Monday, 29 December 2014

Forgotten Futures - New York

What if cities looked like this? The 1920s view of cities of the future was glorious; huge buildings towering into the sky, multi-layer roads, rail and pavements, airships and aircraft, and the bold geometry of art deco.

Sadly this world never came into existence. But what if it had? What would 1950s New York have looked like? I re-imagined this forgotten future based on the view from the Empire State building towards the Grand Central station and the Chrysler building in a world where the 1920s vision of the future came to be.

Software used:
Blender: 3D modeling, texturing, rendering, compositing.
Paint.NET: Final image tweaks.
Inkscape: Texture detailing.

Building a forgotten future; 7 days of 3D modelling in 20 seconds:

Friday, 12 December 2014


Ocean tides are one of the most amazing but overlooked natural wonders of our planet. As the Earth rotates relative to the sun and the moon, their gravity drags the Earth's water about, raising and lowering it in synchrony with the heavens. The importance of tides reaches further than just surfing, sunbathing and shipping: Tides are the reason the moon is drifting away from the Earth at 3.8 cm per year. Tides allow the formation of beaches with rock pools at low tide, that some biologists argue helped the evolution of early life. Tides (of the atmosphere) are the reason a satellite in a low orbit is more likely to burn up on the side of the Earth nearest or opposite to the moon. Tides even influence the time  earthquakes happen.

The explanation of why tides happen is classic high school geography/physics. The gravitational pull of an object is felt more strongly by something close to it. In the case of the Earth, this means that the oceans on the closest side of the Earth to the moon feels a stronger gravitational pull than the Earth as a whole, and the oceans on the far side feel a weaker pull. This means that the oceans on the near side of the Earth are pulled into a bulge (a region of high tide) and the oceans in the far side are also flung outward into a bulge (another region of high tide). This causes high and low tides twice per day. Throw in the similar contribution of the sun's gravitational pull, and it also explains spring tides around the time of the new and full moon.

Of course this is all a bit of a lie to simplify things. Many places have one  high and low tide per day, and a few places even have four. Some places have barely any tide, while others have very large tides where the water level can change by many metres. Why? Because the land gets in the way! It is impossible to have a bulge of water where Africa is, even if the moon was directly over the Sahara. So what does the pattern of tides actually look like?

Something like this:

[Watch in HD on YouTube]

This animation shows sea levels over the course of one day, where orange represents high water level, and blue represents low water level. Instead of the water levels changing because of two big bulges of water, there are instead complex patterns of water level change.

So, how does the simple rotation of the Earth relative to the sun and moon generate such complexity? It is easiest to think about the oceans as containers of water which gently slosh about as the water gets pulled by the gravity of the sun and moon. It is a bit like the sloshing of water you get carrying a glass of water, or when you climb out of a bathtub. The precise pattern of the sloshing depends on many things; the strength and direction of the gravitational force driving the sloshing, the depth of the water, and how the oscillating sloshing movement resonates when it gets trapped against the coastline.

The different water movements that make up the final tidal moment can be broken down by the force that generated them (the sun, the moon) and their frequency (once a day, twice a day). The two biggest contributing movements are a twice daily movement arising from the moon, and a once daily movement due to the combined action of the sun and moon.

These individual movements are mapped through their amplitude (how much the water changes height) and their phase (the relative time of high tide). These maps are surprisingly beautiful! Here are a couple of examples:

These are the patterns of movement of the "M2" part of tides, which is a twice daily water movement arising from the primary action of the moon's gravity. Brightness represents the amplitude, from black (zero amplitude) to white (5 metres amplitude). The coloured lines are a bit more complex. They represent the places where the highest water level occurs due to the M2 tidal component at different times, from red (at 0 hours) through the colours of the spectrum at 1 hour steps.

These are the patterns of movement of the "K1" part of tides, which is a once daily water movement arising from the combined action of the sun's and moon's gravity. Again, brightness represents amplitude, but the amplitudes are smaller and white represents only 2.5 metres. The coloured lines represent the time when highest water level due to the K1 tidal component occur, but this time separated by 2 hour steps.

