Refactor fit_gmm to support weighted particle resampling

[henry2004y]

Updated the fit_gmm method in flekspy/amrex/particle_data.py to automatically detect a "weight" variable in the particle data. If present, the method now performs weighted resampling of the particles before fitting the Gaussian Mixture Model. This ensures that the GMM fit correctly reflects the weighted distribution of the particle population.

This change also implicitly updates extract_core_population to respect particle weights, as it relies on fit_gmm.

Added a regression test to verify the fix.

[gemini-code-assist]

Summary of Changes

Hello @henry2004y, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the fit_gmm function to properly account for particle weights, which is critical for accurately modeling distributions where individual data points have varying importance. By introducing weighted resampling, the Gaussian Mixture Model fit will now be more representative of the true underlying data, directly improving the fidelity of downstream analyses that depend on this model.

Highlights

• Weighted Resampling in GMM: The fit_gmm method in flekspy/amrex/particle_data.py now automatically detects a 'weight' variable in particle data and performs weighted resampling before fitting the Gaussian Mixture Model. This ensures the GMM accurately reflects the weighted distribution of particles.
• Implicit Update to extract_core_population: Due to its reliance on fit_gmm, the extract_core_population method implicitly gains the ability to respect particle weights, leading to more accurate core population extraction.
• New Regression Test: A new regression test (tests/test_weighted_gmm.py) has been added to verify the correct implementation and functionality of weighted GMM fitting.

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[codecov]

Codecov Report

❌ Patch coverage is 96.00000% with 2 lines in your changes missing coverage. Please review.
✅ Project coverage is 78.79%. Comparing base (fc8fb30) to head (8372094).
⚠️ Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
tests/test_weighted_gmm.py	95.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #181      +/-   ##
==========================================
+ Coverage   78.34%   78.79%   +0.45%     
==========================================
  Files          31       32       +1     
  Lines        4341     4391      +50     
==========================================
+ Hits         3401     3460      +59     
+ Misses        940      931       -9     

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[gemini-code-assist]

Code Review

This pull request adds support for weighted particle resampling in fit_gmm, which is a great feature. The implementation is mostly correct, and the new regression test is well-designed.

I've identified a couple of areas for improvement:

1. In flekspy/amrex/particle_data.py, the resampling logic can be made more robust by applying it directly to rdata before any transformations. This avoids potential bugs if a transform function alters the number of particles. I've also suggested adding a check for negative particle weights to prevent runtime errors.
2. In tests/test_weighted_gmm.py, making the random data generation deterministic by using a fixed seed will make the test more reliable.

These changes should improve the robustness and reliability of the new functionality. Overall, this is a valuable addition.