[tonyarkles]

I've thought about that and have written some fun Haskell code in the past but... the other goal is to actually have users :D. I've also considered Lisp, Scheme, and friends to have really easily parseable ASTs.

I jest a bit, but there's a very rich ecosystem of really useful data analysis libraries with Python that do somewhat exist in other ecosystems (R, Julia, etc) but aren't nearly as... I would use the word polish, but a lot of the Python libraries have sharp edges as well. Well trodden might be a better word. My experience with doing heavy data analysis with Python and Julia is that both of them are often going to require some Googling to understand a weird pattern to accomplish something effectively but there's a much higher probability that you're going to find the answer quickly with Python.

I also don't really want to reinvent the universe on the first go.

It has occurred to me that it might be possible to do this in a style similar to org-mode though where it actually doesn't care what the underlying language is and you could just weave a bunch of languages together. Rust code interfacing with some hardware, C++ doing the Kalman filter, Python (via geopandas) doing geospatial computation, and R (via ggplot2) rendering the output. There's a data marshalling issue there of course, which I've also not spent too many cycles thinking about yet :)

Edit: I did copy and paste your comment into my notebook for chewing on while I'm travelling this weekend. Thanks for riffing with me!