Plot custom topographies for MEG sensorsΒΆ

This example exposes the iter_topography function that makes it very easy to generate custom sensor topography plots. Here we will plot the power spectrum of each channel on a topographic layout.

../../_images/sphx_glr_plot_topo_customized_001.png

Out:

Opening raw data file /home/ubuntu/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Current compensation grade : 0
add_eeg_ref defaults to True in 0.13, will default to False in 0.14, and will be removed in 0.15. We recommend to use add_eeg_ref=False and set_eeg_reference() instead.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Band-pass filtering from 1 - 20 Hz
Multiple deprecated filter parameters were used:
phase in 0.13 is "zero-double" but will change to "zero" in 0.14
fir_window in 0.13 is "hann" but will change to "hamming" in 0.14
lower transition bandwidth in 0.13 is 0.5 Hz but will change to "auto" in 0.14
upper transition bandwidth in 0.13 is 0.5 Hz but will change to "auto" in 0.14
The default filter length in 0.13 is "10s" but will change to "auto" in 0.14
Effective window size : 1.705 (s)

# Author: Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)


import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.viz import iter_topography
from mne import io
from mne.time_frequency import psd_welch
from mne.datasets import sample

print(__doc__)

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 20)

picks = mne.pick_types(raw.info, meg=True, exclude=[])
tmin, tmax = 0, 120  # use the first 120s of data
fmin, fmax = 2, 20  # look at frequencies between 2 and 20Hz
n_fft = 2048  # the FFT size (n_fft). Ideally a power of 2
psds, freqs = psd_welch(raw, picks=picks, tmin=tmin, tmax=tmax,
                        fmin=fmin, fmax=fmax)
psds = 20 * np.log10(psds)  # scale to dB


def my_callback(ax, ch_idx):
    """
    This block of code is executed once you click on one of the channel axes
    in the plot. To work with the viz internals, this function should only take
    two parameters, the axis and the channel or data index.
    """
    ax.plot(freqs, psds[ch_idx], color='red')
    ax.set_xlabel = 'Frequency (Hz)'
    ax.set_ylabel = 'Power (dB)'

for ax, idx in iter_topography(raw.info,
                               fig_facecolor='white',
                               axis_facecolor='white',
                               axis_spinecolor='white',
                               on_pick=my_callback):
    ax.plot(psds[idx], color='red')

plt.gcf().suptitle('Power spectral densities')
plt.show()

Total running time of the script: ( 0 minutes 24.709 seconds)

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