CINECA withoutvisualization, is like assembling a jigsaw puzzle … · 2012. 6. 12. · From “Fantasia” – Bruno Munari Imagination for science “Phantasie ist wichtiger als

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Foundations ofScientific Visualization

Ing. Mario ValleCINECA – 11/06/2012

"Computing, and in particular supercomputing,without visualization,is like assembling a jigsaw puzzle in the dark"

Richard Weinberg (1988)

Purpose of computing isinsight,not numbers

Richard HammingNumerical Methods for Scientists and Engineers (1962)

Purpose of computing isinsight,not numbers


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What are theseinsights all about?

It is about recognisingrelationships

"Computing, and in particular supercomputing,without visualization,is like assembling a jigsaw puzzle in the dark"

Richard Weinberg (1988)

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The famous Anscombe’s QuartetProperty Value

Mean of x in each case 9 (exact)Variance of x in each case 11 (exact)Mean of y in each case 7.50 (to 2 decimal places)

Variance of y in each case 4.122 or 4.127 (to 3 decimal places)

Correlation between x and y in each case

0.816 (to 3 decimal places)

Linear regression line in each case

3.00 0.500 (to 2 and 3 decimal places, respectively)

From Wikipedia

Scientific Visualization - Mario Valle - CINECA 11/06/2012

Visualize: what does it means?

Visualize: to form a mental vision, image, or picture of (something not visible or present to sight, or of an abstraction); to make visible to the mind or imagination.

Visualize: the act of putting into a visible form.

The Oxford English Dictionary, 1989

Today path

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The CSCS Data Analysisand VisualizationGroup

[email protected]

http://mariovalle.name/ Why don’t we start here?

ParaView

MayaVi

Spotfire

AVS/Express

Not everything has a meaning…


Collectdata and

information

Try to makesense

of them

Crystalize newknowledge

Actio

n or

com

mun

icat

ion

Havegoal

New inputs

… how do we find meaning?

Car

d et

al.

1999

5

Knowledge is not accumulation

Data ≠ knowledge

Facts ≠ knowledge


Data without meaning

More data do not mean more

knowledge


How do we make sense of data?

Try to makesense

of them

Mental modelsare psychological representations of real, hypothetical or imaginary situations used to understand a specific phenomenon

Models and mental images


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Models and mental images


“All our ideas andconcepts are onlyinternal pictures”

Ludwig Boltzmann(1899)

Logo of a Spanish design agency


Famous mental images

Ernest Rutherford“In the atom there is a nucleus made of protons and neutrons, with electrons orbiting the nucleus like planets in a solar system”

Albert EinsteinWhile still a child, as he was waiting at a train station, watching the clock he wondered what would happen if he moved away so quickly to be «riding a beam of light»

Friedrich August von KekuléBefore publishing in 1875 his theory on the structure of benzene, had a dream in which appeared a snake biting its tail forming a ring

Everyday examples


Mental simulations

They help us understand the workingsof what we model in our mind

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Mental simulation

Help us solve problems

For example: what of the following four models A – D is a rotated version of the one on the left?

Eliot and Smith (1983)


Data images decisions

Try to makesense

of them

Actio

n or

com

mun

icat

ion


Where mental models form?

The concepts so acquired will crystallize as new knowledge by creating connections with existing information

Mental models are formed in the working memory(visual sketchpad)

(Wickens memory and perception model)

What about imagination?

We conceive new ideas usingthe building materials we haveat hand

Leo Leoni, Fish is Fish, Pantheon, 1970

“Imagination is vision running backwards”

S. Greenfield

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Fantasy, invention, creativity think.Imagination sees.

From “Fantasia” – Bruno Munari

Imagination for science

“Phantasie ist wichtiger als Wissen,denn Wissen ist begrenzt”

Albert Einstein


LEGOland Deutschland

Imagination for science


“Imagination ismore importantthan knowledge,because knowledgeis limited”

Albert Einstein

LEGOland Deutschland

What are theseinsights all about?

