Open Source Julia Software - Page 15
Sort By:
Julia Software
Browse free open source Julia Software and projects below. Use the toggles on the left to filter open source Julia Software by OS, license, language, programming language, and project status.
-
Try Google Cloud Risk-Free With $300 in CreditUse your credit across every product. Compute, storage, AI, analytics. When it runs out, 20+ products stay free. You only pay when you choose to.
-
Earn up to 16% annual interest with Nexo.Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.
-
1
DotNET.jl
This package provides interoperability between Julia and .NET apps
This package provides interoperability between Julia and Common Language Runtime, the execution engine of .NET applications. Many languages run on CLR, including C#, Visual Basic .NET and PowerShell. -
2
DynamicalSystems.jl
Award winning software library for nonlinear dynamics timeseries
DynamicalSystems.jl is an award-winning Julia software library for nonlinear dynamics and nonlinear time series analysis. To install DynamicalSystems.jl, run import Pkg; Pkg.add("DynamicalSystems"). To learn how to use it and see its contents visit the documentation, which you can either find online or build locally by running the docs/make.jl file. DynamicalSystems.jl is part of JuliaDynamics, an organization dedicated to creating high-quality scientific software. All implemented algorithms provide a high-level scientific description of their functionality in their documentation string as well as references to scientific papers. The documentation features hundreds of tutorials and examples ranging from introductory to expert usage. -
3
FFTW.jl
Julia bindings to the FFTW library for fast Fourier transforms
This package provides Julia bindings to the FFTW library for fast Fourier transforms (FFTs), as well as functionality useful for signal processing. These functions were formerly a part of Base Julia. Users with a build of Julia based on Intel's Math Kernel Library (MKL) can use MKL for FFTs by setting a preference in their top-level project by either using the FFTW.set_provider!() method, or by directly setting the preference using Preferences.jl. Note that this choice will be recorded for the current project, and other projects that wish to use MKL for FFTs should also set that same preference. Note further that MKL provides only a subset of the functionality provided by FFTW. -
4
FastAI.jl
Repository of best practices for deep learning in Julia
FastAI.jl is a Julia library for training state-of-the-art deep learning models. From loading datasets and creating data preprocessing pipelines to training, FastAI.jl takes the boilerplate out of deep learning projects. It equips you with reusable components for every part of your project while remaining customizable at every layer. FastAI.jl comes with support for common computer vision and tabular data learning tasks, with more to come. -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
5
Fermi.jl
Fermi quantum chemistry program
Fermi.jl is a quantum chemistry framework written in pure Julia. This code is developed at the Center for Computational Quantum Chemistry at the University of Georgia under the supervision of Dr. Justin M. Turney and Prof. Henry F. Schaefer. This work is supported by the U.S. National Science Foundation under grant number CHE-1661604. Fermi focuses on post Hartree--Fock methods. Currently, only restricted references are supported. This is intended as a research code with an ever growing collection of methods implemented in the package itself. However, the Fermi API is designed to make high performance pilot implementations of methods achievable. -
6
FinEtools.jl
Finite Element tools in Julia
FinEtools is a package for basic operations on finite element meshes: Construction, modification, selection, and evaluation of quantities defined on a mesh. Utilities are provided for maintaining mesh-based data (fields), for defining normals and loads, for working with physical units and coordinate systems, and for integrating over finite element meshes. -
7
FiniteDifferences.jl
High accuracy derivatives, estimated via numerical finite differences
FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other. -
8
Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
Flux is an elegant approach to machine learning. It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. Existing Julia libraries are differentiable and can be incorporated directly into Flux models. Cutting-edge models such as Neural ODEs are first class, and Zygote enables overhead-free gradients. GPU kernels can be written directly in Julia via CUDA.jl. Flux is uniquely hackable and any part can be tweaked, from GPU code to custom gradients and layers. -
9
FunSQL.jl
Julia library for compositional construction of SQL queries
FunSQL is a Julia library for the compositional construction of SQL queries. Julia programmers sometimes need to interrogate data with the Structured Query Language (SQL). But SQL is notoriously hard to write in a modular fashion. FunSQL exposes full expressive power of SQL with compositional semantics. FunSQL allows you to build queries incrementally from small independent fragments. This approach is particularly useful for building applications that programmatically construct SQL queries. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
GLM.jl
Generalized linear models in Julia
GLM.jl is a Julia package for fitting linear and generalized linear models (GLMs) with a syntax and functionality familiar to users of R or other statistical environments. It is part of the JuliaStats ecosystem and is tightly integrated with StatsModels.jl for formula handling, and Distributions.jl for specifying error families. The package supports modeling through both formula-based (e.g. @formula) and matrix-based interfaces, allowing both high-level convenience and low-level control. Under the hood, GLM.jl separates the linear predictor and response objects, allowing flexible combinations of link functions, variance structures, and fitting methods. -
11
GPUCompiler.jl
Reusable compiler infrastructure for Julia GPU backends
Reusable compiler infrastructure for Julia GPU backends. This package offers reusable compiler infrastructure and tooling for implementing GPU compilers in Julia. It is not intended for end users! Instead, you should use one of the packages that builds on GPUCompiler.jl, such as CUDA.jl or AMDGPU.jl. -
12
Gadfly
Crafty statistical graphics for Julia
Gadfly is a system for plotting and visualization written in Julia. It is based largely on Hadley Wickhams's ggplot2 for R and Leland Wilkinson's book The Grammar of Graphics. It was Daniel C. Jones' brainchild and is now maintained by the community. -
13
Gaston.jl
A julia front-end for gnuplot
