ENH: allow users to provide custom samplers

[Lucas-Prates]

Pull request type

• Code changes (bugfix, features)
• Code maintenance (refactoring, formatting, tests)

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)

Current behavior

Currently, the user must use distributions that are specified in numpy.

New behavior

Allows the user to build upon the abstract class CustomSampler which must contain two methods: sample and reset_seed. Note that the user may implement very complex samplers that use real data, so long as it conforms to returning the data in the format specified.

Breaking change

• No

Additional information

• Add example to Monte Carlo class usage notebook
• Verify that the seed is being properly reset in parallel Monte Carlo simulations

[Lucas-Prates]

@Gui-FernandesBR, @MateusStano and @phmbressan, I would like, if possible, a design check in this feature. I am not sure if this is what you had in mind.

In order to allow the user to specify his own sampler, I thought of providing an abstract class CustomSampler and allow him to implement two methods: sample and reset_seed. These are completely abstracted away from the Stochastic classes, and we only care about the return value. The sample itself might be very complex and even fetch outside data if the user wants to.

I provided a notebook to test the feature design. There, I implemented a MixtureOfGaussian distribution which is not natively implemented in numpy. (Think of a bimodal gaussian distribution if you want, as exemplified below.

[Gui-FernandesBR]

Plrase update the changelog

[codecov]

Codecov Report

Attention: Patch coverage is 79.41176% with 7 lines in your changes missing coverage. Please review.

Project coverage is 79.84%. Comparing base (4df0b38) to head (f1f7220).
Report is 19 commits behind head on develop.

Files with missing lines	Patch %	Lines
rocketpy/stochastic/stochastic_model.py	75.00%	7 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##           develop     #803      +/-   ##
===========================================
+ Coverage    79.11%   79.84%   +0.72%     
===========================================
  Files           96       98       +2     
  Lines        11575    11958     +383     
===========================================
+ Hits          9158     9548     +390     
+ Misses        2417     2410       -7     

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[MateusStano]

Just missing changelog, then we can merge