#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.
I love graphs from dynamical systems theory and analytical mechanics.
I love those curves in phase space etc.
That makes me want to understand them better.
I have discovered the book "Structure and Interpretation of Classical Mechanics", that teaches mathematical physics and the #programminglanguage#scheme but it looks quite annoying to work in Scheme with my 💻.
So, are there similar books/courses/resources with similar means to an end? Maybe in easier to use programming languages? Like #julialang#julialanguage
(1/6)This time of the year ☃️...Statistical Rethinking 2024 ❤️❤️❤️
This has become a tradition. Like previous Decembers, this week, the 2024 edition of the Statistical Rethinking course was announced. If you are looking to learn Bayesian statistics, I highly recommend checking it out.
Escribir ecuaciones en #LaTeX es bastante pesado. Afortunadamente está el paquete para #Julialang#Latexify.jl. Escribes la fórmula y la transforma en LaTeX de manera muy arreglada. Por ejemplo:
julia> using Latexify
julia> @latexify L = z/(f_hln(10))(10^((T_2-T_ref)/z)-10^((T_1-T_ref)/z))
Is it very straightforward to run Julia code on GPU? Does it need special CUDA rewrites to work?
I do a lot of value function iteration for dynamic programming problems (I need global characterizations, approx around steady state doesn't work). Has anyone done this much in Julia? How's your experience been?
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
STL files give each triangle their own coordinates set. So none of the triangles actually share nodes. So step one, after importing such a mesh, is to merge the nodes. This animation shows vertex normal based "inflation", the left is unmerged, the right is merged.
Today's swirling electrons:
initially located in the halos of galaxies, finally accreted and mixing in a cluster undergoing a merger, and leading to the formation of a (mega) radio halo.
ENZO simulation + #JuliaLang processing, visualisation with SAO Ds9
Seeing the schedule in #julialang conference and the last survey, looks like the community is embracing more the idea of using Rust as a support language.
At first, I was a bit skeptical of the new Modular's #Mojo language.
Having no binaries available (only a playground), and a long history of #Python contenders such as #PyPy or Pyston that never achieved full compatibility... was a huge turnoff.
The amount of Python compilers that never reach 100% compatibility is almost hilarious.
Having seen the tremendous amount of effort behind #Julialang, and how little is its community compared to Python's... adds on top of that.
During the holidays, i.e. from the 23rd to the 7th, I will not work.
I'd like to continue playing with the #julialang and some #pytorch but it's really for pleasure.
I will read some stuff about brains and perception but it'll be Ed Yong's "An Immense World", and perhaps McCulloch and Ashby.
Also, I'm finishing Dune.
If you too have the "privilege" of taking a break, what are you up to?
Here‘s another interesting #julialang, #python, #rstats comparison: „count the number of vowels in a string“. #julia uses an anonymous function as an argument to count(), #python iterates over the string using list comprehension, #rstats does the same but in a vectorized way
Still playing with #julialang. Here's Switzerland in triangles.
@jonocarroll I get a lot of small dark triangles with some images which persist even if I set npts to a high value (5000) and refine as true or false. Any ideas? Thanks again for the package! I'm enjoying seeing what it produces.
Makie is a data visualization ecosystem for the Julia programming language, with high performance and extensibility. It supports various data visualization applications like 2D, 3D, and geospatial plots.