Open Source Data Visualization Software - Page 12
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GitNexus
Zero-Server Code Intelligence Engine
GitNexus is a zero-server code intelligence engine that builds an interactive knowledge graph of a codebase entirely within the user’s browser. The tool allows developers to drop in a GitHub repository or ZIP archive and automatically generate a relational map of files, dependencies, and structural relationships. Its core concept centers on precomputed relational intelligence, enabling AI systems to retrieve comprehensive context with minimal queries. GitNexus includes a built-in Graph RAG agent that helps users explore and understand complex repositories through intelligent navigation and querying. Because the system runs client-side, it emphasizes privacy and portability without requiring backend infrastructure. Overall, GitNexus is designed for developers, code reviewers, and AI tooling builders who need fast, visual codebase comprehension. -
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HomotopyContinuation.jl
A Julia package for solving systems of polynomials
HomotopyContinuation.jl is a Julia package for solving systems of polynomial equations by numerical homotopy continuation. Many models in the sciences and engineering are expressed as sets of real solutions to systems of polynomial equations. We can optimize any objective whose gradient is an algebraic function using homotopy methods by computing all critical points of the objective function. An important special case is when the objective function is the euclidean distance to a given point. An example of an non-algebraic objective function whose derivative is algebraic is the Kullback–Leibler divergence. Homotopy continuation methods allow us to study the conformation space of molecules as for example cyclooctane (CH₂)₈. This molecule consists of eight carbon atoms aligned in a ring, and eight hydrogen atoms, each of which is attached to one of the carbon atoms. -
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InferOpt.jl
Combinatorial optimization layers for machine learning pipelines
InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines. It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives. Typical examples include mixed integer linear programs or graph algorithms. -
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InteractiveDynamics.jl
Fast, general-purpose interactive applications for complex systems
Fast, general-purpose interactive applications for dynamical systems of all kinds, including ODEs, maps, billiards, and agent-based-models. -
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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. -
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JavaCall.jl
Call Java from Julia
Call Java programs from Julia. Julia 1.3.0 through Julia 1.6.2 are tested and guaranteed to work on Linux, macOS, and Windows via continuous integration. Julia 1.6.2 and newer should work on Linux and Windows. The JULIA_COPY_STACKS environment variable should be set to 1 on macOS and Linux, but not Windows. -
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Julia VS Code
Julia extension for Visual Studio Code
This VS Code extension provides support for the Julia programming language. We build on Julia’s unique combination of ease-of-use and performance. Beginners and experts can build better software more quickly, and get to a result faster. With a completely live environment, Julia for VS Code aims to take the frustration and guesswork out of programming and put the fun back in. A hybrid “canvas programming” style combines the exploratory power of a notebook with the productivity and static analysis features of an IDE. VS Code is a powerful editor and customizable to your heart’s content (though the defaults are pretty good too). It has power features like multiple cursors, fuzzy file finding and Vim keybindings. -
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JuliaConnectoR
A functionally oriented interface for calling Julia from R
This R-package provides a functionally oriented interface between R and Julia. The goal is to call functions from Julia packages directly as R functions. Julia functions imported via the JuliaConnectoR can accept and return R variables. It is also possible to pass R functions as arguments in place of Julia functions, which allows callbacks from Julia to R. From a technical perspective, R data structures are serialized with an optimized custom streaming format, sent to a (local) Julia TCP server, and translated to Julia data structures by Julia. The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
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Julog.jl
A Julia package for Prolog-style logic programming
A Julia package for Prolog-style logic programming. -
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KernelAbstractions.jl
Heterogeneous programming in Julia
KernelAbstractions (KA) is a package that enables you to write GPU-like kernels targetting different execution backends. KA is intended to be a minimal and performant library that explores ways to write heterogeneous code. -
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Krylov.jl
A Julia Basket of Hand-Picked Krylov Methods
If you use Krylov.jl in your work, please cite it using the metadata given in CITATION.cff. -
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MPI.jl
MPI wrappers for Julia
This is a basic Julia wrapper for the portable message-passing system Message Passing Interface (MPI). Inspiration is taken from mpi4py, although we generally follow the C and not the C++ MPI API. (The C++ MPI API is deprecated.) MPI is based on a single program, multiple data (SPMD) model, where multiple processes are launched running independent programs, which then communicate as necessary via messages. As the main entry point for users, MPI.jl provides a high-level interface which loosely follows the MPI C API and is described in details in the following sections. The syntax should look familiar if you know MPI already, but some arguments may not be needed (e.g. the type or the number of elements of arrays, which are inferred automatically), others may be placed slightly differently, and others may be optional keyword arguments (e.g. for the index of the root process, or the source and destination of point-to-point communication functions). -
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Makie
Interactive data visualizations and plotting in Julia
Makie is an interactive data visualization and plotting ecosystem for the Julia programming language, available on Windows, Linux, and Mac. The backend packages GLMakie, WGLMakie, CairoMakie and RPRMakie add different functionalities: You can use Makie to interactively explore your data and create simple GUIs in native Windows or web browsers, export high-quality vector graphics or even raytrace with physically accurate lighting. Choose one or more backend packages: GLMakie (interactive OpenGL in native OS windows), WGLMakie (interactive WebGL in browsers, IDEs, notebooks), CairoMakie (static 2D vector graphics and images), and RPRMakie (raytracing). Each backend re-exports all of Makie.jl so you don't have to install or load it explicitly. -
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MeasureTheory.jl
"Distributions" that might not add to one.
