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Showing 27 open source projects for "syntax"
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CommonMark.jl
A CommonMark-compliant parser for Julia
A CommonMark-compliant parser for Julia. -
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DataFramesMeta.jl
Metaprogramming tools for DataFrames
Metaprogramming tools for DataFrames.jl objects to provide more convenient syntax. DataFrames.jl has the functions select, transform, and combine, as well as the in-place select! and transform! for manipulating data frames. DataFramesMeta.jl provides the macros @select, @transform, @combine, @select!, and @transform! to mirror these functions with more convenient syntax. Inspired by dplyr in R and LINQ in C#. -
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Chain.jl
A Julia package for piping a value through transformation expressions
A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality. -
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PythonCall & JuliaCall
Python and Julia in harmony
Bringing Python® and Julia together in seamless harmony. Call Python code from Julia and Julia code from Python via a symmetric interface. Simple syntax, so the Python code looks like Python and the Julia code looks like Julia. Intuitive and flexible conversions between Julia and Python: anything can be converted, you are in control. Fast non-copying conversion of numeric arrays in either direction: modify Python arrays (e.g. bytes, array. array, numpy.ndarray) from Julia or Julia arrays from Python. ... -
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GMT.jl
Generic Mapping Tools Library Wrapper for Julia
...In this sense, all GMT options are put in a single text string that is passed, plus the data itself when it applies, to the gmt() command. However, we also acknowledge that not every one is comfortable with the GMT syntax. This syntax is needed to accommodate the immense pool of options that let you control all details of a figure but that also makes it harder to read/master. -
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Gnuplot.jl
Julia interface to gnuplot
Gnuplot.jl is a simple package able to send both data and commands from Julia to an underlying gnuplot process. Its main purpose it to provide a fast and powerful data visualization framework, using an extremely concise Julia syntax. It also has automatic display of plots in Jupyter, Juno and VS Code. -
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Coluna.jl
Branch-and-Price-and-Cut in Julia
Coluna is a branch-and-price-and-cut framework written in Julia. You write an original MIP that models your problem using the JuMP modeling language and our specific extension BlockDecomposition offers a syntax to specify the problem decomposition. Then, Coluna reformulates the original MIP and optimizes the reformulation using the algorithms you choose. Coluna aims to be very modular and tweakable so that you can define the behavior of your customized branch-and-price-and-cut algorithm. -
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The Julia Programming Language
High-level, 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 computing. Libraries from Python, R, C/Fortran, C++, and Java can also be used. -
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PGFPlotsX.jl
Plots in Julia using the PGFPlots LaTeX package
...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. -
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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 worldwide. It is widely used for data science, research, and production applications, supported by extensive documentation, tutorials, and a free Julia Academy course. ... -
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Compat.jl
Compatibility across Julia versions
...In particular, in cases where it is impossible to write code that works with both the latest Julia master branch and older Julia versions, or impossible to write code that doesn't generate a deprecation warning in some Julia version, the Compat package provides a macro that lets you use the latest syntax in a backward-compatible way. This is primarily intended for use by other Julia packages, where it is important to maintain cross-version compatibility. -
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SimpleTraits.jl
Simple Traits for Julia
This package provides a macro-based implementation of traits, using Tim Holy's trait trick. The main idea behind traits is to group types outside the type-hierarchy and to make dispatch work with that grouping. The difference to Union-types is that types can be added to a trait after the creation of the trait, whereas Union types are fixed after creation. The cool thing about Tim's trick is that there is no performance impact compared to using ordinary dispatch. For a bit of background and a... -
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MPI.jl
MPI wrappers for Julia
...As the main entry point for users, MPI.jl provides a high-level interface which loosely follows the MPI C API and is described in details in the following sections. The syntax should look familiar if you know MPI already, but some arguments may not be needed (e.g. the type or the number of elements of arrays, which are inferred automatically), others may be placed slightly differently, and others may be optional keyword arguments (e.g. for the index of the root process, or the source and destination of point-to-point communication functions). -
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Plots
Powerful convenience for Julia visualizations and data analysis
...If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn. -
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Convex.jl
A Julia package for disciplined convex programming
