def cli(matfiles, savename, rec_type, infosrc):
"""
Convert brainstorm epochs to mne.Epochs object
"""
if infosrc:
if rec_type is 'ds':
from mne.io import read_raw_ctf as read_raw
elif rec_type is 'fif':
from mne.io import Raw as read_raw
with nostdout():
raw_with_info = read_raw(infosrc)
isFirst = True
for fname in matfiles:
with nostdout():
mat_epoch = sio.loadmat(fname)
# click.echo(mat_epoch)
if isFirst:
data = mat_epoch['F']
times = mat_epoch['Time']
# print times[0,-1]
isFirst = False
else:
data = np.dstack((data, mat_epoch['F']))
# click.echo(data.shape)
data = data.transpose((2,0,1))
n_channels = data.shape[1]
sfreq = times.shape[1] / (times[0,-1] + times[0,1])
if infosrc:
if rec_type is 'ds':
from mne.io import read_raw_ctf as read_raw
elif rec_type is 'fif':
from mne.io import Raw as read_raw
with nostdout():
raw_with_info = read_raw(infosrc)
good_info = raw_with_info.info
# click.echo(len(good_info['ch_names']))
ch_types = [channel_type(good_info, idx) for idx in range(n_channels)]
# click.echo(len(ch_types))
info = create_info(ch_names=good_info['ch_names'], sfreq=sfreq, ch_types=ch_types)
else:
ch_types='mag'
info = create_info(n_channels, sfreq, ch_types)
with nostdout():
epochs = EpochsArray(data, info)
epochs.save(savename)
def test_picks_by_channels():
"""Test creating pick_lists."""
rng = np.random.RandomState(909)
test_data = rng.random_sample((4, 2000))
ch_names = ['MEG %03d' % i for i in [1, 2, 3, 4]]
ch_types = ['grad', 'mag', 'mag', 'eeg']
sfreq = 250.0
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
_assert_channel_types(info)
raw = RawArray(test_data, info)
pick_list = _picks_by_type(raw.info)
assert_equal(len(pick_list), 3)
assert_equal(pick_list[0][0], 'mag')
pick_list2 = _picks_by_type(raw.info, meg_combined=False)
assert_equal(len(pick_list), len(pick_list2))
assert_equal(pick_list2[0][0], 'mag')
pick_list2 = _picks_by_type(raw.info, meg_combined=True)
assert_equal(len(pick_list), len(pick_list2) + 1)
assert_equal(pick_list2[0][0], 'meg')
test_data = rng.random_sample((4, 2000))
ch_names = ['MEG %03d' % i for i in [1, 2, 3, 4]]
ch_types = ['mag', 'mag', 'mag', 'mag']
sfreq = 250.0
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
raw = RawArray(test_data, info)
# This acts as a set, not an order
assert_array_equal(pick_channels(info['ch_names'], ['MEG 002', 'MEG 001']),
[0, 1])
# Make sure checks for list input work.
pytest.raises(ValueError, pick_channels, ch_names, 'MEG 001')
pytest.raises(ValueError, pick_channels, ch_names, ['MEG 001'], 'hi')
pick_list = _picks_by_type(raw.info)
assert_equal(len(pick_list), 1)
assert_equal(pick_list[0][0], 'mag')
pick_list2 = _picks_by_type(raw.info, meg_combined=True)
assert_equal(len(pick_list), len(pick_list2))
assert_equal(pick_list2[0][0], 'mag')
# pick_types type check
pytest.raises(ValueError, raw.pick_types, eeg='string')
# duplicate check
names = ['MEG 002', 'MEG 002']
assert len(pick_channels(raw.info['ch_names'], names)) == 1
assert len(raw.copy().pick_channels(names)[0][0]) == 1
def _generate_coherence_data():
"""Create an epochs object with coherence at 22Hz between channels 1 and 3.
A base 10 Hz sine wave is generated for all channels, but with different
phases, which means no actual coherence. A 22Hz sine wave is laid on top
for channels 1 and 3, with the same phase, so there is coherence between
these channels.
"""
ch_names = ['CH1', 'CH2', 'CH3']
sfreq = 50.
info = mne.create_info(ch_names, sfreq, 'eeg')
tstep = 1. / sfreq
n_samples = int(10 * sfreq) # 10 seconds of data
times = np.arange(n_samples) * tstep
events = np.array([[0, 1, 1]]) # one event
# Phases for the signals
phases = np.arange(info['nchan']) * 0.3 * np.pi
# Generate 10 Hz sine waves with different phases
signal = np.vstack([np.sin(times * 2 * np.pi * 10 + phase)
for phase in phases])
data = np.zeros((1, info['nchan'], n_samples))
data[0, :, :] = signal
# Generate 22Hz sine wave at the first and last electrodes with the same
# phase.
signal = np.sin(times * 2 * np.pi * 22)
data[0, [0, -1], :] += signal
return mne.EpochsArray(data, info, events, baseline=(0, times[-1]))
def test_apply_function_verbose():
"""Test apply function verbosity
"""
n_chan = 2
n_times = 3
ch_names = [str(ii) for ii in range(n_chan)]
raw = RawArray(np.zeros((n_chan, n_times)),
create_info(ch_names, 1., 'mag'))
# test return types in both code paths (parallel / 1 job)
assert_raises(TypeError, raw.apply_function, bad_1,
None, None, 1)
assert_raises(ValueError, raw.apply_function, bad_2,
None, None, 1)
assert_raises(TypeError, raw.apply_function, bad_1,
None, None, 2)
assert_raises(ValueError, raw.apply_function, bad_2,
None, None, 2)
# check our arguments
tempdir = _TempDir()
test_name = op.join(tempdir, 'test.log')
set_log_file(test_name)
try:
raw.apply_function(printer, None, None, 1, verbose=False)
with open(test_name) as fid:
assert_equal(len(fid.readlines()), 0)
raw.apply_function(printer, None, None, 1, verbose=True)
with open(test_name) as fid:
assert_equal(len(fid.readlines()), n_chan)
finally:
set_log_file(None)
def test_pick_seeg_ecog():
"""Test picking with sEEG and ECoG
"""
names = 'A1 A2 Fz O OTp1 OTp2 E1 OTp3 E2 E3'.split()
types = 'mag mag eeg eeg seeg seeg ecog seeg ecog ecog'.split()
info = create_info(names, 1024., types)
idx = channel_indices_by_type(info)
assert_array_equal(idx['mag'], [0, 1])
assert_array_equal(idx['eeg'], [2, 3])
assert_array_equal(idx['seeg'], [4, 5, 7])
assert_array_equal(idx['ecog'], [6, 8, 9])
assert_array_equal(pick_types(info, meg=False, seeg=True), [4, 5, 7])
for i, t in enumerate(types):
assert_equal(channel_type(info, i), types[i])
raw = RawArray(np.zeros((len(names), 10)), info)
events = np.array([[1, 0, 0], [2, 0, 0]])
epochs = Epochs(raw, events, {'event': 0}, -1e-5, 1e-5, add_eeg_ref=False)
evoked = epochs.average(pick_types(epochs.info, meg=True, seeg=True))
e_seeg = evoked.copy().pick_types(meg=False, seeg=True)
for l, r in zip(e_seeg.ch_names, [names[4], names[5], names[7]]):
assert_equal(l, r)
# Deal with constant debacle
raw = read_raw_fif(op.join(io_dir, 'tests', 'data',
'test_chpi_raw_sss.fif'), add_eeg_ref=False)
assert_equal(len(pick_types(raw.info, meg=False, seeg=True, ecog=True)), 0)
def test_multitaper_psd():
""" Test multi-taper PSD computation """
import nitime as ni
n_times = 1000
n_channels = 5
data = np.random.RandomState(0).randn(n_channels, n_times)
sfreq = 500
info = create_info(n_channels, sfreq, 'eeg')
raw = RawArray(data, info)
assert_raises(ValueError, psd_multitaper, raw, sfreq, normalization='foo')
ni_5 = (LooseVersion(ni.__version__) >= LooseVersion('0.5'))
norm = 'full' if ni_5 else 'length'
for adaptive, n_jobs in zip((False, True, True), (1, 1, 2)):
psd, freqs = psd_multitaper(raw, adaptive=adaptive,
n_jobs=n_jobs,
normalization=norm)
freqs_ni, psd_ni, _ = ni.algorithms.spectral.multi_taper_psd(
data, sfreq, adaptive=adaptive, jackknife=False)
# for some reason nitime returns n_times + 1 frequency points
# causing the value at 0 to be different
assert_array_almost_equal(psd[:, 1:], psd_ni[:, 1:-1], decimal=3)
assert_array_almost_equal(freqs, freqs_ni[:-1])
def to_mne(self, states=True):
"""Convert data into an MNE Raw object.
Parameters
----------
states : bool
Whether to include state channels in the output.
Returns
-------
raw : instance of MNE Raw
The data in MNE Raw format.
"""
if len(self.ch_names) == 0:
ch_names = ['ch_{}'.format(ii)
for ii in range(self.data.shape[0])]
ch_types = ['eeg'] * len(ch_names)
if states is True:
state_names = ['state_{}'.format(ii)
for ii in range(self.states.shape[0])]
state_types = ['misc'] * len(state_names)
data = np.vstack([self.data, self.states])
else:
state_names = state_types = []
data = self.data
info = mne.create_info(ch_names + state_names, self.sfreq,
ch_types + state_types)
raw = mne.io.RawArray(data, info)
return raw
def test_add_channels():
"""Test tfr splitting / re-appending channel types."""
data = np.zeros((6, 2, 3))
times = np.array([.1, .2, .3])
freqs = np.array([.10, .20])
info = mne.create_info(
['MEG 001', 'MEG 002', 'MEG 003', 'EEG 001', 'EEG 002', 'STIM 001'],
1000., ['mag', 'mag', 'mag', 'eeg', 'eeg', 'stim'])
tfr = AverageTFR(info, data=data, times=times, freqs=freqs,
nave=20, comment='test', method='crazy-tfr')
tfr_eeg = tfr.copy().pick_types(meg=False, eeg=True)
tfr_meg = tfr.copy().pick_types(meg=True)
tfr_stim = tfr.copy().pick_types(meg=False, stim=True)
tfr_eeg_meg = tfr.copy().pick_types(meg=True, eeg=True)
tfr_new = tfr_meg.copy().add_channels([tfr_eeg, tfr_stim])
assert all(ch in tfr_new.ch_names
for ch in tfr_stim.ch_names + tfr_meg.ch_names)
tfr_new = tfr_meg.copy().add_channels([tfr_eeg])
assert all(ch in tfr_new.ch_names
for ch in tfr.ch_names if ch != 'STIM 001')
assert_array_equal(tfr_new.data, tfr_eeg_meg.data)
assert all(ch not in tfr_new.ch_names for ch in tfr_stim.ch_names)
# Now test errors
tfr_badsf = tfr_eeg.copy()
tfr_badsf.info['sfreq'] = 3.1415927
tfr_eeg = tfr_eeg.crop(-.1, .1)
pytest.raises(RuntimeError, tfr_meg.add_channels, [tfr_badsf])
pytest.raises(AssertionError, tfr_meg.add_channels, [tfr_eeg])
pytest.raises(ValueError, tfr_meg.add_channels, [tfr_meg])
pytest.raises(TypeError, tfr_meg.add_channels, tfr_badsf)
def test_mne_c_design():
"""Test MNE-C filter design."""
tempdir = _TempDir()
temp_fname = op.join(tempdir, 'test_raw.fif')
out_fname = op.join(tempdir, 'test_c_raw.fif')
x = np.zeros((1, 10001))
x[0, 5000] = 1.
time_sl = slice(5000 - 4096, 5000 + 4097)
sfreq = 1000.
RawArray(x, create_info(1, sfreq, 'eeg')).save(temp_fname)
tols = dict(rtol=1e-4, atol=1e-4)
cmd = ('mne_process_raw', '--projoff', '--raw', temp_fname,
'--save', out_fname)
run_subprocess(cmd)
h = design_mne_c_filter(sfreq, None, 40)
h_c = read_raw_fif(out_fname)[0][0][0][time_sl]
assert_allclose(h, h_c, **tols)
run_subprocess(cmd + ('--highpass', '5', '--highpassw', '2.5'))
h = design_mne_c_filter(sfreq, 5, 40, 2.5)
h_c = read_raw_fif(out_fname)[0][0][0][time_sl]
assert_allclose(h, h_c, **tols)
run_subprocess(cmd + ('--lowpass', '1000', '--highpass', '10'))
h = design_mne_c_filter(sfreq, 10, None, verbose=True)
h_c = read_raw_fif(out_fname)[0][0][0][time_sl]
assert_allclose(h, h_c, **tols)
def test_orig_units():
"""Test the error handling for original units."""
