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Data visualization libraries Plotly.js is an open-source JavaScript library for creating graphs and powers Plotly.py for Python, as well as Plotly.R for R, MATLAB, Node.js, Julia, and Arduino and a REST API.
Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
VTK consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python.The toolkit is created and supported by the Kitware team. VTK supports a various visualization algorithms including: scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as: implicit modeling, polygon reduction, mesh smoothing, cutting ...
Fully written in Python with additional speed ups in Cython. PySide, open source is a Python binding of the cross-platform GUI toolkit Qt developed by The Qt Company, as part of the Qt for Python project. PyQt, open source (GPL and commercial) is another Python binding of the cross-platform GUI toolkit Qt developed by Riverbank Computing.
Graphviz (short for Graph Visualization Software) is a package of open-source tools initiated by AT&T Labs Research for drawing graphs (as in nodes and edges, not as in bar charts) specified in DOT language scripts having the file name extension "gv". It also provides libraries for software applications to use the tools.
Kivy, open source Python library for developing multitouch application software with a natural user interface (NUI). PyGTK, a popular cross-platform GUI library based on GTK+; furthermore, other GNOME libraries also have bindings for Python; PyQt, another cross-platform GUI library based on Qt; as above, KDE libraries also have bindings
p5 is a Python library that provides high level drawing functionality to quickly create simulations and interactive art using Python. It combines the core ideas of Processing — learning to code in a visual context — with Python's readability to make programming more accessible to beginners, educators, and artists.
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems being addressed.