Convex.jl is a Julia package for Disciplined Convex Programming (DCP). Convex.jl makes it easy to describe optimization problems in a natural, mathematical syntax, and to solve those problems using a variety of different (commercial and open-source) solvers. Convex.jl works by transforming the problem—which possibly has nonsmooth, nonlinear constructions like the nuclear norm, the log determinant, and so forth—into a linear optimization problem subject to conic constraints. This reformulation often involves adding auxiliary variables and is called an "extended formulation", since the original problem has been extended with additional variables. ... -
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Optimization.jl
Mathematical Optimization in Julia
Optimization.jl provides the easiest way to create an optimization problem and solve it. It enables rapid prototyping and experimentation with minimal syntax overhead by providing a uniform interface to >25 optimization libraries, hence 100+ optimization solvers encompassing almost all classes of optimization algorithms such as global, mixed-integer, non-convex, second-order local, constrained, etc. It allows you to choose an Automatic Differentiation (AD) backend by simply passing an argument to indicate the package to use and automatically generates the efficient derivatives of the objective and constraints while giving you the flexibility to switch between different AD engines as per your problem. ... -
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Gridap.jl
Grid-based approximation of partial differential equations in Julia
Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element (FE) discretizations, on structured and unstructured meshes of simplices and n-cubes. It also provides methods for time integration. Gridap is extensible and modular. One can... -
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Reduce.jl
Symbolic parser for Julia language term rewriting using REDUCE algebra
...It is a system for doing scalar, vector and matrix algebra by computer, which also supports arbitrary precision numerical approximation and interfaces to gnuplot to provide graphics. It can be used interactively for simple calculations (as illustrated in the screenshot below) but also provides a full programming language, with a syntax similar to other modern programming languages. REDUCE supports alternative user interfaces including Run-REDUCE, TeXmacs and GNU Emacs. REDUCE (and its complete source code) is available free of charge for most common computing systems, in some cases in more than one version for the same machine. The manual and other support documents and tutorials are also included in the distributions. -
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CUDA.jl
CUDA programming in Julia
High-performance GPU programming in a high-level language. JuliaGPU is a GitHub organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well-positioned to productively program hardware accelerators like GPUs without sacrificing performance. The latest development version of CUDA.jl requires Julia 1.8 or higher. If you are using an older version of Julia, you need to use a previous version of CUDA.jl. This will happen automatically when you install the package using Julia's package manager. -
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JLD.jl
Saving and loading julia variables while preserving native types
JLD, for which files conventionally have the extension .jld, is a widely used format for data storage with the Julia programming language. JLD is a specific "dialect" of HDF5, a cross-platform, multi-language data storage format most frequently used for scientific data. By comparison with "plain" HDF5, JLD files automatically add attributes and naming conventions to preserve type information for each object. For lossless storage of arbitrary Julia objects, the only other complete solution... -
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WhereTraits.jl
Traits for julia: dispatch on whatever you want using where syntax
Welcome to WhereTraits.jl. This package exports one powerful macro @traits with which you can extend Julia's where syntax in order to support traits definitions. In addition, WhereTraits comes with a standardized way how to resolve ambiguities among traits, by defining an order among the traits with @traits_order. Under the hood @traits uses normal function dispatch to achieve the speed and flexibility, however, julia function dispatch can lead to ambiguities. -
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Weave
Scientific reports/literate programming for Julia
Weave is a scientific report generator/literate programming tool for the Julia programming language. It resembles Pweave, knitr, R Markdown, and Sweave. You can write your documentation and code in an input document using Markdown, Noweb or ordinal Julia script syntax, and then use the weave function to execute code and generate an output document while capturing results and figures. Supports various output document formats: HTML, PDF, GitHub markdown, Jupyter Notebook, MultiMarkdown, Asciidoc and reStructuredText. -
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FLoops.jl
Fast sequential, threaded, and distributed for-loops for Julia
Fast sequential, threaded, and distributed for-loops for Julia, fold for humans.FLoops.jl provides a macro @floop. It can be used to generate a fast generic sequential and parallel iteration over complex collections. Furthermore, the loop written in @floop can be executed with any compatible executors. See FoldsThreads.jl for various thread-based executors that are optimized for different kinds of loops. FoldsCUDA.jl provides an executor for GPU. FLoops.jl also provides a simple distributed... -
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Julog.jl
A Julia package for Prolog-style logic programming
A Julia package for Prolog-style logic programming. -
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Soss
Probabilistic programming via source rewriting
Soss is a library for probabilistic programming. Soss and DynamicPPL are both maturing and becoming more complete, so the above will change over time. It's also worth noting that we (the Turing team and I) hope to move toward a natural way of using these systems together to arrive at the best of both.
