## Cloud Eleven: A cloud-based code sharing solution for IgorPro

This post is something of a “how to” guide. The problem is how can you share code with a small team and keep it up-to-date?

For ImageJ, the solution is simple. You can make an ImageJ update site and then push any updated code to the user when they startup ImageJ. For IgorPro, there is no equivalent. Typically I send ipf files to someone and they run the code, but I have to resend them whenever there’s an update. This can cause confusion over which is the latest version.

I’ve tried a bunch of things such as versioning the code (this at least tells you if the person is running an out-of-date version). I also put the code up on GitHub and tell people to pull down the latest version, but again this doesn’t work well. The topic of how to share code comes up perennially on the Igor mailing list and on IgorExchange, so it’s clearly something that people struggle with. I’ve found the solutions offered to be a bit daunting.

My solution is detailed here with some code to make it run.

The details

It is possible to use aliases (shortcuts on Windows) in the User’s WaveMetrics folders to point to external files. Items in the Igor Procedures directory get loaded when you start Igor. Items in User Procedures can get loaded optionally. These aliases sit in the Users Wavemetrics folder (the exact location depends on the Igor Version) and this means that the program itself can get updated without overwriting these files.

So, if aliases are created here that point to a cloud-based repo of Igor Code it can be used to:

1. Optionally load code as the user needs it. Because the code sits in the cloud it can be updated and instantly used by everyone.
2. Force Igor to load a bit of code to make a little menu item so that the user can pick the code they want to load.

To get this working, I made use of Ryotako’s excellent menu loader which was written for loading the “hidden” WaveMetrics procedures. I created a version like that one which makes a menu of our shared code in alphabetical order. I then made a version that allows the code to be grouped by purpose. People in my group told me they prefer this version. Code just needs to be organised in folders for it to work.

The IgorDistro.ipf just needs to be placed in a folder called IP somewhere in a share that users have access to (unless people are contributing to code development, you can make the share read-only). In the same directory that IP sits in, place a folder called UP with all of your code organised into folders. Aliases need to be created that point Igor Procedures folder to IP and User Procedures to UP. That’s it! I’ve tested it on IP7 and IP8, on Windows and Mac, using a shared Dropbox as the cloud repo.

Obviously, the correct IgorPro licences need to be in place to share the code with multiple users.

The post title comes from the pseudonymous LP by Cloud Eleven. A great debut album. The track “Wish I” is worth the price of the record alone (assuming people still buy records).

## Turn That Heartbeat Over Again: comparing wrist and chest-strap HRM

As a geek, the added bonus of exercise is the fun that you can have with the data you’ve generated. A recent conversation on Twitter about the accuracy of wrist-based HRMs got me thinking… how does a wrist-based HRM compare with a traditional chest-strap HRM? Conventional wisdom says that the chest-strap is more accurate, but my own experience of chest-strap HRMs is that they are a bit unreliable. Time to put it to the test.

I have a Garmin Fēnix 5 which records wrist-based HR and I have a Garmin chest-strap which uses ANT+ to transmit. I could pick up the ANT+ signal with a Garmin Edge 800 so that I could record both datasets simultaneously, on the same bike ride. Both the Fēnix and Edge can record GPS and Time (obviously) allowing accurate registration of the data. I also set both devices to receive cadence data via ANT+ from the same cadence/speed sensor so that I could control for (or at least look at) variability in recordings. I rode for a ~1 hr ~32 km to capture enough data. Obviously this is just one trial but the data gives a feel for the accuracy of the two HRMs. Biking, I figured was a fair activity since upper body and wrist movement is minimal, meaning that the contacts for both HRMs are more likely to stay in place than if I was running.

