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EMBO Practical Course: Bioinformatics and Genome Analysis, 5–17 June 2017.


visualization + design

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

contents

  1. ASCII art
  2. proportional spaced fonts
  3. structural character selection
  4. tone-based character selection
  5. fixed string ASCII art
  6. angled text ASCII art
  7. multi-layer ASCII art
  8. recursive ASCII art

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.

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. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DNA helix rendered with string 'dna'. (zoom)

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

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).

Proportional spaced and multi-font 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.

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. (zoom)

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. (zoom)

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. (zoom)

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. (zoom)

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, zoom, )

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. (zoom)

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. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DNA helix rendered with string 'dna'. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
DNA helix rendered with sequence from human chromosome 1. (zoom)

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. (zoom)

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. (zoom)

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. (zoom)

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 (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The above image asciified using 8 weights of 105pt Gotham and the fixed string 'ASCII'. (zoom)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The asciified example above, asciified again using 8 weights of 11pt Gotham and the fixed string 'ASCII'. (zoom)

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news + thoughts

Snowflake simulation

Tue 14-11-2017
Symmetric, beautiful and unique.

Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A few of the beautiful snowflakes generated by the Gravner-Griffeath model. (explore)

Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.

Genes that make us sick

Thu 02-11-2017
Where disease hides in the genome.

My illustration of the location of genes in the human genome that are implicated in disease appears in The Objects that Power the Global Economy, a book by Quartz.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The location of genes implicated in disease in the human genome, shown here as a spiral. (more...)

Ensemble methods: Bagging and random forests

Mon 16-10-2017
Many heads are better than one.

We introduce two common ensemble methods: bagging and random forests. Both of these methods repeat a statistical analysis on a bootstrap sample to improve the accuracy of the predictor. Our column shows these methods as applied to Classification and Regression Trees.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Ensemble methods: Bagging and random forests. (read)

For example, we can sample the space of values more finely when using bagging with regression trees because each sample has potentially different boundaries at which the tree splits.

Random forests generate a large number of trees by not only generating bootstrap samples but also randomly choosing which predictor variables are considered at each split in the tree.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Ensemble methods: bagging and random forests. Nature Methods 14:933–934.

Background reading

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

...more about the Points of Significance column

Classification and regression trees

Mon 16-10-2017
Decision trees are a powerful but simple prediction method.

Decision trees classify data by splitting it along the predictor axes into partitions with homogeneous values of the dependent variable. Unlike logistic or linear regression, CART does not develop a prediction equation. Instead, data are predicted by a series of binary decisions based on the boundaries of the splits. Decision trees are very effective and the resulting rules are readily interpreted.

Trees can be built using different metrics that measure how well the splits divide up the data classes: Gini index, entropy or misclassification error.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Classification and decision trees. (read)

When the predictor variable is quantitative and not categorical, regression trees are used. Here, the data are still split but now the predictor variable is estimated by the average within the split boundaries. Tree growth can be controlled using the complexity parameter, a measure of the relative improvement of each new split.

Individual trees can be very sensitive to minor changes in the data and even better prediction can be achieved by exploiting this variability. Using ensemble methods, we can grow multiple trees from the same data.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

Background reading

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

Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.

...more about the Points of Significance column