Trance opera—Spente le Stellebe dramaticmore quotes

# 3.14: beautiful

DNA on 10th — street art, wayfinding and font

# visualization + design

The 2019 Pi Day art celebrates digits of $\pi$ with hundreds of languages and alphabets. If you're a kid at heart—rejoice—there's a special edition for you!

# $\pi$ Day 2014 Art Posters

2019 $\pi$ has hundreds of digits, hundreds of languages and a special kids' edition.
2018 $\pi$ day
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

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.

All art posters are available for purchase.
I take custom requests.

For the 2014 $\pi$ day, two styles of posters are available: folded paths and frequency circles.

The folded paths show $\pi$ on a path that maximizes adjacent prime digits and were created using a protein-folding algorithm.

The frequency circles colourfully depict the ratio of digits in groupings of 3 or 6. Oh, look, there's the Feynman Point!

## the many paths of $pi$—how to fold numbers

Pi Day art for 2014 is based on the idea of folding the digits of the number into a path. Here one of the most energetically favourable paths is mapped onto a circle — planet π (zoom)

This year's Pi Day art expands on the work from last year, which showed Pi as colored circles on a grid. For those of you who really liked this minimalist depiction of π , I've created something slightly more complicated, but still stylish: Pi digit frequency circles. These are pretty and easy to understand. If you like random distribution of colors (and circles), these are your thing.

But to take drawing Pi a step further, I've experimented with folding its digits into a path. The method used is the same kind used to simulate protein folding. Research into protein folding is very active — the 3-dimensional structure of proteins is necessary for their function. Understanding how structure is affected by changes to underlying sequence is necessary for identifying how things go wrong in a cell.

Folding a protein in 2-dimensions is a difficult challenge. The problem is NP-complete, even when restricted to a lattice. Simulations are used to search for energetically favourable paths. The ultimate goal is to be able to predict the 3-dimensional structure of proteins from its sequence. Images from Wikipedia. (2d folding, 3d shapes)
Enough with proteins, you're here for the art.

## method — folding a number

I will be using the replica exchange Monte Carlo algorithm to create folded paths (download code).

Folding a number | Digits of a number are assigned to a polar (black) or hydrophobic state (red). We search for a path that maximizes the number of neighbours assigned to the hydrophobic (red) state. In this example, the 64 digit number of 7s and 9s has an energy of -42, indicating the path has 42 pairs of neighbouring 7s.

The choice of mapping between digit (0-9) and state (polar, hydrophobic) is arbitrary. I have chosen to assign the prime digits (2, 3, 5, 7) as hydrophobic. Another way can be to use perfect squares (1, 2, 4, 9). I construct the path by assigning each digit to a path node. One can partition π into two (or more) digit groupings (31, 41, 59, 26, ...) as well.

Want more math + art? Look at 2013 Pi Day art, discover the Accidental Similarity Number and other number art. Find humor in my poster of the first 2,000 4s of Pi.

## folding 64 digits of π

Folding Pi | Prime digits in π (2, 3, 5, 7) are assigned a hydrophobic state. The best path is one that maximizes the number of neighbouring prime digits. The path shown here as $E=-23$, indicating 23 neighbouring pairs. A color scheme after the Bauhaus style will be used for the art, with a different scheme for white and black backgrounds.

The quality of the path will depend on how hard you look. Each time the folding simulation is run you run the chance of finding a better solution. For the 64 digits of π shown above, I ran the simulation 500 times and found over 200 paths with the same low energy. It's interesting to note that the path with $E=-22$ was found in <1 second and it took most of the computing time to find the next move.

Below I show 100 paths of 64-digits with $E=-23$, sorted by their aspect ratio.

100 lowest energy paths | These are 100 $E=-23$ 64-digit paths — there are many more paths with this energy. The paths are in increasing order of aspect ratio (width/height). First is 6x14 (0.429) and last is 8x9 (0.889). (zoom)

Running the simulation for 64 digits is very practical — it takes only a few minutes. In a sectino below, I show you how to run your own simulation.

## folding 768 digits of π — the Feynman Point

Let's fold more digits! How about 768 digits — all the way to "...999999". This is the famous The Feynman Point in π where we see the first set of six 9s in row. This happens surprisingly early — at digit 762. In this sequence there are 298 prime digits with the other 470 being composite.

Folding 768 digits of Pi | The best path I could find of the first 768 digits of π with $E=-223$ (width=38, height=52, r=0.73, cm=1, cmabs=13). (zoom)

I have chosen not to emphasize the start and end of the path — finding them is part of the fun (You are haven't fun, aren't you?). The end is easier to spot — the 6 9s stand out. Finding the start, on the other hand, is harder.

## (d,n) points in π — sequences of repeating digits

The Feynman Point is a specific instance of repeating digits, which I call (d,n) points.

You can read more about these locations, where I have enumerated all such locations in the first 268 million digits of π .

## Optimal paths of π up to Feynman Point

Below is a list of the 20 best paths that I've been able to find. They range from $E=-223$ to $E=-219$. I annotate each path with a few geometrical properties, such as width, height, area and so on. In some of the art these properties annotate the path (energy x×y r cm,cmabs).

