Scientific visualization Nelle Varoquaux 0 / 24 What is scientific - - PowerPoint PPT Presentation

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Scientific visualization Nelle Varoquaux 0 / 24 What is scientific - - PowerPoint PPT Presentation

Oct 03, 2023 111 likes •379 views

Scientific visualization Nelle Varoquaux 0 / 24 What is scientific visualisation? Visualisation is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations.

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Scientific visualization

Nelle Varoquaux

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What is scientific visualisation?

“Visualisation is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and

  • computations. Visualisation offers a method for seeing the unseen. It

enriches the process of scientific discovery and fosters profound and unexpected insights.”

Visualisation in Scientific Computing, NSF report, 1987

“For example, about 50 percent of the cerebral cortex of primates is devoted exclusively to visual processing, and the estimated territory for humans is nearly comparable.”

The MIT Encyclopedia of the Cognitive Sciences 1 / 24

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Anscombe’s quartet, 1973

x1 x2 x3 x4 y1 y2 y3 y4 1 10.00 10.00 10.00 8.00 8.04 9.14 7.46 6.58 2 8.00 8.00 8.00 8.00 6.95 8.14 6.77 5.76 3 13.00 13.00 13.00 8.00 7.58 8.74 12.74 7.71 4 9.00 9.00 9.00 8.00 8.81 8.77 7.11 8.84 5 11.00 11.00 11.00 8.00 8.33 9.26 7.81 8.47 6 14.00 14.00 14.00 8.00 9.96 8.10 8.84 7.04 7 6.00 6.00 6.00 8.00 7.24 6.13 6.08 5.25 8 4.00 4.00 4.00 19.00 4.26 3.10 5.39 12.50 9 12.00 12.00 12.00 8.00 10.84 9.13 8.15 5.56 10 7.00 7.00 7.00 8.00 4.82 7.26 6.42 7.91 11 5.00 5.00 5.00 8.00 5.68 4.74 5.73 6.89 What is common to those data sets? Mean of x 9 Variance of x 11 Mean of y 7.50 Variance of y 4.12 Linear regression y = 3. + 0.5x R2 0.666 p-value 0.0021

“The purpose of computing is insights, not numbers.”

Richard Hamming, 1962 2 / 24

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Anscombe’s quartet, 1973

What is common to those data sets? Mean of x 9 Variance of x 11 Mean of y 7.50 Variance of y 4.12 Linear regression y = 3. + 0.5x R2 0.666 p-value 0.0021

“A computer should make both calculations and graphs”

Francis Anscombe (1918-2001) 2 / 24

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Visualization pipeline

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Data type

Quantitative: values or observations that can be measured

  • Continuous (e.g. temperature)
  • Discrete (e.g. number of inhabitants)

Categorical: values or observations that can be sorted into groups or categories

  • Nominal (e.g. nationality)
  • Ordinal (e.g. months)
  • Interval (e.g. age groups)

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Graphical elements

A scientific figure can be fully described by a set of graphic primitives with different attributes:

  • Points, markers, lines, areas, ...
  • Position, color, shape, size, orientation, curvature, ...
  • Helpers, text, axis, ticks, ...
  • Interaction, animation, ...

But who want to describe each individual elements? Describing a figure in terms of such graphic primitives would be a very tedious and complex task. This is exactly where visualization libraries are useful because they will automatize most of the work (more or less depending on the library).

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Visualization types

Data Visualisation catalogue by S. Rebecca

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10 Simple Rules for Better Figures

Nicolas Rougier, Mike Droettboom and Philip Bourne.

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Rule 1: Know your audience

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Rule 2: Identify your message

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Rule 3: Adapt the figure

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Rule 4: Captions are not optional

Optical Illusion The A and B patches are actually the same color even though we perceived them at being different color. 11 / 24

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Rule 5: Do not trust the defaults

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Rule 6: Use color efficiently

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Rule 6b: Above all, do no harm!

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Rule 7: Do not mislead the reader

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Rule 8: Avoid “chart junk”

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Rule 8b: Less is more

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Rule 9: Message trumps beauty

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Rule 10: Get the right tool

  • PDFCrop (remove white borders)

http://pdfcrop.sourceforge.net

  • GraphViz (easy graph)

http://www.graphviz.org

  • ImageMagick (scripted image processing)

http://www.imagemagick.org/script/index.php

  • Gimp (bitmap image manipulation)

https://www.gimp.org

  • Inkscape (vector image manipulation)

https://www.inkscape.org

  • Tikz (scripted vector art)

http://www.texample.net/tikz/examples/all/

  • And many, many, many others

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Enough theory, let’s practice!

https://www.stat.berkeley.edu/˜nelle/teaching/ 2017-visualization/README.html

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Examples of misleading figures

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Misleading figures

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Misleading figures

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Misleading figures

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