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import os.path as op
import numpy as np
from numpy.testing import assert_allclose
import pytest
from mne.datasets import testing
from mne import find_events, Epochs, pick_types
from mne.io import read_raw_fif
from mne.io.constants import FIFF
from mne.label import read_label
from mne.minimum_norm import (read_inverse_operator, apply_inverse_epochs,
prepare_inverse_operator, INVERSE_METHODS)
from mne.minimum_norm.time_frequency import (source_band_induced_power,
source_induced_power,
compute_source_psd,
compute_source_psd_epochs)
from mne.time_frequency.multitaper import psd_array_multitaper
data_path = testing.data_path(download=False)
fname_inv = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-meg-eeg-oct-4-meg-inv.fif')
fname_data = op.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
fname_label = op.join(data_path, 'MEG', 'sample', 'labels', 'Aud-lh.label')
@testing.requires_testing_data
@pytest.mark.parametrize('method', INVERSE_METHODS)
def test_tfr_with_inverse_operator(method):
"""Test time freq with MNE inverse computation."""
tmin, tmax, event_id = -0.2, 0.5, 1
# Setup for reading the raw data
raw = read_raw_fif(fname_data)
events = find_events(raw, stim_channel='STI 014')
inv = read_inverse_operator(fname_inv)
inv = prepare_inverse_operator(inv, nave=1, lambda2=1. / 9., method=method)
raw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=False, exclude='bads')
# Load condition 1
event_id = 1
events3 = events[:3] # take 3 events to keep the computation time low
epochs = Epochs(raw, events3, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
preload=True)
# Compute a source estimate per frequency band
bands = dict(alpha=[10, 10])
label = read_label(fname_label)
# XXX someday we should refactor this so that you don't have to pass
# method -- maybe `prepare_inverse_operator` should add a `method`
# to it and when `prepared=True` the value passed in can be ignored
# (or better, default method=None means "dSPM if unprepared" and if they
# actually pass a value, we check against `inv['method']`)
stcs = source_band_induced_power(epochs, inv, bands, method=method,
n_cycles=2, use_fft=False, pca=True,
label=label, prepared=True)
stc = stcs['alpha']
assert len(stcs) == len(list(bands.keys()))
assert np.all(stc.data > 0)
assert_allclose(stc.times, epochs.times, atol=1e-6)
stcs_no_pca = source_band_induced_power(epochs, inv, bands, method=method,
n_cycles=2, use_fft=False,
pca=False, label=label,
prepared=True)
assert_allclose(stcs['alpha'].data, stcs_no_pca['alpha'].data)
# Compute a source estimate per frequency band
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
preload=True)
freqs = np.arange(7, 30, 2) # define frequencies of interest
power, phase_lock = source_induced_power(
epochs, inv, freqs, label, baseline=(-0.1, 0), baseline_mode='percent',
n_cycles=2, n_jobs=None, method=method, prepared=True)
assert np.all(phase_lock > 0)
assert np.all(phase_lock <= 1)
assert 5 < np.max(power) < 7
@testing.requires_testing_data
@pytest.mark.parametrize('method', INVERSE_METHODS)
@pytest.mark.parametrize('pick_ori', (None, 'normal')) # XXX vector someday?
def test_source_psd(method, pick_ori):
"""Test source PSD computation from raw."""
raw = read_raw_fif(fname_data)
raw.crop(0, 5).load_data()
inverse_operator = read_inverse_operator(fname_inv)
fmin, fmax = 40, 65 # Hz
n_fft = 512
assert inverse_operator['source_ori'] == FIFF.FIFFV_MNE_FREE_ORI
stc, ev = compute_source_psd(
raw, inverse_operator, lambda2=1. / 9., method=method,
fmin=fmin, fmax=fmax, pick_ori=pick_ori, n_fft=n_fft,
overlap=0., return_sensor=True, dB=True)
assert ev.data.shape == (len(ev.info['ch_names']), len(stc.times))
assert ev.times[0] >= fmin
assert ev.times[-1] <= fmax
# Time max at line frequency (60 Hz in US)
assert 58 <= ev.times[np.argmax(np.sum(ev.data, axis=0))] <= 61
assert ev.nave == 2
assert stc.shape[0] == inverse_operator['nsource']
assert stc.times[0] >= fmin
assert stc.times[-1] <= fmax
assert 58 <= stc.times[np.argmax(np.sum(stc.data, axis=0))] <= 61
if method in ('sLORETA', 'dSPM'):
stc_dspm = stc
stc_mne, _ = compute_source_psd(
raw, inverse_operator, lambda2=1. / 9., method='MNE',
fmin=fmin, fmax=fmax, pick_ori=pick_ori, n_fft=n_fft,
overlap=0., return_sensor=True, dB=True)
# normalize each source point by its power after undoing the dB
stc_dspm.data = 10 ** (stc_dspm.data / 10.)
stc_dspm /= stc_dspm.mean()
stc_mne.data = 10 ** (stc_mne.data / 10.)
stc_mne /= stc_mne.mean()
assert_allclose(stc_dspm.data, stc_mne.data, atol=1e-4)
@testing.requires_testing_data
@pytest.mark.parametrize('method', INVERSE_METHODS)
def test_source_psd_epochs(method):
"""Test multi-taper source PSD computation in label from epochs."""
raw = read_raw_fif(fname_data)
inverse_operator = read_inverse_operator(fname_inv)
label = read_label(fname_label)
event_id, tmin, tmax = 1, -0.2, 0.5
lambda2 = 1. / 9.
bandwidth = 8.
fmin, fmax = 0, 100
picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
ecg=True, eog=True, include=['STI 014'],
exclude='bads')
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
events = find_events(raw, stim_channel='STI 014')
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=reject)
# only look at one epoch
epochs.drop_bad()
one_epochs = epochs[:1]
inv = prepare_inverse_operator(inverse_operator, nave=1,
lambda2=1. / 9., method="dSPM")
# return list
stc_psd = compute_source_psd_epochs(one_epochs, inv,
lambda2=lambda2, method=method,
pick_ori="normal", label=label,
bandwidth=bandwidth,
fmin=fmin, fmax=fmax,
prepared=True)[0]
# return generator
stcs = compute_source_psd_epochs(one_epochs, inv,
lambda2=lambda2, method=method,
pick_ori="normal", label=label,
bandwidth=bandwidth,
fmin=fmin, fmax=fmax,
return_generator=True,
prepared=True)
for stc in stcs:
stc_psd_gen = stc
assert_allclose(stc_psd.data, stc_psd_gen.data, atol=1e-7)
# compare with direct computation
stc = apply_inverse_epochs(one_epochs, inv,
lambda2=lambda2, method=method,
pick_ori="normal", label=label,
prepared=True)[0]
sfreq = epochs.info['sfreq']
psd, freqs = psd_array_multitaper(stc.data, sfreq=sfreq,
bandwidth=bandwidth, fmin=fmin,
fmax=fmax)
assert_allclose(psd, stc_psd.data, atol=1e-7)
assert_allclose(freqs, stc_psd.times)
# Check corner cases caused by tiny bandwidth
with pytest.raises(ValueError, match='use a value of at least'):
compute_source_psd_epochs(
one_epochs, inv, lambda2=lambda2, method=method,
pick_ori="normal", label=label, bandwidth=0.01, low_bias=True,
fmin=fmin, fmax=fmax, return_generator=False, prepared=True)
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