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File conversion — Python option

Utility functions for converting between the file formats used across this toolkit. See Installation to set this up.

mat_to_numpy — Load choice file as NumPy array

Loads a .mat triadic choice file and returns a 5-column NumPy array.

Columns: ref, s1, s2, N(s1 chosen), N_repeats (1-indexed)

from rs_tools import mat_to_numpy

array, stim_list = mat_to_numpy("bgca3pt_choices_MC_sess01_10.mat")
print(array.shape)   # (n_trials, 5)
print(stim_list[:5]) # stimulus names

numpy_to_mat — Save NumPy array as choice file

Converts a 5-column NumPy array back to .mat triadic choice format.

from rs_tools import numpy_to_mat

numpy_to_mat(array, "output_choices.mat", stim_list=stim_list)

ooo_to_triadic — Convert odd-one-out to triadic format

Converts an odd-one-out .mat file to standard triadic choice format. Each OOO judgment generates exactly 2 triadic entries.

Input format: s1, s2, s3, N(s1 odd out), N(s2 odd out), N(s3 odd out)

Output format: ref, s1, s2, N(s1 chosen), N_repeats

from rs_tools import ooo_to_triadic

resp, rep, stim_list = ooo_to_triadic("ooo_choices.mat", out_path="triadic_choices.mat")
print(f"{len(resp)} triadic trials from OOO data")

Choice → Coordinates has moved

Fitting a geometric model from a choice file (choices → coordinates) is now documented under Creating RS → Choice → Coordinates, alongside the other ways to create a representational space from perceptual judgments.

For the full function reference, see Python function index.