PARAMOUNT: parallel modal analysis of large datasets

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.