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Fonts + Design
Snellen Optotype Font with Upper and Lowercase characters
In the process of designing my Snellen Eye Chart typographical posters, I came across the Snellen font by Andrew Howlett. I wasn't happy with all the letters, so I made attempts at giving the font an update.
Not being a font designer, I will likely get myself into trouble.
snellen chart posters
While making my Snellen chart series, I entered the rabbit hole of optotype fonts ... and I can't get out!
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The charts don't necessarily use the latest version of my Snellen font design, which fluctuates as my mood about some of the letters changes.
optotype fonts
The optotype requirement is that letters be designed on a 5 × 5 grid, and have constant stroke width. This means that both lower and upper case letters need to share the grid and stroke. To stay compatible with the eyechart paradigm, letters should be as obvious as possible.
Lorrie Frear's article What are Optotypes? Eye Charts in Focus is a great read about optotypes and eye charts.
Snellen Optotype Letter Design
uppercase
The uppercase letter design uses Herman Snellen's original chart as inspiration.
I have modified the design by Andrew Howlett (see below) for some letters. All the changes are relatively minor: more serifs and consistent stroke width for bars on R and K.
lowercase
The lowercase characters should be considered experimental.
The progress of my redesign is shown below. I would greatly appreciate feedback and suggestions!
Download Snellen Optotype Font
The distribution contains both Andrew's version and my redesign.
v7.0 6-mar-2017 — Download Snellen optotype font
version 7 — 6 Feb 2017
Fixed g and e. Thanks to Makeesha Fisher for suggestions.
version 6 — 5 Feb 2017
Adjusted serifs on f, j, l, o, t to extend the full width of the grid. Added a lot more symbols.
version 5 — 4 Feb 2017
Added lowercase, digits and symbols.
version 4 — 23 Feb 2017
Adding digits.
version 3 — 22 Feb 2017
I'm exploring the lowercase characters. I don't know what I want to do with them. Make this into a more standard font in which lowercase letters are smaller, so that letters can fit their roles clearly when text is set in sentence case, or fill out the full optotype grid.
version 2 — 22 Feb 2017
Flushed out some inconsistencies in the uppercase characters. Added serifs to more letters.
Now all the letters occuppy the full 5 × 5 grid, including the I, whose serifs were widened to allow this. While this new uppercase I isn't as pretty as the old one, it makes the entire typeface more consistent to its optotype roots.
Still struggling with the G. In the original version, the descending stroke was cut off in the middle of a grid, which I didn't like.
The S has been fixed—thanks to Elanor Lutz for feedback.
I've color coded the characters slightly differently, drawing attention to ones that I feel need more thought.
The lowercase characters aren't color coded (yet) because ... most of them need help. Primarily, I'm vacillating between making them fill the full size of the 5 × 5 square, just like the uppercase characters, and keeping them confined to a 4 × 4 square, which incurs loss of legibility. If I make the letters the same size, it will be impossible to distinguish lowercase and uppercase characters some cases (e.g. c, i). Perhaps this is desired?
version 1 — 22 Feb 2017
First attempt at lowercase characters.
news + thoughts
`k` index: a weightlighting and Crossfit performance measure
Similar to the `h` index in publishing, the `k` index is a measure of fitness performance.
To achieve a `k` index for a movement you must perform `k` unbroken reps at `k`% 1RM.
The expected value for the `k` index is probably somewhere in the range of `k = 26` to `k=35`, with higher values progressively more difficult to achieve.
In my `k` index introduction article I provide detailed explanation, rep scheme table and WOD example.
Dark Matter of the English Language—the unwords
I've applied the char-rnn recurrent neural network to generate new words, names of drugs and countries.
The effect is intriguing and facetious—yes, those are real words.
But these are not: necronology, abobionalism, gabdologist, and nonerify.
These places only exist in the mind: Conchar and Pobacia, Hzuuland, New Kain, Rabibus and Megee Islands, Sentip and Sitina, Sinistan and Urzenia.
And these are the imaginary afflictions of the imagination: ictophobia, myconomascophobia, and talmatomania.
And these, of the body: ophalosis, icabulosis, mediatopathy and bellotalgia.
Want to name your baby? Or someone else's baby? Try Ginavietta Xilly Anganelel or Ferandulde Hommanloco Kictortick.
When taking new therapeutics, never mix salivac and labromine. And don't forget that abadarone is best taken on an empty stomach.
And nothing increases the chance of getting that grant funded than proposing the study of a new –ome! We really need someone to looking into the femome and manome.
Dark Matter of the Genome—the nullomers
An exploration of things that are missing in the human genome. The nullomers.
Julia Herold, Stefan Kurtz and Robert Giegerich. Efficient computation of absent words in genomic sequences. BMC Bioinformatics (2008) 9:167
Clustering
Clustering finds patterns in data—whether they are there or not.We've already seen how data can be grouped into classes in our series on classifiers. In this column, we look at how data can be grouped by similarity in an unsupervised way.
We look at two common clustering approaches: `k`-means and hierarchical clustering. All clustering methods share the same approach: they first calculate similarity and then use it to group objects into clusters. The details of the methods, and outputs, vary widely.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.
Background reading
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.
What's wrong with pie charts?
In this redesign of a pie chart figure from a Nature Medicine article [1], I look at how to organize and present a large number of categories.
I first discuss some of the benefits of a pie chart—there are few and specific—and its shortcomings—there are few but fundamental.
I then walk through the redesign process by showing how the tumor categories can be shown more clearly if they are first aggregated into a small number groups.
(bottom left) Figure 2b from Zehir et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. (2017) Nature Medicine doi:10.1038/nm.4333