1.1 vis_04 data visualization msc module school of computing ken brodlie semester 1 2004-2005...
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Data VisualizationData Visualization
MSc ModuleSchool of Computing
Ken BrodlieSemester 1 2004-2005
Lecture 1 - Introduction
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VisualizationVisualization
Visualization now seen as key part of modern computing
High performance computing generates vast quantities of data ...
High resolution measurement technology likewise ...– microscopes, scanners, satellites
Information systems involve not only large data sets but also complex connections...
... we need to harness our visual senses to help us understand the data
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Getting StartedGetting Started
What is Visualization? - a definition Where is it useful? - some
applications What is the history? What tools are now available? How are we going to study it?
– MSc in Distributed Multimedia Systems– MSc in Computational Fluid Dynamics
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Data Visualization = Scientific Vis + Information Vis
Data Visualization = Scientific Vis + Information Vis
Scientific Visualization– Numerical data
from science, engineering and medicine
Information Visualization– Numeric and non-
numeric data
Ozone layer around earth Automobile web site- visualizing links
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Scientific Visualization - What is it?
Scientific Visualization - What is it?
Images, animation
Visualization
Reality
Data
Observation Simulation
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Applications - MeteorologyApplications - Meteorology
Pressure at levelsin atmosphere- illustrated by contour lines in aslice plane
Generated bythe Vis5D systemfrom University ofWisconsin (nowVis5d+) Vis5d: http://www.ssec.wisc.edu/~billh/vis5d.html
Vis5d+ : http://vis5d.sourceforge.net
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Applications - MedicineApplications - Medicine
From scanner data, we canvisualize 3D picturesof human anatomy, usingvolume rendering
Generated by VOXELmansoftware from Universityof Hamburg
www.uke.uni-hamburg.de/institute/imdm/idv/index.en.html
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Applications – Climate Prediction
Applications – Climate Prediction
Simulation of 21st century climate evolution
Real-time display of results– temperature, cloud,
precipitation, etc
Massive ensemble of runs : distributed public-resource computing project– see
www.climateprediction.net to participate!
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Applications – Computational Fluid
Dynamics
Applications – Computational Fluid
Dynamics
Flow of air around a car– Vectors and
particle paths illustrate flow
– Coloured slice indicates pressure
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Applications – Computational Fluid
Dynamics
Applications – Computational Fluid
Dynamics Interface
between immiscible fluids– e.g. oil / water
Loops and fingers arise when mixing starts– Rayleigh-Taylor
instability Simulated on
ASCII Blue Pacific (Cook & Dimotakis, 2001)
Interface visualized using a density isosurface
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Applications - Molecular Modelling
Applications - Molecular Modelling
2D potential energy function– molecule
inside a zeolite channel
Displayed as coloured surface (left)– part also
displayed using contour plot (right)
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Applications - Molecular Modelling
Applications - Molecular Modelling
3D potential energy function– three atoms
in a box Displayed as
isosurface (left)– interactive
probe also shows how potential varies between two points (right)
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Visualization BCVisualization BC
Imagination or visualization, and in particular the use of diagrams, has a crucial part to play in scientific investigation.– Rene Descartes, 1637
There are many examples of the use of visualization Before Computers (BC)– graph plots in 10th century– business graphics in 18th century
(Playfair)– contour plots in 18th century (Halley)
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The First VisualizationThe First Visualization
This and following two pictures are taken from BrianCollins ‘Data Visualization - Has it all been seen before?’in ‘Animation and Scientific Visualization’, Academic Press
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The First Business Graphics
The First Business Graphics
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The First Contour MapThe First Contour Map
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Visual ThinkersVisual Thinkers
Many of the great scientists were good at visual thinking:– Leonardo da Vinci– James Clerk Maxwell– Michael Faraday– Albert Einstein
This was often at the expense of verbal skills
Tom West : “In the Mind’s Eye”– See also
http://www.krasnow.gmu.edu/twest/maxwell_visual.html
Maxwell’s clay model nowin New Cavendish Laboratory, Cambridge(picture by Tom West)
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Early Computer Visualization
Early Computer Visualization
From early days of computing, scientists have carried out numerical simulation - and looked to visualizationvisualization to help understand the results.
Visualization systems have evolved in four different styles - all still in use today (so not really history!)
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Subprogram LibrariesSubprogram Libraries
1960 onwards Libraries of
subprograms to draw graphs, contour plots …
Scientists include calls to library routines from within their own code
Leading examples from 1970-1985 era were:– GHOST (UKAEA Culham)– NAG Graphics Library
NAG Graphics : www.nag.co.uk
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Subprogram LibrariesSubprogram Libraries
This style continues today– NAG Graphics
Library still available
– Vtk C++ classes provide modern version of this style
Great flexibility – but need to program
Application Programming Interface
Vtk : www.visualizationtoolkit.org
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Interactive PackagesInteractive Packages
From late 1970 onwards
Menu-driven packages allowing data to be visualized without need to write programs
Example:– gnuplotwww.gnuplot.info
Less flexible, but no programming!
gnuplot
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Interactive PackagesInteractive Packages
Matlab is a powerful system for computation and visualization– Has its own C-like
language
www.mathworks.com
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Visualization TodayVisualization Today
Recent surge of interest in visualization was sparked by an NSF report: Visualization in Scientific
Computing– McCormick, de Fanti and
Brown - 1987 Argued that investment in
high performance computing in US was wasted unless there was corresponding investment in visualization
This motivated a third style of visualization system...
