experimental: support for power spectrum data

[wcxve]

Summary by Sourcery

This pull request introduces support for power spectrum data analysis by implementing the Whittle likelihood function. It includes a custom exponential distribution (BetterExponential) and integrates the new likelihood into the existing inference and plotting framework. It also adds a check to ensure that the quantile residuals are finite.

New Features:

• Adds support for the Whittle likelihood function, enabling power spectrum (periodogram) analysis.

Enhancements:

• Introduces a BetterExponential distribution for improved handling of exponential distributions in the context of the Whittle likelihood.

Tests:

• Adds an assertion to check that the quantile residuals are finite.

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request introduces support for the 'whittle' statistic, enabling the analysis of power spectrum data. It includes a new likelihood function, a custom exponential distribution, and modifications to existing functions to accommodate the new statistic. The changes ensure proper handling of data with Gaussian uncertainties when using the Whittle likelihood and include checks to prevent misuse with Poisson uncertainties.

Updated class diagram for Statistic enum

classDiagram
    class Statistic {
      <<enumeration>>
      chi2
      cstat
      pstat
      pgstat
      wstat
      whittle
    }

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File-Level Changes

Change	Details	Files
Added support for the 'whittle' statistic for power spectrum data, including a new BetterExponential distribution and a whittle likelihood function.	Added 'whittle' to the Statistic type. Implemented BetterExponential distribution. Created whittle likelihood function for power spectrum data. Added 'whittle' to the available likelihood functions. Modified get_counts_data to handle 'whittle' statistic. Modified simulator_factory to handle exponential distributions for 'whittle' statistic. Updated simulate to include the whittle likelihood function. Modified get_stat to include checks for the 'whittle' statistic. Updated _pearson_residuals to handle 'whittle' statistic. Updated quantile_residuals_mle to assert that the residuals are finite. Updated pit to handle 'whittle' statistic.	src/elisa/infer/likelihood.py src/elisa/infer/helper.py src/elisa/plot/data.py src/elisa/infer/fit.py

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[sourcery-ai]

Hey @wcxve - I've reviewed your changes - here's some feedback:

Overall Comments:

• Consider adding a unit test for the new whittle likelihood function to ensure it behaves as expected.
• The BetterExponential class includes a goodness-of-fit term; it would be helpful to add a comment explaining the purpose of this term.

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Documentation: all looks good

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[wcxve]

@codecov-ai-reviewer review

[codecov-ai]

On it! We are reviewing the PR and will provide feedback shortly.

[codecov-ai]

No changes requiring review at this time.