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If you are using a lab computer, it will have MATLAB installed. Verify that it can start successfully (you'll get the
» prompt in the Command Window).
If you are using your own computer, you can download MATLAB from the mathworks.com website. For it to work, you need the username to be 'mvdmlab' and the network.lic license file available from the lab Dropbox. If you want to use MATLAB off-campus, you also may need to connect to the campus network using a VPN (See the uWaterloo VPN page on how to do this). It may also be possible to acquire your own working copy of MATLAB that does not have these restrictions.
This course assumes a basic working knowledge of MATLAB, corresponding roughly to the material in the "Getting Started with MATLAB" Primer. If you are unsure, take a look at the table of contents. If there are things you don't recognize, use the Primer itself, or Chapter 2 of the MATLAB for Neuroscientists book to get up to speed.
GitHub is a system for “distributed version control”: it keeps track of changes to a set of collaboratively edited files, such as pieces of MATLAB code. This is done so that it is easy to share improvements between collaborators. If you are new to GitHub, watch the video under Resources above.
If you don't already have a GitHub account, go to GitHub and sign up.
Download and install the GUI client of your choice; for Windows, I recommend GitHub Windows to get started. For installing Git and setting up GitHub on various operating systems, see GitHub: Set Up Git
Configure your client. For GitHub Windows, you'll first need to sign in with your account, then click Tools > Options. Set the “Default Storage Directory” to something reasonable: on lab computers, this should be something on the D: drive (for example, D:\My_Documents\GitHub\). Also check that your username and e-mail address look ok (I am
Next, on the GitHub website, search for the repository called
Fork it. Notice that this creates your own personal copy of the original repository.
Go back to your local client and hit Refresh. You should now see your forked repo appear, along with the option to
Clone it. Do this. Verify that your local filesystem now contains a
BIOL680 folder in the location you specified in your GitHub client configuration: this is the local version of your forked repo, which you can now make changes to.
readme.md file in the MATLAB editor (if you don't have one, type
edit at the MATLAB command prompt) and add a line to it with your name and date. Save your updated file (notice the
* in the MATLAB editor window that indicates unsaved changes).
In your local GitHub client, hit Refresh (making sure you are looking at the
BIOL680 repo of course). It should indicate that there is now a file to be committed, and highlight the line you added. Write a short commit message, and hit Commit. Notice you now have “unsynced commits”. To upload (push) your change to GitHub, hit
sync. On the GitHub site for this repo, verify that your change appears there.
Important: the change you just made is limited to *your fork* only. The original repo that you forked is *not* automatically updated with your change! To remedy this, select “Pull Requests” on your repo site and choose “New Pull Request”. The resulting page will show the diff of the file in your repo against the same file in the original repo, so it should highlight the change you made. Select “Click to create a Pull Request…”, add a title, and click “Send Pull Request”.
Now, the owner of the original repo (in this case,
mvdm) will need to approve the request before the change is merged into the original repo. You will get a confirmation message by e-mail when this is done.
This process works the same way for changes in the other direction. If the original repo is updated, your fork does not automatically update. If it did, it could have some nasty consequences: someone could break your code without you knowing about it! You are in control of your own fork and need to check for, and approve, any changes you want. A good way of doing this is to Watch the original repo using the button on the GitHub site.
Note: if you are confused or curious about GitHub, or distributed version control in general, a great way to get answers beyond reading the documentation and doing the tutorials is to go for drinks with the folks in the Eliasmith lab!
Using your experience from the previous section, fork and create a local clone of the
vandermeerlab repo. Verify that it exists in your local filesystem before proceeding.
See Setting up MATLAB. Things should be set up so that you have a shortcut that does the following:
cdto your BIOL680 folder
Check that the shortcut works before proceeding.
Use a FTP client such as Filezilla or
WinSCP to connect to the lab FTP server,
mvdmlab-nas1 (220.127.116.11). Configure your FTP client to require “explicit FTP over TLS” and use
BIOL680 as username and password. In the
BIOL680 folder, download the folder
R016-2012-10-08. A good place to put this folder is in
D:\data\promoted\R016\. (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.)
Correct FileZilla configuration is the following:
You will have to be on campus to connect. If you still cannot log in to the server, send me your IP address and I will temporarily enable access for you. IF it still does not work, get the .zip here.
As explained in the Noble paper, create a folder with today's date; I do this within a folder called
daily so to keep things manageable. Create a
sandbox.m file in it and use Cell Mode to check that you can load a data file from the data folder you grabbed (because the loader function is in your path):
%% load data % first, cd to where the data you just grabbed is located [csc,csc_info] = LoadCSC('R016-2012-10-08-CSC02d.ncs'); tvec = Range(csc); raw_LFP = Data(csc); %% plot nSamples = 10000; plot(tvec(1:nSamples),raw_LFP(1:nSamples));
You should replace the comment above with a
cd command to change directory to where your data is located. Do not place the data in your code folder!
If you get no errors and see a nice neural signal, save your
sandbox.m script. Commit and sync to your GitHub fork. If you do get errors, verify that your path is set up correctly (you can type
path to get a listing; it should have the various vandermeerlab folders in it. If not, go back to the Setting up MATLAB steps.)
If you are running Matlab on OS X (and possibly Linux), the above
sandbox.m code will probably fail. This is most likely because the
vandermeerlab codebase downloaded from GitHub calls on low-level Windows functions. The following steps have worked for someone using OS X 10.8, with Matlab R2013a:
extracted folder/binaries/, find the file
Nlx2MatCSC_v3.mexmaci, and rename it to
vandermeerlab/util/neuralynx/for this to work.
sandbox.m should run properly now, and you should see the plot you're supposed to see.
Follow the instructions above for Mac/OS X users, except you may need to recompile the binaries (note that you will need C and C++ compilers installed. Install the
build-essential package on Ubuntu):
PLATFORM=32PCdepending on your architecture, and edit INCLMATLAB and BINMATLAB so that they point to the correct directories for your Matlab installation. If you don't remember, run
locate mexshin the shell and you should see the path.
> rename 's/_v3//' *
This worked on 64 bit Ubuntu with Matlab R2013b.
If you are using a lab computer, only put data and code on the
D:\ drive. This actually has two underlying hard drives (a RAID 1 array in “mirroring” mode) such that if one fails, your data is still available. However, this does not protect against accidentally deleting data, overwriting a key file, any sort of data corruption or damage, et cetera. Some options to minimize the impact of those:
You're set! Just make sure that your
sandbox.m file appears in your repo (not the original that you forked), online on GitHub. I will look at this file to verify that you got to this point OK.