matrices into Parquet database for further analysis<br>
- Visualizing POD modes and coefficients using pyplot</p>
<p><br>
<strong>Using </strong> <strong>PARAMOUNT</strong><br>
<br>
Make sure to install the dependencies by running `pip install -r requirements.txt`<br>
<br>
 </p>
<p>Refer to csv_example to see how to use PARAMOUNT to read CSV files, write the variables of interest into Parquet datasets and inspect the final datasets.<br>
<br>
Refer to svd_example to see how to read Parquet datasets, compute the Singular Value Decomposition, and store the results in Parquet format.<br>
<br>
To visualize the results you can simply read the U, S and V parquet files and your plotting tool of choice. Examples are provided in viz_example.<br>
<br>
<strong>Author and Acknowledgements</strong><br>
<br>
This package is developed by Alireza Ghasemi (<u>alireza.ghasemi@utwente.nl</u>) at University of Twente under the MAGISTER (<u>https://www.magister-itn.eu/</u>) project. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 766264.</p>
%I 4TU.ResearchData