Step-by-step instructions to getting started
- Download and install Anaconda
- Download and install git
- 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.
- 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
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).
- 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.
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.
For most projects (especially those using behavioral data), your laptop will be more than sufficient to run Python
. If you need more heavy lifting, there are a few options:
- ALICE supercomputer @ Leiden Uni
- Get an account, and include a request to be added to the
data_pi-uraiae project space (for IBL data).
- LISA / Cartesius clusters @ SurfSara
- Apply through NWO. Very well managed, but since Leiden does not have a contract with SurfSara you have to apply to extend your account every year.