Interactive Data Visualization in the browser, from Python
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Updated
Jul 28, 2020 - Python
Interactive Data Visualization in the browser, from Python
Graph theory (network) library for visualisation and analysis
js2flowchart - a visualization library to convert any JavaScript code into beautiful SVG flowchart. Learn other’s code. Design your code. Refactor code. Document code. Explain code.
An implementation of the Grammar of Graphics in R
Adds file type icons to Vim plugins such as: NERDTree, vim-airline, CtrlP, unite, Denite, lightline, vim-startify and many more
The BGP swiss army knife of networking
3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL
Visualisation Markdown
A react component to render nice graphs using vis.js
JavaScript architecture diagrams and dependency graphs
A tool to graphically visualize SIMD code
R package for thematic maps
A powerful, format-agnostic, and community-driven Python library for analysing and visualising Earth science data
Preliminary Exploratory Visualisation of Data
Stroom is a highly scalable data storage, processing and analysis platform.
A graph-focused data visualisation and interactive analysis application.
Exploration des données DVF
Visualise velocity data on a leaflet layer
Tutorials on visualizing data using python packages like bokeh, plotly, seaborn and igraph
IDE-like Vim tabline
Data exploration and visualisation for Elasticsearch and Splunk.
Visualizer for large-scale and interactive ray-tracing of neurons
Audio visualizer plugin for obs-studio
Visualisations of data are at the core of every publication of scientific research results. They have to be as clear as possible to facilitate the communication of research. As data can have different formats and shapes, the visualisations often have to be adapted to reflect the data as well as possible. We developed Pylustrator, an interface to directly edit python generated matplotlib graphs to finalize them for publication. Therefore, subplots can be resized and dragged around by the mouse, text and annotations can be added. The changes can be saved to the initial plot file as python code.
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