Part one of our two events for British Science Week was the Sonic Kayak open Hacklab with Kaffe Matthews and Dr. Kirsty Kemp. Amber has reported our findings here, this was the first time we successfully trialled the technology and ideas behind the Sonic Kayak, in future we will be refining them into instruments for experiencing the marine world. More on that soon!
As the egglab camouflage experiment continues, here are some recent examples after 40 or so generations. If you want to take part in a newer experiment, we are currently seeing if a similar approach can evolving motion dazzle camouflage in Dazzle Bug.
Each population of eggs is being evolved against a lot of background images, so it’s interesting to see the different strategies in use – it seems like colour is one of the first things to match, often with some dazzle to break up the outline. Later as you can see in some of these examples, there is some quite accurate background matching happening.
It’s important to say that all of this is done entirely by the perception from tens of thousands of people playing the game – there is no analysis of the images at any point.
When I first started working on the Sonic Bikes project with Kaffe Matthews in 2013 I had just moved to Cornwall, and I used the Penryn river for developing “The swamp that was” installation we made for Ghent. We’ve always talked about bringing this project here, but the various limitations of cycling (fast roads, stupid drivers and ridiculous hills) were always too much of a problem – so we wondered about sonic kayaks, as a distant vague idea. However now, thanks to help from the British Science Association, Feast Cornwall and the Port Eliot Festival they are fast becoming a reality!
We’re also using this opportunity to convert kayaks into instruments for sensing marine microclimates – an area which is currently lacking in scientific knowledge. In order to do this, we need to expand the sonic potential of our current system – moving it from sample playback to a more open ended synthesis approach. We’re running a open hacklab to trial the use of sensors, and actually get out on the water with Kaffe later in the month.
To do all this – and keep it functioning on a Raspberry Pi, we’re using Pure Data. For the moment it seemed most appropriate to stick to the concept of audio zones, previously these defined areas associated with samples that would play back when you were inside of them. The screenshot above is the sonic bike mapping tool – recently rebuilt by Francesca. Using Pure Data we can associated each zone with a specific patch, which leaves the use of samples or not, effects, interpretation of sensor data and any other musical decisions completely open.
The patch above is the first version of the zone patch mixer – it reads OSC messages from the GPS map system (which is written in Lua) and when a patch is triggered, it turns on audio processing for it and gradually fades it up. When the zone is left it fades it down and deactivates it – this way we can have multiple overlayed patches, much like the sample mixing we used before. We can also have loads of different patches as it’s only processing the active ones at any one time, it won’t stress out the Raspberry Pi too much.
I’ve been testing this today by walking around a lot with headphones on – this is a GPS trace, which gives some ideas of the usual problems of GPS (I didn’t actually switch to kayak halfway through, although it thought I did).
It’s workshop time again at Foam Kernow. We’re running a Sonic Kayak development open hacklab with Kaffe Matthews (more on this soon) and a series of tanglebots workshops which will be the finale to the weavingcodes project.
Instead of using my cobbled together homemade interface board, we’re using the pimoroni explorer hat (pro). This comes with some nice features, especially a built in breadboard but also 8 touch buttons, 4 LEDs and two motor drivers. The only downside is that it uses the same power source as the Pi for the motors, so you need to be a little careful as it can reset the Pi if the power draw is too great.
We have a good stock of recycled e-waste robotic toys we’re going to be using to build with (along with some secondhand lego mindstorms):
Also lots of recycled building material from the amazing Cornwall Scrap Store.
In order to keep the workshop balanced between programming and building, and fun for all age groups, we want to use Scratch – rather than getting bogged down with python or similar. In a big improvement to previous versions of the Pi OS, the recent raspbian version (jessie) supports lots of extension hardware including the explorer hat. Things like firing the built in LEDs work ‘out of the box’ like this:
While the two motor controllers (with speed control!) work like this:
The touch buttons were a bit harder to get working as they are not supported by default, so I had to write a scratch driver to do this which you can find here. Once the driver script is running (which launches automatically from the desktop icon), it communicates to scratch over the network and registers the 8 touch buttons as sensors. This means they can be accessed in scratch like so:
Tablet looms have some interesting properties. Firstly, they are very
very old – our neolithic ancestors invented them. Secondly they
are quite straightforward to make and weave but form an extremely
complex structure that incorporates both weaving and braiding (and one I
haven’t managed to simulate correctly yet) – they are also the only form
of weaving that has never been mechanised.
I’ve learned to warp tablets very much by trial and error, so I expect
there are many improvements possible (please let me know), but as I had
to warp a load of tablet looms for the weavecoding workshop in Düsseldorf last
week, I thought I’d document the process here.
