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visualization + math

π Day 2025 Art Posters - TTCAGT: a sequence of digits
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
2025 π DAY | TTCAGT: A sequence of digits. 768 digits of `\pi` as a Sanger sequencing trace of 1,536 peaks. Decode the sequence (BUY ARTWORK)

`\pi` Day 2016 Art Posters


Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2024 π DAY | Explore the garden of digits.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2023 π DAY | Repeated sequence

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2022 π DAY | three one four: a number of digits

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2021 π DAY | Good things grow for those who wait.' edition.


Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2020 π DAY | The piku.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2019 π DAY | Hundreds of digits, hundreds of languages and a special kids' edition.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2018 π DAY | Street maps to new destinations.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2017 π DAY | Imagine the sky in a new way.


Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2016 π APPROXIMATION DAY | What would happen if about right was right.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2016 π DAY | These digits really fall for each other.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2015 π DAY | A transcendental experience.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 π APPROXIMATION DAY | Spirals into roughness.


Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 π DAY | Hypnotizes you into looking.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 π DAY | Come into the fold.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2013 π DAY | Where it started.

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
CIRCULAR π ART | And other distractions.

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.

Most of the art is available for purchase as framed prints and, yes, even pillows. Sleep's never been more important — I take custom requests.

This year's `\pi` day art collection celebrates not only the digit but also one of the fundamental forces in nature: gravity.

In February of 2016, for the first time, gravitational waves were detected at the Laser Interferometer Gravitational-Wave Observatory (LIGO).

The signal in the detector was sonified—a process by which any data can be encoded into sound to provide hints at patterns and structure that we might otherwise miss—and we finally heard what two black holes sound like. A buzz and chirp.

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

this year's theme music

All the art was processed while listening to Roses by Coeur de Pirate, a brilliant female French-Canadian songwriter, who sounds like a mix of Patricia Kaas and Lhasa. The lyrics Oublie-moi (Forget me) are fitting with this year's theme of gravity.

Mais laisse-moi tomber, laisse-nous tomber
Laisse la nuit trembler en moi
Laisse-moi tomber, laisse nous tomber
Cette fois

But let me fall, let us fall
Let the night tremble in me
Let me fall, let us fall
This time

simulating gravity in 2d

The gravitational force between two masses `m_1` located at `(x_1,y_1)` and `m_2` located at `(x_2,y_2)` is given by

$$F = \frac{G m_1 m_2}{r^2} \tag{1} $$

where `r` is the distance between the masses given by

$$r = \sqrt{ \Delta x ^2 + \Delta y ^2 } = \sqrt{ (x_2-x_1)^2 + (y_2-y_1)^2 } \tag{2} $$

The force is directed along the vector formed by `r` and can be decomposed into `x` and `y` components using \begin{align} F_x &= F \frac{ \Delta x}{r} = F \frac{x_2-x_1}{r} \tag{3} \\ F_y &= F \frac{ \Delta y}{r} =F \frac{y_2-y_1}{r} \tag{4} \end{align}

The acceleration of each mass can be obtained using `F = ma` and similarly decomposed into `x` and `y` components \begin{align} a_{1x} &= \frac { F_{1x} }{ m_1} = \frac{G m_2 \Delta x}{r^3} \tag{5} \\ a_{1y} &= \frac { F_{1y} }{ m_1} = \frac{G m_2 \Delta y}{r^3} \tag{6} \\ a_{2x} &= \frac { F_{2x} }{ m_2} = -\frac{G m_1 \Delta x}{r^3} \tag{7} \\ a_{2y} &= \frac { F_{2y} }{ m_2} = -\frac{G m_1 \Delta y}{r^3} \tag{8} \end{align}

When there are `n` masses in the system, the acceleration of mass `i` is the sum of the accelerations due to all other masses \begin{align} a_{ix} &= \sum_{i \ne j} \frac{G m_j \Delta x_{ij}}{r_{ij}^3} \tag{9} \\ a_{iy} &= \sum_{i \ne j} \frac{G m_j \Delta y_{ij}}{r_{ij}^3} \tag{10} \end{align}

The equations of motion for the masses over a period of time `\Delta t` are

\begin{align} \Delta v_x &= \Delta t a_x \tag{11} \\ \Delta v_y &= \Delta t a_y \tag{12} \\ \Delta x &= \Delta t \left( v_x + a_x \frac{\Delta t}{2} \right) \tag{13} \\ \Delta y &= \Delta t \left( v_y + a_y \frac{\Delta t}{2} \right) \tag{14} \end{align}

numerical simulation

There are various ways in which the numerical simulation can be performed. The Euler, Verlet, Runge-Kutta methods are perhaps the most common. I use the Verlet approach.