9

It is about formulating the right question


The scientific discovery processby James Watson

(DNA)EXPERIMENT OR

DATA COLLECTION DATA

Computation orTransformation

Rendered imageInsight

Hypothesis

User

interaction

guesswork


We want to see what we are studying

EXPERIMENT ORDATA COLLECTION DATA



Hypothesis

User

interaction

guesswork

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Self-illustrating phenomena


A lot of data is outside our reach


Or it cannot be replicated

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So we use numbers instead



Hypothesis

User

interaction

guesswork“It is nice to know that the computer

understands the problem.

But I would like to understand it too”Eugene Wigner

(Physicist, 1902-1995)Wigner at the blackboard with Teller


Ideas from numbers alone?

When printed, the

hemoglobin PDB file

outputs 92 pages of

(boring) atomic

coordinates

(PDB ID 1A00)


To understand is to compress

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We see indirectly through images


There is a recurring pattern

Conceptualmodel

DataObject under

studyAcquisition

Render

Black box

It’s the visualization mission To adapt numbers to humans

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Memory limits

The concepts so acquired will crystallize as new knowledge by creating connections with existing information

Mental models are formed in the working memory(visual sketchpad)

(Wickens memory and perception model)

But working memory: Has limited capacity

(7 2 chunks) Disappears in < 30 sec

Ignoring working memory size

SciViz Introduction - Mario Valle - CINECA 14/06/2011

George A. Miller: “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information”


Models drive perception, but…

The “cocktail-party” effect (Moray 1959)

Who callsme?


PARISIN THE

THE SPRING

… they can limit us

Mental models guide us in the discovery of new information and solving problems

But they can also limit us by imposing constraints that do not exist. In this case we also call them preconceived ideas(we do not see what we think we know)

What’s wronghere?

ONCEUPON AA TIME

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Don’t visualize the obvious!


It is a matter of balance

Mental imageryusefulness

Working memorylimits


Using our mind only?

Don Norman (1993) Things That Make Us Smart

“The power of theunaided individualmind is highly overrated.Without external aids, memory, thought,and reasoning areall constrained”


Knowledge externalization

Man differs from animals because he has alwayscreated some extensions to his body and mind

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Example: multiplication

34 x72

------68

238-------2448 0

20

40

60

80

100

120

Mind Paperse

c

Elapsed time

Example: topographic maps


Example: assembly instructions

This is an example of externalization of a process

It is an (external) model that captures time in addition to material objects involved

So why we do not read the instructions and immediately begin to "do"?

Because interaction is much more effective to build a mental model compared to passive reading


To see the unseen…

Since we can not discuss upon the invisible, we must first make it visible

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Our personal supercomputer!Old IBM advertising

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The FBI Homicide data set

X Axisvictim’s age

Y Axisassassin’s age

Colorno. of cases

There are at least five interesting patterns here


Count how many ‘3’ are there

89739057092794057962976509829408028085080830802809850-802808567847298872ty458202094757720021789843890r455790456099272188897594797902855892594573979209


Count how many ‘3’ are there

89739057092794057962976509829408028085080830802809850-802808567847298872ty458202094757720021789843890r455790456099272188897594797902855892594573979209

Pre-attentional perception

“Civilization advances by extending the number of important operations which we can perform without thinking about them”

Alfred North Whitehead

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It is not trivial to say it is here

Form and color are perceived pre-attentionally


And to say it is not here

Form + color are instead perceived sequentially


Perception of visual structures

Why do we perceive three pairs of points and not two triplets?

Why do not we perceive only two

wavy lines?

Why do we perceive a square that is not there?

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Structures simplify perception


But they can also interfere

After Garcia-Mata & Shaffner (1934)

With the ambitious goal of …

S. K. Card,J. D. Mackinlay,B. Shneiderman

Using Vision to Think

Break! (1 of 3)

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Scientific visualization is born

“Visualization offers a methodfor seeing the unseen. Itenriches the process ofscientific discovery and fostersprofound and unexpectedinsights. In many fields it isalready revolutionizing theway scientists do science”

Visualization in Scientific Computing,McCormick et al.

