def run_evoked(subject_id):
subject = "sub%03d" % subject_id
print("processing subject: %s" % subject)
data_path = op.join(meg_dir, subject)
epochs = mne.read_epochs(op.join(data_path, '%s-epo.fif' % subject),
preload=False)
evoked_famous = epochs['face/famous'].average()
evoked_scrambled = epochs['scrambled'].average()
evoked_unfamiliar = epochs['face/unfamiliar'].average()
# Simplify comment
evoked_famous.comment = 'famous'
evoked_scrambled.comment = 'scrambled'
evoked_unfamiliar.comment = 'unfamiliar'
contrast = mne.combine_evoked([evoked_famous, evoked_unfamiliar,
evoked_scrambled], weights=[0.5, 0.5, -1.])
contrast.comment = 'contrast'
faces = mne.combine_evoked([evoked_famous, evoked_unfamiliar], 'nave')
faces.comment = 'faces'
mne.evoked.write_evokeds(op.join(data_path, '%s-ave.fif' % subject),
[evoked_famous, evoked_scrambled,
evoked_unfamiliar, contrast, faces])
# take care of noise cov
cov = mne.compute_covariance(epochs, tmax=0, method='shrunk')
cov.save(op.join(data_path, '%s-cov.fif' % subject))
def apply_STC_epo(fnepo, event, method='MNE', snr=1.0, min_subject='fsaverage',
subjects_dir=None):
from mne import morph_data
from mne.minimum_norm import read_inverse_operator, apply_inverse_epochs
fnlist = get_files_from_list(fnepo)
# loop across all filenames
for fname in fnlist:
fn_path = os.path.split(fname)[0]
name = os.path.basename(fname)
subject = name.split('_')[0]
min_dir = subjects_dir + '/%s' %min_subject
snr = snr
lambda2 = 1.0 / snr ** 2
stcs_path = min_dir + '/stcs/%s/%s/' % (subject,event)
reset_directory(stcs_path)
# fn_inv = fname[:fname.rfind('-ave.fif')] + ',ave-inv.fif'
fn_inv = fn_path + '/%s_epo-inv.fif' %subject
# noise_cov = mne.read_cov(fn_cov)
epo = mne.read_epochs(fname)
epo.pick_types(meg=True, ref_meg=False)
inv = read_inverse_operator(fn_inv)
stcs = apply_inverse_epochs(epo, inv, lambda2, method,
pick_ori='normal')
s = 0
while s < len(stcs):
stc_morph = morph_data(subject, min_subject, stcs[s])
stc_morph.save(stcs_path + '/trial%s_fsaverage'
% (str(s)), ftype='stc')
s = s + 1
def compute_and_save_psd(epochs_fname, fmin=0, fmax=120,
method='welch', n_fft=256, n_overlap=0,
picks=None, proj=False, n_jobs=1, verbose=None):
"""
Load epochs from file,
compute psd and save the result in numpy arrays
"""
import numpy as np
import os
from mne import read_epochs
epochs = read_epochs(epochs_fname)
epochs_meg = epochs.pick_types(meg=True, eeg=False, eog=False, ecg=False)
if method == 'welch':
from mne.time_frequency import psd_welch
psds, freqs = psd_welch(epochs_meg)
elif method == 'multitaper':
from mne.time_frequency import psd_multitaper
psds, freqs = psd_multitaper(epochs_meg)
else:
raise Exception('nonexistent method for psd computation')
path, name = os.path.split(epochs_fname)
base, ext = os.path.splitext(name)
psds_fname = base + '-psds.npz'
# freqs_fname = base + '-freqs.npy'
psds_fname = os.path.abspath(psds_fname)
# print(psds.shape)
np.savez(psds_fname, psds=psds, freqs=freqs)
# np.save(freqs_file, freqs)
return psds_fname
def test_access_by_name():
"""Test accessing epochs by event name and on_missing for rare events
"""
assert_raises(ValueError, Epochs, raw, events, {1: 42, 2: 42}, tmin,
tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, 'foo', tmin, tmax,
picks=picks)
# Test accessing non-existent events (assumes 12345678 does not exist)
event_id_illegal = dict(aud_l=1, does_not_exist=12345678)
assert_raises(ValueError, Epochs, raw, events, event_id_illegal,
tmin, tmax)
# Test on_missing
assert_raises(ValueError, Epochs, raw, events, 1, tmin, tmax,
on_missing='foo')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing='warning')
nw = len(w)
assert_true(1 <= nw <= 2)
Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing='ignore')
assert_equal(len(w), nw)
epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks)
assert_raises(KeyError, epochs.__getitem__, 'bar')
data = epochs['a'].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks,
preload=True)
assert_raises(KeyError, epochs.__getitem__, 'bar')
epochs.save(op.join(tempdir, 'test-epo.fif'))
epochs2 = read_epochs(op.join(tempdir, 'test-epo.fif'))
for ep in [epochs, epochs2]:
data = ep['a'].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
assert_array_equal(epochs2['a'].events, epochs['a'].events)
epochs3 = Epochs(raw, events, {'a': 1, 'b': 2, 'c': 3, 'd': 4},
tmin, tmax, picks=picks, preload=True)
assert_equal(list(sorted(epochs3[['a', 'b']].event_id.values())),
[1, 2])
epochs4 = epochs['a']
epochs5 = epochs3['a']
assert_array_equal(epochs4.events, epochs5.events)
# 20 is our tolerance because epochs are written out as floats
assert_array_almost_equal(epochs4.get_data(), epochs5.get_data(), 20)
epochs6 = epochs3[['a', 'b']]
assert_true(all(np.logical_or(epochs6.events[:, 2] == 1,
epochs6.events[:, 2] == 2)))
assert_array_equal(epochs.events, epochs6.events)
assert_array_almost_equal(epochs.get_data(), epochs6.get_data(), 20)
def calc_inverse_operator(events_id, epochs_fn, fwd_sub_fn, inv_fn, min_crop_t=None, max_crop_t=0):
for cond in events_id.keys():
epochs = mne.read_epochs(epochs_fn.format(cond=cond))
noise_cov = mne.compute_covariance(epochs.crop(min_crop_t, max_crop_t, copy=True))
forward_sub = mne.read_forward_solution(fwd_sub_fn.format(cond=cond))
inverse_operator_sub = make_inverse_operator(epochs.info, forward_sub, noise_cov,
loose=None, depth=None)
write_inverse_operator(inv_fn.format(cond=cond), inverse_operator_sub)
def _calc_epoches(params):
subject, events_id, tmin, tmax = params
out_file = op.join(LOCAL_ROOT_DIR, 'epo', '{}_ecr_nTSSS_conflict-epo.fif'.format(subject))
if not op.isfile(out_file):
events = mne.read_events(op.join(REMOTE_ROOT_DIR, 'events', '{}_ecr_nTSSS_conflict-eve.fif'.format(subject)))
raw = mne.io.Raw(op.join(REMOTE_ROOT_DIR, 'raw', '{}_ecr_nTSSS_raw.fif'.format(subject)), preload=False)
picks = mne.pick_types(raw.info, meg=True)
epochs = find_epoches(raw, picks, events, events_id, tmin=tmin, tmax=tmax)
epochs.save(out_file)
else:
epochs = mne.read_epochs(out_file)
return epochs
def _call_base_epochs_public_api(epochs, tmpdir):
"""Call all public API methods of an (non-empty) epochs object."""
# make sure saving and loading returns the same data
orig_data = epochs.get_data()
export_file = tmpdir.join('test_rt-epo.fif')
epochs.save(str(export_file))
loaded_epochs = read_epochs(str(export_file))
loaded_data = loaded_epochs.get_data()
assert orig_data.shape == loaded_data.shape
assert_allclose(loaded_data, orig_data)
# decimation
epochs_copy = epochs.copy()
epochs_copy.decimate(1)
assert epochs_copy.get_data().shape == orig_data.shape
epochs_copy.info['lowpass'] = 10 # avoid warning
epochs_copy.decimate(10)
assert np.abs(10.0 - orig_data.shape[2] /
epochs_copy.get_data().shape[2]) <= 1
# check that methods that require preloaded data fail
with pytest.raises(RuntimeError):
epochs.crop(tmin=epochs.tmin,
tmax=(epochs.tmin + (epochs.tmax - epochs.tmin) / 2))
with pytest.raises(RuntimeError):
epochs.drop_channels(epochs.ch_names[0:1])
with pytest.raises(RuntimeError):
epochs.resample(epochs.info['sfreq'] / 10)
# smoke test
epochs.standard_error()
avg_evoked = epochs.average()
epochs.subtract_evoked(avg_evoked)
epochs.metadata
epochs.events
epochs.ch_names
epochs.tmin
epochs.tmax
epochs.filename
repr(epochs)
epochs.plot(show=False)
# save time by not calling all plot functions
# epochs.plot_psd(show=False)
# epochs.plot_drop_log(show=False)
# epochs.plot_topo_image()
# epochs.plot_psd_topomap()
# epochs.plot_image()
epochs.drop_bad()
epochs_copy.apply_baseline()
# do not call since we don't want to make assumptions about events
# epochs_copy.equalize_event_counts(epochs.event_id.keys())
epochs_copy.drop([0])
def test_array_epochs():
"""Test creating epochs from array
"""
# creating
rng = np.random.RandomState(42)
data = rng.random_sample((10, 20, 300))
sfreq = 1e3
ch_names = ["EEG %03d" % (i + 1) for i in range(20)]
types = ["eeg"] * 20
info = create_info(ch_names, sfreq, types)
events = np.c_[np.arange(1, 600, 60), np.zeros(10), [1, 2] * 5]
event_id = {"a": 1, "b": 2}
epochs = EpochsArray(data, info, events=events, event_id=event_id, tmin=-0.2)
# saving
temp_fname = op.join(tempdir, "test-epo.fif")
epochs.save(temp_fname)
epochs2 = read_epochs(temp_fname)
data2 = epochs2.get_data()
assert_allclose(data, data2)
assert_allclose(epochs.times, epochs2.times)
assert_equal(epochs.event_id, epochs2.event_id)
assert_array_equal(epochs.events, epochs2.events)
# plotting
import matplotlib
matplotlib.use("Agg") # for testing don't use X server
epochs[0].plot()
# indexing
assert_array_equal(np.unique(epochs["a"].events[:, 2]), np.array([1]))
