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Title:Interactive visualization framework for space plasma data sets
Author(s):Hu, Huizi
Advisor(s):Ilie, Raluca
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):plotly
visualization
python
space science
Abstract:The capability of visualizing numerical data sets to graphs is imperative to our understanding of space physics. However, traditional visualization tools used in the space science community are either proprietary, lack portability or require paid license subscriptions. Therefore we are attempting to resolve these shortcomings by developing cross-platform, free-to-use, easily set up interactive visualization tools that is accessible to any web browsers. This project will showcase five different applications based on a Python framework called Dash developed by plotly. These applications are proved to be capable of visualizing 3D space physics data sets including satellite measurements, ground measurements, or numerical model simulation results. The first two applications use the Cluster II satellite measurement data set provided by ESA, courtesy of Dr.~Elena Kornberg from the Max Plank Institute. The third and the fourth applications use the simulated magnetic field data generated by the Tsyganenko magnetic field model. The fifth application visualizes ion density from an empirical model based on geo-tail data while the final application can plot calculation results from the Polar Wind Outflow Model. By demonstrating the advantages of our visualization tool, we hope to show the significance of a generalized framework, which could benefit the entire space science community. Due to the time constraint of this project, we did not explore all the options provided by Dash. However, we firmly believe this tool has great potential and will be more powerful with additional features such as parallel computing, cloud computing, and the ability to record animations.
Issue Date:2021-04-23
Type:Thesis
URI:http://hdl.handle.net/2142/110689
Rights Information:Copyright 2021 Huizi Hu
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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