Open Source Julia Data Visualization Software

Julia Data Visualization Software

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Browse 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.

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  • 1
    FastAI.jl

    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.
    Downloads: 11 This Week
    Last Update:
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  • 2
    DocOpt.jl

    DocOpt.jl

    Command line arguments parser

    DocOpt.jl is a port of docopt written in the Julia language. docopt generates a command-line arguments parser from human-readable usage patterns. The DocOpt module exports just one function, docopt, which takes multiple arguments but all of them except the first one are optional.
    Downloads: 8 This Week
    Last Update:
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  • 3
    Oceananigans.jl

    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.
    Downloads: 5 This Week
    Last Update:
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  • 4
    LabPlot

    LabPlot

    Data Visualization and Analysis

    LabPlot is a FREE, open source and cross-platform Data Visualization and Analysis software accessible to everyone.
    Downloads: 32 This Week
    Last Update:
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  • 5
    Roots.jl

    Roots.jl

    Root finding functions for Julia

    This package contains simple routines for finding roots, or zeros, of scalar functions of a single real variable using floating-point math. The find_zero function provides the primary interface. The basic call is find_zero(f, x0, [M], [p]; kws...) where, typically, f is a function, x0 a starting point or bracketing interval, M is used to adjust the default algorithms used, and p can be used to pass in parameters. Bisection-like algorithms. For functions where a bracketing interval is known (one where f(a) and f(b) have alternate signs), a bracketing method, like Bisection, can be specified. The default is Bisection, for most floating point number types, employed in a manner exploiting floating point storage conventions. For other number types (e.g. BigFloat), an algorithm of Alefeld, Potra, and Shi is used by default. These default methods are guaranteed to converge. Other bracketing methods are available.
    Downloads: 4 This Week
    Last Update:
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  • 6
    StackExchangeCodes

    StackExchangeCodes

    Codes related to answers on StackExchange Network

    Codes related to answers by Royi Avital on StackExchange Network.
    Downloads: 4 This Week
    Last Update:
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  • 7
    Julia VS Code

    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.
    Downloads: 3 This Week
    Last Update:
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  • 8
    ParallelAccelerator.jl

    ParallelAccelerator.jl

    ParallelAccelerator package, part of the High Performance Scripting

    ParallelAccelerator is a Julia package for speeding up compute-intensive Julia programs. In particular, Julia code that makes heavy use of high-level array operations is a good candidate for speeding up with ParallelAccelerator. With the @acc macro that ParallelAccelerator provides, users may specify parts of a program to accelerate. ParallelAccelerator compiles these parts of the program to fast native code. It automatically eliminates overheads such as array bounds checking when it is safe to do so. It also parallelizes and vectorizes many data-parallel operations. ParallelAccelerator is part of the High Performance Scripting (HPS) project at Intel Labs.
    Downloads: 3 This Week
    Last Update:
    See Project
  • 9
    CUDA.jl

    CUDA.jl

    CUDA programming in Julia

    High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will happen automatically when you install the package using Julia's package manager.
    Downloads: 2 This Week
    Last Update:
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  • 10
    ClimateTools.jl

    ClimateTools.jl

    Climate science package for Julia

    Climate analysis tools in Julia. ClimateTools.jl is a collection of commonly-used tools in Climate science. Basics of climate field analysis are covered, with some forays into exploratory techniques associated with climate scenario design. The package is aimed to ease the typical steps of analysis of climate models outputs and gridded datasets (support for weather stations is a work-in-progress). Climate indices and bias correction functions are coded to leverage the use of multiple threads. To gain maximum performance, use (bash shell Linux/MacOSX) export JULIA_NUM_THREADS=n, where n is the number of threads. To get an idea of the number of threads you can use type (in Julia) Sys.THREADS. This is especially useful for bias correction.
    Downloads: 2 This Week
    Last Update:
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  • 11
    CommonMark.jl

    CommonMark.jl

    A CommonMark-compliant parser for Julia

    A CommonMark-compliant parser for Julia.
    Downloads: 2 This Week
    Last Update:
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  • 12
    JDF.jl

    JDF.jl

    Julia DataFrames serialization format

    JDF is a DataFrames serialization format with the following goals, fast save and load times, compressed storage on disk, enabled disk-based data manipulation (not yet achieved), and support for machine learning workloads, e.g. mini-batch, sampling (not yet achieved). JDF stores a DataFrame in a folder with each column stored as a separate file. There is also a metadata.jls file that stores metadata about the original DataFrame. Collectively, the column files, the metadata file, and the folder is called a JDF "file". JDF.jl is a pure-Julia solution and there are a lot of ways to do nifty things like compression and encapsulating the underlying struture of the arrays that's hard to do in R and Python. E.g. Python's numpy arrays are C objects, but all the vector types used in JDF are Julia data types.
    Downloads: 2 This Week
    Last Update:
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  • 13
    LLVM.jl

    LLVM.jl

    Julia wrapper for the LLVM C API

    A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language.
    Downloads: 2 This Week
    Last Update:
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  • 14
    Manopt.jl

