Dataset set up

download data with python experiments/00_load_dataset.py
- create a data dir under wherever you run it and will use datalad to download the preprocessed data as well as feature spaces needed for fitting semantic encoding models
set neuro1.config.root_dir to where you want to store the data
to make flatmaps, need to set [pycortex filestore] to {root_dir}/ds003020/derivative/pycortex-db/
to run eng1000, need to grab em_data directory from here and move its contents to {root_dir}/em_data
loading responses
- neuro1.data.response_utils function load_response
- loads responses from at {root_dir}/ds003020/derivative/preprocessed_data/{subject}, hwere they are stored in an h5 file for each story, e.g. wheretheressmoke.h5
loading stimulus
- neuro1.features.stim_utils function load_story_wordseqs
- loads textgrids from {root_dir}/ds003020/derivative/TextGrids", where each story has a TextGrid file, e.g. wheretheressmoke.TextGrid`
- uses {root_dir}/ds003020/derivative/respdict.json to get the length of each story

Code install

from the repo directory, start with pip install -e . to locally install the huth package
python 01_fit_encoding.py --subject UTS03 --feature eng1000
- The other optional parameters that encoding.py takes such as sessions, ndelays, single_alpha allow the user to change the amount of data and regularization aspects of the linear regression used.
- This function will then save model performance metrics and model weights as numpy arrays.