assert_equal(len(epochs[:2]), 2)
data[0, 5, 150] = 3000
data[1, :, :] = 0
data[2, 5, 210] = 3000
data[3, 5, 260] = 0
epochs = EpochsArray(
data,
info,
events=events,
event_id=event_id,
tmin=0,
reject=dict(eeg=1000),
flat=dict(eeg=1e-1),
reject_tmin=0.1,
reject_tmax=0.2,
)
assert_equal(len(epochs), len(events) - 2)
assert_equal(epochs.drop_log[0], ["EEG 006"])
assert_equal(len(events), len(epochs.selection))
def evok_epochs(sub_id, session):
""" load a epoched file and average it and save the evoked file
"""
fname = "sub_%d_%s" % (sub_id, session)
f_load = fname + "_tsss_mc_epochs.fif"
f_save = fname + "_tsss_mc_evk.fif"
epochs = mne.read_epochs(f_load)
evoked = epochs.average()
evoked.comment = session
evoked = mne.fiff.pick_types_evoked(evoked, meg='grad', exclude='bads')
evoked.resample(sfreq=250)
evoked.save(f_save)
def calc_evoked(indices, epochs_fname, overwrite_epochs=False, overwrite_evoked=False):
epochs = mne.read_epochs(epochs_fname, preload=False)
print(epochs.events.shape)
for event_name, event_indices in indices.items():
evoked_event_fname = meg.get_cond_fname(meg.EVO, event_name)
epochs_event_fname = meg.get_cond_fname(meg.EPO, event_name)
if not op.isfile(epochs_event_fname) or overwrite_epochs:
print('Saving {} epochs to {}, events num: {}'.format(event_name, epochs_event_fname, len(event_indices)))
event_epochs = epochs[event_indices]
event_epochs.save(epochs_event_fname)
if not op.isfile(evoked_event_fname) or overwrite_evoked:
print('Saving {} evoked to {}'.format(event_name, evoked_event_fname))
mne.write_evokeds(evoked_event_fname, event_epochs.average())
def load(subject, event_type):
with open(op.join(path_data, '%s/behavior_%s.pkl') % (subject, event_type), 'rb') as f:
events = pickle.load(f)
# add explicit conditions
events = complete_behavior(events)
epochs = mne.read_epochs(op.join(path_data, '%s/epochs_%s.fif') % (subject, event_type))
if event_type == 'Target':
epochs.crop(0, .600)
elif event_type == 'Cue':
epochs.crop(0, .900)
elif event_type == 'Probe':
epochs.crop(0, .600)
return epochs, events
def load(subject, event_type):
fname = op.join(path_data, '%s/behavior_%s.hdf5') % (subject, event_type)
events = read_hdf5(fname)
# add explicit conditions
events = complete_behavior(events)
fname = op.join(path_data, '%s/epochs_%s.fif') % (subject, event_type)
epochs = mne.read_epochs(fname)
if event_type == 'Target':
epochs.crop(0, .600)
elif event_type == 'Cue':
epochs.crop(0, .900)
elif event_type == 'Probe':
epochs.crop(0, .600)
return epochs, events
def test_access_by_name():
"""Test accessing epochs by event name and on_missing for rare events
"""
assert_raises(ValueError, Epochs, raw, events, {1: 42, 2: 42}, tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {"a": "spam", 2: "eggs"}, tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {"a": "spam", 2: "eggs"}, tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, "foo", tmin, tmax, picks=picks)
# Test accessing non-existent events (assumes 12345678 does not exist)
event_id_illegal = dict(aud_l=1, does_not_exist=12345678)
assert_raises(ValueError, Epochs, raw, events, event_id_illegal, tmin, tmax)
# Test on_missing
assert_raises(ValueError, Epochs, raw, events, 1, tmin, tmax, on_missing="foo")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing="warning")
nw = len(w)
assert_true(1 <= nw <= 2)
Epochs(raw, events, event_id_illegal, tmin, tmax, on_missing="ignore")
assert_equal(len(w), nw)
epochs = Epochs(raw, events, {"a": 1, "b": 2}, tmin, tmax, picks=picks)
assert_raises(KeyError, epochs.__getitem__, "bar")
data = epochs["a"].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
epochs = Epochs(raw, events, {"a": 1, "b": 2}, tmin, tmax, picks=picks, preload=True)
assert_raises(KeyError, epochs.__getitem__, "bar")
epochs.save(op.join(tempdir, "test-epo.fif"))
epochs2 = read_epochs(op.join(tempdir, "test-epo.fif"))
for ep in [epochs, epochs2]:
data = ep["a"].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
assert_array_equal(epochs2["a"].events, epochs["a"].events)
epochs3 = Epochs(raw, events, {"a": 1, "b": 2, "c": 3, "d": 4}, tmin, tmax, picks=picks, preload=True)
assert_equal(list(sorted(epochs3[["a", "b"]].event_id.values())), [1, 2])
epochs4 = epochs["a"]
epochs5 = epochs3["a"]
assert_array_equal(epochs4.events, epochs5.events)
# 20 is our tolerance because epochs are written out as floats
assert_array_almost_equal(epochs4.get_data(), epochs5.get_data(), 20)
epochs6 = epochs3[["a", "b"]]
assert_true(all(np.logical_or(epochs6.events[:, 2] == 1, epochs6.events[:, 2] == 2)))
assert_array_equal(epochs.events, epochs6.events)
assert_array_almost_equal(epochs.get_data(), epochs6.get_data(), 20)
def test_read_epochs():
event_id = 1
tmin = -0.2
tmax = 0.5
# Setup for reading the raw data
raw = fiff.setup_read_raw(raw_fname)
events = mne.read_events(event_name)
# Set up pick list: MEG + STI 014 - bad channels (modify to your needs)
include = ["STI 014"]
want_meg = True
want_eeg = False
want_stim = False
picks = fiff.pick_types(raw["info"], want_meg, want_eeg, want_stim, include, raw["info"]["bads"])
data, times, channel_names = mne.read_epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0))
def test_access_by_name():
"""Test accessing epochs by event name
"""
assert_raises(ValueError, Epochs, raw, events, {1: 42, 2: 42}, tmin,
tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {'a': 'spam', 2: 'eggs'},
tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, 'foo', tmin, tmax,
picks=picks)
epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks)
assert_raises(KeyError, epochs.__getitem__, 'bar')
data = epochs['a'].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
epochs = Epochs(raw, events, {'a': 1, 'b': 2}, tmin, tmax, picks=picks,
preload=True)
assert_raises(KeyError, epochs.__getitem__, 'bar')
epochs.save(op.join(tempdir, 'test-epo.fif'))
epochs2 = read_epochs(op.join(tempdir, 'test-epo.fif'))
for ep in [epochs, epochs2]:
data = ep['a'].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
assert_array_equal(epochs2['a'].events, epochs['a'].events)
epochs3 = Epochs(raw, events, {'a': 1, 'b': 2, 'c': 3, 'd': 4},
tmin, tmax, picks=picks, preload=True)
assert_equal(list(sorted(epochs3[['a', 'b']].event_id.values())),
[1, 2])
epochs4 = epochs['a']
epochs5 = epochs3['a']
assert_array_equal(epochs4.events, epochs5.events)
# 20 is our tolerance because epochs are written out as floats
assert_array_almost_equal(epochs4.get_data(), epochs5.get_data(), 20)
epochs6 = epochs3[['a', 'b']]
assert_true(all(np.logical_or(epochs6.events[:, 2] == 1,
epochs6.events[:, 2] == 2)))
assert_array_equal(epochs.events, epochs6.events)
assert_array_almost_equal(epochs.get_data(), epochs6.get_data(), 20)
def ep2ts(fif_file):
"""Read fif file with raw data or epochs and save
timeseries to .npy
"""
from mne import read_epochs
from mne import pick_types
from numpy import save
import os.path as op
with nostdout():
epochs = read_epochs(fif_file)
epochs_meg = epochs.pick_types(meg=True, eeg=False, eog=False, ecg=False)
data = epochs_meg.get_data()
save_path = op.abspath('ts_epochs.npy')
save(save_path, data)
return save_path
def inverse_function(sub_id, session):
""" Will calculate the inverse model based dSPM
"""
data_path = "/media/mje/My_Book/Data/MEG/MEG_libet/sub_2_tests"
fname = "sub_%d_%s_tsss_mc" % (sub_id, session)
fname_epochs = data_path + fname + "_epochs.fif"
fname_fwd_meg = data_path + fname + "_fwd.fif"
fname_cov = data_path + fname + "_cov.fif"
fname_inv = data_path + fname + "_inv.fif"
fname_stcs = fname + "_mne_dSPM_inverse"
epochs = mne.read_epochs(fname_epochs)
evoked = epochs.average()
snr = 3.0
lambda2 = 1.0 / snr ** 2
# Load data
forward_meg = mne.read_forward_solution(fname_fwd_meg, surf_ori=True)
noise_cov = mne.read_cov(fname_cov)
# regularize noise covariance
noise_cov = mne.cov.regularize(noise_cov, evoked.info,
mag=0.05, grad=0.05, eeg=0.1, proj=True)
# Restrict forward solution as necessary for MEG
forward_meg = mne.fiff.pick_types_forward(forward_meg, meg=True, eeg=False)
# make an M/EEG, MEG-only, and EEG-only inverse operators
info = evoked.info
inverse_operator_meg = make_inverse_operator(info, forward_meg, noise_cov,
loose=0.2, depth=0.8)
write_inverse_operator(fname_inv, inverse_operator_meg)
# Compute inverse solution
stc = apply_inverse(evoked, inverse_operator_meg, lambda2, "dSPM",
pick_normal=False)
# Save result in stc files
stc.save(fname_stcs)
def load(typ, subject='fsaverage', analysis='analysis', block=999,
download=True, preload=False):
"""Auxiliary saving function."""
