Tag Archives: Igor

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.

commute3d

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.

abcTo 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.

cda

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.

startmidIt’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.

run2016

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.

Tips from the blog X: multi-line commenting in Igor

This is part-tip, part-adventures in code. I found out recently that it is possible to comment out multiple lines of code in Igor and thought I’d put this tip up here.

Multi-line commenting in programming is useful two reasons:

  1. writing comments (instructions, guidance) that last more than one line
  2. the ability to temporarily remove a block of code while testing

In each computer language there is the ability to comment out at least one line of code.

In Igor this is “//”, which comments out the whole line, but no more.

ipcomment1

This is the same as in ImageJ macro language.

ijcomment1

Now, to comment out whole sections in FIJI/ImageJ is easy. Inserting “/*” where you want the comment to start, and then “*/” where it ends, multiple lines later.

ijcomment2

I didn’t think this syntax was available in Igor, and it isn’t really. I was manually adding “//” for each line I wanted to remove, which was annoying. It turns out that you can use Edit > Commentize to add “//” to the start of all selected lines. The keyboard shortcut in IP7 is Cmd-/. You can reverse the process with Edit > Decommentize or Cmd-\.

ipcomment2

There is actually another way. Igor can conditionally compile code. This is useful if for example you write for Igor 7 and Igor 6. You can get compilation of IP7 commands only if the user is running IP7 for example. This same logic can be used to comment out code as follows.

ipcomment3

The condition if 0 is never satisfied, so the code does not compile. The equivalent statement for IP7-specific compilation, is “#if igorversion()>=7”.

So there you have it, two ways to comment out code in Igor. These tips were from IgorExchange.

If you want to read more about commenting in different languages and the origins of comments, read here.

This post is part of a series of tips.

Bateman Writes: Eye of the Tiger

I don’t often write about music at quantixed but I recently caught Survivor’s “Eye of The Tiger” on the radio and thought it deserved a quick post.

Surely everyone knows this song: a kind of catchall motivational tune. It is loved by people in gyms with beach-unready bodies and by presidential hopefuls without permission to use it.

Written specifically for Rocky III after Sylvester Stallone was refused permission by Queen to use “Another One Bites The Dust”, it has that 1980s middle-of-the-road hard-rock-but-not-heavy-metal feel to it. The kind of track that must be filed under “guilty pleasure”. Possibly you love this song. Maybe you get ready to meet your opponents whilst listening to it? If this is you, please don’t read on.

I find it difficult listening to this track because of the timing of the intro. Not sure what I mean?

Here is a waveform of one channel for the intro. Two of the opening phrases are shown underlined. A phrase in this case is: dun, dun-dun-dun, dun-dun-dun, dun-dun-durrrr. Can you see the problem with the second of those two phrases?

tiger1

Still don’t see it? In the second phrase the second of the dun-dun-duns comes in late.

I’ve overlaid the waveform again to compare phrase 1 with phrase 2.

tiger2

The difference is one-eighth (quaver) and it drives me nuts. I think it’s intentional because, well the whole band play the same thing. I don’t think it’s a tape splice error, because the track sounds live and surely someone must have noticed. Finally, they play these phrases again in the outro and that point the timing is correct. No, it’s intentional. Why?

From this page Jim Peterik of Survivor says:

I started doing that now-famous dead string guitar riff and started slashing those chords to the punches we saw on the screen, and the whole song took shape in the next three days.

So my best guess is that the notes were written to match the on-screen action!

The video on YouTube is only at 220 million views (at the time of writing). Give it a listen, if my description of dun-dun-dun’s was not illustrative enough for you.

Notes:

  • The waveform is taken from the Eye of The Tiger album version of the song. I read that the version in the movie is actually the demo version.
  • I loaded it into Igor using SoundLoadWave. I made an average of the stereo channels using MatrixOp and then downsampled the wave from 44.1 kHz so it was easier to move around.

A very occasional series on music. The name Bateman Writes, refers to the obsessive writings of the character Patrick Bateman in Bret Easton Ellis’s novel American Psycho. This serial killer had a penchant for middle of the road rock act Huey Lewis & The News.

