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. "https://youtu.be/uz0q_TKrC84"

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.

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

proper orthogonal decomposition (POD), singular value decomposition (SVD), Spectral Analysis, Parallel Processing, Unsupervised Machine Learning