Repo containing code to reproduce figures for the article on retinotopic connectivity published in PNAS.
**This repository is being populated by cleaned code from a separate private repository. I apologize for any delay in updating this repository; I'm on paternaty leave right now. **
The analyses in this repository were run inside a conda environment that can be created as follows;
conda activate cf_hcp
conda install -c conda-forge nibabel nilearn cifti bottleneck tqdm nistats pingouin
It also depends on some non-conda software from other developers: neuropythy and pycortex, as well as our own prf-fitting package, prfpy.
To install, the following assumes these packages are cloned to the ~/projects/ directory.
cd ~/projects/neuropythy
python setup.py develop
cd ~/projects/prfpy
python setup.py develop
cd ~/projects/pycortex
python setup.py develop
The custom software that is run to produce the figures in the article is structured as a python package, called cfhcpy. This package can be installed from this repository using python setup.py develop. The notebooks in the notebooks folder then run specific parts of the analysis.
The parameters for the analyses are codified in the config.yml file in a format that allows annotated versioning of these analysis parameters. That is, in many cases, to change analyses it should suffice to change the settings in the yaml file without changing the underlying code. For instance, to set up your own version of the analysis, add a system to this yaml file in the systems section. Code, where reasonable, is documented in the numpy docstring format.
This yaml file is read in by the main analysis class, AnalysisBase in cfhcpy/base.py, which can then be used to run per-subject analyses. For group analysis, a similar GroupAnalysis class exists in cfhcpy/group.py.
The fitting of the CF models happens on a SLURM cluster, to which the data are rsynced. To submit jobs and run fitting outside of notebooks, adapt and use the scripts/submit.py and scripts/run.py scripts.