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Visual Programming Systems
Visual Programming Systems
From late 1980s onwards Visualization seen as a sequence of
simple processing steps: eg contouring– read in data– create contour lines– draw contour lines
Systems provide modules implementing simple steps in a visualization pipeline
Scientist uses ‘visual programming’ to connect modules together
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Visual Programming - IRIS Explorer
Visual Programming - IRIS Explorer
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Visual Programming Systems
Visual Programming Systems
Visual programming allows easy experimentation which is what one needs in visualization
Examples are:– IRIS Explorer
www.nag.co.uk
– AVS www.avs.com
– OpenDX (grown from IBM Visualization Data Explorer)
www.opendx.org
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Service-based VisualizationService-based Visualization
The Internet era has introduced a fourth style of system – where a visualization ‘service’ is delivered over the internet using Web technologies
Client-side with Java applets….
www.sdsc.edu/vizwiz
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Service-based VisualizationService-based Visualization
… or server side Here a form on a
web page is used to make a visualization ‘request’
Processed by a visualization system on server and returned to client as VRML
IRIS Explorer SerViswww.visualization.leeds.ac.uk/aqual
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The Four Phases of Visualization Systems
The Four Phases of Visualization Systems
These four phases correlate with four phases in computing generally
Subprogram libraries– begun in era of batch computing
Interactive packages– begun in era of interactive computing, with
terminals connected to host
Visual programming systems– begun in era of workstation computing,
with graphical user interfaces
Service-based visualization– begun in era of internet computing
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Information VisualizationInformation Visualization
Information Visualization– Has emerged over last decade– Building on success of scientific
visualization– Driven by the escalating volumes of
data fuelled by the new technologies (eg supermarket checkouts!) and the accessibility of data via the Internet
– Characterised by large quantities of data – not necessarily numbers – and search for relationships amongst the data …
– … but no absolute dividing line between SciVis and InfoVis
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Outline of the CourseOutline of the Course
Lectures– Monday 10 (Parkinson-B9) ; Friday 9
(LT11) Practical sessions using gnuplot,
IRIS Explorer and xmdvtool under Linux
Background study
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Outline of Lecture CourseData Visualization - I
Outline of Lecture CourseData Visualization - I
Introduction and historical view Fundamental concepts Scientific Visualization techniques
– Scalar data - one value at a point» 1D - graphs, ..» 2D - contour maps, ..» 3D - isosurfaces, volume rendering
– Vector data - many related values at a point» velocity values : flow visualization
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Outline of Lecture CourseData Visualization - II
Outline of Lecture CourseData Visualization - II
Publication of visualization– VRML for 3D web presentation
Visualization Systems Computational steering
– linking simulation and visualization– Grid computing and visualization
Collaborative Visualization– Group working on the Internet
… this will complete the programme for CFD students
… but DMS students continue
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Outline of Lecture Course: Data Visualization - III
Outline of Lecture Course: Data Visualization - III
Web-based visualization– using the Web as a distributed
computing environment Information Visualization
– how to interpret large quantities of data using visualization
– multivariate data
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Practical WorkPractical Work
For DMS and CFD students - use of IRIS Explorer– state of art visualization system– Linux pc’s– practical sessions
For DMS students – xmdvtool (multivariate data)
Publication using the World Wide Web Assessment
– assignments to visualize datasets Experience of other systems
– gnuplot
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Background StudyBackground Study
Reading– mainly recent papers
World Wide Web– IRIS Explorer training materials– generally ... a source of up-to-date
information and examples
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BooksBooks
The Visualization Toolkit (3rd edition)– W Shroeder, K Martin, W Lorensen –
Kitware Inc Introduction to Volume Rendering
– B. Lichtenbelt et al - Prentice Hall (1998) Information Visualization
– R. Spence – Addison-Wesley (2001) Scientific Visualization Tech & Applns
– K W Brodlie et al– Springer Verlag (1992)
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ObjectivesObjectives
To be aware of the value of visualization to gain insight into both numeric data (from science, engineering and medicine for example) …
… and also non-numeric information (such as networks and documents)
To understand the fundamental techniques for data visualization
To be skilled in the use of a state of art visualization system
DMS
DMSCFD
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Keeping in TouchKeeping in Touch
E-mail– [email protected]
Newsgroup for my postings:– local.modules.vis
Newsgroup for your postings:– local.modules.vis.talk
World Wide Web– http://www.comp.leeds.ac.uk/kwb/