I’ll get to heart rate last. First, you can see that the GPS recording is virtually identical between the two units – so that’s a good sign. Second, elevation is pretty similar too. There’s a bit of divergence at the beginning and end of the ride, since those parts are over the same stretch of road, neither device looks totally accurate. I’d be inclined to trust the Fēnix here since has a newer altimeter in it. Third, cadence looks accurate. Remember this data is coming off the same sensor and so any divergence is in how it’s being logged. Finally, heart rate. You can see that at the beginning of the ride, both wrist-based and chest-strap HRMs struggle. Obviously I have no reference here to know which trace is correct, but the chest-strap recording looks like it has an erroneous low reading for several minutes but is otherwise normal. The wrist-based HRM looks like it is reading 120ish bpm values from the start and then snaps into reading the right thing after a while. The chest-strap makes best contact when some perspiration has built up, which might explain its readings. The comparisons are shown below in grey. The correlation is OK but not great. Compared to cadence, the data diverge a lot more which rules out simple logging differences as a cause.

I found a different story in Smart recording mode on the Fēnix. This is a lower frequency recording mode, which is recommended to preserve battery life for long activities.

So what can we see here? Well, the data from the Fēnix are more patchy but even so, the data are pretty similar except for heart rate. The Fēnix performs badly here. Again, you can see the drop out of the chest strap HRM for a few minutes at the start, but otherwise it seems pretty reliable.  The comparison graph for heart rate shows how poorly the wrist-based HRM measures heart rate, in this mode.

OK, this is just two rides, for one person – not exactly conclusive but it gives some idea about the data that are captured with each HRM.

Conclusions

Wrist-based HRM is pretty good (at the higher sampling rate only) especially considering how the technology works, plus chest-strap HRMs can be uncomfortable to wear, so wrist-based HRM may be all you need. If you are training to heart rate zones, or want the best data, chest-strap HRM is more reliable than wrist-based HRM generally. Neither are very good for very short activities (<15 min).

For nerds only

Comparisons like these are quite easy to do in desktop packages like Rubitrack (which I love) or Ascent or others. They tend to mask things like missing data points, they smooth out the data and getting the plots the way you want is not straightforward. So, I took the original FIT files from each unit and used these for processing. There’s a great package for R called cycleRtools. This worked great except for the smart recording data which was sampled irregularly and it turns out and the package requires monotonic sampling. I generated a gpx file and parsed the data for this activity in R using XML. I found this snippet on the web (modified slightly).