# Should work fine
info = create_info(ch_names=['Cz'], sfreq=100, ch_types='eeg')
BaseRaw(info, last_samps=[1], orig_units={'Cz': 'nV'})
# Should complain that channel Cz does not have a corresponding original
# unit.
with pytest.raises(ValueError, match='has no associated original unit.'):
info = create_info(ch_names=['Cz'], sfreq=100, ch_types='eeg')
BaseRaw(info, last_samps=[1], orig_units={'not_Cz': 'nV'})
# Test that a non-dict orig_units argument raises a ValueError
with pytest.raises(ValueError, match='orig_units must be of type dict'):
info = create_info(ch_names=['Cz'], sfreq=100, ch_types='eeg')
BaseRaw(info, last_samps=[1], orig_units=True)
def test_bad_channels():
"""Test exception when unsupported channels are used."""
chs = [i for i in _kind_dict]
data_chs = _DATA_CH_TYPES_SPLIT + ['eog']
chs_bad = list(set(chs) - set(data_chs))
info = create_info(len(chs), 500, chs)
data = np.random.rand(len(chs), 50)
raw = RawArray(data, info)
data = np.random.rand(100, len(chs), 50)
epochs = EpochsArray(data, info)
n_components = 0.9
ica = ICA(n_components=n_components, method='fastica')
for inst in [raw, epochs]:
for ch in chs_bad:
# Test case for only bad channels
picks_bad1 = pick_types(inst.info, meg=False,
**{str(ch): True})
# Test case for good and bad channels
picks_bad2 = pick_types(inst.info, meg=True,
**{str(ch): True})
assert_raises(ValueError, ica.fit, inst, picks=picks_bad1)
assert_raises(ValueError, ica.fit, inst, picks=picks_bad2)
assert_raises(ValueError, ica.fit, inst, picks=[])
def test_filter_picks():
"""Test filtering default channel picks"""
ch_types = ['mag', 'grad', 'eeg', 'seeg', 'misc', 'stim']
info = create_info(ch_names=ch_types, ch_types=ch_types, sfreq=256)
raw = RawArray(data=np.zeros((len(ch_types), 1000)), info=info)
# -- Deal with meg mag grad exception
ch_types = ('misc', 'stim', 'meg', 'eeg', 'seeg')
# -- Filter data channels
for ch_type in ('mag', 'grad', 'eeg', 'seeg'):
picks = dict((ch, ch == ch_type) for ch in ch_types)
picks['meg'] = ch_type if ch_type in ('mag', 'grad') else False
raw_ = raw.pick_types(copy=True, **picks)
# Avoid RuntimeWarning due to Attenuation
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw_.filter(10, 30)
assert_true(len(w) == 1)
# -- Error if no data channel
for ch_type in ('misc', 'stim'):
picks = dict((ch, ch == ch_type) for ch in ch_types)
raw_ = raw.pick_types(copy=True, **picks)
assert_raises(RuntimeError, raw_.filter, 10, 30)
def concatenate_epochs( epoch1, epoch2, ch_name, reversal = False ):
epoch1_arr = epoch1.get_data()
epoch2_arr = epoch2.get_data()
comb_arr = np.concatenate( [epoch1_arr, epoch2_arr] )
comb_arr_sum = np.zeros(len(comb_arr[0][0]))
for idx in range(0, len(comb_arr)):
temp = comb_arr[idx][0]
comb_arr_sum = comb_arr_sum + temp
comb_arr_avg = np.array( [comb_arr_sum/len(comb_arr)] ) # get type 2d array
if reversal:
comb_arr_avg = comb_arr_avg * (-1)
info_comb = mne.create_info(
ch_names = [ch_name]
, sfreq = epoch1.info['sfreq']
, ch_types = 'eeg'
)
evoked = mne.EvokedArray(comb_arr_avg, info_comb, tmin=tmin)
# evoked.plot()
return evoked
def test_continuous_regression_with_overlap():
"""Test regression with overlap correction."""
signal = np.zeros(100000)
times = [1000, 2500, 3000, 5000, 5250, 7000, 7250, 8000]
events = np.zeros((len(times), 3), int)
events[:, 2] = 1
events[:, 0] = times
signal[events[:, 0]] = 1.
effect = hann(101)
signal = np.convolve(signal, effect)[:len(signal)]
raw = RawArray(signal[np.newaxis, :], mne.create_info(1, 100, 'eeg'))
assert_allclose(effect, linear_regression_raw(
raw, events, {1: 1}, tmin=0)[1].data.flatten())
# test that sklearn solvers can be used
from sklearn.linear_model.ridge import ridge_regression
def solver(X, y):
return ridge_regression(X, y, alpha=0.)
assert_allclose(effect, linear_regression_raw(
raw, events, tmin=0, solver=solver)['1'].data.flatten())
# test bad solvers
def solT(X, y):
return ridge_regression(X, y, alpha=0.).T
assert_raises(ValueError, linear_regression_raw, raw, events, solver=solT)
assert_raises(ValueError, linear_regression_raw, raw, events, solver='err')
assert_raises(TypeError, linear_regression_raw, raw, events, solver=0)
def test_multitaper_psd():
"""Test multi-taper PSD computation."""
import nitime as ni
for n_times in (100, 101):
n_channels = 5
data = np.random.RandomState(0).randn(n_channels, n_times)
sfreq = 500
info = create_info(n_channels, sfreq, 'eeg')
raw = RawArray(data, info)
pytest.raises(ValueError, psd_multitaper, raw, sfreq,
normalization='foo')
ni_5 = (LooseVersion(ni.__version__) >= LooseVersion('0.5'))
norm = 'full' if ni_5 else 'length'
for adaptive, n_jobs in zip((False, True, True), (1, 1, 2)):
psd, freqs = psd_multitaper(raw, adaptive=adaptive,
n_jobs=n_jobs,
normalization=norm)
with warnings.catch_warnings(record=True): # nitime integers
freqs_ni, psd_ni, _ = ni.algorithms.spectral.multi_taper_psd(
data, sfreq, adaptive=adaptive, jackknife=False)
assert_array_almost_equal(psd, psd_ni, decimal=4)
if n_times % 2 == 0:
# nitime's frequency definitions must be incorrect,
# they give the same values for 100 and 101 samples
assert_array_almost_equal(freqs, freqs_ni)
with pytest.raises(ValueError, match='use a value of at least'):
psd_multitaper(raw, bandwidth=4.9)
def test_snr():
"""Test trial to trial coherence"""
raw = mne.io.Raw(raw_fname)
sfreq = int(raw.info['sfreq'])
data, times = raw[0, :5 * sfreq]
# Create fake epochs from copies of the raw + noise
n_epochs = 40
noise_amp = .01 * data.max()
data = np.tile(data, [n_epochs, 1, 1])
data += noise_amp * rng.randn(*data.shape)
info = mne.create_info(['ch1'], raw.info['sfreq'], 'eeg')
ev = np.vstack([np.arange(n_epochs),
np.zeros(n_epochs),
np.ones(n_epochs)]).T.astype(int)
epochs = mne.epochs.EpochsArray(data, info, ev)
# Test CC
cc = snr_epochs(epochs, kind='corr')
assert_true((cc > .99).all())
# Test coherence
coh, freqs = snr_epochs(epochs, fmin=2, kind='coh')
assert_true((coh.mean(-1) > .99).all())
# Test random signal
data_rand = 10*rng.randn(*data.shape)
epochs_rand = mne.epochs.EpochsArray(data_rand, info, ev)
cc = snr_epochs(epochs_rand, kind='corr')
assert_true(cc.mean() < .02)
# Test incorrect inputs
assert_raises(ValueError, snr_epochs, epochs, kind='foo')
def write_mnefiff(data, filename):
"""Export data to MNE using FIFF format.
Parameters
----------
data : instance of ChanTime
data with only one trial
filename : path to file
file to export to (include '.mat')
Notes
-----
It cannot store data larger than 2 GB.
The data is assumed to have only EEG electrodes.
It overwrites a file if it exists.
"""
from mne import create_info, set_log_level
from mne.io import RawArray
set_log_level(WARNING)
TRIAL = 0
info = create_info(list(data.axis['chan'][TRIAL]), data.s_freq, ['eeg', ] *
data.number_of('chan')[TRIAL])
UNITS = 1e-6 # mne wants data in uV
fiff = RawArray(data.data[0] * UNITS, info)
if data.attr['chan']:
fiff.set_channel_positions(data.attr['chan'].return_xyz(),
data.attr['chan'].return_label())
fiff.save(filename, overwrite=True)
def creat_mne_raw_object(fname,read_events):
# Read EEG file
data = pd.read_csv(fname)
# get chanel names
ch_names = list(data.columns[1:])
# read EEG standard montage from mne
montage = read_montage('standard_1005',ch_names)
ch_type = ['eeg']*len(ch_names)
data = 1e-6*np.array(data[ch_names]).T
if read_events:
# events file
ev_fname = fname.replace('_data','_events')
# read event file
events = pd.read_csv(ev_fname)
events_names = events.columns[1:]
events_data = np.array(events[events_names]).T
# define channel type, the first is EEG, the last 6 are stimulations
ch_type.extend(['stim']*6)
ch_names.extend(events_names)
# concatenate event file and data
data = np.concatenate((data,events_data))
# create and populate MNE info structure
info = create_info(ch_names,sfreq=500.0, ch_types=ch_type, montage=montage)
info['filename'] = fname
# create raw object
raw = RawArray(data,info,verbose=False)
return raw
def test_pick_bio():
"""Test picking BIO channels."""
names = 'A1 A2 Fz O BIO1 BIO2 BIO3'.split()
types = 'mag mag eeg eeg bio bio bio'.split()
info = create_info(names, 1024., types)
picks_by_type = [('mag', [0, 1]), ('eeg', [2, 3]), ('bio', [4, 5, 6])]
assert_indexing(info, picks_by_type, all_data=False)
def _read_evoked(fname, sensor_mode, info):
""" helper to read evokeds """
data = scio.loadmat(fname, squeeze_me=True)['data']
ch_names = [ch for ch in data['label'].tolist()]
times = data['time'].tolist()
sfreq = 1. / np.diff(times)[0]
if info is None:
info = create_info(
ch_names, ch_types=[sensor_mode] * len(ch_names),
sfreq=sfreq)
else:
info = _hcp_pick_info(info, ch_names)
info['sfreq'] = sfreq
orig_labels = list(data['grad'].tolist()['label'].tolist())
sel = [orig_labels.index(ch) for ch in ch_names]
pos = data['grad'].tolist()['chanpos'].tolist()[sel]
out = list()
comment = ('_'.join(fname.split('/')[-1].split('_')[2:])
.replace('.mat', '')
.replace('_eravg_', '_')
.replace('[', '')
.replace(']', ''))
for key, kind in (('var', 'standard_error'), ('avg', 'average')):
evoked = EvokedArray(
data=data[key].tolist(), info=info, tmin=min(times),
kind=kind, comment=comment)
evoked._set_channel_positions(pos, ch_names)
out.append(evoked)
return out
def file_to_nparray(fname, sfreq=100.0, verbose=False):
"""
Create a mne raw instance from csv file.