The first thing you need to do is make the tablets themselves from
fairly stiff card. You need squares of about 5cm, and holes punched out
from the corners. Beer mats or playing cards are good, I’m just using recycled card here. It saves a bit of time later if you can get the holes lined up when
the tablets are stacked together – but I’ve never managed to do this
very well. A good number of cards to start with are 8 or 10, fewer in
number are easier to manipulate and use less yarn – if you don’t have
much to spare.
The second step is to prepare the warp yarn. You need four separate
balls or cones of wool – it’s easiest to start with four different
colours, although you can make more patterns with double faced weave
(two colours opposite each other on the cards). Fluffy knitting wool
works fine but can catch and be annoying sometimes, cotton is better. In
order to help prevent the yarn getting tangled together which is
probably the biggest problem with this job – it’s a good idea to set
this up so the yarn passes through the back of a chair with the yarn on
the floor like this – it restricts the distance the balls roll as you
pull the yarn.
You need two sticks you can easily loop the warp over, I’m using a cut
broom stick clamped to the chair here. The distance between them determines
how long the final woven band will be, a metre or so is good.
Next you need to thread each of the four warp threads through the
corners of the tablets – each thread needs it’s own corner so be careful
not to mix them up. If the holes line up you can do them all at once,
otherwise it’s one by one.
Here are the tablets with all the corners threaded.
Now we tie the threads together looped over the warp stick furthest from
the yarn, this knot is temporary.
Hook the other end of the warp over the stick on the other side, and
leave one of the tablets half way along. Loop it back again leaving
another tablet – go backwards and forwards repeating.
I can never manage to keep the tablets in order, and usually end up with a mess like this – don’t panic if you get a similar result!
When you have done all of the tablets, quickly check that every warp
pass has a card associated with it and then cut the first knot and tie
the last warp threads to the first.
This gives you a continuous warp, which is good for adjusting the
tension. Group all the cards together and arrange them so the colours
are aligned – rotate them until the same colours are at the top and the bottom.
This is also a good point to check the twist of the tablets so they alternate in
terms of the direction the threads are coming from. This is quite
difficult to explain with text but these images may help. It’s basically a bit easier if they are consistent when you start weaving, then you can see how changing this alters the patterns as you go.
You can actually reorder and flip the tablets at any time, even after
starting weaving – so none of this is critical. It’s handy to equalise
the tension between the warp threads at this point though, so grab all
the cards in alignment, put some tension on the warp and drag them up
and down the warp – if the loops at either end are not too close this
should get the lengths about the same.
Once this is done, tie all the tablets together to preserve their order
Then tie loops of strong cord around both ends of the warp.
Then you’re done – you can pull the ends off the sticks and start tablet
As I needed to transport the tablet looms I wrapped the warps (keeping the upper/lower threads separated) around cardboard tubes to keep the setup from getting tangled up. This seemed to work well:
If you find one of the warp threads is too long and is causing
a tablet to droop when the warp is under tension, you can pull it tight
and tie it back temporarily, after weaving a few wefts it will hold in
The two most important things I’ve learned about weaving – the older the
technique the more forgiving it is to mistakes, and you can never have
too many sticks.
Scientific models are used by researchers in order to understand interactions that are going on around us all the time. They are like microscopes – but rather than observing objects and structures, they focus on specific processes. Models are built from the ground up from mathematical rules that we infer from studying ecosystems, and they allow us to run and re-run experiments to gain understanding, in a way which is not possible using other methods.
I’ve managed to reproduce many of the patterns of co-evolution between the hosts and parasites in the red king model by tweaking the parameters, but the points at which certain patterns emerge is very difficult to pin down. I thought a good way to start building an understanding of this would be to pick random parameter settings (within viable limits) and ‘sweep’ paths between them – looking for any sudden points of change, for example:
This is a row of simulations which are each run for 600 timesteps, with time running downwards. The parasite is red and the host is blue, and both organism types are overlayed so you can see them reacting to each other through time. Each run has a slightly different parameter setting, gradually changing between two settings as endpoints. Halfway through there is a sudden state change – from being unstable it suddenly locks into a stable situation on the right hand side.
I’ve actually mainly been exploring this through sound so far – I’ve built a setup where the trait values are fed into additive synthesis (adding sine waves together). It seems appropriate to keep the audio technique as direct as possible at this stage so any underlying signals are not lost. Here is another parameter sweep image (100 simulations) and the sonified version, which comprises 2500 simulations, overlapped to increase sound density.
