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# $\pi$ Approximation Day Art Posters

2021 $\pi$ reminds us that good things grow for those who wait.' edition.
2019 $\pi$ has hundreds of digits, hundreds of languages and a special kids' edition.
2018 $\pi$ day stitches street maps into new destinations.
2017 $\pi$ day imagines the sky in a new way.

2016 $\pi$ approximation day wonders what would happen if about right was right.
2016 $\pi$ day sees digits really fall for each other.
2015 $\pi$ day maps transcendentally.
2014 $\pi$ approx day spirals into roughness.

2014 $\pi$ day hypnotizes you into looking.
2014 $\pi$ day
2013 $\pi$ day is where it started
Circular $\pi$ art and other distractions

The never-repeating digits of $\pi$ can be approximated by $22/7 = 3.142857$ to within 0.04%. These pages artistically and mathematically explore rational approximations to $\pi$. This 22/7 ratio is celebrated each year on July 22nd. If you like hand waving or back-of-envelope mathematics, this day is for you: $\pi$ approximation day!

Want more math + art? Discover the Accidental Similarity Number. Find humor in my poster of the first 2,000 4s of $\pi$.

The $22/7$ approximation of $\pi$ is more accurate than using the first three digits $3.14$. In light of this, it is curious to point out that $\pi$ Approximation Day depicts $\pi$ 20% more accurately than the official $\pi$ Day! The approximation is accurate within 0.04% while 3.14 is accurate to 0.05%.

## first 10,000 approximations to $\pi$

For each $m=1...10000$ I found $n$ such that $m/n$ was the best approximation of $\pi$. You can download the entire list, which looks like this

$m n m/n relative_error best_seen? 1 1 1.000000000000 0.681690113816 improved 2 1 2.000000000000 0.363380227632 improved 3 1 3.000000000000 0.045070341449 improved 4 1 4.000000000000 0.273239544735 5 2 2.500000000000 0.204225284541 7 2 3.500000000000 0.114084601643 8 3 2.666666666667 0.151173636843 9 4 2.250000000000 0.283802756086 10 3 3.333333333333 0.061032953946 11 4 2.750000000000 0.124647812995 12 5 2.400000000000 0.236056273159 13 4 3.250000000000 0.034507130097 improved 14 5 2.800000000000 0.108732318685 16 5 3.200000000000 0.018591635788 improved 17 5 3.400000000000 0.082253613025 18 5 3.600000000000 0.145915590262 19 6 3.166666666667 0.007981306249 improved 20 7 2.857142857143 0.090543182332 21 8 2.625000000000 0.164436548768 22 7 3.142857142857 0.000402499435 improved 23 7 3.285714285714 0.045875340318 24 7 3.428571428571 0.091348181202 ... 354 113 3.132743362832 0.002816816734 355 113 3.141592920354 0.000000084914 improved 356 113 3.150442477876 0.002816986561 ... 9998 3183 3.141061891298 0.000168946885 9999 3182 3.142363293526 0.000245302310 10000 3183 3.141690229343 0.000031059327$

As the value of $m$ is increased, better approximations are possible. For example, each of $13/4$, $16/5$, $19/6$ and $22/7$ are in turn better approximations of $\pi$. The line includes the $improved$ flag if the approximation is better than others found thus far.

## next best after 22/7

After $22/7$, the next better approximation is at $179/57$.

Out of all the 10,000 approximations, the best one is $355/113$, which is good to 7 digits (6 decimal places).

$pi = 3.1415926 355/113 = 3.1415929$

I've scanned to beyond $m=1000000$ and $355/113$ still remains as the only approximation that returns more correct digits than required to remember it.

## increasingly accurate approximations

Here is a sequence of approximations that improve on all previous ones.

$1 1 1.000000000000 0.681690113816 improved 2 1 2.000000000000 0.363380227632 improved 3 1 3.000000000000 0.045070341449 improved 13 4 3.250000000000 0.034507130097 improved 16 5 3.200000000000 0.018591635788 improved 19 6 3.166666666667 0.007981306249 improved 22 7 3.142857142857 0.000402499435 improved 179 57 3.140350877193 0.000395269704 improved 201 64 3.140625000000 0.000308013704 improved 223 71 3.140845070423 0.000237963113 improved 245 78 3.141025641026 0.000180485705 improved 267 85 3.141176470588 0.000132475164 improved 289 92 3.141304347826 0.000091770575 improved 311 99 3.141414141414 0.000056822190 improved 333 106 3.141509433962 0.000026489630 improved 355 113 3.141592920354 0.000000084914 improved$

For all except one, these approximations aren't all good value for your digits.