"""
# get channel names
# in MNE, this means you must config ith two arrays:
# 1) an array of channel names as strings
# 2) corresponding array of channel types. in our case, all channels are type 'eeg'
ch_names = getChannelNames()
ch_type = ["eeg"] * len(ch_names)
# add one more channel called 'class_label' as type 'stim'
ch_names.extend(["class_label"])
ch_type.extend(["stim"])
# Read EEG file
data = pd.read_table(fname, header=None, names=ch_names)
raw_data = np.array(data[ch_names]).T
# print raw_data.shape
# create and populate MNE info structure
info = create_info(ch_names, sfreq=sfreq, ch_types=ch_type)
info["filename"] = fname
# create raw object
return [raw_data, info]
def compute_bpm(self, y):
raw = RawArray(np.array([y]),
create_info(['channel_0'], self.sampling_frequency,
ch_types=['grad']))
ecg_epochs = mne.preprocessing.find_ecg_events(raw)
bpm = ecg_epochs[2]
return bpm
def test_raw_reject():
"""Test raw data getter with annotation reject."""
info = create_info(['a', 'b', 'c', 'd', 'e'], 100, ch_types='eeg')
raw = RawArray(np.ones((5, 15000)), info)
with warnings.catch_warnings(record=True): # one outside range
raw.annotations = Annotations([2, 100, 105, 148], [2, 8, 5, 8], 'BAD')
data = raw.get_data([0, 1, 3, 4], 100, 11200, 'omit')
assert_array_equal(data.shape, (4, 9900))
# with orig_time and complete overlap
raw = read_raw_fif(fif_fname)
raw.annotations = Annotations([44, 47, 48], [1, 3, 1], 'BAD',
raw.info['meas_date'])
data, times = raw.get_data(range(10), 0, 6000, 'omit', True)
assert_array_equal(data.shape, (10, 4799))
assert_equal(times[-1], raw.times[5999])
assert_array_equal(data[:, -100:], raw[:10, 5900:6000][0])
data, times = raw.get_data(range(10), 0, 6000, 'NaN', True)
assert_array_equal(data.shape, (10, 6000))
assert_equal(times[-1], raw.times[5999])
assert_true(np.isnan(data[:, 313:613]).all()) # 1s -2s
assert_true(not np.isnan(data[:, 614].any()))
assert_array_equal(data[:, -100:], raw[:10, 5900:6000][0])
assert_array_equal(raw.get_data(), raw[:][0])
# Test _sync_onset
times = [10, -88, 190]
onsets = _sync_onset(raw, times)
assert_array_almost_equal(onsets, times - raw.first_samp /
raw.info['sfreq'])
assert_array_almost_equal(times, _sync_onset(raw, onsets, True))
def test_chunk_duration():
"""Test chunk_duration."""
# create dummy raw
raw = RawArray(data=np.empty([10, 10], dtype=np.float64),
info=create_info(ch_names=10, sfreq=1.),
first_samp=0)
raw.info['meas_date'] = 0
raw.set_annotations(Annotations(description='foo', onset=[0],
duration=[10], orig_time=None))
# expected_events = [[0, 0, 1], [0, 0, 1], [1, 0, 1], [1, 0, 1], ..
# [9, 0, 1], [9, 0, 1]]
expected_events = np.atleast_2d(np.repeat(range(10), repeats=2)).T
expected_events = np.insert(expected_events, 1, 0, axis=1)
expected_events = np.insert(expected_events, 2, 1, axis=1)
events, events_id = events_from_annotations(raw, chunk_duration=.5,
use_rounding=False)
assert_array_equal(events, expected_events)
# test chunk durations that do not fit equally in annotation duration
expected_events = np.zeros((3, 3))
expected_events[:, -1] = 1
expected_events[:, 0] = np.arange(0, 9, step=3)
events, events_id = events_from_annotations(raw, chunk_duration=3.)
assert_array_equal(events, expected_events)
def creat_mne_raw_object(fname, read_events = True):
print ("loading data from %s" %fname)
data = pd.read_csv(fname)
ch_names = list(data.columns[1:])
montage = read_montage('standard_1005', ch_names)
ch_type = ['eeg']*len(ch_names)
data = 1e-6*np.array(data[ch_names]).T
if read_events:
ev_fname = fname.replace('_data', '_events')
print (ev_fname)
events = pd.read_csv(ev_fname)
events_names = events.columns[1:]
events_data = np.array(events[events_names]).T
ch_type.extend(['stim']*6)
ch_names.extend(events_names)
data = np.concatenate((data, events_data))
info = create_info(ch_names, sfreq=500.0, ch_types=ch_type, montage=montage)
info['filename'] = fname
raw = RawArray(data, info, verbose=True)
return raw
def get_data(self, series, change=False):
# Get the lift EEG data
ws = self._series_data(series)
lifts = self.lift_info()
montage = mne.channels.read_montage('standard_1005', ws.names.eeg)
ev_names = self.all_event_names()
ch_names = list(ws.names.eeg) + ev_names
X = []
y = []
# loop through the windows, creating a raw object for each one
for win_num, window in enumerate(ws.win):
events = np.zeros((window.eeg.shape[0], len(ev_names)))
row = lifts[(lifts['Run'] == series) & (lifts['Lift'] == win_num + 1)]
if change and np.isnan(row.iloc[0]['PrevW']):
continue
for ev_num, ev_name in enumerate(ev_names):
t = row.iloc[0]['t' + ev_name]
event_rows = np.abs(window.eeg_t - t) <= 0.15 + 1e-10
events[event_rows, ev_num] = 1
data = np.concatenate((1e-6 * window.eeg.T, events.T))
ch_type = ['eeg'] * len(ws.names.eeg) + ['stim'] * len(ev_names)
info = mne.create_info(ch_names, sfreq=500., ch_types=ch_type, montage=montage)
# Return a dictionary, in case we want to incorporate non-EEG data, e.g. previous weight
X.append({'eeg': mne.io.RawArray(data, info, verbose=False)})
#Cast weight to integer, so that we don't have floating point problems
yval = row.iloc[0]['CurW']==row.iloc[0]['PrevW'] if change else row.iloc[0]['CurW']
y.append(yval)
return X, y
def _raw_annot(meas_date, orig_time):
info = create_info(ch_names=10, sfreq=10.)
raw = RawArray(data=np.empty((10, 10)), info=info, first_samp=10)
raw.info['meas_date'] = meas_date
annot = Annotations([.5], [.2], ['dummy'], orig_time)
raw.set_annotations(annotations=annot)
return raw
def test_annotation_property_deprecation_warning():
"""Test that assigning annotations warns and nowhere else."""
with pytest.warns(None) as w:
raw = RawArray(np.random.rand(1, 1), create_info(1, 1))
assert len(w) is 0
with pytest.warns(DeprecationWarning, match='by assignment is deprecated'):
raw.annotations = None
def create_random_epochs(nep, nchan, ntime, sfreq,
nclasses=2, ch_types='eeg'):
data = np.random.randn(nep*nclasses, nchan, ntime*sfreq)
ev = create_random_events(nep, ntime*sfreq, nclasses)
info = mne.create_info([str(i) for i in range(nchan)], sfreq, ch_types)
ep = mne.epochs.EpochsArray(data, info, ev)
return ep
def test_add_channels():
"""Test tfr splitting / re-appending channel types
"""
data = np.zeros((6, 2, 3))
times = np.array([0.1, 0.2, 0.3])
freqs = np.array([0.10, 0.20])
info = mne.create_info(
["MEG 001", "MEG 002", "MEG 003", "EEG 001", "EEG 002", "STIM 001"],
1000.0,
["mag", "mag", "mag", "eeg", "eeg", "stim"],
)
tfr = AverageTFR(info, data=data, times=times, freqs=freqs, nave=20, comment="test", method="crazy-tfr")
tfr_eeg = tfr.pick_types(meg=False, eeg=True, copy=True)
tfr_meg = tfr.pick_types(meg=True, copy=True)
tfr_stim = tfr.pick_types(meg=False, stim=True, copy=True)
tfr_eeg_meg = tfr.pick_types(meg=True, eeg=True, copy=True)
tfr_new = tfr_meg.add_channels([tfr_eeg, tfr_stim], copy=True)
assert_true(all(ch in tfr_new.ch_names for ch in tfr_stim.ch_names + tfr_meg.ch_names))
tfr_new = tfr_meg.add_channels([tfr_eeg], copy=True)
assert_true(ch in tfr_new.ch_names for ch in tfr.ch_names)
assert_array_equal(tfr_new.data, tfr_eeg_meg.data)
assert_true(all(ch not in tfr_new.ch_names for ch in tfr_stim.ch_names))
# Now test errors
tfr_badsf = tfr_eeg.copy()
tfr_badsf.info["sfreq"] = 3.1415927
tfr_eeg = tfr_eeg.crop(-0.1, 0.1)
assert_raises(RuntimeError, tfr_meg.add_channels, [tfr_badsf])
assert_raises(AssertionError, tfr_meg.add_channels, [tfr_eeg])
assert_raises(ValueError, tfr_meg.add_channels, [tfr_meg])
assert_raises(AssertionError, tfr_meg.add_channels, tfr_badsf)
def test_plot_misc_auto():
"""Test plotting of data with misc auto scaling."""
data = np.random.RandomState(0).randn(1, 1000)
raw = RawArray(data, create_info(1, 1000., 'misc'))
raw.plot()
plt.close('all')
def get_PB_data(subject, trial, config):
"""Get the force and position data of a subject and a trial.
Parameters
----------
subject : str
A string of subject ID e.g. 7707.
trial : str
A trail e.g. HighFine..
config : yaml
The configuration file.
Returns
----------
robot_data : array
A numpy array containing total_force
"""
# path of the files
if subject in config['subjects2']:
PB_path = Path(__file__).parents[2] / config[
'exp2_data_path'] / subject / trial / 'PB.csv'
EMG_path = Path(__file__).parents[2] / config[
'exp2_data_path'] / subject / trial / 'EMG.csv'
else:
if trial not in config['comb_trials']:
PB_path = get_trial_path(subject, trial, config, 'PB')
EMG_path = get_trial_path(subject, trial, config, 'EMG')
# read the data
PB_data = np.genfromtxt(
PB_path,
dtype=float,
delimiter=',',
unpack=True,
usecols=[16, 17, 19, 20], # [13, 14]-Fx,Fy; [16,17]-Mx,My
skip_footer=config['skip_footer'],
skip_header=config['skip_header'])
time_data = np.genfromtxt(PB_path,
dtype=str,
delimiter=',',
unpack=True,
usecols=0,
skip_footer=config['skip_footer'],
skip_header=config['skip_header'])
# Moments are used in Amir's experiment. The moment to force conversion scalar provided in the experiment are [-10.0,-10.0]
PB_data[0:2, :] = np.multiply(
PB_data[0:2, :],
np.array([-10, 10
]).reshape(2,
1)) # only when 16, 17 are used instead of 13, 14
# Calculate the forces in the tangential and the normal directions
PB_data = tangential_normal_force_components(PB_data)
PB_data = PCA_force_components(PB_data, wind_len=4)
# get the actual trial start and end time based on the PB and MYO data
if subject in config['subjects2']:
trial_start, trial_end, _ = get_trial_time_exp2(
subject, trial, PB_path, EMG_path, config)
time_data, sfreq = convert_time(time_data)
indices = np.all([time_data >= trial_start, time_data <= trial_end],
axis=0)
time_data = time_data[indices[:, 0]]
PB_data = PB_data[:, indices[:, 0]]
else:
trial_start, trial_end, _ = get_trial_time(subject, trial, config)
time_data, sfreq = convert_time(time_data)
# creating an mne object
info = mne.create_info(
ch_names=['Fx', 'Fy', 'X', 'Y', 'Ft', 'Fn', 'F_pca1', 'F_pca2'],
sfreq=sfreq,
ch_types=['misc'] * 8)
raw = mne.io.RawArray(PB_data, info, verbose=False)
return raw, time_data
def get_raw_emg_exp2(subject, trial, config):
"""Get the raw emg data for a subject and trail from experiment 2
Parameters
----------
subject : str
A string of subject ID e.g. 7707.
trial : str
A trail e.g. HighFine.
config : yaml
The configuration file.
Returns
-------
mne object
A raw mne object.
