Mayavi: 3D visualization of scientific data in Python
Mayavi seeks to provide easy and interactive visualization of 3D data. It does this by the following:
- an (optional) rich user interface with dialogs to interact with all data and objects in the visualization.
- a simple and clean scripting interface in Python, including one-liners, a-la mlab, or object-oriented programming interface.
- harnesses the power of the VTK toolkit without forcing you to learn it.
Additionally Mayavi strives to be a reusable tool that can be embedded in your applications in different ways or combined with the envisage application-building framework to assemble domain-specific tools.
Mayavi is part of the Enthought Tool Suite (ETS).
Mayavi is a general purpose, cross-platform tool for 2-D and 3-D scientific data visualization. Its features include:
- Visualization of scalar, vector and tensor data in 2 and 3 dimensions
- Easy scriptability using Python
- Easy extendability via custom sources, modules, and data filters
- Reading several file formats: VTK (legacy and XML), PLOT3D, etc.
- Saving of visualizations
- Saving rendered visualization in a variety of image formats
- Convenient functionality for rapid scientific plotting via mlab (see mlab documentation)
- See the Mayavi Users Guide for more information.
Unlike its predecessor MayaVi1, Mayavi has been designed with scriptability and extensibility in mind from the ground up. While the mayavi2 application is usable by itself, it may be used as an Envisage plugin which allows it to be embedded in user applications natively. Alternatively, it may be used as a visualization engine for any application.
If you are new to mayavi it is a good idea to read the users guide which should introduce you to how to install and use it. The user guide is available in the docs directory and also available from the mayavi home page.
If you have installed mayavi as described in the previous section you should be able to launch the mayavi2 application and also run any of the examples in the examples directory.
Getting the package
The mayavi codebase can be found in github:
General Build and Installation instructions are available here
Source tarballs for necessary stable ETS packages are available through pypi
Development versions exist in the github Enthought organization
More documentation is available in the online user manual or in
of the sources. This includes a man page for the
application, a users guide in HTML and PDF format and documentation
Examples are all in the
examples directory of the source or the SVN checkout.
The docs and examples do not ship with the binary eggs. The examples directory
also contains some sample data.
The basic test suites for tvtk and mayavi can be run using nose:
nosetests -v tvtk/tests nosetests -v mayavi
The integration tests:
cd integrationtests/mayavi python run.py
Bug tracker, mailing list etc.
The bug tracker is available in github Please provide info and details on platform, python, vtk and gui backends and their versions. If possible, a small example replicating the the problem.
Authors and Contributors
Prabhu Ramachandran: primary author.
Gaël Varoquaux: mlab, icons, many general improvements and maintenance.
Deepak Surti: Upgrade to VTK 5.10.1, VTK 6.x with new pipeline.
Support and code contributions from Enthought Inc.
Patches from many people (see the release notes), including K K Rai and R A Ambareesha for tensor support, parametric source and image data.
Many thanks to all those who have submitted bug reports and suggestions for further enhancements.