Open Source Linux Data Visualization Software - Page 11
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Data Visualization Software for Linux
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Sampler
Tool for shell commands execution, visualization and alerting
Sampler is a real-time monitoring dashboard that allows developers to track various metrics, system information, and custom data sources via a terminal interface. -
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ScientificTypes.jl
An API for dispatching on the "scientific" type of data
This package makes a distinction between machine type and scientific type of a Julia object. The machine type refers to the Julia type being used to represent the object (for instance, Float64). The scientific type is one of the types defined in ScientificTypesBase.jl reflecting how the object should be interpreted (for instance, Continuous or Multiclass). -
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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. -
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Stan.jl
Stan.jl illustrates the usage of the 'single method' packages
A collection of example Stan Language programs demonstrating all methods available in Stan's cmdstan executable (as an external program) from Julia. For most applications one of the "single method" packages, e.g. StanSample.jl, StanDiagnose.jl, etc., is a better choice for day-to-day use. To execute the most important method in Stan ("sample"), use StanSample.jl. Some Pluto notebook examples can be found in the repository. -
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StatProfilerHTML.jl
Show Julia profiling data in an explorable HTML page
This module formats the output from Julia's Profile module into an html rendering of the source function lines and functions, allowing for interactive exploration of any bottlenecks that may exist in your code. -
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StaticTools.jl
Enabling StaticCompiler.jl-based compilation of (some) Julia code
Tools to enable StaticCompiler.jl-based static compilation of Julia code (or more accurately, a subset of Julia which we might call "unsafe Julia") to standalone native binaries by avoiding GC allocations and llvmcall-ing all the things. This package currently requires Julia 1.8 or greater for best results (if in doubt, check which versions are passing CI). Integration tests against StaticCompiler.jl and LoopVectorization.jl are currently run with Julia 1.8 and 1.9 on x86-64 Linux and mac; other platforms and versions may or may not work but will depend on StaticCompiler.jl support. While we'll do our best to keep things working, this package should still be considered experimental at present and necessarily involves a lot of juggling of pointers and such (i.e., "unsafe Julia"). If there are errors in any of the llvmcalls (which we have to use instead of simpler ccalls for things to statically compile smoothly), there could be serious bugs or even undefined behavior. -
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TableView.jl
A Tables.jl compatible table viewer based on ag-grid
TableView.jl is an ag-grid-based table viewer built on WebIO.jl. It can display arbitrarily large tables by lazy-loading additional data when scrolling (this is the default for datasets with more than 10k rows). -
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TaylorSeries.jl
Taylor polynomial expansions in one and several independent variables
A Julia package for Taylor polynomial expansions in one or more independent variables. -
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The NLopt module for Julia
Package to call the NLopt nonlinear-optimization library from Julia
This module provides a Julia-language interface to the free/open-source NLopt library for nonlinear optimization. NLopt provides a common interface for many different optimization algorithms. -
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ThreadsX.jl
Parallelized Base functions
Add prefix ThreadsX. to functions from Base to get some speedup, if supported. The reduce-based functions support any collections that implement SplittablesBase.jl interface including arrays, Dict, Set, and iterator transformations. In particular, these functions support iterator comprehension. ThreadsX.jl is aiming at providing API compatible with Base functions to easily parallelize Julia programs. All functions that exist directly under ThreadsX namespace are public API and they implement a subset of API provided by Base. Everything inside ThreadsX.Implementations is an implementation detail. The public API functions of ThreadsX expect that the data structure and function(s) passed as argument are "thread-friendly" in the sense that operating on distinct elements in the given container from multiple tasks in parallel is safe. For example, ThreadsX.sum(f, array) assumes that executing f(::eltype(array)) and accessing elements as in array[i] from multiple threads is safe. -
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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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UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation
A pure Julia implementation of the Uniform Manifold Approximation and Projection dimension reduction algorithm. The umap function takes two arguments, X (a column-major matrix of shape (n_features, n_samples)), n_components (the number of dimensions in the output embedding), and various keyword arguments. -
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VimBindings.jl
Vim bindings for the Julia REPL
Vim bindings for the Julia REPL. VimBindings.jl is a Julia package which brings vim emulation directly to the Julia REPL. -
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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. With traits these can easily happen if someone defines @traits for the same standard dispatch but using different traits. -
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deck.gl
WebGL2 powered visualization framework