    Manopt.jl

    Optimization on Manifolds in Julia

    Optimization Algorithm on Riemannian Manifolds. A framework to implement arbitrary optimization algorithms on Riemannian Manifolds. Library of optimization algorithms on Riemannian manifolds. Easy-to-use interface for (debug) output and recording values during an algorithm run. Several tools to investigate the algorithms, gradients, and optimality criteria.
    Downloads: 2 This Week
    Last Update:
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  • 15
    OpenCL.jl

    OpenCL.jl

    OpenCL Julia bindings

    Julia interface for the OpenCL parallel computation API. This package aims to be a complete solution for OpenCL programming in Julia, similar in scope to PyOpenCL for Python. It provides a high level API for OpenCL to make programing hardware accelerators, such as GPUs, FPGAs, and DSPs, as well as multicore CPUs much less onerous.
    Downloads: 2 This Week
    Last Update:
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  • 16
    Pluto.jl

    Pluto.jl

    Simple reactive notebooks for Julia plutojl.org

    We are on a mission to make scientific computing more accessible and fun. Writing a notebook is not just about writing the final document, Pluto empowers the experiments and discoveries that are essential to getting there.
    Downloads: 2 This Week
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  • 17
    PyCall.jl

    PyCall.jl

    Package to call Python functions from the Julia language

    Package to call Python functions from the Julia language. This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.
    Downloads: 2 This Week
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  • 18
    VoronoiFVM.jl

    VoronoiFVM.jl

    Solution of nonlinear multiphysics partial differential equations

    Solver for coupled nonlinear partial differential equations (elliptic-parabolic conservation laws) based on the Voronoi finite volume method. It uses automatic differentiation via ForwardDiff.jl and DiffResults.jl to evaluate user functions along with their jacobians and calculate derivatives of solutions with respect to their parameters.
    Downloads: 2 This Week
    Last Update:
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  • 19
    Vulkan.jl

    Vulkan.jl

    Using Vulkan from Julia

    Vulkan.jl is a lightweight wrapper around the Vulkan graphics and compute library. It exposes abstractions over the underlying C interface, primarily geared toward developers looking for a more natural way to work with Vulkan with minimal overhead. It builds upon the core API provided by VulkanCore.jl. Because Vulkan is originally a C specification, interfacing with it requires some knowledge before correctly being used from Julia. This package acts as an abstraction layer, so that you don't need to know how to properly call a C library, while still retaining full functionality. The wrapper is generated directly from the Vulkan Specification.
    Downloads: 2 This Week
    Last Update:
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  • 20
    CausalityTools.jl

    CausalityTools.jl

    Algorithms for detecting associations, dynamical influences

    CausalityTools.jl is a package for quantifying associations and dynamical coupling between datasets, independence testing, and causal inference. Association measures from conventional statistics, information theory, and dynamical systems theory, for example, distance correlation, mutual information, transfer entropy, convergent cross mapping and a lot more. A dedicated API for independence testing, which comes with automatic compatibility with every measure-estimator combination you can think of. For example, we offer the generic SurrogateTest, which is fully compatible with TimeseriesSurrogates.jl, and the LocalPermutationTest for conditional independence testing.
    Downloads: 1 This Week
    Last Update:
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  • 21
    Conda.jl

    Conda.jl

    https://github.com/JuliaPy/Conda.jl

    This package allows one to use conda as a cross-platform binary provider for Julia for other Julia packages, especially to install binaries that have complicated dependencies like Python. conda is a package manager that started as the binary package manager for the Anaconda Python distribution, but it also provides arbitrary packages. Instead of the full Anaconda distribution, Conda.jl uses the miniconda Python environment, which only includes conda and its dependencies.
    Downloads: 1 This Week
    Last Update:
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  • 22
    DiffEqOperators.jl

    DiffEqOperators.jl

    Linear operators for discretizations of differential equations

    DiffEqOperators.jl is a package for finite difference discretization of partial differential equations. It allows building lazy operators for high order non-uniform finite differences in an arbitrary number of dimensions, including vector calculus operators. For the operators, both centered and upwind operators are provided, for domains of any dimension, arbitrarily spaced grids, and for any order of accuracy. The cases of 1, 2, and 3 dimensions with an evenly spaced grid are optimized with a convolution routine from NNlib.jl. Care is taken to give efficiency by avoiding unnecessary allocations, using purpose-built stencil compilers, allowing GPUs and parallelism, etc. Any operator can be concretized as an Array, a BandedMatrix or a sparse matrix.
    Downloads: 1 This Week
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  • 23
    Ferrite.jl

    Ferrite.jl

    Finite element toolbox for Julia

    A simple finite element toolbox written in Julia.
    Downloads: 1 This Week
    Last Update:
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  • 24
    GraphNeuralNetworks.jl

    GraphNeuralNetworks.jl

    Graph Neural Networks in Julia

    GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl.
    Downloads: 1 This Week
    Last Update:
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  • 25
    InferOpt.jl

    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.
    Downloads: 1 This Week
    Last Update:
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