Using the equations of motions above, the Verlet simulation goes as follows

  1. calculate acceleration, `a_1` (eq 9,10)
  2. update position (eq 13,14)
  3. calculate new acceleration, `a_2` (eq 9,10)
  4. update velocity using `(a_1+a_2)/2` (eq 7,8)

The masses are initially uniformly distributed on a circle and given a zero initial velocity or a normally distributed random velocity.

I ran about 10,000 individual simulations with different values of `n` and `k` and collected ones that stood out as pretty.

collisions

The size of a mass is taken to be `s = m^{1/3}`. When two masses, `m_1` and `m_2` come within a distance of `\left( s_1 + s_2 \right)(1-z)` of each other, they collide. Here `z` is a collision margin parameter that I set to either `z=0` or `z=0.25`.

During the collision, a new body is created with mass `M = m_1 + m_2` given a speed that conserves momentum in the collision. \begin{align} v_x &= \frac{m_1 v_{1x} + m_2 v_{2x} }{M} \\ v_y &= \frac{m_1 v_{1y} + m_2 v_{2y} }{M} \end{align}

values

For my simulation, the following values are used

  • `G = 100`
  • mass for each digit, `d` is `(1+d)^k`
  • masses placed on circle with radius `216`
  • when randomized, `(v_x,v_y) \sim N(0,1)`
  • `\Delta t = 0.01`
  • simulation runs for up to 100,000 steps
  • canvas size is `1440 \times 1440`
news + thoughts

Symmetric alternatives to the ordinary least squares regression

Wed 23-07-2025

What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"

This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.

Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.

However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Symmetric alternatives to the ordinary least squares regression. Geometry of quantities minimized in OLS and symmetric regression. OLS minimizes `\Sigma e_y^2` in `Y` ~ `X` and `\Sigma e_x^2` `X` ~ `Y`. Pythagorean regression minimizes AB (magenta). Geometric means regression (GMR) minimizes area of ABP (orange). Orthogonal regression (OR) minimizes HP (blue). (read)

Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.

Beyond Belief Campaign BRCA Art

Wed 11-06-2025

Fuelled by philanthropy, findings into the workings of BRCA1 and BRCA2 genes have led to groundbreaking research and lifesaving innovations to care for families facing cancer.

This set of 100 one-of-a-kind prints explore the structure of these genes. Each artwork is unique — if you put them all together, you get the full sequence of the BRCA1 and BRCA2 proteins.

Propensity score weighting

Mon 17-03-2025

The needs of the many outweigh the needs of the few. —Mr. Spock (Star Trek II)

This month, we explore a related and powerful technique to address bias: propensity score weighting (PSW), which applies weights to each subject instead of matching (or discarding) them.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Propensity score weighting. (read)

Kurz, C.F., Krzywinski, M. & Altman, N. (2025) Points of significance: Propensity score weighting. Nat. Methods 22:638–640.

Happy 2025 π Day—
TTCAGT: a sequence of digits

Thu 13-03-2025

Celebrate π Day (March 14th) and sequence digits like its 1999. Let's call some peaks.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2025 π DAY | TTCAGT: a sequence of digits. The digits of π are encoded into DNA sequence and visualized with Sanger sequencing. (details)

Crafting 10 Years of Statistics Explanations: Points of Significance

Sun 09-03-2025

I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player

Points of Significance is an ongoing series of short articles about statistics in Nature Methods that started in 2013. Its aim is to provide clear explanations of essential concepts in statistics for a nonspecialist audience. The articles favor heuristic explanations and make extensive use of simulated examples and graphical explanations, while maintaining mathematical rigor.

Topics range from basic, but often misunderstood, such as uncertainty and P-values, to relatively advanced, but often neglected, such as the error-in-variables problem and the curse of dimensionality. More recent articles have focused on timely topics such as modeling of epidemics, machine learning, and neural networks.

In this article, we discuss the evolution of topics and details behind some of the story arcs, our approach to crafting statistical explanations and narratives, and our use of figures and numerical simulations as props for building understanding.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Crafting 10 Years of Statistics Explanations: Points of Significance. (read)

Altman, N. & Krzywinski, M. (2025) Crafting 10 Years of Statistics Explanations: Points of Significance. Annual Review of Statistics and Its Application 12:69–87.

Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentrePHSA
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