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# art is science is art

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

# Christopher Hitchens—Out of Letters

## ASCII Art

The images shown here were created as part of my ASCII Art project, which extends ASCII art to include

• 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

Applying the code to images of Hitchens was motivated by my own deep love of Hitchens and a typographic portrait of Christopher Hitchens, created out of Gill Sans letters by Miles Chic at Capilano University.

This adds to my growing shrines to Hitchens, including Merry Hitchmas! and hitchslap t-shirts.

## Christopher Hitchens in Letters and Words

All images are generated using Gotham, with up to 8 weights (Extra Light to Ultra). Each image includes size and characters used for the image. I give the absolute type size, though only useful to know in relative terms to the size of the image and other images drawn with the same method. The color of text in each layer is the same—black— but font weight may vary.

Some images are generated using more than one layer of ASCII. In some cases the characters used in each layer are different.

## basic ascii

As the font size is reduced, greater detail and contrast can be achieved.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 43pt set: . x 8 : * @ - \ | _space_ / (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 29pt set: . x 8 : * @ - \ | _space_ / (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 17pt set: . x 8 : * @ - \ | _space_ / (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: . x 8 : * @ - \ | _space_ / (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)

By setting the image with a fixed string, such as a short quote or longer body of text, detail is lost but the ASCII representation takes on more meaning.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: godisnotgreat (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 29pt set: godisnotgreat (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: Hitchslap 11 (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 5 layers (11pt -6deg, 17pt 6deg, 29pt -3deg, 43pt 3deg, 59pt 0deg) set: godisnotgreat (source from Christian Witkin/Twelve/Grand Central Publishing) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 7pt set: . x 8 : * @ - \ | _space_ / (source from Gasper Tringale) (zoom, source)

## multi-layered ascii art

Images take on detail when multiple rotated layers of text is used. Each of the images below is composed of more than one layer, starting with a 2-layer image which uses the uppercase alphabet at 0 and 90 degrees.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers (0deg, 90deg) set: A-Z (source from HBO) (zoom, source)

Meaning can be added to the image by using different text in each layer. In the examples below, I set the same image using the pair "Godisnotgreat" (at 0 degrees) and "religionpoisonseverything" (at 90 degrees). In the second example, I use the unlikely combination of "Jesus" and "Mohammad"—inspired by Jesus and Mo.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers set: Godisnotgreat. 0deg Relionpoisonseverything. 90deg (source from HBO) (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers set: Jesus 0deg Mohammad 90deg (source from HBO) (zoom, source)

When rotated layers contain punctuation, very high level of detail can be achieved.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers set: . : - + * _space_ 0deg / 90deg (zoom, source)

The image below is made out of layers that contain only forward (/) and back (\) slashes.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers set: \ / (zoom, source)

The image below is made using only the period character in three layers rotated at -45, 0 and 45 degrees. Although the image looks like a pixelated version of the original—it is more than that. It is a typeset representation that uses 8 weights of Gotham. Character spacing between periods is informed by font metrics.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers set: . (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 2 layers set: 8 X x | ^ : . - = + ' " @ \ / | * ~ , # (source from The Australian) (zoom, source)

## hitchens at the podium

The three images below show the difference between using a variety of punctuation characters and setting an image using a block of text. The first image uses "8 X x" and common punctuation.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: 8 X x | ^ : . - = + ' " @ \ / | * ~ , # (zoom, source)

I use hitchslap 9 for the first image below, and all the hitchslaps for the second image. When setting an image in using a block of text, the choice of character at any position is fixed and only the font weight is allowed to vary. When the text is relatively short (e.g. hitchslap 9 is 544 characters and is repeated 50 times in the image), rivers of space appear in the image.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: Hitchslap 9 (zoom, source)
Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: all Hitchslaps (zoom, source)

In both cases, the image is very recognizable.

## recursive ascii art

When an image of text is set with the text itself, you have recursive ASCII art. Below is hitchslap 2, set with itself. In the image, the font is Gotham and the text used to asciify the image is also Gotham.

It makes ordinary moral people, compels them, forces them, in some cases orders them do disgusting wicked unforgivable things. There's no expiation for the generations of misery and suffering that religion has inflicted in this way and continues to inflict. And I still haven't heard enough apology for it. — Christopher Hitchens

The quote is 307 characters long and is repeated 391 times in the image.

Christopher Hitchens ASCII Art. method: Gotham font, 8 weights, 11pt set: Hitchslap 2 (source from hitchslap 2) (zoom, source)

In principle, the process of asciifying text with text can be repeated, by using the asciified image as input for asciification with progressively smaller text.

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# Intuitive Design

Thu 03-11-2016

Appeal to intuition when designing with value judgments in mind.

Figure clarity and concision are improved when the selection of shapes and colors is grounded in the Gestalt principles, which describe how we visually perceive and organize information.

One of the Gestalt principles tells us that the magenta and green shapes will be perceived as as two groups, overriding the fact that the shapes within the group might be different. What the principle does not tell us is how the reader is likely to value each group. (read)

The Gestalt principles are value free. For example, they tell us how we group objects but do not speak to any meaning that we might intuitively infer from visual characteristics.

Nature Methods Points of View column: Intuitive Design. (read)

This month, we discuss how appealing to such intuitions—related to shapes, colors and spatial orientation— can help us add information to a figure as well as anticipate and encourage useful interpretations.

Krzywinski, M. (2016) Points of View: Intuitive Design. Nature Methods 13:895.

# Regularization

Fri 04-11-2016

Constraining the magnitude of parameters of a model can control its complexity.

This month we continue our discussion about model selection and evaluation and address how to choose a model that avoids both overfitting and underfitting.

Ideally, we want to avoid having either an underfitted model, which is usually a poor fit to the training data, or an overfitted model, which is a good fit to the training data but not to new data.

Nature Methods Points of Significance column: Regularization (read)

Regularization is a process that penalizes the magnitude of model parameters. This is done by not only minimizing the SSE, $\mathrm{SSE} = \sum_i (y_i - \hat{y}_i)^2$, as is done normally in a fit, but adding to this minimized quantity the sum of the mode's squared parameters, $\mathrm{SSE} + \lambda \sum_i \hat{\beta}^2_i$.

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

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: Classifier evaluation. Nature Methods 13:603-604.

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

# Model Selection and Overfitting

Fri 04-11-2016

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.

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: 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.