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May 23, 2024 · Mayavi is a general purpose, cross-platform tool for 2-D and 3-D scientific data visualization. Its features include: See the Mayavi Users Guide for more information. Unlike its predecessor MayaVi1, Mayavi has been designed with …

May 23, 2024 · Welcome, this is the user guide for Mayavi, a application and library for interactive scientific data visualization and 3D plotting in Python. You want to use an interactive application to visualize your data in 3D? Read the Mayavi application section.

May 23, 2024 · This section details the various ways of installing Mayavi. If you already have Mayavi up and running, you can skip this section. By itself Mayavi is not a difficult package to install but its dependencies are unfortunately rather heavy.

Mayavi2 is a general purpose, cross-platform tool for 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 extendibility via custom sources, modules, and …

May 23, 2024 · This section describes the mlab API, for use of Mayavi as a simple plotting in scripts or interactive sessions. This is the main entry point for people interested in doing 3D plotting à la Matlab or IDL in Python.

The Mayavi scientific data 3-dimensional visualizers. copied from cf-staging / mayavi

Apr 9, 2025 · Mayavi can be used as a fully integrated and interactive 3D plotting tool in a GUI application. Using the event model behind Traits and TVTK, all the different properties of a visualization can be changed dynamically, including the data visualized itself.

- Update to Mayavi 4.6.0 + Features - Use new pyface toolkit - Qt5 support. This is critical for Python 3.x as this is the only toolkit that works with Python 3.x. + Fixes - Update installation documentation. - Fix warning message when updating mlab_source data without scalars/vectors. - Fix nbextension and missing bug with global.

May 23, 2024 · A MayaVi example of how to generate an unstructured grid dataset using numpy arrays. Also shown is a way to visualize this data with mayavi2. The script can be run like so: