=======
MayaVi2
=======


Vision
======

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).  The mayavi home page is:
https://svn.enthought.com/enthought/wiki/MayaVi


.. _MayaVi2: https://svn.enthought.com/enthought/wiki/MayaVi


Description
===========

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. 

More information is available at the mayavi home page:
https://svn.enthought.com/enthought/wiki/MayaVi


.. _MayaVi1: http://mayavi.sf.net


Quick start
===========

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 `enthought.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
===================

Source tarballs for all stable ETS packages are available at

 http://code.enthought.com/enstaller/eggs/source

General Build and Installation instructions for ETS are available here:

 https://svn.enthought.com/enthought/wiki/Build
 https://svn.enthought.com/enthought/wiki/Install

To install mayavi using eggs you will need to install the `enthought.mayavi`
egg with all its dependencies as described in the above links.


Documentation
==============

More documentation is available in the `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`. 

The documentation is also available from the mayavi home page.


Examples
========

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.


Test suite
==========

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.


Bug tracker, mailing list etc.
==============================

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 enthought-dev@mail.enthought.com mailing list.


Authors and Contributors
========================

Prabhu Ramachandran: primary author.

Gaël Varoquaux: new mlab, icons, many general improvements and suggestions.

K K Rai and R A Ambareesha: Patches for tensor support, parametric source and image data. 


Many thanks to all those who have submitted bug reports and suggestions for
further enhancements.