# get file name
fname = paths(typ, subject=subject, analysis=analysis, block=block)
# check if file exists
if not op.exists(fname) and download:
client.download(fname)
# different data format depending file type
if typ == 'behavior':
from base import read_events
out = read_events(fname)
elif typ == 'sss':
out = Raw(fname, preload=preload)
elif typ in ['epo_block', 'epochs', 'epochs_decim', 'epochs_vhp']:
out = read_epochs(fname, preload=preload)
elif typ in ['cov']:
from mne.cov import read_cov
out = read_cov(fname)
elif typ in ['fwd']:
from mne import read_forward_solution
out = read_forward_solution(fname, surf_ori=True)
elif typ in ['inv']:
from mne.minimum_norm import read_inverse_operator
out = read_inverse_operator(fname)
elif typ in ['evoked', 'decod', 'decod_tfr', 'score', 'score_tfr',
'evoked_source']:
with open(fname, 'rb') as f:
out = pickle.load(f)
elif typ == 'morph':
from scipy.sparse import csr_matrix
loader = np.load(fname)
out = csr_matrix((loader['data'], loader['indices'], loader['indptr']),
shape=loader['shape'])
elif typ in ['score_source', 'score_pval']:
out = np.load(fname)
else:
raise NotImplementedError()
return out
def _calc_inverse(params):
subject, epochs, overwrite = params
epo = op.join(REMOTE_ROOT_DIR, 'ave', '{}_ecr_nTSSS_conflict-epo.fif'.format(subject))
fwd = op.join(REMOTE_ROOT_DIR, 'fwd', '{}_ecr-fwd.fif'.format(subject))
local_inv_file_name = op.join(LOCAL_ROOT_DIR, 'inv', '{}_ecr_nTSSS_conflict-inv.fif'.format(subject))
if os.path.isfile(local_inv_file_name) and not overwrite:
inverse_operator = read_inverse_operator(local_inv_file_name)
print('inv already calculated for {}'.format(subject))
else:
if epochs is None:
epochs = mne.read_epochs(epo)
noise_cov = mne.compute_covariance(epochs.crop(None, 0, copy=True))
inverse_operator = None
if not os.path.isfile(fwd):
print('no fwd for {}'.format(subject))
else:
forward = mne.read_forward_solution(fwd)
inverse_operator = make_inverse_operator(epochs.info, forward, noise_cov,
loose=None, depth=None)
write_inverse_operator(local_inv_file_name, inverse_operator)
return inverse_operator
def run_time_frequency(subject_id):
print("processing subject: %s" % subject_id)
subject = "sub%03d" % subject_id
data_path = op.join(meg_dir, subject)
epochs = mne.read_epochs(op.join(data_path, '%s-epo.fif' % subject))
faces = epochs['face']
idx = [faces.ch_names.index('EEG070')]
power_faces, itc_faces = mne.time_frequency.tfr_morlet(
faces, freqs=freqs, return_itc=True, n_cycles=n_cycles, picks=idx)
power_scrambled, itc_scrambled = mne.time_frequency.tfr_morlet(
epochs['scrambled'], freqs=freqs, return_itc=True, n_cycles=n_cycles,
picks=idx)
power_faces.save(op.join(data_path, '%s-faces-tfr.h5' % subject),
overwrite=True)
itc_faces.save(op.join(data_path, '%s-itc_faces-tfr.h5' % subject),
overwrite=True)
power_scrambled.save(op.join(data_path, '%s-scrambled-tfr.h5' % subject),
overwrite=True)
itc_scrambled.save(op.join(data_path, '%s-itc_scrambled-tfr.h5' % subject),
overwrite=True)
def apply_inverse_oper(fnepo, tmin=-0.2, tmax=0.8, subjects_dir=None):
'''
Apply inverse operator
Parameter
---------
fnepo: string or list
The epochs file with ECG, EOG and environmental noise free.
tmax, tmax:float
The time period (second) of each epoch.
'''
# Get the default subjects_dir
from mne import make_forward_solution
from mne.minimum_norm import make_inverse_operator, write_inverse_operator
fnlist = get_files_from_list(fnepo)
# loop across all filenames
for fname in fnlist:
fn_path = os.path.split(fname)[0]
name = os.path.basename(fname)
subject = name.split('_')[0]
subject_path = subjects_dir + '/%s' % subject
fn_trans = fn_path + '/%s-trans.fif' % subject
fn_cov = fn_path + '/%s_empty-cov.fif' % subject
fn_src = subject_path + '/bem/%s-oct-6-src.fif' % subject
fn_bem = subject_path + '/bem/%s-5120-5120-5120-bem-sol.fif' % subject
fn_inv = fn_path + '/%s_epo-inv.fif' % subject
epochs = mne.read_epochs(fname)
epochs.crop(tmin, tmax)
epochs.pick_types(meg=True, ref_meg=False)
noise_cov = mne.read_cov(fn_cov)
fwd = make_forward_solution(epochs.info, fn_trans, fn_src, fn_bem)
fwd['surf_ori'] = True
inv = make_inverse_operator(epochs.info, fwd, noise_cov, loose=0.2,
depth=0.8, limit_depth_chs=False)
write_inverse_operator(fn_inv, inv)
def global_RMS(sub, session, baseline=500, selection="Vertex"):
""" make global RMS
baseline is in indexes
"""
f_load = "sub_%d_%s_tsss_mc_epochs.fif" % (sub, session)
epochs = mne.read_epochs(f_load)
if selection is not None:
selection = mne.viz._clean_names(mne.read_selection(selection))
data_picks = mne.epochs.pick_types(epochs.info, meg='grad',
exclude='bads', selection=None)
else:
data_picks = mne.epochs.pick_types(epochs.info, meg='grad',
exclude='bads')
data = epochs.get_data()[:, data_picks, :]
data = np.sqrt(np.square(data.mean(axis=0)))
data = data.mean(axis=0)
baseline_std = data[:baseline].std().mean()
grms = data/baseline_std
return grms
def run_time_decoding(subject_id, condition1, condition2):
subject = "sub%03d" % subject_id
data_path = os.path.join(meg_dir, subject)
epochs = mne.read_epochs(os.path.join(data_path, '%s-epo.fif' % subject))
# We define the epochs and the labels
n_cond1 = len(epochs[condition1])
n_cond2 = len(epochs[condition2])
y = np.r_[np.ones((n_cond1, )), np.zeros((n_cond2, ))]
epochs = mne.concatenate_epochs([epochs[condition1],
epochs[condition2]])
epochs.apply_baseline()
# Let us restrict ourselves to the occipital channels
from mne.selection import read_selection
ch_names = [ch_name.replace(' ', '') for ch_name
in read_selection('occipital')]
epochs.pick_types(meg='mag').pick_channels(ch_names)
# Now we fit and plot the time decoder
from mne.decoding import TimeDecoding
times = dict(step=0.005) # fit a classifier only every 5 ms
# Use AUC because chance level is same regardless of the class balance
td = TimeDecoding(predict_mode='cross-validation',
times=times, scorer='roc_auc')
td.fit(epochs, y)
# let's save the scores now
a_vs_b = '%s_vs_%s' % (os.path.basename(condition1),
os.path.basename(condition2))
fname_td = os.path.join(data_path, '%s-td-auc-%s.mat'
% (subject, a_vs_b))
from scipy.io import savemat
savemat(fname_td, {'scores': td.score(epochs),
'times': td.times_['times']})
def test_array_epochs():
"""Test creating epochs from array
"""
# creating
rng = np.random.RandomState(42)
data = rng.random_sample((10, 20, 50))
sfreq = 1e3
ch_names = ['EEG %03d' % (i + 1) for i in range(20)]
types = ['eeg'] * 20
info = create_info(ch_names, sfreq, types)
events = np.c_[np.arange(1, 600, 60),
np.zeros(10),
[1, 2] * 5]
event_id = {'a': 1, 'b': 2}
epochs = EpochsArray(data, info, events=events, event_id=event_id,
tmin=-.2)
# saving
temp_fname = op.join(tempdir, 'epo.fif')
epochs.save(temp_fname)
epochs2 = read_epochs(temp_fname)
data2 = epochs2.get_data()
assert_allclose(data, data2)
assert_allclose(epochs.times, epochs2.times)
assert_equal(epochs.event_id, epochs2.event_id)
assert_array_equal(epochs.events, epochs2.events)
# plotting
import matplotlib
matplotlib.use('Agg') # for testing don't use X server
epochs[0].plot()
# indexing
assert_array_equal(np.unique(epochs['a'].events[:, 2]), np.array([1]))
assert_equal(len(epochs[:2]), 2)
def test_access_by_name():
"""Test accessing epochs by event name
"""
assert_raises(ValueError, Epochs, raw, events, {1: 42, 2: 42}, tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {"a": "spam", 2: "eggs"}, tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, {"a": "spam", 2: "eggs"}, tmin, tmax, picks=picks)
assert_raises(ValueError, Epochs, raw, events, "foo", tmin, tmax, picks=picks)
epochs = Epochs(raw, events, {"a": 1, "b": 2}, tmin, tmax, picks=picks)
assert_raises(KeyError, epochs.__getitem__, "bar")
data = epochs["a"].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
epochs = Epochs(raw, events, {"a": 1, "b": 2}, tmin, tmax, picks=picks, preload=True)
assert_raises(KeyError, epochs.__getitem__, "bar")
epochs.save(op.join(tempdir, "test-epo.fif"))
epochs2 = read_epochs(op.join(tempdir, "test-epo.fif"))
for ep in [epochs, epochs2]:
data = ep["a"].get_data()
event_a = events[events[:, 2] == 1]
assert_true(len(data) == len(event_a))
assert_array_equal(epochs2["a"].events, epochs["a"].events)
epochs3 = Epochs(raw, events, {"a": 1, "b": 2, "c": 3, "d": 4}, tmin, tmax, picks=picks, preload=True)
epochs4 = epochs["a"]
epochs5 = epochs3["a"]
assert_array_equal(epochs4.events, epochs5.events)
# 20 is our tolerance because epochs are written out as floats
assert_array_almost_equal(epochs4.get_data(), epochs5.get_data(), 20)
epochs6 = epochs3[["a", "b"]]
assert_true(all(np.logical_or(epochs6.events[:, 2] == 1, epochs6.events[:, 2] == 2)))
assert_array_equal(epochs.events, epochs6.events)
assert_array_almost_equal(epochs.get_data(), epochs6.get_data(), 20)
def apply_ica(subject, run, session):
print("Processing subject: %s" % subject)
# Construct the search path for the data file. `sub` is mandatory
subject_path = op.join('sub-{}'.format(subject))
# `session` is optional
if session is not None:
subject_path = op.join(subject_path, 'ses-{}'.format(session))
subject_path = op.join(subject_path, config.kind)
bids_basename = make_bids_basename(subject=subject,
session=session,
task=config.task,
acquisition=config.acq,
run=None,
processing=config.proc,
recording=config.rec,
space=config.space
)
fpath_deriv = op.join(config.bids_root, 'derivatives',
config.PIPELINE_NAME, subject_path)
fname_in = \
op.join(fpath_deriv, bids_basename + '-epo.fif')
fname_out = \
op.join(fpath_deriv, bids_basename + '_cleaned-epo.fif')
# load epochs to reject ICA components
epochs = mne.read_epochs(fname_in, preload=True)
print("Input: ", fname_in)
print("Output: ", fname_out)
# load first run of raw data for ecg /eog epochs
print(" Loading one run from raw data")
bids_basename = make_bids_basename(subject=subject,
session=session,
task=config.task,
acquisition=config.acq,
run=config.runs[0],
processing=config.proc,
recording=config.rec,
space=config.space
)
if config.use_maxwell_filter:
raw_fname_in = \
op.join(fpath_deriv, bids_basename + '_sss_raw.fif')
else:
raw_fname_in = \
op.join(fpath_deriv, bids_basename + '_filt_raw.fif')
raw = mne.io.read_raw_fif(raw_fname_in, preload=True)
# run ICA on MEG and EEG
picks_meg = mne.pick_types(raw.info, meg=True, eeg=False,
eog=False, stim=False, exclude='bads')
picks_eeg = mne.pick_types(raw.info, meg=False, eeg=True,
eog=False, stim=False, exclude='bads')
all_picks = {'meg': picks_meg, 'eeg': picks_eeg}
for ch_type in config.ch_types:
report = None
print(ch_type)
picks = all_picks[ch_type]
# Load ICA
fname_ica = \
op.join(fpath_deriv, bids_basename + '_%s-ica.fif' % ch_type)
print('Reading ICA: ' + fname_ica)
ica = read_ica(fname=fname_ica)
pick_ecg = mne.pick_types(raw.info, meg=False, eeg=False,
ecg=True, eog=False)
# ECG
# either needs an ecg channel, or avg of the mags (i.e. MEG data)
ecg_inds = list()
if pick_ecg or ch_type == 'meg':
picks_ecg = np.concatenate([picks, pick_ecg])
# Create ecg epochs
if ch_type == 'meg':
reject = {'mag': config.reject['mag'],
'grad': config.reject['grad']}
elif ch_type == 'eeg':
reject = {'eeg': config.reject['eeg']}
ecg_epochs = create_ecg_epochs(raw, picks=picks_ecg, reject=reject,
baseline=(None, 0), tmin=-0.5,
tmax=0.5)
ecg_average = ecg_epochs.average()
ecg_inds, scores = \
ica.find_bads_ecg(ecg_epochs, method='ctps',
threshold=config.ica_ctps_ecg_threshold)
del ecg_epochs
report_fname = \
op.join(fpath_deriv,
bids_basename + '_%s-reject_ica.html' % ch_type)
report = Report(report_fname, verbose=False)
# Plot r score
report.add_figs_to_section(ica.plot_scores(scores,
exclude=ecg_inds,
show=config.plot),
captions=ch_type.upper() + ' - ECG - ' +
'R scores')
# Plot source time course
report.add_figs_to_section(ica.plot_sources(ecg_average,
exclude=ecg_inds,
show=config.plot),
captions=ch_type.upper() + ' - ECG - ' +
'Sources time course')
# Plot source time course
report.add_figs_to_section(ica.plot_overlay(ecg_average,
exclude=ecg_inds,
show=config.plot),
captions=ch_type.upper() + ' - ECG - ' +
'Corrections')
else:
# XXX : to check when EEG only is processed
print('no ECG channel is present. Cannot automate ICAs component '
'detection for ECG!')