ACM SIGGRAPH, 1987


Visualization definition…

“[Visualization is] the use ofcomputer-supported, interactive,visual representations of data toamplify cognition...”

Card, Mackinlay and Shneiderman

“[Visualization is] the use ofcomputer-supported, interactive,visual representations of data toamplify cognition...”

Card, Mackinlay and Shneiderman


… condensed

“Discover the unexpected,describe and explain the expected”

National Visualization and Analytics Center™Pacific Northwest National Laboratory Scientific Visualization - Mario Valle - CINECA 11/06/2012

X

X X

XX

X

X

X X

X

X

The actions of dr. John Snow during a cholera epidemic in London in 1854 gives us an early example of the use of visualization for data analysis

Discover the unexpected

1) Collect and visualize dataRed crosses:

water pumpsBlack points:

deaths

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2) Analyze the resulting graphical representation


X

X X

XX

X

X

X X

X

X


3) Act guided by theanalysis’ results

Dr. Snow removedthe pump handleand the epidemic died out


X

X X

XX

X

X

X X

X

X


Visualization is not recent

Taken from: Brian Collins, Data Visualization - Has it all been seen before?in Animation and Scientific Visualization, Academic Press


Visualization and business

William Playfair (1759-1823) in 1786 in London published The Commercial and Political Atlas

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Today technologies change...

…but not the goal

And the goal is understanding

Purpose of computing is insight,not numbers


Purpose of visualization is insight,not pretty pictures

Stuart K. Card, Jock Mackinlay, Ben ShneidermanUsing Vision to Think (1999)

Richard Hamming in 1948


How do we reach it?

Using color,shape, interaction,spatial relationship and visual metaphors


Using spatial relationships

CraigStats – San Francisco Cost of Rent Heatmap (http://mullinslab2.ucsf.edu/craigstats/)

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Using spatial relationships

Robertson, G. G., et al (1991). Cam trees: animated 3D visualizations of hierarchical information.

Using shapes(isosurface on lobster Xray density volume)

RbCl phase transitionSimulation by Stefano Leoni – MPI Dresden

(visualization done using STM4)

Adding shapes Remove unneeded details

UCSF Chimera

Simplifying shapes

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Changing time into spaceRedesigned animation of a “Numerically Modeled Severe Storm” by Edward Tufte & Colleen Bushell

The original “Numerically Modeled Severe Storm” movie

Using graphic similaritiesSometimes the (physical or real world) data have a direct translation into graphic objects

For example the air flow around an object is represented as“streamlines”

Using metaphors for abstract data smartmoney.com/map-of-the-market

www.newsmap.jpScientific Visualization - Mario Valle - CINECA 11/06/2012

Sometimes they are too much…

Botanical Visualization of Huge Hierarchies (Kleiberg, et al, 2001)

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There is no “Holy Grail”

Smartsystem

Your data

Perfectvisualizations!

Don’t visualize without thinking

You must remember that the number of pixels on the screen is limited and the bandwidth of human vision is huge, but not unlimited.

But also you must remember that visualization is useless without human creativity and without helping serendipity

CERN STAR detector

Humans are better…

And you can not automate human creativity (especially in the use of tools)

… because often machines do not “get it”

The human aspect is crucial

Perception, serendipity, pattern recognition and the tacit knowledge we posses make visualization a distinctive human activity

What the computer sees

What we see

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Pivotal role in discovering




Hypothesis

User

interaction

guesswork

VISUALIZATION

Scientist makes discoveries

Visualization is only an internal interface in the discovery cycle

What visualization is not


Visualization and graphics

“I always thought visualization is just creating beautiful images”

The aim is to improve understanding, not to create illusions or surprise

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Visualization and presentation

Visualization goal is not (only) to transmit information

SciViz Introduction - Mario Valle - CINECA 14/06/2011

Visualization ≠ Data Mining

Data Mining:

“Analysis of data in a database using automated toolswhich look for trends or anomalies without knowledge of the meaning of the data”

It is not scientific illustration

Visualization does not interpret artistically a scientific phenomenon, it shows it as such (as far as chosen visual metaphors allow)

Visualization scientist:an artist with a cognitive goal

Artists and scientists share a similar goal: to make the invisible visible

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Visualization dual role

“Discover the unexpected,describe and explain the expected”

National Visualization and Analytics Center™Pacific Northwest National Laboratory

Explore/UnderstandResearcher extracts meaning from data

Present/CommunicateResearcher transmits this meaning


How to trivialize visualization

ModelCompute

Visualize


How to trivialize visualization

“The Fastest Path from Data to Presentation”

… indeed!

