🩹 Disallow xarray 0.21.0 in setup.cfg

[s-weigand]

Currently our ASV workflow fails since xarray==0.21.0 had a mishap forgetting to add packaging as a dependency.
Happens to the best of us, especially since packaging piggybacks on pytest so you won't see this error in any test environment, and even in our docs something added it, so they pass.

Anyway, to have our benchmarks work and go on with our stuff, let's just disallow this version until everything is sorted out.

If xarray decides to make a post-release, we can simply revert this change. If they rather choose to make a bugfix release it will stay in.

Change summary

• 🩹 Disallow xarray 0.21.0

Checklist

• ✔️ Passing the tests (mandatory for all PR's)

[github-actions]

👈 Launch a binder notebook on branch s-weigand/pyglotaran/disallow-xarray-0.21.0

[sonarcloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
0 Security Hotspots
0 Code Smells

No Coverage information
No Duplication information

[codecov]

Codecov Report

Merging #976 (c915d45) into main (a7f67d4) will not change coverage.
The diff coverage is n/a.

@@          Coverage Diff          @@
##            main    #976   +/-   ##
=====================================
  Coverage   85.6%   85.6%           
=====================================
  Files         90      90           
  Lines       4883    4883           
  Branches     916     916           
=====================================
  Hits        4181    4181           
  Misses       552     552           
  Partials     150     150           

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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 v0.5.1 vs. main

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [96b42630]       [c915d45d]
     <v0.5.1>                   
       62.7±0.8ms         64.0±2ms     1.02  BenchmarkOptimize.time_optimize(False, False, False)
        81.4±40ms        94.7±30ms    ~1.16  BenchmarkOptimize.time_optimize(False, False, True)
         64.8±2ms         60.0±1ms     0.93  BenchmarkOptimize.time_optimize(False, True, False)
        68.7±20ms         66.5±3ms     0.97  BenchmarkOptimize.time_optimize(False, True, True)
         78.7±2ms         76.3±3ms     0.97  BenchmarkOptimize.time_optimize(True, False, False)
        94.7±30ms        83.2±40ms    ~0.88  BenchmarkOptimize.time_optimize(True, False, True)
         75.6±1ms         77.7±1ms     1.03  BenchmarkOptimize.time_optimize(True, True, False)
        82.5±40ms        92.7±40ms    ~1.12  BenchmarkOptimize.time_optimize(True, True, True)
             203M             205M     1.01  IntegrationTwoDatasets.peakmem_optimize
       2.17±0.05s       1.99±0.05s     0.92  IntegrationTwoDatasets.time_optimize

Benchmark diff main vs. PR

Parametrized benchmark signatures:

BenchmarkOptimize.time_optimize(index_dependent, grouped, weight)

All benchmarks:

       before           after         ratio
     [a7f67d4a]       [c915d45d]
         62.3±2ms         64.0±2ms     1.03  BenchmarkOptimize.time_optimize(False, False, False)
         67.6±3ms        94.7±30ms    ~1.40  BenchmarkOptimize.time_optimize(False, False, True)
         64.5±1ms         60.0±1ms     0.93  BenchmarkOptimize.time_optimize(False, True, False)
        98.3±40ms         66.5±3ms    ~0.68  BenchmarkOptimize.time_optimize(False, True, True)
         73.5±2ms         76.3±3ms     1.04  BenchmarkOptimize.time_optimize(True, False, False)
        84.6±30ms        83.2±40ms     0.98  BenchmarkOptimize.time_optimize(True, False, True)
       74.8±0.8ms         77.7±1ms     1.04  BenchmarkOptimize.time_optimize(True, True, False)
         106±30ms        92.7±40ms    ~0.87  BenchmarkOptimize.time_optimize(True, True, True)
             201M             205M     1.02  IntegrationTwoDatasets.peakmem_optimize
       2.02±0.05s       1.99±0.05s     0.98  IntegrationTwoDatasets.time_optimize

[jsnel]

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