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

"""
Plot sensor-level DSMs
======================

This example demonstrates how to visualize representational dissimilarity
matrices (DSMs) computed from EEG data. We will compute them using a spatial
searchlight and plot the DSM computed for each searchlight-patch.

"""
# Import required packages
import numpy as np
import mne
import mne_rsa

###############################################################################
# MNE-Python contains a build-in data loader for the kiloword dataset. We use
# it here to read it as 960 epochs. Each epoch represents the brain response to
# a single word, averaged across all the participants. For this example, we
# speed up the computation, at a cost of temporal precision, by downsampling
# the data from the original 250 Hz. to 100 Hz.

data_path = mne.datasets.kiloword.data_path(verbose=True)
epochs = mne.read_epochs(data_path / 'kword_metadata-epo.fif')
epochs = epochs.resample(100)

###############################################################################
# We now compute DSMs using a spatial searchlight with a radius of 45
# centimeters.

# This will create a generator for the DSMs
dsms = mne_rsa.dsm_epochs(
    epochs,                     # The EEG data
    dist_metric='correlation',  # Metric to compute the EEG DSMs
    spatial_radius=45,          # Spatial radius of the searchlight patch
    temporal_radius=None,       # Perform only spatial searchlight
    tmin=0.15, tmax=0.25,       # To save time, only analyze this time interval
)

# Unpack the generator into a NumPy array so we can plot it
dsms = np.array(list(dsms))

# Visualize the DSMs.
mne_rsa.viz.plot_dsms_topo(dsms, epochs.info, cmap='magma')