An active role for visualization

ModelCompute

Visualize

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User-in-the-loop

ModelCompute

Visualize

“And roughly the only mechanism for suggesting questions is exploratory”

A conversation with John W. Tukey and Elizabeth TukeyLuisa T. Fernholz and Stephan Morgenthaler

Statistical ScienceVolume 15, Number 1 (2000), 79‐94

Classical data analysis

We have a model of our phenomena under study


problem

data

hypothesis

analysis

conclusions

We use quantitative methods to prove or disprove our hypothesis(confirmative data analysis)

Exploratory data analysis

We do not start from an established model


problem

data

analysis

model

conclusions

We focus on the data“Know your data”

We try various graphicalmethods looking for the(hidden) model in an exploration-driven,evolutionary way

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Atoms per cell

Fing

erpr

int

cuto

ffGeneral law

Cry

stal

Fp –

Par

amet

ric

stu

dy

sup

po

rt

Visualization reveals anomaly


Correlation of MODIS measurements on two satellites (Terra and Aqua)


What’s strange here?

What seems strange, wrong, curious (or interesting ...) in this dataset?

Was a bug in my reader for the XYZ chemistry format

50050

Ar -0.15295E+00 0.42954E-02 0.67474E-01 0.30441E+00 0.78083E+00Ar 0.60071E+00 0.83520E+00 -0.81022E-01 0.41648E+00Ar 0.20854E+00 0.59037E+00 0.81560E+00 0.33224E+00Ar 0.72689E+00 -0.21910E+00 0.53502E+00 0.28482E+00Ar -0.12988E+00 -0.24638E-01 0.16533E+01 0.30124E+00. . .

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Visualization process

Conceptualmodel

DataObject under

study

Datamodel

Acquisition

Assumptions &interpretationAlgorithms

Render

Chemistry Visualization Tools in an Integrated Discovery Cycle -Mario Valle - EGEE'07 - 01/10/2007

The real visualization cycle 1. Guess the data format2. Select the visualization tool3. Try to load the data4. Grumble5. Retry data loading6. Select at random a

visualization technique7. Navigate around the scene8. Scratch your head9. Try to remember what you

want to see10.Use another technique11.Try to have another idea12.Chase the right paper13.Etcetera, etcetera …


Visualization is important

“The average girl would rather havebeauty than brains because sheknows that the average man cansee much better than he can think”

— Ladies' Home Journal (circa 1900)


Break! (2 of 3)

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Viz always starts from data

Conceptualmodel

DataObject under

study

Datamodel

Acquisition

Assumptions &interpretationAlgorithms

Render

We are drowing in data


Data, not information Visually organized data

Per Capita Income

Col

leg

e D

egre

e %

33

Organized to extract information

Per Capita Income

Col

leg

e D

egre

e %

Data – information – knowledge

Wisdomis the ability to reliably predictwhat will happen

Knowledgeis understanding what happens

Informationare organized data that enablesinterpretation and insight

Data

is a disorganized collection of facts

Your jobYour job


Do you remember GIGO?GIGO /gi:'goh/ [acronym]1. Garbage In, Garbage Out: usually said in response to users who complain that a program didn't “do the right thing” when given imperfect input or otherwise mistreated in some way.

2. Garbage In, Gospel Out: this more recent expansion is a sardonic comment on the tendency human beings have to put excessive trust in “computerized” data.

