Скачать или смотреть Space Science with Python - Near-Earth Objects #5: Orbital Elements

GitHub Link of today's session: https://github.com/ThomasAlbin/Astron...

Last time, we converted a single parameter, namely the Absolute Magnitude, to the NEOs' corresponding, physical diameters. But NEOs are not only defined by the physical appearance and chemical composition. Dynamical properties are important to identify and understand e.g., observational biases.

This time we will use the library Seaborn that allows us to generate several plots, histograms and Kernel-Density Estimators with only a few lines of code; enabling us to dive deeply into the multi-dimensional world of orbital elements.

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How to setup a local dev environment:    • Space Science with Python - Part 2: Setup ...  

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Content
0:00 Introduction
1:30 Cell #1 - #4: Setting things up
2:50 Cell #5: Seaborn
5:10 Cell #6: Absolute Magnitude distribution
6:22 Cell #7: Absolute Magnitude by NEO types
9:50 Cell #8: Semi-Major Axis KDE plot
12:30 Cell #9: Multi-Grid Plot (orbital elements)
18:54 Summary & Outlook
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There is a lot to do and to learn and I hope you will join the journey. Meanwhile, if you have questions or ideas, reach out to me via:

Twitter:   / mrastrothomas  
Reddit:   / mrastrothomas  
GitHub: https://github.com/ThomasAlbin

Or drop a comment!

Talk to you later,
Thomas