# EOG
pick_eog = mne.pick_types(raw.info, meg=False, eeg=False,
ecg=False, eog=True)
eog_inds = list()
if pick_eog.any():
print('using EOG channel')
picks_eog = np.concatenate([picks, pick_eog])
# Create eog epochs
eog_epochs = create_eog_epochs(raw, picks=picks_eog, reject=None,
baseline=(None, 0), tmin=-0.5,
tmax=0.5)
eog_average = eog_epochs.average()
eog_inds, scores = ica.find_bads_eog(eog_epochs, threshold=config.ica_ctps_eog_threshold)
del eog_epochs
params = dict(exclude=eog_inds, show=config.plot)
# Plot r score
report.add_figs_to_section(ica.plot_scores(scores, **params),
captions=ch_type.upper() + ' - EOG - ' +
'R scores')
# Plot source time course
report.add_figs_to_section(ica.plot_sources(eog_average, **params),
captions=ch_type.upper() + ' - EOG - ' +
'Sources time course')
# Plot source time course
report.add_figs_to_section(ica.plot_overlay(eog_average, **params),
captions=ch_type.upper() + ' - EOG - ' +
'Corrections')
report.save(report_fname, overwrite=True, open_browser=False)
else:
print('no EOG channel is present. Cannot automate ICAs component '
'detection for EOG!')
ica_reject = (list(ecg_inds) + list(eog_inds) +
list(config.rejcomps_man[subject][ch_type]))
# now reject the components
print('Rejecting from %s: %s' % (ch_type, ica_reject))
epochs = ica.apply(epochs, exclude=ica_reject)
print('Saving cleaned epochs')
epochs.save(fname_out)
if report is not None:
fig = ica.plot_overlay(raw, exclude=ica_reject, show=config.plot)
report.add_figs_to_section(fig, captions=ch_type.upper() +
' - ALL(epochs) - Corrections')
if config.plot:
epochs.plot_image(combine='gfp', group_by='type', sigma=2.,
cmap="YlGnBu_r", show=config.plot)
# 999 Heartbeats
# 998 Blinks
# Randomize subjects - this will help when submitting multiple jobs in Beluga
# Select channel type to create the topographies on
ch_type = 'eeg'
n_skipped_subjects = 0
iSubject = 1
for subject in subjects:
frontalEOGChannel = 'E22'
# Load preprocessed mne data from Guillaume
fname = os.path.join(data_path, subject, 'RestingState_Blinks_epo.fif')
epochs = mne.read_epochs(fname, proj=True, preload=True, verbose=None)
if len(
epochs
) < 50: # Only take into account the cases when the algorithm "PROBABLY" detects blinks correctly
subject = subject.lower() + "01" # BIDS compliance
#eo.plot_image(picks=[frontalEOGChannel])
## Do some preprocessing on the epochs - jitter the center so the model doesn't learn the position
epochs_preprocessed = epochs.crop(tmin=-0.4 + np.random.random() * 0.1,
tmax=0.40 + np.random.random() * 0.1,
include_tmax=True)
# Check if resampling is needed
epochs_preprocessed = epochs_preprocessed.resample(100)
import mne
base_dir = "C:/Users/muellena/Desktop/Desktop/Experiment Somatosensorik/SOMA Daten/"
proc_dir = base_dir + "proc/"
subjs = ["SOM_20"]
#runs = ["1","2","3"]
runs = ["2"]
for sub in subjs:
for run in runs:
mepo = mne.read_epochs(proc_dir + sub + "_" + run + "_m-epo.fif")
ica = mne.preprocessing.ICA(n_components=0.95,
max_iter=500,
method="picard")
ica.fit(mepo)
ica.save(proc_dir + sub + "_" + run + "_prepro-ica.fif")
sub=sub,
sp=spacing))
freq = freq_table[sub]
freqs = [freq-1, freq, freq+1]
print(freqs)
all_bads = []
epos = []
epo_names = []
epo_conds = []
epo_cond_names = []
for run_idx,run in enumerate(runs):
wav_epos = []
for wav_idx, wav_name in enumerate(wavs):
epo_name = "{dir}nc_{sub}_{run}_{wav}_hand-epo.fif".format(
dir=proc_dir, sub=sub, run=run, wav=wav_name)
epo = mne.read_epochs(epo_name)
all_bads += epo.info["bads"]
epos.append(epo)
wav_epos.append(epo)
epo_names.append("{}_{}".format(run,wav_name))
epo_conds.append(mne.concatenate_epochs(wav_epos))
epo_cond_names.append(run)
for x in epos:
x.info["bads"] = all_bads
x.info["dev_head_t"] = epos[0].info["dev_head_t"]
epo = mne.concatenate_epochs(epos)
csd = csd_morlet(epo, frequencies=freqs, n_jobs=n_jobs, n_cycles=cycles, decim=3)
csd = csd.mean()
fwd_name = "{dir}nc_{sub}_{sp}-fwd.fif".format(dir=proc_dir, sub=sub, sp=spacing)
fwd = mne.read_forward_solution(fwd_name)
ch = range(1, 307) # Channels of interest
mags = range(2, 306, 3)
grads = range(0, 306, 3) + range(1, 306, 3)
for subj in subjlist:
fpath = froot + subj + '/'
print 'Running Subject', subj
save_raw_name = subj + '_ABR-epo.fif'
if os.path.isfile(fpath + save_raw_name):
preEpoched = True
print 'Epoched data is already available on disk!'
print 'Loading data from:', fpath + save_raw_name
epochs = mne.read_epochs(fpath + save_raw_name, verbose='DEBUG')
Fs = epochs.info['sfreq']
x = epochs.get_data()
times = epochs.times
else:
preEpoched = False
fifs = fnmatch.filter(os.listdir(fpath), subj + '_CABR_raw.fif')
print 'No pre-epoched data found, looking for raw files'
print 'Viola!', len(fifs), 'files found!'
