Here we are now at the middle of the fourth large part of this talk.get nowheremore quotes

# at a glance

The Outbreak Poems — artistic emissions in a pandemic

# Converting, finding, clustering and naming colors

These color resources are a side project and provided absolutely free to use with no restrictions. If you find them useful, your donation would be a way to say thanks.

## Convert colors and white points between color spaces

The $colorconvert$ (read documentation) converts colors between color spaces, white points and RGB working spaces.

$colorconvert$ is very useful for analyzing and transforming color coordinates. The output can be easily parsed by downstream scripts or imported into a spreadsheet. You can read colors from a file.

For example, you can use $colorconvert$ to get the RGB and XYZ color coordinates of all white points at each color temperature in the range 4,000–25,000 K.

$bin/colorconvert -from 4000K -to RGB255,RGBhex,xyz -oneline bin/colorconvert -from 4050K -to RGB255,RGBhex,xyz -oneline ... bin/colorconvert -from 25000K -to RGB255,RGBhex,xyz -oneline$

See the $bin/whitepoints.sh$ script.

The tool has support for the following color spaces: RGB XYZ xyY Lab LCHab Luv LCHuv HSL HSV CMY CMYK YCbCr YPbPr YUV YIQ LMS, RGB working spaces: 601, 709, Adobe, Adobe RGB (1998), Apple, Apple RGB, BestRGB, Beta RGB, BruceRGB, CIE, CIE ITU, CIE Rec 601, CIE Rec 709, ColorMatch, DonRGB4, ECI, Ekta Space PS5, NTSC, PAL, PAL/SECAM, ProPhoto, SMPTE, SMPTE-C, WideGamut, sRGB white points: A B C D50 D55 D65 D75 D93 E F11 F2 F7 4000K-25000K.

$# convert an RGB color to all color spaces > bin/colorconvert -from 41,171,226 RGB255 41 171 226 RGBhex 29ABE2 RGB 0.161 0.671 0.886 XYZ 0.292 0.351 0.772 xyY 0.206 0.248 0.351 Lab 65.815 -15.265 -37.260 LCHab 65.815 40.266 -112.279 Luv 65.815 -42.283 -57.421 LCHuv 65.815 71.309 -126.367 HSL 197.838 0.761 0.524 HSV 197.838 0.819 0.886 CMY 0.839 0.329 0.114 CMYK 0.725 0.216 0 0.114 YCbCr 148.295 156.939 98.634 YPbPr 0.604 0.129 -0.131 YUV 0.604 0.113 -0.161 YIQ 0.604 -0.197 0.007 LMS 0.230 0.410 0.782 # convert a Lab color to RGB255 and RGBhex using Adobe RGB working space # with single-line CSV output > bin/colorconvert -from lab,50,-25,50 -to RGB255,RGBhex -oneline -csv -to_rgb Adobe RGB255 109,128,39 RGBhex 6D8027 # get coordinates of white point for 4000K in RGB255 and RGBhex # with single-line CSV output > bin/colorconvert -from 4000K -to RGB255,RGBhex -oneline -csv RGB255 255,248,187 RGBhex FFF8BB # convert colors from file > bin/colorconvert -from spectral.15.txt -to RGBhex -oneline spectral-15-div-1 RGBhex 9E0142 spectral-15-div-2 RGBhex D53E4F spectral-15-div-3 RGBhex D7191C ...$

## Snap to colors and find colors

The Gretag Macbeth color checker represented in sRGB D65 colors. Colors from RGB coordinates of the Macbeth colorchecker by D. Pascale (download colors).

If you want to find specific colors in an image or snap colors to a set of reference colors then my $colorsnap$ application is for you (read documentation).

This application is useful if you want to figure out what fraction of an image is occupied by a specific color (or color range). The color clustering provided by the colorsummarizer is not useful in this case since the clustering does not key off specific colors. $colorsnap$ will also report the average color of all snapped colors for each reference color and the $\Delta E$ of the reference and average.

No shape or position analysis is performed whatsoever. Each pixel is snapped independently.

The application is written in Perl and runs natively on Linux. I've also created a compiled binary for Windows—no need to install Perl. It generates plain-text reports about color proportions, making it perfect for scripting and reports and analyzing the colors in a large number of images.

$# man page > bin/colorsnap -man # snap tucan image and create snap image and histogram image > bin/colorsnap -file tucan.jpg -delta_e_max 25 -snap -bar$

As usual, for windows replace $/$ in filepaths with $\$.

