Add PixelResponseNonUniformity effect for fixed per-pixel gain variation (PRNU)

[ShannonS00]

Summary

• Adds a new PixelResponseNonUniformity effect in effects/electronics/noise.py that models per-pixel gain
  variation (PRNU) by multiplying each pixel by a (per pixel) gain factor drawn from N(1, prnu_std)
• The gain map is generated once per detector and cached, reflecting that PRNU
  is a fixed physical property of the detector chip
• prnu_std accepts a scalar float or a dict keyed by detector ID, for later implementation of different values per detector type (e.g. H2RG & GeoSnap) in the instrument packages
• Defaults set to prnu_std=0.01 (1%)

[codecov]

Codecov Report

❌ Patch coverage is 79.41176% with 7 lines in your changes missing coverage. Please review.
✅ Project coverage is 74.86%. Comparing base (02976b3) to head (be4928b).
⚠️ Report is 9 commits behind head on main.

Files with missing lines	Patch %	Lines
scopesim/effects/electronic/noise.py	79.41%	7 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #886      +/-   ##
==========================================
+ Coverage   74.82%   74.86%   +0.03%     
==========================================
  Files          69       69              
  Lines        8869     8903      +34     
==========================================
+ Hits         6636     6665      +29     
- Misses       2233     2238       +5     

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[teutoburg]

Please add:

• A more meaningful title (PR titles are part of the documentation as they become changelog entries)
• Some unit tests to to verify that this is doing what it should (or indeed runs at all)

I'm drafting this in the meantime...

[teutoburg]

Would this fully close your #885?

[oczoske]

Ideas for further tests (to improve patch coverage):

•    with pytest.raises(TypeError):
            PixelResponseNonUniformity(prnu_std="not a number nor a dict")
  

• before an apply_to:

    with pytest.raises(RuntimeError):
             prnu.plot()

• after an apply_to:

      assert isinstance(prnu.plot(), plt.Figure)

[oczoske]

Not sure it's a gain variation or rather a QE variation or a combination of both. But let's not split hairs.