for k, fif in enumerate(fifs):
fifs[k] = fpath + fif
# Load data and read event channel
raw = mne.io.Raw(fifs, preload=True, add_eeg_ref=False)
raw.set_channel_types({'EMG061': 'eeg'})
raw.info['bads'] += ['MEG0223', 'MEG1623']
# Scipy supports read and write of matlab files. You can save your single
# trials with:
from scipy import io # noqa
io.savemat('epochs_data.mat', dict(epochs_data=epochs_data), oned_as='row')
##############################################################################
# or if you want to keep all the information about the data you can save your
# epochs in a fif file:
epochs.save('sample-epo.fif')
##############################################################################
# and read them later with:
saved_epochs = mne.read_epochs('sample-epo.fif')
##############################################################################
# Compute evoked responses for auditory responses by averaging and plot it:
evoked = epochs['aud_l'].average()
print(evoked)
evoked.plot(time_unit='s')
##############################################################################
# .. topic:: Exercise
#
# 1. Extract the max value of each epoch
max_in_each_epoch = [e.max() for e in epochs['aud_l']] # doctest:+ELLIPSIS
print(max_in_each_epoch[:4]) # doctest:+ELLIPSIS
def run_evoked(subject):
print("Processing subject: %s" % subject)
meg_subject_dir = op.join(config.meg_dir, subject)
# load epochs to reject ICA components
extension = '-epo'
fname_in = op.join(meg_subject_dir, config.base_fname.format(**locals()))
epochs = mne.read_epochs(fname_in, preload=True)
extension = 'cleaned-epo'
fname_out = op.join(meg_subject_dir, config.base_fname.format(**locals()))
print("Input: ", fname_in)
print("Output: ", fname_out)
# load first run of raw data for ecg /eog epochs
raw_list = list()
print(" Loading one run from raw data")
extension = config.runs[0] + '_sss_raw'
raw_fname_in = op.join(meg_subject_dir,
config.base_fname.format(**locals()))
raw = mne.io.read_raw_fif(raw_fname_in, preload=True)
# run ICA on MEG and EEG
picks_meg = mne.pick_types(raw.info,
meg=True,
eeg=False,
eog=False,
stim=False,
exclude='bads')
picks_eeg = mne.pick_types(raw.info,
meg=False,
eeg=True,
eog=False,
stim=False,
exclude='bads')
all_picks = {'meg': picks_meg, 'eeg': picks_eeg}
if config.eeg:
ch_types = ['meg', 'eeg']
else:
ch_types = ['meg']
for ch_type in ch_types:
print(ch_type)
picks = all_picks[ch_type]
# Load ICA
fname_ica = op.join(
meg_subject_dir,
'{0}_{1}_{2}-ica.fif'.format(subject, config.study_name, ch_type))
print('Reading ICA: ' + fname_ica)
ica = read_ica(fname=fname_ica)
pick_ecg = mne.pick_types(raw.info,
meg=False,
eeg=False,
ecg=True,
eog=False)
# ECG
# either needs an ecg channel, or avg of the mags (i.e. MEG data)
if pick_ecg or ch_type == 'meg':
picks_ecg = np.concatenate([picks, pick_ecg])
# Create ecg epochs
if ch_type == 'meg':
reject = {
'mag': config.reject['mag'],
'grad': config.reject['grad']
}
elif ch_type == 'eeg':
reject = {'eeg': config.reject['eeg']}
ecg_epochs = create_ecg_epochs(raw,
picks=picks_ecg,
reject=reject,
baseline=(None, 0),
tmin=-0.5,
tmax=0.5)
ecg_average = ecg_epochs.average()
# XXX I had to lower the threshold for ctps (default 0.25), otherwise it does not
# find any components
# check how this behaves on other data
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs,
method='ctps',
threshold=0.1)
del ecg_epochs
report_name = op.join(
meg_subject_dir,
'{0}_{1}_{2}-reject_ica.html'.format(subject,
config.study_name,
ch_type))
report = Report(report_name, verbose=False)
# Plot r score
report.add_figs_to_section(ica.plot_scores(scores,
exclude=ecg_inds),
captions=ch_type.upper() + ' - ECG - ' +
'R scores')
# Plot source time course
report.add_figs_to_section(ica.plot_sources(ecg_average,
exclude=ecg_inds),
captions=ch_type.upper() + ' - ECG - ' +
'Sources time course')
# Plot source time course
report.add_figs_to_section(ica.plot_overlay(ecg_average,
exclude=ecg_inds),
captions=ch_type.upper() + ' - ECG - ' +
'Corrections')
else:
print('no ECG channel!')
# EOG
pick_eog = mne.pick_types(raw.info,
meg=False,
eeg=False,
ecg=False,
eog=True)
if pick_eog.any():
print('using EOG channel')
picks_eog = np.concatenate([picks, pick_eog])
# Create eog epochs
eog_epochs = create_eog_epochs(raw,
picks=picks_eog,
reject=None,
baseline=(None, 0),
tmin=-0.5,
tmax=0.5)
eog_average = eog_epochs.average()
eog_inds, scores = ica.find_bads_eog(eog_epochs, threshold=3.0)
del eog_epochs
# Plot r score
report.add_figs_to_section(ica.plot_scores(scores,
exclude=eog_inds),
captions=ch_type.upper() + ' - EOG - ' +
'R scores')
# Plot source time course
report.add_figs_to_section(ica.plot_sources(eog_average,
exclude=eog_inds),
captions=ch_type.upper() + ' - EOG - ' +
'Sources time course')
# Plot source time course
report.add_figs_to_section(ica.plot_overlay(eog_average,
exclude=eog_inds),
captions=ch_type.upper() + ' - EOG - ' +
'Corrections')
report.save(report_name, overwrite=True, open_browser=False)
else:
print('no EOG channel!')
ica_reject = (list(ecg_inds) + list(eog_inds) +
list(config.rejcomps_man[subject][ch_type]))
# now reject the components
print('Rejecting from ' + ch_type + ': ' + str(ica_reject))
epochs = ica.apply(epochs, exclude=ica_reject)
print('Saving epochs')
epochs.save(fname_out)
report.add_figs_to_section(ica.plot_overlay(raw.copy(),
exclude=ica_reject),
captions=ch_type.upper() +
' - ALL(epochs) - ' + 'Corrections')
if config.plot:
epochs.plot_image(combine='gfp',
group_by='type',
sigma=2.,
cmap="YlGnBu_r")
epochs.resample(300) # let's save a version without dropping based on SSPs red_epochs = epochs.copy() no_interest = np.nonzero(red_epochs.events[:, 2] < 13)[0] red_epochs.drop_epochs(no_interest) print 'Saving epochs and evoked data...' evokeds = [red_epochs[name].average() for name in ['STI-correct', 'STI-incorrect']] mne.write_evokeds(dir_out + subj + '_stop_parsed_matched_BP1-100_DS300-ave.fif', evokeds) new_fname = dir_out + subj + '_stop_parsed_matched_BP1-100_DS300-epo.fif.gz' red_epochs.save(new_fname) # now we save a version after cleaning with SSPs # grab SSP vectors from previous cleanup sessions epochs_fname = clean_dir + subj + '_stop_parsed_matched_clean_BP1-35_DS120-epo.fif.gz' epochs35 = mne.read_epochs(epochs_fname, proj=True) bad_epochs = [i for i, j in enumerate(epochs35.drop_log) if len(j) > 0] epochs.drop_epochs(bad_epochs) epochs.info['projs'] = epochs35.info['projs'] # removing the epochs we don't want, need to do it again because indices # changed after removing bad epochs based on SSP no_interest = np.nonzero(epochs.events[:, 2] < 13)[0] epochs.drop_epochs(no_interest) # make averaged file and save final result print 'Saving epochs and evoked data with optional SSP operators...' evokeds = [epochs[name].average() for name in ['STI-correct', 'STI-incorrect']] mne.write_evokeds(dir_out + subj + '_stop_parsed_matched_clean_BP1-100_DS300-ave.fif', evokeds) new_fname = dir_out + subj + '_stop_parsed_matched_clean_BP1-100_DS300-epo.fif.gz' epochs.save(new_fname)
## change data files dir
os.chdir('/projects/MINDLAB2011_24-MEG-readiness/scratch')
### SETUP DATA ####
sessions = ["plan", "classic"]
subs = [2]
for sub in subs:
for session in sessions:
f_load = "sub_%d_%s_tsss_mc_epochs.fif" %(sub, session)
f_save = "sub_%d_%s" % (sub, session)
print f_load
print f_save
epochs = mne.read_epochs(f_load)
exec("%s=%s" % (f_save, "epochs"))
sub_2_classic.resample(sfreq=500, n_jobs=n_jobs)
sub_2_plan.resample(sfreq=500, n_jobs=n_jobs)
#sub_2_interupt.resample(sfreq=500, n_jobs=n_jobs)
cmb_A = sub_2_classic[:, :, 250:-250]
cmb_B = sub_2_plan[:, :, 250:-250]
#cmb_C = sub_2_interupt[:, :, 250:-250]
###############################################################################
# Decoding in sensor space using a linear SVM
n_times = len(epochs.times)
# Take only the data channels (here the gradiometers)
data_picks = mne.fiff.pick_types(sub_2_plan.info, meg='grad', exclude='bads')
import pandas as pd
import os.path as op
import matplotlib.pyplot as plt
from scipy.signal import hilbert, savgol_filter
subjects = ['s1', 's2', 's3']
study_path = '/Users/lpen/Documents/wake_sleep/study/'
ref = 'avg' # reference: 'avg' (average) or 'bip' (bipolar)
subject = 's1'
# Prepare data --------
# load
file_name = '%s_wake_%s_16s-epo.fif' % (subject, ref)
file_path = op.join(study_path, subject, 'data', 'epochs', file_name)
epochs = mne.read_epochs(file_path)
# apply filter
bandpass = [90, 95]
epochs.filter(bandpass[0], bandpass[1], fir_design='firwin')
# get data
data_mat = epochs.get_data() # 3D matrix: epochs x channels x samples
sfreq = int(epochs.info['sfreq'])
pd.set_option('display.expand_frame_repr',
False) # to show channel info in wide format
ch_info = pd.read_csv(op.join(study_path, 'varios', 'chan_info_all.csv'))
subj_ch_info = ch_info.loc[(ch_info.Subject == subject)
& (ch_info.Condition == 'wake')]
print(subj_ch_info)
def main(ecog_lp,savefolder,averagePower,stat_method):
'''
Use MNE to compute spectrograms from ECoG trials (with false positive trials removed, if desired).
'''
#Load parameters
alpha = config.constants_compute_tfr['alpha']
n_perms = config.constants_compute_tfr['n_perms']
pad_val = config.constants_compute_tfr['pad_val'] #(sec) used for padding each side of epochs and cropped off later
freqs = config.constants_compute_tfr['freqs']
decim = config.constants_compute_tfr['decim']
baselineT = config.constants_compute_tfr['baseline_vals'] B#aseline times to subtract off (in sec, centered around events)
#Find all epoch files
fnames_all = natsort.natsorted(glob.glob(ecog_lp+'*_epo.fif'))
for fname in fnames_all:
#Load epoch data
ep_dat = mne.read_epochs(fname)
epoch_times = [ep_dat.times.min()+pad_val, ep_dat.times.max()-pad_val]
bad_chans = ep_dat.info['bads'].copy() #store for later
ep_dat.info['bads'] = []
#Remove false positive events
if 'false_pos' in ep_dat.metadata.columns:
bad_ev_inds = np.nonzero(ep_dat.metadata['false_pos'].values)[0]
ep_dat.drop(bad_ev_inds)
#Compute TFR
power = compute_tfr(ep_dat,epoch_times,freqs = freqs,crop_val=pad_val,decim=decim)
power._metadata = ep_dat.metadata.copy() #add in metadata
#Parameter updates
power_ave_masked=power.copy()
if averagePower:
power_ave_masked = power_ave_masked.average() #remove epoch dimension for output
else:
alpha = np.nan #no stats if not averaging
#Calculate and subtract baseline (for each channel), computing stats if desired
baseidx = np.nonzero(np.logical_and(power.times>=baselineT[0],power.times<=baselineT[1]))[0]
for chan in range(power.data.shape[1]):
curr_power = tfr_subtract_baseline(power,chan,baseidx,compute_mean=True)
curr_masked_power_ave = tfr_boot_sig_mask(curr_power,baseidx,n_perms,alpha,stat_method,averagePower)
#Return masked data to original variable
if averagePower:
power_ave_masked.data[chan,...]=curr_masked_power_ave
else:
curr_masked_power_ave = np.moveaxis(curr_masked_power_ave,-1,0)
power_ave_masked.data[:,chan,...]=curr_masked_power_ave
del curr_masked_power_ave, curr_power
#Save result
file_prefix = fname.split('/')[-1][:-8]
if not averagePower:
saveName = file_prefix+'_allEpochs_tfr.h5'
else:
saveName = file_prefix+'_ave_tfr.h5'
power_ave_masked.info['bads'] = bad_chans.copy() #add bad channel list back in
power_ave_masked.save(savefolder+saveName, overwrite=True)