"""
# path of the PB file
# if subject in config['subjects2']:
# trial_path = Path(__file__).parents[2] / config['exp2_data_path'] / subject / trial / 'PB.csv'
# else:
# trial_path = get_trial_path(subject, trial, config, 'PB')
# path of the files
# if subject in config['subjects2']:
PB_path = Path(__file__).parents[2] / config[
'exp2_data_path'] / subject / trial / 'PB.csv'
EMG_path = Path(__file__).parents[2] / config[
'exp2_data_path'] / subject / trial / 'EMG.csv'
# else:
# PB_path = get_trial_path(subject, trial, config, 'PB')
# EMG_path = get_trial_path(subject, trial, config, 'EMG')
# path of the EMG file
filepath = Path(
__file__).parents[2] / config['exp2_data_path'] / subject / trial
if filepath.exists():
for file in filepath.iterdir():
if (file.name.split('.')[0] == 'EMG'):
# Get the time from EMG.csv
time = np.genfromtxt(file,
dtype=None,
delimiter=',',
unpack=True,
skip_footer=config['skip_footer'],
skip_header=config['skip_header'],
usecols=0,
encoding=None)
# Get the EMG data
EMG_data = np.genfromtxt(file,
dtype=float,
delimiter=',',
unpack=True,
skip_footer=config['skip_footer'],
skip_header=config['skip_header'],
usecols=np.arange(1, 9),
encoding=None)
# get the actual trial start and end time based on the PB and MYO data
if subject in config['subjects2']:
trial_start, trial_end, _ = get_trial_time_exp2(
subject, trial, PB_path, EMG_path, config)
else:
trial_start, trial_end, _ = get_trial_time(
subject, trial, config)
time_EMG, sfreq = convert_time(time)
indices = np.all(
[time_EMG >= trial_start, time_EMG <= trial_end], axis=0)
time_EMG = time_EMG[indices[:, 0]]
EMG_data = EMG_data[:, indices[:, 0]]
# Create mne raw object
info = mne.create_info(ch_names=[
'emg_1', 'emg_2', 'emg_3', 'emg_4', 'emg_5', 'emg_6',
'emg_7', 'emg_8'
],
ch_types=['misc'] * EMG_data.shape[0],
sfreq=sfreq)
# Create mne raw file
raw = mne.io.RawArray(EMG_data, info, verbose=False)
# Additional information
raw.info['subject_info'] = subject
return raw, [trial_start[:], trial_end[:]]
else:
return [], []
def load_muse_csv_as_raw(filename,
sfreq=256.,
ch_ind=[0, 1, 2, 3],
stim_ind=5,
replace_ch_names=None):
"""Load CSV files into a Raw object.
Args:
filename (str or list): path or paths to CSV files to load
Keyword Args:
subject_nb (int or str): subject number. If 'all', load all
subjects.
session_nb (int or str): session number. If 'all', load all
sessions.
sfreq (float): EEG sampling frequency
ch_ind (list): indices of the EEG channels to keep
stim_ind (int): index of the stim channel
replace_ch_names (dict or None): dictionary containing a mapping to
rename channels. Useful when an external electrode was used.
Returns:
(mne.io.array.array.RawArray): loaded EEG
"""
n_channel = len(ch_ind)
raw = []
for fname in filename:
# read the file
data = pd.read_csv(fname, index_col=0)
# name of each channels
ch_names = list(data.columns)[0:n_channel] + ['Stim']
if replace_ch_names is not None:
ch_names = [
c if c not in replace_ch_names.keys() else replace_ch_names[c]
for c in ch_names
]
# type of each channels
ch_types = ['eeg'] * n_channel + ['stim']
montage = read_montage('standard_1005')
# get data and exclude Aux channel
data = data.values[:, ch_ind + [stim_ind]].T
# convert in Volts (from uVolts)
data[:-1] *= 1e-6
# create MNE object
info = create_info(ch_names=ch_names,
ch_types=ch_types,
sfreq=sfreq,
montage=montage)
raw.append(RawArray(data=data, info=info))
# concatenate all raw objects
raws = concatenate_raws(raw)
return raws
def annotate_motion_artifacts(raw,
pos,
disp_thr=0.01,
velo_thr=0.03,
gof_thr=0.99,
return_stat_raw=False):
"""Find and annotate periods of high HPI velocity and high HPI distance."""
annot = Annotations([], [], [])
info = raw.info
# grab initial cHPI locations
# point sorted in hpi_results are in mne device coords
chpi_locs_dev = sorted([d for d in info['hpi_results'][-1]['dig_points']],
key=lambda x: x['ident'])
chpi_locs_dev = np.array([d['r'] for d in chpi_locs_dev])
# chpi_locs_dev[0] -> LPA
# chpi_locs_dev[1] -> NASION
# chpi_locs_dev[2] -> RPA
chpi_static_head = apply_trans(info['dev_head_t'], chpi_locs_dev)
time = pos[:, 0]
n_hpi = chpi_static_head.shape[0]
quats = pos[:, 1:7]
chpi_moving_head = np.array(
[_apply_quat(quat, chpi_locs_dev, move=True) for quat in quats])
# compute displacements
hpi_disp = chpi_moving_head - np.tile(chpi_static_head, (len(time), 1, 1))
hpi_disp = np.sqrt((hpi_disp**2).sum(axis=-1))
# compute velocities
hpi_velo = chpi_moving_head[1:, :, :] - chpi_moving_head[:-1, :, :]
hpi_velo = np.sqrt((hpi_velo**2).sum(axis=-1))
hpi_velo /= np.tile(time[1:] - time[:-1], (n_hpi, 1)).transpose()
hpi_velo = np.concatenate((np.zeros((1, n_hpi)), hpi_velo), axis=0)
if disp_thr is not None:
art_mask = hpi_disp > disp_thr
annot += _annotations_from_mask(time, art_mask,
'Bad-motion-dist>%0.3f' % disp_thr)
if velo_thr is not None:
art_mask = hpi_velo > velo_thr
annot += _annotations_from_mask(time, art_mask,
'Bad-motion-velo>%0.3f' % velo_thr)
if gof_thr is not None:
art_mask = pos[:, 7] <= gof_thr
annot += _annotations_from_mask(time, art_mask,
'Bad-chpi_gof>%0.3f' % gof_thr)
tmp = 1000 * hpi_disp.max(axis=0)
_fmt = '\tHPI00 - %0.1f'
for i in range(1, n_hpi):
_fmt += '\n\tHPI%02d' % (i) + ' - %0.1f'
logger.info('CHPI MAX Displacments (mm):')
logger.info(_fmt % tuple(tmp))
tmp = 1000 * hpi_velo.max(axis=0)
logger.info('CHPI Velocity Displacments (mm/sec):')
logger.info(_fmt % tuple(tmp))
raw_hpi = None
if return_stat_raw:
n_times = len(raw.times)
# build full time data arrays
start_idx = raw.time_as_index(time, use_rounding=True)
end_idx = raw.time_as_index(np.append(time[1:], raw.times[-1]),
use_rounding=True)
data_pos = np.zeros((2 * n_hpi, n_times))
for t_0, t_1, disp_val, velo_val in zip(start_idx, end_idx, hpi_disp,
hpi_velo):
t_slice = slice(t_0, t_1)
data_pos[:n_hpi, t_slice] = np.tile(disp_val, (t_1 - t_0, 1)).T
data_pos[n_hpi:, t_slice] = np.tile(velo_val, (t_1 - t_0, 1)).T
ch_names = []
ch_names_ = []
for i in range(n_hpi):
ch_names.append('HPI%02d_disp_pos' % i)
ch_names_.append('HPI%02d_velo_pos' % i)
ch_names.extend(ch_names_)
# build raw object!
info = create_info(ch_names=ch_names,
ch_types=np.repeat('misc', len(ch_names)),
sfreq=raw.info['sfreq'])
raw_hpi = RawArray(data_pos, info)
return annot, raw_hpi
pick_ori=None)
raw_stc = apply_inverse_raw(raw,
inv,
lambda2=1.0 / (2**snr_raw),
method=source_method,
label=label,
pick_ori=None)
ch_names.append(label.name)
evoked_stcs.append(evoked_stc.data.mean(0))
raw_stcs.append(raw_stc.data.mean(0))
evoked_stcs = np.array(evoked_stcs)
raw_stcs = np.array(raw_stcs)
# Create info for raw_source and evoked_source
info_raw = mne.create_info(ch_names=ch_names,
ch_types=['stim'] + ['mag'] * len(labels),
sfreq=600)
info_evoked = mne.create_info(ch_names=ch_names[1:],
ch_types=['mag'] * len(labels),
sfreq=600)
raw_source = mne.io.RawArray(raw_stcs, info_raw)
evoked_source = mne.EvokedArray(evoked_stcs, info_evoked)
# save raw_source and evoked_source
results_folder = op.join(path_data, subject, 'source_signal')
if not op.isdir(results_folder):
os.mkdir(results_folder)
fname = op.join(results_folder, '%s_raw_source.fif' % subject)
raw_source.save(fname, overwrite=True)
fname = op.join(results_folder, '%s_evoked_source.fif' % subject)
evoked_source.save(fname)
eval_events_path = "/arquivos/Documents/eeg_data/doutorado_cleison/true_labels/A01E.csv" # raw = mne.io.read_raw_eeglab(data_train_path) data, fs = loadBiosig(data_train_path) data = nanCleaner(data) ch_names = ['Fz', 'EEG1', 'EEG2', 'EEG3', 'EEG4', 'EEG5', 'EEG6', 'EEG-C3', 'EEG7', 'EEG-Cz', 'EEG8', 'EEG-C4', 'EEG9', 'EEG10', 'EEG11', 'EEG12', 'EEG13', 'EEG14', 'EEG15', 'EEG-Pz', 'EEG16', 'EEG17', 'EOG1', 'EOG2', 'EOG3' ] sfreq = fs ch_types = ['eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eeg', 'eog', 'eog', 'eog' ] info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types) raw = mne.io.RawArray(data, info) events_list = readEvents(train_events_path) info_stim = mne.create_info(ch_names=['stim_clean'], sfreq=raw.info['sfreq'], ch_types=['stim']) info_stim['buffer_size_sec'] = raw.info['buffer_size_sec'] data_dum = np.zeros([1, data.shape[1]]) raw_stim = mne.io.RawArray(data_dum, info=info_stim) raw.add_channels([raw_stim]) raw.add_events(events_list, stim_channel = None) # Processing beggining: tmin, tmax = -1.5, 3.5 # time before event, time after event
def test_io():
"""Test TFR IO capacities."""
from pandas import DataFrame
tempdir = _TempDir()
fname = op.join(tempdir, 'test-tfr.h5')
data = np.zeros((3, 2, 3))
times = np.array([.1, .2, .3])
freqs = np.array([.10, .20])
info = mne.create_info(['MEG 001', 'MEG 002', 'MEG 003'], 1000.,
['mag', 'mag', 'mag'])
tfr = AverageTFR(info,
data=data,
times=times,
freqs=freqs,
nave=20,
comment='test',
method='crazy-tfr')
tfr.save(fname)
tfr2 = read_tfrs(fname, condition='test')
assert_array_equal(tfr.data, tfr2.data)
assert_array_equal(tfr.times, tfr2.times)
assert_array_equal(tfr.freqs, tfr2.freqs)
assert_equal(tfr.comment, tfr2.comment)
assert_equal(tfr.nave, tfr2.nave)
pytest.raises(IOError, tfr.save, fname)
tfr.comment = None
tfr.save(fname, overwrite=True)
assert_equal(read_tfrs(fname, condition=0).comment, tfr.comment)
tfr.comment = 'test-A'
tfr2.comment = 'test-B'
fname = op.join(tempdir, 'test2-tfr.h5')
write_tfrs(fname, [tfr, tfr2])
tfr3 = read_tfrs(fname, condition='test-A')
assert_equal(tfr.comment, tfr3.comment)
assert (isinstance(tfr.info, mne.Info))
tfrs = read_tfrs(fname, condition=None)
assert_equal(len(tfrs), 2)
tfr4 = tfrs[1]
assert_equal(tfr2.comment, tfr4.comment)
pytest.raises(ValueError, read_tfrs, fname, condition='nonono')
# Test save of EpochsTFR.
n_events = 5
data = np.zeros((n_events, 3, 2, 3))
# create fake metadata
rng = np.random.RandomState(42)
rt = np.round(rng.uniform(size=(n_events, )), 3)
trialtypes = np.array(['face', 'place'])
trial = trialtypes[(rng.uniform(size=(n_events, )) > .5).astype(int)]
meta = DataFrame(dict(RT=rt, Trial=trial))
# fake events and event_id
events = np.zeros([n_events, 3])
events[:, 0] = np.arange(n_events)
events[:, 2] = np.ones(n_events)
event_id = dict(a=1)
tfr = EpochsTFR(info,
data=data,
times=times,
freqs=freqs,
comment='test',
method='crazy-tfr',
events=events,
event_id=event_id,
metadata=meta)
tfr.save(fname, True)
read_tfr = read_tfrs(fname)[0]
assert_array_equal(tfr.data, read_tfr.data)
assert_metadata_equal(tfr.metadata, read_tfr.metadata)
assert_array_equal(tfr.events, read_tfr.events)
assert_equal(tfr.event_id, read_tfr.event_id)
def test_tfr_multitaper():
"""Test tfr_multitaper."""
