Python

Enabling MATLAB in Jupyter notebooks on Linux

Posted on by Eryk Walczak

Introduction

In my previous post I showed how to enable MATLAB in Jupyter notebooks on Windows. Now it’s time for GNU/Linux (Ubuntu).

My main issue with enabling new kernel was having initially installed two Anacondas and two Python versions (2.7 and 3.5). After a lot of frustration, I decided to remove both Anacondas and have a clear install of the latest Anaconda with Python 2.7 and 3.5. In this tutorial I assume that Jupyter and MATLAB are already installed on your system.

Using the right environment

Although the official MATLAB website states that Python-MATLAB engine works with Python 2.7, 3.4, 3.5 and 3.6, I struggled to install it using Python 3.5. If you try to install it with a 3.5 version, you will see the following error:

OSError: MATLAB Engine for Python supports Python version 2.7, 3.3 and 3.4, but your version of Python is 3.5

The error makes it obvious that you need an older version of Python. I decided to use 2.7. To do that, I created another environment with Python 2.7:

conda create -n py27 python=2.7 anaconda

The guidelines to managing Python environments are here.

The next step was checking what environments were available:

conda info --envs

And activating Python 2.7 (py27):

source activate py27

Install Python-MATLAB engine

To install the engine connecting both languages: go to your MATLAB folder, find the Python engine folder and install setup.py. This can be done in the following way:

Change your working directory to where your MATLAB lives:
cd "MATLABROOT/extern/engines/python"

If you don’t know where your MATLAB is installed, use:
locate matlab

Then install the engine (it will only work with MATLAB >=2014b):

sudo python setup.py install

And the latest remaining dependencies:

sudo pip install -U metakernel
sudo pip install -U matlab_kernel
sudo pip install -U pymatbridge

That should do the job. Now open new Jupyter notebook:

jupyter notebook

To check whether you can find MATLAB among the available engines (top right corner):

Now check whether you can actually run the notebook. Initially, when I tried using Python 3.5, I could see MATLAB among the options but the kernels would die each time I tried running the MATLAB code. Moving to Python 2.7, as described in this tutorial, solved the problem.

If all works fine then the following notebook should generate correctly:

Even though I’m getting the MetaKernelApp error, the notebook continues to work correctly:
[MetaKernelApp] ERROR | No such comm target registered: jupyter.widget.version

To leave the environment used to run the notebook, simply type:

source deactivate

Notes

Initially, I struggled a bit with making it all work so in the meantime I also tried installing Octave (a free equivalent of MATLAB). I’m not sure whether that installation helped me with running MATLAB within Jupyter.

While trying to install the engine I came across several errors. I guess that most of them were related to my OS configuration and all of them were solved by searching for the error message. One of the errors was:

Error:
[I 00:58:19.847 NotebookApp] KernelRestarter: restarting kernel (3/5)
/home/eub/anaconda3/bin/python: No module named matlab_kernel

This was due to installing the Python engine in the wrong environment (i.e. my default Python 3.5). It was solved by activating Python 2.7 and using it to install the Python-MATLAB engine.

I think that there is an alternative way to activate MATLAB in Jupyter, without Anaconda, would be to explicitly point the installer to the Python version that supports the Py-MATLAB engine.

In my case:
sudo ~/anaconda/pkgs/python-2.7.13-0/bin/python2.7 setup.py install

You might also want install the engine in a non-default location. In that case, MATLAB has a solution to that problem and suggested installing Python in the home directory.

There is another Jupyter kernel (imatlab) engine that supposedly works with Python 3.5 and MATLAB R2016b+ but I haven’t tested it myself. As long as my current configuration works, I’m not planning to go through the hell of installing dependencies again.

Sending serial triggers from PsychoPy to ETG-4000 fNIRS

Posted on by Eryk Walczak

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.