You'll be working with another reviewer to read and run the code, make sure it fills a basic checklist which usually only takes a few hours, and beyond that whatever youd like to focus on. Both of these are collaborative review processes where the goal is to help these packages be usable, well documented, and maintainable for the overall health of free scientific software.
Its fun, I promise! Happy to answer questions and boosts welcome.
Edit: feel free to volunteer as a reply here, DM me, or commenting on those issues! Anyone is welcome! Some experience with the language required, but other than that I can coach you through the rest.
Let's explore solutions, together with leading experts Anita Graser 🌻 from AIT Austrian Institute of Technology GmbH, Edzer Pebesma from Westfälische Wilhelms-Universität Münster, Maarten Pronk from Deltares, Lorena Abad Crespo from Paris Lodron Universität Salzburg and Tomislav Hengl. Join this interactive discussion, ask us your questions, and answer ours online #Slido. https://opengeohub.org/event/discussion-forum/
I regularly use and love #Typescript. I used to use #Python the most – it’s what I learned in and I am more interested in backends than frontends. I also am regularly using and really enjoying #Kotlin (so much better than #Java). But truly Typescript is bae.
#Julia is a joy to work with. Very much like Python but more powerful. If it had the library support Python or #JVM has I would probably prefer to use Julia for backends.
But Typescript really changed the game and now that’s probably my favorite language not just because of the language itself but because it has web dominance. Until I can write #WASM with Python or Kotlin or Rust, and I’m building #web applications, TS is my lingua franca.
This video https://youtu.be/8ynsN4nJxzU does a great job comparing some of the languages I know and love (#rstats, #julia) with #apl (and others) and really spells out the benefits of combinators and array operations. I suspect you could (but wouldn't) translate each of the glyphs to an R function but thinking about the composition this way definitely seems like an eye-opener.
Really interesting approach from Paul Goulart at #RustSciComp23 mixing #julia and #rust with the takeaway (paraphrased)
"Julia is great for debugging the math problems, Rust is great for debugging the code problems, and supporting both of those at once Is a lot less work than doing all of it twice"
languages that are involved in some sort of data analysis and processing (#sql, #clang /c++) are doing very well. Not sure what to make of #Python; are ppl in #AI seeing through the reality is a scripting over extremely performant c/c++ and that there are other lang that can glue as well? #golang & #Julia are ⬆️
Just read the news that HBO Max is cancelling Julia, just as the show was really getting good.
Why not... it's critically acclaimed, has incredibly reviews with both critics and the audience, a 96% on Rotten Tomatoes. That makes all the sense in the world for HBO to cancel it.
I made some cards with DataFrames.jl and Luxor.jl. In previous years I used to calculate the data using Astro libraries, but recently I discovered that NASA supply all the relevant data in CSV format. 😂
I posted an #introduction but then moved to fediscience.org, so let me introduce myself again.
I'm a #computational neuroscientist at Boston University. I make circuit models of cognitive-emotional interaction in the #limbic system. I shifted to #Julia from Matlab recently (and I love it!).
On the birdsite I tend to post a mix of neuroscience, philosophy, history, math, literature and... not-quite-serious musings.
I'm interested in public scientific communication and education too.
For the first day of #AdventOfCode living in UTC+0, I actually have to be up at stupid o'clock anyway. In the interests of speed, which I usually do not care about, given the opportunity I shall start day 1 in #perl in which the thing will take moments. I'll clean it up into #raku because, well, one ought to. I'll then start again in #Julia because that's what I want to learn this time around.
I see a lot of exotic things, or rust, or R. Where's my perl tribesfolk?!
I find it WILDLY FRUSTRATING implementing contrast coding in Julia.
If I use ContrastCoding(), I can specify my own contrast matrices (yay!) but I can't label them. So the regression output just reuses my actual factor levels to label an actual model term that means something like, say, 'mean of levels A and B vs. mean of levels C and D'. Or whatever. To interpret my model, I must make physical notes on a piece of paper about what each term means.