library(XML)
library(plyr)
filename <- "myfile.gpx"
gpx.raw <- xmlTreeParse(filename, useInternalNodes = TRUE)
rootNode <- xmlRoot(gpx.raw)
gpx.rawlist <- xmlToList(rootNode)trk gpx.list <- unlist(gpx.rawlist[names(gpx.rawlist) == "trkseg"], recursive = FALSE) gpx <- do.call(rbind.fill, lapply(gpx.list, function(x) as.data.frame(t(unlist(x)), stringsAsFactors=F))) names(gpx) <- c("ele", "time", "temp", "hr", "cad", "lat", "lon") Otherwise: library(cycleRtools) edge <- as.data.frame(read_fit(file = file.choose(), format = TRUE, CP = NULL, sRPE = NULL)) write.csv(edge, file = "edge.csv", row.names = F) The resulting data frames could be saved out as csv and read into Igor to make the plots. I wrote a quick function in Igor to resample all datasets at 1 Hz. The plots and layout were generated by hand. The post title comes from “Turn That Heartbeat Over Again” by Steely Dan from Can’t Buy A Thrill ## Adventures in Code VI: debugging and silly mistakes This deserved a bit of further explanation, due to the stupidity involved. “Debugging is like being the detective in a crime movie where you are also the murderer.” – Filipe Fortes My code was giving an unexpected result and I was having a hard time figuring out the problem. The unexpected result was that a resampled set of 2D coordinates were not being rotated randomly. I was fortunate to be able to see this otherwise I would have never found this bug and probably would’ve propagated the error to other code projects. I narrowed down the cause but ended up having to write some short code to check that it really did cause the error. I was making a rotation matrix and then using it to rotate a 2D coordinate set by matrix multiplication. The angle was randomised in the loop. What could go wrong? I looked at that this: theta = pi*enoise(1) rotMat = {{cos(theta),-sin(theta)},{sin(theta),cos(theta)}} and thought “two lines – pah – it can be done in one!”. Since the rotation matrix is four numbers [-1,1], I thought “I’ll just pick those numbers at random, I just want a random angle don’t I?” rotMat = enoise(1) Why doesn’t an alarm go off when this happens? A flashing sign saying “are you sure about that?”… My checks showed that a single point at 1,0 after matrix multiplication with this method gives. When it should give And it’s so obvious when you’ve seen why. The four numbers in the rotation matrix are, of course, not independent. I won’t make that mistake again and I’m going to try to think twice when trying to save a line of code like in the future! Part of a series on computers and coding. ## 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. 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. PCA/ALL/SEVC/SRMT/SCMT xCoord,yCoord 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! ## Parallel lines: new paper on modelling mitotic microtubules in 3D We have a new paper out! You can access it here. The people This paper really was a team effort. Faye Nixon and Tom Honnor are joint-first authors. Faye did most of the experimental work in the final months of her PhD and Tom came up with the idea for the mathematical modelling and helped to rewrite our analysis method in R. Other people helped in lots of ways. George did extra segmentation, rendering and movie making. Nick helped during the revisions of the paper. Ali helped to image samples… the list is quite long. The paper in a nutshell We used a 3D imaging technique called SBF-SEM to see microtubules in dividing cells, then used computers to describe their organisation. What’s SBF-SEM? Serial block face scanning electron microscopy. This method allows us to take an image of a cell and then remove a tiny slice, take another image and so on. We then have a pile of images which covers the entire cell. Next we need to put them back together and make some sense of them. How do you do that? We use a computer to track where all the microtubules are in the cell. In dividing cells – in mitosis – the microtubules are in the form of a mitotic spindle. This is a machine that the cell builds to share the chromosomes to the two new cells. It’s very important that this process goes right. If it fails, mistakes can lead to diseases such as cancer. Before we started, it wasn’t known whether SBF-SEM had the power to see microtubules, but we show in this paper that it is possible. We can see lots of other cool things inside the cell too like chromosomes, kinetochores, mitochondria, membranes. We made many interesting observations in the paper, although the focus was on the microtubules. So you can see all the microtubules, what’s interesting about that? The interesting thing is that our resolution is really good, and is at a large scale. This means we can determine the direction of all the microtubules in the spindle and use this for understanding how well the microtubules are organised. Previous work had suggested that proteins whose expression is altered in cancer cause changes in the organisation of spindle microtubules. Our computational methods allowed us to test these ideas for the first time. Resolution at a large scale, what does that mean? The spindle is made of thousands of microtubules. With a normal light microscope, we can see the spindle but we can’t tell individual microtubules apart. There are improvements in light microscopy (called super-resolution) but even with those improvements, right in the body of the spindle it is still not possible to resolve individual microtubules. SBF-SEM can do this. It doesn’t have the best resolution available though. A method called Electron Tomography has much higher resolution. However, to image microtubules at this large scale (meaning for one whole spindle), it would take months or years of effort! SBF-SEM takes a few hours. Our resolution is better than light microscopy, worse than electron tomography, but because we can see the whole spindle and image more samples, it has huge benefits. What mathematical modelling did you do? Cells are beautiful things but they are far from perfect. The microtubules in a mitotic spindle follow a pattern, but don’t do so exactly. So what we did was to create a “virtual spindle” where each microtubule had been made perfect. It was a bit like “photoshopping” the cell. Instead of straightening the noses of actresses, we corrected the path of every microtubule. How much photoshopping was needed told us how imperfect the microtubule’s direction was. This measure – which was a readout of microtubule “wonkiness” – could be done on thousands of microtubules and tell us whether cancer-associated proteins really cause the microtubules to lose organisation. The publication process The paper is published in Journal of Cell Science and it was a great experience. Last November, we put up a preprint on this work and left it up for a few weeks. We got some great feedback and modified the paper a bit before submitting it to a journal. One reviewer gave us a long list of useful comments that we needed to address. However, the other two reviewers didn’t think our paper was a big enough breakthrough for that journal. Our paper was rejected*. This can happen sometimes and it is frustrating as an author because it is difficult for anybody to judge which papers will go on to make an impact and which ones won’t. One of the two reviewers thought that because the resolution of SBF-SEM is lower than electron tomography, our paper was not good enough. The other one thought that because SBF-SEM will not surpass light microscopy as an imaging method (really!**) and because EM cannot be done live (the cells have to be fixed), it was not enough of a breakthrough. As I explained above, the power is that SBF-SEM is between these two methods. Somehow, the referees weren’t convinced. We did some more work, revised the paper, and sent it to J Cell Sci. J Cell Sci is a great journal which is published by Company of Biologists, a not-for-profit organisation who put a lot of money back into cell biology in the UK. They are preprint friendly, they allow the submission of papers in any format, and most importantly, they have a fast-track*** option. This allowed me to send on the reviews we had and including our response to them. They sent the paper back to the reviewer who had a list of useful comments and they were happy with the changes we made. It was accepted just 18 days after we sent it in and it was online 8 days later. I’m really pleased with the whole publishing experience with J Cell Sci. * I’m writing about this because we all have papers rejected. There’s no shame in that at all. Moreover, it’s obvious from the dates on the preprint and on the JCS paper that our manuscript was rejected from another journal first. ** Anyone who knows something about microscopy will find this amusing and/or ridiculous. *** Fast-track is offered by lots of journals nowadays. It allows authors to send in a paper that has been reviewed elsewhere with the peer review file. How the paper has been revised in light of those comments is assessed by at the Editor and one peer reviewer. Parallel lines is of course the title of the seminal Blondie LP. I have used this title before for a blog post, but it matches the topic so well. ## Adventures in Code V: making a map of Igor functions I’ve generated a lot of code for IgorPro. Keeping track of it all has got easier since I started using GitHub – even so – I have found myself writing something only to discover that I had previously written the same thing. I was thinking that it would be good to make a list of all functions that I’ve written to locate long lost functions. This question was brought up on the Igor mailing list a while back and there are several solutions – especially if you want to look at dependencies. However, this two liner works to generate a file called funcfile.txt which contains a list of functions and the ipf file that they are appear in. grep "^[ \t]*Function" *.ipf | grep -oE '[ \t]+[A-Za-z_0-9]+\(' | tr -d " " | tr -d "(" > output for i in cat output; do grep -ie "i" *.ipf | grep -w "Function" >> funcfile.txt ; done

