Search Results for "big data visualization" - Page 16
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Showing 391 open source projects for "big data visualization"
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Strategems
Quantitative systematic trading strategy development and backtesting
Strategems is a Julia package aimed at simplifying and streamlining the process of developing, testing, and optimizing algorithmic/systematic trading strategies. This package is inspired in large part by the quantstrat1,2 package in R, adopting a similar general structure to the building blocks that make up a strategy. Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward... -
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Cubature.jl
One- and multi-dimensional adaptive integration routines for Julia
This module provides one- and multi-dimensional adaptive integration routines for the Julia language, including support for vector-valued integrands and facilitation of parallel evaluation of integrands, based on the Cubature Package by Steven G. Johnson. Adaptive integration works by evaluating the integrand at more and more points until the integrand converges to a specified tolerance (with the error estimated by comparing integral estimates with different numbers of points). The Cubature... -
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julia-mode
Julia support in Emacs
Emacs major mode for the Julia programming language. -
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Cxx.jl
The Julia C++ Interface
The Julia C++ Foreign Function Interface (FFI) and REPL. Now, this package provides an out-of-box installation experience on 64-bit Linux, macOS and Windows. -
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Immerse.jl
Dive deeper into your data with interactive graphics
Dive deeper into your data with interactive graphics. Immerse is a wrapper that adds graphical interactivity to Julia's plots. Currently, Immerse supports Gadfly. The toolbar at the top supports saving your figure to a file, zooming and panning, and lasso selection. Zooming and panning uses the defaults set by GtkUtilities. The left mouse button allows you to rubberband-select a zoom region. Use your mouse wheel or arrow-keys to pan or change the zoom level. Double-click, or press the 1:1... -
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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... -
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Julia plugin for the IntelliJ Platform
Julia Plugin for IntelliJ IDEA
This is a work in progress, some features are implemented partially, there may be performance and stability problems. Install IntelliJ IDEA (or other JetBrains IDEs), open Settings | Plugins | Browse repositories, install Julia plugin, and create a Julia project. -
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TensorFlow.jl
A Julia wrapper for TensorFlow
A wrapper around TensorFlow, a popular open-source machine learning framework from Google. -
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julia-vim
Vim support for Julia.
Vim support for Julia. This plug-in adds some functionality to substitute LaTeX code sequences (e.g. \alpha) with corresponding Unicode symbols (e.g. α). By default, these substitutions must be triggered explicitly by pressing the Tab key, as in the Julia command line (the REPL); however, an automatic, as-you-type mode can also be activated, and a method based on keymap is also available. This feature also works in command mode, e.g. when searching the files with the / or ? commands, but the... -
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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. -
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Plotly.jl
A Julia interface to the plot.ly plotting library and cloud services
A Julia interface to the plot.ly plotting library and cloud services. Plotting functions provided by this package are identical to PlotlyJS. Please consult its documentation. In fact, the package depends on PlotlyJS.jl and reexports all the methods. -
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TerminalMenus.jl
Simple interactive menus for the terminal (Now ships with Julia!)
This package has been merged into the Julia standard library. As such, you probably just want to using REPL.TerminalMenus and skip the Installation instructions. The RadioMenu allows the user to select one option from the list. The request function displays the interactive menu and returns the index of the selected choice. If a user presses 'q' or ctrl-c, request will return a -1. -
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BERT
Connector for Excel and the programming languages R and Julia
BERT is a tool for connecting Excel with the statistics language R. Specifically, it’s designed to support running R functions from Excel spreadsheet cells. In Excel terms, it’s for writing User-Defined Functions (UDFs) in R. All you have to do is write the function. Everything else – loading the function into Excel, managing parameters, and handling type conversion – is done automatically for you. It really could not be any easier. BERT also has a console that you can use to control Excel... -
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HttpServer.jl
Basic, non-blocking HTTP server in Julia
This is a basic, non-blocking HTTP server in Julia. You can write a basic application using just this if you're happy dealing with values representing HTTP requests and responses directly. For a higher-level view, you could use Mux. If you'd like to use WebSockets as well, you'll need to grab WebSockets.jl. -
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Merlin.jl
Deep Learning for Julia
Merlin is a deep learning framework written in Julia. It aims to provide a fast, flexible and compact deep learning library for machine learning. Merlin is tested against Julia 1.0 on Linux, OS X, and Windows (x64). -
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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...