#!/usr/bin/env python
# coding: utf-8

"""
Construct a model RDM
=====================

This example shows how to create RDMs from arbitrary data. A common use case for this is
to construct a "model" RDM to RSA against the brain data. In this example, we will
create a RDM based on the length of the words shown during an EEG experiment.

Authors
-------
Marijn van Vliet <marijn.vanvliet@aalto.fi>
Stefan Appelhoff <stefan.appelhoff@mailbox.org>
"""

# Import required packages
import mne
import mne_rsa

########################################################################################
# MNE-Python contains a build-in data loader for the kiloword dataset, which is used
# here as an example dataset. Since we only need the words shown during the experiment,
# which are in the metadata, we can pass ``preload=False`` to prevent MNE-Python from
# loading the EEG data, which is a nice speed gain.

data_path = mne.datasets.kiloword.data_path(verbose=True)
epochs = mne.read_epochs(data_path / "kword_metadata-epo.fif", preload=False)

# Show the metadata of 10 random epochs
print(epochs.metadata.sample(10))

########################################################################################
# Now we are ready to create the "model" RDM, which will encode the difference in length
# between the words shown during the experiment.

rdm = mne_rsa.compute_rdm(epochs.metadata.NumberOfLetters, metric="euclidean")

# Plot the RDM
fig = mne_rsa.plot_rdms(rdm, title="Word length RDM")
fig.set_size_inches(3, 3)  # Make figure a little bigger to show axis properly