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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– kwb@comp.leeds.ac.uk

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/