cerebellum_connectivity

Install pyenv using Homebrew:

$ brew update
$ brew install pyenv

Add pyenv init to your shell:

$ echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi' >> ~/.bash_profile
$ source ~/.bash_profile

Install the required version of python:

$ pyenv install 3.7.0

Installing the Required Python Packages

This project uses pipenv for virtual environment and python package management.

Ensure pipenv is installed globally:

$ brew install pipenv

Navigate to the top-level directory in cerebellum_connectivity and install the packages from the Pipfile.lock. This will automatically create a new virtual environment for you and install all requirements using the correct version of python.

$ pipenv install

Additional packages to download (not currently pipenv installable)

There are two repos on Github that you will need to clone for this project:

Add the following repo to your PATH: $ git clone [email protected]:DiedrichsenLab/surfAnalysisPy.git

Add the following repo to your PATH: $ git clone [email protected]:DiedrichsenLab/SUITPy.git

Note: You can do the latter by adding the following to the .env file in the top-level directory of cerebellum_connectivity $ PYTHONPATH="DIRECTORY_NAME:DIRECTORY_NAME:$PYTHONPATH" $ export PYTHONPATH

Activating the virtual environment in terminal:

$ pipenv shell

NOTE: To deactivate the virtual environment when you are done working, simply type exit

Adding kernel to jupyter notebook:

$ python -m ipykernel install --user --name connectivity

Running connectivity models:

And example of how to run a set of connectivity models + evaluate them can be found in 
connectivity/scripts/script_ridge.py or script_mk.py 

NOTE: Log trained models on this shared google doc: https://docs.google.com/spreadsheets/d/1SnuGiH42E20QrcQvT5nle8aRJEvnVjMoYjZ9WuJvz0k/edit#gid=0

Project Organization

Data Organization:

├── data
|   └── fs_LR_32 <- freesurfer atlases
│   └── sc1 <- Data from study 1
│       └── anatomicals          
│       └── GLM_firstlevel_7
│       └── GLM_firstlevel_8
│       └── imaging_data
│       └── conn_models
│       └── suit
│       └── surfaceFreesurfer
│       └── surfaceWB
│       └── beta_roi
│   └── sc2 <- Data from study 2         
│       └── GLM_firstlevel_7
│       └── GLM_firstlevel_8
│       └── conn_models
│       └── suit
│       └── beta_roi    

Repository structure:

├── LICENSE
├── Makefile                 <- Makefile with commands like `make data` (not yet implemented)
├── README.md                <- The top-level README for developers using this project.
│
├── docs                     <- A default Sphinx project; see sphinx-doc.org for details (not yet implemented)
│
│
├── notebooks                <- Jupyter notebooks. Naming convention is a number (for ordering),
│                               the creator's initials, and a short `-` delimited description, e.g.
│                               `1.0-mk-visualize_maps`.
│
├── references               <- Manuals, and all other explanatory materials (not yet implemented)
│
├── reports                  <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures              <- Generated graphics and figures to be used in reporting
│
├── Pipfile                  <- The Pipfile for reproducing the analysis environment, e.g.
│                               install all packages with `pipenv install`, check existing packages `pipenv graph`
│
├── setup.py                 <- makes project pip installable (pip install -e .) so connectivity package can be imported
├── connectivity             <- Source code for use in this project.
│   ├── __init__.py          <- Makes connectivity a Python module
│   ├── constants.py         <- Global defauls (not a class!) and default directories   
│   ├── io.py                <- Module for Import/Output .mat, .h5, .json files
│   ├── data.py              <- Module containing the Dataset class 
│   ├── models.py            <- Module for defining different models classes (mostly derived from sklearn, but potentially with mixin )
|   ├── run.py               <- Module defining function to traind and evaluate a model on a set of subjects - uses `train_config` and `eval_config` to set the parameters
│   └── visualize.py         <- Module to map results to the surface and visualize them. 

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