You can hear quite a few shifts and discontinuities between different branching patterns that emerge at different points – writing this I realise an animated version might be a good idea to try too.
Stereo is done by slightly changing one parameter (the host tradeoff curve) across the left and right channels – so it gives the changes a sense of direction, and you are actually hearing 5000 simulations being run in total, in both ears. All the code so far (very experimental at present) is here. The next thing to do is to take a step back and think about the best way to invite people in to experience this strange world.
Here are some more tests:
A new project begins, on the subject of ecology and evolution of infectious disease. This one is a little different from a lot of Foam Kernow’s citizen science projects in that the subject is theoretical research – and involves mathematical simulations of populations of co-evolving organisms, rather than the direct study of real ones in field sites etc.
The simulation, or model, we are working with is concerned with the co-evolution of parasites and their hosts. Just as in more commonly known simulations of predators and prey, there are complex relationships between hosts and parasites – for example if parasites become too successful and aggressive the hosts start to die out, in turn reducing the parasite populations. Hosts can evolve to resist infection, but this has an overhead that starts to become a disadvantage when most of a population is free of parasites again.
Over time these relationships shift and change, and this happens in different patterns depending on the starting conditions. Little is known about the categorisation of these patterns, or even the range of relationships possible. The models used to simulate them are still a research topic in their own right, so in this project we are hoping to explore different ways people can both control a simulation (perhaps with an element of visual live programming), and also experience the results in a number of ways – via a sonifications, or game world. The eventual, ambitious aim – is to provide a way for people to feedback their discoveries into the research.
Archaeologists can read a woven artifact created thousands of years ago, and from its structure determine the actions performed in the right order by the weaver who created it. They can then recreate the weaving, following in their ancestor’s ‘footsteps’ exactly.
This is possible because a woven artifact encodes time digitally, weft by weft. In most other forms of human endeavor, reverse engineering is still possible (e.g. in a car or a cake) but instructions are not encoded in the object’s fundamental structure – they need to be inferred by experiment or indirect means. Similarly, a text does not quite represent its writing process in a time encoded manner, but the end result. Interestingly, one possible self describing artifact could be a musical performance.
Looked at this way, any woven pattern can be seen as a digital record of movement performed by the weaver. We can create the pattern with a notation that describes this series of actions (a handweaver following a lift plan), or move in the other direction like the archaeologist, recording a given notation from an existing weave.
One of the potentials of weaving I’m most interested in is being able to demonstrate fundamentals of software in threads – partly to make the physical nature of computation self evident, but also as a way of designing new ways of learning and understanding what computers are.
If we take the code required to make the pattern in the weaving above:
(twist 3 4 5 14 15 16)
(twist 4 15)
(twist 4 8 11 15)
(twist 8 11)
(twist 9 10)
(twist 9 10)
(twist 9 10)
(twist 8 11)
We can “compile” it into a binary form which describes each instruction – the exact process for this is irrelevant, but here it is anyway – an 8 bit encoding, packing instructions and data together:
8bit instruction encoding:
Action Direction Count/Tablet ID (5 bit number)
0 1 2 3 4 5 6 7
weave: 01 (1)
rotate: 10 (2)
twist: 11 (3)
If we compile the code notation above with this binary system, we can then read the binary as a series of tablet weaving card flip rotations (I’m using 20 tablets, so we can fit in two instructions per weft):
0 1 6 7 10 11 15
0 1 5 7 10 11 14 15 16
0 1 4 5 6 7 10 11 13
1 6 7 10 11 15
0 1 5 7 11 17
0 1 5 10 11 14
0 1 4 6 7 10 11 14 15 16 17
0 1 2 3 4 5 6 7 11 12 15
0 1 4 10 11 14 16
1 6 10 11 14 17
0 1 4 6 11 16
0 1 4 7 10 11 14 16
1 2 6 10 11 14 17
0 1 4 6 11 12 16
0 1 4 7 10 11 14 16
If we actually try weaving this (by advancing two turns forward/backward at a time) we get this mess:
The point is that (assuming we’ve made no mistakes) this weave represents *exactly* the same information as the pattern does – you could extract the program from the messy encoded weave, follow it and recreate the original pattern exactly.
The messy pattern represents both an executable, as well as a compressed form of the weave – taking up less space than the original pattern, but looking a lot worse. Possibly this is a clue too, as it contains a higher density of information – higher entropy, and therefore closer to randomness than the pattern.
The graphviz visualisations we’ve been using for quipu have quite a few limitations, as they tend to make very large images, and there is limited control over how they are drawn. It would be better to be able to have more of an overview of the data, also rendering the knots in the right positions with the pendants being the right length.