For example, the tucan image below was snapped to the 24 Gretag Macbeth colorchecker colors—each color in the original image was matched to the closest color in the colorchecker.

Original image.
Image snapped to nearest Gretag Macbeth colorchecker color.

When snapping to reference colors you can impose a maximum color difference, as measured by $\Delta E$.

Image snapped to nearest Gretag Macbeth colorchecker color within $\Delta E \le 25$. (zoom)
Image snapped to nearest Gretag Macbeth colorchecker color within $\Delta E \le 10$. (zoom)

The application also generates a plain-text report of the color distribution—great for scripting and reports.

$black_2 rgbref 49 49 51 rgbavg 41 45 18 dE 19.7 n 17322 0.105 0.105 0.199 ***** blue rgbref 35 63 147 rgbavg - - - dE - n 0 0.000 0.000 0.000 blue_flower rgbref 130 128 176 rgbavg - - - dE - n 0 0.000 0.000 0.000 blue_sky rgbref 91 122 156 rgbavg - - - dE - n 0 0.000 0.000 0.000 bluish_green rgbref 92 190 172 rgbavg 79 164 145 dE 9.6 n 716 0.004 0.004 0.008 cyan rgbref 0 136 170 rgbavg 50 139 143 dE 17.8 n 139 0.001 0.001 0.002 dark_skin rgbref 116 81 67 rgbavg 85 36 10 dE 22.5 n 655 0.004 0.004 0.008 foliage rgbref 90 108 64 rgbavg 68 83 15 dE 16.5 n 86944 0.529 0.529 1.000 *********************** green rgbref 67 149 74 rgbavg 82 136 34 dE 15.9 n 1390 0.008 0.008 0.016 light_skin rgbref 199 147 129 rgbavg 219 150 131 dE 7.8 n 61 0.000 0.000 0.001 magenta rgbref 193 84 151 rgbavg - - - dE - n 0 0.000 0.000 0.000 moderate_red rgbref 198 82 97 rgbavg 176 87 80 dE 13.6 n 490 0.003 0.003 0.006 neutral_3.5 rgbref 82 84 86 rgbavg 73 81 77 dE 5.0 n 268 0.002 0.002 0.003 neutral_5 rgbref 121 121 122 rgbavg 111 120 113 dE 6.1 n 91 0.001 0.001 0.001 neutral_6.5 rgbref 161 163 163 rgbavg 149 164 142 dE 13.3 n 99 0.001 0.001 0.001 neutral_8 rgbref 200 202 202 rgbavg 200 205 171 dE 18.0 n 51 0.000 0.000 0.001 orange rgbref 224 124 47 rgbavg 218 79 6 dE 21.6 n 3853 0.023 0.023 0.044 * orange_yellow rgbref 230 162 39 rgbavg 186 145 4 dE 14.3 n 4466 0.027 0.027 0.051 * pure_black rgbref 0 0 0 rgbavg 24 17 7 dE 7.9 n 7856 0.048 0.048 0.090 ** pure_white rgbref 255 255 255 rgbavg - - - dE - n 0 0.000 0.000 0.000 purple rgbref 94 58 106 rgbavg 32 42 80 dE 20.8 n 2 0.000 0.000 0.000 purplish_blue rgbref 68 91 170 rgbavg - - - dE - n 0 0.000 0.000 0.000 red rgbref 180 49 57 rgbavg 139 27 18 dE 15.6 n 3512 0.021 0.021 0.040 * white_9.5 rgbref 245 245 243 rgbavg 251 251 202 dE 23.8 n 371 0.002 0.002 0.004 yellow rgbref 238 198 20 rgbavg 239 217 43 dE 10.1 n 24507 0.149 0.149 0.282 ******** yellow_green rgbref 159 189 63 rgbavg 169 194 36 dE 11.3 n 11707 0.071 0.071 0.135 ****$

You can uses this application for quick and easy image color analysis. For example, by using the Google maps traffic density colors, you can snap the colors in a Google map to these colors (disregarding all others) to get a sense of the fraction of streets that are busy.

