Mayavi docs: http://docs.enthought.com/mayavi/tvtk
BSD 3 ClauseMayavi 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 Quick start
If you are new to Mayavi it is a good idea to read the Installation
By itself Mayavi is not a difficult package to install but its dependencies are unfortunately rather heavy. However, many of these dependencies are now available as wheels on PyPI. The two critical dependencies are,
The latest VTK wheels are available on all the major platforms (Windows, MacOS, and Linux), but only for 64 bit machines. Python 3.x is fully supported on all these operating systems and Python 2.7.x on MacOS and Linux. If you are out of luck, and your platform is not supported then you will need to install VTK yourself using your particular distribution as discussed in the PyQt5 and wheels are available for this. On 2.7.x you have more options, and can use PyQt4, and traitsui, Latest stable release
As of the latest release, i.e. 4.6.0 and above, if you are using Python 3.x and are on a 64 bit machine, installation via ipywidgets and pip or your favorite package manager.
If you want to install the latest version of Mayavi from github, you can simply do the following:
$ git clone https://github.com/enthought/mayavi.git $ cd mayavi $ pip install -r requirements.txt $ pip install PyQt5 # replace this with any supported toolkit $ python setup.py install # or develop
Add the jupyter nbextensions using the instructions in the section above and you should be good to go.
More documentation is available in the https://github.com/prabhuramachandran/mayavi-tutorial
Here are some tutorial videos that you can watch to learn Mayavi:
Examples are all in the examples directory of the source or the git clone.
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
The bug tracker is available in Mayavi-users mailing list. This is used by some folks and is not too active. Another mailing list that may be of use is the Authors and Contributors
Core contributors:
Prabhu Ramachandran: primary author.
Previous contributors:
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.
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