These are just the two largest components of tides, there are many complex contributing factors: M2: principal semi-diurnal lunar, S2: principal semi-diurnal solar, N2: larger semi-diurnal elliptical lunar, K2: declinational semi-diurnal solar/lunar, 2N2: second-order semi-diurnal elliptical lunar, K1: principal diurnal solar/lunar, O1: principal lunar, P1: principal diurnal solar, Q1: larger diurnal elliptical lunar. Each of these components has similarly beautiful patterns of movement.

Software used:
ImageJ: HAMTIDE tital data plotting.

Tuesday, 8 July 2014

3D Wood Grain

Using a block of wood and a plane Keith Skretch made something amazing. He snapped a picture of the wood, then planed a thin layer off, snapped another picture, planed another layer off, and repeated this hundreds of times. In the resulting timelapse/stop motion video you fly through the wood structure, and can see knots and grain in the wood ripple by.

Waves of Grain from Keith Skretch on Vimeo
To my computational image analysis eyes, the truly amazing thing about this video is contains the detailed three dimensional map of the internal structure of blocks of wood; that these blocks of wood have been digitally immortalised!
Let's look at just one of the blocks of wood:
 The series of images 29-36 seconds through Waves of Grain

So what can you do with this data? Well you can reproject to give you a virtual view of what the left and the front sides of the blocks of wood would have looked like:

That's quite cool, but doesn't capture the power of having the full 3D information. The more powerful thing you can do is do a virtual cuts through anywhere you want in the block of wood. You can cut it somewhere in the middle to take a look at the internal structure... The yellow lines mark where the virtual slices were made:
That's also quite cool, but still doesn't capture the power of having all that 3D data. You can also reslice the image at any orientation that you want; it doesn't have to be neat orthogonal lines:

Again, quite cool. But you can still do more. Because this is now a purely digital representation of this block of wood you can display it in ways that would be physically impossible to make. Instead of just looking at the outside of the block...

... you can now look inside.

This 3D reconstruction lets you see how the growth rings appear in three dimensions, showing exactly where the grain runs. It lets you see how the knot, which is where a branch grew from the tree, cuts through the growth rings in a distinctive way. It lets you see pretty much everything about the internal structure of the wood!

This kind of approach is used all over biology, and is normally called something like serial sectioning. You can use it for everything from reconstructing a whole person by histology and a light microscope to a single cell by electron microscopy.

Software used:
ImageJ: 3D reconstructions

Thursday, 3 July 2014

3D Lightning 2

About a year ago two redditors happened to take a photo of the same lightning bolt, but from different places, and I use them to make a 3D reconstruction: 3D Lightning.

Well, it happened again!
The two source images.

This time the lightning bolt struck one World Trade Center (Freedom Tower), and two people got a shot of it from over the river. A little adjustment for the rotation of the image and some guestimation of their approximate locations let me work out that there was very little vertical shift between their locations, but quite a large horizontal shift.

Just like last time, a 100% accurate reconstruction isn't possible. You need to know the exact locations and elevations of the people, and field of view of the cameras used, to do this precisely. However, just like last time, a rough reconstruction is possible where the difference in horizontal position of part of the lightning bolt between the two images is proportional to the distance from the people taking the photos.

The approximate 3D reconstruction.

After grabbing the coordinates from the photos it was just a matter of plugging them into Blender to make an approximate 3D reconstruction.

Software used:
ImageJ: Image analysis.
Blender: 3D modelling and rendering.

Sunday, 8 June 2014


Many classic games like Transport TycoonRollercoaster Tycoon and Theme Hospital have pixel art graphics using a limited number of colours. These graphics are tricky to draw and take a lot of skill, especially when trying to draw accurate 3D shapes from different angles and getting the perspective and shading right.

So I made PixelTool to help out!

What is PixelTool?

PixelTool is an online voxel-based tool for drawing isometric pixel art graphics. To use it you modify a 3D volume of voxels; picking 8-bit colours for each of the voxels and leaving the background as the 'magic blue' which is transparent.

It takes the voxel data and does a pixel-perfect rendering of it into 3D and adding lighting and shadowing, but still sticking to the starting 8-bit colour palette.

Slices through the voxel data of a piece of heavy hauling equipment for OpenTTD

The corresponding rendered image of the voxel block.

Blowing up the voxels in the rendered image by 4 times lets you see what is going on in a bit more detail:

PixelTool isn't just a cheap imitation of 3D rendering software, it is a dedicated tool streamlined to making isometric sprites for classic 8-bit games.

