Open Source Julia Data Visualization Software
Sort By:
Julia Data Visualization Software
View 452 business solutionsBrowse free open source Julia Data Visualization Software and projects below. Use the toggles on the left to filter open source Julia Data Visualization Software by OS, license, language, programming language, and project status.
-
Earn up to 15% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
Earn up to 15% 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
MarketData.jl
Time series market data
The MarketData package provides open-source financial data for research and testing. The data is from Quandl and is free end-of-day stock data. It is public domain without restrictions. The TimeSeries TimeArray data structure is used to store the data, but conversion to other data structures, including DataFrames and AxisArrays, is supported. -
2
Yggdrasil
Collection of builder repositories for BinaryBuilder.jl
This repository contains recipes for building binaries for Julia packages using BinaryBuilder.jl. -
3
MATLAB.jl
Calling MATLAB in Julia through MATLAB Engine
The MATLAB.jl package provides an interface for using MATLAB® from Julia using the MATLAB C api. In other words, this package allows users to call MATLAB functions within Julia, thus making it easy to interoperate with MATLAB from the Julia language. You cannot use MATLAB.jl without having purchased and installed a copy of MATLAB® from MathWorks. This package is available free of charge and in no way replaces or alters any functionality of MathWorks's MATLAB product. -
4
LiveServer.jl
Simple development server with live-reload capability for Julia
This is a simple and lightweight development web-server written in Julia, based on HTTP.jl. It has live-reload capability, i.e. when modifying a file, every browser (tab) currently displaying the corresponding page is automatically refreshed. -
GoAnywhere Managed File Transfer (MFT)GoAnywhere MFT provides secure managed file transfer for enterprises. Deployable on-premise, in the cloud, or in hybrid environments, GoAnywhere MFT software enables organizations to exchange data among employees, customers, and trading partners, as well as between systems, securely. GoAnywhere MFT was a recipient of the Cybersecurity Excellence Award for Secure File Transfer.
-
5
PlotlyJS
Julia library for plotting with plotly.js
Julia interface to plotly.js visualization library. This package constructs plotly graphics using all local resources. To interact or save graphics to the Plotly cloud, use the Plotly package. -
6
The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose computing. Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
7
DoubleFloats.jl
Math with more good bits
Math with 85+ accurate bits. Extended precision float and complex types. -
8
DFTK.jl
Density-functional toolkit
The density-functional toolkit, DFTK for short, is a collection of Julia routines for experimentation with plane-wave density-functional theory (DFT). The unique feature of this code is its emphasis on simplicity and flexibility with the goal of facilitating algorithmic and numerical developments as well as interdisciplinary collaboration in solid-state research. -
9
DataFrames.jl
In-memory tabular data in Julia
DataFrames.jl is a powerful Julia package for working with in-memory tabular data. It provides a familiar, flexible, and efficient interface for handling datasets, making it easy to load, manipulate, join, and analyze structured data. With syntax inspired by data frames in R and pandas in Python, it offers intuitive tools while taking advantage of Julia’s speed and type system. The package is actively maintained by the JuliaData community, with contributions from over 200 developers worldwide. It is widely used for data science, research, and production applications, supported by extensive documentation, tutorials, and a free Julia Academy course. Cited in academic literature and trusted by practitioners, DataFrames.jl is one of the core libraries for Julia’s growing data ecosystem. -
The most user-friendly sales commission software for revenue-focused teamsRated as #1 sales compensation management software, Everstage helps businesses streamline commission administration, boost sales performance and improve ROI with actionable insights. Top features: No-code plan designer, detailed commission statements, advanced commission forecasting, quota management, queries & approval workflows, deferred commissions (ASC606), BI-powered reporting, and more.
-
10
MLJBase.jl
Core functionality for the MLJ machine learning framework
Repository for developers that provides core functionality for the MLJ machine learning framework. MLJ is a Julia framework for combining and tuning machine learning models. This repository provides core functionality for MLJ. -
11
ChainRulesCore
AD-backend agnostic system defining custom forward and reverse rules
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system. The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself. This will allow your package to be used with ChainRules.jl, which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives. -
12
Clang.jl
C binding generator and Julia interface to libclang
This package provides a Julia language wrapper for libclang: the stable, C-exported interface to the LLVM Clang compiler. The libclang API documentation provides background on the functionality available through libclang, and thus through the Julia wrapper. The repository also hosts related tools built on top of libclang functionality. -
13
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. -
14
MathLink.jl
Julia language interface for Mathematica/Wolfram Engine
This package provides access to Mathematica/Wolfram Engine via the MathLink library, now renamed to Wolfram Symbolic Transfer Protocol (WSTP). -
15
LabPlot
Data Visualization and Analysis
LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone. -
16
Compose.jl
Declarative vector graphics
Compose is a vector graphics library for Julia. It forms the basis for the statistical graphics system Gadfly. Compose is a declarative vector graphics system written in Julia. It's designed to simplify the creation of complex graphics and serves as the basis of the Gadfly data visualization package. -
17
FromFile.jl
Julia enhancement proposal (Julep) for implicit per file module
This package exports a macro @from, which can be used to import objects from files. The hope is that you will never have to write include again. FromFile is a Julia Language package. To install FromFile, please open Julia's interactive session (known as REPL) and press ] key in the REPL to use the package mode. -
18
HDF5.jl
Save and load data in the HDF5 file format from Julia
HDF5 stands for Hierarchical Data Format v5 and is closely modeled on file systems. In HDF5, a "group" is analogous to a directory, a "dataset" is like a file. HDF5 also uses "attributes" to associate metadata with a particular group or dataset. HDF5 uses ASCII names for these different objects, and objects can be accessed by Unix-like pathnames, e.g., "/sample1/tempsensor/firsttrial" for a top-level group "sample1", a subgroup "tempsensor", and a dataset "firsttrial". For simple types (scalars, strings, and arrays), HDF5 provides sufficient metadata to know how each item is to be interpreted. For example, HDF5 encodes that a given block of bytes is to be interpreted as an array of Int64, and represents them in a way that is compatible across different computing architectures. However, to preserve Julia objects, one generally needs additional type information to be supplied, which is easy to provide using attributes. -
19
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. -
20
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. -
21
MAT.jl
Julia module for reading MATLAB files
Julia module for reading MATLAB files. Read and write MATLAB files in Julia. This library can read MATLAB .mat files, both in the older v4/v5/v6/v7 format, as well as the newer v7.3 format. -
22
ONNX.jl
Read ONNX graphs in Julia
ONNX.jl is in the process of a total reconstruction and currently supports saving & loading graphs as a Umlaut.Tape. When possible, functions from NNlib or the standard library are used, but no conversion to Flux is implemented yet. See resnet18.jl for a practical example of graph loading. -
23
ReservoirComputing.jl
Reservoir computing utilities for scientific machine learning (SciML)
ReservoirComputing.jl provides an efficient, modular and easy-to-use implementation of Reservoir Computing models such as Echo State Networks (ESNs). For information on using this package please refer to the stable documentation. Use the in-development documentation to take a look at not-yet-released features. -
24
ArviZ.jl
Exploratory analysis of Bayesian models with Julia
ArviZ.jl (pronounced "AR-vees") is a Julia package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, model checking, comparison and diagnostics. -
25
Bayesian Julia
Bayesian Statistics using Julia and Turing
Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. Mostly notorious is null-hypothesis significance testing (NHST) based on p-values. Bayesian statistics incorporate uncertainty (and prior knowledge) by allowing probability statements about parameters.