The International Language of Screaming

A couple of recent projects have meant that I had to get to grips more seriously with R and with MATLAB. Regular readers will know that I am a die-hard IgorPro user. Trying to tackle a new IDE is a frustrating experience, as anyone who has tried to speak a foreign language will know. The speed with which you can do stuff (or get your point across) is very slow. Not only that, but… if you could just revert to your mother tongue it would be so much easier…

What I needed was something like a Babel Fish. As I’m sure you’ll know, this fish is the creation of Douglas Adams. It allows instant translation of any language. The only downside is that you have to insert the fish into your ear.

The closest thing to the Babel Fish in computing is the cheat sheet. These sheets are typically a huge list of basic commands that you’ll need as you get going. I found a nice page which had cheat sheets which allowed easy interchange between R, MATLAB and python. There was no Igor version. Luckily, a user on IgorExchange had taken the R and MATLAB page and added some Igor commands. This was good, but it was a bit rough and incomplete. I took this version, formatted it for GitHub flavored markdown, and made some edits.

The repo is here. I hope it’s useful for others. I learned a lot putting it together. If you are an experienced user of R, MATLAB or IGOR (or better still can speak one or more of these languages), please fork and make edits or suggest changes via GitHub issues, or by leaving a comment on this page if you are not into GitHub. Thanks!

R-MATLAB-IGOR-CheatSheet

Here is a little snapshot to whet your appetite. Bon appetit!

cssnapshot

 

The post title is taken from “The International Language of Screaming” by Super Furry Animals from their Radiator LP. Released as a single, the flip-side had a version called NoK which featured the backing tracking to the single. Gruff sings the welsh alphabet with no letter K.

The Digital Cell: Statistical tests

Statistical hypothesis testing, commonly referred to as “statistics”, is a topic of consternation among cell biologists.

This is a short practical guide I put together for my lab. Hopefully it will be useful to others. Note that statistical hypothesis testing is a huge topic and one post cannot hope to cover everything that you need to know.

What statistical test should I do?

To figure out what statistical test you need to do, look at the table below. But before that, you need to ask yourself a few things.

  • What are you comparing?
  • What is n?
  • What will the test tell you? What is your hypothesis?
  • What will the p value (or other summary statistic) mean?

If you are not sure about any of these things, whichever test you do is unlikely to tell you much.

The most important question is: what type of data do you have? This will help you pick the right test.

  • Measurement – most data you analyse in cell biology will be in this category. Examples are: number of spots per cell, mean GFP intensity per cell, diameter of nucleus, speed of cell migration…
    • Normally-distributed – this means it follows a “bell-shaped curve” otherwise called “Gaussian distribution”.
    • Not normally-distributed – data that doesn’t fit a normal distribution: skewed data, or better described by other types of curve.
  • Binomial – this is data where there are two possible outcomes. A good example here in cell biology would be a mitotic index measurement (the proportion of cells in mitosis). A cell is either in mitosis or it is not.
  • Other – maybe you have ranked or scored data. This is not very common in cell biology. A typical example here would be a scoring chart for a behavioural effect with agreed criteria (0 = normal, 5 = epileptic seizures). For a cell biology experiment, you might have a scoring system for a phenotype, e.g. fragmented Golgi (0 = is not fragmented, 5 = is totally dispersed). These arbitrary systems are a not a good idea. Especially, if the person scoring is unblinded to the experimental procedure. Try to come up with an unbiased measurement procedure.

 

What do you want to do? Measurement

(Normal)

Measurement

(not Normal)

Binomial

 

Describe one group Mean, SD Median, IQR Proportion
Compare one group to a value One-sample t-test Wilcoxon test Chi-square
Compare two unpaired groups Unpaired t-test Wilcoxon-Mann-Whitney two-sample rank test Fisher’s exact test

or Chi-square

Compare two paired groups Paired t-test Wilcoxon signed rank test McNemar’s test
Compare three or more unmatched groups One-way ANOVA Kruskal-Wallis test Chi-square test
Compare three or more matched groups Repeated-measures ANOVA Friedman test Cochran’s Q test
Quantify association between two variables Pearson correlation Spearman correlation
Predict value from another measured variable Simple linear regression Nonparametric regression Simple logistic regression
Predict value from several measured or binomial variables Multiple linear (or nonlinear) regression Multiple logistic regression

Modified from Table 37.1 (p. 298) in Intuitive Biostatistics by Harvey Motulsky, 1995 OUP.

