Improve ordering in k_matrix involved_compartments function

[jsnel]

Avoiding the use of set to improve ordering

Refactor at least guarantees better than random ordering using list(set(x))

TODO: further improve ordering to use initial_concentrations ordering

Checklist

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

Closes issues

closes #787

[github-actions]

👈 Launch a binder notebook on branch jsnel/pyglotaran/fix/787_ordered_involved_compartments

[codecov]

Codecov Report

Merging #788 (2a76a0b) into staging (dd0ce80) will increase coverage by 0.0%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##           staging    #788   +/-   ##
=======================================
  Coverage     83.9%   83.9%           
=======================================
  Files           75      75           
  Lines         4185    4186    +1     
  Branches       752     754    +2     
=======================================
+ Hits          3512    3513    +1     
  Misses         538     538           
  Partials       135     135           

Impacted Files	Coverage Δ
glotaran/builtin/megacomplexes/decay/k_matrix.py	94.9% <100.0%> (+<0.1%)	⬆️

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[joernweissenborn]

This should work for now.

Will be revised in 0.6

[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
7 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

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [dc00e6da]       [2a76a0bd]
     <v0.4.0>                   
-        44.0±1ms       33.7±0.6ms     0.77  BenchmarkOptimize.time_optimize(False, False, False)
-         244±4ms         40.2±2ms     0.17  BenchmarkOptimize.time_optimize(False, False, True)
-      63.2±0.8ms       55.0±0.8ms     0.87  BenchmarkOptimize.time_optimize(False, True, False)
-        64.6±1ms         57.1±1ms     0.88  BenchmarkOptimize.time_optimize(False, True, True)
         45.6±2ms       41.7±0.6ms     0.92  BenchmarkOptimize.time_optimize(True, False, False)
-         241±2ms        51.8±20ms     0.21  BenchmarkOptimize.time_optimize(True, False, True)
         64.6±1ms       64.9±0.4ms     1.00  BenchmarkOptimize.time_optimize(True, True, False)
         68.4±3ms         70.1±3ms     1.02  BenchmarkOptimize.time_optimize(True, True, True)
             179M             177M     0.99  IntegrationTwoDatasets.peakmem_create_result
             195M             193M     0.99  IntegrationTwoDatasets.peakmem_optimize
-         207±3ms          170±3ms     0.82  IntegrationTwoDatasets.time_create_result
-      4.35±0.01s        1.59±0.1s     0.36  IntegrationTwoDatasets.time_optimize