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ASCII Art—Proportional Spacing, Tone/Structure Mapping and Fixed Strings

After finding a typographic portrait of Christopher Hitchens, created out of Gill Sans letters by Miles Chic at Capilano University, I thought to resurrect software I wrote a long time ago that converts images into letters and expanding traditional ASCII art by using

  • proportionally spaced fonts
  • a variety of font weights in a single image
  • both tone and structure of the image to select characters
  • fixed strings to render an image in legible text

1 · Download code

asciifyimage-0.02.tgz

This is a Perl script and requires Imager. See README in the archive for instructions. I cannot provide installation support, but welcome questions and ideas about the method.

1.1 · Examples

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Part of the Pioneer plaque rendered with the sequence of human chromosome 1, using 8 weights of Gotham.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DNA helix rendered with string 'dna'.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The Mona Lisa rendered with fixed string 'monalisa'.

2 · ASCII Art

The representation of images by characters—ASCII art—has a long history. ASCII art extends the emoticon (or smiley) to represent a larger piece of work. Typically, the works use a fixed-space font (e.g. Courier), originally designed for display on a terminal. Despite the sophistication of computer graphics today, ASCII art continues to have a strong following with new work continually added to public online galleries.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Community contributions to ASCII Art Museum. ASCII art can vary from simple cartoon-like depictions to photorealistic interpretations. (source, zoom)

Photos and paintings can be ASCIIfied using a tone-based approach and automated methods exist to do this (Paul D. O’Grady and Scott T. Rickard (2008) Automatic ASCII Art Conversion of Binary Images Using Non-Negative Constraints).

Many artists generate new creations, exclusive to the medium. Typically this kind of ASCII art is based on the interpretation of structure rather than tone—this method has also been automated (Xuemiao Xu, Linling Zhang, Tien-Tsin Wong (2010) Structure-based ASCII Art).

3 · Proportionally-spaced and multi-font ASCII art

I have written code to generate ASCII art from images by using proportional spaced fonts.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Fixed width fonts (e.g. Pragmata) are popular. ASCII art can be extended to proportionally spaced fonts (e.g. Gotham). More than one weight (or font) can be used to add contrast.

Below is an example of how Pragmata and Gotham can be used to different effect to render an image. When a proportional spaced font is used, the ASCII shape can more fully fill the image.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Comparison of fixed and proportional spaced fonts in ASCII art. Employing multiple weights adds contrast. The grey background is added to emphasize the original image. (zoom)

Let's see how these methods work on a real image. Many ASCII art Mona Lisa versions exist. Below, I render the Mona Lisa with Pragmata, Gotham Book and 8 weights of Gotham.

3.1 · Structural character selection

Two-tone shapes like the S in the figure above require selecting characters that match the structure of the image. (e.g. "|" matches vertical lines). For a given character and image position there are four distinct match possibilities—a combination of whether the character and image have a signal at a position. I show this in the figure below.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Finding the best character involves maximizing overlap (s1, s3) and minimizing penalty (s2, s4).

By maximizing scores derived from matches (s1, s3) and minimizing any penalties (s2, s4), a character is identified based on maximal coverage of the image region and minimum coverage of areas that are blank.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Ink artwork, or thresholded bitmaps in which there are only two tone values, are approximated using structural matching. Here I compare the method of O'Grady and Rickard with my naive structural matching.

When proportional text is used, edges are better approximated, such as in the Homer Simpson example below which uses Gotham Book.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
For this image, 17pt text matches the detail well.

3.2 · Tone-based character selection

Images that are not two-tone require that we match both structure and tone. Structure is approximated by the choice of character, while tone by choice of font weight. To select the best character based on tone, the character's average tone is compared to the average tone of the section of the image to which it is being compared.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Heavier weights are used to match dark areas of the image.

It is possible to combine both structure and tone metrics in character selection. Below is an example of how an image with both tone and structure is interpreted as the tone and structure score weights are varied. The balance between these two metrics can be very hard to find—it greatly depends on the image. Tone-based mapping works well when font size is small and the image is viewed from larger distance—in this case, characters play the role of individual pixels with varying brightness. Structure-based mapping works with larger type and closer viewing distance.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A tone:structure ratio of 1:0.5 works well for the Star Trek logo.

Continuous tone bitmaps are an idea application of multi-font ASCII art—images no longer need to be thresholded or dithered.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Applying both tone and structure character selection metrics to a greyscale image. (source, )

4 · Other varieties of ASCII art

4.1 · Fixed-string ASCII art

ASCII art is generated by dividing the image into a grid and finding the letter (the choice of characters is often expanded to include punctuation) that best matches the grid section. Typically, for each grid the entire set of allowable characters is sampled. Instead, we can limit the choice of character by successively sampling from a fixed string.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Fixed string ASCII art limits the choice of characters available at each grid. Characters can be drawn from a short string (e.g. 'ilovegotham') or from a larger corpus (e.g. Wikipedia entry for Mona Lisa). The string can be contiguous within the image, or locally within the font.

Here is the Mona Lisa rendered with the fixed string "monalisa" using 8 weights of Gotham.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Fixed string ASCII art rendering of Mona Lisa.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DNA helix rendered with string 'dna'.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DNA helix rendered with sequence from human chromosome 1.

4.2 · Angled text ASCII art

Things get even more interesting when the text is angled.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
By applying rotations to the input and output images, the image can be approximated by angled text.

4.3 · Multi-layer ASCII art

The image can be textured with multiple layers of ASCII art. In the example below, four layers of text are used, each with a different font size.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Part of the Pioneer plaque rendered with the sequence of human chromosome 1, using 4 layers of sizes (17pt, 33pt, 59pt and 93pt) and 8 weights of Gotham.

Instead of varying size, the angle of the text can be changed among layers. This results in a pattern reminiscent of a halftone.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Part of the Pioneer plaque rendered with the sequence of human chromosome 1, using 4 layers with different text rotation (-45, -15, 15, 45 degrees) and 8 weights of Gotham.

4.4 · Recursive ASCII art

An image can be asciified several times, with each iteration the asciified output of the previous step used as input for the next. At each step, the font size should be reduced to s → √s.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
ASCII, set in Gotham Ultra
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The above image asciified using 8 weights of 105pt Gotham and the fixed string 'ASCII'.
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The asciified example above, asciified again using 8 weights of 11pt Gotham and the fixed string 'ASCII'.
news + thoughts

How Analyzing Cosmic Nothing Might Explain Everything

Thu 18-01-2024

Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
How Analyzing Cosmic Nothing Might Explain Everything. Text by Michael Lemonick (editor), art direction by Jen Christiansen (Senior Graphics Editor), source: SDSS

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”

voids

Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.

Sloan Digital Sky Survey Data Release 12

constellation figures

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)

stars

Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).

cosmology

H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

Error in predictor variables

Tue 02-01-2024

It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle

In regression, the predictors are (typically) assumed to have known values that are measured without error.

Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Error in predictor variables. (read)

Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.

Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. Nat. Methods 20.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nat. Methods 12:999–1000.

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

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

Convolutional neural networks

Tue 02-01-2024

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).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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 20:1269–1270.

Background reading

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.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
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.

Background reading

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

Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
Google whack “vicissitudinal corporealization”
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