What do “paired/unpaired” and “matched/unmatched” mean?

Most of the data you will get in cell biology is unpaired or unmatched. Individual cells are measured and you have say, 20 cells in the control group and 18 different cells in the test group. These are unpaired (or unmatched in the case of more than one test group) because the cells are different in each group. If you had the same cell in two (or more) groups, the data would be paired (or matched). An example of a paired dataset would be where you have 10 cells that you treat with a drug. You take a measurement from each of them before treatment and a measurement after. So you have paired measurements: one for cell A before treatment, one after; one for cell B before and after, and so on.

How to do some of these tests in IgorPRO

The examples below assume that you have values in waves called data0, data1, data2,… substitute the wavenames for your actual wave names.

Is it normally distributed?

The simplest way is to plot them and see. You can plot out your data using Analysis>Histogram… or Analysis>Packages>Percentiles and BoxPlot… Another possibility is to look at skewness or kurtosis of the dataset (you can do this with WaveStats, see below)

However, if you only have a small number of measurements, or you want to be sure, you can do a test. There are several tests you can do (Kolmogorov-Smirnoff, Jarque-Bera, Shapiro-Wilk). The easiest to do and most intuitive (in Igor) is Shapiro-Wilk.


StatsShapiroWilkTest data0

If p < 0.05 then the data are not normally distributed. Statistical tests on normally distributed data are called parametric, while those on non-normally distributed data are non-parametric.

Describe one group

To get the mean and SD (and lots of other statistics from your data):


Wavestats data0

To get the median and IQR:


StatsQuantiles/ALL data0

The mean and sd are also stored as variables (V_avg, V_sdev). StatsQuantiles calculates V_median, V_Q25, V_Q75, V_IQR, etc. Note that you can just get the median by typing Print StatsMedian(data0) or – in Igor7 – Print median(data0). There is often more than one way to do something in Igor.

Compare one group to a value

It is unlikely that you will need to do this. In cell biology, most of the time we do not have hypothetical values for comparison, we have experimental values from appropriate controls. If you need to do this:


StatsTTest/CI/T=1 data0

Compare two unpaired groups

Use this for normally distributed data where you have test versus control, with no other groups. For paired data, use the additional flag /PAIR.


StatsTTest/CI/T=1 data0,data1

For the non-parametric equivalent, if n is large computation takes a long time. Use additional flag /APRX=2. If the data are paired, use the additional flag /WSRT.


StatsWilcoxonRankTest/T=1/TAIL=4 data0,data1

For binomial data, your waves will have 2 points. Where point 0 corresponds to one outcome and point 1, the other. Note that you can compare to expected values here, for example a genetic cross experiment can be compared to expected Mendelian frequencies. To do Fisher’s exact test, you need a 2D wave representing a contingency table. McNemar’s test for paired binomial data is not available in Igor

StatsChiTest/S/T=1 data0,data1

If you have more than two groups, do not do multiple versions of these tests, use the correct method from the table.

Compare three or more unmatched groups

For normally-distributed data, you need to do a 1-way ANOVA followed by a post-hoc test. The ANOVA will tell you if there are any differences among the groups and if it is possible to investigate further with a post-hoc test. You can discern which groups are different using a post-hoc test. There are several tests available, e.g. Dunnet’s is useful where you have one control value and a bunch of test conditions. We tend to use Tukey’s post-hoc comparison (the /NK flag also does Newman-Keuls test).


StatsAnova1Test/T=1/Q/W/BF data0,data1,data2,data3
StatsTukeyTest/T=1/Q/NK data0,data1,data2,data3

The non-parametric equivalent is Kruskal-Wallis followed by a multiple comparison test. Dunn-Holland-Wolfe method is used.