Source: Jargon File 4.2.0


Data horror stories

The Mars Climate Orbiter crashed in September 1999 because of a "silly mistake": wrong units in a program (mixture of pounds and kilograms)

The hole in Ozone layer over Antarctica left undetected for extended period because data was considered anomalous by software since it was out of the specified range

One of the Pelton turbine simulation at CSCS produced water jets that exerted (unphysical) negative pressure on the turbine buckets

34


Bizarre errors in public data

...

ATOM 47 N6 A A 2 2.068 5.433 -2.482

...

ATOM 59 1H6 A A 2 1.160 5.722 -2.818

ATOM 60 2H6 A A 2 2.901 5.700 -2.985

...

...

ATOM 47 N6 A A 2 2.068 5.433 -2.482

...

ATOM 59 1H6 A A 2 1.160 5.722 -2.818

ATOM 60 2H6 A A 2 2.901 5.700 2.985

...

PDB 1EW1 – model 4

Nice images hide errors

Sca

le e

xpan

ded

22

tim

es!

NASA Venus flying over


Know your data!

KNOW YOUR DATA!

GIGO

AlpTransit excavation

Data drive visualization

If you do not know what the data represent this visualization is as good as any other

Instead, if you know the data you can create a visualization that communicates and helps understanding

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Data are not single …

Different kind and different origin data can:

Create an interpretative context for other data Suggest correlations Making clear cause-effect relationship

In short, they make available new information. And this is called Data Fusion


3D Data Fusion

Data Fusion exampleCharts

2D GIS view


Characterize the data

Data have a physical format (file layout, data structures, coding of variables, etc.)

This format does not influence the visualization techniques that can be applied to the data

The physical format only affects performance, file size, etc.

Clearly it plays a role in the choice of the visualization tool to be used (availability of readers)

They have also a logical format (numer of dimensions, time dependency, associated geometry, etc.)

This is the only format you want to consider to define a visualization


The data physical format

Data derived from various sources: File, database, etc. Computing codes Real-time sensors

There are standard formats for data interchange... NetCDF, HDF5, Plot3D, CGNS Tiff, Jpeg, DICOM, FITS, VOTable (based on XML) DXF, ShapeFiles, DEM

… but almost always the developers express their creativity in inventing new data formats

A visualization work can burn 50% – 90% of the time just to create a reader for the new format.

Often are not specified the reference system, the units of measurement, etc.

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Logical data format

Geometry Geometrydimensionality

Datadimensionality

Data kind

Timedependency

Componentsnumber and

location

DATA

Scientific visualization data has always an associated geometry

Scatter data From points to surfaces/volumes


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Unstructured grids

Uniform grids

Structured grids


Scattered(no geometry)

Connected(has geometry)

Structured

Unstructured

Uniform

RectilinearCurvilinear

DATA

Types of geometry (mesh)

No spatialassociation

1D, 2D, 3D, 2D in 3D

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Cell types (VTK) / Mesh types (AVS)

Point Unstructured

Uniform Rectilinear Structured

VTK AVS/Express


Scattered(no geometry)

Connected(has geometry)

DATA

Data associated to geometry

No spatialassociation

On nodes

On nodes or cells

SimpleMulti-component

StaticTime dependent

ScalarVector (2D, 3D)TensorMultidimentional


From data type to visualizationVector data on a unstructuredgrid

Scalar on uniform grid

Multi-component scalar in 1D

Data are an important asset!

Data are no more disposable!

Scientific Data Management

Lawrence Livermore National LaboratoryCDC 7600 Disk Farm

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Correctly manage them…

http://www.phdcomics.com/comics/archive.php?comicid=1323 Scientific Visualization - Mario Valle - CINECA 11/06/2012

Break! (3 of 3)


Visualization by example1. A simple data table2. Multidimensional Data: the campaign of

Napoleon in Russia as seen by Charles Minard3. A scalar variable on a surface4. A scalar defined on all points of a volume5. A vector field, like air velocity around a car or

winds over Europe6. Hierarchical information, such as the structure

of a file system or the segmentation of marketing data

7. Unusual visualizationsScientific Visualization - Mario Valle - CINECA 11/06/2012

Example 1

Here is a simple data table…

time(min) temp(ºC)0 253 276 299 3112 3215 32

40


A possible representation

ABC analyzer warm-up

25

27

29

31

33

0 3 6 9 12 15 18

time from power-on (min)

tem

pera

ture

(°C

)

Visualizzare per comunicare - Mario Valle - CINECA 16/06/2010

Same data – different info

t(time)=15', T(temperature)=32º;

t=0', T=25º; t=6', T=29º; t=3',

T=27º; t=12', T=32º; t=9', T=31º

time(min) temp(ºC)0 253 276 299 3112 3215 32

temp(ºC) time(min)25 027 329 631 932 1232 15


Why should not use these?