del power
###############################################################################
# See the :ref:`tut-epochs-dataframe` tutorial for many more examples of the
# :meth:`~mne.Epochs.to_data_frame` method.
#
#
# Loading and saving ``Epochs`` objects to disk
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# :class:`~mne.Epochs` objects can be loaded and saved in the ``.fif`` format
# just like :class:`~mne.io.Raw` objects, using the :func:`mne.read_epochs`
# function and the :meth:`~mne.Epochs.save` method. Functions are also
# available for loading data that was epoched outside of MNE-Python, such as
# :func:`mne.read_epochs_eeglab` and :func:`mne.read_epochs_kit`.
epochs.save('saved-audiovisual-epo.fif', overwrite=True)
epochs_from_file = mne.read_epochs('saved-audiovisual-epo.fif', preload=False)
###############################################################################
# The MNE-Python naming convention for epochs files is that the file basename
# (the part before the ``.fif`` or ``.fif.gz`` extension) should end with
# ``-epo`` or ``_epo``, and a warning will be issued if the filename you
# provide does not adhere to that convention.
#
# As a final note, be aware that the class of the epochs object is different
# when epochs are loaded from disk rather than generated from a
# :class:`~mne.io.Raw` object:
print(type(epochs))
print(type(epochs_from_file))
###############################################################################
elec = 65
def nextpow2(n):
m_f = np.log2(n)
m_i = np.ceil(m_f)
return int(np.log2(2**m_i))
output_dir = 'your output directory for time-frequency results'
data_path = 'your path to all your epoched files'
for epochs in glob.glob(os.path.join(data_path, '*.fif')):
EEG = mne.read_epochs(epochs)
for E in range(1, elec):
tmin = min(EEG.times)
tmax = max(EEG.times)
min_frex = 1
max_frex = 30
num_frex = 30
time = np.arange(-1, 1.0001, 1 / EEG.info['sfreq'])
pnts = int(EEG.info['sfreq'] * (tmax + (tmin * -1)) + 1)
frex = np.logspace(np.log10(min_frex), np.log10(max_frex), num_frex)
cycles = np.logspace(np.log10(3), np.log10(10),
num_frex) / (2 * np.pi * frex)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 10 01:35:05 2020
@author: wexu
"""
import mne
from config_SYN_Human import study_path,EEG_data_path,exp
import os
import json
from mne.preprocessing import ICA
for ID in exp.index:
fname=os.path.join(study_path,ID+'.edf')
epochs=mne.read_epochs(fname.replace(".edf", "_cleaned-epo.fif"))
ica = ICA(method='fastica',n_components=0.99,max_iter=1000)
ica.fit(epochs)
ica.save(fname.replace(".edf", "-ica.fif"))
def pipeline(self, band_name, ext='_ica-raw.fif', stim_channel='UPPT001'):
"""Pipeline of standard operations
Args:
band_name ({str}): Band name of filter raw
ext ({str}, optional): The extend name of interest files. Defaults to '_ica-raw.fif'.
stim_channel ({str}, optional): The channel name of stimuli. Defaults to 'UPPT001'.
"""
# Prepare memory stuffs ------------------------------------------------------
# Raw name
memory_name = f'{self.running_name}-{band_name}-epo.fif'
memory_path = os.path.join(MEMORY_DIR, memory_name)
# Denoise name
memory_denoise_name = f'{self.running_name}-{band_name}-denoise-epo.fif'
memory_denoise_path = os.path.join(MEMORY_DIR, memory_denoise_name)
# Clean name
memory_clean_name = [
f'{self.running_name}-{band_name}-clean-epo.fif',
f'{self.running_name}-{band_name}-clean-lags.pkl'
]
memory_clean_path = [
os.path.join(MEMORY_DIR, memory_clean_name[0]),
os.path.join(MEMORY_DIR, memory_clean_name[1])
]
# Get raw -------------------------------------------------------------------
raw_dir = os.path.join(RAW_DIR, self.running_name)
self.raw = self._get_raw(raw_dir, ext)
# Raw epochs ----------------------------------------------------------------
try:
assert (self.use_memory)
# Recall epochs from memory
self.epochs = mne.read_epochs(memory_path)
prompt(f'Raw epochs are recalled from memory: {self.epochs}')
except:
# Filter raw
l_freq, h_freq = self.parameters['bands'][band_name]
self._filter_raw(l_freq=l_freq, h_freq=h_freq)
# Get epochs
self.epochs = self._get_epochs(stim_channel)
# Remember if [use_memory]
if self.use_memory:
self.epochs.save(memory_path)
# Denoise epochs ------------------------------------------------------------
try:
assert (self.use_memory)
# Recall denoise epochs from memory
self.denoise_epochs = mne.read_epochs(memory_denoise_path)
prompt(
f'Denoise epochs are recalled from memory: {self.denoise_epochs}'
)
except:
# Denoise epoch
self.denoise_epochs = self._denoise(self.epochs.copy())
# Remember if [use_memory]
if self.use_memory:
self.denoise_epochs.save(memory_denoise_path)
# Remove button effect ------------------------------------------------------
try:
assert (self.use_memory)
# Recall clean epochs and lags from memory
self.clean_epochs = mne.read_epochs(memory_clean_path[0])
with open(memory_clean_path[1], 'rb') as f:
self.paired_lags_timelines = pickle.load(f)
prompt(
f'Clean epochs are recalled from memory: {self.clean_epochs}')
except:
# Remove button effect
clean_epochs, paired_lags_timelines = self._remove_button_effect()
self.clean_epochs = clean_epochs
self.paired_lags_timelines = paired_lags_timelines
# Remember if [use_memory]
if self.use_memory:
self.clean_epochs.save(memory_clean_path[0])
with open(memory_clean_path[1], 'wb') as f:
pickle.dump(self.paired_lags_timelines, f)
fname = data_dir + '/%s_event_order.txt' % subj
fid = open(fname, 'r')
events = [line.rstrip() for line in fid]
fid.close()
if len(events) > 688:
fname = behavior_dir + '/%s_m_triaOrder.txt' % subj
else:
fname = behavior_dir + '/%s_triaOrder.txt' % subj
fid = open(fname, 'r')
behavior = [line.rstrip() for line in fid]
fid.close()
epochs_fname = data_dir + subj + '_stop_parsed_BP1-100_DS300-epo.fif.gz'
epochs = mne.read_epochs(epochs_fname)
new_events = epochs.events.copy()
# check how many events match starting from the first one
cnt = 0
match = True
while cnt < min(len(behavior), len(events)) and match:
match = behavior[cnt].find(events[cnt]) >= 0
if match:
new_events[cnt, 2] = new_event_ids[behavior[cnt]]
cnt += 1
print 'Last matched event for %s: %d (Beh: %d, Eve: %d)' % (
subj, cnt, len(behavior), len(events))
# we need to drop events that were not matched
if len(behavior) < len(events):
picks=EEG_chs,
average=False)
return morlet
fmin = 2
fmax = 45
n_bins = 45
##Compute_PSD
for subj in SUBJ_LIST:
for task in TASK_LIST:
for run in RUN_LIST[task]:
epochs_name, epochs_path = get_pareidolia_bids(FOLDERPATH,
subj,
task,
run,
stage='epo_long_AR',
cond=None)
epochs = mne.read_epochs(epochs_path)
epochs = epochs.crop(-1.5, 8)
#Downsampling
epochs = epochs.copy().resample(500, npad='auto')
morlet = compute_morlet(epochs, fmin, fmax, n_bins)
morlet.apply_baseline(mode='ratio', baseline=(None, -0.1))
psds_file, psds_path = get_pareidolia_bids(
FOLDERPATH, subj, task, run, stage='Morlet_halfcycle_500Hz')
morlet.save(psds_path, overwrite=True)
from hypyp.ext.mpl3d.mesh import Mesh
from hypyp.ext.mpl3d.camera import Camera
#MNE
import mne
#HyPyP
from hypyp import prep # need pip install https://api.github.com/repos/autoreject/autoreject/zipball/master
from hypyp import analyses
from hypyp import stats
from hypyp import viz
path="C:\\Users\\kathr\\OneDrive\\Documents\\GitHub\\EEG---Special-course"
os.chdir(path)
epo1 = mne.read_epochs('13_epochs_224a_resampled-epo.fif', preload = True)
epo2 = mne.read_epochs('13_epochs_224b_resampled-epo.fif', preload = True)
epo1.set_channel_types({'1-EXG2':'eog', '1-EXG3':'eog', '1-EXG4':'eog', '1-EXG5':'eog', '1-EXG6':'eog', '1-EXG7':'eog', '1-EXG8':'eog'})
epo2.set_channel_types({'1-EXG2':'eog', '1-EXG3':'eog', '1-EXG4':'eog', '1-EXG5':'eog', '1-EXG6':'eog', '1-EXG7':'eog', '1-EXG8':'eog'})
epo1.set_montage('biosemi64')
epo2.set_montage('biosemi64')
#Equal number of epochs
mne.epochs.equalize_epoch_counts([epo1, epo2])
#Sampling rate
sampling_rate = epo1.info['sfreq']
for [si, sbj] in enumerate(subjs):
subject = 'Sub%02d' % sbj
sub_dir = op.join(C.cov_path, subject)
if not op.exists(sub_dir):
os.mkdir(sub_dir)
for st_duration in C.MF['st_duration']:
for origin in C.MF['origin']:
## read epochs
epo_fname = C.fname_epo(C, subject, st_duration, origin)
epochs = mne.read_epochs(epo_fname, preload=True)
for method in C.cov_methods: # doesn't work with 'auto'
# covariance matrix (filter with wildcard)
cov_fname = C.fname_cov(C, subject, st_duration, origin,
method, '*')
# method may be underspecified, since it may be ranked differently for different subjects
cov_fname = glob.glob(cov_fname)[
0] # be careful if multiple options present
cov = mne.read_cov(cov_fname)
# hack to create filenames taking into account muliptle '.' in paths/names
fstem = cov_fname.split('.')[0:-1] # remove suffix'
print 'running for condition ', mycond
if filtered:
fn_list = glob.glob(
op.join(
subjects_dir, subject,
'MEG/*bcc,nr,fibp1-45,ar,*,evt_%s_bc-epo.fif' %
mycond))
else:
fn_list = glob.glob(
op.join(subjects_dir, subject,
'MEG/*bcc,nr,ar,*,evt_%s_bc-epo.fif' % mycond))
print len(fn_list), ' files for this condition.'
