Tag: code

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I’ve tried a bunch of things such as versioning the code (this at least tells you if the person is running an out-of-date version).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.

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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).

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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!

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Part of a series on computers and coding.

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Frankly, Mr. Shankly

I read about Antonio Sánchez Chinchón’s clever approach to use the Travelling Salesperson algorithm to generate some math-art in R. The follow up was even nicer in my opinion, Pencil Scribbles. The subject was Boris Karloff as the monster in Frankenstein. I was interested in running the code (available here and here), so I thought I’d run it on a famous scientist.

By happy chance one of the most famous scientists of the 20th Century, Rosalind Franklin, shares a nominative prefix with the original subject. There is also a famous portrait of her that I thought would work well.

I first needed needed to clear up the background because it was too dark.

Now to run the TSP code.

The pencil scribbles version is nicer I think.

The R scripts basically ran out-of-the-box. I was using a new computer that didn’t have X11quartz on it nor the packages required, but once that they were installed I just needed to edit the line to use a local file in my working directory. The code just ran. The outputs FrankyTSP and Franky_scribbles didn’t even need to be renamed, given my subject’s name.

Thanks to Antonio for making the code available and so easy to use.

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The post title comes from “Frankly, Mr. Shankly” by The Smiths which appears on The Queen is Dead. If the choice of post title needs an explanation, it wasn’t a good choice…

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Adventures in Code IV: correcting filenames

A large amount of time doing data analysis is the process of cleaning, importing, reorganising and generally not actually analysing data but getting it ready to analyse. I’ve been trying to get over the idea to non-coders in the group that strict naming conventions (for example) are important and very helpful to the poor person who has to deal with the data.

missingplot

Things have improved a lot and dtatsets that used to take a few hours to clean up are now pretty much straightforward. A recent example is shown here. Almost 200 subconditions are plotted out and there is only one missing graph. I suspect the blood sugar levels were getting low in the person generating the data… the cause was a hyphen in the filename and not an underscore.

These data are read into Igor from CSVs outputted from Imaris. Here comes the problem: the folder and all files within it have the incorrect name.

There are 35 files in each folder and clearly this needs a computer to fix, even if it were just one foldersworth at fault. The quickest way is to use the terminal and there are lots of ways to do it.

Now, as I said the problem is that the foldername and filenames both need correcting. Most terminal commands you can quickly find online actually fail because they try to rename the file and folder at the same time, and since the folder with the new name doesn’t exist… you get an error.

The solution is to rename the folders first and then the files.


find . -type d -maxdepth 2 -name "oldstring*" | while read FNAME; do mv "$FNAME" "${FNAME//oldstring/newstring}"; done
find . -type f -maxdepth 3 -name "oldstring*.csv" | while read FNAME; do mv "$FNAME" "${FNAME//oldstring/newstring}"; done

A simple tip, but effective and useful. HT this gist

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Part of a series on computers and coding

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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.

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This post is part of a series of tips.

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Calendars and Clocks

This is a quick post about the punch card feature on GitHub. This is available from Graphs within each repo and is also directly accessible via the API.

I was looking at the punch card for two of my projects: one is work related and the other, more of a kind of hobby. The punch cards were different (the work one had way more commits, 99, than the hobby, 22). There was an interesting pattern to them. Here they are overlaid. Green is the work repo. Purple is the hobby.

punchcard

It says something about my working day. There’s times when I don’t do any committing, i.e. weekends during the day and most early evenings. What was interesting was that I was pretty stringent about doing hobby stuff only at set times: first thing over a coffee, over lunch, or in the evenings.

As self analysis goes, this is pretty lightweight compared to this terrifying post by Stephen Wolfram.

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The post title is taken from “Calendars and Clocks” from The Coral’s debut LP

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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!

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

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The Digital Cell: Getting Started

More on the theme of “The Digital Cell“: using quantitative, computational approaches in cell biology.

So you want to get started? Well, the short version of this post is:

Find something that you need to automate and get going!

Programming

http://www.instruction-manuals.co.uk/imageIM/four/seven/bbc.gif
http://www.instruction-manuals.co.uk/imageIM/four/seven/bbc.gif

I make no claim to be a computer wizard. My first taste of programming was the same as anyone who went to school in the UK in the 1980s: BBC Basic. Although my programming only went as far as copying a few examples from the book (right), this experience definitely reduced the “fear of the command line”. My next encounter with coding was to learn HTML when I was an undergraduate. It was not until I was a postdoc that I realised that I needed to write scripts in order get computers to do what I wanted them to do for my research.

Image analysis

I work in cell biology. My work involves a lot of microscopy. From the start, I used computer-based methods to quantify images. My first paper mentions quantifying images, but it wasn’t until I was a PhD student that I first used NIH Image (as it was called then) to extract quantitative information from confocal micrographs. I was also introduced to IgorPRO (version 3!) as a PhD student, but did no programming. That came later. As a postdoc, we used Scanalytics’ IPLab and Igor (as well as a bit of ImageJ as it had become). IPLab had an easy scripting language and it was in this program that I learned to write macros for analysis. At this time there were people in the lab who were writing software in IgorPro and MATLAB. While I didn’t pick up programming in IgorPRO or MATLAB then, it made me realise what was possible.