25

27

29

31

33

0 3 6 9 12 15

time

tem

p

25

27

2931

32

32

HistogramValue by category

Pie chartParts of the whole

Age histogram (live)

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Data categories and charts

Variables are classified as:• Nominal (Milan, Rome,…)• Ordinal (Jan, Feb,…)• Quantitative (3.14, 7.68,…)

Continuous data, evolution

Data grouping

Parts of a wholeScientific Visualization - Mario Valle - CINECA 11/06/2012

If the goal is to compare groups

010203040

50607080

20 - 40 40 - 60Age range

Impo

rtan

ce (%

)

ABCD

01020304050607080

A B C D

Factor

Impo

rtan

ce (%

)

20 - 4040 - 60

Age range20-40 40-60

A 70% 50%B 37% 45%C 10% 23%D 50% 35%


Quantitative perception

Why to convey quantitative information the popular pie charts are worse than histograms?

Because quantitative perception has its own rules


Perception accuracy

Accuracy Ranking of Quantitative Perceptual Tasks Estimated(Mackinlay 1988 from Cleveland & McGill) ø

42

Visualizzare per comunicare - Mario Valle - CINECA 16/06/2010

Chartjunk

In addition to obscure the graph with the photo, this visualization is using an inappropriate method to convey quantitative information (annual snowfall).

In fact the use of shapes is not even contemplated in the classification of Mackinlaybecause the quantitative perception is totally inaccurate


Example 2

“My name is Charles Joseph Minard, and I wantto proclaim that the Napoleon’s campaign inRussia was a useless massacre”

“I want to represent the size of the army, thedirection of travel, the geographical positionwith the corresponding date and temperature. Iwant to show this way the size of the carnageand the futility of the military campaign”


Data to be visualizedlonc latc city lont temp days date lonp latp surviv dir div24.0 55.0 Kowno 37.6 0 6 Oct 18 24.0 54.9 340000 A 125.3 54.7 Wilna 36.0 0 6 Oct 24 24.5 55.0 340000 A 126.4 54.4 Smorgoni 33.2 -9 16 Nov 9 25.5 54.5 340000 A 126.8 54.3 Molodexno 32.0 -21 5 Nov 14 26.0 54.7 320000 A 127.7 55.2 Gloubokoe 29.2 -11 10 27.0 54.8 300000 A 127.6 53.9 Minsk 28.5 -20 4 Nov 28 28.0 54.9 280000 A 128.5 54.3 Studienska 27.2 -24 3 Dec 1 28.5 55.0 240000 A 128.7 55.5 Polotzk 26.7 -30 5 Dec 6 29.0 55.1 210000 A 129.2 54.4 Bobr 25.3 -26 1 Dec 7 30.0 55.2 180000 A 130.2 55.3 Witebsk 30.3 55.3 175000 A 130.4 54.5 Orscha 32.0 54.8 145000 A 130.4 53.9 Mohilow 33.2 54.9 140000 A 132.0 54.8 Smolensk 34.4 55.5 127100 A 133.2 54.9 Dorogobouge 35.5 55.4 100000 A 134.3 55.2 Wixma 36.0 55.5 100000 A 134.4 55.5 Chjat 37.6 55.8 100000 R 136.0 55.5 Mojaisk 37.5 55.7 98000 R 137.6 55.8 Moscou 37.0 55.0 97000 R 136.6 55.3 Tarantino 36.8 55.0 96000 R 136.5 55.0 Malo-jarosewli 35.4 55.3 87000 R 1

34.3 55.2 55000 R 1 33.3 54.8 37000 R 132.0 54.6 24000 R 130.4 54.4 20000 R 129.2 54.4 20000 R 1

(etc.)


