resources
visualizations
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lunch break
Distractions and amusements, with a sandwich and coffee.
visualization + design
`pi` Day 2013 Art Posters
On March 14th celebrate `\pi` Day. Hug `\pi`—find a way to do it.
For those who favour `\tau=2\pi` will have to postpone celebrations until July 26th. That's what you get for thinking that `\pi` is wrong. I sympathize with this position and have `\tau` day art too!
If you're not into details, you may opt to party on July 22nd, which is `\pi` approximation day (`\pi` ≈ 22/7). It's 20% more accurate that the official `\pi` day!
Finally, if you believe that `\pi = 3`, you should read why `\pi` is not equal to 3.
2013 was the first year in which I made `\pi` day art. It was a year of dots and love.
René Hansen has created an interactive version of this year's posters! Why not go to the Feynman point directly!
shimmering dots
The posters explore the relationship between adjacent digits in `\pi`, which are encoded by color using the scheme shown above. The design appears to shimmer due to the luminance effect. In some versions of the poster, adjacent identical (or similar) digits are connected by lines.
The recipe for each poster is included in its figure legend. It gives the color of the `i`th outer and inner circles. `\pi_i` is used to represent the `i`th digit of `\pi`. For example, the recipe
`\pi_i` / `\pi_{i+1}`
corresponds to the case where outer circle color encodes the `i`th digit and the inner circle color encodes the next digit `i+1`th. In this scheme, inner and outer circles of adjacent positions have the same color.
The posters were generated automatically with a Perl script that generated SVG files. Post processing and layout was done in Illustrator. If you are interested in depicting your favourite number this way, let me know.
The design was inspired by the beautiful AIDS posters by Elena Miska.
love in `\pi`—you can find it here
I calculated `pi` to 13,099,586 digits and then I found love.
It's fun to look for digits or look for words in `\pi`.
Just don't get carried away. Because `\pi` is likely normal in base 10, all words and all patterns appear in it, somewhere.
I wanted to know the first time that "love" appears in `\pi`. When encoded using the scheme a=0, b=1, ..., z=25, "love" is the digit sequence 1114214.
This sequence appears first at position 13,099,586 (...892199163111142148187311392...). And, of course, infinitely many times after that.
Curiously, "hate" (0700194) appears well before love, at digit 514,717. In the first 200,000,000 digit "hate" appears 23 times, 6 times more than "love".
If you use the scheme a=1, b=2, ..., z=26, then "love" becomes 1215225. This is first seen at 6,317,696 (...610311912912152256606850141...).
news + thoughts
Scientific data visualization: Aesthetic for diagrammatic clarity
The scientific process works because all its output is empirically constrained.
My chapter from The Aesthetics of Scientific Data Representation, More than Pretty Pictures, in which I discuss the principles of data visualization and connect them to the concept of "quality" introduced by Robert Pirsig in Zen and the Art of Motorcycle Maintenance.
Yearning for the Infinite — Aleph 2
Discover Cantor's transfinite numbers through my music video for the Aleph 2 track of Max Cooper's Yearning for the Infinite (album page, event page).
I discuss the math behind the video and the system I built to create the video.
Hidden Markov Models
Everything we see hides another thing, we always want to see what is hidden by what we see.
—Rene Magritte
A Hidden Markov Model extends a Markov chain to have hidden states. Hidden states are used to model aspects of the system that cannot be directly observed and themselves form a Markov chain and each state may emit one or more observed values.
Hidden states in HMMs do not have to have meaning—they can be used to account for measurement errors, compress multi-modal observational data, or to detect unobservable events.
In this column, we extend the cell growth model from our Markov Chain column to include two hidden states: normal and sedentary.
We show how to calculate forward probabilities that can predict the most likely path through the HMM given an observed sequence.
Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Hidden Markov Models. Nature Methods 16:795–796.
Background reading
Altman, N. & Krzywinski, M. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.
Hola Mundo Cover
My cover design for Hola Mundo by Hannah Fry. Published by Blackie Books.
Curious how the design was created? Read the full details.
Markov Chains
You can look back there to explain things,
but the explanation disappears.
You'll never find it there.
Things are not explained by the past.
They're explained by what happens now.
—Alan Watts
A Markov chain is a probabilistic model that is used to model how a system changes over time as a series of transitions between states. Each transition is assigned a probability that defines the chance of the system changing from one state to another.
Together with the states, these transitions probabilities define a stochastic model with the Markov property: transition probabilities only depend on the current state—the future is independent of the past if the present is known.
Once the transition probabilities are defined in matrix form, it is easy to predict the distribution of future states of the system. We cover concepts of aperiodicity, irreducibility, limiting and stationary distributions and absorption.
This column is the first part of a series and pairs particularly well with Alan Watts and Blond:ish.
Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.
1-bit zoomable gigapixel maps of Moon, Solar System and Sky
Places to go and nobody to see.
Exquisitely detailed maps of places on the Moon, comets and asteroids in the Solar System and stars, deep-sky objects and exoplanets in the northern and southern sky. All maps are zoomable.
