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Top 21 Julia Julialang Projects
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Project mention: Ask HN: I just want to have fun programming again | news.ycombinator.com | 2023-02-08
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InfluxDB
Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.
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LanguageServer.jl
An implementation of the Microsoft Language Server Protocol for the Julia language.
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Project mention: Is Postgresql integration well supported in Julia? | reddit.com/r/Julia | 2022-09-27
That's deprecated for LibPQ, which in my experience works very well. 'Coverage' means the proportion of the library's source code that executes at least once when the unit tests are run - a simple measure of how well tested the code is.
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SeaPearl.jl
Julia hybrid constraint programming solver enhanced by a reinforcement learning driven search.
Project mention: [D] Combinatorial optimization - what ML approaches are available and which are the most appropriate? | reddit.com/r/MachineLearning | 2022-06-19 -
Project mention: Starlight.jl – A game engine written in Julia | reddit.com/r/linux_gaming | 2022-06-06
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SonarQube
Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.
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DynamicalBilliards.jl
An easy-to-use, modular, extendable and absurdly fast Julia package for dynamical billiards in two dimensions.
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CoherentNoise.jl
A comprehensive suite of coherent noise algorithms and composable tools for manipulating them.
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Glyphy.jl
I will look for you. I will find you. And I will print you. (If you're a Unicode glyph...)
Project mention: Finding Unicode characters easily... A little REPL utility | reddit.com/r/Julia | 2022-06-20 -
> you just have to keep in your head all the methods that are expected to exist for a given type.
Technically, you don't need to keep that in your head :-) The general approach is to define generic functions and also write docs about how to extend those functions to satisfy interface requirements.
Perhaps not too surprisingly, many of the Julia community people also want to have some official interface support directly from the language. Before that, several open-source projects were spawned to address that gap e.g. here is a shameless plug about my package:
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MuladdMacro.jl
This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem
But if what you want is automatic FMA, then why carry along every other possible behavior with it? Just because you want FMA, suddenly NaNs are turned into Infs, subnormal numbers go to zero, handling of sin(x) at small values is inaccurate, etc? To me that's painting numerical handling in way too broad of strokes. FMA also only increases numerical accuracy, it doesn't decrease numerical accuracy, so bundling it with unsafe transformations makes one uncertain now whether it has improved or decreased accuracy.
For reference, to handle this well we use MuladdMacro.jl which is a semantic transformation that turns x*y+z into muladd expressions, and it does not recurse into functions so it does not change the definitions of the callers inside of the macro scope.
https://github.com/SciML/MuladdMacro.jl
This is something that will always increase performance and accuracy (performance because muladd in Julia is an FMA that is only applied if hardware FMA exists, effectively never resorting to a software FMA emulation) because it's targeted to do only a transformation that has that property.
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GenericArpack.jl
A pure Julia translation of the Arpack library for eigenvalues and eigenvectors but for any numeric types. (Symmetric only right now)
For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
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Project mention: Question: How to make method globally accessible (for interface/protocols/polymorphic types) and avoid "per-module" extension? | reddit.com/r/Julia | 2022-07-19
This is the usual way of doing things in Julia, and is why there are packages like StatsAPI.jl.
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2023-02-11.
Julia Julialang related posts
- Language Server does not detect local Modules
- [D] Combinatorial optimization - what ML approaches are available and which are the most appropriate?
- julia coding | lsp-julia
- TypeDB Client for Julia
- Went from days to minutes by using Dask and Spark – what else should I know?
- ERROR: UndefinedVarError: OneHotEncode not defined
- I created an Emacs package to statically lint Julia files (using StaticLint.jl)
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Index
What are some of the best open-source Julialang projects in Julia? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | OnlineStats.jl | 745 |
| 2 | Agents.jl | 538 |
| 3 | LanguageServer.jl | 317 |
| 4 | KernelAbstractions.jl | 219 |
| 5 | LibPQ.jl | 193 |
| 6 | SeaPearl.jl | 130 |
| 7 | Starlight.jl | 124 |
| 8 | DynamicalBilliards.jl | 95 |
| 9 | LatticeQCD.jl | 91 |
| 10 | Lathe.jl | 74 |
| 11 | CoherentNoise.jl | 53 |
| 12 | TypeDBClient.jl | 51 |
| 13 | Glyphy.jl | 49 |
| 14 | BinaryTraits.jl | 47 |
| 15 | MuladdMacro.jl | 37 |
| 16 | SparkSQL.jl | 23 |
| 17 | GenericArpack.jl | 23 |
| 18 | StatsAPI.jl | 12 |
| 19 | NumericalAlgorithms.jl | 11 |
| 20 | advent-of-code | 6 |
| 21 | BioMart.jl | 2 |
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