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Encube: large-scale comparative visulisation of sets of multidimensional data
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README.md

encube

Large-scale comparative visualisation and analysis of sets of multidimensional data

Copyright (c) 2015, Dany Vohl, David G. Barnes, Christopher J. Fluke, Govinda Poudel, Nellie Georgiou-Karistianis, Amr H. Hassan, Yuri Benovitski, Tsz Ho Wong, Owen Kaluza, Toan D. Nguyen, C. Paul Bonnington. All rights reserved.

Authors: Dany Vohl - dvohl (at) swin.edu.au, David G. Barnes - david.g.barnes (at) monash.edu, Chris J. Fluke - cfluke (at) swin.edu.au, Yuri Benovitski - ybenovitski (at) bionicsinstitute.org, Owen Kaluza - owen.kaluza ( at ) monash.edu, Toan D. Nguyen toan.nguyen (at) monash.edu.

encube is licensed under the GNU General Public License (version 3) as published by the Free Software Foundation. Find more at http://www.gnu.org/licenses/.

Dependencies:

S2PLOT : https://github.com/mivp/s2plot

S2VOLSURF: https://github.com/mivp/s2volsurf

CFITSIO (for astronomical data) : http://heasarc.gsfc.nasa.gov/fitsio/fitsio.html

Configuration

See build setup examples in the scripts repository: typical S2PLOT builds. See configuration files example in the config repository.

e.g. "app_type": "astro_fits" or "brain_xrw"

How to use it:

build s2hd (encube-PR): scripts/build-VERSION.

start encube_PR via encube_M python encube_m.py config/config_file.json 0

start interactive unit server (webserver), which will create an instance of encube_M and connect to encube_PR python webserver.py config/config_file.json

Open your browser and go to http://localhost:8000

Note:

Tractography shader for IMAGE-HD currently supported on Linux only.

Reference note:

We would appreciate it if research outcomes using encube would provide the following acknowledgement:

"Visual analytics of multidimensional data was conducted with encube."

and a reference to

Dany Vohl, David G. Barnes, Christopher J. Fluke, Govinda Poudel, Nellie Georgiou-Karistianis, Amr H. Hassan, Yuri Benovitski, Tsz Ho Wong, Owen L Kaluza, Toan D. Nguyen, C. Paul Bonnington. (2016) Large-scale comparative visualisation of sets of multidimensional data. PeerJ Computer Science 2:e88 https://doi.org/10.7717/peerj-cs.88

Acknowlegements:

This work was enabled and supported by the Monash Immersive Visualisation Platform http://monash.edu/mivp.

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