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
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.
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.
🎂It's my birthday!🎂
To celebrate, I'm... Working to build a friendly, diverse #DataScience community at https://r4ds.io, just like I do every day! It'd make my day if you supported our efforts at https://r4ds.io/donate !
Every so often I try re-installing R on termux on android to see if it's fixed (some .so doesn't link, even using the latest instructions) but no luck. Last night I was reminded that UserLAnd is another option and hurrah! #rstats, #julialang, #python, and more at my fingertips wherever I go! Great for quick syntax checks/comparisons. Add a BT keyboard and it's a tiny dev machine in my pocket.
Idk, managing to actually dispatch the parallel tasks of my #Julialang code to the cores of Leonardo at CINECA (currently 4th HPC machine in the world), by calling the slurm task manager with ClusterManagers.jl (👉 https://github.com/JuliaParallel/ClusterManagers.jl) feels very good.
My routine is not meant to scale on more than 32 cores at the moment; however it means that I should be able to effectively use Julia also for massive parallel postprocessing on the computing nodes.
👍 @julialang #astrodon#astronomy
I wanted to follow along the juliaacademy course "Julia for Data Science", but the first tutorial shows how to activate an environment (?) but in the end I have no idea how to reliably work in an already installed JupyterLab, Julia and packages version compatible, and all in a specific environment for this project.
I've been installing and uninstalling Julia 1.6, 1.9 and 1.10, deleting them from PATH, etc.
With Python is not straightforward, but I got it.
I decided I will use #Plutojl and manually recreate all the courses notebooks. For the sake of learning, it's definitely better than just running readymade cells, plus it's in line with the MIT Julia course.
But it doesn't feel right, I feel there's a huge gap in my understanding, and likely a gap in the tutorials too.
Also, spending 2 days without proper coding, just running around bugs, is super frustrating, just feels like time lost forever to no cause.
#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 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. 😂
@julialang
Optimization.jl has many options for automatic differentiation, such as AutoZygote() and AutoEnzyme().
They are very useful, but are there any pros and cons for each option?
The Mathematical Engineering of Deep Learning is a new book by Benoit Liquet, Sarat Moka, and Yoni Nazarathy.
As its name implies, it focuses on the mathematical engineering of #deeplearning and covers topics such as:
✅ Foundation of machine learning and deep learning
✅ Optimization algorithms
✅ Convolutional neural networks
✅ Transformers
✅ Generative models
✅ Diffusion models
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?
(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.