deck.gl is designed to simplify high-performance, WebGL-based visualization of large data sets. Users can quickly get impressive visual results with minimal effort by composing existing layers, or leveraging deck.gl's extensible architecture to address customer needs. deck.gl maps data (usually an array of JSON objects) into a stack of visual layers - e.g. icons, polygons, texts; and look at them with views: e.g. map, first-person, orthographic. deck.gl handles a number of challenges out of the box. Deck.gl is designed to be highly customizable. All layers come with flexible APIs to allow programmatic control of each aspect of the rendering. 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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jill
Command line installer of the Julia Language
On Linux, the best way to install Julia is to use the Generic Linux Binaries. And while all Linux users love manually downloading, unpacking, and linking their software, this script does it for you. -
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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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leafmap
A Python package for interactive mapping and geospatial analysis
A Python package for geospatial analysis and interactive mapping in a Jupyter environment. Leafmap is a Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment. It is a spin-off project of the geemap Python package, which was designed specifically to work with Google Earth Engine (GEE). However, not everyone in the geospatial community has access to the GEE cloud computing platform. Leafmap is designed to fill this gap for non-GEE users. It is a free and open-source Python package that enables users to analyze and visualize geospatial data with minimal coding in a Jupyter environment, such as Google Colab, Jupyter Notebook, and JupyterLab. Leafmap is built upon several open-source packages, such as folium and ipyleaflet (for creating interactive maps), WhiteboxTools and whiteboxgui (for analyzing geospatial data), and ipywidgets (for designing interactive graphical user interface [GUI]). -
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libCEED
CEED Library: Code for Efficient Extensible Discretizations
libCEED provides fast algebra for element-based discretizations, designed for performance portability, run-time flexibility, and clean embedding in higher-level libraries and applications. It offers a C99 interface as well as bindings for Fortran, Python, Julia, and Rust. While our focus is on high-order finite elements, the approach is mostly algebraic and thus applicable to other discretizations in factored form, as explained in the user manual and API implementation portion of the documentation. One of the challenges with high-order methods is that a global sparse matrix is no longer a good representation of a high-order linear operator, both with respect to the FLOPs needed for its evaluation, as well as the memory transfer needed for a matvec. Thus, high-order methods require a new "format" that still represents a linear (or more generally non-linear) operator, but not through a sparse matrix. -
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IMPORTANT NOTICE: This project has moved to GitHub: https://github.com/sarahtattersall/PIPE Releases will be posted here, but please check on GitHub for the most recent activity. Create, model and analyse Petri nets with a standards-compliant Petri net tool. PIPE2 is the active fork of the Platform Independent Petri net Editor project, which originated at Imperial College London.
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pfstools - a set of programs for reading, writing, manipulating and viewing high-dynamic range (HDR) images and video frames. pfscalibration - photometric calibration of cameras and HDR images from multiple exposures. pfstmo - tone mapping operators.
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AbstractFFTs.jl
A Julia framework for implementing FFTs
A general framework for fast Fourier transforms (FFTs) in Julia. This package is mainly not intended to be used directly. Instead, developers of packages that implement FFTs (such as FFTW.jl or FastTransforms.jl) extend the types/functions defined in AbstractFFTs. This allows multiple FFT packages to co-exist with the same underlying fft(x) and plan_fft(x) interface. -
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Accessors.jl
Update immutable data
The goal of Accessors.jl is to make updating immutable data simple. It is the successor of Setfield.jl. -
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Algorithm Visualizer
Interactive Online Platform that Visualizes Algorithms from Code
Hacker Scripts is a light-hearted collection of small automation and demo scripts that solve amusing everyday tasks or illustrate quick integrations with external services. The repo collects short programs (originally a set of shell and Ruby scripts) and many community contributed ports in other languages to show “how you might automate X” — for example sending a quick SMS, firing off an email, or triggering a coffee maker — with examples and scheduling snippets included. The README explains each example, lists required environment variables or credentials (e.g., Twilio/Gmail where applicable), and gives cron examples so readers can run the scripts in a real environment. The project is intentionally informal and educational: it’s meant for experimentation, learning language-interop, and having fun rather than production-grade automation. Many implementations exist across languages (shell, Ruby, Python, Node, PowerShell, Go, Java, and more) and contributors are encouraged to add further -
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AppleAccelerate.jl
Julia interface to the macOS Accelerate framework
Julia interface to the macOS Accelerate framework. This provides a Julia interface to some of the macOS Accelerate frameworks. At the moment, this package provides access to Accelerate BLAS and LAPACK using the libblastrampoline framework, an interface to the array-oriented functions, which provide a vectorized form for many common mathematical functions. The performance is significantly better than using standard libm functions in some cases, though there does appear to be some reduced accuracy.