Using MATLAB in Jupyter notebooks on Windows

After using R notebooks for a while I found it really unintuitive to use MATLAB in IDE. I read that it’s possible to use MATLAB with IPython but the instructions seemed a bit out of date. When I tried to follow them, I still could not run MATLAB with Jupyter (spin-off from IPython).

I wanted to conduct analyses of electroencephalographic (EEG) activity and the best plug-ins to do it (EEGLAB and ERPLAB) were written in MATLAB. I still wanted to use a programming notebook so I had to combine Jupyter and MATLAB.

I spent a bit of time setting it all up so I thought it might be worthwhile to share the process. Initially, I had three version of MATLAB (2011a, 2011b, and 2016b) and two versions of Python (2.7 and 3.3). This did not make my life easier of Windows 7.

Eventually, I only kept the installation of MATLAB 2016b to avoid problems with paths pointing to other versions. MATLAB’s Python engine works only with MATLAB 2014b or later so keeping the older versions could only cause problems.

Instructions

  • Install Anaconda (2.7)
  • Install MATLAB (>=2014b) – if you are a student then it’s very likely that your university bought a license. There is also a free MATLAB-like language called Octave, but I have not used with Jupyter. Apparently, it is possible to combine Octave with Jupyter. I’m going to focus exclusively on MATLAB in this post.
  • Install MATLAB’s Python engine – run as admin and follow the steps on the official site.
  • Once the engine was installed. I could move to installing metakernel, matlab_kernel, and pymatbridge. Go to Anaconda prompt (run as admin) and run pip install metakernel
  • In the Anaconda prompt run pip install matlab_kernel – this will use the development version of the MATLAB kernel.
  • Run pip install pymatbridge to install a connector between Python and MATLAB.
  • … voilĂ !

    MATLAB should now be available in the list of available languages.
    Once you choose it, you can start using it in a Jupyter notebook:

    Issues
    Obviously, thing were not always this smooth. Initially, I ran into problems with installing MATLAB’s Python engine. The official website suggested running the following code:
    cd "matlabroot\extern\engines\python"
    python setup.py install

    Which I did but it resulted in an error:

    Luckily, the error message was clear so I had to point Python to run the 64-bit version. I double-checked my versions with:
    import platform
    platform.architecture()

    Which returned 64-bit as expected:

    Using a command with full path to Python solved the problem:

    Summary
    I hope this will be useful. I have been messing with other issues which were pretty specific to my system so I did not include them here. Hopefully, these instructions will be enough to make MATLAB work with Jupyter.

    PS: I have also tried using MATLAB with Jupyter on Ubuntu, but this topic probably deserves another blog post.

    How to add code coverage (codecov) to your R package?

    During the development of another R package I wasted a bit of time figuring out how to add code coverage to my package. I had the same problem last time so I decided to write up the procedure step-by-step.

    Provided that you’ve already written an R package, the next step is to create tests. Luckily, devtools package makes setting up both testing and code coverage a breeze.

    Let’s start with adding an infrastructure for tests with devtools:
    library(devtools)
    use_testthat()

    Then add a test file of your_function() to your tests folder:
    use_test("your_function")

    Then add the scaffolding for the code coverage (codecov)
    use_coverage(pkg = ".", type = c("codecov"))

    After running this code you will get a code that can be added to your README file to display a codecov badge. In my case it’s the following:
    [![Coverage Status](https://img.shields.io/codecov/c/github/erzk/PostcodesioR/master.svg)](https://codecov.io/github/erzk/PostcodesioR?branch=master)

    This will create a codecov.yml file that needs to be edited by adding:
    comment: false
    language: R
    sudo: false
    cache: packages
    after_success:
    - Rscript -e 'covr::codecov()'

    Now log in to codecov.io using the GitHub account. Give codecov access to the project where you want to cover the code. This should create a screen where you can see a token which needs to be copied:

    Once this is completed, go back to R and run the following commands to use covr:

    install.packages("covr")
    library(covr)
    codecov(token = "YOUR_TOKEN_GOES_HERE")

    The last line will connect your package to codecov. If the whole process worked, you should be able to see a percentage of coverage in your badge, like this:

    Click on it to see which functions are not fully covered/need more test:

    I hope this will be useful and will save a lot of frustrations.

    Interactive plot of fNIRS data

    The easiest way to plot ETG-4000 data in R is by using plot_ETG4000() from fnirsr package. However, if you want to explore your data in more detail, then an interactive plot is more appropriate.

    I used dygraphs package to create the chart below. In case of using many channels, the colours in the legend can get a bit mixed up like in my example. I haven’t figured out yet how to add a custom colour palette that could deal with multiple channels.

    One way or another, this code snippet should be enough to start generating interactive charts. I haven’t added the interactive chart to the main plotting function (i.e. plot_ETG4000) but I might do it in future releases.

    The code used to generate the chart is here:

    PS: The dygraph generated correctly in the interactive window, when using R notebooks, and when knitting. When I Saved as Web Page from RStudio, I got a header error that I had to clean by removing a tag (<!DOCTYPE html>) from the generated html file.

    fnirsr – Fixing bugs, Travis CI, and detrending

    I haven’t worked on fnirsr (my R package for analysing fNIRS data) for a while so I thought it’s time for some improvements. I read a great introduction to Travis CI and decided to make it work this time. After running R CMD check (and devtools::check()) several times to fix multiple bugs, I finally got to see that lovely green badge 🙂

    The package still needs more testing, but so far it does its job. On top of that, I finally added a function that removes a linear trend from an fNIRS signal:

    For more details and the latest updates see the project’s GitHub page.
    CRAN, here I come!