# loop across chops per condition per subject (done for better ICA)
chop_epochs_list = []
for fn_epo in fn_list:
epochs = mne.read_epochs(fn_epo, preload=True)
chop_epochs_list.append(epochs)
os.remove(fn_epo)
final_epochs = mne.concatenate_epochs(chop_epochs_list)
final_epochs_name = fn_epo.rsplit(
'ar')[0] + 'ar,evt_%s_bc-epo.fif' % mycond # jumeg epocher
print 'saving combined epochs as ', final_epochs_name
final_epochs.save(final_epochs_name)
if check_epochs:
# for manual checking and identifying bad epochs
if filtered:
fn_list = glob.glob(
subjects_dir +
'*[0-9]/MEG/*rfDC,bcc,nr,fibp1-45,ar,evt_*bc-epo.fif')
else:
def test_anonymize(tmpdir):
"""Test that sensitive information can be anonymized."""
pytest.raises(TypeError, anonymize_info, 'foo')
# Fake some subject data
raw = read_raw_fif(raw_fname)
raw.set_annotations(
Annotations(onset=[0, 1],
duration=[1, 1],
description='dummy',
orig_time=None))
first_samp = raw.first_samp
expected_onset = np.arange(2) + raw._first_time
assert raw.first_samp == first_samp
assert_allclose(raw.annotations.onset, expected_onset)
# test mne.anonymize_info()
events = read_events(event_name)
epochs = Epochs(raw, events[:1], 2, 0., 0.1, baseline=None)
_test_anonymize_info(raw.info.copy())
_test_anonymize_info(epochs.info.copy())
# test instance methods & I/O roundtrip
for inst, keep_his in zip((raw, epochs), (True, False)):
inst = inst.copy()
subject_info = dict(his_id='Volunteer', sex=2, hand=1)
inst.info['subject_info'] = subject_info
inst.anonymize(keep_his=keep_his)
si = inst.info['subject_info']
if keep_his:
assert si == subject_info
else:
assert si['his_id'] == '0'
assert si['sex'] == 0
assert 'hand' not in si
# write to disk & read back
inst_type = 'raw' if isinstance(inst, BaseRaw) else 'epo'
fname = 'tmp_raw.fif' if inst_type == 'raw' else 'tmp_epo.fif'
out_path = tmpdir.join(fname)
inst.save(out_path, overwrite=True)
if inst_type == 'raw':
read_raw_fif(out_path)
else:
read_epochs(out_path)
# test that annotations are correctly zeroed
raw.anonymize()
assert raw.first_samp == first_samp
assert_allclose(raw.annotations.onset, expected_onset)
assert raw.annotations.orig_time == raw.info['meas_date']
stamp = _dt_to_stamp(raw.info['meas_date'])
assert raw.annotations.orig_time == _stamp_to_dt(stamp)
raw.info['meas_date'] = None
raw.anonymize(daysback=None)
with pytest.warns(RuntimeWarning, match='None'):
raw.anonymize(daysback=123)
assert raw.annotations.orig_time is None
assert raw.first_samp == first_samp
assert_allclose(raw.annotations.onset, expected_onset)
if t_by_t:
n_pnts = 50
else:
n_pnts = 1
allscores = np.ndarray((n_pnts,24))
perm_allscores = np.ndarray((n_pnts,24,nperms))
max_perm_allscores = np.ndarray((24,nperms))
allscores_pval = np.ndarray((n_pnts,24))
for subj in np.arange(0,24):
dat_file = ('S%02d_EB-epo' % (subj+1))
dat_file = op.join(filepath, dat_file + '.fif')
epochs = mne.read_epochs(dat_file)
epochs.crop(tmin=0, tmax=1)
epochs.resample(50)
X0 = epochs['motor'].get_data()
# dat_file = ('S%02d_EB-epo' % (subj+1))
# dat_file = op.join(filepath, dat_file + '.fif')
# epochs = mne.read_epochs(dat_file)
# epochs.crop(tmin=0, tmax=1)
# epochs.resample(50)
X1 = epochs['non-motor'].get_data()
X = np.concatenate((X0,X1),axis=0)
y = np.concatenate((np.zeros(X0.shape[0]),np.ones(X1.shape[0])),axis=0)
if t_by_t:
def test_channel_name_limit(tmpdir, monkeypatch, fname):
"""Test that our remapping works properly."""
#
# raw
#
if fname.endswith('fif'):
raw = read_raw_fif(fname)
raw.pick_channels(raw.ch_names[:3])
ref_names = []
data_names = raw.ch_names
else:
assert fname.endswith('.ds')
raw = read_raw_ctf(fname)
ref_names = [
raw.ch_names[pick]
for pick in pick_types(raw.info, meg=False, ref_meg=True)
]
data_names = raw.ch_names[32:35]
proj = dict(data=np.ones((1, len(data_names))),
col_names=data_names[:2].copy(),
row_names=None,
nrow=1)
proj = Projection(data=proj,
active=False,
desc='test',
kind=0,
explained_var=0.)
raw.add_proj(proj, remove_existing=True)
raw.info.normalize_proj()
raw.pick_channels(data_names + ref_names).crop(0, 2)
long_names = ['123456789abcdefg' + name for name in raw.ch_names]
fname = tmpdir.join('test-raw.fif')
with catch_logging() as log:
raw.save(fname)
log = log.getvalue()
assert 'truncated' not in log
rename = dict(zip(raw.ch_names, long_names))
long_data_names = [rename[name] for name in data_names]
long_proj_names = long_data_names[:2]
raw.rename_channels(rename)
for comp in raw.info['comps']:
for key in ('row_names', 'col_names'):
for name in comp['data'][key]:
assert name in raw.ch_names
if raw.info['comps']:
assert raw.compensation_grade == 0
raw.apply_gradient_compensation(3)
assert raw.compensation_grade == 3
assert len(raw.info['projs']) == 1
assert raw.info['projs'][0]['data']['col_names'] == long_proj_names
raw.info['bads'] = bads = long_data_names[2:3]
good_long_data_names = [
name for name in long_data_names if name not in bads
]
with catch_logging() as log:
raw.save(fname, overwrite=True, verbose=True)
log = log.getvalue()
assert 'truncated to 15' in log
for name in raw.ch_names:
assert len(name) > 15
# first read the full way
with catch_logging() as log:
raw_read = read_raw_fif(fname, verbose=True)
log = log.getvalue()
assert 'Reading extended channel information' in log
for ra in (raw, raw_read):
assert ra.ch_names == long_names
assert raw_read.info['projs'][0]['data']['col_names'] == long_proj_names
del raw_read
# next read as if no longer names could be read
monkeypatch.setattr(meas_info, '_read_extended_ch_info',
lambda x, y, z: None)
with catch_logging() as log:
raw_read = read_raw_fif(fname, verbose=True)
log = log.getvalue()
assert 'extended' not in log
if raw.info['comps']:
assert raw_read.compensation_grade == 3
raw_read.apply_gradient_compensation(0)
assert raw_read.compensation_grade == 0
monkeypatch.setattr( # restore
meas_info, '_read_extended_ch_info', _read_extended_ch_info)
short_proj_names = [
f'{name[:13 - bool(len(ref_names))]}-{len(ref_names) + ni}'
for ni, name in enumerate(long_data_names[:2])
]
assert raw_read.info['projs'][0]['data']['col_names'] == short_proj_names
#
# epochs
#
epochs = Epochs(raw, make_fixed_length_events(raw))
fname = tmpdir.join('test-epo.fif')
epochs.save(fname)
epochs_read = read_epochs(fname)
for ep in (epochs, epochs_read):
assert ep.info['ch_names'] == long_names
assert ep.ch_names == long_names
del raw, epochs_read
# cov
epochs.info['bads'] = []
cov = compute_covariance(epochs, verbose='error')
fname = tmpdir.join('test-cov.fif')
write_cov(fname, cov)
cov_read = read_cov(fname)
for co in (cov, cov_read):
assert co['names'] == long_data_names
assert co['bads'] == []
del cov_read
#
# evoked
#
evoked = epochs.average()
evoked.info['bads'] = bads
assert evoked.nave == 1
fname = tmpdir.join('test-ave.fif')
evoked.save(fname)
evoked_read = read_evokeds(fname)[0]
for ev in (evoked, evoked_read):
assert ev.ch_names == long_names
assert ev.info['bads'] == bads
del evoked_read, epochs
#
# forward
#
with pytest.warns(None): # not enough points for CTF
sphere = make_sphere_model('auto', 'auto', evoked.info)
src = setup_volume_source_space(
pos=dict(rr=[[0, 0, 0.04]], nn=[[0, 1., 0.]]))
fwd = make_forward_solution(evoked.info, None, src, sphere)
fname = tmpdir.join('temp-fwd.fif')
write_forward_solution(fname, fwd)
fwd_read = read_forward_solution(fname)
for fw in (fwd, fwd_read):
assert fw['sol']['row_names'] == long_data_names
assert fw['info']['ch_names'] == long_data_names
assert fw['info']['bads'] == bads
del fwd_read
#
# inv
#
inv = make_inverse_operator(evoked.info, fwd, cov)
fname = tmpdir.join('test-inv.fif')
write_inverse_operator(fname, inv)
inv_read = read_inverse_operator(fname)
for iv in (inv, inv_read):
assert iv['info']['ch_names'] == good_long_data_names
apply_inverse(evoked, inv) # smoke test
def split_epochs(LOGS_DIR,
subj,
bloc,
lobound=None,
hibound=None,
save_epochs=False):
'''
This functions allows to use the logfile to split the epochs obtained in the epo.fif file.
It works by comparing the timestamps of IN and OUT events to the timestamps in the epo file events
Ultimately, we want to compute our features on all epochs then split them as needed. That's why we gonna use IN and OUTidx.
