Open Source Data Visualization Software - Page 22
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Data Visualization Software
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ScikitLearn.jl
Julia implementation of the scikit-learn API
The scikit-learn Python library has proven very popular with machine learning researchers and data scientists in the last five years. It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. ScikitLearn.jl brings these capabilities to Julia. Its primary goal is to integrate both Julia- and Python-defined models together into the scikit-learn framework. -
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ScottPlot
Interactive Plotting Library for .NET
ScottPlot is a free and open-source plotting library for .NET that makes it easy to interactively display large datasets. Line plots, bar charts, pie graphs, scatter plots, and more can be created with just a few lines of code. The ScottPlot Cookbook demonstrates how to create line plots, bar charts, pie graphs, scatter plots, and more with just a few lines of code. The ScottPlot Demo is a click-to-run application that demonstrates mouse-interactive versions of every cookbook recipe. ScottPlot does not require a GUI so it can be used to create plots in cloud applications. The images below are regenerated hourly using Azure Functions to update star metrics from the GitHub API and download count from the NuGet API. The data is plotted with ScottPlot and saved as an image file in blob storage. -
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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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Spark.jl
Julia binding for Apache Spark
A Julia interface to Apache Spark. Spark.jl provides an interface to Apache Spark™ platform, including SQL / DataFrame and Structured Streaming. It closely follows the PySpark API, making it easy to translate existing Python code to Julia. Spark.jl supports multiple cluster types (in client mode), and can be considered as an analog to PySpark or RSpark within the Julia ecosystem. It supports running within on-premise installations, as well as hosted instances such as Amazon EMR and Azure HDInsight. -
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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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Temporal.jl
Time series implementation for the Julia language
This package provides a flexible & efficient time series class, TS, for the Julia programming language. While still early in development, the overarching goal is for the class to be able to slice & dice data with the rapid prototyping speed of R's xts and Python's pandas packages, while retaining the performance one expects from Julia. See the documentation for a more in-depth look at the package and some of the pain points it may solve when doing technical research with time series data. -
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TensorCast.jl
It slices, it dices, it splices
This package lets you work with multi-dimensional arrays in index notation, by defining a few macros which translate this to broadcasting, permuting, and reducing operations. -
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The PyPlot module for Julia
Plotting for Julia based on matplotlib.pyplot
This module provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy). (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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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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Tullio.jl
Tullio is a very flexible einsum macro
Tullio is a very flexible einsum macro. It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough arrays. -
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Vulkan.jl
Using Vulkan from Julia
Vulkan.jl is a lightweight wrapper around the Vulkan graphics and compute library. It exposes abstractions over the underlying C interface, primarily geared toward developers looking for a more natural way to work with Vulkan with minimal overhead. It builds upon the core API provided by VulkanCore.jl. Because Vulkan is originally a C specification, interfacing with it requires some knowledge before correctly being used from Julia. This package acts as an abstraction layer, so that you don't need to know how to properly call a C library, while still retaining full functionality. The wrapper is generated directly from the Vulkan Specification. -
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XELFViewer
ELF file viewer/editor for Windows, Linux and MacOS
XELFViewer is a graphical viewer and editor for Executable and Linkable Format files. It provides a cross-platform environment for inspecting binaries commonly used on Linux and other Unix-like systems. The tool is intended for reverse engineering, binary research, and low-level software analysis rather than ordinary file browsing. Users can examine ELF structures and navigate the data contained inside 32-bit and 64-bit binaries. Its architecture shares components with a larger suite of hexadecimal, disassembly, format-parsing, and analysis utilities. The project includes source and build workflows for Windows, Linux, and macOS. This makes ELF inspection available from multiple operating systems without requiring the analyzed executable to run on the host platform. -
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awesome-single-cell
Community-curated list of software packages and data resources
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc. List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data. Find bimodal, unimodal, and multimodal features in your data. Ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. An analytical framework for big-scale single cell data. Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction. -
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billboard.js
Re-usable, easy interface JavaScript chart library based on D3.js
billboard.js is a re-usable, easy interface JavaScript chart library, based on D3.js. The name "billboard" comes from the famous "billboard chart" which everybody knows. billboard.js provides the easiest way to create a 'chart' instantly. Chart generation is super easy. With extensive options, you can create a chart instantly! Yes, billboard.js works on D3 v4+; what everybody was waiting for! All of the code was written as an ESM(ES Module) with ES6+ syntax. Combine hundreds of options to satisfy your needs. Checkout the examples to see in action! Play with the diverse options generated on the fly! If you want to use 'billboard.js' without installation, load files directly from one of the CDN providers. -
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echarts-for-react
