astropy VS anaconda-issues

Astropy [0] lives at the heart of most work. It has a Python interface, often backed by Fortran and C++ extension modules. If you use Astropy, you're indirectly using libraries like ERFA [6] and cfitsio [7] which are in C/Fortran.

I personally end up doing a lot of work that uses the HEALPix sky tesselation, so I use healpy [2] as well.

Openorb is perhaps a good example of a pure-Fortran package that I use quite. frequently for orbit propagation [3].

In C, there's Rebound [4] (for N-body simulations) and ASSIST [5] (which extends Rebound to use JPL's pre-calculated positions of major perturbers, and expands the force model to account for general relativity).

There are many more, these are just ones that come to mind from frequent usage in the last few months.

[0] https://www.astropy.org/

Users interested in a broader range of astronomical tools beyond coordinate transformations may be interested in https://www.astropy.org/ and its affiliated packages.

I'm working on a pure Rust approximation of astropy. Up til now, I was able to recreate the intent by looking at an external API, but I'm moving on to functionality that I don't understand enough to implement without basically copying the code. Astropy uses the BSD-3 license, and it wraps the ERFA library which uses a custom license. My project currently uses the MIT license. My PR is here - my question is have I attributed everything correctly, or is there anything I need to change for everything to be above-board?

For folks who might be interested in astronomical calculations but who don't want to roll their own library, astropy (https://www.astropy.org/) is widely used by professional astronomers.

I agree with the other commenter. Check out their github. If you’re looking to build your skills long term (and have some experience with python) it’s worth checking out astropy and their fits file handling routines.

Google search of the issue also turned up this thread: https://github.com/ContinuumIO/anaconda-issues/issues/72

I found this post on github that describes the same issue I'm facing but the solutions didn't help.

A scientist typically wouldn't write web backend, a sysadmin doesn't do a lot of statistical stuff, etc.

A small startup might do well to make their MVP in Python, but as the code grows the implicit costs (of using Python) do too.

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In re: Rust, sorry I wasn't clear above. I don't mean that Rust is a glue language, I mean that people write e.g. grep replacements in it and things like that. Python does systems programming by being glue, Rust does it by being, well, Rust. It makes sense to me that Rust libs would get Python wrappers, but it also seems to me that that adds to my argument: Python is good for small glue, but crunchy things (like grep) should be written in e.g. Rust or Go or something.

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One other things about Python is that the packaging & distribution "story" is ridiculous now. The people in charge of that call themselves the Python Packaging Authority (which name, given what they're doing, reminds me of Brazil the movie) and they seem to me to be running amok, cargo-culting the crap out of what should be a pretty simple and straightforward problem. I could go on but I feel a rant brewing, so I'll cut it off there.

It's not just the PyPA folks that are having problems packaging and distributing Python. The Conda folks ship Tkinter in a broken state for five years now: https://github.com/ContinuumIO/anaconda-issues/issues/6833 That's the default GUI system that ships with the Python Standard Library.

Compare and contrast with Rust's Cargo, or Nim's Nimble, or Erlang's Rebar, etc.

If so, you can check this solution. I've encountered this same issue when using Anaconda or Conda. https://github.com/ContinuumIO/anaconda-issues/issues/6833

https://github.com/ContinuumIO/anaconda-issues/issues/1206#issuecomment-258672013