🩹 Fix matrix provider

[jsnel]

This PR is meant to fix an issue with grouping inadvertently introduced in PR #1175

Change summary

• 🩹 Fix the matrix provider alignment/reduction ('grouping') issues introduced in Move index dependent calculation to megacomplexes for speed-up #1175 (🩹 Fix matrix provider #1190)

Checklist

• ✔️ Passing the tests (mandatory for all PR's)
• 🚧 Added changes to changelog (mandatory for all PR's)

• 🧪 Adds new tests for the feature (mandatory for ✨ feature and 🩹 bug fix PR's)

Closes issues

closes #XXXX

[sourcery-ai]

Sourcery Code Quality Report

❌  Merging this PR will decrease code quality in the affected files by 0.94%.

Quality metrics	Before	After	Change
Complexity	7.21 ⭐	7.60 ⭐	0.39 👎
Method Length	63.91 🙂	64.91 🙂	1.00 👎
Working memory	9.30 🙂	9.38 🙂	0.08 👎
Quality	63.46% 🙂	62.52% 🙂	-0.94% 👎

Other metrics	Before	After	Change
Lines	692	703	11

Changed files	Quality Before	Quality After	Quality Change
glotaran/optimization/matrix_provider.py	63.46% 🙂	62.52% 🙂	-0.94% 👎

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

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
glotaran/optimization/matrix_provider.py	MatrixProvider.combine_megacomplex_matrices	19 😞	203 😞	11 😞	39.11% 😞	Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/matrix_provider.py	MatrixProvider.apply_relations	14 🙂	181 😞	13 😞	41.51% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/matrix_provider.py	MatrixProviderLinked.align_matrices	13 🙂	179 😞	12 😞	44.11% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/matrix_provider.py	MatrixProviderLinked.calculate_aligned_matrices	11 🙂	192 😞	12 😞	44.78% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/matrix_provider.py	MatrixProviderUnlinked.calculate_full_matrices	10 🙂	119 🙂	13 😞	51.60% 🙂	Extract out complex expressions

Legend and Explanation

The emojis denote the absolute quality of the code:

• ⭐ excellent
• 🙂 good
• 😞 poor
• ⛔ very poor

The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request.

---

Please see our documentation here for details on how these metrics are calculated.

We are actively working on this report - lots more documentation and extra metrics to come!

Help us improve this quality report!

[github-actions]

👈 Launch a binder notebook on branch jsnel/pyglotaran/experiment/fix_matrix_provider

[github-actions]

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

Benchmark diff v0.6.0 vs. main

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [6c3c390e]       [a1bdb839]
     <v0.6.0>                   
!      54.4±0.6ms           failed      n/a  BenchmarkOptimize.time_optimize(False, False, False)
!       58.7±30ms           failed      n/a  BenchmarkOptimize.time_optimize(False, False, True)
!      55.3±0.4ms           failed      n/a  BenchmarkOptimize.time_optimize(False, True, False)
!       60.7±30ms           failed      n/a  BenchmarkOptimize.time_optimize(False, True, True)
!        65.7±1ms           failed      n/a  BenchmarkOptimize.time_optimize(True, False, False)
!      71.3±0.8ms           failed      n/a  BenchmarkOptimize.time_optimize(True, False, True)
!        65.7±1ms           failed      n/a  BenchmarkOptimize.time_optimize(True, True, False)
!        72.0±6ms           failed      n/a  BenchmarkOptimize.time_optimize(True, True, True)
             203M             210M     1.03  IntegrationTwoDatasets.peakmem_optimize
-      1.97±0.06s       1.18±0.03s     0.60  IntegrationTwoDatasets.time_optimize

Benchmark diff main vs. PR

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [a1bdb839]       [5927e23f]
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, False, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, False, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, True, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, True, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, False, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, False, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, True, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, True, True)
             210M             205M     0.98  IntegrationTwoDatasets.peakmem_optimize
       1.18±0.03s       1.17±0.02s     0.99  IntegrationTwoDatasets.time_optimize

[codecov]

Codecov Report

Base: 87.6% // Head: 87.6% // Increases project coverage by +0.0% 🎉

Coverage data is based on head (5927e23) compared to base (a1bdb83).
Patch coverage: 100.0% of modified lines in pull request are covered.

Additional details and impacted files

@@          Coverage Diff          @@
##            main   #1190   +/-   ##
=====================================
  Coverage   87.6%   87.6%           
=====================================
  Files        104     104           
  Lines       4947    4954    +7     
  Branches     820     821    +1     
=====================================
+ Hits        4337    4344    +7     
  Misses       493     493           
  Partials     117     117           

Impacted Files	Coverage Δ
glotaran/optimization/matrix_provider.py	94.0% <100.0%> (+0.1%)	⬆️

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[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
No Duplication information

[ism200]

thank you for fixing the bug

[ism200]

thank you for fixing the bug

[s-weigand]

@jsnel Thanks a lot for debugging this
I tested it with the case study that brought the problem to light and this fix restores the results from before #1175

🎉