def test_get_inst_data():
"""Test _get_inst_data."""
raw = read_raw_fif(fname_raw)
raw.crop(tmax=1.)
assert_equal(_get_inst_data(raw), raw._data)
raw.pick_channels(raw.ch_names[:2])
epochs = _segment_raw(raw, 0.5)
assert_equal(_get_inst_data(epochs), epochs._data)
evoked = epochs.average()
assert_equal(_get_inst_data(evoked), evoked.data)
evoked.crop(tmax=0.1)
picks = list(range(2))
freqs = np.array([50., 55.])
n_cycles = 3
tfr = tfr_morlet(evoked, freqs, n_cycles, return_itc=False, picks=picks)
assert_equal(_get_inst_data(tfr), tfr.data)
assert_raises(TypeError, _get_inst_data, 'foo')
def test_get_inst_data():
"""Test _get_inst_data."""
raw = read_raw_fif(fname_raw)
raw.crop(tmax=1.)
assert_equal(_get_inst_data(raw), raw._data)
raw.pick_channels(raw.ch_names[:2])
epochs = _segment_raw(raw, 0.5)
assert_equal(_get_inst_data(epochs), epochs._data)
evoked = epochs.average()
assert_equal(_get_inst_data(evoked), evoked.data)
evoked.crop(tmax=0.1)
picks = list(range(2))
freqs = np.array([50., 55.])
n_cycles = 3
tfr = tfr_morlet(evoked, freqs, n_cycles, return_itc=False, picks=picks)
assert_equal(_get_inst_data(tfr), tfr.data)
pytest.raises(TypeError, _get_inst_data, 'foo')
def make_csd_rest_approx(raw,
frequencies,
events=None,
event_id=None,
tmin=None,
tmax=None,
n_jobs=1,
n_cycles=7.,
decim=4,
segment_length=1.):
'''Approximate CSD for continuous signals by segmenting and computing CSD
on segments.
This function is not used in the "Three time NO" paper - it servers as a
comparison.
Parameters
----------
raw : mne Raw object
Instance of Raw object to use in CSD computation.
frequencies : list of float
List of frequencies to compute CSD for.
events :
Events array in mne format. Optional.
event_id :
Events to use in segmenting. Optional.
tmin :
Time start. If ``events`` was passed then ``tmin`` is with respect to
each event onset.
tmax :
Time end. If ``events`` was passed then ``tmax`` is with respect to
each event onset.
n_jobs :
Number of jobs to use. Defaults to 1.
n_cycles :
Number of cycles to use when computing cross spectral density. Defaults
to 7.
decim :
Decimation factor in time of the cross spectral density result.
Defaults to 4.
segment_length : float
Length of segments to which the signal of interest is divided. Defaults
to 1.
'''
from mne.epochs import _segment_raw
events, tmin, tmax = _deal_with_csd_inputs(tmin, tmax, events, event_id)
sfreq = raw.info['sfreq']
windows_list = list()
for event_idx in range(events.shape[0]):
event_onset = events[event_idx, 0] / sfreq
this_tmin = event_onset + tmin
this_tmax = event_onset + tmax
raw_crop = raw.copy().crop(this_tmin, this_tmax)
windows = _segment_raw(raw_crop,
segment_length=segment_length,
preload=True,
verbose=False)
if len(windows._data) > 0:
windows_list.append(windows)
windows_list = mne.concatenate_epochs(windows_list)
return mne.time_frequency.csd_morlet(windows_list,
frequencies=frequencies,
n_jobs=n_jobs,
n_cycles=n_cycles,
verbose=False,
decim=decim)
