Open Source Linux Data Visualization Software - Page 6
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Data Visualization Software for Linux
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Intelligent testing agents | Checksum.aiCoding agents write the code. Checksum runs it—continuously testing against real APIs, real data, real edge cases—before it ever reaches production.
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Ditto Edge Server is a lightweight standalone server for resource-constrained edge environments, based on the core Ditto Edge SDK.Ditto's Edge SDK is the only thing your edge devices need to ensure your application is operational in any environment, regardless of network conditions.
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DynamicalBilliards.jl
An easy-to-use, modular, extendable and absurdly fast Julia package
A Julia package for dynamical billiard systems in two dimensions. The goals of the package is to provide a flexible and intuitive framework for fast implementation of billiard systems of arbitrary construction. -
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Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
This is a package containing the Julia bindings for Enzyme. This is very much a work in progress and bug reports/discussion is greatly appreciated. Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM. It is highly-efficient and its ability perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools. -
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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 executor. -
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FastGaussQuadrature.jl
Julia package for Gaussian quadrature
A Julia package to compute n-point Gauss quadrature nodes and weights to 16-digit accuracy and in O(n) time. So far the package includes gausschebyshev(), gausslegendre(), gaussjacobi(), gaussradau(), gausslobatto(), gausslaguerre(), and gausshermite(). This package is heavily influenced by Chebfun. -
Electronic Lab Notebook (ELN) SoftwareeLabJournal is an all-in-one Electronic Lab Notebook (ELN) software that includes sample tracking and protocol management modules.
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Ferrite.jl
Finite element toolbox for Julia
A simple finite element toolbox written in Julia. -
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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. The best case for forward-mode differentiation is a function that maps a scalar to a vector. -
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HCubature.jl
Pure-Julia multidimensional h-adaptive integration
The HCubature module is a pure-Julia implementation of multidimensional "h-adaptive" integration. then hcubature(f, a, b) computes the integral, adaptively subdividing the integration volume into smaller and smaller pieces until convergence is achieved to the desired tolerance (specified by optional rtol and atol keyword arguments. Because hcubature is written purely in Julia, the integrand f(x) can return any vector-like object (technically, any type supporting +, -, * real, and norm: a Banach space). You can integrate real, complex, and matrix-valued integrands, for example. Note that HCubature assumes that your function f(x) can be computed at arbitrary points in the integration domain. (This is the ideal way to do numerical integration.) If you instead have f(x) precomputed at a fixed set of points, such as a Cartesian grid, you will need to use some other method (e.g. Trapz.jl for a multidimensional trapezoidal rule). -
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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 appears to be Julia's serializer, which can be accessed via the serialize and deserialize commands. However, because the serializer is also used for inter-process communication, long-term backward compatibility is currently uncertain. (The JLDArchives repository exists to test the compatibility of older JLD file formats.) If you choose to save data using the serializer, please use the file extension .jls to distinguish the files from .jld files. -
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LineSearches.jl
Line search methods for optimization and root-finding
Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package. -
Quality and compliance software for growing life science companiesAutomate gap analysis across FDA, ISO 13485, MDR, and 28+ regulatory standards. Cross-map evidence once, reuse across submissions. Get real-time risk alerts and board-ready dashboards, so you can expand into new markets with confidence
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LinearSolve.jl
High-Performance Unified Interface for Linear Solvers in Julia
LinearSolve.jl is a unified interface for the linear solving packages of Julia. It interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems, with options for large-scale systems. If you run into any performance issues, please file an issue. -
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Makie
Interactive data visualizations and plotting in Julia
Makie is an interactive data visualization and plotting ecosystem for the Julia programming language, available on Windows, Linux, and Mac. The backend packages GLMakie, WGLMakie, CairoMakie and RPRMakie add different functionalities: You can use Makie to interactively explore your data and create simple GUIs in native Windows or web browsers, export high-quality vector graphics or even raytrace with physically accurate lighting. Choose one or more backend packages: GLMakie (interactive OpenGL in native OS windows), WGLMakie (interactive WebGL in browsers, IDEs, notebooks), CairoMakie (static 2D vector graphics and images), and RPRMakie (raytracing). Each backend re-exports all of Makie.jl so you don't have to install or load it explicitly. -
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NNlib.jl
Neural Network primitives with multiple backends
This package provides a library of functions useful for neural networks, such as softmax, sigmoid, batched multiplication, convolutions and pooling. Many of these are used by Flux.jl, which loads this package, but they may be used independently. -
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PGFPlotsX.jl
