http://github.enthought.com/mayavi/mayavi
MayaVi2_ 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 Mayavi2 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).
MayaVi2 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 MayaVi2 Users Guide for more information.
Unlike its predecessor MayaVi1, Mayavi2 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.
General Build and Installation instructions for ETS are available here:
http://github.enthought.com/mayavi/mayavi/installation.html
Source tarballs for all stable ETS packages are available at
http://code.enthought.com/enstaller/eggs/source
More documentation is available in the online user manual,
http://github.enthought.com/mayavi/mayavi
or in docs directory of the sources. This includes a man page for the mayavi2 application, a users guide in HTML and PDF format and documentation for mlab.
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 test suite may be run like so (on a bash shell):
cd tests for i in test*.py; do python $i; done
Use a similar line for your particular shell.
The bug tracker is available as part of the trac interface here:
https://svn.enthought.com/enthought/
To submit a bug you will necessarily have to register at the site. Click on the "register" link at the top right on the above page to register. Or login if you already have registered. Once you are registered you may file a bug by creating a new ticket.
Alternatively, you can post on the [email protected] mailing list.
Core contribtuors:
Prabhu Ramachandran: primary author.
Gaël Varoquaux: mlab, icons, many general improvements and maintainance.
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.