Traffic conditions in downtown Vancouver. (zoom)
Image snapped to traffic density colors within $\Delta E \le 20$. Because some feature icons use a the same or very similar color to traffic conditions, they also appear in the snapped image. (zoom)

$dred rgb 119 39 35 n 203 0.018 0.001 0.028 green rgb 128 211 117 n 7210 0.656 0.026 1.000 ****************************** lred rgb 224 76 62 n 992 0.090 0.004 0.138 **** orange rgb 242 155 92 n 2581 0.235 0.009 0.358 **********$

You can use $colorsnap$ to convert artwork to a different palette. For example, below are the colors of the subway lines in New York City, Paris and London.

Colors used by the New York MTA subway lines.

Colors used by the Paris metro lines.

Colors used by the London underground lines.

Colors used by the Tokyo subway lines.

Below is an image of Times Square in New York City snapped to the New York City subway line colors

Times Square in New York City. (zoom)
Times Square in New York City rendered using NYC MTA subway line colors. (zoom)

The Granger rainbow gives a concrete example of how snapping works.

This rainbow is a color calibration image and contains all the RGB colors—here resized as a small image so strictly not all colors are present.

Granger rainbow. (zoom)
Granger rainbow snapped to Gretag Macbeth colorchecker colors. (zoom)

Granger rainbow snapped to subway lines colors from four cities. (zoom)

One way of deriving the reference colors is to use $colorsummarizer$ (see below) to cluster colors in oine image and then using the average cluster color as the input for $colorsnap$ for a different image. Let's try this with the tucan image with Munch's Scream as the reference.

$k=8$ means clusters for Munch's Scream (download colors).

$k=32$ means clusters for Munch's Scream (download colors).

Above are the swatches for each of the $k=8$ and $k=32$ clusters of The Scream as analyzed by my $colorsummarizer$ image clustering tool. Below are the $colorsnap$ results for the tucan image using $k=8$ cluster colors.

$barley_corn rgbref 185 153 98 rgbavg 110 139 41 dE 34.5 n 3047 0.019 0.019 0.057 * brown_derby rgbref 88 68 52 rgbavg 57 66 15 dE 24.1 n 34665 0.211 0.211 0.643 ************ brown_grey rgbref 143 131 105 rgbavg 84 116 46 dE 32.2 n 2403 0.015 0.015 0.045 brown_sugar rgbref 142 103 69 rgbavg 82 89 15 dE 29.7 n 53898 0.328 0.328 1.000 ******************** cello rgbref 57 80 97 rgbavg 60 76 83 dE 6.7 n 49 0.000 0.000 0.001 jungle_green rgbref 40 41 37 rgbavg 36 37 15 dE 12.2 n 25368 0.154 0.154 0.471 ********* limed_ash rgbref 94 102 92 rgbavg 66 127 107 dE 20.8 n 1387 0.008 0.008 0.026 rob_roy rgbref 215 168 77 rgbavg 215 194 34 dE 27.4 n 43683 0.266 0.266 0.810 ****************$
Tucan snapped to $k=8$ means clusters of Edvard Munch's Scream. (zoom)
Tucan snapped to $k=8$ means clusters of Edvard Munch's Scream. Displayed is the average of the snapped colors for each reference. (zoom)
Tucan snapped to $k=32$ means clusters of Edvard Munch's Scream. (zoom)

## Color summarizer

The color summarizer generates statistical color summaries of images. Whereas $colorsnap$ (see above) calculates how close the colors in an image match a set of reference colors, $colorsummarizer$ finds $k$ such reference colors for which the difference compared to the image colors is minimum.

It reports average RGB, HSV, LAB and LCH color components as well as histograms and individual pixel values for these color spaces. Comes with useful web API for all your automation needs.

Yes! I support LCH, which is extremely useful in generating color ramps and, in general, talking about perceptual aspects of color that are intuitive.

My color summarizer reports the representative colors in an image by grouping colors into clusters of similar colors and reporting the average color in each cluster. This is useful in image identification and comparison.

The color summarizer also identifies representative colors in the image by using k-means clustering to group colors into clusters. The centers of each cluster are also reported by name, using my large database of named colors.

Below is an example of a detailed color report of an image—an adorable Fiat 126p I found while it was screaming out its color against the fading background of Havana.

My color summarizer generates statistical color summaries of images, including a poetic list of words used to describe the colors.

## Adobe Swatches for Brewer Palettes

All the Brewer palettes at a glance.

The Brewer color palettes are an excellent source for perceptually uniform color palettes. I provide Adobe Swatches for all colors in the Brewer Palettes.

I also provide a short talk to help you understand why these palettes are important.

## Color Palettes for Color Blindness

Color blindness is a thing. You should worry about it when you're designing and especially when you're encoding information.

