👌🎨 Wrap model section in result markdown in details tag for notebooks

[s-weigand]

This change makes the markdown rendering of the result in notebooks/ipython less overwhelming by hiding the model in a details tag which can be expanded.

Requested by @ism200

Before

After

Hidden

Expanded

Change summary

• 👌🎨 Wrap model section in result markdown in details tag for notebooks
• 🧪 Added test for details tag in ipython render but not in call to markdown

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)

[sourcery-ai]

Sourcery Code Quality Report

❌  Merging this PR will decrease code quality in the affected files by 1.84%.

Quality metrics	Before	After	Change
Complexity	2.90 ⭐	3.34 ⭐	0.44 👎
Method Length	41.40 ⭐	43.65 ⭐	2.25 👎
Working memory	6.06 ⭐	6.41 🙂	0.35 👎
Quality	80.35% ⭐	78.51% ⭐	-1.84% 👎

Other metrics	Before	After	Change
Lines	343	358	15

Changed files	Quality Before	Quality After	Quality Change
glotaran/project/result.py	76.96% ⭐	73.98% 🙂	-2.98% 👎
glotaran/project/test/test_result.py	83.60% ⭐	82.78% ⭐	-0.82% 👎

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

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
glotaran/project/result.py	Result.markdown	10 🙂	201 😞	14 😞	42.01% 😞	Try splitting into smaller methods. Extract out complex expressions

Legend and Explanation

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• 😞 poor
• ⛔ very poor

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

---

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[github-actions]

👈 Launch a binder notebook on branch s-weigand/pyglotaran/shorter-result-markdown

[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]       [ff66d8ae]
     <v0.6.0>                   
         69.1±1ms       71.8±0.9ms     1.04  BenchmarkOptimize.time_optimize(False, False, False)
         117±40ms         75.8±5ms    ~0.65  BenchmarkOptimize.time_optimize(False, False, True)
         70.0±2ms         70.2±2ms     1.00  BenchmarkOptimize.time_optimize(False, True, False)
         117±40ms        93.9±40ms    ~0.80  BenchmarkOptimize.time_optimize(False, True, True)
         89.1±1ms         85.3±3ms     0.96  BenchmarkOptimize.time_optimize(True, False, False)
         95.8±2ms         91.5±2ms     0.96  BenchmarkOptimize.time_optimize(True, False, True)
         87.9±5ms         85.8±2ms     0.98  BenchmarkOptimize.time_optimize(True, True, False)
        96.2±30ms        94.1±30ms     0.98  BenchmarkOptimize.time_optimize(True, True, True)
             207M             205M     0.99  IntegrationTwoDatasets.peakmem_optimize
       2.20±0.03s       2.14±0.07s     0.97  IntegrationTwoDatasets.time_optimize

Benchmark diff main vs. PR

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [ff66d8ae]       [fd99c000]
       71.8±0.9ms         72.3±2ms     1.01  BenchmarkOptimize.time_optimize(False, False, False)
         75.8±5ms         161±50ms    ~2.13  BenchmarkOptimize.time_optimize(False, False, True)
         70.2±2ms         69.2±1ms     0.99  BenchmarkOptimize.time_optimize(False, True, False)
        93.9±40ms         142±40ms    ~1.51  BenchmarkOptimize.time_optimize(False, True, True)
         85.3±3ms         85.0±1ms     1.00  BenchmarkOptimize.time_optimize(True, False, False)
         91.5±2ms         94.4±6ms     1.03  BenchmarkOptimize.time_optimize(True, False, True)
         85.8±2ms         91.1±2ms     1.06  BenchmarkOptimize.time_optimize(True, True, False)
        94.1±30ms        96.4±10ms     1.02  BenchmarkOptimize.time_optimize(True, True, True)
             205M             209M     1.02  IntegrationTwoDatasets.peakmem_optimize
       2.14±0.07s       2.17±0.03s     1.01  IntegrationTwoDatasets.time_optimize

[jsnel]

Requesting a different name for the model_is_detail option. Either model_in_detail' or something like compact_view` or something.

[codecov]

Codecov Report

Merging #1098 (fd99c00) into main (ff66d8a) will increase coverage by 0.0%.
The diff coverage is 100.0%.

@@          Coverage Diff          @@
##            main   #1098   +/-   ##
=====================================
  Coverage   87.1%   87.1%           
=====================================
  Files        101     101           
  Lines       5339    5341    +2     
  Branches     995     996    +1     
=====================================
+ Hits        4654    4656    +2     
  Misses       530     530           
  Partials     155     155           

Impacted Files	Coverage Δ
glotaran/project/result.py	90.8% <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
0.7% Duplication