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# art is science is art

Visualizaiton workshop at UBC B.I.G. Research Day. 11 May 2016

# visualization + design

The 2016 Pi Day art imagines the digits of Pi as physical masses collapsing under gravity and is featured in the article The Gravity of Pi at the Scientific American SA Visual blog.

# The art of Pi ($\pi$), Phi ($\phi$) and $e$

2016 $\pi$ day
2015 $\pi$ day
2014 $\pi$ approx day
2014 $\pi$ day
2014 $\pi$ day
2013 $\pi$ day
Circular $\pi$ art

Numbers are a lot of fun. They can start conversations—the interesting number paradox is a party favourite: every number must be interesting because the first number that wasn't would be very interesting! Of course, in the wrong company they can just as easily end conversations.

The art here is my attempt at transforming famous numbers in mathematics into pretty visual forms, start some of these conversations and awaken emotions for mathematics—other than dislike and confusion

Like music with numbers? Try Angels at My Door (Una), Pt vs Ys (Yoshinori Sunahara), 2wicky (Hooverphonic), One (Aimee Mann), Straight to Number One (Touch and Go), 99 luftbaloons (Nena).

Numerology is bogus, but art based on numbers can be beautiful. Proclus got it right when he said (as quoted by M. Kline in Mathematical Thought from Ancient to Modern Times)

Wherever there is number, there is beauty.

2,258 digits of $\phi$, 3,855 digits of $e$ and 3,628 digits of $\pi$ in 6 level treemaps. Uniform line thickness. Bauhaus prime colors in Piet Mondrian style. (2015 $\pi$ day posters, BUY ARTWORK)
All art posters are available for purchase.
I take custom requests.

## the numbers π, φ and e

The consequence of the interesting number paradox is that all numbers are interesting. But some are more interesting than others—how Orwellian!

All animals are equal, but some animals are more equal than others.
—George Orwell (Animal Farm)

Numbers such as $\pi$ (or $\tau$ if you're a revolutionary), $\phi$, $e$, $i = \sqrt{-1}$, and $0$ have captivated imagination. Chances are at least one of them appears in the next physics equation you come across.

$π φ e$
$= 3.14159 26535 89793 23846 26433 83279 50288 41971 69399 ... = 1.61803 39887 49894 84820 45868 34365 63811 77203 09179 ... = 2.71828 18284 59045 23536 02874 71352 66249 77572 47093 ...$

Of these three transcendental numbers, $\pi$ (3.14159265...) is the most well known. It is the ratio of a circle's circumference to its diameter ($d = \pi r$) and appears in the formula for the area of the circle ($a = \pi r^2$).

2,258 digits of $\phi$, 3,855 digits of $e$ and 3,628 digits of $\pi$ in 6 level treemaps. Uniform line thickness. Bauhaus prime colors in Piet Mondrian style. (2016 $\pi$ day posters, BUY ARTWORK)

The Golden Ratio ($\phi$, 1.61803398...) is the attractive proportion of values $a > b$ that satisfy ${a+b}/2 = a/b$, which solves to $a/b = {1 + \sqrt{5}}/2$.

The last of the three numbers, $e$ (2.71828182...) is Euler's number and also known as the base of the natural logarithm. It, too, can be defined geometrically—it is the unique real number, $e$, for which the function $f(x) = e^x$ has a tangent of slope 1 at $x=0$. Like $\pi$, $e$ appears throughout mathematics. For example, $e$ is central in the expression for the normal distribution as well as the definition of entropy. And if you've ever heard of someone talking about log plots ... well, there's $e$ again!

Two of these numbers can be seen together in mathematics' most beautiful equation, the Euler identity: $e^{i\pi} = -1$. The tau-oists would argue that this is even prettier: $e^{i\tau} = 1$.

## accidentally similar

Did you notice how the 13th digit of all three numbers is the same (9)? This accidental similarity generates its own number—the Accidental Similarity Number (ASN).

VIEW ALL

# Analyzing Outliers: Influential or Nuisance?

Fri 08-04-2016

Some outliers influence the regression fit more than others.