Plots in Julia using the PGFPlots LaTeX package
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. -
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PairPlots.jl
Beautiful and flexible vizualizations of high dimensional data
Beautiful and flexible visualizations of high-dimensional data. This package produces pair plots, otherwise known as corner plots or scatter plot matrices: grids of 1D and 2D histograms that allow you to visualize high-dimensional data. Pair plots are an excellent way to visualize the results of MCMC simulations, but are also a useful way to visualize correlations in general data tables. The default styles of this package roughly reproduce the output of the Python library corner.py for a single series and chainconsumer.py for multiple series. If these are not to your tastes, the package aims to be highly configurable. -
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Parquet.jl
Julia implementation of Parquet columnar file format reader
A parquet file or dataset can be loaded using the read_parquet function. A parquet dataset is a directory with multiple parquet files, each of which is a partition belonging to the dataset. -
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Parsers.jl
fast parsing machinery for basic types in Julia
A collection of type parsers and utilities for Julia. Installation: at the Julia REPL, import Pkg; Pkg.add("Parsers") Parsers is maintained collectively by the JuliaData collaborators. Responsiveness to pull requests and issues can vary, depending on the availability of key collaborators. -
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PeriodicTable.jl
Periodic Table for Julians
A very simple package for accessing elements in the Periodic Table. PeriodicTable.jl provides a Julia interface to a small database of element properties for all of the elements in the periodic table. In particular PeriodicTable exports a global variable called elements, which is a collection of Element data structures. -
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PolyChaos.jl
Julia package to construct orthogonal polynomials
PolyChaos is a collection of numerical routines for orthogonal polynomials written in the Julia programming language. Starting from some non-negative weight (aka an absolutely continuous nonnegative measure). -
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ProtoBuf.jl
Julia protobuf implementation
This is a Julia package that provides a compiler and a codec for Protocol Buffers. Protocol Buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data. -
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SymbolicNumericIntegration.jl
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
SymbolicNumericIntegration.jl is a hybrid symbolic/numerical integration package that works on the Julia Symbolics expressions. -
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Tulip.jl
Interior-point solver in pure Julia
Tulip is an open-source interior-point solver for linear optimization, written in pure Julia. It implements the homogeneous primal-dual interior-point algorithm with multiple centrality corrections and therefore handles unbounded and infeasible problems. Tulip’s main feature is that its algorithmic framework is disentangled from linear algebra implementations. This allows to seamless integration of specialized routines for structured problems. -
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jlpkg
A command line interface (CLI) for Pkg, Julia's package manager
A command line interface (CLI) to Pkg, Julia's package manager. -
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just-dashboard
Dashboards using YAML or JSON files
Dashboards using YAML or JSON files. Create a public GitHub gist with a file named dashboard.yml or dashboard.json (depending on your preferred format) As your dashboard is just data, you can generate it instead of repeating yourself. You can do that by generating the YAML or JSON file yourself, or you can use jq queries in your YAML file. And one with a dashboard that contains a component that can fetch the data from other other gist and turn it into 3 different charts. Using the same principle, you can also loads parts from your dashboard from other files, or just JSON/CSV data for specific charts. Suppose you are only interested in comparing foods by how much they contain of a single macronutrient. However, you want to be able to decide which macronutrient. -
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ngx-datatable
A feature-rich yet lightweight data-table crafted for Angular
ngx-datatable is an Angular component for presenting large and complex data. It has all the features you would expect from any other table but in a light package with no external dependencies. The table was designed to be extremely flexible and light; it doesn't make any assumptions about your data or how you: filter, sort or page it. It was built for modern browsers using TypeScript, CSS3 and HTML5 and Angular 8.0.0. This is the sister project of the angular-data-table that is designed for Angular 1.x. Intelligent Column Width Algorithms (Force-fill & Flex-grow). Fixed AND Fluid height. Left and Right Column Pinning. Row Detail View. Decoupled theme'ing with included Google Material theme. Light codebase / No external dependencies. AoT Compilation Support. Universal Support. Cell & Row Selection. -
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wavesurfer.js
Navigable waveform built on Web Audio and Canvas
wavesurfer.js is a customizable audio waveform visualization, built on top of Web Audio API and HTML5 Canvas. With wavesurfer.js you can create anything from an HTML5 audio player to a sophisticated DJ application. wavesurfer.js works only in modern browsers supporting Web Audio. It will fallback to Audio Element without graphics in other browsers (IE 11 and lower). You can also try wavesurfer.swf which is a Flash-based fallback. The audio will start playing as you press play. A thin line will be displayed until the whole audio file is downloaded and decoded to draw the waveform. Web Audio needs the whole file to decode it in the browser. You can however load pre-decoded waveform data to draw the waveform immediately. wavesurfer.js runs on modern browsers supporting Web Audio, including Firefox, Chrome, Safari (desktop and mobile) and Opera.
