Add performance benchmarking scripts

[EwoutH]

This is a cherry pick from #1978, without the CI. This can be reviewed and merged first, and then the CI can be built on top.

General usage (once this is merged to main):

1. Go to the main branch.
2. Run global_benchmarks.py
3. Go to your development branch
4. Run global_benchmarks.py again
5. Run compare_timings.py

It should output a table like this:

Model	Size	Init time [95% CI]	Run time [95% CI]
SchellingModel	small	🔵 +5.7% [+1.7%, +10.8%]	🔵 +5.8% [+1.1%, +12.0%]
SchellingModel	large	🔵 -1.0% [-2.1%, -0.2%]	🔵 -0.5% [-2.1%, +1.2%]
WolfSheep	small	🔵 +0.0% [-0.6%, +0.6%]	🔵 -0.0% [-0.5%, +0.4%]
WolfSheep	large	🔴 +211.2% [+172.4%, +255.0%]	🔵 +4.6% [-8.0%, +22.4%]
BoidFlockers	small	🔵 +0.1% [-9.7%, +11.6%]	🔵 +3.1% [-0.7%, +7.4%]
BoidFlockers	large	🔵 -11.9% [-25.2%, -1.8%]	🟢 -18.2% [-31.6%, -4.7%]

Positive indicates increate in runtime, negative a decrease. As for the colors:
If the 95% confidence interval is

• fully below -3%: 🟢
• fully above +3%: 🔴
• else: 🔵

Open points

1. I'm not 100% sure the seeds work correctly. Especially the fluctuations in WolfSheep are unexpectedly large sometimes. I questions if the runs are 100% deterministic. If someone want to dive in, please.
2. The models are copied from ABMFrameworksComparison and not following our own code standards for Black and pre-commit. I'm thinking of excluding them from it.
3. I'm using bootstrapping to circumvent small sample sizes. If someone with a statistics background can say if it's good enough for this use case, please do.

Curious if this is useful and what everybody thinks!

[rht]

It makes sense to decouple the CI issue from the rest, which already works.

[rht]

I fixed the Ruff and Codespell issues, and am merging this PR as a checkpoint.

[EwoutH]

Thanks for the fixes and merging!