For example, $179/57$ requires you to remember 5 digits but only gets you 3 digits of $\pi$ correct (3.14).

Only $355/113$ gets you more digits than you need to remember—you need to memorize 6 but get 7 (3.141592) out of the approximation!

You could argue that $22/7$ and $355/113$ are the only approximations worth remembering. In fact, go ahead and do so.

## approximations for large $m$ and $n$

It's remarkable that there is no better $m/n$ approximation after $355/113$ for all $m \le 10000$.

What do we find for $m > 10000$?

Well, we have to move down the values of $m$ all the way to 52,163 to find $52163/16604$. But for all this searching, our improvement in accuracy is miniscule—0.2%!

$pi 3.141592653589793238 m n m/n relative_error 355 113 3.1415929203 0.00000008491 52163 16604 3.1415923873 0.00000008474$

After 52,162 there is a slew improvements to the approximation.

$104348 33215 3.1415926539 0.000000000106 208341 66317 3.1415926534 0.0000000000389 312689 99532 3.1415926536 0.00000000000927 833719 265381 3.141592653581 0.00000000000277 1146408 364913 3.14159265359 0.000000000000513 3126535 995207 3.141592653588 0.000000000000364 4272943 1360120 3.1415926535893 0.000000000000129 5419351 1725033 3.1415926535898 0.00000000000000705 42208400 13435351 3.1415926535897 0.00000000000000669 47627751 15160384 3.14159265358977 0.00000000000000512 53047102 16885417 3.14159265358978 0.00000000000000388 58466453 18610450 3.14159265358978 0.00000000000000287$

I stopped looking after $m=58,466,453$.

Despite their accuracy, all these approximations require that you remember more or equal the number of digits than they return. The last one above requires you to memorize 17 (9+8) digits and returns only 14 digits of $\pi$.

The only exception to this is $355/113$, which returns 7 digits for its 6.

You can download the first 175 increasingly accurate approximations, calculated to extended precision (up to $58,466,453/18,610,450$).

news + thoughts

# Convolutional neural networks

Thu 17-08-2023

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman

Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.

The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).

Nature Methods Points of Significance column: Convolutional neural networks. (read)

Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:.

Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

# Neural network primer

Tue 10-01-2023

Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon

In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.

Nature Methods Points of Significance column: Neural network primer. (read)

Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.

We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

# Cell Genomics cover

Mon 16-01-2023

Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.

Designed in collaboration with with Carlos Urzua.

Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).

Browse my gallery of cover designs.

A catalogue of my journal and magazine cover designs. (more)

Thu 05-01-2023

My cover design on the 6 January 2023 Science Advances issue depicts DNA sequencing read translation in high-dimensional space. The image showss 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes.

My Science Advances cover that encodes sequence onto hypercubes (volume 9, issue 1, 6 January 2023). (more)

Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.

Browse my gallery of cover designs.

A catalogue of my journal and magazine cover designs. (more)

# Regression modeling of time-to-event data with censoring

Thu 17-08-2023

If you sit on the sofa for your entire life, you’re running a higher risk of getting heart disease and cancer. —Alex Honnold, American rock climber

In a follow-up to our Survival analysis — time-to-event data and censoring article, we look at how regression can be used to account for additional risk factors in survival analysis.

We explore accelerated failure time regression (AFTR) and the Cox Proportional Hazards model (Cox PH).

Nature Methods Points of Significance column: Regression modeling of time-to-event data with censoring. (read)

Dey, T., Lipsitz, S.R., Cooper, Z., Trinh, Q., Krzywinski, M & Altman, N. (2022) Points of significance: Regression modeling of time-to-event data with censoring. Nature Methods 19:1513–1515.

# Music video for Max Cooper's Ascent

Tue 25-10-2022

My 5-dimensional animation sets the visual stage for Max Cooper's Ascent from the album Unspoken Words. I have previously collaborated with Max on telling a story about infinity for his Yearning for the Infinite album.

I provide a walkthrough the video, describe the animation system I created to generate the frames, and show you all the keyframes

Frame 4897 from the music video of Max Cooper's Asent.

The video recently premiered on YouTube.

Renders of the full scene are available as NFTs.