Want to play some more?
Test PixelTool out online here:
Grab the source HTML/javascript code here:
Download this example of voxel data here:
Join the discussion here:

Tuesday, 20 May 2014

Jurassic Wedding

You will have seen the instant internet classic of a dinosaur crashing a wedding... I got married this year and just had to do the same. Fortunately my wife agreed! I am a biochemist, but cloning a dinosaur to crash my wedding would have been a bit of a challenge, so I had to stick to the graphics approach instead.

So how do you get a dinosaur to crash your wedding?

Step 1: Recruit an understanding wedding photographer and guests for a quick running photoshoot. Make sure everyone is screaming and staring at something imaginary!

Step 2: Recruit a dinosaur. A virtual one will do, and I used this excellent freely available Tyrannosaurus rex model for blender.

Step 3: Get some dynamic posing going on! Most 3D graphics software uses a system called 'rigging' to add bones to a 3D model to make it poseable. This is exactly what I did, and with 17 bones (three for each leg, seven for the tail, two for the body and neck and two for the head and jaw) I made our pet T. rex poseable.

 The bone system

The posed result

Step 4: Get the T. rex into the scene. By grabbing the EXIF data from the running photo I found that it was shot with a 70mm focal length lens. By setting up a matching camera in blender and tweaking its position I made the camera position match perspective between the view of the T. rex and the running people.

Step 5: Making the dino look good. A 3D model is just a mesh of points in 3D space. To get it looking good texturing and lighting need to be added. For this project they also need to match the photo. Matching the lighting is particularly important, and I used Google maps and the time the photo was taken to match up where the sun was as accurately as possible.

The T. rex wireframe

Textured with a flat grey texture.

With a detail bump texture and accurate lighting.

With colours, detail texture and lighting.

 Step 6: Layering it all together. To fit into the scene the dinosaur must sit into the picture in 3D; in front of some object and behind others. To do this I just made a copy of some of the guests which need to sit in front of the dinosaur and carefully cut around them. The final result is then just layering the pictures together.

So there you go! 6 steps to make your own wedding dinosaur disaster photo!

Software used:
Blender: 3D modelling and rendering.
Paint.NET: Final layering of the image.

Monday, 28 April 2014

A Year in The Life of a Computer

What does a year in the life of a computer look like?

Well, something like the map below! This is a map every bit of of mouse movement, every mouse click and every keyboard press I have done on my home and work computer over every day of a whole year.

2013-2014 [click for a bigger view]

To make it I wrote a little python script using pyHook to grab inputs in Windows, which I compiled to an .exe using py2exe. I set this up so that it starts recording the mouse movement, clicks, and keyboard presses after I log into my home or work computer. After 2 years it had collected nearly 10 Gb of data! This was far too much to look through by hand, so I wrote a second set of scripts to plot it to an image.

So what does it all mean? Well the map breaks down a bit like a normal calendar, with days of the week running from the top to the bottom of the map, and successive weeks running from left to right. The years and months are marked at the top of the map.

Within each day my computer activity is broken down by time. Time runs from the top to the bottom of each day, from midnight to midnight. Coloured speckles on the dark background indicate computer activity. It is easy to see that I use computers a lot, with a chunk of time from around midnight to 7 am when I am normally asleep, then smatterings of activity from around 8 am to midnight when I am at work or awake at home.

Different types of computer activity are shown in different colours.

The structure within each of the colours also contains information; distance in the horizontal direction corresponds to horizontal mouse position across my two screens (for mouse movement) which mouse button was clicked (for mouse clicks) and which key was pressed (for keyboard presses).

2012-2013 [click for a bigger view]

In these maps of usage some interesting structures jump out; you can spot the type of work I was doing with my computer based on the type of mouse and keyboard activity:

This is usage on a day where I was writing my PhD thesis. The keyboard (cyan) has loads of activity, while the mouse (magenta) did relatively little.

This is a day where I was mainly using Blender for 3D graphics. The mouse (magenta) has huge levels of activity, centred on just the left hand screen). The keyboard is hardly active except for the control and shift keys, which light up as a single column of bright cyan pixels.