Thanks to Thomas Braun on the mailing list for the idea. I have converted it to work on grep (BSD grep) 2.5.1-FreeBSD which runs on macOS. Use the terminal, cd to the directory containing your ipf files and run it. Enjoy!

EDIT: I did a bit more work on this idea and it has now expanded to its own repo. Briefly, funcfile.txt is converted to tsv and then parsed – using Igor – to json. This can be displayed using some d3.js magic.

Part of a series with code snippets and tips.

## Realm of Chaos

Caution: this post is for nerds only.

I watched this numberphile video last night and was fascinated by the point pattern that was created in it. I thought I would quickly program my own version to recreate it and then look at patterns made by more points.

I didn’t realise until afterwards that there is actually a web version of the program used in the video here. It is a bit limited though so my code was still worthwhile.

A fractal triangular pattern can be created by:

1. Setting three points
2. Picking a randomly placed seed point
3. Rolling a die and going halfway towards the result
4. Repeat last step

If the first three points are randomly placed the pattern is skewed, so I added the ability to generate an equilateral triangle. Here is the result.

and here are the results of a triangle through to a decagon.

All of these are generated with one million points using alpha=0.25. The triangle, pentagon and hexagon make nice patterns but the square and polygons with more than six points make pretty uninteresting patterns.

Watching the creation of the point pattern from a triangular set is quite fun. This is 30000 points with a frame every 10 points.

Here is the code.

Some other notes: this version runs in IgorPro. In my version, the seed is set at the centre of the image rather than a random location. I used the random allocation of points rather than a six-sided dice.

The post title is taken from the title track from Bolt Thrower’s “Realm of Chaos”.

