Random walk series for plot benchmarks

[nikolausWest]

Adds a "Gaussian Random Walk" method to the plot dashboard benchmark.

Also fixes "sim_time" to be in seconds and be consistent with the frequency argument.

• Python:
  • just py-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000 --series-type gaussian-random-walk --save plot_gauss_py.rrd
  • just py-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000 --series-type sin-uniform --save plot_uniform_py.rrd
• Rust:
  • just rs-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000 --series-type gaussian-random-walk --save plot_gauss_rs.rrd
  • just rs-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000 --series-type sin-uniform --save plot_uniform_rs.rrd
• C++:
  • just cpp-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000 --series-type gaussian-random-walk --save plot_gauss_cpp.rrd
  • just cpp-plot-dashboard --num-plots 10 --num-series-per-plot 5 --num-points-per-series 5000 --freq 1000 --series-type sin-uniform --save plot_uniform_cpp.rrd

Checklist

• I have read and agree to Contributor Guide and the Code of Conduct
• I've included a screenshot or gif (if applicable)
• I have tested the web demo (if applicable):
  • Using newly built examples: app.rerun.io
  • Using examples from latest main build: app.rerun.io
  • Using full set of examples from nightly build: app.rerun.io
• The PR title and labels are set such as to maximize their usefulness for the next release's CHANGELOG

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[teh-cmc]

Saving a gaussian random walk to file

Args:

--num-plots 10
--num-series-per-plot 5
--num-points-per-series 5000
--freq 1000
--series-type gaussian-random-walk

Python:

logged 25550 scalars over 1.0s (freq=25543.508Hz, expected=50000.0Hz, load=754.952%)
logged 27350 scalars over 1.0s (freq=27339.616Hz, expected=50000.0Hz, load=236.44%)
logged 27950 scalars over 1.001s (freq=27932.918Hz, expected=50000.0Hz, load=214.696%)
logged 29350 scalars over 1.001s (freq=29331.363Hz, expected=50000.0Hz, load=205.755%)
logged 29850 scalars over 1.001s (freq=29832.375Hz, expected=50000.0Hz, load=207.829%)
logged 30250 scalars over 1.001s (freq=30207.163Hz, expected=50000.0Hz, load=201.035%)
logged 29800 scalars over 1.002s (freq=29752.806Hz, expected=50000.0Hz, load=227.308%)
logged 29900 scalars over 1.001s (freq=29878.672Hz, expected=50000.0Hz, load=231.528%)
logged 20000 scalars over 0.676s (freq=29582.716Hz, expected=50000.0Hz, load=211.143%)

Rust:

logged 50050 scalars over 1.000423729s (freq=50028.801Hz, expected=50000.000Hz, load=41.337%)
logged 50000 scalars over 1.000452354s (freq=49977.393Hz, expected=50000.000Hz, load=38.216%)
logged 50000 scalars over 1.000493239s (freq=49975.350Hz, expected=50000.000Hz, load=49.272%)
logged 50000 scalars over 1.000597015s (freq=49970.167Hz, expected=50000.000Hz, load=26.132%)
logged 49950 scalars over 1.000550035s (freq=49922.541Hz, expected=50000.000Hz, load=44.966%)

C++:

logged 50050 scalars over 1.00029s (freq=50035.6Hz, expected=50000Hz, load=72.1643%)
logged 50000 scalars over 1.00012s (freq=49993.9Hz, expected=50000Hz, load=92.0067%)
logged 50050 scalars over 1.00099s (freq=50000.6Hz, expected=50000Hz, load=73.7993%)
logged 50000 scalars over 1.00012s (freq=49994.2Hz, expected=50000Hz, load=58.2881%)
logged 49900 scalars over 0.998841s (freq=49957.9Hz, expected=50000Hz, load=77.5741%)