Data visualization on experimental data utilizing "seaborn", "matplotlib", "pandas" to generate visually engaging graphs
The results were generated using the data gathered during collision avoidance experimentations by varying algorithms as [1 or 2] and number of agent as [4, 9, 15, or 25]. Matplotlib and Seaborn packages were used to generate the below results along with numpy adn randas to make the computations efficient.
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Line Plot: This plot represent the variation of in range obstacles for each agent during the experiment's duration.
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Box Plot: This plot represents the total number of collisions during each experiment.
If already the large files have been committed and got the error then the first commit must be undone
Using git desktop can be faster on windows
git reset
git lfs track '*.csv'
git add .
git commit -m "Commit Message"
git push