def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(ref_data is None)
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True): # weight tables
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(ref_data is None)
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True): # weight tables
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
def test_set_eeg_reference():
""" Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
# Separate EEG channels from other channel types
picks_eeg = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
picks_other = pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=True, exclude='bads')
# Get the raw EEG data and other channel data
raw_eeg_data = raw[picks_eeg][0]
raw_other_data = raw[picks_other][0]
# Get the rereferenced EEG data and channel other
reref_eeg_data = reref[picks_eeg][0]
unref_eeg_data = reref_eeg_data + ref_data
# Undo rereferencing of EEG channels
reref_other_data = reref[picks_other][0]
# Check that both EEG data and other data is the same
assert_allclose(raw_eeg_data, unref_eeg_data, 1e-6, atol=1e-15)
assert_allclose(raw_other_data, reref_other_data, 1e-6, atol=1e-15)
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
def test_set_eeg_reference():
""" Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
# Separate EEG channels from other channel types
picks_eeg = pick_types(raw.info, meg=False, eeg=True, exclude='bads')
picks_other = pick_types(raw.info,
meg=True,
eeg=False,
eog=True,
stim=True,
exclude='bads')
# Get the raw EEG data and other channel data
raw_eeg_data = raw[picks_eeg][0]
raw_other_data = raw[picks_other][0]
# Get the rereferenced EEG data and channel other
reref_eeg_data = reref[picks_eeg][0]
unref_eeg_data = reref_eeg_data + ref_data
# Undo rereferencing of EEG channels
reref_other_data = reref[picks_other][0]
# Check that both EEG data and other data is the same
assert_array_equal(raw_eeg_data, unref_eeg_data)
assert_array_equal(raw_other_data, reref_other_data)
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
raw.info["projs"] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info["projs"]))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info["projs"]))
assert_true(ref_data is None)
# Test setting an average reference when one was already present
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ["EEG 001", "EEG 002"], copy=True)
assert_true(reref.info["custom_ref_applied"])
_test_reference(raw, reref, ref_data, ["EEG 001", "EEG 002"])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ["EEG 001", "EEG 002"], copy=False)
assert_true(raw is reref)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(ref_data is None)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
# Setting an average reference on a dataset that already contains a custom
# reference should fail
assert_raises(RuntimeError, set_eeg_reference, reref)
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(ref_data is None)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
# Setting an average reference on a dataset that already contains a custom
# reference should fail
assert_raises(RuntimeError, set_eeg_reference, reref)
# if i<ntrials-1:
# for j in np.arange(0,19,1):
# true_eves = np.insert(true_eves, i*20+1, [true_eves_bk[i,0] + (j+1)*4096*2, 0, true_eves_bk[i,2]], axis=0)
#
## true_eves[i*20+1,0] = true_eves[i*20,0] + (j+1)*4096
# elif i == ntrials-1:
# for j in np.arange(0,19,1):
# true_eves = np.append(true_eves, np.asarray([true_eves_bk[-1,0] + (j+1)*4096*2, 0, true_eves_bk[-1,2]]).reshape(1,3),axis=0)
#
#true_eves = true_eves[true_eves[:,0].argsort()]
refChannels = ['EXG1', 'EXG2']
print 'Re-referencing data to', refChannels
(raw, ref_data) = set_eeg_reference(raw, refChannels, copy=False)
raw.filter(l_freq=70, h_freq=1500, picks=np.arange(0, 32, 1), n_jobs=8)
if subj == 'C015':
raw.info['bads'] = ['A20']
#elif subj == 'C012':
# raw.info['bads'] = ['A3','A6','A7','A23','A24', 'A25','A27', 'A28']
if subj == 'S007':
raw.info['bads'] = ['A17', 'A18'] # for S007
trig_list = np.unique(true_eves_bk[:,2])
n_trigs = trig_list.shape[0]
conds = {}
def importbdf(bdfname, nchans=34, refchans=['EXG1', 'EXG2'],
hptsname=None, mask=255, extrachans=[],
exclude=None, verbose=None):
"""Wrapper around mne-python to import BDF files
Parameters
----------
bdfname - Name of the biosemi .bdf filename with full path
nchans - Number of EEG channels (including references)
(Optional) By default, 34 (32 + 2 references)
refchans - list of strings with reference channel names
(Optional) By default ['EXG1','EXG2'].
Use None for average reference.
hptsname - Name of the electrode position file in .hpts format with path
(Optional) By default a 32 channel Biosemi layout is used. If
the nchans is >= 64 and < 96, a 64 channel Biosemi layout is
used. If nchans >= 96, a 96 channel biosemi layout is used.
Formats other than .hpts will also likely work, but behavior
may vary.
mask - Integer mask to use for trigger channel (Default is 255).
extrachans - Additional channels other than EEG and EXG that may be in the
bdf file. These will be marked as MISC in mne-python.
