cineca withoutvisualization, is like assembling a jigsaw puzzle … · 2012. 6. 12. · from...
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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
Richard HammingNumerical Methods for Scientists and Engineers (1962)
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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
http://mariovalle.name/ Why don’t we start here?
ParaView
MayaVi
Spotfire
AVS/Express
Not everything has a meaning…
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
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Knowledge is not accumulation
Data ≠ knowledge
Facts ≠ knowledge
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Data without meaning
More data do not mean more
knowledge
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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Models and mental images
Scientific Visualization - Mario Valle - CINECA 11/06/2012
“All our ideas andconcepts are onlyinternal pictures”
Ludwig Boltzmann(1899)
Logo of a Spanish design agency
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Mental simulations
They help us understand the workingsof what we model in our mind
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Data images decisions
Try to makesense
of them
Actio
n or
com
mun
icat
ion
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
LEGOland Deutschland
Imagination for science
Scientific Visualization - Mario Valle - CINECA 11/06/2012
“Imagination ismore importantthan knowledge,because knowledgeis limited”
Albert Einstein
LEGOland Deutschland
What are theseinsights all about?
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It is about formulating the right question
Scientific Visualization - Mario Valle - CINECA 11/06/2012
The scientific discovery processby James Watson
(DNA)EXPERIMENT OR
DATA COLLECTION DATA
Computation orTransformation
Rendered imageInsight
Hypothesis
User
interaction
guesswork
Scientific Visualization - Mario Valle - CINECA 11/06/2012
We want to see what we are studying
EXPERIMENT ORDATA COLLECTION DATA
Computation orTransformation
Rendered imageInsight
Hypothesis
User
interaction
guesswork
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Self-illustrating phenomena
Scientific Visualization - Mario Valle - CINECA 11/06/2012
A lot of data is outside our reach
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Or it cannot be replicated
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
So we use numbers instead
EXPERIMENT ORDATA COLLECTION DATA
Rendered imageInsight
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Ideas from numbers alone?
When printed, the
hemoglobin PDB file
outputs 92 pages of
(boring) atomic
coordinates
(PDB ID 1A00)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
To understand is to compress
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
We see indirectly through images
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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”
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Models drive perception, but…
The “cocktail-party” effect (Moray 1959)
Who callsme?
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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!
Scientific Visualization - Mario Valle - CINECA 11/06/2012
It is a matter of balance
Mental imageryusefulness
Working memorylimits
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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”
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Knowledge externalization
Man differs from animals because he has alwayscreated some extensions to his body and mind
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
Example: multiplication
34 x72
------68
238-------2448 0
20
40
60
80
100
120
Mind Paperse
c
Elapsed time
Example: topographic maps
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Count how many ‘3’ are there
89739057092794057962976509829408028085080830802809850-802808567847298872ty458202094757720021789843890r455790456099272188897594797902855892594573979209
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
It is not trivial to say it is here
Form and color are perceived pre-attentionally
Scientific Visualization - Mario Valle - CINECA 11/06/2012
And to say it is not here
Form + color are instead perceived sequentially
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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 - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
… 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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
2) Analyze the resulting graphical representation
Discover the unexpected
X
X X
XX
X
X
X X
X
X
Scientific Visualization - Mario Valle - CINECA 11/06/2012
3) Act guided by theanalysis’ results
Dr. Snow removedthe pump handleand the epidemic died out
Discover the unexpected
X
X X
XX
X
X
X X
X
X
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Visualization is not recent
Taken from: Brian Collins, Data Visualization - Has it all been seen before?in Animation and Scientific Visualization, Academic Press
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Visualization and business
William Playfair (1759-1823) in 1786 in London published The Commercial and Political Atlas
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
Today technologies change...
…but not the goal
And the goal is understanding
Purpose of computing is insight,not numbers
Richard HammingNumerical Methods for Scientists and Engineers (1962)
Purpose of visualization is insight,not pretty pictures
Stuart K. Card, Jock Mackinlay, Ben ShneidermanUsing Vision to Think (1999)
Richard Hamming in 1948
Scientific Visualization - Mario Valle - CINECA 11/06/2012
How do we reach it?
Using color,shape, interaction,spatial relationship and visual metaphors
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Using spatial relationships
CraigStats – San Francisco Cost of Rent Heatmap (http://mullinslab2.ucsf.edu/craigstats/)
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
EXPERIMENT ORDATA COLLECTION DATA
Computation orTransformation
Rendered imageInsight
Hypothesis
User
interaction
guesswork
VISUALIZATION
Scientist makes discoveries
Visualization is only an internal interface in the discovery cycle
What visualization is not
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
How to trivialize visualization
ModelCompute
Visualize
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Correlation of MODIS measurements on two satellites (Terra and Aqua)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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 …
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Break! (2 of 3)
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
Viz always starts from data
Conceptualmodel
DataObject under
study
Datamodel
Acquisition
Assumptions &interpretationAlgorithms
Render
We are drowing in data
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Data, not information Visually organized data
Per Capita Income
Col
leg
e D
egre
e %
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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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
3D Data Fusion
Data Fusion exampleCharts
2D GIS view
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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Unstructured grids
Uniform grids
Structured grids
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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%
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Quantitative perception
Why to convey quantitative information the popular pie charts are worse than histograms?
Because quantitative perception has its own rules
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Perception accuracy
Accuracy Ranking of Quantitative Perceptual Tasks Estimated(Mackinlay 1988 from Cleveland & McGill) ø
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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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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”
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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.)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
The result
From: E. Tufte 1983
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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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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:
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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
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Vis
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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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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)
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Image courtesy CRS4
Example: rainbow colormap
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Image courtesy CRS4
Example: luminance only
Something appears
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Image courtesy CRS4
Example: “data driven” colormap
Phenomena is clearly visible
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Non linear scale usage
Line
ar s
cale
(def
ault)
Hyp
erbo
lic s
cale
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Sawtooth colormapShows the characteristics of the data, not its values (because it is not bijective)
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Surface displacement
ø
Altitude (m)
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
Other representations
ø Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Isosusurface or isovolume?
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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:
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
The use of perspective
Brunelleschi: La Chiesa di Santo Spirito
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
Perspective distorts perception
Perspective
Orthographic projection
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Depth cueing
Without With depth cueing
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
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TR 1
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199
9
Scientific Visualization - Mario Valle - CINECA 11/06/2012
… solved moving to 2D
From: SSC San Diego – TR 1795 – March 1999
Scientific Visualization - Mario Valle - CINECA 11/06/2012
3D (shape) + 2D (spatial layout)
ø
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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
Containment
Using an unusual structure
Connection
Treemaps techniqueMy workingdirectory
One Million Items Treemap
Overview and details
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ø Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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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Scientific Visualization - Mario Valle - CINECA 11/06/2012
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…
Scientific Visualization - Mario Valle - CINECA 11/06/2012
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!