1/ As someone who enjoys, thinks constantly about, and almost romanticizes tool building, I really liked this article by @mZargham and @gablerjonathan. It talks about something that's been bothering me for a while and I agree with: there's a tooling gap.https://medium.com/block-science/cadcad-filling-a-critical-gap-in-open-source-data-science-fcd0d3faa8ed …
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2/ I wrote my dissertation's ABM in Cythonized Python. But, it was a bit weird in that, the agents weren't implemented via a classical object-oriented way. Instead, I used numpy arrays with agent IDs.
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3/ The way I wrote it I left myself room to turn it into something that looked like classical OO programming — think Django's data descriptor model but with numpy data representation. Then, I could "cleverly" compile it to a computational graph via tf or JAX. (Didn't get to it.)
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4/ Why is this useful? Because iterative and interactive programming and analysis is incomparably more productive. Executing and interrogating simulations in a
@ProjectJupyter with a wonderful experience.1 reply 0 retweets 1 likeShow this thread -
5/ I think MASON / Repast / Swarm are great in that they're performant. But, inspection (even with JVM bridges to REPLs) feels too low resolution.
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@netlogo is pretty fantastic in that it's has "full" IDE and you can ship people your simulation really easily. But, again, interrogation is coarse. (Plus, I don't want to write in LOGO.)1 reply 0 retweets 1 likeShow this thread -
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@badnetworker and@JackieKazil did a great job with MESA. ABM's in Jupyter /#IPython — that feel's good! But, writing ABMs in python for richer models demands Cython (or sometimes happily@anacondainc's numba), which slows *me* down. https://mesa.readthedocs.io/en/master/2 replies 0 retweets 3 likesShow this thread -
I imagine that one day in the future parts of mesa might move to a lower level of are over written with an extensible package.
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