Gaston is a Julia package for plotting. It provides an interface to gnuplot, a powerful plotting package available on all major platforms. The current stable release is v1.1.0, and it has been tested with Julia LTS (1.6) and stable (1.8), on Linux. Gaston should work on any platform that runs gnuplot. -
14
Genie.jl
The highly productive Julia web framework
Genie Framework includes all you need to quickly build production-ready web applications with Julia. Develop Julia backends, create beautiful web UIs, build data applications and dashboards, integrate with databases and set up high-performance web services and APIs. Genie Builder is a free VSCode plugin for quickly building Julia apps without writing frontend code. Drag and drop UI components such as text, sliders, plots, and data tables onto a canvas, and connect them to the variables in the backend code. Genie.jl is the backbone of Genie Framework: the complete solution for developing modern full-stack web applications in Julia. Genie.jl includes key features like the webserver, the flexible templating engine with support for HTML, JSON, Markdown, and Julia views, caching, (encrypted) cookies and sessions, forms handling, and the powerful router. Genie.jl uses the familiar MVC architecture, follows industry best practices, and comes with lots of useful code generators. -
15
Geodesy.jl
Work with points defined in various coordinate systems
Geodesy is a Julia package for working with points in various world and local coordinate systems. The primary feature of Geodesy is to define and perform coordinate transformations in a convenient and safe framework, leveraging the CoordinateTransformations package. Transformations are accurate and efficient and implemented in native Julia code (with many functions being ported from Charles Karney's GeographicLib C++ library), and some common geodetic datums are provided for convenience. -
16
HttpServer.jl
Basic, non-blocking HTTP server in Julia
This is a basic, non-blocking HTTP server in Julia. You can write a basic application using just this if you're happy dealing with values representing HTTP requests and responses directly. For a higher-level view, you could use Mux. If you'd like to use WebSockets as well, you'll need to grab WebSockets.jl. -
17
Immerse.jl
Dive deeper into your data with interactive graphics
Dive deeper into your data with interactive graphics. Immerse is a wrapper that adds graphical interactivity to Julia's plots. Currently, Immerse supports Gadfly. The toolbar at the top supports saving your figure to a file, zooming and panning, and lasso selection. Zooming and panning uses the defaults set by GtkUtilities. The left mouse button allows you to rubberband-select a zoom region. Use your mouse wheel or arrow-keys to pan or change the zoom level. Double-click, or press the 1:1 button, to restore the full view. -
18
ImplicitDifferentiation.jl
Automatic differentiation of implicit functions
ImplicitDifferentiation.jl is a package for automatic differentiation of functions defined implicitly, i.e., forward mappings. Those for which automatic differentiation fails. Reasons can vary depending on your backend, but the most common include calls to external solvers, mutating operations or type restrictions. Those for which automatic differentiation is very slow. A common example is iterative procedures like fixed point equations or optimization algorithms. -
19
Indicators.jl
Financial market technical analysis & indicators in Julia
Indicators is a Julia package offering efficient implementations of many technical analysis indicators and algorithms. This work is inspired by the TTR package in R and the Python implementation of TA-Lib, and the ultimate goal is to implement all of the functionality of these offerings (and more) in Julia. This package has been written to be able to interface with both native Julia Array types, as well as the TS time series type from the Temporal package. Contributions are of course always welcome for wrapping any of these functions in methods for other types and/or packages out there, as are suggestions for other indicators to add to the lists. -
20
Interpolations.jl
Fast, continuous interpolation of discrete datasets in Julia
This package implements a variety of interpolation schemes for the Julia language. It has the goals of ease of use, broad algorithmic support, and exceptional performance. Currently, this package supports B-splines and irregular grids. The API has been designed with the intent to support more options. Initial support for Lanczos interpolation was recently added. Pull requests are more than welcome! It should be noted that the API may continue to evolve over time. -
21
JET.jl
An experimental code analyzer for Julia
JET employs Julia's type inference system to detect potential bugs and type instabilities. JET is tightly coupled to the Julia compiler, and so each JET release supports a limited range of Julia versions. See the Project.toml file for the range of supported Julia versions. The Julia package manager should install a version of JET compatible with the Julia version you are running. If you want to use JET on unreleased version of Julia where compatibility with JET is yet unknown, clone this git repository and dev it, such that Julia compatibility is ignored. -
22
JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
23
Javis
Julia Animations and Visualizations
Javis: Julia Animations and Visualizations. Javis makes generating simple animations a breeze! Want to learn more? Check out our documentation for tutorials, our contributing guidelines, and the mission of Javis.jl. We have a live Zulip stream that you can join to discuss Javis with other Javis users. -
24
Julia plugin for the IntelliJ Platform
Julia Plugin for IntelliJ IDEA
This is a work in progress, some features are implemented partially, there may be performance and stability problems. Install IntelliJ IDEA (or other JetBrains IDEs), open Settings | Plugins | Browse repositories, install Julia plugin, and create a Julia project. -
25
JuliaWorkshop
Intensive Julia workshop that takes you from zero to hero
This is an intensive workshop for the Julia language, composed out of three 2-hour segments. It targets people already familiar with programming, so that the established basics such as for-loops are skipped through quickly and efficiently. Nevertheless, it assumes only rudimentary programming familiarity and does explain concepts that go beyond the basics. The goal of the workshop is to take you from zero to hero (regarding Julia): even if you know nothing about Julia, by the end you should be able to use it like a pro. The material has been updated during July-December 2023 to Julia v1.9+ and the corresponding latest stable versions of used packages
