🚀🩹 v0.4.2 release preparation and bugfix backport for result consistency checking purposes

[jsnel]

Maintenance release v0.4.2 with bugfix backport for result consistency checking purposes

🩹🚧 Backport of bugfix #927 discovered in PR #860 related to initial_concentration normalization when saving results (#935).

It was found in #860 that the a_matrix when calculated for result saving was calculated without normalized initial_concentrations while the calculation for the optimization was (correctly) using the normalized version. Fixing this bug in #927 then breaks result consistency checking for some case studies, thus requiring backporting of this bugfix to the 0.4 maintenance branch which we continue to use for result/consistency checking.

Checklist

• ✔️ Passing the tests (mandatory for all PR's)
• 👌 Link to issue issue (mandatory for ✨ feature and 🩹 bug fix PR's)
• 🧪 Adds new tests for the feature (mandatory for ✨ feature and 🩹 bug fix PR's)
• 📚 Adds documentation of the feature

[github-actions]

👈 Launch a binder notebook on branch jsnel/pyglotaran/maintenance/v0.4-bugfix-a_matrix

[codecov]

Codecov Report

Merging #935 (8f5f91a) into maintenance/v0.4.1 (21ba272) will not change coverage.
The diff coverage is 100.0%.

@@                Coverage Diff                 @@
##           maintenance/v0.4.1    #935   +/-   ##
==================================================
  Coverage                81.8%   81.8%           
==================================================
  Files                      79      79           
  Lines                    3924    3924           
  Branches                  692     719   +27     
==================================================
  Hits                     3212    3212           
  Misses                    593     593           
  Partials                  119     119           

Impacted Files	Coverage Δ
glotaran/__init__.py	100.0% <100.0%> (ø)
...iltin/models/kinetic_image/kinetic_image_result.py	85.7% <100.0%> (ø)

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[github-actions]

Benchmark is done. Checkout the benchmark result page.
Benchmark differences below 5% might be due to CI noise.

Benchmark diff

· Did not find results for commit 2c2c6053920778ae7aff075f11333152af5d82fb

[jsnel]

The first test run of this PR ( a83bcb3) is expected to fail on the comparison results consistency check because a bugfix was committed which changes how the results are saved (the a_matrix and derived DAS in the results was previously saved using the normalized initial_concentrations, this was a bug fixed in this PR). After confirming this, the 'gold standard' can be updated to reflect these changes.

[update] Results confirming failing tests:

• Log files: logs_9176.zip
• pyglotaran_examples reference for result comparison

[sourcery-ai]

Sourcery Code Quality Report

Merging this PR leaves code quality unchanged.

Quality metrics	Before	After	Change
Complexity	0.67 ⭐	0.67 ⭐	0.00
Method Length	24.50 ⭐	24.50 ⭐	0.00
Working memory	4.33 ⭐	4.33 ⭐	0.00
Quality	91.44% ⭐	91.44% ⭐	0.00%

Other metrics	Before	After	Change
Lines	22	22	0

Changed files	Quality Before	Quality After	Quality Change
glotaran/init.py	91.44% ⭐	91.44% ⭐	0.00%

Here are some functions in these files that still need a tune-up:

File	Function	Complexity	Length	Working Memory	Quality	Recommendation

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[sonarcloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
0.0% Duplication

[jsnel]

After the update we find that the result comparison still fails but only for a singular data point in one of the examples (log below). Digging a little deeper (looking at the plot) we find that the point of failure is at a place were the spectral value in question is very small (~-1.497), compared to the overall maximum of the spectrum (~7500). However, the spectra are compared locally (per wavelength) and the absolute tolerance for the comparison between results is scaled with the local value of the data, meaning that the tolerance for a point close to zero is much tighter than the tolerance for a point near the maximum. This is a point where the result comparison could be further refined, but for now we accept this as a PASS ✔️ and this PR may be merged.

FAILED .github/test_result_consistency.py::test_result_data_var_consistency[study_fluorescence-decay_associated_spectra]

E       AssertionError: Result data_var data mismatch: 'decay_associated_spectra' in 'dataset1.nc'.
E         With sum of absolute difference: 0.0039020775839004873 and shape: (49, 5)
E         Mean difference: 1.592684728122648e-05
E         
E       assert False
E        +  where False = <function allclose.<locals>._allclose at 0x7f40599e5790>(<xarray.DataArray 'decay_associated_spectra' (spectral: 49, component: 5)>\narray([[-145.581345,   52.590453,   21.6330...7\n    rate      (component) float64 ...\n    lifetime  (component) float64 ...\nDimensions without coordinates: component, <xarray.DataArray 'decay_associated_spectra' (spectral: 49, component: 5)>\narray([[-145.581346,   52.590453,   21.6330...7\n    rate      (component) float64 ...\n    lifetime  (component) float64 ...\nDimensions without coordinates: component, atol=array([[1.73546487e-05, 6.26927054e-06, 2.57886253e-06, 2.17881660e-06,\n        6.32769907e-06],\n       [1.50005268e-0...     5.91504505e-06],\n       [5.92400043e-05, 4.45607905e-05, 4.22466039e-06, 1.01596965e-04,\n        6.42848991e-06]]), rtol=1e-05, print_fail=20)

.github/test_result_consistency.py:221: AssertionError
----------------------------- Captured stdout call -----------------------------
allclose first 1 failures:
  (40, 1): -1.4969902223364386 -1.4969635695838823
=========================== short test summary info ============================
FAILED .github/test_result_consistency.py::test_result_data_var_consistency[study_fluorescence-decay_associated_spectra]