sfreq = 200.0
ch_names = ['SIM0001', 'SIM0002']
ch_types = ['grad', 'grad']
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
n_times = int(sfreq) # Second long epochs
n_epochs = 3
seed = 42
rng = np.random.RandomState(seed)
noise = 0.1 * rng.randn(n_epochs, len(ch_names), n_times)
t = np.arange(n_times, dtype=np.float) / sfreq
signal = np.sin(np.pi * 2. * 50. * t) # 50 Hz sinusoid signal
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
dat = noise + signal
reject = dict(grad=4000.)
events = np.empty((n_epochs, 3), int)
first_event_sample = 100
event_id = dict(sin50hz=1)
for k in range(n_epochs):
events[k, :] = first_event_sample + k * n_times, 0, event_id['sin50hz']
epochs = EpochsArray(data=dat,
info=info,
events=events,
event_id=event_id,
reject=reject)
freqs = np.arange(35, 70, 5, dtype=np.float)
power, itc = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0)
power2, itc2 = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
decim=slice(0, 2))
picks = np.arange(len(ch_names))
power_picks, itc_picks = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
picks=picks)
power_epochs = tfr_multitaper(epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
return_itc=False,
average=False)
power_averaged = power_epochs.average()
power_evoked = tfr_multitaper(epochs.average(),
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
return_itc=False,
average=False).average()
print(power_evoked) # test repr for EpochsTFR
# Test channel picking
power_epochs_picked = power_epochs.copy().drop_channels(['SIM0002'])
assert_equal(power_epochs_picked.data.shape, (3, 1, 7, 200))
assert_equal(power_epochs_picked.ch_names, ['SIM0001'])
pytest.raises(ValueError,
tfr_multitaper,
epochs,
freqs=freqs,
n_cycles=freqs / 2.,
return_itc=True,
average=False)
# test picks argument
assert_array_almost_equal(power.data, power_picks.data)
assert_array_almost_equal(power.data, power_averaged.data)
assert_array_almost_equal(power.times, power_epochs.times)
assert_array_almost_equal(power.times, power_averaged.times)
assert_equal(power.nave, power_averaged.nave)
assert_equal(power_epochs.data.shape, (3, 2, 7, 200))
assert_array_almost_equal(itc.data, itc_picks.data)
# one is squared magnitude of the average (evoked) and
# the other is average of the squared magnitudes (epochs PSD)
# so values shouldn't match, but shapes should
assert_array_equal(power.data.shape, power_evoked.data.shape)
pytest.raises(AssertionError, assert_array_almost_equal, power.data,
power_evoked.data)
tmax = t[np.argmax(itc.data[0, freqs == 50, :])]
fmax = freqs[np.argmax(power.data[1, :, t == 0.5])]
assert (tmax > 0.3 and tmax < 0.7)
assert not np.any(itc.data < 0.)
assert (fmax > 40 and fmax < 60)
assert (power2.data.shape == (len(picks), len(freqs), 2))
assert (power2.data.shape == itc2.data.shape)
# Test decim parameter checks and compatibility between wavelets length
# and instance length in the time dimension.
pytest.raises(TypeError,
tfr_multitaper,
epochs,
freqs=freqs,
n_cycles=freqs / 2.,
time_bandwidth=4.0,
decim=(1, ))
pytest.raises(ValueError,
tfr_multitaper,
epochs,
freqs=freqs,
n_cycles=1000,
time_bandwidth=4.0)
from mne.time_frequency import (tfr_multitaper, tfr_stockwell, tfr_morlet,
tfr_array_morlet)
from mne.viz import centers_to_edges
print(__doc__)
###############################################################################
# Simulate data
# -------------
#
# We'll simulate data with a known spectro-temporal structure.
sfreq = 1000.0
ch_names = ['SIM0001', 'SIM0002']
ch_types = ['grad', 'grad']
info = create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
n_times = 1024 # Just over 1 second epochs
n_epochs = 40
seed = 42
rng = np.random.RandomState(seed)
noise = rng.randn(n_epochs, len(ch_names), n_times)
# Add a 50 Hz sinusoidal burst to the noise and ramp it.
t = np.arange(n_times, dtype=np.float64) / sfreq
signal = np.sin(np.pi * 2. * 50. * t) # 50 Hz sinusoid signal
signal[np.logical_or(t < 0.45, t > 0.55)] = 0. # Hard windowing
on_time = np.logical_and(t >= 0.45, t <= 0.55)
signal[on_time] *= np.hanning(on_time.sum()) # Ramping
data = noise + signal
def test_scalings_int():
"""Test that auto scalings access samples using integers."""
raw = RawArray(np.zeros((1, 500)), create_info(1, 1000., 'eeg'))
raw.plot(scalings='auto')
def test_plot_raw_psd():
"""Test plotting of raw psds."""
raw = _get_raw()
# normal mode
raw.plot_psd(average=False)
# specific mode
picks = pick_types(raw.info, meg='mag', eeg=False)[:4]
raw.plot_psd(tmax=None, picks=picks, area_mode='range', average=False,
spatial_colors=True)
raw.plot_psd(tmax=20., color='yellow', dB=False, line_alpha=0.4,
n_overlap=0.1, average=False)
plt.close('all')
ax = plt.axes()
# if ax is supplied:
pytest.raises(ValueError, raw.plot_psd, ax=ax, average=True)
raw.plot_psd(tmax=None, picks=picks, ax=ax, average=True)
plt.close('all')
ax = plt.axes()
with pytest.raises(ValueError, match='2 axes must be supplied, got 1'):
raw.plot_psd(ax=ax, average=True)
plt.close('all')
ax = plt.subplots(2)[1]
raw.plot_psd(tmax=None, ax=ax, average=True)
plt.close('all')
# topo psd
ax = plt.subplot()
raw.plot_psd_topo(axes=ax)
plt.close('all')
# with channel information not available
for idx in range(len(raw.info['chs'])):
raw.info['chs'][idx]['loc'] = np.zeros(12)
with pytest.warns(RuntimeWarning, match='locations not available'):
raw.plot_psd(spatial_colors=True, average=False)
# with a flat channel
raw[5, :] = 0
for dB, estimate in itertools.product((True, False),
('power', 'amplitude')):
with pytest.warns(UserWarning, match='[Infinite|Zero]'):
fig = raw.plot_psd(average=True, dB=dB, estimate=estimate)
ylabel = fig.axes[1].get_ylabel()
ends_dB = ylabel.endswith('mathrm{(dB)}$')
if dB:
assert ends_dB, ylabel
else:
assert not ends_dB, ylabel
if estimate == 'amplitude':
assert r'fT/cm/\sqrt{Hz}' in ylabel, ylabel
else:
assert estimate == 'power'
assert '(fT/cm)²/Hz' in ylabel, ylabel
ylabel = fig.axes[0].get_ylabel()
if estimate == 'amplitude':
assert r'fT/\sqrt{Hz}' in ylabel
else:
assert 'fT²/Hz' in ylabel
# test reject_by_annotation
raw = _get_raw()
raw.set_annotations(Annotations([1, 5], [3, 3], ['test', 'test']))
raw.plot_psd(reject_by_annotation=True)
raw.plot_psd(reject_by_annotation=False)
plt.close('all')
# test fmax value checking
with pytest.raises(ValueError, match='not exceed one half the sampling'):
raw.plot_psd(fmax=50000)
# test xscale value checking
with pytest.raises(ValueError, match="Invalid value for the 'xscale'"):
raw.plot_psd(xscale='blah')
# gh-5046
raw = read_raw_fif(raw_fname, preload=True).crop(0, 1)
picks = pick_types(raw.info)
raw.plot_psd(picks=picks, average=False)
raw.plot_psd(picks=picks, average=True)
plt.close('all')
raw.set_channel_types({'MEG 0113': 'hbo', 'MEG 0112': 'hbr',
'MEG 0122': 'fnirs_raw', 'MEG 0123': 'fnirs_od'},
verbose='error')
fig = raw.plot_psd()
assert len(fig.axes) == 10
plt.close('all')
# gh-7631
data = 1e-3 * np.random.rand(2, 100)
info = create_info(['CH1', 'CH2'], 100)
raw = RawArray(data, info)
picks = pick_types(raw.info, misc=True)
raw.plot_psd(picks=picks, spatial_colors=False)
plt.close('all')
###############################################################################
# First we import what we need for this example.
import numpy as np
import mne
from pyprep.noisy import Noisydata
###############################################################################
# Now let's make some arbitrary MNE raw object for demonstration purposes.
sfreq = 1000.
n_chans = 6
ch_names = ['Fpz', 'Fz', 'FCz', 'Cz', 'Pz', 'Oz']
info = mne.create_info(ch_names=ch_names,
sfreq=sfreq,
ch_types=['eeg'] * n_chans)
time = np.arange(0, 60, 1. / sfreq) # 60 seconds of recording
X = np.random.random((n_chans, time.shape[0]))
raw = mne.io.RawArray(X, info)
print(raw)
###############################################################################
# Assign the mne object to the :class:`Noisydata` class. The resulting object
# will be the place where all following methods are performed.
nd = Noisydata(raw)
###############################################################################
# Find all bad channels and print a summary
data_cls = np.asarray(cls_all)
data_pln = np.asarray(pln_all)
# Setup data for epochs and cross validation
X = np.vstack([data_cls, data_pln])
y = np.concatenate([np.zeros(len(data_cls)), np.ones(len(data_pln))])
cv = StratifiedKFold(n_splits=7, shuffle=True)
# Create epochs to use for classification
n_trial, n_chan, n_time = X.shape
events = np.vstack((range(n_trial), np.zeros(n_trial, int), y.astype(int))).T
chan_names = ['MEG %i' % chan for chan in range(n_chan)]
chan_types = ['mag'] * n_chan
sfreq = 250
info = create_info(chan_names, sfreq, chan_types)
epochs = EpochsArray(data=X, info=info, events=events, verbose=False)
epochs.times = selected_times[:n_time]
# make classifier
clf = LogisticRegression(C=0.0001)
# fit model and score
gat = GeneralizationAcrossTime(
scorer="roc_auc", cv=cv, predict_method="predict")
gat.fit(epochs, y=y)
gat.score(epochs, y=y)
# Save model
joblib.dump(gat, data_path + "decode_time_gen/gat_deg_bin.jl")
def test_annotations():
"""Test annotation class."""
raw = read_raw_fif(fif_fname)
onset = np.array(range(10))
duration = np.ones(10)
description = np.repeat('test', 10)
dt = datetime.utcnow()
meas_date = raw.info['meas_date']
# Test time shifts.
for orig_time in [None, dt, meas_date[0], meas_date]:
annot = Annotations(onset, duration, description, orig_time)
assert_raises(ValueError, Annotations, onset, duration, description[:9])
assert_raises(ValueError, Annotations, [onset, 1], duration, description)
assert_raises(ValueError, Annotations, onset, [duration, 1], description)
# Test combining annotations with concatenate_raws
raw2 = raw.copy()
orig_time = (meas_date[0] + meas_date[1] * 0.000001 +
raw2.first_samp / raw2.info['sfreq'])
annot = Annotations(onset, duration, description, orig_time)
raw2.annotations = annot
assert_array_equal(raw2.annotations.onset, onset)
concatenate_raws([raw, raw2])
raw.annotations.delete(-1) # remove boundary annotation
assert_array_almost_equal(onset + 20., raw.annotations.onset, decimal=2)
assert_array_equal(annot.duration, raw.annotations.duration)
assert_array_equal(raw.annotations.description, np.repeat('test', 10))
# Test combining with RawArray and orig_times
data = np.random.randn(2, 1000) * 10e-12
sfreq = 100.
info = create_info(ch_names=['MEG1', 'MEG2'], ch_types=['grad'] * 2,
sfreq=sfreq)
info['meas_date'] = 0
raws = []
for i, fs in enumerate([12300, 100, 12]):
raw = RawArray(data.copy(), info, first_samp=fs)
ants = Annotations([1., 2.], [.5, .5], 'x', fs / sfreq)
raw.annotations = ants
raws.append(raw)
raw = RawArray(data.copy(), info)
raw.annotations = Annotations([1.], [.5], 'x', None)
raws.append(raw)
raw = concatenate_raws(raws)
boundary_idx = np.where(raw.annotations.description == 'BAD boundary')[0]
assert_equal(len(boundary_idx), 3)
raw.annotations.delete(boundary_idx)
assert_array_equal(raw.annotations.onset, [1., 2., 11., 12., 21., 22.,
31.])
