Improvements to application of clp_penalties (equal area)

[jsnel]

This fix reproduces the area penalty behavior that was in v0.4.1 when multiple datasets are involved.

The 'equal area penalty' is calculated on a per dataset basis.

Change summary

• Calculate in calculate_clp_penalties the clp penalties on a per dataset level

Checklist

• ✔️ Passing the tests (mandatory for all PR's)
• 👌 Closes issue (mandatory for ✨ feature and 🩹 bug fix PR's)

Closes issues

closes #800

[github-actions]

👈 Launch a binder notebook on branch jsnel/pyglotaran/refactor/800_clp_penalties_equal_area

[codecov]

Codecov Report

Merging #801 (b3e85b6) into staging (fe7d75c) will increase coverage by 0.0%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##           staging    #801   +/-   ##
=======================================
  Coverage     84.4%   84.5%           
=======================================
  Files           75      75           
  Lines         4195    4200    +5     
  Branches       755     756    +1     
=======================================
+ Hits          3544    3549    +5     
  Misses         518     518           
  Partials       133     133           

Impacted Files	Coverage Δ
glotaran/analysis/problem_grouped.py	96.0% <ø> (ø)
glotaran/analysis/problem_ungrouped.py	93.8% <ø> (ø)
glotaran/model/clp_penalties.py	31.9% <ø> (ø)
glotaran/analysis/util.py	91.3% <100.0%> (+0.3%)	⬆️

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[joernweissenborn]

@jsnel i officially grant you code ownership of this function 😉

[lgtm-com]

This pull request introduces 1 alert when merging b309dd0 into fe7d75c - view on LGTM.com

new alerts:

• 1 for Wrong number of arguments in a call

[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
9 Code Smells

No Coverage information
0.0% Duplication

[sourcery-ai]

Sourcery Code Quality Report

❌  Merging this PR will decrease code quality in the affected files by 0.28%.

Quality metrics	Before	After	Change
Complexity	6.22 ⭐	6.24 ⭐	0.02 👎
Method Length	80.66 🙂	81.92 🙂	1.26 👎
Working memory	10.38 😞	10.41 😞	0.03 👎
Quality	62.84% 🙂	62.56% 🙂	-0.28% 👎

Other metrics	Before	After	Change
Lines	1070	1105	35

Changed files	Quality Before	Quality After	Quality Change
glotaran/analysis/problem_grouped.py	57.20% 🙂	57.20% 🙂	0.00%
glotaran/analysis/problem_ungrouped.py	67.78% 🙂	67.75% 🙂	-0.03% 👎
glotaran/analysis/util.py	67.90% 🙂	66.64% 🙂	-1.26% 👎

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

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
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_grouped.py	GroupedProblem._index_independent_residual	8 ⭐	137 😞	14 😞	49.89% 😞	Try splitting into smaller methods. Extract out complex expressions
glotaran/analysis/problem_grouped.py	GroupedProblem._index_dependent_residual	8 ⭐	123 😞	15 😞	50.28% 🙂	Try splitting into smaller methods. Extract out complex expressions

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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.

---

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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]       [b3e85b61]
     <v0.4.0>                   
-     42.7±0.09ms       31.8±0.1ms     0.74  BenchmarkOptimize.time_optimize(False, False, False)
-       245±0.9ms       37.0±0.4ms     0.15  BenchmarkOptimize.time_optimize(False, False, True)
-      63.1±0.4ms       52.6±0.3ms     0.83  BenchmarkOptimize.time_optimize(False, True, False)
-      64.8±0.3ms       56.2±0.3ms     0.87  BenchmarkOptimize.time_optimize(False, True, True)
       42.9±0.1ms       41.0±0.3ms     0.96  BenchmarkOptimize.time_optimize(True, False, False)
-       244±0.7ms        79.5±40ms     0.33  BenchmarkOptimize.time_optimize(True, False, True)
       62.8±0.3ms       63.4±0.3ms     1.01  BenchmarkOptimize.time_optimize(True, True, False)
       64.8±0.3ms        67.4±40ms     1.04  BenchmarkOptimize.time_optimize(True, True, True)
             179M             177M     0.98  IntegrationTwoDatasets.peakmem_create_result
             196M             192M     0.98  IntegrationTwoDatasets.peakmem_optimize
-         201±2ms          159±2ms     0.79  IntegrationTwoDatasets.time_create_result
-      4.34±0.03s       1.61±0.06s     0.37  IntegrationTwoDatasets.time_optimize