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Module 1: Setting up


  • Set up a working MATLAB installation with appropriate path shortcuts
  • Use GitHub to acquire the analysis code we will use
  • Perform some elementary GitHub operations (pull, add, commit, push)
  • Create a well-designed folder structure for your work, and be aware of naming conventions (in process, promoted data)
  • Connect to the lab database, download a data set, and test your path setup



Installing MATLAB

At MBL, MATLAB should already be installed on lab computers. Verify it starts correctly; you should see its main window open up, including a panel called “Command Window” greeting you with a prompt (»). At Dartmouth, follow these instructions to install MATLAB. Release 2018a seems to work.

Setting up Git

Next, we need to obtain some existing MATLAB code that we will build on in this module. To do this, we will use GitHub.

GitHub is a system for “distributed version control”: it keeps track of changes to a set of files, such as pieces of MATLAB code, with one or more contributors. This system makes it easy to keep track of evolving code, and to share improvements between collaborators. Typical scenarios in which such version control is useful include, for instance, if you want to run the exact code that you used to generate some figure a while ago, but you've since made changes to the code; or the same analysis suddenly gives a different result and you want to track down what change caused it. If you are new to GitHub, you can watch the video under Resources above to get an overall idea of how it works and why it is useful.

Meanwhile, download and install the Git client of your choice if you don't already have one installed. This is a piece of software that will allow you to talk to GitHub, which is where the code is actually stored. For Windows, you can get git here. For more detailed info on different Git clients and setting up Git on other operating systems, see GitHub: Set Up Git.

Cloning the module codebase

Now we are ready to use Git to create a local copy (“clone”) of the module codebase. On Windows, open a PowerShell, which you can do by typing PowerShell in the search box of the Start menu. Once open, note your working directory (displayed at the prompt of your now opened shell), and change it to a different location if you prefer, using the ``cd`` command. Once you are in an appropriate location, such as a new folder named GitHub on your local machine, type git clone, which will create a new folder nsb2022 in your working directory.

Now, verify that the above steps have resulted in the creation of a nsb2022 folder with various subfolders and files in it, indicating that you have a local copy of the codebase. Because Git is tracking the contents of this folder, it is now easy to “pull” the latest version from GitHub:

git pull

This “pull” should do nothing, because you already have the latest version. The basic idea is that you can stay up-to-date easily as well as contribute to the codebase so that everyone else can benefit. As you might expect, that part is known as a “push”, which we will do in the next step.

A first commit and push

To complete this part, sign up for a GitHub account if you don't already have one, and post your username in the module Slack channel so I can give you permission to write to the code repository.

First, if you haven't “done a pull” recently, do one now before starting the next step.

Open the file in the nsb2022 folder. The .md extension is for Markdown, a lightweight set of commands to format text (syntax reference is here).

Add your name to the list, and save the file. Then navigate to the nsb2022 repository in a shell and type git status. Git should notice the change, but it says that this change is not yet “staged for commit”. In other words, git is not tracking this file. Let's fix this:

git add
git commit -m "Added name to list in README file"

If you now do a git status you will see that you are ahead of the origin (the online repository) by 1 commit. This makes sense because you just made a change. Let's push this by doing git push. If you get an “access denied” type error, let me (mvdm) know and I will give you permission. If everything goes to plan you should now be able to see the updated README file on GitHub.

A schematic of these basic operations (pull, commit, push) is shown below, using the amazing DokuWiki plugin for GraphViz:

What happens if in between your pull and push someone else pushes a change? In that case you cannot push your changes unless you do a pull first and resolve any conflicts. In any case, you should make a habit of doing a pull first before starting to edit anything, in order to minimize conflicts.

Using GitHub to acquire the FieldTrip toolbox

Using your experience from the previous section, create a local clone of the FieldTrip toolbox. If you are using the command line (shell), make sure that you cd to your GitHub folder, i.e. that you are not within some other project such as nsb2022, before cloning. If things worked correctly you should have fieldtrip and nsb2022 folders within your GitHub folder; not a fieldtrip folder within your nsb2022 folder!

This toolbox is useful for the analysis of local field potential (LFP) data. Be aware that it is about 1.2GB in size!

Note how using GitHub to obtain FieldTrip not only ensures you have the most recent version, but also enables you to easily incorporate future changes, revert to previous versions, etc. using pull and other git tools.

Configuring MATLAB to use the code from GitHub

Now, we need to tell MATLAB where to find all this code we have just obtained. Open MATLAB and create a shortcut (2017b and earlier) or a Favorite (2018a+) titled something like “Neural Data Analysis”. The code for the shortcut should be

restoredefaultpath; clear classes; % start with a clean slate
cd('D:\My_Documents\GitHub\nsb2022\code-matlab\shared'); % or, wherever your code is located -- NOTE \shared subfolder!
p = genpath(pwd); % create list of all folders from here
% can optionally add FieldTrip here when needed (commented out for now)
% cd('D:\My_Documents\GitHub\FieldTrip'); % or whatever you chose, obviously
% ft_defaults;
% rmpath('D:\My_Documents\GitHub\GitHub\fieldtrip\external\signal\') % needed to preserve use of MATLAB filtfilt.m

This ensures that whenever you click this button, you have a clean path (the set of folders, other than the current working directory, whose contents MATLAB can access) of only the MATLAB default plus your local versions of the two GitHub repositories.

