Step-by-step instructions to getting started
- Download and install Anaconda
- Download and install git. See here for an excellent instroduction.
- Download and install VSCode, including the AutoPilot and Remote extensions (the latter for working on the ALICE cluster)
- Continue to setup your environment for analyzing IBL data (and select
iblenv
as your interpreter in VSCode)
SSH setup (locally)
- Pin the Anaconda Prompt to the Start bar, right-click on Properties, and change
%HOMEPATH
to the path where your code lives (e.g. C:\Users\username\Documents\code\
).
- Connect your GitHub repo with SSH. Note: the university’s Dell machines have a habit of looking for ssh keys in the P-Drive
/p//.ssh/
, whereas you probably want to keep your keys in /c//Users//username//.ssh
. To change where SSH looks for keys, add an environment variable called HOME
, and set it to C:\Users\username
. Running ssh-keygen
as a test, it should then prompt you to save your keys on the C-drive. If you’ve already created your ssh keys and they are on the P-drive, simply move them.
- MobaXTerm is your best bet for using ssh on Windows (for transferring data).
Python and data analysis references
- Know your way around the command line
- Understand what a virtual environment is
- Understand Git and GitHub
- Learn the basics of Python
- Think about the structure of data
- Once your code runs, make it better!
Open data, open code, open science
- Might there be a dataset or a tool out there that does what you need? Check out this list of lists.
Computing resources
For most projects (especially those using behavioral data), your laptop will be more than sufficient to run Python. If you need more heavy lifting, we use the ALICE supercomputer @ Leiden Uni
- Get an account (see special form for FSW users) and include a request to be added to the
data_pi-uraiae
project space (for IBL data).
ALICE setup
- See here to connect using ssh, and here for Windows-specific instructions with custom keynames.
- Warning: your home directory only has 15GB of space. This fills up quickly if you install your conda environments there (just
iblenv
is 2.5GB!). So before you create your first conda env, run mkdir ~/data1/.conda; ln -fs data1/.conda
. Keep only code in your home directory, and everything else (data, figures) in the lab’s shared project space or your own data folder.
- It’s helpful to use the VSCode Remote Mode for development and debugging (to avoid a Git commit history full of 1-typo fixes as you’re getting your local code to now run on ALICE). See here for instructions.