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Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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Braket.jl
Experimental Julia implementation of the Amazon Braket SDK
Braket.jl is not an officially supported AWS product. This package is a Julia implementation of the Amazon Braket SDK allowing customers to access Quantum Hardware and Simulators. This is experimental software, and support may be discontinued in the future. For a fully supported SDK, please use the Python SDK. We may change, remove, or deprecate parts of the API when making new releases. Please review the CHANGELOG for information about changes in each release. -
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Run Page
Make your own running home page
GitHub Actions manages automatic synchronization of runs and generation of new pages. Gatsby-generated static pages, fast. Support for Vercel (recommended) and GitHub Pages automated deployment. React Hooks. Mapbox for map display. Supports most sports apps such as nike strava. Automatically backup gpx data for easy backup and uploading to other software. -
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PairPlots.jl
Beautiful and flexible vizualizations of high dimensional data
Beautiful and flexible visualizations of high-dimensional data. This package produces pair plots, otherwise known as corner plots or scatter plot matrices: grids of 1D and 2D histograms that allow you to visualize high-dimensional data. Pair plots are an excellent way to visualize the results of MCMC simulations, but are also a useful way to visualize correlations in general data tables. The default styles of this package roughly reproduce the output of the Python library corner.py for a... -
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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... -
Combine Jira and SCM data to improve team performanceJellyfish is the leading Engineering Management Platform, providing complete visibility into engineering organizations, the work they do, and how they operate. By analyzing engineering signals from Git and Jira, qualitative team feedback, and contextual business data from roadmapping, incident response, HR, calendar, and collaboration tools, Jellyfish enables engineering leaders to align engineering decisions with business initiatives and deliver the right software, efficiently, on time. With Jellyfish, engineering leaders can focus their teams on what matters most to the business, driving strategic decisions and delivering results.
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Digital Earth Australia notebooks
Repository for Digital Earth Australia Jupyter Notebooks
The knowledge hub brings together information about Digital Earth Australia’s products and services, allowing you to utilize our free and open-source satellite imagery archive. Browse our catalog of data products to find supporting information and ways to access the data. The Digital Earth Australia notebooks and tools repository (dea-notebooks) hosts Jupyter Notebooks, Python scripts and workflows for analyzing Digital Earth Australia (DEA) satellite data and derived products. This... -
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electricityMap
A real-time visualisation of the CO2 emissions of electricity
Real-time visualization of the Greenhouse Gas (in terms of CO2 equivalent) footprint of electricity consumption built with d3.js and mapbox GL. Real-time data is defined as a data source with an hourly (or better) frequency, delayed by less than 2hrs. It should provide a breakdown by generation type. Often fossil fuel generation (coal/gas/oil) is combined under a single heading like 'thermal' or 'conventional', this is not a problem. Citizens should not be responsible for the emissions... -
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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. -
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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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nb-clean
Clean Jupyter notebooks of outputs, metadata, and empty cells
nb-clean cleans Jupyter notebooks of cell execution counts, metadata, outputs, and (optionally) empty cells, preparing them for committing to version control. It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines.... -
Melis Platform is an enterprise-grade Low Code Platform simplifying app creation, management, and delivery.Melis is a new generation of Content Management System and eCommerce platform to achieve and manage websites from a single web interface easy to use while offering the best of open source technology.
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its... -
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TIGRE
TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox
TIGRE is an open-source toolbox for fast and accurate 3D tomographic reconstruction for any geometry. Its focus is on iterative algorithms for improved image quality that have all been optimized to run on GPUs (including multi-GPUs) for improved speed. It combines the higher-level abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make it both fast and easy to use. TIGRE is free to download and distribute: use it, modify it, add to it, and share it. Our... -
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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... -
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UnROOT.jl
Native Julia I/O package to work with CERN ROOT files objects
UnROOT.jl is a reader for the CERN ROOT file format written entirely in Julia, without any dependence on ROOT or Python. -
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Tokenize.jl
Tokenization for Julia source code
Tokenize is a Julia package that serves a similar purpose and API as the tokenize module in Python but for Julia. This is to take a string or buffer containing Julia code, perform lexical analysis and return a stream of tokens. -
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CounterfactualExplanations.jl
A package for Counterfactual Explanations and Algorithmic Recourse
CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs. -
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Circuitscape.jl
Algorithms from circuit theory to predict connectivity
Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over the... -
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GoldenCheetah
Performance Software for Cyclists, Runners, Triathletes and Coaches
Analyze using summary metrics like BikeStress, TRIMP, or RPE. Extract insight via models like Critical Power and W'bal. Track and predict performance using models like Banister and PMC. Optimize aerodynamics using Virtual Elevation. Train indoors with ANT and BTLE trainers. Upload and Download with many cloud services including Strava, Withings, and Today's Plan. Import and export data to and from a wide range of bike computers and file formats. Track body measures, and equipment use and set... -
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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... -
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Sundials.jl
Julia interface to Sundials, including a nonlinear solver
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. -
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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. -
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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... -
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Graphs.jl
An optimized graphs package for the Julia programming language
The goal of Graphs.jl is to offer a performant platform for network and graph analysis in Julia, following the example of libraries such as NetworkX in Python. Offers a set of simple, concrete graph implementations – SimpleGraph (for undirected graphs) and SimpleDiGraph (for directed graphs), an API for the development of more sophisticated graph implementations under the AbstractGraph type, and a large collection of graph algorithms with the same requirements as this API. -
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InteractiveViz.jl
Interactive visualization tools for Julia
Julia already has a rich set of plotting tools in the form of the Plots and Makie ecosystems, and various backends for these. So why another plotting package? InteractiveViz is not a replacement for Plots or Makie, but rather a graphics pipeline system developed on top of Makie. It has a few objectives. To provide a simple API to visualize large or possibly infinite datasets (tens of millions of data points) easily. To enable interactivity, and be responsive even with large amounts of data.... -
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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. -
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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...
