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.
@exa just impressions but there are a couple of talks about Rust and Julia in the next JuliaCon, I remember also the last survey show a percentage of Julia Developers are also interested in Rust as a second language and then you have discussion like using Rust for some things where Julia still is weak
@indymnv ooh, interesting. Like, I could join the crowd asking for actual sub-NP static typing algorithm, we got bitten by the typesystem so many times... (but I'm biased towards haskell instead of rust :) having typeclasses would also solve SOOO MUCH of various other gripes, incl. compilation/load speed).
@metin Yes, probably*. You may have it already in Blender (I know Meshlab has it too for instance). It is called HC (Humphrey’s Classes) smoothing. Here is the paper defining the algorithm, it is easy enough to implement: https://doi.org/10.1111/1467-8659.00334/.
Email me if you can't access it (kevin.moerman@universityofgalway.ie).
It is not formally volume preserving but it aims to avoid the shape distortions seen for normal Laplacian smoothing by pushing nodes back a bit.
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.
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
@kevinmoerman oooooh yes, we started doing 100% as mandatory for all new projects like a year ago. It helps SO MUCH.
Now we try to cover the 100% with Literate-rendered docs, which is also very useful, kinda guarantees that everything in the package is up-to-date documented.
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 :)
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.
(2/3) The Makie's ecosystem is based on the following four graphic engines:
✅ GLMakie - using OpenGL engine on the backend
✅ CairoMakie - uses Cairo.jl to draw vector graphics to SVG and PDF
✅ WGLMakie - is the web-based backend, which is mostly implemented in Julia applications to generate the HTML and JavaScript for displaying the plots
✅ RPRMakie - is a ray tracing backend using AMD's RadeonProRender, used for geospatial applications
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?
#Julia#Julialang@julialang
I have a question:
I am using HDF5.jl to open an hdf5 file; the file is organised in >100 datasets, written by the creator in a rather unpredictable way.
How do I automatically open all
datasets in the sequence, without knowing their name beforehand? (with the final goal of recreating a single long array, by appending the content of each dataset - this I know how to do).
thanks in advance
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
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
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