raw.annotations.delete(2)
assert_array_equal(raw.annotations.onset, [1., 2., 12., 21., 22., 31.])
raw.annotations.append(5, 1.5, 'y')
assert_array_equal(raw.annotations.onset, [1., 2., 12., 21., 22., 31., 5])
assert_array_equal(raw.annotations.duration, [.5, .5, .5, .5, .5, .5, 1.5])
assert_array_equal(raw.annotations.description, ['x', 'x', 'x', 'x', 'x',
'x', 'y'])
# Test concatenating annotations with and without orig_time.
raw = read_raw_fif(fif_fname)
last_time = raw.last_samp / raw.info['sfreq']
raw2 = raw.copy()
raw.annotations = Annotations([45.], [3], 'test', raw.info['meas_date'])
raw2.annotations = Annotations([2.], [3], 'BAD', None)
raw = concatenate_raws([raw, raw2])
raw.annotations.delete(-1) # remove boundary annotation
assert_array_almost_equal(raw.annotations.onset, [45., 2. + last_time],
decimal=2)
def test_resample():
"""Test resample (with I/O and multiple files)
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 3, False)
raw.load_data()
raw_resamp = raw.copy()
sfreq = raw.info['sfreq']
# test parallel on upsample
raw_resamp.resample(sfreq * 2, n_jobs=2, npad='auto')
assert_equal(raw_resamp.n_times, len(raw_resamp.times))
raw_resamp.save(op.join(tempdir, 'raw_resamp-raw.fif'))
raw_resamp = Raw(op.join(tempdir, 'raw_resamp-raw.fif'), preload=True)
assert_equal(sfreq, raw_resamp.info['sfreq'] / 2)
assert_equal(raw.n_times, raw_resamp.n_times / 2)
assert_equal(raw_resamp._data.shape[1], raw_resamp.n_times)
assert_equal(raw._data.shape[0], raw_resamp._data.shape[0])
# test non-parallel on downsample
raw_resamp.resample(sfreq, n_jobs=1, npad='auto')
assert_equal(raw_resamp.info['sfreq'], sfreq)
assert_equal(raw._data.shape, raw_resamp._data.shape)
assert_equal(raw.first_samp, raw_resamp.first_samp)
assert_equal(raw.last_samp, raw.last_samp)
# upsampling then downsampling doubles resampling error, but this still
# works (hooray). Note that the stim channels had to be sub-sampled
# without filtering to be accurately preserved
# note we have to treat MEG and EEG+STIM channels differently (tols)
assert_allclose(raw._data[:306, 200:-200],
raw_resamp._data[:306, 200:-200],
rtol=1e-2, atol=1e-12)
assert_allclose(raw._data[306:, 200:-200],
raw_resamp._data[306:, 200:-200],
rtol=1e-2, atol=1e-7)
# now check multiple file support w/resampling, as order of operations
# (concat, resample) should not affect our data
raw1 = raw.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw4 = raw.copy()
raw1 = concatenate_raws([raw1, raw2])
raw1.resample(10., npad='auto')
raw3.resample(10., npad='auto')
raw4.resample(10., npad='auto')
raw3 = concatenate_raws([raw3, raw4])
assert_array_equal(raw1._data, raw3._data)
assert_array_equal(raw1._first_samps, raw3._first_samps)
assert_array_equal(raw1._last_samps, raw3._last_samps)
assert_array_equal(raw1._raw_lengths, raw3._raw_lengths)
assert_equal(raw1.first_samp, raw3.first_samp)
assert_equal(raw1.last_samp, raw3.last_samp)
assert_equal(raw1.info['sfreq'], raw3.info['sfreq'])
# test resampling of stim channel
# basic decimation
stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
assert_allclose(raw.resample(8., npad='auto')._data,
[[1, 1, 0, 0, 1, 1, 0, 0]])
# decimation of multiple stim channels
raw = RawArray(2 * [stim], create_info(2, len(stim), 2 * ['stim']))
assert_allclose(raw.resample(8., npad='auto')._data,
[[1, 1, 0, 0, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 1, 0, 0]])
# decimation that could potentially drop events if the decimation is
# done naively
stim = [0, 0, 0, 1, 1, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
assert_allclose(raw.resample(4., npad='auto')._data,
[[0, 1, 1, 0]])
# two events are merged in this case (warning)
stim = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.resample(8., npad='auto')
assert_true(len(w) == 1)
# events are dropped in this case (warning)
stim = [0, 1, 1, 0, 0, 1, 1, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.resample(4., npad='auto')
assert_true(len(w) == 1)
# test resampling events: this should no longer give a warning
stim = [0, 1, 1, 0, 0, 1, 1, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
events = find_events(raw)
raw, events = raw.resample(4., events=events, npad='auto')
assert_equal(events, np.array([[0, 0, 1], [2, 0, 1]]))
# test copy flag
stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
raw_resampled = raw.resample(4., npad='auto', copy=True)
assert_true(raw_resampled is not raw)
raw_resampled = raw.resample(4., npad='auto', copy=False)
assert_true(raw_resampled is raw)
# resample should still work even when no stim channel is present
raw = RawArray(np.random.randn(1, 100), create_info(1, 100, ['eeg']))
raw.info['lowpass'] = 50.
raw.resample(10, npad='auto')
assert_equal(raw.info['lowpass'], 5.)
assert_equal(len(raw), 10)
# read event file
labels= pd.read_csv(events_fname)
clean=data.drop(['id' ], axis=1)#remove id
labels=labels.drop(['id' ], axis=1)#remove id
return clean,labels
data,labels = prepare_data_train('/Users/camasa/Documents/University/Grad/DM_Pro/train/subj1_series2_data.csv')
data =np.asarray(data.astype(float))
labels =np.asarray(labels.astype(float))
# Some information about the channels
ch_names = ['Fp1', 'Fp2','F7','F3','Fz','F4','F8','FC5','FC1','FC2','FC6','T7',
'C3','Cz','C4','T8','TP9','CP5','CP1','CP2','CP6','TP10','P7','P3','Pz',
'P4','P8','PO9','O1','Oz','O2','PO10']
# Sampling rate
sfreq = 500 # Hz
# Create the info structure needed by MNE
info = mne.create_info(ch_names, sfreq, ch_types= 'eeg')
# Finally, create the Raw object
raw = mne.io.RawArray(np.transpose(data), info)
#data, times = raw[:30, int(sfreq * 1):int(sfreq * 3)]
# Plot it!
#plt.plot(times, data.T)
#data.plot()
#__reset__()
import numpy as np import mne import os dir_path = os.getcwd() # Read the CSV file as a NumPy array data = np.loadtxt(dir_path + '/tmp/edf.csv', delimiter=',') # Some information about the channels ch_names = ['CH 1', 'CH 2', 'CH 3'] # TODO: finish this list # Sampling rate of the Nautilus machine sfreq = 500 # Hz # Create the info structure needed by MNE info = mne.create_info(ch_names, sfreq) # Finally, create the Raw object raw = mne.io.RawArray(data, info) # Plot it! raw.plot()
mat = loadmat(mne.datasets.misc.data_path() + '/ecog/sample_ecog.mat')
ch_names = mat['ch_names'].tolist()
elec = mat['elec'] # electrode positions given in meters
# Now we make a montage stating that the sEEG contacts are in head
# coordinate system (although they are in MRI). This is compensated
# by the fact that below we do not specicty a trans file so the Head<->MRI
# transform is the identity.
montage = mne.channels.make_dig_montage(ch_pos=dict(zip(ch_names, elec)),
coord_frame='head')
print('Created %s channel positions' % len(ch_names))
###############################################################################
# Now that we have our electrode positions in MRI coordinates, we can create
# our measurement info structure.
info = mne.create_info(ch_names, 1000., 'ecog', montage=montage)
###############################################################################
# We can then plot the locations of our electrodes on our subject's brain.
#
# .. note:: These are not real electrodes for this subject, so they
# do not align to the cortical surface perfectly.
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
fig = plot_alignment(info,
subject='sample',
subjects_dir=subjects_dir,
surfaces=['pial'])
mne.viz.set_3d_view(fig, 200, 70)
###############################################################################
def make_random_mne_object(sfreq=1000.0,
t_secs=600,
n_freq_comps=5,
freq_range=[10, 60]):
"""Make a random MNE object to use for testing.
Parameters
----------
sfreq : float
Sampling frequency in Hz.
t_secs : int
Recording length in seconds.
n_freq_comps : int
Number of signal components summed to make a signal.
freq_range : list, len==2
Signals will contain freqs from this range.
Returns
-------
raw : mne raw object
The mne object for performing the tests.
n_freq_comps : int
freq_range : list, len==2
"""
t = np.arange(0, t_secs, 1.0 / sfreq)
signal_len = t.shape[0]
ch_names = [
"Fpz",
"AFz",
"Fz",
"FCz",
"Cz",
"CPz",
"Pz",
"POz",
"Oz",
"C1",
"C2",
"C3",
"C4",
"C5",
"C6",
]
ch_types = ["eeg" for chn in ch_names]
n_chans = len(ch_names)
# Make a random signal
signal = np.zeros((n_chans, signal_len))
low = freq_range[0]
high = freq_range[1]
for chan in range(n_chans):
# Each channel signal is a sum of random freq sine waves
for freq_i in range(n_freq_comps):
freq = RNG.randint(low, high, signal_len)
signal[chan, :] += np.sin(2 * np.pi * t * freq)
signal *= 1e-6 # scale to Volts
# Make mne object
info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
raw = mne.io.RawArray(signal, info)
return raw, n_freq_comps, freq_range
for t, w_time in enumerate(centered_w_times):
# Center the min and max of the window
w_tmin = w_time - w_size / 2.
w_tmax = w_time + w_size / 2.
# Crop data into time-window of interest
X = epochs.copy().crop(w_tmin, w_tmax).get_data()
# Save mean scores over folds for each frequency and time window
tf_scores[freq, t] = np.mean(cross_val_score(estimator=clf,
X=X,
y=y,
scoring='roc_auc',
cv=cv,
n_jobs=1),
axis=0)
###############################################################################
# Plot time-frequency results
# Set up time frequency object
av_tfr = AverageTFR(create_info(['freq'], sfreq), tf_scores[np.newaxis, :],
centered_w_times, freqs[1:], 1)
chance = np.mean(y) # set chance level to white in the plot
av_tfr.plot([0],
vmin=chance,
title="Time-Frequency Decoding Scores",
cmap=plt.cm.Reds)
fs = 1000
filename = data_dir + 'fingerflex/data/' + str(sid) + '/1_fingerflex.mat'
mat = scipy.io.loadmat(filename)
data = np.transpose(mat['data']) # (46, 610040)
chn_num = data.shape[0]
flex = np.transpose(mat['flex']) #(5, 610040)
cue = np.transpose(mat['cue']) # (1, 610040)
data = np.concatenate((data, cue, flex), axis=0) # (47, 610040) / (52, 610040)
chn_names = np.append(["ecog"] * chn_num,
["stim", "thumb", "index", "middle", "ring", "little"])
chn_types = np.append(["ecog"] * chn_num,
["stim", "emg", "emg", "emg", "emg", "emg"])
info = mne.create_info(ch_names=list(chn_names),
ch_types=list(chn_types),
sfreq=fs)
raw = mne.io.RawArray(data, info)
events = mne.find_events(raw, stim_channel='stim')
events = events - [0, 0, 1]
'''
verify the events are picked up correctly.
