egg changed the topic of #kspacademia to: https://git.io/JqLs2 | Dogs are cats. Spiders are cat interferometers. | Document well, for tomorrow you may get mauled by a ネコバス. | <UmbralRaptor> egg|nomz|egg: generally if your eyes are dewing over, that's not the weather. | <ferram4> I shall beat my problems to death with an engineer. | We can haz pdf | Logs: https://esper.irclog.whitequark.org/kspacademia
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<raptop> !wpn
* galois gives raptop a convex octohedron
<raptop> !wpn -add:adj stop
<galois> Added adj 'stop'
<raptop> !wpn -add:adj go
<galois> Added adj 'go'
<raptop> !wpn -add:adj yield
<galois> Added adj 'yield'
<raptop> this feels relevant to mofh and/or egg|anbo|egg https://twitter.com/kanjyu_reimari/status/1387542386247614469
<UmbralRaptop> And yes, the MASCARA survey is another example of astronomers naming things
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* raptop has an intensely silly though -- passenger and cargo transport across the great lakes via ekranoplane
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* raptop feels like %s/ / /g is entirely too useful for editing some papers
<egg|cell|egg> MASCARA?
<galois> MASCARA: Multi-site All-Sky CAmeRA
<raptop> see!
<egg|cell|egg> O_o
<raptop> SnoopJ's bot idea would be fun
<raptop> !8 Is multi-nya catputing a good idea?
<galois> raptop: no
* raptop feels like SnoopJ would have an opinion on how good/bad these are
<galois> title: NumPy tutorials — NumPy Tutorials
<SnoopJ> they're probably good (numpy's docs generally are quality), but won't teach you much numpy heh
<SnoopJ> I feel like the main thing people are missing in numpy knowledge is just like...what's going on with ndarrays, how to express things idiomatically over them (avoiding much more expensive alternatives that may be more intuitive if you don't know about the difference)
<galois> title: 1.4. NumPy: creating and manipulating numerical data — Scipy lecture notes
<raptop> thanks
<galois> title: Python Numpy Tutorial (with Jupyter and Colab)
<SnoopJ> although both of those are more "cookbooky" I guess
<SnoopJ> raptop, if there's anything in particular you wanna know or have a reference for, I have some parts of the numpy docs bookmarked as particularly-useful references
<raptop> probably just stuff for making array operations not excessively expensive
<galois> title: Indexing — NumPy v1.20 Manual
<SnoopJ> the first one because broadcasting operations are in general fast: loops over ndarray are basically always wrong
<SnoopJ> second is useful because advanced indexing is *guaranteed* to copy, and copies are slow
<SnoopJ> sometimes people will write code that ends up copying something big a bunch of times when they'd be better served by making a single copy and operating over that or something
* raptop >_>;;
<SnoopJ> oh uh also numba is really good now?
<SnoopJ> I don't really use it, but it's good
<SnoopJ> it understands ndarrays pretty well and can seriously accelerate some of those anti-idiom loops
<SnoopJ> mostly because it turns the loop into not-Python heh
<raptop> not-Python... FORTRAN?
<SnoopJ> machine instructions
<SnoopJ> @numba.jit is basically "read this Python source and get it into LLVM IR and then LLVM go brrrr"
<SnoopJ> with some trickery to help it infer types blah blah
<SnoopJ> but the end result is that you get code that isn't running inside the interpreter which can be just miserably slow
<SnoopJ> `for val in some_array: ... # so much interpreter code has to run to even iterate this array, let alone what happens inside the loop`