🩹Fix for ufunc with more than 32 operands (Sourcery refactored)

[sourcery-ai]

Pull Request #1184 refactored by Sourcery.

Since the original Pull Request was opened as a fork in a contributor's
repository, we are unable to create a Pull Request branching from it.

To incorporate these changes, you can either:

1. Merge this Pull Request instead of the original, or

2. Ask your contributor to locally incorporate these commits and push them to
   the original Pull Request

   Incorporate changes via command line

   git fetch https://github.com/glotaran/pyglotaran pull/1184/head
   git merge --ff-only FETCH_HEAD
   git push

NOTE: As code is pushed to the original Pull Request, Sourcery will
re-run and update (force-push) this Pull Request with new refactorings as
necessary. If Sourcery finds no refactorings at any point, this Pull Request
will be closed automatically.

See our documentation here.

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[sourcery-ai]

Sourcery Code Quality Report

❌  Merging this PR will decrease code quality in the affected files by 0.09%.

Quality metrics	Before	After	Change
Complexity	5.11 ⭐	5.10 ⭐	-0.01 👍
Method Length	52.61 ⭐	53.07 ⭐	0.46 👎
Working memory	8.89 🙂	8.89 🙂	0.00
Quality	68.17% 🙂	68.08% 🙂	-0.09% 👎

Other metrics	Before	After	Change
Lines	616	633	17

Changed files	Quality Before	Quality After	Quality Change
glotaran/optimization/data_provider.py	68.17% 🙂	68.08% 🙂	-0.09% 👎

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

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
glotaran/optimization/data_provider.py	DataProvider.__init__	8 ⭐	229 ⛔	16 ⛔	39.60% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/data_provider.py	DataProviderLinked.align_weights	16 🙂	142 😞	10 😞	48.91% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/data_provider.py	DataProvider.add_model_weight	7 ⭐	149 😞	12 😞	52.50% 🙂	Try splitting into smaller methods. Extract out complex expressions
glotaran/optimization/data_provider.py	DataProviderLinked.align_groups	3 ⭐	120 😞	8 🙂	67.66% 🙂	Try splitting into smaller methods
glotaran/optimization/data_provider.py	DataProvider.get_axis_slice_from_interval	3 ⭐	82 🙂	10 😞	68.57% 🙂	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 glotaran/pyglotaran/sourcery/pull-1184

[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
0.0% Duplication

[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]       [86e01909]
     <v0.6.0>                   
!      66.2±0.3ms           failed      n/a  BenchmarkOptimize.time_optimize(False, False, False)
!       73.6±30ms           failed      n/a  BenchmarkOptimize.time_optimize(False, False, True)
!        68.3±1ms           failed      n/a  BenchmarkOptimize.time_optimize(False, True, False)
!       80.1±40ms           failed      n/a  BenchmarkOptimize.time_optimize(False, True, True)
!        83.0±1ms           failed      n/a  BenchmarkOptimize.time_optimize(True, False, False)
!       91.0±30ms           failed      n/a  BenchmarkOptimize.time_optimize(True, False, True)
!        93.2±9ms           failed      n/a  BenchmarkOptimize.time_optimize(True, True, False)
!        115±40ms           failed      n/a  BenchmarkOptimize.time_optimize(True, True, True)
             206M             206M     1.00  IntegrationTwoDatasets.peakmem_optimize
-      2.24±0.04s       1.47±0.02s     0.66  IntegrationTwoDatasets.time_optimize

Benchmark diff main vs. PR

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [86e01909]       [6f5bc385]
           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)
             206M             208M     1.01  IntegrationTwoDatasets.peakmem_optimize
       1.47±0.02s       1.48±0.01s     1.01  IntegrationTwoDatasets.time_optimize