Added Tess' machine-learning on RXTE data notebook contents into the existing RXTE notebook

[DavidT3]

HEASARC notebook review

Didn't seem to make sense having another notebook for the machine learning aspect, like there was in the SciServer cookbooks, as a big chunk of it was replicated (the acquisition of the spectra etc.)

So in ingesting that notebook I have added it as a section to the existing RXTE spectral analysis notebook - the combination lets us do some interesting things looking at how model-independent classification compares with fitted model parameters as well.

Re-written the code, and added a lot more commentary.

Critical review criteria

The author of the pull request should make an effort to go through these check points and ensure that their submission satisfies each point - reviewers will also compare to these checklists.

Science review checklist

• Does using high-energy data make up a significant part of the tutorial?
• Is there a use case in the introduction that motivates the code?
• Does the code do what the introduction says it is going to do?
• Is it scientifically accurate?
• Have all necessary references to literature been included?

Formatting checklist

• Did you base your notebook on the HEASARC-tutorials template?
• Are all sections in the HEASARC-tutorial template included in your notebook?
• Is the notebook title compact and informative? It will be the name of the notebook on the HEASARC website!
• Have you populated the notebook front-matter (the metadata at the top of the notebook)?
• Is the kernel specified in the front-matter (e.g., heasoft, sas, ciao) correct for the notebook?
• Have you added an entry for your notebook in the *_index.md file for the containing directory?

Tech review checklist

• Documentation:
  • Is every function documented?
  • Do all code cells have corresponding narratives/comments?
  • Are all code comments of a purely technical nature? All narratives should be in Markdown cells.
  • Did you populate the 'Runtime' section?
• Notebook execution, error handling, etc.:
  • Does the notebook run end-to-end, out of the box?
  • Are errors handled appropriately, with try/except statements that are narrow in scope?
  • Have warnings been dealt with appropriately, preferably by updating the code to avoid them (i.e., not by simply silencing them)?
• Efficiency:
  • Is data accessed from the cloud where possible?
  • Is the code parallelized where possible?
  • If the notebook is intended to be scaled up, does it do that efficiently?
  • Is memory usage optimized where possible?
• Cleanup:
  • Have blocks of code that need to be re-used been turned into functions and placed in the 'global setup'-'function' section?
  • Has unused code been removed (e.g., unused functions and commented-out lines)?
  • Are comment lines wrapped so all fit within a max of 90 - 100 characters per line?
  • Do plots use color-blind friendly palettes for plotting? Try this simulator for a visual check.