Note

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Morlet Wavelet spectrogram plot

Below is a code sample for plotting wavelet spectrograms

from ieeg.viz.ensemble import chan_grid
from bids import BIDSLayout
from ieeg.navigate import channel_outlier_marker, trial_ieeg, outliers_to_nan
from ieeg.io import raw_from_layout
from ieeg.calc.scaling import rescale
from ieeg.timefreq.utils import wavelet_scaleogram, crop_pad
from ieeg.viz.parula import parula_map
import numpy as np
import mne

Load Data

bids_root = mne.datasets.epilepsy_ecog.data_path()
layout = BIDSLayout(bids_root)
filt = raw_from_layout(layout, subject="pt1", preload=True,
                      extension=".vhdr")

Extracting parameters from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_task-ictal_ieeg.vhdr...
Setting channel info structure...
Reading events from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_task-ictal_events.tsv.
Reading channel info from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_task-ictal_channels.tsv.
Reading electrode coords from /home/docs/mne_data/MNE-epilepsy-ecog-data/sub-pt1/ses-presurgery/ieeg/sub-pt1_ses-presurgery_space-fsaverage_electrodes.tsv.
/home/docs/checkouts/readthedocs.org/user_builds/ieeg-pipelines/checkouts/latest/ieeg/io.py:283: RuntimeWarning: DigMontage is only a subset of info. There are 3 channel positions not present in the DigMontage. The channels missing from the montage are:

['RQ1', 'RQ2', 'N/A'].

Consider using inst.rename_channels to match the montage nomenclature, or inst.set_channel_types if these are not EEG channels, or use the on_missing parameter if the channel positions are allowed to be unknown in your analyses.
  whole_raw = read_raw_bids(bids_path=BIDS_path, verbose=verbose)
/home/docs/checkouts/readthedocs.org/user_builds/ieeg-pipelines/checkouts/latest/ieeg/io.py:283: RuntimeWarning: Unable to map the following column(s) to to MNE:
outcome: S
engel_score: 1.0
ilae_score: 2.0
date_follow_up: n/a
ethnicity: 0.0
years_follow_up: 3.0
site: NIH
clinical_complexity: 1.0
  whole_raw = read_raw_bids(bids_path=BIDS_path, verbose=verbose)
Reading 0 ... 269079  =      0.000 ...   269.079 secs...

Crop raw data to minimize processing time

new = filt.copy()

# Mark channel outliers as bad
new.info['bads'] = channel_outlier_marker(new, 5)

# Exclude bad channels
good = new.copy().drop_channels(new.info['bads'])
good.load_data()

# Remove intermediates from mem
del new

outlier round 1 channels: ['N/A']
outlier round 2 channels: ['N/A', 'RQ2']
outlier round 3 channels: ['N/A', 'RQ2', 'AST2']
outlier round 4 channels: ['N/A', 'RQ2', 'AST2', 'AD3']
outlier round 5 channels: ['N/A', 'RQ2', 'AST2', 'AD3', 'PD4']
outlier round 6 channels: ['N/A', 'RQ2', 'AST2', 'AD3', 'PD4', 'G32']

Calculate Spectra

for epoch, t in zip(
        ("onset", "offset"),
        ((-1, 0), (-1, 1)),):
    times = [None, None]
    times[0] = t[0] - 0.5
    times[1] = t[1] + 0.5
    trials = trial_ieeg(good, epoch, times, preload=True)
    outliers_to_nan(trials, outliers=10)
    spec = wavelet_scaleogram(trials, n_jobs=1, decim=int(
        good.info['sfreq'] / 200))
    crop_pad(spec, "0.5s")
    if epoch == "onset":
        base = spec.copy()
        continue
    spec_a = rescale(spec, base, copy=True, mode='ratio').average(
        lambda x: np.nanmean(x, axis=0), copy=True)
    spec_a._data = np.log10(spec_a._data) * 20

Used Annotations descriptions: [np.str_('AD1-4, ATT1,2'), np.str_('AST1,3'), np.str_('G16'), np.str_('PD'), np.str_('SLT1-3'), np.str_('offset'), np.str_('onset')]
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 1 events and 2001 original time points ...
0 bad epochs dropped
Data is self data: True
[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  12 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  24 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  31 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  60 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  84 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  92 out of  92 | elapsed:    0.1s finished
Used Annotations descriptions: [np.str_('AD1-4, ATT1,2'), np.str_('AST1,3'), np.str_('G16'), np.str_('PD'), np.str_('SLT1-3'), np.str_('offset'), np.str_('onset')]
Not setting metadata
1 matching events found
No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 1 events and 3001 original time points ...
0 bad epochs dropped
Data is self data: True
[Parallel(n_jobs=1)]: Done   1 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done   4 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done   7 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  12 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  24 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  31 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done  60 tasks      | elapsed:    0.2s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.2s
[Parallel(n_jobs=1)]: Done  84 tasks      | elapsed:    0.2s
[Parallel(n_jobs=1)]: Done  92 out of  92 | elapsed:    0.2s finished
Applying baseline correction (mode: ratio)

Plot data

chan_grid(spec_a, vlim=(-2, 20), cmap=parula_map)
  • , G1, G2, G3, G4, G5, G6, G7, G8, G9, G10, G13, G14, G15, G16, G17, G18, G19, G20, G21, G22, G23, G11, G12, G24, G25, G26, G27, G28, G29, G30, G31, ATT1, ATT2, ATT3, ATT4, ATT5, ATT6, ATT7, ATT8, PLT1, PLT2, PLT3, PLT4, PLT5, PLT6, AST1, AST3, AST4, PST1, PST2, PST3, PST4, AD1, AD2, AD4, PD1, PD2, PD3, RQ1, SF1
  • , SF2, SF3, SF4, SF5, SF6, IF1, IF2, IF3, IF4, IF5, IF6, ILT1, ILT2, ILT3, ILT4, MLT1, MLT2, MLT3, MLT4, SLT1, SLT2, SLT3, SLT4
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[<Figure size 1200x1800 with 60 Axes>, <Figure size 1200x1800 with 60 Axes>]

Total running time of the script: (0 minutes 25.905 seconds)

Estimated memory usage: 1563 MB

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