Specify as list of names.
excllude - List of channel names to exclude from importing
verbose - bool, str, int, or None (Optional)
The verbosity of messages to print. If a str, it can be either DEBUG,
INFO, WARNING, ERROR, or CRITICAL.
Returns
-------
raw - MNE raw data object of rereferences and preloaded data
eves - Event list (3 column array as required by mne.Epochs)
Requires
--------
mne-python module > release 0.7
"""
# Default HPTS file
if(hptsname is None):
anlffr_root = os.path.dirname(sys.modules['anlffr'].__file__)
if nchans >= 64 and nchans < 96:
logger.info('Number of channels is greater than 64.'
' Hence loading a 64 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi64'
montage = read_montage(kind=hptsname, path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans >= 96:
logger.info('Number of channels is greater than 96.'
' Hence loading a 96 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi96'
montage = read_montage(kind=hptsname, path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans == 2:
logger.info('Number of channels is 2.'
'Guessing ABR montage.')
montage = None
misc = []
else:
logger.info('Loading a default 32 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi32'
montage = read_montage(kind=hptsname, path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
misc += extrachans
raw = edf.read_raw_edf(bdfname, montage=montage, preload=True,
misc=misc, exclude=exclude, stim_channel='Status')
# Rereference
if refchans is not None:
sys.stdout.write('Re-referencing data to: ' + ' '.join(refchans))
(raw, ref_data) = set_eeg_reference(raw, refchans, copy=False)
raw.info['bads'] += refchans
else:
# Add average reference operator for possible use later
ave_ref_operator = make_eeg_average_ref_proj(raw.info, activate=False)
raw = raw.add_proj(ave_ref_operator)
eves = find_events(raw, shortest_event=1, mask=mask)
return (raw, eves)
def importbdf(bdfname,
nchans=34,
refchans=['EXG1', 'EXG2'],
hptsname=None,
mask=255,
extrachans=[],
exclude=None,
verbose=None):
"""Wrapper around mne-python to import BDF files
Parameters
----------
bdfname - Name of the biosemi .bdf filename with full path
nchans - Number of EEG channels (including references)
(Optional) By default, 34 (32 + 2 references)
refchans - list of strings with reference channel names
(Optional) By default ['EXG1','EXG2'].
Use None for average reference.
hptsname - Name of the electrode position file in .hpts format with path
(Optional) By default a 32 channel Biosemi layout is used. If
the nchans is >= 64 and < 96, a 64 channel Biosemi layout is
used. If nchans >= 96, a 96 channel biosemi layout is used.
Formats other than .hpts will also likely work, but behavior
may vary.
mask - Integer mask to use for trigger channel (Default is 255).
extrachans - Additional channels other than EEG and EXG that may be in the
bdf file. These will be marked as MISC in mne-python.
Specify as list of names.
excllude - List of channel names to exclude from importing
verbose - bool, str, int, or None (Optional)
The verbosity of messages to print. If a str, it can be either DEBUG,
INFO, WARNING, ERROR, or CRITICAL.
Returns
-------
raw - MNE raw data object of rereferences and preloaded data
eves - Event list (3 column array as required by mne.Epochs)
Requires
--------
mne-python module > release 0.7
"""
# Default HPTS file
if (hptsname is None):
anlffr_root = os.path.dirname(sys.modules['anlffr'].__file__)
if nchans >= 64 and nchans < 96:
logger.info('Number of channels is greater than 64.'
' Hence loading a 64 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi64'
montage = read_montage(kind=hptsname,
path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans >= 96:
logger.info('Number of channels is greater than 96.'
' Hence loading a 96 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi96'
montage = read_montage(kind=hptsname,
path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
else:
if nchans == 2:
logger.info('Number of channels is 2.'
'Guessing ABR montage.')
montage = None
misc = []
else:
logger.info('Loading a default 32 channel montage.')
hptspath = os.path.join(anlffr_root, 'helper/sysfiles/')
hptsname = 'biosemi32'
montage = read_montage(kind=hptsname,
path=hptspath,
transform=True)
misc = ['EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8']
misc += extrachans
raw = edf.read_raw_edf(bdfname,
montage=montage,
preload=True,
misc=misc,
exclude=exclude,
stim_channel='Status')
# Rereference
if refchans is not None:
sys.stdout.write('Re-referencing data to: ' + ' '.join(refchans))
(raw, ref_data) = set_eeg_reference(raw, refchans, copy=False)
raw.info['bads'] += refchans
else:
# Add average reference operator for possible use later
ave_ref_operator = make_eeg_average_ref_proj(raw.info, activate=False)
raw = raw.add_proj(ave_ref_operator)
eves = find_events(raw, shortest_event=1, mask=mask)
return (raw, eves)
