🩹 Fix coherent artifact crash for index dependent models (Sourcery refactored)

[sourcery-ai]

Pull Request #808 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:

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   the original Pull Request

   Incorporate changes via command line

   git fetch https://github.com/glotaran/pyglotaran pull/808/head
   git merge --ff-only FETCH_HEAD
   git push

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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.

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[github-actions]

👈 Launch a binder notebook on branch glotaran/pyglotaran/sourcery/pull-808

[sourcery-ai]

Sourcery Code Quality Report

Merging this PR leaves code quality unchanged.

Quality metrics	Before	After	Change
Complexity	6.49 ⭐	6.49 ⭐	0.00
Method Length	68.67 🙂	68.67 🙂	0.00
Working memory	9.68 🙂	9.68 🙂	0.00
Quality	63.27% 🙂	63.27% 🙂	0.00%

Other metrics	Before	After	Change
Lines	1505	1505	0

Changed files	Quality Before	Quality After	Quality Change
glotaran/analysis/problem.py	80.36% ⭐	80.36% ⭐	0.00%
glotaran/analysis/problem_grouped.py	57.20% 🙂	57.20% 🙂	0.00%
glotaran/analysis/problem_ungrouped.py	67.75% 🙂	67.75% 🙂	0.00%
glotaran/analysis/simulation.py	64.20% 🙂	64.20% 🙂	0.00%
glotaran/analysis/test/test_optimization.py	34.60% 😞	34.60% 😞	0.00%

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

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
glotaran/analysis/test/test_optimization.py	test_optimization	19 😞	509 ⛔	15 😞	25.13% 😞	Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
glotaran/analysis/problem_grouped.py	GroupedProblem._append_to_grouped_bag	12 🙂	269 ⛔	23 ⛔	29.09% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/analysis/problem_ungrouped.py	UngroupedProblem._calculate_residual	13 🙂	214 ⛔	12 😞	41.40% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/analysis/problem_grouped.py	GroupedProblem.init_bag	10 🙂	174 😞	16 ⛔	41.86% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/analysis/problem.py	Problem._add_weight	15 🙂	134 😞	11 😞	48.74% 😞	Try splitting into smaller methods. Extract out complex expressions

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[sonarcloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
1 Code Smell

No Coverage information
0.0% Duplication

[codecov]

Codecov Report

Merging #810 (619bd4f) into staging (9892b6e) will increase coverage by 0.0%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##           staging    #810   +/-   ##
=======================================
  Coverage     84.5%   84.5%           
=======================================
  Files           75      75           
  Lines         4198    4201    +3     
  Branches       756     757    +1     
=======================================
+ Hits          3550    3553    +3     
  Misses         515     515           
  Partials       133     133           

Impacted Files	Coverage Δ
glotaran/analysis/problem.py	90.9% <100.0%> (ø)
glotaran/analysis/problem_grouped.py	96.0% <100.0%> (ø)
glotaran/analysis/problem_ungrouped.py	93.8% <100.0%> (ø)
glotaran/analysis/simulation.py	82.9% <100.0%> (ø)
...coherent_artifact/coherent_artifact_megacomplex.py	73.5% <100.0%> (+1.5%)	⬆️
glotaran/model/dataset_model.py	82.0% <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

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [dc00e6da]       [619bd4f4]
     <v0.4.0>                   
-      43.1±0.3ms       31.7±0.2ms     0.74  BenchmarkOptimize.time_optimize(False, False, False)
-       246±0.4ms       37.4±0.7ms     0.15  BenchmarkOptimize.time_optimize(False, False, True)
-      63.1±0.4ms       52.6±0.3ms     0.83  BenchmarkOptimize.time_optimize(False, True, False)
       65.1±0.2ms       57.1±0.8ms    ~0.88  BenchmarkOptimize.time_optimize(False, True, True)
       43.0±0.3ms       41.6±0.5ms     0.97  BenchmarkOptimize.time_optimize(True, False, False)
-       246±0.8ms        47.1±30ms     0.19  BenchmarkOptimize.time_optimize(True, False, True)
       63.0±0.3ms       63.9±0.3ms     1.01  BenchmarkOptimize.time_optimize(True, True, False)
       65.0±0.3ms        86.8±30ms    ~1.34  BenchmarkOptimize.time_optimize(True, True, True)
             179M             180M     1.00  IntegrationTwoDatasets.peakmem_create_result
             197M             199M     1.01  IntegrationTwoDatasets.peakmem_optimize
-         205±1ms          164±3ms     0.80  IntegrationTwoDatasets.time_create_result
-      4.29±0.02s        1.51±0.1s     0.35  IntegrationTwoDatasets.time_optimize

[s-weigand]

See #811