# -*- coding: utf-8 -*- """ .. _ex-evoked-topomap: ======================================== Plotting topographic maps of evoked data ======================================== Load evoked data and plot topomaps for selected time points using multiple additional options. """ # Authors: Christian Brodbeck # Tal Linzen # Denis A. Engeman # MikoĊ‚aj Magnuski # Eric Larson # Alex Rockhill # # License: BSD-3-Clause # %% # sphinx_gallery_thumbnail_number = 5 import numpy as np import matplotlib.pyplot as plt from mne.datasets import sample from mne import read_evokeds print(__doc__) path = sample.data_path() fname = path / 'MEG' / 'sample' / 'sample_audvis-ave.fif' # load evoked corresponding to a specific condition # from the fif file and subtract baseline condition = 'Left Auditory' evoked = read_evokeds(fname, condition=condition, baseline=(None, 0)) # %% # Basic :func:`~mne.viz.plot_topomap` options # ------------------------------------------- # # We plot evoked topographies using :func:`mne.Evoked.plot_topomap`. The first # argument, ``times`` allows to specify time instants (in seconds!) for which # topographies will be shown. We select timepoints from 50 to 150 ms with a # step of 20ms and plot magnetometer data: times = np.arange(0.05, 0.151, 0.02) evoked.plot_topomap(times, ch_type='mag') # %% # If times is set to None at most 10 regularly spaced topographies will be # shown: evoked.plot_topomap(ch_type='mag') # %% # We can use ``nrows`` and ``ncols`` parameter to create multiline plots # with more timepoints. all_times = np.arange(-0.2, 0.5, 0.03) evoked.plot_topomap(all_times, ch_type='mag', ncols=8, nrows='auto') # %% # Instead of showing topographies at specific time points we can compute # averages of 50 ms bins centered on these time points to reduce the noise in # the topographies: evoked.plot_topomap(times, ch_type='mag', average=0.05) # %% # We can plot gradiometer data (plots the RMS for each pair of gradiometers) evoked.plot_topomap(times, ch_type='grad') # %% # Additional :func:`~mne.viz.plot_topomap` options # ------------------------------------------------ # # We can also use a range of various :func:`mne.viz.plot_topomap` arguments # that control how the topography is drawn. For example: # # * ``cmap`` - to specify the color map # * ``res`` - to control the resolution of the topographies (lower resolution # means faster plotting) # * ``contours`` to define how many contour lines should be plotted evoked.plot_topomap(times, ch_type='mag', cmap='Spectral_r', res=32, contours=4) # %% # If you look at the edges of the head circle of a single topomap you'll see # the effect of extrapolation. There are three extrapolation modes: # # - ``extrapolate='local'`` extrapolates only to points close to the sensors. # - ``extrapolate='head'`` extrapolates out to the head circle. # - ``extrapolate='box'`` extrapolates to a large box stretching beyond the # head circle. # # The default value ``extrapolate='auto'`` will use ``'local'`` for MEG sensors # and ``'head'`` otherwise. Here we show each option: extrapolations = ['local', 'head', 'box'] fig, axes = plt.subplots(figsize=(7.5, 4.5), nrows=2, ncols=3) # Here we look at EEG channels, and use a custom head sphere to get all the # sensors to be well within the drawn head surface for axes_row, ch_type in zip(axes, ('mag', 'eeg')): for ax, extr in zip(axes_row, extrapolations): evoked.plot_topomap(0.1, ch_type=ch_type, size=2, extrapolate=extr, axes=ax, show=False, colorbar=False, sphere=(0., 0., 0., 0.09)) ax.set_title('%s %s' % (ch_type.upper(), extr), fontsize=14) fig.tight_layout() # %% # More advanced usage # ------------------- # # Now we plot magnetometer data as topomap at a single time point: 100 ms # post-stimulus, add channel labels, title and adjust plot margins: fig = evoked.plot_topomap(0.1, ch_type='mag', show_names=True, colorbar=False, size=6, res=128) fig.subplots_adjust(left=0.01, right=0.99, bottom=0.01, top=0.88) fig.suptitle('Auditory response') # %% # We can also highlight specific channels by adding a mask, to e.g. mark # channels exceeding a threshold at a given time: # Define a threshold and create the mask mask = evoked.data > 1e-13 # Select times and plot times = (0.09, 0.1, 0.11) mask_params = dict(markersize=10, markerfacecolor='y') evoked.plot_topomap(times, ch_type='mag', mask=mask, mask_params=mask_params) # %% # Or by manually picking the channels to highlight at different times: times = (0.09, 0.1, 0.11) _times = ((np.abs(evoked.times - t)).argmin() for t in times) significant_channels = [ ('MEG 0231', 'MEG 1611', 'MEG 1621', 'MEG 1631', 'MEG 1811'), ('MEG 2411', 'MEG 2421'), ('MEG 1621')] _channels = [np.in1d(evoked.ch_names, ch) for ch in significant_channels] mask = np.zeros(evoked.data.shape, dtype='bool') for _chs, _time in zip(_channels, _times): mask[_chs, _time] = True evoked.plot_topomap(times, ch_type='mag', mask=mask, mask_params=mask_params) # %% # Interpolating topomaps # ---------------------- # We can also look at the effects of interpolating our data. Perhaps, we # might have data per channel such as the impedance of each electrode that # we don't want interpolated, or we just want to visualize the data without # interpolation as a sanity check. We can use ``image_interp='nearest'`` to # prevent any interpolation or ``image_interp='linear'`` to do a linear # interpolation instead of the default cubic interpolation. # %% # The default cubic interpolation is the smoothest and is great for # publications. evoked.plot_topomap(times, ch_type='eeg', image_interp='cubic') # %% # The linear interpolation might be helpful in some cases. evoked.plot_topomap(times, ch_type='eeg', image_interp='linear') # %% # The nearest (Voronoi, no interpolation) interpolation is especially helpful # for debugging and seeing the values assigned to the topomap unaltered. evoked.plot_topomap(times, ch_type='eeg', image_interp='nearest', contours=0) # %% # Animating the topomap # --------------------- # # Instead of using a still image we can plot magnetometer data as an animation, # which animates properly only in matplotlib interactive mode. # sphinx_gallery_thumbnail_number = 9 times = np.arange(0.05, 0.151, 0.01) fig, anim = evoked.animate_topomap( times=times, ch_type='mag', frame_rate=2, blit=False)