Python plot_topo_tfr Examples

Example #1

File: test_viz.py Project: emanuele/mne-python

def test_plot_topo_tfr():
    """Test plotting of TFR
    """
    # Make a fake dataset to plot
    n_freqs = 11
    con = np.random.randn(n_chan, n_freqs, len(epochs.times))
    freqs = np.arange(n_freqs)
    # Show topography of connectivity from seed
    plot_topo_tfr(epochs, con, freqs, layout)

Example #2

def test_plot_topo_tfr():
    """Test plotting of TFR
    """
    # Make a fake dataset to plot
    n_freqs = 11
    con = np.random.randn(n_chan, n_freqs, len(epochs.times))
    freqs = np.arange(n_freqs)
    # Show topography of connectivity from seed
    plot_topo_tfr(epochs, con, freqs, layout)

Example #3

File: plot_cwt_sensor_connectivity.py Project: Anevar/mne-python

# Use 'MEG 2343' as seed
seed_ch = 'MEG 2343'
picks_ch_names = [raw.ch_names[i] for i in picks]

# Create seed-target indices for connectivity computation
seed = picks_ch_names.index(seed_ch)
targets = np.arange(len(picks))
indices = seed_target_indices(seed, targets)

# Define wavelet frequencies and number of cycles
cwt_frequencies = np.arange(7, 30, 2)
cwt_n_cycles = cwt_frequencies / 7.

# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info['sfreq']  # the sampling frequency
con, freqs, times, _, _ = spectral_connectivity(epochs, indices=indices,
    method='wpli2_debiased', mode='cwt_morlet', sfreq=sfreq,
    cwt_frequencies=cwt_frequencies, cwt_n_cycles=cwt_n_cycles, n_jobs=2)

# Mark the seed channel with a value of 1.0, so we can see it in the plot
con[np.where(indices[1] == seed)] = 1.0

# Show topography of connectivity from seed
import matplotlib.pyplot as plt
title = 'WPLI2 - Visual - Seed %s' % seed_ch

layout = mne.find_layout(epochs.info, 'meg')  # use full layout
plot_topo_tfr(epochs, con, freqs, layout=layout, title=title)
plt.show()

Example #4

# Create seed-target indices for connectivity computation
seed = picks_ch_names.index(seed_ch)
targets = np.arange(len(picks))
indices = seed_target_indices(seed, targets)

# Define wavelet frequencies and number of cycles
cwt_frequencies = np.arange(7, 30, 2)
cwt_n_cycles = cwt_frequencies / 7.

# Run the connectivity analysis using 2 parallel jobs
sfreq = raw.info['sfreq']  # the sampling frequency
con, freqs, times, _, _ = spectral_connectivity(
    epochs,
    indices=indices,
    method='wpli2_debiased',
    mode='cwt_morlet',
    sfreq=sfreq,
    cwt_frequencies=cwt_frequencies,
    cwt_n_cycles=cwt_n_cycles,
    n_jobs=2)

# Mark the seed channel with a value of 1.0, so we can see it in the plot
con[np.where(indices[1] == seed)] = 1.0

# Show topography of connectivity from seed
import matplotlib.pyplot as plt
layout = read_layout('Vectorview-all')
title = 'WPLI2 - Visual - Seed %s' % seed_ch
plot_topo_tfr(epochs, con, freqs, layout, title=title)
plt.show()