ENH: Implementing Multivariate Rejection Sampling (MRS) in RocketPy

[Lucas-Prates]

Pull request type

• Code changes (bugfix, features)

Checklist

• Tests for the changes have been added (if needed)
• Docs have been reviewed and added / updated
• Lint (black rocketpy/ tests/) has passed locally
• All tests (pytest tests -m slow --runslow) have passed locally
• CHANGELOG.md has been updated (if relevant)
• RST documentation

New behavior

This PR implements the MRS requested in #162 and described in RocketPy paper.

Breaking change

• No

Additional information

It is possibly a good idea to use setters when distribution_dict is modified by the user. Otherwise, the sample method can provide incorrect results due to attributes computed in the initialization.

[codecov]

Codecov Report

Attention: Patch coverage is 17.30769% with 86 lines in your changes missing coverage. Please review.

Project coverage is 75.92%. Comparing base (83aa20e) to head (3a3c9e6).
Report is 9 commits behind head on develop.

Files with missing lines	Patch %	Lines
...ketpy/simulation/multivariate_rejection_sampler.py	17.30%	86 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##           develop     #738      +/-   ##
===========================================
- Coverage    76.42%   75.92%   -0.50%     
===========================================
  Files           95       97       +2     
  Lines        11090    11198     +108     
===========================================
+ Hits          8475     8502      +27     
- Misses        2615     2696      +81     

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

[Lucas-Prates]

This PR is ready for a first "Design Review." I would like to get your opinion if this implementation provides what you think on how the user should use the MRS.

Albeit a implementation as a function seems natural, I implemented as a class because:

1. the function would be humongous;
2. if the user wants to resample several times, the data monte carlo data is only read once from the harddrive.

It currently works as follows:

0. Input: monte carlo filepath prefix, mrs filepath prefix, distribution dictionary;
1. Load input and output data from a monte carlo simulation into memory (python objects - lists of jsons);
2. To avoid having to read data twice, while loading, precompute some important properties required in the sampler algorithm;
3. Select and save iteratively accepted samples;
4. Output: files are saved in the same "scheme" as the MonteCarlo simulation.

I provided a quick and dirty notebook, which will be removed, just to show how the class is being used at the moment.

[Gui-FernandesBR]

I don't have much time so I'll be short

• The class dies after you sample the data once, this makes it pointless to have a class
• Instead I'd remove the sample dictionary from arguments.
• First read the data during initialization. Then you use a function to set the variables you are going to allow be varied. This way u can already anticipate which variables may be varied.
• We want almost instant results for a MonteCarlo simulation after using MRS.
• Other thing is that the user must supply the original pdf. Could we possile estimate this from data? (imagine 70k)
• Finally, plotting is crucial for MRS, or even tables. We'd love to see more on that later.

[phmbressan]

This line should be inside a finally block, I believe. The same applies to the method load_output.

[phmbressan]

If possible, it is better practice to handle paths with the pathlib standard library. Check out the parallel monte carlo PR as an example of handling the filename in the MonteCarlo class.