This month our column addresses the effect that outliers have on linear regression.

You may be surprised, but not all outliers have the same influence on the fit (e.g. regression slope) or inference (e.g. confidence or prediction intervals). Outliers with large leverage—points that are far from the sample average—can have a very large effect. On the other hand, if the outlier is close to the sample average, it may not influence the regression slope at all.

Nature Methods Points of Significance column: Analyzing Outliers: Influential or Nuisance? (read)

Quantities such as Cook's distance and the so-called hat matrix, which defines leverage, are useful in assessing the effect of outliers.

Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple Linear Regression Nature Methods 12:999-1000.

# Typographical posters of bird songs

Mon 28-03-2016

Chirp, chirp, chirp but much better looking.

The song of the Northern Flicker, Black-capped Chickadee, Olive-sided Flycatcher and Red-eyed Vireo. Sweet to the eye and ear. (details)

If you like these, check out my other typographical art posters.

# Happy 2016 Pi Day—gravity of $\pi$

Mon 14-03-2016

Celebrate $\\pi$ Day (March 14th) with colliding digits in space. This year, I celebrate the detection of gravitational waves at the LIGO lab and simulate the effect of gravity on masses created from the digits of $\\pi$.

1,000 digits of $\pi$ under the influence of gravity. (details)

Some strange things can happen.

44 digits of $\pi$ under the influence of gravity. (details)

The art is featured in the Gravity of Pi article on the Scientific American SA Visual blog.

Check out art from previous years: 2013 $\\pi$ Day and 2014 $\\pi$ Day and 2015 $\\pi$ Day.

# Neural Circuit Diagrams

Sun 13-03-2016

Use alignment and consistency to untangle complex circuit diagrams.

This month we apply the ideas presented in our column about drawing pathways to neural circuit diagrams. Neural circuits are networks of cells or regions, typically with a large number of variables, such as cell and neurotransmitter type.

Nature Methods Points of View column: Neural circuit diagrams. (read)

We discuss how to effectively route arrows, how to avoid pitfalls of redundant encoding and suggest ways to encorporate emphasis in the layout.

Hunnicutt, B.J. & Krzywinski, M. (2016) Points of View: Neural circuit diagrams. Nature Methods 13:189.

Hunnicutt, B.J. & Krzywinski, M. (2016) Points of Viev: Pathways. Nature Methods 13:5.

Wong, B. (2010) Points of Viev: Gestalt principles (part 1). Nature Methods 7:863.

Wong, B. (2010) Points of Viev: Gestalt principles (part 2). Nature Methods 7:941.

# Pathways

Mon 04-01-2016

Apply visual grouping principles to add clarity to information flow in pathway diagrams.

We draw on the Gestalt principles of connection, grouping and enclosure to construct practical guidelines for drawing pathways with a clear layout that maintains hierarchy.

Nature Methods Points of View column: Pathways. (read)

We include tips about how to use negative space and align nodes to emphasizxe groups and how to effectively draw curved arrows to clearly show paths.

Hunnicutt, B.J. & Krzywinski, M. (2016) Points of Viev: Pathways. Nature Methods 13:5.

Wong, B. (2010) Points of Viev: Gestalt principles (part 1). Nature Methods 7:863.

Wong, B. (2010) Points of Viev: Gestalt principles (part 2). Nature Methods 7:941.

# Multiple Linear Regression

Mon 04-01-2016

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

This month we continue with the topic of regression and expand the discussion of simple linear regression to include more than one variable. As it turns out, although the analysis and presentation of results builds naturally on the case with a single variable, the interpretation of the results is confounded by the presence of correlation between the variables.

By extending the example of the relationship of weight and height—we now include jump height as a second variable that influences weight—we show that the regression coefficient estimates can be very inaccurate and even have the wrong sign when the predictors are correlated and only one is considered in the model.

Nature Methods Points of Significance column: Multiple Linear Regression. (read)

Care must be taken! Accurate prediction of the response is not an indication that regression slopes reflect the true relationship between the predictors and the response.

Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.