## Elevation: accuracy of a Garmin Edge 800 GPS device

I use a Garmin 800 GPS device to log my cycling activity. including my commutes. Since I have now built up nearly 4 years of cycling the same route, I had a good dataset to look at how accurate the device is.

I wrote some code to import all of the rides tagged with commute in rubiTrack 4 Pro (technical details are below). These tracks needed categorising so that they could be compared. Then I plotted them out as a gizmo in Igor Pro and compared them to a reference data set which I obtained via GPS Visualiser.

The reference dataset is black. Showing the “true” elevation at those particular latitude and longitude coordinates. Plotted on to that are the commute tracks coloured red-white-blue according to longitude. You can see that there are a range of elevations recorded by the device, apart from a few outliers they are mostly accurate but offset. This is strange because I have the elevation of the start and end points saved in the device and I thought it changed the altitude it was measuring to these elevation positions when recording the track, obviously not.

To look at the error in the device I plotted out the difference in the measured altitude at a given location versus the true elevation. For each route (to and from work) a histogram of elevation differences is shown to the right. The average difference is 8 m for the commute in and 4 m for the commute back. This is quite a lot considering that all of this is only ~100 m above sea level. The standard deviation is 43 m for the commute in and 26 m for the way back.

This post at VeloViewer comparing GPS data on Strava from pro-cyclists riding the St15 of 2015 Giro d’Italia sprang to mind. Some GPS devices performed OK, whereas others (including Garmin) did less well. The idea in that post is that rain affects the recording of some units. This could be true and although I live in a rainy country, I doubt it can account for the inaccuracies recorded here. Bear in mind that that stage was over some big changes in altitude and my recordings, very little. On the other hand, there are very few tracks in that post whereas there is lots of data here.

It’s interesting that the data is worse going in to work than coming back. I do set off quite early in the morning and it is colder etc first thing which might mean the unit doesn’t behave as well for the commute to work. Both to and from work tracks vary most in lat/lon recordings at the start of the track which suggests that the unit is slow to get an exact location – something every Garmin user can attest to. Although I always wait until it has a fix before setting off. The final two plots show what the beginning of the return from work looks like for location accuracy (travelling east to west) compared to a midway section of the same commute (right). This might mean the the inaccuracy at the start determines how inaccurate the track is. As I mentioned, the elevation is set for start and end points. Perhaps if the lat/lon is too far from the endpoint it fails to collect the correct elevation.

Conclusion

I’m disappointed with the accuracy of the device. However, I have no idea whether other GPS units (including phones) would outperform the Garmin Edge 800 or even if later Garmin models are better. This is a good but limited dataset. A similar analysis would be possible on a huge dataset (e.g. all strava data) which would reveal the best and worst GPS devices and/or the best conditions for recording the most accurate data.

Technical details

I described how to get GPX tracks from rubiTrack 4 Pro into Igor and how to crunch them in a previous post. I modified the code to get elevation data out from the cycling tracks and generally made the code slightly more robust. This left me with 1,200 tracks. My commutes are varied. I frequently go from A to C via B and from C to A via D which is a loop (this is what is shown here). But I also go A to C via D, C to A via B and then I also often extend the commute to include 30 km of Warwickshire countryside. The tracks could be categorized by testing whether they began at A or C (this rejected some partial routes) and then testing whether they passed through B or D. These could then be plotted and checked visually for any routes which went off course, there were none. The key here is to pick the right B and D points. To calculate the differences in elevation, the simplest thing was to get GPS Visualiser to tell me what the elevation should be for all the points I had. I was surprised that the API could do half a million points without complaining. This was sufficient to do the rest. Note that the comparisons needed to be done as lat/lon versus elevation because due to differences in speed, time or trackpoint number lead to inherent differences in lat/lon (and elevation). Note also due to the small scale I didn’t bother converting lat/lon into flat earth kilometres.

The post title comes from “Elevation” by Television, which can be found on the classic “Marquee Moon” LP.