    Sending serial triggers from PsychoPy to ETG-4000 fNIRS

    In the process of designing my latest experiment in PsychoPy I realised that setting up the serial port connection is not the most obvious thing to do. I wrote this tutorial so that others (and future me) won’t have to waste time reinventing the wheel.

    After creating an initial version of my experiment (looping over a wav file) I tried to figure out how to send a relevant trigger to my fNIRS. Luckily, the PsychoPy tutorial has a section about using a serial port, but after reading this I still wasn’t sure how to use the code with my script. After a quick brainstorm with the lab technician, we figured out that a simple script (see below) is indeed sending triggers to the fNIRS:

    To better understand the arguments of the serial.Serial class please consult the pySerial documentation.
    The argument (‘COM1’) is the name of the serial port used. The second argument is the Baud rate, it should be the same as the Baud rate used in the Parameter/External settings of the ETG-4000:

    The last line of the script is sending the signal through the serial port. In this example, it is “A ” followed by Python’s string literal for a Carriage Return. That was one of the strings expected by ETG-4000, i.e. it was on the list previously set up in Parameter/Stim Measurement:

    The easiest way for me to test whether I was sending correct signals was to use the Communication Test in the External tab (see the second screenshot). Once the test is started, you can run the Python script to test whether the serial signals are coming through.

    If the triggers work as expected, the code sections for serial triggers can be embedded in the experiment. It can be done via GUI or code editor. That’s where to add code using GUI:

    The next step is adding the triggers for the beginning, each stimulus/block, and the end of recording.

    Here is an example of an experiment using serial port triggers to delimit blocks of stimuli and individual stimuli.

    Due to the sampling rate, I had to add delay between the triggers delimiting the blocks, otherwise they would not be captured accurately. The triggers for block sections had to be send consecutively because the triggers cannot be interspersed (not sure if that’s because of my settings). For instance, AABBAA is fine, but ABABAB is not.

    fnirsr – An R package to analyse ETG-4000 fNIRS data

    As I mentioned in my previous post, I am trying to get my head around analysing fNIRS data collected using Hitachi ETG-4000. The output of a recording session with ETG-4000 can be saved as a raw csv file (see the example). This file seems to be pretty straightforward to parse: the top section is a header, and raw data starts at line 41.

    I created a set of basic R functions that can deal with the initial stages of the analysis and I wrapped them in an R-package. It is still a very early alpha (or rather pre-alpha), as the documentation is still sparse and no unit tests were made. I only have several raw csv files and they seemed to work fine with my functions but I’m not sure how robust they are.
    Anyway, I think it will be useful to release it even in the early stage and work on the functions as time goes by.

    The package can be found on GitHub and it can be installed with the following command:

    devtools::install_github("erzk/fnirsr")

    A vignette (Rmd) is here.

    HTML vignette:

    I couldn’t find any other R packages that would deal with these files so feel free to contact me if you work(ed) on something similar. Pull requests are encouraged.

    Loading and plotting nirs data in R

    Recently I started to learn how to use Hitachi ETG-4000 functional near-infrared spectroscopy (fNIRS) for my research. Very quickly I found out that, as usual in neuroscience, the main data analysis packages are written in MATLAB.

    I couldn’t find any script to analyse fNIRS data in R so I decided to write it myself. Apparently there are some Python options, like MNE or NinPy so I will look into them in future.

    ETG-4000 records data in a straightforward(ish) .csv files but the most popular MATLAB package for fNIRS data analysis (HOMER2) expects .nirs files.

    There is a ready-made MATLAB script that transforms Hitachi data into the nirs format but it’s only available in MATLAB. I will skip the transformation step for now, and will work only with a .nirs file.

    The file I used (Simple_Probe.nirs) comes from the HOMER2 package. It is freely available in the package, but I uploaded it here to make the analysis easier to reproduce.

    My code is here:

    This will produce separate time series plots for each channel with overlapping triggers, e.g.:

    The entire analysis workflow:

    Other files:
    RMarkdown file
    html report

    I hope this helps.
    More to follow.

    Scraping the Performance History at the Royal Opera House

    As a fan of both open data and ROH I was excited to find the article promising that the ROH’s data might be opened. However, this article was published in 2014 and the only open data about ROH that I could find was the Royal Opera House Collections Database. Its format is far from being user-friendly, i.e. there are no cleaned csv (or even xlsx) files, but luckily the structure of the entire website is fairly predictable. This meant I managed to write a basic scraper to extract the high level performance data. Unfortunately the database only contained data for the years 1946-2012.

    The result is this interactive dashboard created in Tableau:

    Highlights:

      The number of performances in 1993 (159) started to approach the peak previously reached in 1951 (168 performances).
      1998 and 1999 were years when ROH was being reconstructed.
      The matinees started to become more popular in the noughties.
      1968 was the last year of performances as the Covent Garden Opera Company.
      Tosca was the most popular opera in the studied period with 187 performances.
      La bohème was the most popular matinee.
      The Kirov Opera was the third most popular company performing at the ROH with 33 appearances. After the name change to Mariinsky Theatre, there were additional four performances by this company.

    Here’s the scraper code:

    UK broadband speeds – embedding Tableau dashboards into Rmd files

    I found a new dataset about UK broadband speeds and I started analysing it in R. However, after cleaning the data, I thought that creating a dashboard with Shiny would take me too much time so I moved to Tableau. I wanted to keep my analyses in one place so I embedded the dashboard into the output html document (see below).

    Initially I thought that RMarkdown can’t generate embedded Tableau visualizations because the iframe in my report seemed blank after knitting the report. I had to open the generated in the browser to see the iframe filled with Tableau dashboard.

    RMarkdown file is available here.