It is quite scary how much information can be gleaned from these maps of computer activity. Without knowing which programs were open or which keyboard keys were being pressed it is still easy to work out where I have been, when I have been working, and the kind of things I was doing on my computer. Similar data can be collected remotely; particularly if an internet company tracks when and where you use the internet.

Stop for a second and think about the companies you interact with, and the data mining they can do. Think how much they can learn about you and your habits; Google and the websites you visit, your phone company and when and who you text and call, the supermarket you shop in and what you buy. These companies can work out what you are interested in, what you like and dislike, when you are awake and when you are asleep. This is big data, and it is valuable and it is powerful. Big data is how Target knew a man's teenage daughter was pregnant before he did!

Software used:
pyHook and py2exe: Data logging.
ImageJ: Data plotting.
Inkscape: Plot annotation.

Thursday, 17 April 2014

Tree of Plants

Everyone knows what plants are like; they have leaves and roots, flowers and seeds. Or do they? All of these classic features of plants are actually relatively recent developments in plant evolution. Conifers don't have flowers, ferns don't have seeds or flowers and moss doesn't have leaves, roots, seeds or flowers! Leaves, roots, flowers and seeds are all features that evolved as plants adapted, starting at something like seaweed, to life on the land.

This term's issue of Phenotype has a bit of a focus on plants, and my research comic for this issue focuses on how plants evolved and adapted to land. You can download a pdf of this feature here, the full issue for the summer (Trinity) term will be available soon here.

While I was making this I started reconsidering just what the plant life cycle looks like, as a classic school education about how plants reproduce isn't very accurate! The classic teaching is that the pollen produced by a flower is like sperm in mammals (and humans), and the ovum in the flower is like the egg in mammals. In fact pollen and the developing seed are more like small haploid multicellular organisms, gametophytes, that used to be free living. If you go back through evolutionary time towards ferns then the gametophyte is a truly independent multicellular organism. Go back further still and the bryophytes spend most of their time as the gametophyte.

If you imagine the same evolutionary history for humans then it is easy to see how different this life cycle is to animals; if the ancestors of humans had a life cycle similar to ferns then, roughly speaking, ovaries and testicles would be free-living organisms that sprout a full grown human once fertilisation successfully occurs. I can't help but think that would have been a little strange!

Software used:
Autodesk Sketchbook Pro: Drawing the cells.
Inkscape: Page layout.

Thursday, 10 April 2014

Cells and Worms - 2. The Shirt

Last post I talked about how seeing how many worms overlap if you drop them on a patch of ground, how (somehow) this was vaguely related to my scientific research, and that the simulation of this process even generates quite nice pictures. If you thought that was geeky, then this takes geekyness to a whole new level!

Part of my research has been into the shapes of trypanosome parasites. Trypanosomes that cause disease in people are fairly widely known (you might have heard of sleeping sickness, Chagas disease, or leishmaniasis) but trypanosomes don't just infect people. Trypanosome species have also been found infecting animals from sharks to penguins, crocodiles to elephants. There is even one species named after Steve Irwin (the crocodile hunter) that infects koalas!

A scanning electron microscope image of Trypanosoma brucei, the trypanosome which causes sleeping sickness.

In short, I did some research to test whether there were particular characteristic shapes of trypanosomes (length, width, etc.) that look like they might help the parasite survive in the bloodstream of different host animals. I made a big database of properties of trypanosome shape and, using the scripts I made to draw nicely tesselated trypanosome shapes I talked about in the last post, I put together a compelling summary of just how varied trypanosome shapes from different host species are are:

The science behind this picture suggests some interesting adaptation to help the parasites swim within their host bloodstream, but that's enough about the science. To me this pattern was just begging to be on a shirt, an abstract design with a biological twist!

Spoonflower is a fantastic online service where you can order customised fabric, wallpaper and other prints. So that is exactly what I did, and after some sewing (that I didn't do myself) I am now the proud owner of the world's only 100% scientifically accurate trypanosome shirt, featuring 27 different trypanosome species.

Scientists always say that research can take you down unexpected paths. This path from wriggly worms, through an image generating script, through research into trypanosome shape, to the world's only trypanosome shirt was quite an unexpected one!

Software used:
ImageJ: Automated trypanosome drawing.
Inkscape: Conversion to vector graphics for printing.

