Predictive Clinical Neuroscience software toolkit (formerly nispat).

Methods for normative modelling, spatial statistics and pattern recognition. Documentation, including tutorials can be found on readthedocs. Click on the docs button above to visit the site.

i) install anaconda3 ii) create enviornment with "conda create --name <env_name>" iii) activate environment by "source activate <env_name>" iv) install required conda packages

conda install pip pandas scipy

v) install PCNtoolkit (plus dependencies)

pip install pcntoolkit

Make sure conda is available on the system. Otherwise install it first from https://www.anaconda.com/

conda --version

Create a conda environment in a shared location

conda create -y python==3.8.3 numpy mkl blas --prefix=/shared/conda/<env_name>

Activate the conda environment

conda activate /shared/conda/<env_name>

Install other dependencies

conda install -y pandas scipy 

Install pip dependencies

pip --no-cache-dir install nibabel sklearn torch glob3 

Clone the repo

git clone https://github.com/amarquand/PCNtoolkit.git

install in the conda environment

cd PCNtoolkit/
python3 setup.py install

Test

python -c "import pcntoolkit as pk;print(pk.__file__)"

For normative modelling, functionality is handled by the normative.py script, which can be run from the command line, e.g.

# python normative.py -c /path/to/training/covariates -t /path/to/test/covariates -r /path/to/test/response/variables /path/to/my/training/response/variables

For more information, please see the following resources:

• documentation
• developer documentation
• a tutorial and worked through example on a real-world dataset