IVIM_fit

Preprocessing diffusion MRI data and fitting IVIM models

How to use locally:

Clone the repository in Sherlock using git clone https://github.com/PostonLab/IVIM_fit.git. cd into the cloned repository, then cd into the ivim_fit directory within the repository. You should see a run.py file there.
```
git clone https://github.com/PostonLab/IVIM_fit.git
```
```
cd ivim_fit
```
Install poetry version 1.8.5 using python3 -m pip install poetry==1.8.5
```
python3 -m pip install poetry==1.8.5
```
Install dependencies with poetry install, then use poetry shell to activate the virtual environment.
```
poetry install
```
```
poetry shell
```
First run the workflow dry-run with python ./run.py <path to bids data> <path to output> participant -np.
```
./run.py <insert path to your bids data> <insert path to your output> participant -np
```
If the dry run is successful, we can run the workflow with python ./run.py <path to bids data> <path to output> participant -c all --use-singularity. Where -c is for number of cores you want to use. all will use everything available.
```
python ./run.py <path to bids data> <path to output> participant -c all --use-singularity
```
When running for the first time, it will install all the singularity containers.

How to run in Sherlock cluster at Stanford:

Clone the repository in Sherlock using git clone https://github.com/PostonLab/IVIM_fit.git. cd into the cloned repository, then cd into the ivim_fit directory within the repository. You should see a run.py file there.
```
git clone https://github.com/PostonLab/IVIM_fit.git
```
```
cd ivim_fit
```
(If you see the error: fatal: unable to access 'https://github.com/PostonLab/IVIM_fit.git/': error setting certificate verify locations: CAfile: /etc/ssl/certs/ca-certificates.crt CApath: none, first manually point git to the CA bundle with git config --global http.sslCAInfo /etc/ssl/certs/ca-bundle.crt, then try git clone again.)
```
git config --global http.sslCAInfo /etc/ssl/certs/ca-bundle.crt
```
Activate an interractive shell using salloc
```
salloc
```
Load python3 using ml python/3.9.0
```
ml python/3.9.0
```
Create a python environment python3.9 -m venv <name>
```
python3.9 -m venv <name>
```
Activate the environment source <venv_name>/bin/activate
```
source <venv_name>/bin/activate
```
Install dependencies with poetry install. [If poetry version 1.8.5 is not already installed, install it using python3 -m pip install poetry==1.8.5, then rerun poetry install (recommended), or wget -O - https://install.python-poetry.org | python3 - --version 1.8.5]. If needed install six package using python3 -m pip install six)
```
poetry install
```
If poetry not already installed:
```
python3 -m pip install poetry==1.8.5
```
Deactivate the existing python environment using deactivate. Then use poetry shell to activate the new ivim virtual environment.
```
deactivate
```
```
poetry shell
```
Navigate to ivim_fit directory using cd ivim_fit where you will see the run.py file.
```
cd ivim_fit
```
First run the workflow dry-run with python ./run.py <path to bids data> <path to output> participant -np.
```
python ./run.py <path to bids data> <path to output> participant -np
```
If the dry run is successful, we can run the workflow with python ./run.py <path to bids data> <path to output> participant -c all --use-singularity. Where -c is for number of cores you want to use. all will use everything available.
```
python ./run.py <path to bids data> <path to output> participant -c all --use-singularity
```
If running again, first load python 3.9 using ml python/3.9.0, and then run poetry shell to activae the python environment.
```
ml python/3.9.0
```
```
poetry shell
```

How to contribute:

Clone the repository with git clone https://github.com/PostonLab/IVIM_fit.git
```
git clone https://github.com/PostonLab/IVIM_fit.git
```
Checkout a branch and make changes.
Before doing git add ., run poe quality. This will automatically run formatting and make necessary changes.
If everything looks pretty, proceed with pushing changes.
Go to github and make a pull request for your changes.

Acknowledgement

This project utilizes code adapted from the snakedwi pipeline ([https://github.com/akhanf/snakedwi]), an open-source project for diffusion MRI preprocessing. Their work provided a valuable foundation for building the preprocessing steps of IVIM_fit pipeline.