a=np.asarray([i for i in events if i[2]==1])
fig,ax=plt.subplots()
ax.plot(cue[0,:111080])
for i in a[:6]:
ax.axvline(x=i[0],linewidth=1,color='r',linestyle='--')
'''
event1 = events[(events[:, 2] == 0)]
event2 = events[(events[:, 2] == 1)]
g2 = np.swapaxes(
np.array([[
y_df.query(
'metric_type=="{}" & subj_id=="{}" & channel=="{}"'.format(
metric_type, s, ch))['env'].values for ch in CHANNELS
] for s in y_df.query('fb_type=="FBMock"')['subj_id'].unique()]),
2, 1)
from mne.stats import spatio_temporal_cluster_test
from mne import create_info
from mne.channels import read_montage, find_ch_connectivity
cnk = find_ch_connectivity(
create_info(CHANNELS, 250, 'eeg', read_montage('standard_1005')),
'eeg')[0]
t_obs, clusters, cluster_pv, h0 = spatio_temporal_cluster_test(
[g1, g2], 138, stat_fun=rankstat, tail=1, connectivity=cnk)
cluster_pv
good_cluster_inds = np.where(cluster_pv < 0.05)[0]
for i_clu, clu_idx in enumerate(good_cluster_inds[:10]):
# unpack cluster information, get unique indices
time_inds, space_inds = np.squeeze(clusters[clu_idx])
ch_inds = np.unique(space_inds)
time_inds = np.unique(time_inds)
# get topography for F stat
eeg=True,
mindist=5.0,
n_jobs=None)
print(fwd)
##############################################################################
# From here on, standard inverse imaging methods can be used!
#
# Infant MRI surrogates
# ---------------------
# We don't have a sample infant dataset for MNE, so let's fake a 10-20 one:
ch_names = \
'Fz Cz Pz Oz Fp1 Fp2 F3 F4 F7 F8 C3 C4 T7 T8 P3 P4 P7 P8 O1 O2'.split()
data = np.random.RandomState(0).randn(len(ch_names), 1000)
info = mne.create_info(ch_names, 1000., 'eeg')
raw = mne.io.RawArray(data, info)
##############################################################################
# Get an infant MRI template
# ^^^^^^^^^^^^^^^^^^^^^^^^^^
# To use an infant head model for M/EEG data, you can use
# :func:`mne.datasets.fetch_infant_template` to download an infant template:
subject = mne.datasets.fetch_infant_template('6mo', subjects_dir, verbose=True)
##############################################################################
# It comes with several helpful built-in files, including a 10-20 montage
# in the MRI coordinate frame, which can be used to compute the
# MRI<->head transform ``trans``:
fname_1020 = op.join(subjects_dir, subject, 'montages', '10-20-montage.fif')
cos = np.cos(times * 10)
sinX2 = sin * 2
cosX2 = cos * 2
# Numpy array of size 4 X 10000.
data = np.array([sin, cos, sinX2, cosX2])
# Definition of channel types and names.
ch_types = ['mag', 'mag', 'grad', 'grad']
ch_names = ['sin', 'cos', 'sinX2', 'cosX2']
###############################################################################
# Create an :class:`info <mne.Info>` object.
# It is also possible to use info from another raw object.
info = mne.create_info(ch_names=ch_names, sfreq=sfreq, ch_types=ch_types)
###############################################################################
# Create a dummy :class:`mne.io.RawArray` object
raw = mne.io.RawArray(data, info)
# Scaling of the figure.
# For actual EEG/MEG data different scaling factors should be used.
scalings = {'mag': 2, 'grad': 2}
raw.plot(n_channels=4,
scalings=scalings,
title='Data from arrays',
show=True,
block=True)
def test_rejection(buffer_size):
"""Test rejection."""
event_id, tmin, tmax = 1, 0.0, 0.5
sfreq = 1000
ch_names = ['Fz', 'Cz', 'Pz', 'STI 014']
raw_tmax = 5
info = create_info(ch_names=ch_names,
sfreq=sfreq,
ch_types=['eeg', 'eeg', 'eeg', 'stim'])
raw_array = np.random.randn(len(ch_names), raw_tmax * sfreq)
raw_array[-1, :] = 0
epoch_start_samples = np.arange(raw_tmax) * sfreq
raw_array[-1, epoch_start_samples] = event_id
reject_threshold = np.max(raw_array) - np.min(raw_array) + 1
reject = {'eeg': reject_threshold}
epochs_to_reject = [1, 3]
epochs_to_keep = np.setdiff1d(np.arange(len(epoch_start_samples)),
epochs_to_reject)
expected_drop_log = [tuple() for _ in range(len(epoch_start_samples))]
for cur_epoch in epochs_to_reject:
raw_array[1, epoch_start_samples[cur_epoch]] = reject_threshold + 1
expected_drop_log[cur_epoch] = (ch_names[1], )
expected_drop_log = tuple(expected_drop_log)
raw = RawArray(raw_array, info)
events = find_events(raw, shortest_event=1, initial_event=True)
picks = pick_types(raw.info, eeg=True)
epochs = Epochs(raw,
events,
event_id=event_id,
tmin=tmin,
tmax=tmax,
baseline=None,
picks=picks,
preload=True,
reject=reject)
epochs_data = epochs.get_data()
assert len(epochs) == len(epoch_start_samples) - len(epochs_to_reject)
assert_array_equal(epochs_data[:, 1, 0],
raw_array[1, epoch_start_samples[epochs_to_keep]])
assert epochs.drop_log == expected_drop_log
assert_array_equal(epochs.selection, epochs_to_keep)
rt_client = MockRtClient(raw)
rt_epochs = RtEpochs(rt_client,
event_id,
tmin,
tmax,
picks=picks,
baseline=None,
isi_max=0.5,
find_events=dict(initial_event=True),
reject=reject)
rt_epochs.start()
rt_client.send_data(rt_epochs,
picks,
tmin=0,
tmax=raw_tmax,
buffer_size=buffer_size)
assert len(rt_epochs) == len(epochs_to_keep)
assert rt_epochs.drop_log == expected_drop_log
assert_array_equal(rt_epochs.selection, epochs_to_keep)
rt_data = rt_epochs.get_data()
assert rt_data.shape == epochs_data.shape
assert_array_equal(rt_data, epochs_data)
def test_find_events():
"""Test find events in raw file."""
events = read_events(fname)
raw = read_raw_fif(raw_fname, preload=True)
# let's test the defaulting behavior while we're at it
extra_ends = ['', '_1']
orig_envs = [os.getenv('MNE_STIM_CHANNEL%s' % s) for s in extra_ends]
os.environ['MNE_STIM_CHANNEL'] = 'STI 014'
if 'MNE_STIM_CHANNEL_1' in os.environ:
del os.environ['MNE_STIM_CHANNEL_1']
events2 = find_events(raw)
assert_array_almost_equal(events, events2)
# now test with mask
events11 = find_events(raw, mask=3, mask_type='not_and')
with pytest.warns(RuntimeWarning, match='events masked'):
events22 = read_events(fname, mask=3, mask_type='not_and')
assert_array_equal(events11, events22)
# Reset some data for ease of comparison
raw._first_samps[0] = 0
raw.info['sfreq'] = 1000
raw._update_times()
stim_channel = 'STI 014'
stim_channel_idx = pick_channels(raw.info['ch_names'],
include=[stim_channel])
# test digital masking
raw._data[stim_channel_idx, :5] = np.arange(5)
raw._data[stim_channel_idx, 5:] = 0
# 1 == '0b1', 2 == '0b10', 3 == '0b11', 4 == '0b100'
pytest.raises(TypeError, find_events, raw, mask="0", mask_type='and')
pytest.raises(ValueError, find_events, raw, mask=0, mask_type='blah')
# testing mask_type. default = 'not_and'
assert_array_equal(
find_events(raw, shortest_event=1, mask=1, mask_type='not_and'),
[[2, 0, 2], [4, 2, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=2, mask_type='not_and'),
[[1, 0, 1], [3, 0, 1], [4, 1, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=3, mask_type='not_and'),
[[4, 0, 4]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=4, mask_type='not_and'),
[[1, 0, 1], [2, 1, 2], [3, 2, 3]])
# testing with mask_type = 'and'
assert_array_equal(
find_events(raw, shortest_event=1, mask=1, mask_type='and'),
[[1, 0, 1], [3, 0, 1]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=2, mask_type='and'),
[[2, 0, 2]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=3, mask_type='and'),
[[1, 0, 1], [2, 1, 2], [3, 2, 3]])
assert_array_equal(
find_events(raw, shortest_event=1, mask=4, mask_type='and'),
[[4, 0, 4]])
# test empty events channel
raw._data[stim_channel_idx, :] = 0
assert_array_equal(find_events(raw), np.empty((0, 3), dtype='int32'))
raw._data[stim_channel_idx, :4] = 1
assert_array_equal(find_events(raw), np.empty((0, 3), dtype='int32'))
raw._data[stim_channel_idx, -1:] = 9
assert_array_equal(find_events(raw), [[14399, 0, 9]])
# Test that we can handle consecutive events with no gap
raw._data[stim_channel_idx, 10:20] = 5
raw._data[stim_channel_idx, 20:30] = 6
raw._data[stim_channel_idx, 30:32] = 5
raw._data[stim_channel_idx, 40] = 6
assert_array_equal(find_events(raw, consecutive=False),
[[10, 0, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(
find_events(raw, consecutive=True),
[[10, 0, 5], [20, 5, 6], [30, 6, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw),
[[10, 0, 5], [20, 5, 6], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw, output='offset', consecutive=False),
[[31, 0, 5], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(
find_events(raw, output='offset', consecutive=True),
[[19, 6, 5], [29, 5, 6], [31, 0, 5], [40, 0, 6], [14399, 0, 9]])
pytest.raises(ValueError,
find_events,
raw,
output='step',
consecutive=True)
assert_array_equal(
find_events(raw, output='step', consecutive=True, shortest_event=1),
[[10, 0, 5], [20, 5, 6], [30, 6, 5], [32, 5, 0], [40, 0, 6],
[41, 6, 0], [14399, 0, 9], [14400, 9, 0]])
assert_array_equal(find_events(raw, output='offset'),
[[19, 6, 5], [31, 0, 6], [40, 0, 6], [14399, 0, 9]])
assert_array_equal(find_events(raw, consecutive=False, min_duration=0.002),
[[10, 0, 5]])
assert_array_equal(find_events(raw, consecutive=True, min_duration=0.002),
[[10, 0, 5], [20, 5, 6], [30, 6, 5]])
assert_array_equal(
find_events(raw,
output='offset',
consecutive=False,
min_duration=0.002), [[31, 0, 5]])
assert_array_equal(
find_events(raw, output='offset', consecutive=True,
min_duration=0.002), [[19, 6, 5], [29, 5, 6], [31, 0, 5]])
assert_array_equal(find_events(raw, consecutive=True, min_duration=0.003),
[[10, 0, 5], [20, 5, 6]])
# test find_stim_steps merge parameter
raw._data[stim_channel_idx, :] = 0
raw._data[stim_channel_idx, 0] = 1
raw._data[stim_channel_idx, 10] = 4
raw._data[stim_channel_idx, 11:20] = 5
assert_array_equal(
find_stim_steps(raw, pad_start=0, merge=0, stim_channel=stim_channel),
[[0, 0, 1], [1, 1, 0], [10, 0, 4], [11, 4, 5], [20, 5, 0]])
assert_array_equal(
find_stim_steps(raw, merge=-1, stim_channel=stim_channel),
[[1, 1, 0], [10, 0, 5], [20, 5, 0]])
assert_array_equal(
find_stim_steps(raw, merge=1, stim_channel=stim_channel),
[[1, 1, 0], [11, 0, 5], [20, 5, 0]])
# put back the env vars we trampled on
for s, o in zip(extra_ends, orig_envs):
if o is not None:
os.environ['MNE_STIM_CHANNEL%s' % s] = o
# Test with list of stim channels
raw._data[stim_channel_idx, 1:101] = np.zeros(100)
raw._data[stim_channel_idx, 10:11] = 1
raw._data[stim_channel_idx, 30:31] = 3
stim_channel2 = 'STI 015'
stim_channel2_idx = pick_channels(raw.info['ch_names'],
include=[stim_channel2])
raw._data[stim_channel2_idx, :] = 0
raw._data[stim_channel2_idx, :100] = raw._data[stim_channel_idx, 5:105]
events1 = find_events(raw, stim_channel='STI 014')
events2 = events1.copy()
events2[:, 0] -= 5
events = find_events(raw, stim_channel=['STI 014', stim_channel2])
assert_array_equal(events[::2], events2)
assert_array_equal(events[1::2], events1)
# test initial_event argument
info = create_info(['MYSTI'], 1000, 'stim')
data = np.zeros((1, 1000))
raw = RawArray(data, info)
data[0, :10] = 100
data[0, 30:40] = 200
assert_array_equal(find_events(raw, 'MYSTI'), [[30, 0, 200]])
assert_array_equal(find_events(raw, 'MYSTI', initial_event=True),
[[0, 0, 100], [30, 0, 200]])
list_el = [str('PZ'), str('PO5'), str('PO3'), str('POz'), str('PO4'), str('PO6'), str('O1'), str('Oz'),
str('O2')] # Electrodes to use
vec_ind_el = df_location[df_location['Label'].isin(list_el)].index # Vector with indexes of electrodes to use
vec_ind_el = df_location['Label'].index
ind_ref_el = df_location['Electrode'][df_location['Label'] == 'Cz'].index[0] # Index of reference electrode 'Cz'
ind_oz = df_location['Electrode'][df_location['Label'] == 'Oz'].index[0]
ind_frq = 7
## Load and create Montage
df_montage = pd.read_csv(os.path.join(dir_data, 'montage.DAT'), sep='\t', header=None)
df_montage.columns = ['Channel', 'x', 'y', 'z']
df_montage['Channel'] = df_montage['Channel'].str.strip()
mne_montage = mne.channels.make_dig_montage(df_montage[3:])
mne_montage.ch_names = df_location['Label'].tolist()
n_channels = len(df_montage['Channel'][3:])
fake_info = mne.create_info(ch_names=mne_montage.ch_names, sfreq=250.,
ch_types='eeg')
N_pre = int(0.5 * 250) # pre stim
N_delay = int(0.140 * 250) # SSVEP delay
N_stim = int(5 * 250) # stimulation
N_start = N_pre + N_delay - 1
N_stop = N_start + N_stim
### Plot average over all subjects
Nb = 6
Ns = 35
Nf = 40
Ne = 9
mat_proc = np.zeros([Ns * Nb, len(vec_ind_el), N_stim])
s = 0
def _to_mne():
x_mne = []
for k in range(n_suj):
info = create_info(roi[k].tolist(), sf)
x_mne += [EpochsArray(x[k], info, tmin=times[0], verbose=False)]
return x_mne
def test_montage():
"""Test making montages."""