StatsKSTest/T=1/Q data0,data1,data2,data3
StatsNPMCTest/T=1/DHW/Q data0,data1,data2,data3

Compare three or more matched groups

It’s unlikely that this kind of data will be obtained in a typical cell biology experiment.

StatsANOVA2RMTest/T=1 data0,data1,data2,data3

There are also operations for StatsFriedmanTest and StatsCochranTest.

Correlation

Straightforward command for two waves or one 2D wave. Waves (or columns) must be of the same length


StatsCorrelation data0

At this point, you probably want to plot out the data and use Igor’s fitting functions. The best way to get started is with the example experiment, or just display your data and Analysis>Curve Fitting…

Hazard and survival data

In the lab we have, in the past, done survival/hazard analysis. This is a bit more complex and we used SPSS and would do so again as Igor does not provide these functions.

Notes for use

Screen Shot 2016-07-12 at 14.18.18The good news is that all of this is a lot more intuitive in Igor 7! There is a new Menu item called Statistics, where most of these functions have a dialog with more information. In Igor 6.3 you are stuck with the command line. Igor 7 will be out soon (July 2016).

  • Note that there are further options to most of these commands, if you need to see them
    • check the manual or Igor Help
    • or type ShowHelpTopic “StatsMedian” in the Command Window (put whatever command you want help with between the quotes).
  • Extra options are specified by “flags”, these are things like “/Q” that come after the command. For example, /Q means “quiet” i.e. don’t print the output into the history window.
  • You should always either print the results to the history or put them into a table so that we can check them. Note that the table gets over written if you do the same test with different data, so printing in this case is a good idea.
  • The defaults in Igor are setup OK for our needs. For example, Igor does two-tailed comparison, alpha = 0.05, Welch’s correction, etc.
  • Most operations can handle waves of different length (or have flags set to handle this case).
  • If you are used to doing statistical tests in Excel, you might be wondering about tails and equal variances. The flags are set in the examples to do two-tailed analysis and unequal variances are handled by Welch’s correction.
  • There’s a school of thought that says that using non-parametric tests is best to be cautious. These tests are not as powerful and so it is best to use parametric tests (t test, ANOVA) when you can.

Part of a series on the future of cell biology in quantitative terms.

The Digital Cell: Getting started with IgorPRO

This post follows on from “Getting Started“.

In the lab we use IgorPRO for pretty much everything. We have many analysis routines that run in Igor, we have scripts for processing microscope metadata etc, and we use it for generating all figures for our papers. Even so, people in the lab engage with it to varying extents. The main battle is that the use of Excel is pretty ubiquitous.

I am currently working on getting more people in the lab started with using Igor. I’ve found that everyone is keen to learn. The approach so far has been workshops to go through the basics. This post accompanies the first workshop, which is coupled to the first few pages of the Manual. If you’re interested in using Igor read on… otherwise you can skip to the part where I explain why I don’t want people in the lab to use Excel.

IgorPro is very powerful and the learning curve is steep, but the investment is worth it.

WaveMetrics_IGOR_Pro_LogoThese are some of the things that Igor can do: Publication-quality graphics, High-speed data display, Ability to handle large data sets, Curve-fitting, Fourier transforms, smoothing, statistics, and other data analysis, Waveform arithmetic, Matrix math, Image display and processing, Combination graphical and command-line user interface, Automation and data processing via a built-in programming environment, Extensibility through modules written in the C and C++ languages. You can even play games in it!

The basics

The first thing to learn is about the objects in the Igor environment and how they work.There are four basic objects that all Igor users will encounter straight away.

  • Waves
  • Graphs
  • Tables
  • Layouts

All data is stored as waveforms (or waves for short). Waves can be displayed in graphs or tables. Graphs and tables can be placed in a Layout. This is basically how you make a figure.

The next things to check out are the command window (which displays the history), the data browser and the procedure window.

