🩹 Transform standard error if parameter has non_negative constraint

[jsnel]

Correctly report standard error in the case that non_negative contraint is used.

Change summary

• 👌Improve reporting of standard error in case of non_negative constraint in the parameter

Checklist

• ✔️ Passing the tests (mandatory for all PR's)
• 🚧 Added changes to changelog (mandatory for all PR's)
• 👌 Closes issue (mandatory for ✨ feature and 🩹 bug fix PR's)
• 🧪 Adds new tests for the feature (mandatory for ✨ feature and 🩹 bug fix PR's)
• 📚 Adds documentation of the feature

[github-actions]

👈 Launch a binder notebook on branch jsnel/pyglotaran/fix/correct_t_values_with_non_negative_params

[github-actions]

Benchmark is done. Checkout the benchmark result page.
Benchmark differences below 5% might be due to CI noise.

Benchmark diff v0.7.0 vs. main

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [9adf4a49]       [1e583f11]
     <v0.7.0>                   
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, False, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, False, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, True, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, True, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, False, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, False, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, True, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, True, True)
             216M             219M     1.01  IntegrationTwoDatasets.peakmem_optimize
       1.60±0.04s       1.58±0.08s     0.98  IntegrationTwoDatasets.time_optimize

Benchmark diff main vs. PR

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [1e583f11]       [2a278b6c]
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, False, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, False, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, True, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(False, True, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, False, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, False, True)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, True, False)
           failed           failed      n/a  BenchmarkOptimize.time_optimize(True, True, True)
             219M             221M     1.01  IntegrationTwoDatasets.peakmem_optimize
       1.58±0.08s       1.58±0.03s     1.00  IntegrationTwoDatasets.time_optimize

[codecov]

Codecov Report

Patch coverage: 71.4% and project coverage change: -0.1 ⚠️

Comparison is base (1e583f1) 88.5% compared to head (2a278b6) 88.5%.

Additional details and impacted files

@@           Coverage Diff           @@
##            main   #1320     +/-   ##
=======================================
- Coverage   88.5%   88.5%   -0.1%     
=======================================
  Files        107     107             
  Lines       5122    5128      +6     
  Branches     960     962      +2     
=======================================
+ Hits        4538    4542      +4     
- Misses       470     471      +1     
- Partials     114     115      +1     

Impacted Files	Coverage Δ
glotaran/optimization/optimizer.py	80.8% <71.4%> (-0.8%)	⬇️

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[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
0.0% Duplication

[ism200]

thanks

[s-weigand]

LGTM 👍