The images shown here were created as part of my ASCII Art project, which extends ASCII art to include
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.
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.
As the font size is reduced, greater detail and contrast can be achieved.
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.
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.
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.
When rotated layers contain punctuation, very high level of detail can be achieved.
The image below is made out of layers that contain only forward (/) and back (\) slashes.
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.
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.
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.
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.
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.
Choose your own dust adventure!
Nobody likes dusting but everyone should find dust interesting.
Working with Jeannie Hunnicutt and with Jen Christiansen's art direction, I created this month's Scientific American Graphic Science visualization based on a recent paper The Ecology of microscopic life in household dust.
Barberan A et al. (2015) The ecology of microscopic life in household dust. Proc. R. Soc. B 282: 20151139.
A very large list of named colors generated from combining some of the many lists that already exist (X11, Crayola, Raveling, Resene, wikipedia, xkcd, etc).
For each color, coordinates in RGB, HSV, XYZ, Lab and LCH space are given along with the 5 nearest, as measured with ΔE, named neighbours.
I also provide a web service. Simply call this URL with an RGB string.
It is possible to predict the values of unsampled data by using linear regression on correlated sample data.
This month, we begin our column with a quote, shown here in its full context from Box's paper Science and Statistics.
In applying mathematics to subjects such as physics or statistics we make tentative assumptions about the real world which we know are false but which we believe may be useful nonetheless. The physicist knows that particles have mass and yet certain results, approximating what really happens, may be derived from the assumption that they do not. Equally, the statistician knows, for example, that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive results which match, to a useful approximation, those found in the real world.
—Box, G. J. Am. Stat. Assoc. 71, 791–799 (1976).
This column is our first in the series about regression. We show that regression and correlation are related concepts—they both quantify trends—and that the calculations for simple linear regression are essentially the same as for one-way ANOVA.
While correlation provides a measure of a specific kind of association between variables, regression allows us to fit correlated sample data to a model, which can be used to predict the values of unsampled data.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Simple Linear Regression Nature Methods 12:999-1000.
Altman, N. & Krzywinski, M. (2015) Points of significance: Association, correlation and causation Nature Methods 12:899-900.
Krzywinski, M. & Altman, N. (2014) Points of significance: Analysis of variance (ANOVA) and blocking. Nature Methods 11:699-700.
Correlation implies association, but not causation. Conversely, causation implies association, but not correlation.
This month, we distinguish between association, correlation and causation.
Association, also called dependence, is a very general relationship: one variable provides information about the other. Correlation, on the other hand, is a specific kind of association: an increasing or decreasing trend. Not all associations are correlations. Moreover, causality can be connected only to association.
We discuss how correlation can be quantified using correlation coefficients (Pearson, Spearman) and show how spurious corrlations can arise in random data as well as very large independent data sets. For example, per capita cheese consumption is correlated with the number of people who died by becoming tangled in bedsheets.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Association, correlation and causation Nature Methods 12:899-900.