TY - DATA
T1 - PARAMOUNT: parallel modal analysis of large datasets
PY - 2022/11/28
AU - Alireza Ghasemi
AU - Jim Kok
UR - https://data.4tu.nl/articles/software/PARAMOUNT_parallel_modal_analysis_of_large_datasets/20089760/1
DO - 10.4121/20089760.v1
KW - proper orthogonal decomposition (POD)
KW - singular value decomposition (SVD)
KW - Spectral Analysis
KW - Parallel Processing
KW - Unsupervised Machine Learning
N2 - <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>
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<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>
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Refer to svd_example to see how to read Parquet datasets, compute the Singular Value Decomposition, and store the results in Parquet format.<br>
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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>
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<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>
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