The raw data is approximately 37.5 GBs. Additionally, the complete analysis will - in total - produce another approximately 190 GBs of data written to disk. Please ensure your machine is equippted with sufficient storage space.

Since large matrices must be computed and held in memory, systems should have at least 90 GBs of accessible RAM. We strongly encourage running on a machine with 120 GBs of accessible RAM to emulate development environment.

1. Clone and move into the project repository: git clone https://github.com/berkeley-stat159/project-lambda.git && cd project-lambda
2. Install python dependencies with pip: pip install -r requirements.txt
3. Code depends on an internally provided python module module called stat159lambda. Ensure this module is on your python path with export PYTHONPATH='<absolute-path-to-repository>/project-lambda/code'.
   For example, export PYTHONPATH='/Users/alondaks/project-lambda/code'. We strongly encourage adding your export statement to your .bashrc while running this project to ensure the environement variable is defined in all shells.

Data for a single subject is ~7.5 GBs due to 2 hour scan at 7 Tesla resolution. The preprocessing scripts are therefore memory intensive. Reproducing preprocessing code should be on a machine with 70+ GBs. In addition to downloading raw data, make data from the root project directory will download all preprocessed data. Setting a unix environment variable USE_CACHED_DATA will instruct scripts if they reexecute preprocessing or not. USE_CACHED_DATA='True' will bypass any presprocessing as long as the resulting preprocessed file exists in data/processed/. USE_CACHED_DATA='False' will recalculate preprocessed files even if they exist in data/processed/. USE_CACHED_DATA will default 'True'.

• Alon Daks (alondaks)
• Jordeen Chang (jodreen)
• Lisa Ann Yu (lisaannyu)
• Ying Luo (yingtluo)