Team Members: Alon Daks, Lisa Ann Yu, Jordeen Chang, Ying Luo

All code is packaged in a python module called stat159lambda.
Ensure this module is on your python path with export PYTHONPATH='<path-to-repository>/code'.
For example, export PYTHONPATH='/Users/alondaks/project-lambda/code'.

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'.