This collection of Julia scripts automatically generates C header files from Julia functions marked with the @ccallable macro. This is useful when creating Julia libraries that need to be called from C/C++ code.
The main generator script that provides a simple and robust approach to generating C headers. It uses a manual definition approach where you explicitly define function signatures, avoiding complex AST parsing.
Features:
preface: Posting these online since it sounds like these notes are somewhat interesting based on a few folks I've shared with. These are semi-rough notes that I basically wrote for myself in case I ever needed to revisit this fix, so keep that in mind.
I recently bought an LG ULTRAGEAR monitor secondhand off of a coworker. I really love it and it's been great so far, but I ran into some minor issues with it in Linux. It works great on both Mac and Windows, but on Linux it displays just a black panel until I use the second monitor to go in and reduce the refresh rate down to 60 Hz.
This has worked decent so far but there's some issues:
| #Helpers: | |
| "Given some IR generates a MethodInstance suitable for passing to infer_ir!, if you don't already have one with the right argument types" | |
| function get_toplevel_mi_from_ir(ir, _module::Module) | |
| mi = ccall(:jl_new_method_instance_uninit, Ref{Core.MethodInstance}, ()); | |
| mi.specTypes = Tuple{ir.argtypes...} | |
| mi.def = _module | |
| return mi | |
| end | |
| "run type inference and constant propagation on the ir" |
| @recipe(Barplot3D) do scene | |
| Attributes( | |
| color = theme(scene, :markercolor), | |
| colormap = theme(scene, :colormap), | |
| ) | |
| end | |
| Makie.conversion_trait(::Type{<:Barplot3D}) = Makie.DiscreteSurface() | |
| function Makie.plot!(p::Barplot3D) |
| [Security] | |
| EAP-Method=TLS | |
| EAP-Identity=<your harvard email> | |
| EAP-TLS-CACert=/var/lib/iwd/usertrustrsacertificationauthority.cer | |
| EAP-TLS-ClientCert=/var/lib/iwd/harvard_eduroam.p12 | |
| EAP-TLS-ClientKey=/var/lib/iwd/harvard_eduroam.p12 | |
| EAP-TLS-ClientKeyPassphrase=<your certificate password> | |
| EAP-TLS-Phase2-Method=Tunneled-PAP | |
| EAP-TLS-Phase2-Identity=<your harvard email> | |
| EAP-TLS-Phase2-Password=<your harvard login password> |
| # Add this file to ~/.julia/config/ (mkdir config if necessary) | |
| try | |
| using Coverage | |
| catch e | |
| @warn "Error initializing Coverage: trying install" exception = (e, catch_backtrace()) | |
| using Pkg | |
| Pkg.add("Coverage") | |
| end |
Since writing out ROOT files is currently not possible with UnROOT, one can write out Arrow files
directly from an UnROOT.LazyTree object which can be read back in julia.
With some consideration of the chunking, this won't use much memory.
using UnROOT
using Arrow
using Tables
treename = "Events"
filename = "18BCCE71-15B8-194B-8738-EC993C8DD3BD.root"
The Jax developers optimized a differential equation benchmark in this issue which used DiffEqFlux.jl as a performance baseline. The Julia code from there was updated to include some standard performance tricks and is the benchmark code here. Thus both codes have been optimized by the library developers.
| # Electromagnetic simulation at varying timescales - simulation code to make this animation: | |
| # https://twitter.com/bencbartlett/status/1369396941730312193 | |
| # | |
| # Animation idea was inspired by u/cenit997's (Twitter: @__cenit) Reddit post: | |
| # https://www.reddit.com/r/Python/comments/hxmhai/a_simulation_of_how_an_incoherent_light_source/ | |
| #--- | |
| using LinearAlgebra, Base.Threads, BenchmarkTools, LoopVectorization, CSV, DataFrames, Printf, Plots |