Category Archives: Julia

ElectronDisplay.jl v1.0.0 Released

By: Tony Lian

Re-posted from: https://www.queryverse.org//2020/02/05/electrondisplay-v1.0.0/

ElectronDisplay.jl is a package that provides a simple UI based on Electron for displaying plots and tabular data. It works with most plotting packages and almost all data structures that represent tabular data in the Julia ecosystem. The package is a core piece of the Queryverse.

What is new?

There are a bunch of bugfixes as well as some new features in v1.0.0. The main new feature is the plotgallery, an interactive plot display UI that is written in TypeScript and based on React. It supports displaying multiple plots in the same window simultaniously, even if those plots are created with different plotting packages. For example, you can display a Gadfly.jl figure and then plot a VegaLite.jl plot, or multiple VegaLite.jl plots, and they would show in a single window instead of opening a new window each time or replacing the old plots.

Some functionalities in detail

  • Press CTRL + C on Windows and Linux or Command + C on macOS to copy the current figure to the system clipboard
  • Press arrow keys to switch between plots quickly and delete/backspace to remove a graph
  • Supports Vega, VegaLite, and several image formats (such as PNG, JPG, and SVG)
  • Support viewport-sized plots that resize according to the window size

Plotgallery screenshot

You can have a look at the detailed release notes here.

Contributions are welcomed

ElectronDisplay.jl is still a new package that needs help from the community to resolve the current issues that it has. In fact, as an apprentice of the Undergraduate Research Apprentice Program supervised by David Anthoff, I have been working on several issues on this project over the past few weeks after I made the initial pull request. If you are interested in making contributions to the project, you can have a look at Issues for ElectronDisplay.jl, or Issues for Plotgallery.

Julia Computing Brings Machine Learning in Julia Workshop to Prague

Prague, Czech Republic – Julia Computing’s Avik Sengupta (VP Engineering) and Coillte’s Kevin O’Brien (Forestry Resource Modeller) will lead a workshop on “Machine Learning in Julia” at the Machine Learning Prague conference on March 20, 2020.

Julia is the fastest high-performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and other scientific and numeric computing applications. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++.

Julia works with GPUs, TPUs, multithreading and parallel processing to deliver seamless unlimited scalability from a single CPU to thousands of nodes, cores and threads in the public or private cloud. Julia has run at petascale on 9,300 Knights Landing (KNL) nodes with 650,000 cores and 1.3 million threads to analyze over 56 terabytes of data using Cori, one of the ten largest and most powerful supercomputers in the world.

Julia has been downloaded more than 13 million times and is used at more than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software and the 2019 Sidney Fernbach Award.

Julia is used by hundreds of firms worldwide in engineering, biotechnology, pharmaceutical research, aviation, manufacturing and medicine for image and pattern recognition, flight path planning, risk analysis, optimization and more. Examples include:

  • Path BioAnalytics: Path BioAnalytics is a computational biotech company using precision medicine for drug discovery and development and treatment of disease. By switching to Julia, Path BioAnalytics decreased computation 65x, increased accuracy 55% and significantly reduced their code base.

  • Lincoln Labs: Lincoln Labs works with the US Federal Aviation Administration (FAA) to develop and deploy the next generation Airborne Collision Avoidance System (ACAS-X). They use Julia to compute 650 billion decision points within an optimized logic table to identify failures. Julia reduced the time required to conduct these computations by several years.

  • Brazilian National Institute for Space Research: Brazil’s space mission planning research institute uses Julia to plan space missions. They leverage Julia’s superior speed and ease of use to build a simulator, create multidisciplinary design optimization (MDO) tools for space mission planning and design a attitude and orbit control subsystem (AOCS).

  • Contextflow: Contextflow uses Julia for artificial intelligence to search and analyze medical images to improve the speed and accuracy of medical diagnosis and treatment. Julia reduced the time required for image searching from up to 20 minutes to less than 2 seconds.

  • Aviva: One of Europe’s largest insurers uses Julia for risk analysis, including Solvency II compliance. According to Tim Thornham, Aviva’s Director of Financial Modeling, “Solvency II compliant models in Julia are one thousand times faster, use 93% fewer lines of code and took one-tenth the time to implement” compared with their legacy system.

About Julia Computing

Julia Computing was founded in 2015 by all the creators of Julia to provide products including JuliaTeam, JuliaSure and JuliaRun to businesses and researchers using Julia.