Skip to main content

Python interface to LavaVu OpenGL 3D scientific visualisation utilities

Project description

# logo

Build Status Deploy Status DOI Binder

A scientific visualisation tool with a python interface for fast and flexible visual analysis.

Documentation available here LavaVu Documentation

examplevis

LavaVu development is supported by the Monash Immersive Visualisation Plaform and the Simulation, Analysis & Modelling component of the NCRIS AuScope capability.

The acronym stands for: lightweight, automatable visualisation and analysis viewing utility, but "lava" is also a reference to its primary application as a viewer for geophysical simulations. It was also chosen to be unique enough to find the repository with google.

The project started in the gLucifer1 framework for visualising geodynamics simulations. The OpenGL visualisation module was separated from the simulation and sampling libraries and became a more general purpose visualisation tool. gLucifer continues as a set of sampling tools for Underworld simulations as part of the Underworld2 code. LavaVu provides the rendering library for creating 2d and 3d visualisations to view this sampled data, inline within interactive IPython notebooks and offline through saved visualisation databases and images/movies.

As a standalone tool it is a scriptable 3D visualisation tool capable of producing publication quality high res images and video output from time varying data sets along with HTML5 3D visualisations in WebGL. Rendering features include correctly and efficiently rendering large numbers of opaque and transparent points and surfaces and volume rendering by GPU ray-marching. There are also features for drawing vector fields and tracers (streamlines).

Control is via python and a set of simple verbose scripting commands along with mouse/keyboard interaction. GUI components can be generated for use from a web browser via the python "control" module and a built in web server.

A native data format called GLDB is used to store and visualisations in a compact single file, using SQLite for storage and fast loading. A small number of other data formats are supported for import (OBJ surfaces, TIFF stacks etc). Further data import formats are supported with python scripts, with the numpy interface allowing rapid loading and manipulation of data.

A CAVE2 virtual reality mode is provided by utilising Omegalib (http://github.com/uic-evl/omegalib) to allow use in Virtual Reality and Immersive Visualisation facilities, such as the CAVE2 at Monash, see (https://github.com/mivp/LavaVR). Side-by-side and quad buffer stereoscopic 3D support is also provided for other 3D displays.

This repository

This is the public source code repository for all development on the project. Development happens in the "master" branch with stable releases tagged, so if you just check out master, be aware that things can be unstable or broken from time to time.

How do I get set up?

It's now in the python package index, so you can install with pip:

pip install --user lavavu

If you don't have pip available, you can try sudo easy_install pip or just install Anaconda, which comes with pip and a whole lot of other useful packages for scientific work with python. Once in anaconda pip install lavavu will install the package.

Currently no binaries are provided and the installer needs to compile the library, so on Linux you may need some developer tools and headers first, eg: for Ubuntu: sudo apt install build-essential libgl1-mesa-dev libx11-dev zlib1g-dev

To try it out:

python
> import lavavu
> lv = lavavu.Viewer() #Create a viewer
> lv.test()            #Plot some sample data
> lv.interactive()     #Open an interactive viewer window

Alternatively, clone this repository with git and build from source:

  git clone https://github.com/lavavu/LavaVu
  cd LavaVu
  make -j4

If all goes well the viewer will be built, try running with: ./lavavu/LavaVu

Dependencies

  • OpenGL and Zlib, present on most systems, headers may need to be installed
  • To use with python requires python 2.7+ and NumPy
  • For video output, requires: libavcodec, libavformat, libavutil, libswscale (from FFmpeg / libav)
  • To build the python interface from source requires swig (http://www.swig.org/)

Who do I talk to?

For further documentation / examples, see the online documentation

Included libraries

In order to avoid as many external dependencies as possible, the LavaVu sources include files from the following public domain or open source libraries, many thanks to the authors for making their code available!