The result

From: E. Tufte 1983

43

Data without geometry

But not all data have an associatedgeometry (number of accesses to a web site, marketing data, etc.). Some other have only a topology (tree, graph)Then to view them you must give them a geometric structure


Field traditional subdivision

Scientific visualization

Focused primarily on physical data such as the human body, the earth, molecules, temperature and so on. Mature field, but “constrained” by reality.

Information visualization

Focused primarily on (multidimensional) non-physical data such as abstract texts, hierarchies, and statistical data. It is a young field and thus more free and creative. Business applications make it well-funded.

InfoViz is not very intuitive… A understandeable example

• Versicolor• Setosa

• Virginica

R.A.Fisher’sIris Dataset(1936)

Species:

44

Unavoidable subdivision? No!

Scientific visualization

Focused primarily on physical data.

Scientific visualization is informative

Information visualization

Focused primarily on (multidimensional) non-physical data.

Information visualization is scientific

But contamination pays back …

Win

ner

of t

he

Vis

ual

izat

ion

C

on

test

(IE

EE V

is2

00

4)

45

ø Scientific Visualization - Mario Valle - CINECA 11/06/2012

Example 3I should represent a continuous scalar variable on a surface (here: values of altitude). The surface can exist in 2D or it could be the external surface of a 3D object


Pseudocolor mapping

The colormap converts numerical values into colors to be visualized

In the figure here a rainbow colormap has been used

This technique is widely used, but human perception has a strong impact on the outcome


Monochrome scale

For example, a monochromatic colormap shows finer details compared to the previous one

(This could explain why radiologists are struggling to move from plates to computer screens)

46


Perceptually tuned scale

Details are even better if you choose a color scale that respects the perceptual characteristics of the human eye

The colormap here varies hue, saturation and value (HSV)from: (0.66, 0, 0)to: (0.00, 1, 1)


Image courtesy CRS4

Example: rainbow colormap


Image courtesy CRS4

Example: luminance only

Something appears


Image courtesy CRS4

Example: “data driven” colormap

Phenomena is clearly visible

47

Non linear scale usage

Line

ar s

cale

(def

ault)

Hyp

erbo

lic s

cale


Sawtooth colormapShows the characteristics of the data, not its values (because it is not bijective)


Select according to purpose

The choice of the map must be consistent with the goal to be achieved:

Show value

Segment

Highlight

Contrast

www.colorbrewer.org


Surface displacement

ø

Altitude (m)

48


Other representations


Example 4

I have a scalar data defined on all points of a volume. An example is the temperature inside a metal block or the concentration of pollutants in a certain volume of space

Problem: Understand what is inside the

block Figure out how to display 3D

on the two-dimensional screen


Slicing

The volume is cut with an arbitrary plane or with a plane parallel to one of the coordinated axis (orthoslice)

The variable values are then displayed on the plane with the use of pseudocolor mapping


Isosurfaces

An isosurface is a surface that passes through the points of the volume where the scalar variable has a given value

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Isosurfaces

An isosurface is a surface that passes through the points of the volume where the scalar variable has a given value

Eventually the value of another scalar variable defined in the volume can be mapped on the isosurface as color to show correlations between the two variables


Isosusurface or isovolume?


Volume rendering

The volume is displayed directly without having to convert it to a surface representation

The metaphor used is that of a block of translucent glass

The critical factor here is the choice of the transfer function which maps the values to color and transparency


Nested isosurfaces

Semitransparent isosurfaces foster the understanding of the structure of the volume without having to resort to heavier techniques such as volume rendering

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Animation

1.The orthosliceposition and axis

2.The isusurface level3.The volume

rendering parameters

The use of animation helps to build a mental model of the volume's contentsIn the movie you see changed:


Depth perception

1. How do you transmit a spatial sensation through a medium like the two-dimensional video screen?

2. How do you overcome the intrinsic perception problems linked to a three-dimensional scene (position ambiguity and occlusion)?