Essential IgorPro

  • Tables are not spreadsheets! Most important thing to understand. Tables are just a way of displaying a wave. They may look like a spreadsheet, but they are not.
  • Igor is case insensitive.
  • Spaces. Igor can handle spaces in names of objects, but IMO are best avoided.
  • Igor is 0-based not 1-based
  • Logical naming and logical thought – beginners struggle with this and it’s difficult to get this right when you are working on a project, but consistent naming of objects makes life easier.
  • Programming versus not programming – you can get a long way without programming but at some point it will be necessary and it will save you a lot of time.

Pretty soon, you will go beyond the four basic objects and encounter other things. These include: Numeric and string variables, Data folders, Notebooks, Control panels, 3D plots – a.k.a. gizmo, Procedures.

Getting started guide

Getting started guide

Why don’t we use Excel?

  • Excel can’t make high quality graphics for publication.
    • We do that in Igor.
    • So any effort in Excel is a waste of time.
  • Excel is error-prone.
    • Too easy for mistakes to be introduced.
    • Not auditable. Tough/impossible to find mistakes.
    • Igor has a history window that allows us to see what has happened.
  • Most people don’t know how to use it properly.
  • Not good for biological data – Transcription factor Oct4 gets converted to a date.
  • Limited to 1048576 rows and 16384 columns.
  • Related: useful link describing some spreadsheet crimes of data entry.

But we do use Excel a lot

  • Excel is useful for quick calculations and for preparing simple charts to show at lab meeting.
  • Same way that Powerpoint is OK to do rough figures for lab meeting.
  • But neither are publication-quality.
  • We do use Excel for Tracking Tables, Databases(!) etc.

The transition is tough, but worth it

Writing formulae in Excel is straightforward, and the first thing you will find is that to achieve the same thing in Igor is more complicated. For example, working out the mean for each row in an array (a1:y20) in Excel would mean typing =AVERAGE(A1:y1) in cell z1 and copying this cell down to z20. Done. In Igor there are several ways to do this, which itself can be unnerving. One way is to use the Waves Average panel. You need to know how this works to get it to do what you want.

But before you turn back, thinking I’ll just do this in Excel and then import it… imagine you now want to subtract a baseline value from the data, scale it and then average. Imagine that your data are sampled at different intervals. How would you do that? Dealing with those simple cases in Excel is difficult-to-impossible. In Igor, it’s straightforward.

Resources for learning more Igor:

  • Igor Help – fantastic resource containing the manual and more. Access via Help or by typing ShowHelpTopic “thing I want to search for”.
  • Igor Manual – This PDF is available online or in Applications/Igor Pro/Manual. This used to be a distributed as a hard copy… it is now ~3000 pages.
  • Guided Tour of IgorPro – this is a great way to start and will form the basis of the workshops.
  • Demos – Igor comes packed with Demos for most things from simple to advanced applications.
  • IgorExchange – Lots of code snippets and a forum to ask for advice or search for past answers.
  • Igor Tips – I’ve honestly never used these, you can turn on tips in Igor which reveal help on mouse over.
  • Igor mailing list – topics discussed here are pretty advanced.
  • Introduction to IgorPRO from Payam Minoofar is good. A faster start to learning to program that reading the manual.
  • Hands-on experience!

Part of a series on the future of cell biology in quantitative terms.

Everything In Its Right Place

Something that has driven me nuts for a while is the bug in FIJI/ImageJ when making montages of image stacks. This post is about a solution to this problem.

What’s a montage?

You have a stack of images and you want to array them in m rows by n columns. This is useful for showing a gallery of each frame in a movie or to separate the channels in a multichannel image.

What’s the bug/feature in ImageJ?

If you select Image>Stacks>Make Montage… you can specify how you want to layout your montage. You can specify a “border” for this. Let’s say we have a stack of 12 images that are 300 x 300 pixels. Let’s arrange them into 3 rows and 4 columns with 0 border.

Screen Shot 2016-07-06 at 14.21.53So far so good. We have an image that is 1200 x 900. But it looks a bit rubbish, we need some grouting (white pixel space between the images). We don’t need a border, but let’s ignore that for the moment. So the only way to do this in ImageJ is to specify a border of 8 pixels.

