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D ATA V ISUALISATION CRICOS provider 00111D Christopher Fluke AusGO/AAO Observational Techniques Workshop 2014.

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Presentation on theme: "D ATA V ISUALISATION CRICOS provider 00111D Christopher Fluke AusGO/AAO Observational Techniques Workshop 2014."— Presentation transcript:

1 D ATA V ISUALISATION CRICOS provider 00111D Christopher Fluke AusGO/AAO Observational Techniques Workshop 2014

2 Question 1. Talk to the person next to you, and discuss what visualisation means to you. 2-3 minutes

3 What is Visualisation? The process of creating [computer-generated] images in order to gain new understanding or insight into data. DataScience Display Technology Interaction Software Visualisation-enabled Knowledge Discovery Publication

4 A Data Life Cycle 4 Collect Data Filter/Modify Data Characterise Data Display Data Interpret Data Publish/Pres ent Data

5 Why Visualise? How do you write an algorithm to find something that you don’t know is there?

6 The Ultimate Visualisation System

7 Never Forget… There are many things we do not know about the way the human visualisation system works Not everyone sees the world in quite the same way: –Colour blindness –Stereo blindness Our visual system is good at identifying shapes –Face recognition –Nephelococcygia

8 What do you see?

9 Qualitative Visual inspection Comparative Side-by-side comparison Data overlays Quantitative Selection Statistics Intuitive Interaction Increasing complexity Increasing scientific value? Visualisation Taxonomy Hypothesis Testing

10 Three-dimensional Visualisation Qualitative – easy Look at data NGC 628 in H I Data: THINGS survey Vis: S2PLOT, Volume Render, 256x256x72 voxels

11 Three-dimensional Visualisation Qualitative – easy Look at data Table 3. Hassan & Fluke (2011), PASA NGC 628 in H I Data: THINGS survey Vis: S2PLOT, Volume Render, 256x256x72 voxels

12 Three-dimensional Visualisation Qualitative – easy Look at data Comparative – harder Model + data Duchamp source-finder catalogue overlaid on volume rendering. Data: Ursa Major galaxy cluster at 21cm (V.Kilborn) Image: Hassan, Fluke, Barnes, 2011, ADASS XX

13 Three-dimensional Visualisation Qualitative – easy Look at data Comparative – harder Model + data Quantitative – hardest Dynamic selection Statistics “Operators” NGC 628 in H I Data: THINGS survey Vis: S2PLOT, Volume Render, 256x256x72 voxels What is the [median|average|maxim um|…] flux in this 3D region?

14 The Development of Astronomy Visualisation Making sense of the sky Recording to remember Exploration and discovery

15

16 Zodiac of Dendera (Ptolemaic Period? 300 BCE-30 BCE)

17 Bayeux Tapestry (c.1070s) Credit: Wikimedia Commons “They wonder at the star” (Halley’s Comet)

18 Uranometria: Bayer (1601) Linda Hall Library of Science, Engineering and Technology First accurate grid for star positions

19 CfA2 Redshift Survey (1986) Three-dimensional structure of the Universe

20 Toomre & Toomre, 1972, ApJ, 178, 623 © American Astronomical Society Visualisation is very important for numerical data

21 Types of Astronomical Data Brunner et al. (2001): Imaging data: 2D, narrow , fixed epoch Catalogs: secondary parameters determined from processing (coordinates, fluxes, sizes, etc). Spectroscopic data and products (e.g. redshifts, chemical composition, etc). Studies in the time domain - moving objects, variable and transient sources (synoptic surveys) Numerical simulations from theory They each pose their own problems for effective visualisation

22 Scientific Visualisation Physical Geometric Information Visualisation Abstract Multi-dimensional Presentation Graphics Publications Education & Public Outreach Astronomy Visualisation Types of visualisations

23 Visual Elements Points –Point size –Point colour Symbols/glyphs/markers –Symbol size –Symbol colour Lines/contours –Line thickness –Line style –Line colour Polygons/surfaces –Colour –Texture Vector Data –Vector Plots –Directed glyphs –Length, colour, thickness Meshes/Volume data –Isosurfaces Value Colour –Volume rendering Data range Transfer function 23

24 2D Contour Lines

25 Vector Field 25

26 Volume visualisations Points Splats Isosurfac e Volume Render

27 Colour Used correctly, colour enhances comprehension Used incorrectly, colour reduces comprehension “Optical Nervous System” –Or “How the inside of your head feels” –From a lecture by Alan Watts ( ) –Interpreted by David McConville (Elumenati) –http://www.youtube.com/watch?v=R3ozwTRepqM 27

28 Colour Maps We can use colour to represent value by providing a colour map Need to know minimum and maximum data value –Out of range values? –Number of steps? 28 Credit: Wikipedia Commons

29 Colour Maps: N = 1000 steps 29 Hue based Saturation based

30 Tints, Shades, Tones Add white Add black Add grey

31 Think about the visualisation software/tools that you have used. Now choose one of these packages. b) What is this software’s best/most useful feature to you? c) “If I could change one thing about this package it would be…” Discuss your answer with your neighbours, and find out whether the software they use might help you. 5 minutes

32 Reading List Brunner, R.J., Djorgovski, S.G., Prince, T.A., Szalay, A.S., 2001, Massive Datasets in Astronomy, arXiv:astro-ph/ Farmer, R.S., 1934, Celestial Cartography, PASP, 50, 34 Fluke, C.J., Bourke, P.D., O’Donovan, D., 2006, Future Directions in Astronomy Visualization, PASA, 23, 12 Globus, A., Raible, E., 1994, Fourteen Ways to Say Nothing with Scientific Visualization, Computer, 27, 86 Hassan, A.H., Fluke, C.J., 2011, Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era, PASA, 28, 150 Norris, R.P., 1994, The Challenge of Astronomical Visualisation, ADASS III, ASP Conference Series, 61, eds. D.R.Crabtree, R.J.Hanisch, J.Barnes, p.51


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