Meet the pixelquipu!
These are drawn using a python script which reads the Harvard Quipu Database and renders quipu structure using the correct colours. The knots are shown as a single pixel attached to the pendant, with a colour code of red as single knot, green for a long knot and blue as a figure of eight knot (yellow is unknown or missing). The value of the knot sets the brightness of the pixel. The colour variations for the pendants are working, but no difference between twisted and alternating colours, also no twist direction is visualised yet.
Another advantage of this form of rendering is that we can draw data entropy within the quipu in order to provide a different view of how the data is structured, as a attempt to uncover hidden complexity. This is done hierarchically so a pendant’s entropy is that of its data plus all the sub-pendants, which seemed most appropriate given the non-linear form that the data takes.
We can now look at some quipus in more detail – what was the purpose of the red and grey striped pendants in the quipu below? They contain no knots, are they markers of some kind? This also seems to be a quipu where the knots do not follow the decimal coding pattern that we understand, they are mostly long knots of various values.
There also seems to be data stored in different kinds of structure in the same quipu – the collection of sub-pendants below in the left side presumably group data in a more hierarchical manner than the right side, which seems much more linear – and also a colour change emphasises this.
Read left to right, this long quipu below seems very much like you’d expect binary data to look – some kind of header information or preamble, followed by a repeating structure with local variation. The twelve groups of eight grey pendants seem redundant – were these meant to be filled in later? Did they represent something important without containing any knots? We will probably never know.
The original thinking of the pixelquipu was to attempt to fit all the quipus on a single page for viewing, as it represents them with the absolute minimum pixels required. Here are both pendant colour and entropy shown for all 247 quipu we have the data for:
As we have so little idea what the Inca are telling us in their Quipu, it seems appropriate to add a cryptanalysis approach to our toolkit of inquiry. One of the first things that a cryptanalyst will do when inspecting an unknown system is to visualise it’s entropy in order to get a handle on any structures or patterns in the underlying information. This concept comes from Claude Shannon’s work on information theory in the 40’s, where he proved that information obeys fundamental laws of physics. The concept that information and “cyberspace” may not be as intangible and otherworldly as we might believe (in fact is grounded in physical reality along with everything else) is one of the recurring themes of the weavingcodes project.
Shannon’s innovation was to separate the concepts of data quantity from information value, and he claims that information is equivalent to surprise – the more surprising a piece of data is, the more information it contains. Conversely a piece of information which we expect to hear by definition doesn’t really tell us very much. The potential for some data to be surprising (or more specifically it’s potential to reduce our uncertainty) can be measured statistically, with a quantity he called entropy, as it is analogous to states in thermodynamic systems.
Shannon defined a generalised communication system, which is handy to give us a way of reasoning about our situation in relation to the Inca. Our main unknown is the source of the messages they are sending us, are they accounting information, calendars or stories? We know a bit more about the transmitters of the messages, the khipukamayuq – the knot makers and quipu keepers. At the time Shannon was working on information theory, he was part of the start of the movement away from analogue, continuous signals and towards digital signals – with advantages that they are highly resistant to noise and can be carried further and combined together to increase bandwidth. Quipu are also mainly comprised of digital information – the type of a knot, the number of turns it’s comprised of or the twist direction of a thread are all discreet (either one thing or another) and therefore highly robust to material decay or decomposition. We can still ‘read’ them confidently after 500 years or more without the digital signal they represent being degraded too badly, if only we could understand it. At the same time, none of us working on this have access to a real quipu, so our receivers are the archaeologists and historians who study them, and compile archives such as the Harvard Quipu Archive we are using.
Although entropy is a very simplistic approach mathematically, it’s main use is to give us a tool for measuring the comparative information carrying potential of data which we have no idea about. Here are all the quipu in the Harvard database in order of average entropy bits they contain (only listing every other quipu ID):
This graph is calculated by making lists of all the discreet data of the same type, e.g. knot value, type, tying direction, pendant colours and ply direction (ignoring lengths and knot positions as these are continuous) – then calculating Shannon entropy on histograms for each one and adding them together.
We can also compare different types of information against one another, for example the main data we currently understand has some specific meaning are the knot values, partly derived from the knot type (long, single or figure of eight), which represent a decimal notation. If we compare the entropy of these we can expect them to have roughly similar average amounts of information:
The meanings of colours, ply and structure are largely unknown. Here are the knot values compared with the colours:
And this is pendant ply direction compared with knot values for each quipu:
At this point the most useful aspect of this work is to give us some outliers to inspect visually and sonically – more on that soon.