This repository contains a python implementation of the key analyses in the manuscript by Guo Jiahui^†*, Ma Feilong^†*, Matteo Visconti di Oleggio Castello, Samuel A. Nastase, James V. Haxby, & M. Ida Gobbini ^* (2021). https://doi.org/10.1101/2021.11.17.469009

A standard computer with enough RAM to support the in-memory operations. Demos should finish in seconds.

Our code has been tested on macOS: Big Sur (11.6.8)

The code was writen in Python 3, Python packages required for running those analyses include numpy, scipy, and rpy2.

R package vegan is required for running the variance partitioning analysis.

Running our code only requires a standard Python3 and R environment with dependencies as above. No extra installation is required.

The data folder contains RDMs and masks needed for the analyses, and the code folder contains functions and example scripts for the key analyses. Each subfolder in the data folder also contains a readme.txt file to explain the content of the data files.

The sample data can be downloaded from the Release of the repository: https://github.com/GUO-Jiahui/not_so_fast/releases/download/1.0.0/data.zip

This repository is covered under the MIT License.