Displaying 1 to 13 from 13 results

jQuery-Mapael - jQuery plugin based on raphael.js that allows you to display dynamic vector maps


The complete documentation is available on Mapael website (repository: 'neveldo/mapael-documentation').Additional maps are stored in the repository 'neveldo/mapael-maps'.

textures - Textures.js is a JavaScript library for creating SVG patterns


Textures.js is a javascript library for creating SVG patterns. Made on top of d3.js, it is designed for data visualization. Read more on http://riccardoscalco.github.io/textures/.




leaflet-dvf - Leaflet Data Visualization Framework


The Leaflet Data Visualization Framework (DVF) is an extension to the Leaflet JavaScript mapping library. The primary goal of the framework is to simplify data visualization and thematic mapping using Leaflet - making it easier to turn raw data into compelling maps.

django-rest-pandas - 📊📈 Serves up Pandas dataframes via the Django REST Framework for use in client-side (i


Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats) for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. This capability can often be leveraged by sending users to the same URL that your visualization code uses internally to load the data.

ngx-charts-builder - 🚀 Chart Builder for ngx-charts!


Run npm start for a dev server. Navigate to http://localhost:4200/. The app will automatically reload if you change any of the source files.Run npm run build to build the project. The build artifacts will be stored in the dist/ directory. Use the -prod flag for a production build.


python-opengl - Python & OpenGL for Scientific Visualization (Open Access Book)


Python and OpenGL have a long but complicated story. It used to be really easy to program something using the fixed-pipeline and libraries such as Pyglet but things became more difficult with the introduction of the dynamic graphic pipeline in 2004. The goal of this book is to reconciliate Python programmers with OpenGL, providing both an introduction to modern OpenGL and a set of basic and advanced techniques in order to achieve both fast, scalable & beautiful scientific visualizations. The book uses the GLES 2.0 API which is the most simple API for accessing the programmable graphic pipeline. It does not cover up-to-date OpenGL techniques but it is sufficient to achieve great visualisation. In fact, modern OpenGL allows to control pretty much everything in the pipeline and the goal of this book is to explain several techniques dedicated to scientific visualisation such as isolines, markers, colormaps, arbitrary transformations but there are actually many more techniques to be discovered and explained in this open-access book. And of course, everything will be fast and beautiful. This book is open-access (i.e. it's free to read at this address) because I believe knowledge should be free. However, if you think the book is worth a few dollars (5€ or 10€), you can use Paypal to make payment. This money will help me to travel to Python conferences and to write other books as well. If you don't have money, it's fine. Just enjoy the book and spread the word about it.

gephi-tutorials - Open and collaborative tutorials for Gephi


written in asciidoc to ease the conversion to pdf, html, slides, etc. tutorials use a lot of charts. I use Google Drawings for that. Very practical for online editing, and live updates: charts made on Google Drawing can be embedded in web documents, so that any correction brought to a chart is instantaneously reflected in the web versions of the tutorials. Great stuff.

bs-d3 - Experimental d3 4.x bindings for BuckleScript


WIP d3 4.x bindings for Bucklescript. Currently some of the typings are still quite loose/permissive, if you have any suggestions to tighten them up in an idiomatic OCaml way, please file an issue or PR.

fancy-minard - Use ggplot and R to do fancy things with Minard's famous plot of Napoleon's 1812 retreat from Russia


For whatever reason, I decided to start reading Tolstoy's War and Peace (via Audible) the week I had to turn in my dissertation. I still have a dozen or so hours to go, but the book has been amazing. I had no idea what it was about going into it, and was delighted to find that the "war" parts of the book deal with the Napolonic wars—both his 1804–1805 campaign in the War of the Third Coalition (like the Battle of Austerlitz), and his 1812 campaign to invade Russia, from whence we get Tchaikovsky's 1812 Overture. I knew nothing about these wars and Tolstoy's descriptions are incredible and gripping. It's been especially exciting because I'm preparing a course on data visualization this fall and had been looking forward to using Charles Minard's famous plot about Napoleon's 1812 winter retreat from Moscow, where the Grande Armée dropped from 422,000 to 10,000 troops.