## Colours Running Out: Analysis of 2016 running

Towards the end of 2015, I started distance running. I thought it’d be fun to look at the frequency of my runs over the course of 2016.

Most of my runs were recorded with a GPS watch. I log my cycling data using Rubitrack, so I just added my running data to this. This software is great but to do any serious number crunching, other software is needed. Yes, I know that if I used strava I can do lots of things with my data… but I don’t. I also know that there are tools for R to do this, but I wrote something in Igor instead. The GitHub repo is here. There’s a technical description below, as well as some random thoughts on running (and cycling).

The animation shows the tracks I recorded as 2016 rolled by. The routes won’t mean much to you, but I can recognise most of them. You can see how I built up the distance to run a marathon and then how the runs became less frequent through late summer to October. I logged 975 km with probably another 50 km or so not logged.

Technical description

To pull the data out of rubiTrack 4 Pro is actually quite difficult since there is no automated export. An applescript did the job of going through all the run activities and exporting them as gpx. There is an API provided by Garmin to take the data straight from the FIT files recorded by the watch, but everything is saved and tagged in rubiTrack, so gpx is a good starting point. GPX is an xml format which can be read into Igor using XMLutils XOP written by andyfaff. Previously, I’ve used nokogiri for reading XML, but this XOP keeps everything within Igor. This worked OK, but I had some trouble with namespaces which I didn’t resolve properly and what is in the code is a slight hack. I wrote some code which imported all the files and then processed the time frame I wanted to look at. It basically looks at a.m. and p.m. for each day in the timeframe. Igor deals with date/time nicely and so this was quite easy. Two lookups per day were needed because I often went for two runs per day (run commuting). I set the lat/lon at the start of each track as 0,0. I used the new alpha tools in IP7 to fade the tracks so that they decay away over time. They disappear with 1/8 reduction in opacity over a four day period. Igor writes out to mov which worked really nicely, but wordpress can’t host movies, so I added a line to write out TIFFs of each frame of the animation and assembled a nice gif using FIJI.

Getting started with running

Getting into running was almost accidental. I am a committed cyclist and had always been of the opinion: since running doesn’t improve aerobic cycling performance (only cycling does that), any activity other than cycling is a waste of time. However, I realised that finding time for cycling was getting more difficult and also my goal is to keep fit and not to actually be a pro-cyclist, so running had to be worth a try. Roughly speaking, running is about three times more time efficient compared to cycling. One hour of running approximates to three hours of cycling. I thought, I would just try it. Over the winter. No more than that. Of course, I soon got the running bug and ran through most of 2016. Taking part in a few running events (marathon, half marathons, 10K). A quick four notes on my experience.

1. The key thing to keeping running is staying healthy and uninjured. That means building up distance and frequency of running very slowly. In fact, the limitation to running is the body’s ability to actually do the distance. In cycling this is different, as long as you fuel adequately and you’re reasonably fit, you could cycle all day if you wanted. This not true of running, and so, building up to doing longer distances is essential and the ramp up shouldn’t be rushed. Injuries will cost you lost weeks on a training schedule.
2. There’s lots of things “people don’t tell you” about running. Blisters and things everyone knows about, but losing a toenail during a 20 km run? Encountering runner’s GI problems? There’s lots of surprises as you start out. Joining a club or reading running forums probably helps (I didn’t bother!). In case you are wondering, the respective answers are getting decent shoes fitted and well, there is no cure.
3. Going from cycling to running meant going from very little upper body mass to gaining extra muscle. This means gaining weight. This is something of a shock to a cyclist and seems counterintuitive, since more activity should really equate to weight loss. I maintained cycling through the year, but was not expecting a gain of ~3 kilos.
4. As with any sport, having something to aim for is essential. Training for training’s sake can become pointless, so line up something to shoot for. Sign up for an event or at least have an achievement (distance, average speed) in your mind that you want to achieve.

So there you have it. I’ll probably continue to mix running with cycling in 2017. I’ll probably extend the repo to do more with cycling data if I have the time.

The post title is taken from “Colours Running Out” by TOY from their eponymous LP.