Wednesday, 9 April 2014

Cells and Worms - 1. The Theory

If you scatter 100 worms on a patch of soil 1 meter by 1 meter how many worms will fall on top of another worm? This might seem like a really pointless question, but it is surprisingly relevant to biological research using microscopes. It's also a surprisingly hard question to answer because worms are very wriggly! However, even this dry, theoretical, research problem provides the tools for making fun illustrations...

My work involves a lot of automated image analysis; taking a picture from a microscope and automatically analysing it to extract scientific data. To make sure an automated analysis is reliable you have to think about all the likely problems that might turn up, and with cells and microscopes a common problem is when two cells are lying on top of each other. The problems this causes are easy to imagine; if there are two cells with one nucleus lying on top of each other then it might look like one cell with two nuclei.

For some types of cells it is quite easy to work out how likely two are to touch or lie partly on top of each other when they are scattered randomly over a microscope slide. An example of an easy case is where all cells are circular and the same size; the approximate calculation is quite simple. Unfortunately the cells I work on are more worm-like in shape, about 17 microns long and 2 wide... if you scatter these cells over a slide how many will end up touching?

To work out the answer simulation is vital; the maths is just too complicated to do it analytically. A simulation of worm-like shapes proved to be quite simple:
  1. Pick a random starting point, direction and curvature.
  2. Start drawing a curved line from that point.
  3. Occasionally re-randomise the curvature.
  4. Stop once you have reached the length of the cell.
  5. Draw the profile of the cell shape along that curve.
Following these simple rules and tweaking the parameters (e.g. the minimum and maximum curvature, frequency of randomising curvature, etc.) gives a simple algorithm for drawing a worm-like shape. With a bit of tweaking it could draw cells that look like trypanosomes. Using this drawing tool it was possible to measure the chance of a cell touching or lying on top of another cell already on the microscope slide. Just repeat the drawing process thousands of times and detect whether the newly drawn cell intersects with any previously drawn ones. Problem solved.

This process gave me the answer I needed, but it also provided a tool for drawing trypanosome-like shapes. Better than that, it was easy to adapt it to make sure no two cells overlapped and they fitted neatly together over the image... And just like that a dry, theoretical, research problem turned into a beautiful image:

This was also easy to adapt to other worm-like shapes, like earthworms:

Software used:

Thursday, 3 April 2014


Human cheek cells are a classic subject of school microscopy. It is easy to collect some by gently scraping the inside of your cheek. This is a high resolution phase contrast image of one of my cheek cells, put together using focus stacking of a 4 by 4 montage of 57 focus slices using one of my ImageJ macros. The detail of the nuclear structure, the granular contents of the cytoplasm and the structured surface of the cell really jump out.

This cell is quite large for mammalian cells, about 75 μm across, and around 10 times larger than the single cell Leishmania parasite I currently do much of my research on. If you have sharp eyesight you can even see human cheek cells by eye (although only just) when they are spread on a slide.

Like most mammalian cells, cheek cells are essentially transparent. If you use a microscope in the most basic way, essentially as a giant magnifying glass, shining light straight through the sample towards your eye, you see something like this:

Bright field micrograph of a human cheek cell.

This picture has even had the contrast artificially enhanced. Practically it is tough to even find the cells on the slide and get them in focus!

For many years the best alternative was oblique or dark field microscopy. Here you deliberately avoid shining light straight through the sample, and instead make sure that only light scattered by structures in the sample can collected by the objective lens and get up to your eye.

Dark field micrograph of a human cheek cell.

Images by dark field microscopy can be hard to interpret, and are typically limited to fairly low resolution.

More complex methods based on interference of light travelling through the sample were developed in the 20th Century. These methods, phase contrast and differential interference contrast, were a revolution. They allowed completely new approaches for looking at the biology of cells, particularly live cells and dynamic processes like cell division. They were such a revolution that the inventor of phase contrast microscopy, Frits Zernik, was awarded the Nobel prize in Physics in 1953 for this work.

Phase contrast micrograph of a human cheek cell.

DIC micrograph of a human cheek cell.

It was not until the development of the famous green fluorescence protein, for fluorescence microscopy in live cells in the 1990s, that there was another discovery which improved the capacity for live cell microscopy to the same extent as phase contrast and DIC.

Software used: