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 scikit-learn 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