Twenty — minutes — maybe — more.choose four wordsmore quotes

# circles: beautiful

In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017

# visualization + design

The 2018 Pi Day art celebrates the 30th anniversary of $\pi$ day and connects friends stitching road maps from around the world. Pack a sandwich and let's go!

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

2018 $\pi$ day shrinks the world and celebrates road trips by stitching streets from around the world together. In this version, we look at the boonies, burbs and boutique of $\pi$ by drawing progressively denser patches of streets. Let's go places.
2017 $\pi$ day
2016 $\pi$ approximation day
2016 $\pi$ day
2015 $\pi$ day
2014 $\pi$ approx day
2014 $\pi$ day
2013 $\pi$ day
Circular $\pi$ art

This section contains various art work based on $\pi$, $\phi$ and $e$ that I created over the years.

Some of the numerical art reveals interesting and unexpected observations. For example, the sequence 999999 in π at digit 762 called the Feynman Point. Or that if you calculate π to 13,099,586 digits you will find love.

$\pi$ day art and $\pi$ approximation day art is kept separate.

For some time I have been thinking about creating minimalist typographical art based on the digits of $\pi$. The $i$-ness $\pi$ project was one of my first forays into this kind of art.

For each $i$th digit in $\pi$, $\pi_i$, its $i$-ness is the average of the distance between $\pi_i$ and its $n$ neighbouring digits.

The $i$-ness is a general property based on the neighbours of a digit. When $i$ is assigned a fixed value (e.g., $i=4$) then the average distance is calculated relative to 4 instead of $\pi_i$.

Thanks to Lance Bailey for suggesting this idea.

## example

Consider the starting sequence in $\pi = 31415...$. The 4 neighbours of the 3rd digit $\pi_3=4$ are (3, 1, 1, 5). The differences between its neighbours and $\pi_3 = 4$ are $-1$, $-3$, $-3$ and $1$. So at this position, the average is the $i$-ness and is $-1.5$ with a standard deviation of $1.7$.

If we calculate the difference relative to a fixed number (e.g. 5), we get the 5-ness of $\pi$. For the position $\pi_3$ the 5-ness is the average of the differences between the neighbour digits and 5. These differences are $-2$, $-4$, $-4$ and 0. The average value here is $-2.5$ with a standard deviation of $1.9$.

The iness of $\pi$. (posters)

In the $i$-ness of $\pi$ poster shown above, the average is mapped onto a color and the standard deviation onto size.

Based on the color you can tell how far away from 4 the neighbours are. Brown 4s have smaller neighbours and blue/green 4s have larger ones, on average.

## design

The type face of the main digits is Gotham. Index annotation is set in The Sans Mono Condensed Light and the neighbour and statistics annotations in Inconsolata.

VIEW ALL

# Find and snap to colors in an image

Sat 29-12-2018

One of my color tools, the $colorsnap$ application snaps colors in an image to a set of reference colors and reports their proportion.

Below is Times Square rendered using the colors of the MTA subway lines.

Colors used by the New York MTA subway lines.

Times Square in New York City.
Times Square in New York City rendered using colors of the MTA subway lines.
Granger rainbow snapped to subway lines colors from four cities. (zoom)

# Take your medicine ... now

Wed 19-12-2018

Drugs could be more effective if taken when the genetic proteins they target are most active.

Design tip: rediscover CMYK primaries.

More of my American Scientific Graphic Science designs

Ruben et al. A database of tissue-specific rhythmically expressed human genes has potential applications in circadian medicine Science Translational Medicine 10 Issue 458, eaat8806.

# Predicting with confidence and tolerance

Wed 07-11-2018
I abhor averages. I like the individual case. —J.D. Brandeis.

We focus on the important distinction between confidence intervals, typically used to express uncertainty of a sampling statistic such as the mean and, prediction and tolerance intervals, used to make statements about the next value to be drawn from the population.

Confidence intervals provide coverage of a single point—the population mean—with the assurance that the probability of non-coverage is some acceptable value (e.g. 0.05). On the other hand, prediction and tolerance intervals both give information about typical values from the population and the percentage of the population expected to be in the interval. For example, a tolerance interval can be configured to tell us what fraction of sampled values (e.g. 95%) will fall into an interval some fraction of the time (e.g. 95%).

Nature Methods Points of Significance column: Predicting with confidence and tolerance. (read)

Altman, N. & Krzywinski, M. (2018) Points of significance: Predicting with confidence and tolerance Nature Methods 15:843–844.

Krzywinski, M. & Altman, N. (2013) Points of significance: Importance of being uncertain. Nature Methods 10:809–810.

# 4-day Circos course

Wed 31-10-2018

A 4-day introductory course on genome data parsing and visualization using Circos. Prepared for the Bioinformatics and Genome Analysis course in Institut Pasteur Tunis, Tunis, Tunisia.

Composite of the kinds of images you will learn to make in this course.

# Oryza longistaminata genome cake

Mon 24-09-2018

Data visualization should be informative and, where possible, tasty.

Stefan Reuscher from Bioscience and Biotechnology Center at Nagoya University celebrates a publication with a Circos cake.

The cake shows an overview of a de-novo assembled genome of a wild rice species Oryza longistaminata.

Circos cake celebrating Reuscher et al. 2018 publication of the Oryza longistaminata genome.

# Optimal experimental design

Tue 31-07-2018
Customize the experiment for the setting instead of adjusting the setting to fit a classical design.

The presence of constraints in experiments, such as sample size restrictions, awkward blocking or disallowed treatment combinations may make using classical designs very difficult or impossible.

Optimal design is a powerful, general purpose alternative for high quality, statistically grounded designs under nonstandard conditions.

Nature Methods Points of Significance column: Optimal experimental design. (read)

We discuss two types of optimal designs (D-optimal and I-optimal) and show how it can be applied to a scenario with sample size and blocking constraints.

Smucker, B., Krzywinski, M. & Altman, N. (2018) Points of significance: Optimal experimental design Nature Methods 15:599–600.