Paintball’s Coming Home: generating Damien Hirst spot paintings

A few days ago, I read an article about Damien Hirst’s new spot paintings. I’d forgotten how regular the spots were in the original spot paintings from the 1990s (examples are on his page here). It made me think that these paintings could be randomly generated and so I wrote a quick piece of code to do this (HirstGenerator).

I used Hirst’s painting ‘Abalone Acetone Powder’ (1991), which is shown on this page as photographed by Alex Hartley. A wrote some code to sample the colours of this image and then a script to replicate it. The original is shown below  © Damien Hirst and Science Ltd. Click them for full size.

and then this is the replica:

Now that I had a palette of the colours used in the original. It was simple to write a generator to make spot paintings where the spots are randomly assigned.

The generator can make canvasses at whatever size is required.

The code can be repurposed to make spot paintings with different palettes from his other spot paintings or from something else. So there you have it. Generative Hirst Spot Paintings.

For nerds only

My original idea was to generate a palette of unique colours from the original painting. Because of the way I sampled them, each spot is represented once in the palette. This means the same colour as used by the artist is represented as several very similar but nonidentical colours in the palette. My original plan was to find the euclidean distances between all spots in RGB colour space and to establish a distance cutoff to decide what is a unique colour.

That part was easy to write but what value to give for the cutoff was tricky. After some reading, it seems that other colour spaces are better suited for this task, e.g. converting RGB to a CIE colour space. For two reasons, I didn’t pursue this. First, quantixed coding is time-limited. Second. assuming that there is something to the composition of these spot paintings (and they are not a con trick) the frequency of spots must have artistic merit and so they should be left in the palette for sampling in the generated pictures. The representation of the palette in RGB colour space had an interesting pattern (shown in the GIF above).

The post title comes from “Paintball’s Coming Home” by Half Man Half Biscuit from Voyage To The Bottom Of The Road. Spot paintings are kind of paintballs, but mostly because I love the title of this song.

Scoop: some practical advice

So quantixed occasionally gets correspondence from other researchers asking for advice. A recent email came from someone who had been “scooped”. What should they do?

Before we get into this topic we have to define what we mean by being scooped.

In the most straightforward sense being scooped means that an article appeared online before you managed to get your article online.

You were working on something that someone else was also working on – maybe you knew about this or not and vice versa – but they got their work out before you did. They are the scooper and you are the scoopee.

There is another use of the term, primarily used in highly competitive fields, which define the act of scooping as the scooper have gained some unfair advantage to make the scoop. In the worst case, this can be done by receiving your article to review confidentially and then delaying your work while using your information to accelerate their own work (Ginsparg, 2016).

However it happens, the scoop can classified as an overscoop or an underscoop. An overscoop is where the scooper has much more data and a far more complete story. Maybe the scooper’s paper appears in high profile journal while the scoopee was planning on submitting to a less-selective journal.  Perhaps the scooper has the cell data, an animal model, the biochemical data and a crystal structure; while the scoopee had some nice data in cells and a bit of biochemistry. An underscoop is where a key observation that the scoopee was building into a full paper is partially revealed. The scoopee could have more data or better quality results and maybe the full mechanism, but the scooper’s paper gives away a key detail (Mole, 2004).

All of these definitions are different from the journalistic definition which simply means “the scoop” is the big story. What the science and journalistic term share is the belief that being second with a story is worthless. In science, being second and getting the details right is valuable and more weight should be given that it currently is. I think follow-up work is valued by the community, but it is fair to say that it is unlikely to receive the same billing and attention as the scooper’s paper.

How often does scooping actually happen?

To qualify as being scooped, you need to have a paper that you are preparing for publication when the other paper appears. If you are not at that point, someone else was just working on something similar and they’ve published a paper. They haven’t scooped you. This is easiest to take when you have just had an idea or have maybe done a few experiments and then you see a paper on the same thing. It must’ve been a good idea! The other paper has saved you some time! Great. Move on. The problem comes when you have invested a lot of time doing a whole bunch of work and then the other paper appears. This is very annoying, but to reiterate, you haven’t really been scooped if you weren’t actually at the point of preparing your work for publication.

As you might have gathered, I am not even sure scooping is a real thing. For sure the fear of being scooped is real. And there are instances of scooping happening. But most of the time the scoopee has not actually been scooped. And even then, the scoopee does not just abandon their work.

So what is the advice to someone who has discovered that they have been scooped?

Firstly, don’t panic! The scoopers paper is not going to go away and you have to deal with the fact you now have the follow up paper. It can be hard to change your mindset, but you must rewrite your paper to take their work into account. Going into denial mode and trying to publish your work as though the other paper doesn’t exist is a huge mistake.

Second, read their work carefully. I doubt that the scooper has left you with no room for manoeuvre. Even in the case of the overscoop, you probably still have something that the other paper doesn’t have that you can still salvage. There’s bound to be some details on which your work does not agree and this can feature in your paper. If it’s an underscoop, you have even less to worry about. There will be a way forward – you just need to identify it and move on.

The main message is that “being scooped” is not the end. You just need to figure out your way forward.

How do I stop it from happening to me?

Be original! It’s a truism that if you are working on something interesting, it’s likely that someone else is too. And if you work in a highly competitive area, there might be many groups working on the same thing and it is more likely that you will be scooped. Some questions are obvious next steps and it might be worth thinking twice about pursuing them. This is especially true if you come up with an idea based on a paper you’ve read. Work takes so long to appear that the lab who published that paper is likely far ahead of you.

Having your own niche gives the best protection. If you have carved out your own question you probably have the lead and will be associated with work in this area anyway. Other labs will back off. If you have a highly specialised method, again you can contribute in ways that others can’t and so your chances of being scooped decrease.

Have a backup plan. Do you have a side project which you can switch to if too much novelty is taken away from your main project? You can insulate yourself from scoop damage by not working on projects that are all-or-nothing. Horror stories about scooping in structural biology (which is all about “the big reveal”) are commonplace. Investing energy in alternative approaches or new assays as well as getting a structure might help here.

If you find out about competition, maybe from a poster or a talk at a meeting, you need to evaluate whether it is worth carrying on. If you can, talk to the other lab. Most labs do not want to compete and would prefer to collaborate or at least co-ordinate submission of manuscripts.

Use preprints! If you deposit your work on a preprint server, you get a DOI and a date stamp. You can prove that your work existed on that date and in what form. This is ultimate protection against being scooped. If someone else’s work appears online before you do this, then as I said above, you haven’t really been scooped. If work appears and you already have a DOI, well, then you haven’t been scooped either. Some journals see things this way. For example, EMBO J have a scoop protection policy that states that the preprint deposition timestamp is the date at which priority is assessed.

The post title is taken from “Scoop” by The Auctioneers. I have this track on an extended C86 3-Disc set.

Measured Steps: Garmin step adjustment algorithm

I recently got a new GPS running watch, a Garmin Fēnix 5. As well as tracking runs, cycling and swimming, it does “activity tracking” – number of steps taken in a day, sleep, and so on. The step goals are set to move automatically and I wondered how it worked. With a quick number crunch, the algorithm revealed itself. Read on if you are interested how it works.

Step screen on the Garmin Fēnix 5

The watch started out with a step target of 7500 steps in one day. I missed this by 2801 and the target got reduced by 560 to 6940 for the next day. That day I managed 12480, i.e. 5540 over the target. So the target went up by 560 to 7500. With me so far? Good. So next I went over the target and it went up again (but this time by 590 steps). I missed that target by a lot and the target was reduced by 530 steps. This told me that I’d need to collect a bit more data to figure out how the goal is set. Here are the first few days to help you see the problem.

Actual steps Goal Deficit/Surplus Adjustment for Tomorrow
4699 7500 -2801 -560
12480 6940 5540 560
10417 7500 2917 590
2726 8090 -5364 -530
6451 7560 -1109 -220
8843 7340 1503 150
8984 7490 1494 300
9216 7790 1426 290

The data is available for download as a csv via the Garmin Connect website. After waiting to accumulate some more data, I plotted out the adjustment vs step deficit/surplus. The pattern was pretty clear.

There are two slopes here that pass through the origin. It doesn’t matter what the target was, the adjustment applied is scaled according to how close to the target I was, i.e. the step deficit or surplus. There was either a small (0.1) or large (0.2) scaling used to adjust the step target for the next day, but how did the watch decide which scale to use?

The answer was to look back at the previous day’s activity as well as the current day.

So if today you exceeded the target and you also exceeded the target yesterday then you get a small scale increase. Likewise if you fell short today and yesterday, you get a small scale decrease. However, if you’ve exceeded today but fell short yesterday, your target goes up by the big scaling. Falling short after exceeding yesterday is rewarded with a big scale decrease. The actual size of the decrease depends on the deficit or surplus on that day. The above plot is coloured according to the four possibilities described here.

I guess there is a logic to this. The goal could quickly get unreachable if it increased by 20% on a run of two days exceeding the target, and conversely, too easy if the decreases went down rapidly with consecutive inactivity. It’s only when there’s been a swing in activity that the goal should get moved by the large scaling. Otherwise, 10% in the direction of attainment is fine.

I have no idea if this is the algorithm used across all of Garmin’s watches or if other watch manufacturer’s use different target-setting algorithms.

The post title comes from “Measured Steps” by Edsel from their Techniques of Speed Hypnosis album.

Esoteric Circle

Many projects in the lab involve quantifying circular objects. Microtubules, vesicles and so on are approximately circular in cross section. This quick post is about how to find the diameter of these objects using a computer.

So how do you measure the diameter of an object that is approximately circular? Well, if it was circular you would measure the distance from one edge to the other, crossing the centre of the object. It doesn’t matter along which axis you do this. However, since these objects are only approximately circular, it matters along which axis you measure. There are a couple of approaches that can be used to solve this problem.

Principal component analysis

The object is a collection of points* and we can find the eigenvectors and eigenvalues of these points using principal component analysis. This was discussed previously here. The 1st eigenvector points along the direction of greatest variance and the 2nd eigenvector is normal to the first. The order of eigenvectors is determined by their eigenvalues. We use these to rotate the coordinate set and offset to the origin.

Now the major axis of the object is aligned to the x-axis at y=0 and the minor axis is aligned with the y-axis at x=0 (compare the plot on the right with the one on the left, where the profiles are in their original orientation – offset to zero). We can then find the absolute values of the axis crossing points and when added together these represent the major axis and minor axis of the object. In Igor, this is done using a oneliner to retrieve a rotated set of coords as the wave M_R.


To find the crossing points, I use Igor’s interpolation-based level crossing functions. For example, storing the aggregated diameter in a variable called len.

FindLevel/Q/EDGE=1/P m1c0, 0
len = abs(m1c1(V_LevelX))
FindLevel/Q/EDGE=2/P m1c0, 0
len += abs(m1c1(V_LevelX))

This is just to find one axis (where m1c0 and m1c1 are the 1st and 2nd columns of a 2-column wave m1) and so you can see it is a bit cumbersome.

Anyway, I was quite happy with this solution. It is unbiased and also tells us how approximately circular the object is (because the major and minor axes tell us the aspect ratio or ellipticity of the object). I used it in Figure 2 of this paper to show the sizes of the coated vesicles. However, in another project we wanted to state what the diameter of a vesicle was. Not two numbers, just one. How do we do that? We could take the average of the major and minor axes, but maybe there’s an easier way.

Polar coordinates

The distance from the centre to every point on the edge of the object can be found easily by converting the xy coordinates to polar coordinates. To do this, we first find the centre of the object. This is the centroid \((\bar{x},\bar{y})\) represented by

\(\bar{x} = \frac{1}{n}\sum_{i=1}^{n} x_{i} \) and \(\bar{y} = \frac{1}{n}\sum_{i=1}^{n} y_{i} \)

for n points and subtract this centroid from all points to arrange the object around the origin. Now, since the xy coords are represented in polar system by

\(x_{i} = r_{i}\cos(\phi) \) and \(y_{i} = r_{i}\sin(\phi) \)

we can find r, the radial distance, using

\(r_{i} = \sqrt{x_{i}^{2} + y_{i}^{2}}\)

With those values we can then find the average radial distance and report that.

There’s something slightly circular (pardon the pun) about this method because all we are doing is minimising the distance to a central point initially and then measuring the average distance to this minimised point in the latter step. It is much faster than the PCA approach and would be insensitive to changes in point density around the object. The two methods would probably diverge for noisy images. Again in Igor this is simple:

Make/O/N=(dimsize(m1,0)-1)/FREE rW

rW[] = sqrt(m1[p][0]^2 + m1[p][1]^2)

len = 2 * mean(rW)
Here again, m1 is the 2-column wave of coords and the diameter of the object is stored in len.

How does this compare with the method above? The answer is kind of obvious, but it is equidistant between the major and minor axes. Major axis is shown in red and minor axis shown in blue compared with the mean radial distance method (plotted on the y-axis). In places there is nearly a 10 nm difference which is considerable for objects which are between 20 and 35 nm in diameter. How close is it to the average of the major and minor axis? Those points are in black and they are very close but not exactly on y=x.

So for simple, approximately circular objects with low noise, the ridiculously simple polar method gives us a single estimate of the diameter of the object and this is much faster than the more complex methods above. For more complicated shapes and noisy images, my feeling is that the PCA approach would be more robust. The two methods actually tell us two subtly different things about the shapes.

Why don’t you just measure them by hand?

In case there is anyone out there wondering why a computer is used for this rather than a human wielding the line tool in ImageJ… there are two good reasons.

  1. There are just too many! Each image has tens of profiles and we have hundreds of images from several experiments.
  2. How would you measure the profile manually? This approach shows two unbiased methods that don’t rely on a human to draw any line across the object.

* = I am assuming that the point set is already created.

The post title is taken from “Esoteric Circle” by Jan Garbarek from the LP of the same name released in 1969. The title fits well since this post is definitely esoteric. But maybe someone out there is interested!