Screen Shot 2016-07-06 at 14.22.27Looks a lot better. Ok there’s a border around the outside, which is no use, but it looks good. But wait a minute! Check out the size of the image (1204 x 904). This is only 4 pixels bigger in x and y, yet we added all that grouting, what’s going on?

The montage is not pixel perfect.

Screen Shot 2016-07-06 at 14.23.38

So the first image is not 300 x 300 any more. It is 288 x 288. Hmmm, maybe we can live with losing some data… but what’s this?

Screen Shot 2016-07-06 at 14.24.49

The next image in the row is not even square! It’s 292 x 288. How much this annoys you will depend on how much you like things being correct… The way I see it, this is science, if we don’t look after the details, who will? If I start with 300 x 300 images, it’s not too much to ask to end up with 300 x 300 images, is it? I needed to fix this.

Solutions

I searched for a while for a solution. It had clearly bothered other people in the past, but I guess people just found their own workaround.

ImageJ solution for multichannel array

So for a multichannel image, where the grayscale images are arrayed next to the merge, I wrote something in ImageJ to handle this. These macros are available here. There is a macro for doing the separation and arraying. Then there is a macro to combine these into a bigger figure.

Igor solution

For the exact case described above, where large stacks need to be tiled out into and m x n array, I have to admit I struggled to write something for ImageJ and instead wrote something for IgorPRO. Specifying 3 rows, 4 columns and a grout of 8 pixels gives the correct TIFF 1224 x 916, with each frame showing in full and square. The code is available here, it works for 8 bit greyscale and RGB images.

Screen Shot 2016-07-06 at 14.55.31

I might update the code at some point to make sure it can handle all data types and to allow labelling and adding of a scale bar etc.

The post title is taken from “Everything In Its Right Place” by Radiohead from album Kid A.

Adventures in code II

I needed to generate a uniform random distribution of points inside a circle and, later, a sphere. This is part of a bigger project, but the code to do this is kind of interesting. There were no solutions available for IgorPro, but stackexchange had plenty of examples in python and mathematica. There are many ways to do this. The most popular seems to be to generate a uniform random set of points in a square or cube and then discard those that are greater than the radius away from the origin. I didn’t like this idea, because I needed to extend it to spheroids eventually, and as I saw it the computation time saved was minimal.

Here is the version for points in a circle (radius = 1, centred on the origin).

circleCode

This gives a nice set of points, 1000 shown here.

pointsCircle

And here is the version inside a sphere. This code has variable radius for the sphere.

sphereCode

The three waves (xw,yw,zw) can be concatenated and displayed in a Gizmo. The code just plots out the three views.

pointsSphere

My code uses var + enoise(var) to get a random variable from 0,var. This is because enoise goes from -var to +var. There is an interesting discussion about whether this is a truly flat PDF here.

This is part of a bigger project where I’ve had invaluable help from Tom Honnor from Statistics.

This post is part of a series on esoterica in computer programming.

Weak Superhero: how to win and lose at Marvel Top Trumps

Top Trumps is a card game for children. The mind can wander when playing such games with kids… typically, I start thinking: what is the best strategy for this game? But also, as the game drags on: what is the quickest way to lose?

Since Top Trumps is based on numerical values with simple outcomes, it seemed straightforward to analyse the cards and to simulate different scenarios to look at these questions.

PackMany Top Trumps variants exist, but the pack I’ll focus on is Marvel Universe “Who’s Your Hero?” made by Winning Moves (cat. No.: 3399736). Note though that the approach can probably be adapted to handle any other Top Trumps set.

There are 30 cards featuring Marvel characters. Each card has six categories:

  1. Strength
  2. Skill
  3. Size
  4. Wisecracks
  5. Mystique
  6. Top Trumps Rating.

What is the best card and which one is the worst?

VictoriesIn order to determine this I pulled in all the data and compared each value to every other card’s value, and repeated this per category (code is here, the data are here). The scaling is different between category, but that’s OK, because the game only uses within field comparisons. This technique allowed me to add up how many cards have a lower value for a certain field for a given card, i.e. how many cards would that card beat. These victories could then be summed across all six fields to determine the “winningest card”.