1 Stegman, D.R., Moresi, L., Turnbull, R., Giordani, J., Sunter, P., Lo, A. and S. Quenette, gLucifer: Next Generation Visualization Framework for High performance computational geodynamics, 2008, Visual Geosciences
2 Ruijters, Daniel & ter Haar Romeny, Bart & Suetens, Paul. (2008). Efficient GPU-Based Texture Interpolation using Uniform B-Splines. J. Graphics Tools. 13. 61-69.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

lavavu_osmesa-1.8.83-cp312-cp312-manylinux_2_28_x86_64.whl (74.0 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.28+ x86-64

lavavu_osmesa-1.8.83-cp311-cp311-manylinux_2_28_x86_64.whl (74.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.28+ x86-64

lavavu_osmesa-1.8.83-cp310-cp310-manylinux_2_28_x86_64.whl (74.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.28+ x86-64

lavavu_osmesa-1.8.83-cp39-cp39-manylinux_2_28_x86_64.whl (74.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.28+ x86-64

lavavu_osmesa-1.8.83-cp38-cp38-manylinux_2_28_x86_64.whl (74.0 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.28+ x86-64

File details

Details for the file lavavu_osmesa-1.8.83-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for lavavu_osmesa-1.8.83-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 179f0ef4f03763e500031feeba9be730a9e294b5399fd874863cf49edf01d1af
MD5 74610e2449ef3e855484bb678a39ba9f
BLAKE2b-256 a890c2ed168f24e7782eefacd1f51255425af2b83fae72ac8397c31dfcac1e3d

See more details on using hashes here.

Provenance

The following attestation bundles were made for lavavu_osmesa-1.8.83-cp312-cp312-manylinux_2_28_x86_64.whl:

Publisher: osmesa_deploy.yaml on lavavu/LavaVu

Attestations:

File details

Details for the file lavavu_osmesa-1.8.83-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for lavavu_osmesa-1.8.83-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c40b8f69046250c20ea3e712b1ba81828470b825a350d1f9f69fded5081ef28a
MD5 4de9ee818ad7fd86403b88cf3b6b5737
BLAKE2b-256 cca31158637a38f37b606db312f769be6e16ce381fe91fc73446aaadffd1123d

See more details on using hashes here.

Provenance

The following attestation bundles were made for lavavu_osmesa-1.8.83-cp311-cp311-manylinux_2_28_x86_64.whl:

Publisher: osmesa_deploy.yaml on lavavu/LavaVu

Attestations:

File details

Details for the file lavavu_osmesa-1.8.83-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for lavavu_osmesa-1.8.83-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 acafa3a915a7b0f9d45b4f30ccfc5c67d5837f44cb80acc30536b99bd9ce1b17
MD5 f8da9b036d33f09affec056ee22882da
BLAKE2b-256 b4a73d5689a06d3d868e95e5e3c107768cfc469143ac698de9340f3005f85c31

See more details on using hashes here.

Provenance

The following attestation bundles were made for lavavu_osmesa-1.8.83-cp310-cp310-manylinux_2_28_x86_64.whl:

Publisher: osmesa_deploy.yaml on lavavu/LavaVu

Attestations:

File details

Details for the file lavavu_osmesa-1.8.83-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for lavavu_osmesa-1.8.83-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4e6c9d0ef599e6d0da5308b7d6690bddb23de599b7b2085649c2352d8e551fcb
MD5 0a8252533ee82b72b4e051c6d37a4a39
BLAKE2b-256 50e39717af0ce0333b1cefa1717785ecad2529fd810f1f8309cc0945808b2553

See more details on using hashes here.

Provenance

The following attestation bundles were made for lavavu_osmesa-1.8.83-cp39-cp39-manylinux_2_28_x86_64.whl:

Publisher: osmesa_deploy.yaml on lavavu/LavaVu

Attestations:

File details

Details for the file lavavu_osmesa-1.8.83-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for lavavu_osmesa-1.8.83-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4cefe5c77e32ebaeee25bad8a2665bc2f4caac6fcf84678c4ab004fc35f70936
MD5 a39356ce163bfd27981041e6d57297c4
BLAKE2b-256 9ae2dfcb373aae6572234cee971c4e4b67f957f18b92e57b08e50c2e84edd21c

See more details on using hashes here.

Provenance

The following attestation bundles were made for lavavu_osmesa-1.8.83-cp38-cp38-manylinux_2_28_x86_64.whl:

Publisher: osmesa_deploy.yaml on lavavu/LavaVu

Attestations:

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page