Dataset set up

to quickstart, just download the responses / wordsequences for 3 subjects from the encoding scaling laws paper
- this is all the data you need if you only want to analyze 3 subjects and don't want to make flatmaps
to run eng1000, need to grab em_data directory from here and move its contents to {root_dir}/em_data
for more, 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
to make flatmaps, need to set [pycortex filestore] to {root_dir}/ds003020/derivative/pycortex-db/

Code install

pip install ridge_utils (for full control, could alternatively pip install -e ridge_utils_frozen from the repo directory)
pip install -e . from the repo directory to locally install the neuro package
set neuro.config.root_dir/data to where you put all the data
- loading responses
  - neuro.data.response_utils function load_response
  - loads responses from at {neuro.config.root_dir}/ds003020/derivative/preprocessed_data/{subject}, where they are stored in an h5 file for each story, e.g. wheretheressmoke.h5
- loading stimulus
  - ridge_utils.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
python experiments/02_fit_encoding.py
- This script takes many relevant arguments through argparse

builds on the dataset repo from the huth lab. See that wonderful repo!
uses data from openneuro.
builds on code from encoding-model-scaling-laws, which is the repo for the paper "Scaling laws for language encoding models in fMRI" (antonello, vaidya, & huth, 2023). See the cool results there!
it also copies a lot of code from the repo for SASC