The cumulative victories can be used to rank the cards and a category plot illustrates how “winningness” is distributed throughout the deck.

As an aside: looking at the way the scores for each category are distributed is interesting too. Understanding these distributions and the way that each are scaled gives a better feel for whether a score of say 2 in Wisecracks is any good (it is).

Wasp SMvIMThe best card in the deck is Iron Man. What is interesting is that Spider-Man has the designation Top Trump (see card), but he’s actually second in terms of wins over all other cards. Head-to-head, Spider-Man beats Iron Man in Skill and Mystique. They draw on Top Trumps Rating. But Iron Man beats Spider-Man on the three remaining fields. So if Iron Man comes up in your hand, you are most likely to defeat your opponent.

At the other end of the “winningest card” plot, the worst card, is Wasp. Followed by Ant Man and Bucky Barnes. There needs to be a terrible card in every Top Trump deck, and Wasp is it. She has pitiful scores in most fields. And can collectively only win 9 out of (6 * 29) = 174 contests. If this card comes up, you are pretty much screwed.

What about draws? VictoriesFSIt’s true that a draw doesn’t mean losing and the active player gets another turn, so a draw does have some value. To make sure I wasn’t overlooking this with my system of counting victories, I recalculated the values using a Football League points system (3 points for a win, 1 point for a draw and 0 for a loss). The result is the same, with only some minor changes in the ranking.

I went with the first evaluation system in order to simulate the games.

I wrote a first version of the code that would printout what was happening so I could check that the simulation ran OK. Once that was done, it was possible to call the function that runs the game, do this multiple (1 x 10^6) times and record who won (player 1 or player 2) and for how many rounds each game lasted.

A typical printout of a game (first 9 rounds) is shown here. So now I could test out different strategies: What is the best way to win and what is the best way to lose?

Strategy 1: pick your best category and play

S_bestIf you knew which category was the most likely to win, you could pick that one and just win every game? Well, not quite. If both players take this strategy, then the player that goes first has a slight advantage and wins 57.8% of the time. The games can go on and on, the longest is over 500 rounds. I timed a few rounds and it worked out around 15 s per round. So the longest game would take just over 2 hours.

Strategy 2: pick one category and stick with it

S_stickThis one requires very little brainpower and suits the disengaged adult: just keep picking the same category. In this scenario, Player 1 just picks strength every time while Player 2 picks their best category. This is a great way to lose. Just 0.02% of games are won using this strategy.

Strategy 3: pick categories at random

S_randomThe next scenario was to just pick random categories. I set up Player 1 to do this and play against Player 2 picking their best category. This means 0.2% of wins for Player 1. The games are over fairly quickly with the longest of 1 x 10^6 games stretching to 200 rounds.

If both players take this strategy, it results in much longer games (almost 2000 rounds for the longest). The player-goes-first advantage disappears and the wins are split 49.9 to 50.1%.

Strategy 4: pick your worst category

S_worstHow does all of this compare with selecting the worst category? To look at this I made Player 2 take this strategy, while Player 1 picked the best category. The result was definitive, it is simply not possible for Player 2 to win. Player 1 wins 100% of all 1 x 10^6 games. The games are over in less than 60 rounds, with most being wrapped up in less than 35 rounds. Of course this would require almost as much knowledge of the deck as the winning strategy, but if you are determined to lose then it is the best strategy.

 

The hand you’re dealt

Head-to-head, the best strategy is to pick your best category (no surprise there), but whether you win or lose depends on the cards you are dealt. I looked at which player is dealt the worst card Wasp and at the outcome. The split of wins for player 1 (58% of games) are with 54% of those, Player 2 stated with Wasp. Being dealt this card is a disadvantage but it is not the kiss of death. This analysis could be extended to look at the outcome if the n worst cards end up in your hand. I’d predict that this would influence the outcome further than just having Wasp.

So there you have it: every last drop of fun squeezed out of a children’s game by computational analysis. At quantixed, we aim to please.

The post title is taken from “Weak Superhero” by Rocket From The Crypt off their debut LP “Paint As A Fragrance” on Headhunter Records