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PGFPlotsX is a Julia package to generate publication quality figures using the LaTeX library PGFPlots. It is similar in spirit to the package PGFPlots.jl but it tries to have a very close mapping to the PGFPlots API as well as minimize the number of dependencies. The fact that the syntax is similar to the TeX version means that examples from Stack Overflow and the PGFPlots manual can easily be incorporated in the Julia code.

https://github.com/KristofferC/PGFPlotsX.jlTags | pgfplots julia latex plotting |

Implementation | Julia |

License | MIT |

Platform |

matlab2tikz is a MATLAB(R) script to convert native MATLAB(R) figures to TikZ/Pgfplots figures that integrate seamlessly in LaTeX documents. To download the official releases and rate matlab2tikz, please visit its page on FileExchange.

matlab tikz pgfplots octave matlab2tikz latexfor native inclusion into LaTeX documents. matplotlib2tikz works with both Python 2 and Python 3.

tikz matplotlib pgfplots latexJulia.jl aggregates and curates decibans of knowledge resources for programming in Julia, an all-purpose programming language that addresses the needs of high-performance numerical analysis and computational science. For Base packages, check if the package you seek is listed in the built-in package manager on github, or check METADATA for registered Julia packages, then use the built-in package manager to install it after checking the requirements for respective versions. Pkg3.jl is an alpha next-generation package manager for Julia that creates a Manifest.toml file that records the exact versions of each dependency and their transitive dependencies.

julia julialang awesome-listPowerful convenience for Julia visualizations and data analysis

julia plotting visualization julia-languageThe well-optimized DifferentialEquations solvers benchmark as the some of the fastest implementations, using classic algorithms and ones from recent research which routinely outperform the "standard" C/Fortran methods, and include algorithms optimized for high-precision and HPC applications. At the same time, it wraps the classic C/Fortran methods, making it easy to switch over to them whenever necessary. It integrates with the Julia package sphere, for example using Juno's progress meter, automatic plotting, built-in interpolations, and wraps other differential equation solvers so that many different methods for solving the equations can be accessed by simply switching a keyword argument. It utilizes Julia's generality to be able to solve problems specified with arbitrary number types (types with units like Unitful, and arbitrary precision numbers like BigFloats and ArbFloats), arbitrary sized arrays (ODEs on matrices), and more. This gives a powerful mixture of speed and productivity features to help you solve and analyze your differential equations faster. For information on using the package, see the stable documentation. Use the latest documentation for the version of the documentation which contains the un-released features.

differential-equations differentialequations julia ode sde pde dae stochastic dde spde delay monte-carlo-simulation stochastic-processes stochastic-differential-equations delay-differential-equations partial-differential-equations differential-algebraic-equations simulation numerical-integration dynamical-systemsJulia support for Vim. The full documentation is available from Vim: after installation, you just need to type :help julia-vim.

julia vim vim-plugin unicode latexCoCalc offers collaborative calculation in the cloud. This includes working with the full (scientific) Python stack, SageMath, Julia, R, Octave, and more. It also offers capabilities to author documents in LaTeX, R/knitr or Markdown, storing and organizing files, a web-based Linux Terminal, communication tools like a chat, course management and more. You can easily use CoCalc on your own computer for free by running a Docker image.

sagemath cloud jupyter-notebook jupyter latex r octave markdown mathjax terminal coffeescript postgresql nodejs mathematics gap pari juliaPythonTeX provides fast, user-friendly access to Python from within LaTeX. It allows Python code entered within a LaTeX document to be executed, and the results to be included within the original document. It also provides syntax highlighting for code within LaTeX documents via the Pygments syntax highlighter. PythonTeX also provides support for Ruby, Julia, Octave, Sage, Bash, and Rust. Support for additional languages is coming soon.

pgfplots - A TeX package to draw normal and/or logarithmic plots directly in TeX in two and three dimensions with a user-friendly interface and pgfplotstable - a TeX package to round and format numerical tables. Examples in manuals and/or on web site.

jl ("JSON lambda") is a tiny functional language for querying and manipulating JSON. Binary releases for Linux and OS X are available here.

json command-line-tool command-line haskellJulia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below. New developers may find the notes in CONTRIBUTING helpful to start contributing to the Julia codebase.

julia julia-language programming-language scientific-computing high-performance-computing numerical-computation machine-learningThe Julia base package is pretty big, although at the same time, there are lots of other packages around to expand it with. The result is that on the whole, it is impossible to give a thorough overview of all that Julia can do in just a few brief exercises. Therefore, I had to adopt a little 'bias', or 'slant' if you please, in deciding what to focus on and what to ignore. Julia is a technical computing language, although it does have the capabilities of any general purpose language and you'd be hard-pressed to find tasks it's completely unsuitable for (although that does not mean it's the best or easiest choice for any of them). Julia was developed with the occasional reference to R, and with an avowed intent to improve upon R's clunkiness. R is a great language, but relatively slow, to the point that most people use it to rapid prototype, then implement the algorithm for production in Python or Java. Julia seeks to be as approachable as R but without the speed penalty.

julia learning-julia language learning learning-by-doing julia-language julialang data-science statistics technical-computing hpc scientific-computingThis 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. Within Julia, just use the package manager to run Pkg.add("PyCall") to install the files. Julia 0.5 or later is required.

Several MIT courses involving numerical computation, including 18.06, 18.303, 18.330, 18.335/6.337, 18.337/6.338, and 18.338, are beginning to use Julia, a fairly new language for technical computing. This page is intended to supplement the Julia documentation with some simple tutorials on installing and using Julia targeted at MIT students. See also our Julia cheatsheet listing a few basic commands, as well as the Learn Julia in Y minutes tutorial page. In particular, we will be using Julia in the IJulia browser-based enviroment, which leverages your web browser and IPython to provide a rich environment combining code, graphics, formatted text, and even equations, with sophisticated plots via Matplotlib.

Wisp : Wisp Is Scala Plotting is a console-centric plotting library for scala. It focuses on existing web-based plotting libraries, and strives to bring the power and flexibility of web-based plotting tools to the scala console, while preserving an at-your-finger-tips feel readily found in matlab, R, and many other languages. Wisp is open source, and we hope to get involvement from the community. We'd love to get some pull requests. Also, even if you don't have a fix, feel free to report bugs or just request new features through the github issue tracker.

Run scripts based on file name, a selection of code, or by line number. NOTE: Some grammars may require you to install a custom language package.

script runner bash behat-feature coffeescript coffeescript-(literate) cucumber-(gherkin) elixir f# fsharp f* fstar haskell julia latex mongodb newlisp powershell rspec ruby-on-rails run code lammpsUntil an issue with one of our dependencies is resolved, LightGraphs will not work with any Julia 0.7 or 1.0 version that has been built from source on OSX or other systems with a compiler more modern than GCC7. If you use LightGraphs with Julia 0.7 or 1.0, please download a Julia binary. LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type.

julia graph lightgraphs graph-theory graph-generation graph-analytics graph-algorithmsgnuplot is a command-driven interactive function plotting program. It can be used to plot functions and data points in both two- and three-dimensional plots in many different formats. It is designed primarily for the visual display of scientific data.

graphics chart visualization plotting-engine
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