When I started my own group I discovered that IPLab had been acquired by BD Biosciences and then stripped out. I had hundreds of useless scripts and needed a new solution. ImageJ had improved enormously by this time and so this became our default image analysis program. The first data analysis package I bought was IgorPro (version 6) and I have stuck with it since then. In a future post, I will probably return to whether or not this was a good path.

Getting started with programming

Around 2009, I was still unable to program properly. I needed a macro for baseline subtraction – something really simple – and realised I didn’t know how to do it. We didn’t have just one or two traces to modify, we had hundreds. This was simply not possible by hand. It was this situation that made me realise I needed to learn to program.

…having a concrete problem that is impossible to crack any other way is the best motivator for learning to program.

This might seem obvious, but having a concrete problem that is impossible to crack any other way is the best motivator for learning to program. I know many people who have decided they “want to learn to code” or they are “going to learn to use R”. This approach rarely works. Sitting down and learning this stuff without sufficient motivation is really tough. So I would advise someone wanting to learn programming to find something that needs automation and just get going. Just get something to work!

Don’t worry (initially) about any of the following:

  • What program/language to use – as long as it is possible, just pick something and do it
  • If your code is ugly or embarrassing to show to an expert – as long as it runs, it doesn’t matter
  • About copy-and-pasting from examples – it’s OK as long as you take time to understand what you are doing, this is a quick way to make progress. Resources such as stackoverflow are excellent for this
  • Bugs – you can squish them, they will frustrate you, but you might need some…
  • Help – ask for help. Online forums are great, experts love showing off their knowledge. If you have local expertise, even better!

Once you have written something (and it works)… congratulations, you are a computer programmer!

IMG_2206Seriously, that is all there is to it. OK, it’s a long way to being a good programmer or even a competent one, but you have made a start. Like Obi Wan Kenobi says: you’ve taken your first step into a larger world.

So how do you get started with an environment like IgorPro? This will be the topic for next time.

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

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The Digital Cell: Workflow

The future of cell biology, even for small labs, is quantitative and computational. What does this mean and what should it look like?

My group is not there yet, but in this post I’ll describe where we are heading. The graphic below shows my current view of the ideal workflow for my lab.

Workflow

The graphic is pretty self-explanatory, but to walk you through:

  • A lab member sets up a microscopy experiment. We have standardised procedures/protocols in a lab manual and systems are in place so that reagents are catalogued to minimise error.
  • Data goes straight from the microscope to the server (and backed-up). Images and metadata are held in a database and object identifiers are used for referencing in electronic lab notebooks (and for auditing).
  • Analysis of the data happens with varying degrees of human intervention. The outputs of all analyses are processed automatically. Code for doing these steps in under version control using git (github).
  • Post-analysis the processed outputs contain markers for QC and error checking. We can also trace back to the original data and check the analysis. Development of code happens here too, speeding up slow procedures via “software engineering”.
  • Figures are generated using scripts which are linked to the original data with an auditable record of any modification to the image.
  • Project management, particularly of paper writing is via trello. Writing papers is done using collaborative tools. Everything is synchronised to enable working from any location.
  • This is just an overview and some details are missing, e.g. backup of analyses is done locally and via the server.

Just to reiterate, that my team are not at this point yet, but we are reasonably close. We have not yet implemented three of these things properly in my group, but in our latest project (via collaboration) the workflow has worked as described above.

The output is a manuscript! In the future I can see that publication of a paper as a condensed report will give way to making the data, scripts and analysis available, together with a written summary. This workflow is designed to allow this to happen easily, but this is the topic for another post.

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

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The Digital Cell

If you are a cell biologist, you will have noticed the change in emphasis in our field.

At one time, cell biology papers were – in the main – qualitative. Micrographs of “representative cells”, western blots of a “typical experiment”… This descriptive style gave way to more quantitative approaches, converting observations into numbers that could be objectively assessed. More recently, as technology advanced, computing power increased and data sets became more complex, we have seen larger scale analysis, modelling, and automation begin to take centre stage.

This change in emphasis encompasses several areas including (in no particular order):

  • Statistical analysis
  • Image analysis
  • Programming
  • Automation allowing analysis at scale
  • Reproducibility
  • Version control
  • Data storage, archiving and accessing large datasets
  • Electronic lab notebooks
  • Computer vision and machine learning
  • Prospective and retrospective modelling
  • Mathematics and physics

The application of these areas is not new to biology and has been worked on extensively for years in certain areas. Perhaps most obviously by groups that identified themselves as “systems biologists”, “computational biologists”, and people working on large-scale cell biology projects. My feeling is that these methods have now permeated mainstream (read: small-scale) cell biology to such an extent that any groups that want to do cell biology in the future have to adapt in order to survive. It will change the skills that we look for when recruiting and it will shape the cell biologists of the future. Other fields such as biophysics and neuroscience are further through this change, while others have yet to begin. It is an exciting time to be a biologist.

I’m planning to post occasionally about the way that our cell biology research group is working on these issues: our solutions and our problems.

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

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