PARAMOUNT: parallel modal analysis of large datasets

doi:10.4121/20089760.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/20089760
Datacite citation style:
Ghasemi Khourinia, Alireza; Kok, Jim (2022): PARAMOUNT: parallel modal analysis of large datasets. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/20089760.v1
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Software

PARAMOUNT: parallel modal analysis of large datasets

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  here.

Features
 

- Distributed processing of data on local machines or clusters using Dask Distributed
- Reading CSV files in glob format from specified folders
- Extracting relevant columns from CSV files and writing Parquet database for each specified variable
- Distributed computation of Proper Orthogonal Decomposition (POD)
- Writing U, S and V matrices into Parquet database for further analysis
- Visualizing POD modes and coefficients using pyplot


Using  PARAMOUNT

Make sure to install the dependencies by running `pip install -r requirements.txt`

 

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.

Refer to svd_example to see how to read Parquet datasets, compute the Singular Value Decomposition, and store the results in Parquet format.

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.

Author and Acknowledgements

This package is developed by Alireza Ghasemi (alireza.ghasemi@utwente.nl) at University of Twente under the MAGISTER (https://www.magister-itn.eu/) 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.

history
  • 2022-06-20 first online
  • 2022-11-28 published, posted
publisher
4TU.ResearchData
format
Python files (.py) Requirements file (.txt)
funding
  • 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
organizations
University of Twente, Faculty of Engineering Technology (ET), Thermal Engineering (TE)

DATA

files (1)