Feature: Damped Oscillation Megacomplex

[joernweissenborn]

This PR adds the DampedOscillationMegacomplex model to pyglotaran.

Change summary

• Add DampedOscillationMegacomplex as type damped-oscillation
• Small refactor in analysis.util and builtin/megacomplex/decay

Checklist

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

[github-actions]

👈 Launch a binder notebook on branch joernweissenborn/pyglotaran/feature/damped_osc_megacmplx

[codecov]

Codecov Report

Merging #764 (239df81) into staging (d2b297b) will increase coverage by 0.0%.
The diff coverage is 86.2%.

@@           Coverage Diff           @@
##           staging    #764   +/-   ##
=======================================
  Coverage     83.9%   83.9%           
=======================================
  Files           73      75    +2     
  Lines         4089    4185   +96     
  Branches       736     752   +16     
=======================================
+ Hits          3431    3512   +81     
- Misses         528     538   +10     
- Partials       130     135    +5     

Impacted Files	Coverage Δ
...n/builtin/megacomplexes/decay/decay_megacomplex.py	82.5% <ø> (-0.3%)	⬇️
...mped_oscillation/damped_oscillation_megacomplex.py	83.5% <83.5%> (ø)
glotaran/analysis/util.py	90.9% <100.0%> (+<0.1%)	⬆️
...iltin/megacomplexes/damped_oscillation/__init__.py	100.0% <100.0%> (ø)
glotaran/builtin/megacomplexes/decay/irf.py	66.2% <100.0%> (+1.9%)	⬆️
glotaran/builtin/megacomplexes/decay/util.py	70.3% <100.0%> (ø)
glotaran/model/model.py	85.6% <100.0%> (ø)

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[lgtm-com]

This pull request fixes 3 alerts when merging a6c90f7 into 5efcf0c - view on LGTM.com

fixed alerts:

• 2 for Unused local variable
• 1 for Comparison using is when operands support `__eq__`

[sonarcloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
0.0% Duplication

[sourcery-ai]

Sourcery Code Quality Report

✅  Merging this PR will increase code quality in the affected files by 3.00%.

Quality metrics	Before	After	Change
Complexity	4.78 ⭐	3.43 ⭐	-1.35 👍
Method Length	71.62 🙂	70.93 🙂	-0.69 👍
Working memory	9.52 🙂	9.13 🙂	-0.39 👍
Quality	64.39% 🙂	67.39% 🙂	3.00% 👍

Other metrics	Before	After	Change
Lines	585	606	21

Changed files	Quality Before	Quality After	Quality Change
glotaran/analysis/util.py	62.73% 🙂	67.90% 🙂	5.17% 👍
glotaran/analysis/test/models.py	66.10% 🙂	66.88% 🙂	0.78% 👍

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

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
glotaran/analysis/util.py	apply_relations	8 ⭐	121 😞	10 😞	58.41% 🙂	Try splitting into smaller methods. Extract out complex expressions
glotaran/analysis/util.py	find_overlap	8 ⭐	71 🙂	13 😞	60.15% 🙂	Extract out complex expressions
glotaran/analysis/util.py	retrieve_clps	7 ⭐	104 🙂	10 😞	61.56% 🙂	Extract out complex expressions
glotaran/analysis/util.py	calculate_matrix	8 ⭐	80 🙂	10 😞	64.01% 🙂	Extract out complex expressions
glotaran/analysis/util.py	combine_matrix	5 ⭐	82 🙂	10 😞	66.60% 🙂	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!

[jsnel]

Reviewed ok, tested against the beta doas example found here at the time of the review.

[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]       [239df816]
     <v0.4.0>                   
-      57.1±0.3ms       40.8±0.5ms     0.71  BenchmarkOptimize.time_optimize(False, False, False)
-         324±4ms         52.0±1ms     0.16  BenchmarkOptimize.time_optimize(False, False, True)
-        82.3±1ms       70.1±0.2ms     0.85  BenchmarkOptimize.time_optimize(False, True, False)
         85.4±1ms         81.3±1ms     0.95  BenchmarkOptimize.time_optimize(False, True, True)
       57.9±0.9ms       55.2±0.5ms     0.95  BenchmarkOptimize.time_optimize(True, False, False)
-         323±3ms        68.1±20ms     0.21  BenchmarkOptimize.time_optimize(True, False, True)
         83.2±1ms         89.5±3ms     1.08  BenchmarkOptimize.time_optimize(True, True, False)
         85.0±2ms         96.8±3ms    ~1.14  BenchmarkOptimize.time_optimize(True, True, True)
             182M             181M     0.99  IntegrationTwoDatasets.peakmem_create_result
             197M             198M     1.00  IntegrationTwoDatasets.peakmem_optimize
          268±4ms          211±4ms    ~0.79  IntegrationTwoDatasets.time_create_result
-         5.45±0s       1.68±0.05s     0.31  IntegrationTwoDatasets.time_optimize