Stereoscopic vision


The use of perspective

Brunelleschi: La Chiesa di Santo Spirito

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Perspective distorts perception

Perspective

Orthographic projection


Depth cueing

Without With depth cueing


Using the right lighting

Frontal light Light from left

Shadow usage

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Artificial depth cuesDrop lines / projections Can confuse the scene

Occlusion More natural But hide part of the data

Better: animation & interaction


Interaction overcomes 3D ambiguity

Illusion caused by the fixed point of view

The famous Ames Room optical illusion

3D is better …

Top view

Front view

Side view

Reconstruct the 3D shape starting from these 2D projections

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… to understand shapes 3D disvantage…

From

: SSC

San

Die

go –

TR 1

795

–M

arch

199

9


… solved moving to 2D

From: SSC San Diego – TR 1795 – March 1999


3D (shape) + 2D (spatial layout)

ø

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Example 5

Representation of a vector field as the air velocity in the volume around a car or winds over Europe.

Streamlines

MeteoSwiss Weather Forecast computed daily at CSCSWinds visualized using Lagrangian-Eulerian Advection

Example 6

How do you represent hierarchical information, such as directory trees or segmentation of marketing data?

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Use of containment relationships instead of connecting nodes to represent the hierarchy:

Each node in the tree occupies an area

Child nodes are contained within the parent node


Containment

Using an unusual structure

Connection

Treemaps techniqueMy workingdirectory

One Million Items Treemap

Overview and details

ww

w.in

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t.co

m/d

emos

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cery

/


Sometimes you have to come up with a new visualization technique or move away from the usual representational spaces for the data in question.For example, rearranging or recombining the dataset (the famous pseudo random generator RANDU IBM):

Example 7

One-dimensional sequence Points in 3Dx1,x2,x3,x4,x5,x6… (x1,x2,x3), (x4,x5,x6), …

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Stanislaw Ulam idea

The numbers are arranged in a spiral, with primes indicated by a special color instead of the more usual matrix layout.

This construction was first made by Polish-American mathematician StanislawUlam in 1963 while doodling during a boring talk at a scientific meeting.

Unusual representation

Prime numbers ona spiral path

Usual matrix representation of prime numbers < 480.000


Periodic and angular dataPeriodic phenomena benefit from a spiral representation. Each revolution represents a period, so that data with the same phase appear to be aligned.

From: Interactive Visualization of Serial Periodic DataJohn V. Carlis and Joseph A. Konstan

Similarly to represent an angle (e.g. phase) you can use the circle of shades. The singularities become immediately visible.

From: Michael BerryRandom phases – “Visions of Science Photographic Awards 2002” ø


How do I choose?

1. Consider the problem to be solved, the goal to achieve and the context in which you operate

2. The type of data you need to visualize

3. The tool you have at hand, and the visualization techniques that it provides

To make a great visualization, you

should do great science

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Getting started

1. First, use your own eyes to look around,to look actively.Copy good examples.“Talent imitate, genius steal” (Thomas Eliot)

2. Use your curiosity: what wouldhappen if I use here a diagram?

3. Explore some tools.Unfortunately does not exist a “Photoshop”or a for visualization…


Few (free) tools

GnuplotGrace

R2D charting

ParaViewMayaVi

Scientific Visualization

XmdvToolGgobi

OrangeMultidimensional Visualization

http://mariovalle.name/SciViz/tools.html

Purpose of visualization is insight,not pretty pictures

Stuart K. Card, Jock Mackinlay, Ben ShneidermanUsing Vision to Think (1999)

Start with anythingBut start!

And don’t forget…

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… my beginner book (in Italian)

It is an introduction to scientific visualization

Book details:

http://mariovalle.name/libro/

You can explore a gallery of images and techniques on:

…/immagini.html

http://mariovalle.name/[email protected]

See youtomorrow!

Documents

CINECA withoutvisualization, is like assembling a jigsaw puzzle … · 2012. 6. 12. · From “Fantasia” – Bruno Munari Imagination for science “Phantasie ist wichtiger als