Open Source Julia Data Visualization Software

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
    AppleAccelerate.jl

    AppleAccelerate.jl

    Julia interface to the macOS Accelerate framework

    Julia interface to the macOS Accelerate framework. This provides a Julia interface to some of the macOS Accelerate frameworks. At the moment, this package provides access to Accelerate BLAS and LAPACK using the libblastrampoline framework, an interface to the array-oriented functions, which provide a vectorized form for many common mathematical functions. The performance is significantly better than using standard libm functions in some cases, though there does appear to be some reduced accuracy.
    Downloads: 12 This Week
    Last Update:
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  • 2
    MLJBase.jl

    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.
    Downloads: 2 This Week
    Last Update:
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  • 3
    JuliaFEM.jl

    JuliaFEM.jl

    The JuliaFEM software library is a framework

    The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The basic design principle is: that everything is nonlinear. All physics models are nonlinear from which the linearization are made as special cases.
    Downloads: 1 This Week
    Last Update:
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  • 4
    AbstractFFTs.jl

    AbstractFFTs.jl

    A Julia framework for implementing FFTs

    A general framework for fast Fourier transforms (FFTs) in Julia. This package is mainly not intended to be used directly. Instead, developers of packages that implement FFTs (such as FFTW.jl or FastTransforms.jl) extend the types/functions defined in AbstractFFTs. This allows multiple FFT packages to co-exist with the same underlying fft(x) and plan_fft(x) interface.
    Downloads: 0 This Week
    Last Update:
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  • 5
    Clapeyron

    Clapeyron

    Framework for the development and use of fluid-thermodynamic models

    Welcome to Clapeyron! This module provides both a large library of thermodynamic models and a framework for one to easily implement their own models. Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.
    Downloads: 0 This Week
    Last Update:
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  • 6
    ConcurrentSim.jl

    ConcurrentSim.jl

    Discrete event process oriented simulation framework written in Julia

    A discrete event process-oriented simulation framework written in Julia inspired by the Python library SimPy. One of the longest-lived Julia packages (originally under the name SimJulia).
    Downloads: 0 This Week
    Last Update:
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  • 7
    InvertibleNetworks.jl

    InvertibleNetworks.jl

    A Julia framework for invertible neural networks

    Building blocks for invertible neural networks in the Julia programming language.
    Downloads: 0 This Week
    Last Update:
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  • 8
    ReTest.jl

    ReTest.jl

    Testing framework for Julia

    ReTest is a testing framework for Julia allowing defining tests in source files, whose execution is deferred and triggered on demand. This is useful when one likes to have definitions of methods and corresponding tests close to each other. This is also useful for code that is not (yet) organized as a package, and where one doesn't want to maintain a separate set of files for tests. Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for running only part of the test suite of a package. For example, if you made a change related to addition, and included "addition" in the description of the corresponding testsets, you can easily run only these tests.
    Downloads: 0 This Week
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