#SimulatedUniverses
a new visualisation experiment to show how a full simulated volume (100 Megaparsec cubic) gets progressively filled by cosmic web structures if I keep adding a layer after another along the line of sight - here using 64 layers of 1.56 Megaparsec each.
This is baryonic matter.
the visualisation is done using #Julialang in parallel using 16 cores on my MAC - the input simulation has 1024^3 cells.
Really sorry for the spam, but I'm hosting a #data event soon and I need to fill a bowl with data jokes. I have few but need MANY more. Here's an example of what I'm looking for:
"A query walks into a bar, sees two tables and goes 'can I join these'?"
🙏 PLEASE collective hive-mind of the fediverse, give me your best!
We can't replace them, but we welcome anyone looking for a friendly, inclusive community to join us at the Data Science Learning Community (@DSLC) https://DSLC.io
📢 Finally out the recording of the #JuliaCon 2023 talk we presented at MIT this July!
So grateful for the opportunity to present our package at such a wonderfully organized global conference with so many brilliant #JuliaLang users and developers!
Implementing "Dijkstra's algorithm" or "mesh distance marching". Computes the on-surface dist from one point to all others (i.e. like a heat spreading across the mesh).
From a seed point, we could add the furthest point to the list and reiterate, compute dist, find furthest point, add this point to list, repeat... This will produce an (approximately) equidistant or geodesic set of points. Handy for remeshing, but it does look like David's got the measles. #julialang
I wonder how #Mojo can get away with lies regarding its latest benchmark.
Fake news in tech are actually a threat?
Tech entertainers like #ThePrimagen only make things worse by promoting these fake news without validation.
Then he is quoted in a later Mojo blog post spreading bullshit about #RustLang and ignoring #JuliaLang. But debunking posts like this are ignored:
My university dropped the campus wide MATLAB license around August last year. It is amusing to see the effect on my GitHub contribution chart. But then I picked up #julialang and now there is more than a recovery :)
My weekend side-project: at long last I set some time aside to learn the basics of #julialang. The learning process was a tonne of fun. The thing where it somehow sprawled into a three-part series of blog posts... not so much fun. Anyway here they are... first one is me wrestling with some basic features in language
By the time I hit the third post I was kind of exhausted, so it's a bit, um, telegraphic in places. But nevertheless it was sort of nice to get a sense of (one tool for) data visualisation in #julialang
Second one is a bit more practical, and looks at how to do basic data wrangling using the DataFrames package. Not surprisingly as an #rstats person learning #julialang, a big part of this was me trying to find a pipe-centric workflow that I like. In the end I decided I kind of like the combination of the Julia base pipe and anonymous functions as a workflow
The R4DS Online Learning Community has thousands of members, hundreds of which are active on our Slack every week. You might be wondering: Why not charge those learners? Why is the Community funded through donations?
One of my favorite hard #scifi topics that I love to work within (even if I am terrifically starved for time to do much about this at the moment) are wormhole networks.
They have interesting geometry constraints once you add real star maps, #optimization challenges when it becomes time to figure out how many connections you need to connect what places while observing construction and shape constraints…
it is the kind of #graph computer science that tickles my brain extra being scifi nerdy
In particular, simulating n-agent circumstances with improving technologies (wormhole projection speeds and ranges in a single connection creation) would be interesting, both to study optimal networks that satisfy the most constraints from all the parties, and just letting some organically emerge with quirks and bad choices that play off of each other.
Maybe something to use #julialang for, though I have to say, when it comes to really optimizing the configuration, I lack know-how
Getting there. These images show tests of a triangulation algorithm I've developed that uses Delaunay triangulation. It features mostly equilateral triangles except for at the boundaries.
I kept my twtr account for a while because brands I occasionally reach out to were still exclusively there. It’s now no longer the case so I put the account down for real :)
@ayo Thanks for building this @anze3db, I've already found some great accounts to follow! A couple of things that would make it even better, the Nix language uses a regex that matches people mentioning Unix or *nix, so it has a lot of false positives. Secondly, I'd love to see #JuliaLang added to the list of languages, and to add JuliaLang.social to the index :)