Apache ECharts components for React wrapper
echarts-for-react is a React wrapper for Apache ECharts that lets developers render ECharts visualizations inside React applications. It provides a simple component-based interface where users pass an ECharts option object to generate charts. The package supports common React integration needs, including themes, styles, loading states, chart-ready callbacks, event binding, and renderer options. It also exposes access to the underlying ECharts instance, which lets developers use the full ECharts API when needed. The project includes guidance for importing ECharts modules manually to reduce bundle size and for using it with Next.js. Overall, it is a practical bridge between React projects and the full charting power of Apache ECharts. -
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geemap
A Python package for interactive geospaital analysis and visualization
A Python package for interactive geospatial analysis and visualization with Google Earth Engine. Geemap is a Python package for geospatial analysis and visualization with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. Compared with the comprehensive documentation and interactive IDE (i.e., GEE JavaScript Code Editor) of the GEE JavaScript API, the GEE Python API has relatively little documentation and limited functionality for visualizing results interactively. The geemap Python package was created to fill this gap. -
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jnotebook_reader
An awesome viewer to browse and render Jupyter Notebooks
An awesome viewer to browse and render Jupyter Notebooks from local, Amazon S3, Google Cloud Storage, or MinIO. Browse and render Jupyter Notebooks from local, Amazon S3, Google Cloud Storage, or MinIO. Register and access multiple directories(or buckets) at the same time. Show and allow to config ToC(Table of Contents) included in your Jupyter Notebook. Hide/show all codes in your Jupyter Notebook interactively. Generate a permanent link about your Jupyter Notebook. Download your Jupyter Notebook in one click. -
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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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nw_wrld
nw_wrld is an event-driven sequencer for triggering visuals
nw_wrld is a procedurally generated world-building engine tailored for game developers and interactive storytellers who want to craft rich, random yet coherent environments without hand-crafting every detail. It uses noise functions and modular terrain algorithms to generate expansive maps, diverse biomes, and layered features like rivers, mountain ranges, forests, and resource nodes. The system is designed to be extensible, letting developers plug in new generation rules or tweak parameters with real-time previews so they can iterate rapidly on world design. It also includes utilities to derive metadata from worlds, such as climate distributions, strategic points of interest, and navigable paths, which can be consumed by gameplay systems or AI agents. For teams building games or simulations, nw_wrld provides a reusable foundation that reduces upfront world design costs while enabling endless variety. -
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oneAPI.jl
Julia support for the oneAPI programming toolkit.
Julia support for the oneAPI programming toolkit. oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux. This package is still under significant development, so expect bugs and missing features. -
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prettymaps
A small set of Python functions to draw pretty maps from OpenStreetMap
A Python package to draw maps with customizable styles from OpenStreetMap data. Created using the osmnx, matplotlib, shapely and vsketch packages. -
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rayshader
R Package for 2D and 3D mapping and data visualization
This is an R package designed for producing beautiful and interactive 2D and 3D visualizations — especially maps and terrain renderings — using elevation/gridded data and ray-tracing / hill-shading methods. At its core, rayshader takes a matrix of elevations and applies shading, texture, ambient occlusion, overlays, and light modeling (ray shade, lambertian shading, etc.) to produce realistic relief maps. Users can rotate, zoom, and animate the scenes or script camera trajectories programmatically. It supports outputting high-quality renders via path tracing (using a companion package) and also offers depth-of-field (“cinematic blur”) effects to bring visual focus into scenes. It allows layering relational data (roads, points, polygons) on top of the shaded terrain, so you can combine spatial data overlays with the 3D model. The package can export models to 3D formats like STL or OBJ for 3D printing or external rendering. -
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repo2docker GitHub Action
A GitHub action to build data science environment images
Trigger repo2docker to build a Jupyter enabled Docker image from your GitHub repository and push this image to a Docker registry of your choice. This will automatically attempt to build an environment from configuration files found in your repository. Images generated by this action are automatically tagged with both latest and <SHA> corresponding to the relevant commit SHA on GitHub. Both tags are pushed to the Docker registry specified by the user. If an existing image with the latest tag already exists in your registry, this Action attempts to pull that image as a cache to reduce uncessary build steps. -
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tota11y
An accessibility (a11y) visualization toolkit
tota11y helps visualize how your site performs with assistive technologies. The process of testing for accessibility (a11y) is often tedious and confusing. In many cases, developers must have some prior accessibility knowledge in order to make sense of the results. Instead, tota11y aims to reduce this barrier of entry by helping visualize accessibility violations (and successes), while educating on best practices. tota11y is a single JavaScript file that inserts a small button in the bottom corner of your document. The toolbar consists of several plugins that each provide their own functionality. Many of these plugins "annotate" elements on the page. Sometimes to show their existence, other times to point out when something's wrong. tota11y makes it easy to spot some of the most common accessibility violations made by page authors today. -
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A collection of OpenGL samples: It doesn't contain a lot of comments, nor does it use the features for complex effects, but they still show how to set up all the different new features.