Sets of representative hues and tones that are indistinguishable to individuals with different kinds of color blindness. The rectangle below the each color pair shows how the colors appear to someone with color blindness.

I provide some background on color blindness and give options for choosing 7-, 12- and 15-color palettes that are colorblind safe.

(left) Colors grouped by equivalence of perception in deuteranopes. Each of the two hues is represented in six different brightness and chroma combinations. (right) One of the subsets of colors on the left that are reasonably distinct in both deuteranopia and protanopia. To tritanopes, three of the pairs are difficult to distinguish.

## List of Named Colors

Probably the world's largest list of named colors.

With more than 8,300 colors, even a mantis shrimp would be impressed. You can finally imagine a color you can't even imagine and name it!

Use my list of named colors to name the colors in the Google logo: dodger blue, cinnabar, amber and medium emerland green.

The color name list is hooked into the color summarizer's clustering. You can get a list of words, derived from the color names, that describes an image.

The color summarizer returns words that qualitatively describe the image.

## color proportions in country flags

A visual survey of the color proportions in flags of 256 countries.

(right) 256 country flags as concentric circles showing the proportions of each color in the flag. (left) Unique flags sorted by similarity.

Flags are depicted by concentric rings whose thickness is a function of the amount of that color in the flag.

I make the flag color catalog available, as well as similarity scores based on color proportions for each flag pair, so you can run your own analysis.

# "This data might give you a migrane"

Tue 06-10-2020

An in-depth look at my process of reacting to a bad figure — how I design a poster and tell data stories.

A poster of high BMI and obesity prevalence for 185 countries.

# He said, he said — a word analysis of the 2020 Presidential Debates

Thu 01-10-2020

Building on the method I used to analyze the 2008, 2012 and 2016 U.S. Presidential and Vice Presidential debates, I explore word usagein the 2020 Debates between Donald Trump and Joe Biden.

Analysis of word usage by parts of speech for Trump and Biden reveals insight into each candidate.

# Points of Significance celebrates 50th column

Mon 24-08-2020

We are celebrating the publication of our 50th column!

To all our coauthors — thank you and see you in the next column!

Nature Methods Points of Significance: Celebrating 50 columns of clear explanations of statistics. (read)

# Uncertainty and the management of epidemics

Mon 24-08-2020

When modelling epidemics, some uncertainties matter more than others.

Public health policy is always hampered by uncertainty. During a novel outbreak, nearly everything will be uncertain: the mode of transmission, the duration and population variability of latency, infection and protective immunity and, critically, whether the outbreak will fade out or turn into a major epidemic.

The uncertainty may be structural (which model?), parametric (what is $R_0$?), and/or operational (how well do masks work?).

This month, we continue our exploration of epidemiological models and look at how uncertainty affects forecasts of disease dynamics and optimization of intervention strategies.

Nature Methods Points of Significance column: Uncertainty and the management of epidemics. (read)

We show how the impact of the uncertainty on any choice in strategy can be expressed using the Expected Value of Perfect Information (EVPI), which is the potential improvement in outcomes that could be obtained if the uncertainty is resolved before making a decision on the intervention strategy. In other words, by how much could we potentially increase effectiveness of our choice (e.g. lowering total disease burden) if we knew which model best reflects reality?

This column has an interactive supplemental component (download code) that allows you to explore the impact of uncertainty in $R_0$ and immunity duration on timing and size of epidemic waves and the total burden of the outbreak and calculate EVPI for various outbreak models and scenarios.

Nature Methods Points of Significance column: Uncertainty and the management of epidemics. (Interactive supplemental materials)

Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Uncertainty and the management of epidemics. Nature Methods 17.

Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: Modeling infectious epidemics. Nature Methods 17:455–456.

Bjørnstad, O.N., Shea, K., Krzywinski, M. & Altman, N. (2020) Points of significance: The SEIRS model for infectious disease dynamics. Nature Methods 17:557–558.

# Cover of Nature Genetics August 2020

Mon 03-08-2020

Our design on the cover of Nature Genetics's August 2020 issue is “Dichotomy of Chromatin in Color” . Thanks to Dr. Andy Mungall for suggesting this terrific title.

Dichotomy of Chromatin in Color. Nature Genetics, August 2020 issue. (read more)

The cover design accompanies our report in the issue Gagliardi, A., Porter, V.L., Zong, Z. et al. (2020) Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes. Nature Genetics 52:800–810.