Probabilistic programming and statistical computing are vibrant areas in the development of the Julia programming language, but the underlying infrastructure dramatically predates recent developments. The goal of MeasureTheory.jl is to provide Julia with the right vocabulary and tools for these tasks. In this package we introduce well-chosen foundational primitives centered around the notion of measure, density and conditional probability with powerful combinators and transforms intended to power and unify work on probabilistic programming and statistical computing within Julia. Check out our JuliaCon 2021 article detailing our ideas for and with this package. -
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Memento.jl
A flexible logging library for Julia
Memento is a flexible hierarchical logging library for Julia. -
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ModelingToolkitStandardLibrary.jl
A standard library of components to model the world and beyond
The ModelingToolkit Standard Library is a standard library of components to model the world and beyond. -
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Molly.jl
Molecular simulation in Julia
Much of science can be explained by the movement and interaction of molecules. Molecular dynamics (MD) is a computational technique used to explore these phenomena, from noble gases to biological macromolecules. Molly.jl is a pure Julia package for MD, and for the simulation of physical systems more broadly. The package is described in a talk at Enzyme Conference 2023 and an earlier talk at the JuliaMolSim minisymposium at JuliaCon 2022. Slides are also available for a tutorial in September 2023. -
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NetCDF.jl
NetCDF support for the julia programming language
NetCDF support for the Julia programming language, there is a high-level and a medium-level interface for writing and reading netcdf files. The dimensions "x1" and "t" of the variable are called "x1" and "t" in this example. If the dimensions do not exist yet in the file, they will be created. The dimension "x1" will be of length 10 and have the values 11..20, and the dimension "t" will have length 20 and the attribute "units" with the value "s". -
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Observable Plot
A concise API for exploratory data visualization
Observable Plot is a free, open-source JavaScript library to help you quickly visualize tabular data. It has a concise and (hopefully) memorable API to foster fluency — and plenty of examples to learn from and copy-paste. In the spirit of show don’t tell, below is a scatterplot of the height and weight of Olympic athletes (sourced from Matt Riggott), constructed using a dot mark. We assign columns of data (such as weight) to visual properties (such as the dot’s x), and Plot infers the rest. You can configure much more, if needed, but Plot’s goal is to help you get a meaningful visualization quickly. Plot employs a layered grammar of graphics inspired by Vega-Lite, ggplot2, Wilkinson’s Grammar of Graphics, and Bertin’s Semiology of Graphics. Plot rejects a chart typology in favor of marks, scales, and transforms. Plot can be readily extended in JavaScript, whether to define a channel, a transform, or even a custom mark. -
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Oceananigans.jl
Julia software for fast, friendly, flexible fluid dynamics on CPUs
Oceananigans is a fast, friendly, flexible software package for finite volume simulations of the nonhydrostatic and hydrostatic Boussinesq equations on CPUs and GPUs. It runs on GPUs (wow, fast!), though we believe Oceananigans makes the biggest waves with its ultra-flexible user interface that makes simple simulations easy, and complex, creative simulations possible. -
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OpenSCAD Graph Editor
OpenSCAD Graph Editor
OpenSCAD Graph Editor is a graphical user interface for OpenSCAD that allows you to create 3D models by connecting nodes in a graph. The editor will automatically generate OpenSCAD code for you. It integrates with the OpenSCAD program so you can preview your models in real-time while you are editing the graph. Please check the manual to find out how to install and use the program. -
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OptimalTransport.jl
Optimal transport algorithms for Julia
This package provides some Julia implementations of algorithms for computational optimal transport, including the Earth-Mover's (Wasserstein) distance, Sinkhorn algorithm for entropically regularized optimal transport as well as some variants or extensions. Notably, OptimalTransport.jl provides GPU acceleration through CUDA.jl and NNlibCUDA.jl. -
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Oxygen.jl
A breath of fresh air for programming web apps in Julia
A breath of fresh air for programming web apps in Julia. Oxygen is a micro-framework built on top of the HTTP.jl library. Breathe easy knowing you can quickly spin up a web server with abstractions you're already familiar with. -
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PETSc.jl
Julia wrappers for the PETSc library
This package provides a low level interface for PETSc and allows combining julia features (such as automatic differentiation) with the PETSc infrastructure and nonlinear solvers. -
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Parquet.jl
Julia implementation of Parquet columnar file format reader
A parquet file or dataset can be loaded using the read_parquet function. A parquet dataset is a directory with multiple parquet files, each of which is a partition belonging to the dataset.
