Setup an instance#

Install#

Dependencies#

yarn install
conda env create -f environment.yml

Once the conda environment is created, activate it with

conda activate brain-cockpit

Updating dependencies after installation#

Dependencies in package.json and environment.yml might evolve quickly. In order to update your local environment, run the following commands:

yarn install
conda env update --file environment.yml

Generate assets (3D meshes)#

This command generates fsaverage meshes from nilearn and stores them in ./public/assets

python bc_utils/gifti_to_gltf.py

Download IBC contrasts#

Projected contrasts are available at /storage/store2/work/athual/data/ibc_surface_conditions_db.zip. You most likely want to download and unzip this archive locally:

scp username@drago2:/storage/store2/work/athual/data/ibc_surface_conditions_db.zip /path/to/archive
unzip /path/to/ibc_surface_conditions_db.zip

Overwrite default environment variables#

Default environment variables are initiated in .env. You can overwrite these by creating a .env.development.local file suited to your needs. In particular, you most likely want to set DATA_PATH to point to downloaded IBC contrasts.

If you need a more custom .env files setup, check out all other possibilities allowed by create-react-app.

Run#

Application in dev mode#

In separate prompts:

  • start the frontend with yarn start

  • start the backend with python main.py (using your brain-cockpit conda env)

Application in production mode#

In separate prompts:

  • build the frontend with yarn build

  • start the backend with python main.py --env production (using your brain-cockpit conda env)

Custom utilitaries#

Functional images resampling#

This will resample functional images contained in SLICE_DATA_PATH, to later be displayed in brain-cockpit.

python bc_utils/resample_functional_images.py --env {development, production}