$# e - energy, as positive number # x,y - path width and height # r - aspect ratio = x/y # area - area (x*y) # cm - center of mass |(sum(x),sum(y))|/n and |(sum(|x|),sum(|y|))|/n # dend - distance between start and end of path 0 e 223 size 37 51 r 0.725 area 1887 cm 1.9 13.4 dend 24.4 1 e 222 size 36 44 r 0.818 area 1584 cm 17.3 18.8 dend 10.4 2 e 221 size 37 50 r 0.740 area 1850 cm 7.6 14.0 dend 16.3 3 e 221 size 70 36 r 1.944 area 2520 cm 1.0 17.3 dend 30.1 4 e 221 size 41 55 r 0.745 area 2255 cm 17.9 20.6 dend 29.5 5 e 221 size 50 49 r 1.020 area 2450 cm 20.8 22.1 dend 34.1 6 e 221 size 61 35 r 1.743 area 2135 cm 11.4 18.2 dend 15.0 7 e 221 size 53 45 r 1.178 area 2385 cm 14.7 18.1 dend 18.8 8 e 221 size 32 52 r 0.615 area 1664 cm 14.0 18.1 dend 33.8 9 e 220 size 46 70 r 0.657 area 3220 cm 26.6 27.8 dend 27.3 10 e 220 size 55 55 r 1.000 area 3025 cm 5.1 16.8 dend 15.0 11 e 220 size 58 34 r 1.706 area 1972 cm 9.3 14.6 dend 43.4 12 e 220 size 62 50 r 1.240 area 3100 cm 30.6 31.4 dend 33.4 13 e 220 size 41 45 r 0.911 area 1845 cm 15.4 17.6 dend 19.2 14 e 220 size 47 51 r 0.922 area 2397 cm 25.6 26.7 dend 16.0 15 e 220 size 38 52 r 0.731 area 1976 cm 13.1 15.9 dend 23.6 16 e 220 size 57 46 r 1.239 area 2622 cm 20.7 22.7 dend 51.7 17 e 220 size 43 57 r 0.754 area 2451 cm 21.3 23.3 dend 29.6 18 e 219 size 45 45 r 1.000 area 2025 cm 16.5 18.2 dend 33.1 19 e 219 size 51 46 r 1.109 area 2346 cm 16.0 19.2 dend 44.4$

As you can see, the dimensions of the paths vary greatly. Low energy paths are not necessarily symmetrical. Paths with a small $cm$ are balanced around their center. Paths with $r$≈1 are confined in a square boundary. Paths with small $dend$ have their start and end points close to one another.

## planet π — path lattice on a circle

The art would not be complete if we didn't somehow try to further force things into a circle! The path lattice is rectangular, but can be deformed into an ellipse or circle using the following transformation

$[(x'),(y')] = [(x sqrt(1-y^2/2)),(y sqrt(1-x^2/2)) ]$

Deforming the path lattice | A path of π on a square lattice is blasphemous! Here the path is transformed to either an ellipse (preserving the path's aspect ratio) or a circle. So much better.
Planet π | Let's go there. The 64-digit path shown here has $E=-219$. (zoom)
VIEW ALL

# Yearning for the Infinite — Aleph 2

Mon 18-11-2019

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).

Yearning for the Infinite, Max Cooper at the Barbican Hall, London. Track Aleph 2. Video by Martin Krzywinski. Photo by Michal Augustini. (more)

I discuss the math behind the video and the system I built to create the video.

# Hidden Markov Models

Mon 18-11-2019

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.

Nature Methods Points of Significance column: Hidden Markov Models. (read)

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.

Altman, N. & Krzywinski, M. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.

# Hola Mundo Cover

Sat 21-09-2019

My cover design for Hola Mundo by Hannah Fry. Published by Blackie Books.

Hola Mundo by Hannah Fry. Cover design is based on my 2013 $\pi$ day art. (read)

Curious how the design was created? Read the full details.

# Markov Chains

Tue 30-07-2019

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.

Nature Methods Points of Significance column: Markov Chains. (read)

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

Mon 22-07-2019

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.

3.6 gigapixel map of the near side of the Moon, annotated with 6,733. (details)
100 megapixel and 10 gigapixel map of the Solar System on 20 July 2019, annotated with 758k asteroids, 1.3k comets and all planets and satellites. (details)
100 megapixle and 10 gigapixel map of the Northern Celestial Hemisphere, annotated with 44 million stars, 74,000 deep-sky objects and 3,000 exoplanets. (details)
100 megapixle and 10 gigapixel map of the Southern Celestial Hemisphere, annotated with 69 million stars, 88,000 deep-sky objects and 1000 exoplanets. (details)

# Quantile regression

Sat 01-06-2019
Quantile regression robustly estimates the typical and extreme values of a response.

Quantile regression explores the effect of one or more predictors on quantiles of the response. It can answer questions such as "What is the weight of 90% of individuals of a given height?"

Nature Methods Points of Significance column: Quantile regression. (read)

Unlike in traditional mean regression methods, no assumptions about the distribution of the response are required, which makes it practical, robust and amenable to skewed distributions.

Quantile regression is also very useful when extremes are interesting or when the response variance varies with the predictors.

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nature Methods 16:451–452.