Mayavi: 3D Visualization of Scientific Data

By Prabhu Ramachandran, Gaël Varoquaux. 2010

575 citations recorded by the source corpus.

Mayavi is a general purpose, open source 3D scientific visualization package that is tightly integrated with the rich ecosystem of Python scientific packages. Mayavi provides a continuum of tools for developing scientific applications, ranging from interactive and script-based data visualization in Python to full-blown custom end-user applications.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Graying the black box: Understanding DQNs (2016, methodology): “In order to capture the game dynamics, DQN represents a state by a sequence of history frames and pads initial states with zero frames.”
The Virtual Brain: a simulator of primate brain network dynamics (2013, methodology): “Additionally, for shell users there is a collection of plotting tools available based on matplotlib and mayavi (Ramachandran and Varoquaux, 2011).”
Inverse design of 3d molecular structures with conditional generative neural networks (2021, unknown): “All molecules shown in figures have been rendered with the 3d visualization package Mayavi66.”
Not so lumpy after all: modelling the depletion of dark matter subhaloes by Milky Way-like galaxies (2017, methodology): “This work also made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013), matplotlib (Hunter 2007), numpy (van der Walt et al. 2011), scipy (Jones et al. 01 ), ipython (Perez & Granger 2007), Mayavi (Ramachandran & Varoquaux 2011), and NASA’s As”
Estimation of 3D vegetation density with Terrestrial Laser Scanning data using voxels. A sensitivity analysis of influencing parameters (2017, methodology): “(b and c were plotted using Mayavi software (Ramachandran and Varoquaux, 2011)).”
Tesseroids: Forward-modeling gravitational fields in spherical coordinates (2016, methodology): “We used the following Python language libraries to perform the error analysis and generate figures: pandas by McKinney (2010), matplotlib by Hunter (2007) for 2D figures and maps, andMayavi by Ramachandran and Varoquaux (2011) for 3D figures.”