:!: When setting your path in MATLAB to add the shared folder only and not a parent folder such as nsb2022. Adding the entire nsb2022 folder will result in an error when you try to run the LoadCSC command later in the module!

Optional: if you don't like the .git folders in your path, you can get clever with regular expressions to remove these:

p = regexprep(p,'D.*?\.git.*?;','');

Establish a sensible folder structure

So far, you have local GitHub repository clones added to MATLAB's path. But as you work on a project, you will write your own analysis code. You will also have data files to work with; some that you download as part of these modules, and some that you may have collected yourself. It is important to consider where all of these files will go, and how you will manage them. I recommend using three separate locations:

  • GitHub folders. Files in here you only change (or add) when you can improve what is already there. This content is backed up and version-controlled (i.e. you can see the complete history of changes and revert to any version you want) through the GitHub system. These files can be shared by multiple different projects, including working through these modules, analysis related to the data you collect, and perhaps a PhD project! For me, this folder is in D:\My_Documents\GitHub\.
  • Project folders. Each project has a home folder which holds the code for that project. As explained in the Noble paper linked to above, it can be helpful to create a new folder for each day you work on the project. If you find you are copying certain functions or snippets of code from day to day, those should be moved to the shared folder. It is critical that the contents of this folder are backed up in case of computer failure. I use Dropbox for this, so an example project folder I have is D:\My_Documents\Dropbox\projects\OccasionSettingNAccRecording\. As an alternative, you may also set up a GitHub repository of your own (it's free) so that you can track your progress. Either way, the important point is that you can always find what you did on a given date – this should work together with your lab (analysis) notes where you keep track of issues, progress, paste figures, et cetera.
  • Data folders. Data, both raw and preprocessed, should live in a different place: D:\data\ in my case. This is because different projects may access the same data, and because backup strategies for data are typically different than for code.

With this trifold division, when you want to work on a project, you would click the appropriate MATLAB shortcut for it first. Following the example above, this should add the appropriate GitHub folders to the path. Next, the shared folder of the project is also added to the path. Data is generally not added to the path, because some data files in different folders may have the same name. Then, you create a new folder with today's date, and you are ready to go! Note also that if you want someone else to be able to replicate your results, you need to tell them what path you used.

There are several situations when it is appropriate to move code from your project folder to a GitHub folder:

  • you improve a piece of code that was already on GitHub
  • you have a new piece of code in the shared project folder that is proving useful
  • you reach a milestone, such as an analysis that tests a certain hypothesis

If you are an owner or collaborator of a GitHub repository, you will be able to push changes you make. I will enable this for you on the course repository (if you email me – you need to do this for the editing of the readme file, above, to work), but to be accepted as a collaborator on a large project such as FieldTrip, you will need to show your work to the owners first (as can be done by creating a Fork or branch and and issuing a pull request).

Grab an example data session

Next, let's get some data! At MBL, go the NS&B share and find the tutorial_data folder (within the MouseModule folder). At Dartmouth or elsewhere, you'll need to connect to the lab server. E-mail MvdM for instructions on how to do that.

For this module you will need the R016-2012-10-08 folder (containing data from one recording session), which you can find in the \promoted\R016 folder on the share. Copy this folder onto your own computer. A good place to put it is in something like D:\data\promoted\ (Rxxx indicate different rats, followed by the date of each session). As mentioned, in general you want to keep your data separate from your code; for instance, multiple analysis projects may use the same data, so you don't want to duplicate it.

The choice of the folder name promoted indicates that these are data folders for which preprocessing is completed. As will be explained further in Module 2 and others, preprocessing typically includes the renaming of raw data files, annotation, spike sorting, and a few other steps. In general, it is useful to keep promoted data separate from data still in process.

Verify things are working

As explained in the Noble paper linked to above, create a folder with today's date in your project folder. Create a sandbox.m file in it, click your previously made shortcut to set up the paths, and use Cell Mode in the MATLAB editor (type edit in the Command Window if you don't have one open yet) to check that you can load a data file:

%% load data
cd('D:\Data\R016\R016-2012-10-08'); % replace this with where you saved the data
cfg = [];
cfg.fc = {'R016-2012-10-08-CSC02d.ncs'}; % cell array with filenames to load
csc = LoadCSC(cfg);

When you execute the above cell (Ctrl+Enter when it is selected in the Editor; Command+Enter on OS X), you should get:

LoadCSC: Loading 1 files...
LoadCSC: R016-2012-10-08-CSC02d.ncs 44/10761 bad blocks found (0.41%).
>> csc
csc = 
     tvec: [5498360x1 double]
     data: [1x5498360 double]
    label: {'R016-2012-10-08-CSC02d.ncs'}
      cfg: [1x1 struct]
>> csc.cfg
ans = 
      history: [1x1 struct]
          hdr: {[1x1 struct]}
      ExpKeys: [1x1 struct]
    SessionID: 'R016-2012-10-08'

What you have loaded is in fact a local field potential recorded from the rat ventral striatum. The different file types and data fields above will be explained in more detail in the next module. For now, let's just take a peek at the data:

xlim([1338.6 1339.2]);

You should see some interesting oscillations – we will explore these in detail in upcoming modules. If you see this, you have successfully completed this module!

analysis/nsb2019/week1.txt · Last modified: 2022/06/27 17:02 by mvdm