Lips that taste of tears, they say, are the best for kissing.get crankymore quotes

# gotham: exciting

UCD Computational and Molecular Biology Symposium, Dublin, Ireland. 1-2 Dec 2016.

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

## contents

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

Part of the Pioneer plaque rendered with the sequence of human chromosome 1, using 8 weights of Gotham. (zoom)
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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Fixed string ASCII art rendering of Mona Lisa. (zoom)
DNA helix rendered with string 'dna'. (zoom)
DNA helix rendered with sequence from human chromosome 1. (zoom)

## angled text ASCII art

Things get even more interesting when the text is angled.

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.

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.

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.

ASCII, set in Gotham Ultra (zoom)
The above image asciified using 8 weights of 105pt Gotham and the fixed string 'ASCII'. (zoom)
The asciified example above, asciified again using 8 weights of 11pt Gotham and the fixed string 'ASCII'. (zoom)

VIEW ALL

# Model Selection and Overfitting

Tue 13-09-2016

With four parameters I can fit an elephant and with five I can make him wiggle his trunk. —John von Neumann.

By increasing the complexity of a model, it is easy to make it fit to data perfectly. Does this mean that the model is perfectly suitable? No.

When a model has a relatively large number of parameters, it is likely to be influenced by the noise in the data, which varies across observations, as much as any underlying trend, which remains the same. Such a model is overfitted—it matches training data well but does not generalize to new observations.

Nature Methods Points of Significance column: Model Selection and Overfitting (read)

We discuss the use of training, validation and testing data sets and how they can be used, with methods such as cross-validation, to avoid overfitting.

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

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: Logistic regression. Nature Methods 13:541-542.

# Classifier Evaluation

Tue 13-09-2016

It is important to understand both what a classification metric expresses and what it hides.

We examine various metrics use to assess the performance of a classifier. We show that a single metric is insufficient to capture performance—for any metric, a variety of scenarios yield the same value.

Nature Methods Points of Significance column: Classifier Evaluation (read)

We also discuss ROC and AUC curves and how their interpretation changes based on class balance.

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: Logistic regression. Nature Methods 13:541-542.

# Happy 2016 $\pi$ Approximation, roughly speaking

Sun 24-07-2016

Today is the day and it's hardly an approximation. In fact, $22/7$ is 20% more accurate of a representation of $\pi$ than $3.14$!

Time to celebrate, graphically. This year I do so with perfect packing of circles that embody the approximation.

By warping the circle by 8% along one axis, we can create a shape whose ratio of circumference to diameter, taken as twice the average radius, is 22/7.

If you prefer something more accurate, check out art from previous $\pi$ days: 2013 $\pi$ Day and 2014 $\pi$ Day, 2015 $\pi$ Day, and 2016 $\pi$ Day.

# Logistic Regression

Tue 13-09-2016

Regression can be used on categorical responses to estimate probabilities and to classify.

The next column in our series on regression deals with how to classify categorical data.

We show how linear regression can be used for classification and demonstrate that it can be unreliable in the presence of outliers. Using a logistic regression, which fits a linear model to the log odds ratio, improves robustness.

Nature Methods Points of Significance column: Logistic regression? (read)

Logistic regression is solved numerically and in most cases, the maximum-likelihood estimates are unique and optimal. However, when the classes are perfectly separable, the numerical approach fails because there is an infinite number of solutions.

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