tempdir = _TempDir()
inputs = dict(
sfp='FidNz 0 9.071585155 -2.359754454\n'
'FidT9 -6.711765 0.040402876 -3.251600355\n'
'very_very_very_long_name -5.831241498 -4.494821698 4.955347697\n'
'Cz 0 0 8.899186843',
csd=
'// MatLab Sphere coordinates [degrees] Cartesian coordinates\n' # noqa: E501
'// Label Theta Phi Radius X Y Z off sphere surface\n' # noqa: E501
'E1 37.700 -14.000 1.000 0.7677 0.5934 -0.2419 -0.00000000000000011\n' # noqa: E501
'E3 51.700 11.000 1.000 0.6084 0.7704 0.1908 0.00000000000000000\n' # noqa: E501
'E31 90.000 -11.000 1.000 0.0000 0.9816 -0.1908 0.00000000000000000\n' # noqa: E501
'E61 158.000 -17.200 1.000 -0.8857 0.3579 -0.2957 -0.00000000000000022', # noqa: E501
mm_elc=
'# ASA electrode file\nReferenceLabel avg\nUnitPosition mm\n' # noqa:E501
'NumberPositions= 68\n'
'Positions\n'
'-86.0761 -19.9897 -47.9860\n'
'85.7939 -20.0093 -48.0310\n'
'0.0083 86.8110 -39.9830\n'
'-86.0761 -24.9897 -67.9860\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
m_elc='# ASA electrode file\nReferenceLabel avg\nUnitPosition m\n'
'NumberPositions= 68\nPositions\n-.0860761 -.0199897 -.0479860\n' # noqa:E501
'.0857939 -.0200093 -.0480310\n.0000083 .00868110 -.0399830\n'
'.08 -.02 -.04\n'
'Labels\nLPA\nRPA\nNz\nDummy\n',
txt='Site Theta Phi\n'
'Fp1 -92 -72\n'
'Fp2 92 72\n'
'very_very_very_long_name -92 72\n'
'O2 92 -90\n',
elp='346\n'
'EEG\t F3\t -62.027\t -50.053\t 85\n'
'EEG\t Fz\t 45.608\t 90\t 85\n'
'EEG\t F4\t 62.01\t 50.103\t 85\n'
'EEG\t FCz\t 68.01\t 58.103\t 85\n',
hpts='eeg Fp1 -95.0 -3. -3.\n'
'eeg AF7 -1 -1 -3\n'
'eeg A3 -2 -2 2\n'
'eeg A 0 0 0',
bvef='<?xml version="1.0" encoding="UTF-8" standalone="yes"?>\n'
'<!-- Generated by EasyCap Configurator 19.05.2014 -->\n'
'<Electrodes defaults="false">\n'
' <Electrode>\n'
' <Name>Fp1</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-72</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>1</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>Fz</Name>\n'
' <Theta>45</Theta>\n'
' <Phi>90</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>2</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F3</Name>\n'
' <Theta>-60</Theta>\n'
' <Phi>-51</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>3</Number>\n'
' </Electrode>\n'
' <Electrode>\n'
' <Name>F7</Name>\n'
' <Theta>-90</Theta>\n'
' <Phi>-36</Phi>\n'
' <Radius>1</Radius>\n'
' <Number>4</Number>\n'
' </Electrode>\n'
'</Electrodes>',
)
# Get actual positions and save them for checking
# csd comes from the string above, all others come from commit 2fa35d4
poss = dict(
sfp=[[0.0, 9.07159, -2.35975], [-6.71176, 0.0404, -3.2516],
[-5.83124, -4.49482, 4.95535], [0.0, 0.0, 8.89919]],
mm_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.08681, -0.03998], [-0.08608, -0.02499, -0.06799]],
m_elc=[[-0.08608, -0.01999, -0.04799], [0.08579, -0.02001, -0.04803],
[1e-05, 0.00868, -0.03998], [0.08, -0.02, -0.04]],
txt=[[-26.25044, 80.79056, -2.96646], [26.25044, 80.79056, -2.96646],
[-26.25044, -80.79056, -2.96646], [0.0, -84.94822, -2.96646]],
elp=[[-48.20043, 57.55106, 39.86971], [0.0, 60.73848, 59.4629],
[48.1426, 57.58403, 39.89198], [41.64599, 66.91489, 31.8278]],
hpts=[[-95, -3, -3], [-1, -1., -3.], [-2, -2, 2.], [0, 0, 0]],
bvef=[[-26.266444, 80.839803, 5.204748e-15],
[3.680313e-15, 60.104076, 60.104076],
[-46.325632, 57.207392, 42.500000],
[-68.766444, 49.961746, 5.204748e-15]],
)
for key, text in inputs.items():
kind = key.split('_')[-1]
fname = op.join(tempdir, 'test.' + kind)
with open(fname, 'w') as fid:
fid.write(text)
montage = read_montage(fname)
if kind in ('sfp', 'txt'):
assert ('very_very_very_long_name' in montage.ch_names)
assert_equal(len(montage.ch_names), 4)
assert_equal(len(montage.ch_names), len(montage.pos))
assert_equal(montage.pos.shape, (4, 3))
assert_equal(montage.kind, 'test')
if kind == 'csd':
dtype = [('label', 'S4'), ('theta', 'f8'), ('phi', 'f8'),
('radius', 'f8'), ('x', 'f8'), ('y', 'f8'), ('z', 'f8'),
('off_sph', 'f8')]
try:
table = np.loadtxt(fname, skip_header=2, dtype=dtype)
except TypeError:
table = np.loadtxt(fname, skiprows=2, dtype=dtype)
poss['csd'] = np.c_[table['x'], table['y'], table['z']]
if kind == 'elc':
# Make sure points are reasonable distance from geometric centroid
centroid = np.sum(montage.pos, axis=0) / montage.pos.shape[0]
distance_from_centroid = np.apply_along_axis(
np.linalg.norm, 1, montage.pos - centroid)
assert_array_less(distance_from_centroid, 0.2)
assert_array_less(0.01, distance_from_centroid)
assert_array_almost_equal(poss[key], montage.pos, 4, err_msg=key)
# Test reading in different letter case.
ch_names = [
"F3", "FZ", "F4", "FC3", "FCz", "FC4", "C3", "CZ", "C4", "CP3", "CPZ",
"CP4", "P3", "PZ", "P4", "O1", "OZ", "O2"
]
montage = read_montage('standard_1020', ch_names=ch_names)
assert_array_equal(ch_names, montage.ch_names)
# test transform
input_strs = [
"""
eeg Fp1 -95.0 -31.0 -3.0
eeg AF7 -81 -59 -3
eeg AF3 -87 -41 28
cardinal 2 -91 0 -42
cardinal 1 0 -91 -42
cardinal 3 0 91 -42
""", """
Fp1 -95.0 -31.0 -3.0
AF7 -81 -59 -3
AF3 -87 -41 28
FidNz -91 0 -42
FidT9 0 -91 -42
FidT10 0 91 -42
"""
]
# sfp files seem to have Nz, T9, and T10 as fiducials:
# https://github.com/mne-tools/mne-python/pull/4482#issuecomment-321980611
kinds = ['test_fid.hpts', 'test_fid.sfp']
for kind, input_str in zip(kinds, input_strs):
fname = op.join(tempdir, kind)
with open(fname, 'w') as fid:
fid.write(input_str)
montage = read_montage(op.join(tempdir, kind), transform=True)
# check coordinate transformation
pos = np.array([-95.0, -31.0, -3.0])
nasion = np.array([-91, 0, -42])
lpa = np.array([0, -91, -42])
rpa = np.array([0, 91, -42])
fids = np.vstack((nasion, lpa, rpa))
trans = get_ras_to_neuromag_trans(fids[0], fids[1], fids[2])
pos = apply_trans(trans, pos)
assert_array_equal(montage.pos[0], pos)
assert_array_equal(montage.nasion[[0, 2]], [0, 0])
assert_array_equal(montage.lpa[[1, 2]], [0, 0])
assert_array_equal(montage.rpa[[1, 2]], [0, 0])
pos = np.array([-95.0, -31.0, -3.0])
montage_fname = op.join(tempdir, kind)
montage = read_montage(montage_fname, unit='mm')
assert_array_equal(montage.pos[0], pos * 1e-3)
# test with last
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
_set_montage(info, montage)
pos2 = np.array([c['loc'][:3] for c in info['chs']])
assert_array_equal(pos2, montage.pos)
assert_equal(montage.ch_names, info['ch_names'])
info = create_info(montage.ch_names, 1e3,
['eeg'] * len(montage.ch_names))
evoked = EvokedArray(data=np.zeros((len(montage.ch_names), 1)),
info=info,
tmin=0)
# test return type as well as set montage
assert (isinstance(evoked.set_montage(montage), type(evoked)))
pos3 = np.array([c['loc'][:3] for c in evoked.info['chs']])
assert_array_equal(pos3, montage.pos)
assert_equal(montage.ch_names, evoked.info['ch_names'])
# Warning should be raised when some EEG are not specified in montage
info = create_info(montage.ch_names + ['foo', 'bar'], 1e3,
['eeg'] * (len(montage.ch_names) + 2))
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
# Channel names can be treated case insensitive
info = create_info(['FP1', 'af7', 'AF3'], 1e3, ['eeg'] * 3)
_set_montage(info, montage)
# Unless there is a collision in names
info = create_info(['FP1', 'Fp1', 'AF3'], 1e3, ['eeg'] * 3)
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage)
assert len(info['dig']) == 5 # 2 EEG w/pos, 3 fiducials
montage.ch_names = ['FP1', 'Fp1', 'AF3']
info = create_info(['fp1', 'AF3'], 1e3, ['eeg', 'eeg'])
assert (info['dig'] is None)
with pytest.warns(RuntimeWarning, match='position specified'):
_set_montage(info, montage, set_dig=False)
assert (info['dig'] is None)
# test get_pos2d method
montage = read_montage("standard_1020")
c3 = montage.get_pos2d()[montage.ch_names.index("C3")]
c4 = montage.get_pos2d()[montage.ch_names.index("C4")]
fz = montage.get_pos2d()[montage.ch_names.index("Fz")]
oz = montage.get_pos2d()[montage.ch_names.index("Oz")]
f1 = montage.get_pos2d()[montage.ch_names.index("F1")]
assert (c3[0] < 0) # left hemisphere
assert (c4[0] > 0) # right hemisphere
assert (fz[1] > 0) # frontal
assert (oz[1] < 0) # occipital
assert_allclose(fz[0], 0, atol=1e-2) # midline
assert_allclose(oz[0], 0, atol=1e-2) # midline
assert (f1[0] < 0 and f1[1] > 0) # left frontal
# test get_builtin_montages function
montages = get_builtin_montages()
assert (len(montages) > 0) # MNE should always ship with montages
assert ("standard_1020" in montages) # 10/20 montage
assert ("standard_1005" in montages) # 10/05 montage