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Showing 385 open source projects for "you"
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Matplot++
Matplot++: A C++ Graphics Library for Data Visualization
Data visualization can help programmers and scientists identify trends in their data and efficiently communicate these results with their peers. Modern C++ is being used for a variety of scientific applications, and this environment can benefit considerably from graphics libraries that attend the typical design goals toward scientific data visualization. Besides the option of exporting results to other environments, the customary alternatives in C++ are either non-dedicated libraries that... -
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Java Tablesaw
Java dataframe and visualization library
Tablesaw is a dataframe and visualization library that supports loading, cleaning, transforming, filtering, and summarizing data. If you work with data in Java, it may save you time and effort. Tablesaw also supports descriptive statistics and can be used to prepare data for working with machine learning libraries like Smile, Tribuo, H20.ai, DL4J. Import data from RDBMS, Excel, CSV, TSV, JSON, HTML, or Fixed Width text files, whether they are local or remote (http, S3, etc.) ... -
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GPUArrays
Reusable array functionality for Julia's various GPU backends
...This package is the counterpart of Julia's AbstractArray interface, but for GPU array types: It provides functionality and tooling to speed-up development of new GPU array types. This package is not intended for end users! Instead, you should use one of the packages that builds on GPUArrays.jl, such as CUDA.jl, oneAPI.jl, AMDGPU.jl, or Metal.jl. -
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FileTrees.jl
Parallel file processing made easy
...Make up a file tree in memory, create some data to go with each file (in parallel), write the tree to disk (in parallel). FileTrees is a set of tools to lazy-load, process and save file trees. Built-in parallelism allows you to max out all threads and processes that Julia is running with. Files and subtrees in a file tree can have any value attached to them, you can map and reduce over these values, or combine them by merging or collapsing trees or subtrees. When computing lazy trees, these values are held in distributed memory and operated on in parallel. -
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Observable Plot
A concise API for exploratory data visualization
You can configure much more, if needed, but Plot’s goal is to help you get a meaningful visualization quickly. Plot employs a layered grammar of graphics inspired by Vega-Lite, ggplot2, Wilkinson’s Grammar of Graphics, and Bertin’s Semiology of Graphics. Plot rejects a chart typology in favor of marks, scales, and transforms. Plot can be readily extended in JavaScript, whether to define a channel, a transform, or even a custom mark. -
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DFTK.jl
Density-functional toolkit
The density-functional toolkit, DFTK for short, is a collection of Julia routines for experimentation with plane-wave density-functional theory (DFT). The unique feature of this code is its emphasis on simplicity and flexibility with the goal of facilitating algorithmic and numerical developments as well as interdisciplinary collaboration in solid-state research. -
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FunSQL.jl
Julia library for compositional construction of SQL queries
...Julia programmers sometimes need to interrogate data with the Structured Query Language (SQL). But SQL is notoriously hard to write in a modular fashion. FunSQL exposes full expressive power of SQL with compositional semantics. FunSQL allows you to build queries incrementally from small independent fragments. This approach is particularly useful for building applications that programmatically construct SQL queries. -
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JET.jl
An experimental code analyzer for Julia
...JET is tightly coupled to the Julia compiler, and so each JET release supports a limited range of Julia versions. See the Project.toml file for the range of supported Julia versions. The Julia package manager should install a version of JET compatible with the Julia version you are running. If you want to use JET on unreleased version of Julia where compatibility with JET is yet unknown, clone this git repository and dev it, such that Julia compatibility is ignored. -
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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... -
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ReTest.jl
Testing framework for Julia
...Filtering run testsets with a Regex, which is matched against the descriptions of testsets. This is useful for running only part of the test suite of a package. For example, if you made a change related to addition, and included "addition" in the description of the corresponding testsets, you can easily run only these tests. -
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deck.gl
WebGL2 powered visualization framework
...All core classes such are easily extendable by the users to address custom use cases. deck.gl is part of vis.gl, an OpenJS Foundation project. Read the contribution guidelines if you are interested in contributing. -
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Delta
A viewer for git and diff output
Code evolves, and we all spend time studying diffs. Delta aims to make this both efficient and enjoyable: it allows you to make extensive changes to the layout and styling of diffs, as well as allowing you to stay arbitrarily close to the default git/diff output. Language syntax highlighting with color themes. Within-line highlights based on a Levenshtein edit inference algorithm. Git style strings (foreground color, background color, font attributes) are supported for >20 stylable elements. ... -
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CondaPkg.jl
Add Conda dependencies to your Julia project
...CondaPkg will install these dependencies into a Conda environment specific to the current Julia project. Hence dependencies are isolated from other projects or environments. Functions like add, rm, status exist to edit the dependencies programmatically. Or you can do pkg> conda add some_package to edit the dependencies from the Pkg REPL. -
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ChainRulesCore
AD-backend agnostic system defining custom forward and reverse rules
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system. The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself. This will allow your package to be used with ChainRules.jl, which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives. -
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The PyPlot module for Julia
Plotting for Julia based on matplotlib.pyplot
...(See also PythonPlot.jl for a version of PyPlot.jl using the alternative PythonCall.jl package.) This package takes advantage of Julia's multimedia I/O API to display plots in any Julia graphical backend, including as inline graphics in IJulia. Alternatively, you can use a Python-based graphical Matplotlib backend to support interactive plot zooming etcetera. -
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Plots
Powerful convenience for Julia visualizations and data analysis
...It sits above other backends, like GR, PythonPlot, PGFPlotsX, or Plotly, connecting commands with implementation. 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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F2
An elegant, interactive and flexible charting library for mobile
...Covering business scenarios such as Ant membership, Alipay monthly bills, personal total assets, etc., it helps you understand your consumption data faster and better through visualization. -
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Ferrite.jl
Finite element toolbox for Julia
A simple finite element toolbox written in Julia. -
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RevoGrid
Powerful virtual data grid smartsheet with advanced customization
...Rapid prototyping with intuitive and extendable codebase. Agility to build robust data-driven applications in no time. Quickly build elegant and efficient data grids. Lightweight yet powerful architecture lets you easily scale as your needs grow. -
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gt R
Easily generate information-rich, publication-quality tables from R
...These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting it to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. -
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Optimization.jl
Mathematical Optimization in Julia
...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. Additionally, Optimization.jl takes care of passing problem-specific information to solvers that can leverage it such as the sparsity pattern of the hessian or constraint jacobian and the expression graph. -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD). While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation, but ForwardDiff may still be a good choice if x is not too large, as it is much simpler. ... -
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Semiotic
A data visualization framework combining React & D3
...Each object represents a line. Every object needs a coordinates property with the array of points for that line. The points will be rendered in the order of that array. You can use a key other than coordinates by changing the lineDataAccessor props. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function... -
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PythonCall & JuliaCall
Python and Julia in harmony
...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. Helpful wrappers: interpret Python sequences, dictionaries, arrays, dataframes and IO streams as their Julia counterparts, and vice versa.