HERE WE KEEP ONLY CORRECT TRIALS
'''
epo_path, epo_filename = get_SAflow_bids(FOLDERPATH,
subj,
bloc,
'epo',
cond=None)
### Find logfile to extract VTC
log_file = find_logfile(subj, bloc, os.listdir(LOGS_DIR))
VTC, INbounds, OUTbounds, INzone, OUTzone = get_VTC_from_file(
LOGS_DIR + log_file, lobound=lobound, hibound=hibound)
### Find events, split them by IN/OUT and start epoching
preproc_path, preproc_filename = get_SAflow_bids(FOLDERPATH,
subj,
bloc,
stage='preproc_raw',
cond=None)
raw = read_raw_fif(preproc_filename,
preload=False) #, min_duration=2/epochs.info['sfreq'])
events = mne.find_events(raw, min_duration=2 / raw.info['sfreq'])
INevents, OUTevents = split_events_by_VTC(INzone, OUTzone, events)
print('event length : {}'.format(len(events)))
INidx = []
OUTidx = []
epo_events = mne.read_events(
epo_filename) # get events from the epochs file (so no resp event)
# the droping of epochs is a bit confused because we have to get indices from the current cleaned epochs file
for idx, ev in enumerate(epo_events):
if ev[0] in INevents[:, 0]: #compare timestamps
INidx.append(idx)
if ev[0] in OUTevents[:, 0]:
OUTidx.append(idx)
INidx = np.array(INidx)
OUTidx = np.array(OUTidx)
if save_epochs == True:
epo_idx = np.array(range(len(epo_events)))
IN_todrop = np.delete(epo_idx, INidx) # drop all epochs EXCEPT INidx
OUT_todrop = np.delete(epo_idx, OUTidx)
INepochs = mne.read_epochs(epo_filename, preload=False)
INepochs = INepochs.drop(indices=IN_todrop)
OUTepochs = mne.read_epochs(epo_filename, preload=False)
OUTepochs = OUTepochs.drop(indices=OUT_todrop)
if lobound == None:
INpath, INfilename = get_SAflow_bids(FOLDERPATH,
subj,
bloc,
'epo',
cond='IN')
else:
INpath, INfilename = get_SAflow_bids(FOLDERPATH,
subj,
bloc,
'epo',
cond='IN{}'.format(lobound *
100))
if hibound == None:
OUTpath, OUTfilename = get_SAflow_bids(FOLDERPATH,
subj,
bloc,
'epo',
cond='OUT')
else:
OUTpath, OUTfilename = get_SAflow_bids(FOLDERPATH,
subj,
bloc,
'epo',
cond='OUT{}'.format(
hibound * 100))
INepochs.save(INfilename)
OUTepochs.save(OUTfilename)
return INidx, OUTidx
"NEM_32": "NAG83",
"NEM_33": "FAO18_fa",
"NEM_34": "KER27",
"NEM_35": "MUN79",
"NEM_36": "BRA52_fa",
"NEM_37": "EAM67"
}
# # sub_dict = {"NEM_21":"WKI71_fa","NEM_22":"EAM11","NEM_23":"FOT12","NEM_24":"BII41","NEM_26":"ENR41",
# "NEM_27":"HIU14","NEM_28":"WAL70","NEM_29":"KIL72","NEM_30":"DIU11","NEM_31":"BLE94",
# "NEM_32":"NAG83","NEM_33":"FAO18_fa","NEM_34":"KER27","NEM_35":"MUN79","NEM_36":"BRA52_fa",
# "NEM_37":"EAM67"} # these are waiting for the next batch
# # sub_dict = {"NEM_10":"GIZ04","NEM_11":"WOO07","NEM_12":"TGH11","NEM_14":"FIN23","NEM_15":"KIL13","NEM_16":"KIO12","NEM_17":"DEN59","NEM_18":"SAG13","NEM_19":"ALC81","NEM_20":"PAG48",} # these are done
for meg, mri in sub_dict.items():
# load and prepare the MEG data
rest = mne.read_epochs("{dir}nc_{sub}_1_ica-epo.fif".format(dir=meg_dir,
sub=meg))
ton = mne.read_epochs("{dir}nc_{sub}_2_ica-epo.fif".format(dir=meg_dir,
sub=meg))
epo_exp = mne.read_epochs("{dir}nc_{sub}_exp-epo.fif".format(dir=meg_dir,
sub=meg))
neg = epo_exp['negative']
pos = epo_exp['positive']
# override head_position data to append sensor data (just for calculating CSD !)
rest.info['dev_head_t'] = ton.info['dev_head_t']
epo_bas = mne.concatenate_epochs([rest, ton])
# make and save CSDs for 1-90 Hz range
frequencies = np.linspace(1, 90, num=90)
csd_exp = csd_morlet(epo_exp,
frequencies=frequencies,
n_jobs=8,
n_cycles=7,
img_type = 'orig'
fs_subj = subj + '_an' if img_type == 'anony' else subj
epo_path = op.join(study_path, 'source_stim', subj, 'epochs', 'fif')
conds = glob.glob(epo_path + '/*-epo.fif')
fwd_fname = op.join(study_path, 'source_stim', subj, 'source_files',
img_type, '%s-fwd.fif' % fs_subj)
if not op.isfile(fwd_fname):
fwd = make_fwd_solution(conds[0], fs_subj, study_path)
else:
fwd = mne.read_forward_solution(fwd_fname)
for cond_fname in conds:
eeg_epo = mne.read_epochs(cond_fname)
eeg_epo.interpolate_bads(reset_bads=True)
# eeg_epo.filter(None, 40)
cov = mne.compute_covariance(
eeg_epo, method='shrunk', tmin=-0.5,
tmax=-0.3) # use method='auto' for final computation
evoked = eeg_epo.average()
evoked.plot(spatial_colors=True)
mne.viz.plot_evoked_topo(evoked)
evoked.plot_topomap(np.linspace(-0.005, 0.005, 11))
dist_dis = source_loc(evoked,
cov,
fwd,
subj,
i for i in os.listdir(filedir)
if 'Subject' in i and 'Test' not in i and '.' not in i
]
rmvSubjList = []
SubjList = [
'Subject%d' % (n + 1) for n in np.arange(len(SubjDir)) if 'Subject%d' %
(n + 1) not in rmvSubjList and os.path.exists(filedir + '/Subject%d/SF' %
(n + 1))
]
SubjN = len(SubjList)
del SubjDir
#- setting some parameters -#
os.chdir(filedir + '/' + SubjList[0] + '/' + ExpID + '/Datafiles/EpochData')
Epochs = mne.read_epochs('ProperEpochData-epo.fif', preload=True)
epochs = Epochs.copy().pick_types(meg=True)
conditions = list(epochs.event_id.keys())
conditions2 = [i for i in conditions if i != 'target']
times = epochs.times
del Epochs, epochs
MRIsubject = 'fsaverage'
subjects_dir = ''
src = mne.read_source_spaces(subjects_dir + '/' + MRIsubject +
'/bem/%s-%s-src.fif' %
(MRIsubject, useFsaveModel))
nuseVerts = src[-1]['nuse']
def test_read_write_epochs():
"""Test epochs from raw files with IO as fif file
"""
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0))
evoked = epochs.average()
data = epochs.get_data()
epochs_no_id = Epochs(raw, pick_events(events, include=event_id), None, tmin, tmax, picks=picks, baseline=(None, 0))
assert_array_equal(data, epochs_no_id.get_data())
eog_picks = pick_types(raw.info, meg=False, eeg=False, stim=False, eog=True, exclude="bads")
eog_ch_names = [raw.ch_names[k] for k in eog_picks]
epochs.drop_channels(eog_ch_names)
assert_true(len(epochs.info["chs"]) == len(epochs.ch_names) == epochs.get_data().shape[1])
data_no_eog = epochs.get_data()
assert_true(data.shape[1] == (data_no_eog.shape[1] + len(eog_picks)))
# test decim kwarg
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
epochs_dec = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), decim=4)
assert_equal(len(w), 1)
data_dec = epochs_dec.get_data()
assert_array_equal(data[:, :, epochs_dec._decim_idx], data_dec)
evoked_dec = epochs_dec.average()
assert_array_equal(evoked.data[:, epochs_dec._decim_idx], evoked_dec.data)
n = evoked.data.shape[1]
n_dec = evoked_dec.data.shape[1]
n_dec_min = n // 4
assert_true(n_dec_min <= n_dec <= n_dec_min + 1)
assert_true(evoked_dec.info["sfreq"] == evoked.info["sfreq"] / 4)
# test IO
epochs.save(op.join(tempdir, "test-epo.fif"))
epochs_read = read_epochs(op.join(tempdir, "test-epo.fif"))
assert_array_almost_equal(epochs_read.get_data(), epochs.get_data())
assert_array_equal(epochs_read.times, epochs.times)
assert_array_almost_equal(epochs_read.average().data, evoked.data)
assert_equal(epochs_read.proj, epochs.proj)
bmin, bmax = epochs.baseline
if bmin is None:
bmin = epochs.times[0]
if bmax is None:
bmax = epochs.times[-1]
baseline = (bmin, bmax)
assert_array_almost_equal(epochs_read.baseline, baseline)
assert_array_almost_equal(epochs_read.tmin, epochs.tmin, 2)
assert_array_almost_equal(epochs_read.tmax, epochs.tmax, 2)
assert_equal(epochs_read.event_id, epochs.event_id)
epochs.event_id.pop("1")
epochs.event_id.update({"a:a": 1}) # test allow for ':' in key
epochs.save(op.join(tempdir, "foo-epo.fif"))
epochs_read2 = read_epochs(op.join(tempdir, "foo-epo.fif"))
assert_equal(epochs_read2.event_id, epochs.event_id)
# add reject here so some of the epochs get dropped
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0), reject=reject)
epochs.save(op.join(tempdir, "test-epo.fif"))
# ensure bad events are not saved
epochs_read3 = read_epochs(op.join(tempdir, "test-epo.fif"))
assert_array_equal(epochs_read3.events, epochs.events)
data = epochs.get_data()
assert_true(epochs_read3.events.shape[0] == data.shape[0])
# test copying loaded one (raw property)
epochs_read4 = epochs_read3.copy()
assert_array_almost_equal(epochs_read4.get_data(), data)
# test equalizing loaded one (drop_log property)
epochs_read4.equalize_event_counts(epochs.event_id)
epochs.drop_epochs([1, 2], reason="can we recover orig ID?")
epochs.save("test-epo.fif")
epochs_read5 = read_epochs("test-epo.fif")
assert_array_equal(epochs_read5.selection, epochs.selection)
assert_array_equal(epochs_read5.drop_log, epochs.drop_log)
# Test that one can drop channels on read file
epochs_read5.drop_channels(epochs_read5.ch_names[:1])
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
epochs_badname = op.join(tempdir, "test-bad-name.fif.gz")
epochs.save(epochs_badname)
read_epochs(epochs_badname)
assert_true(len(w) == 2)
import numpy as np
import os
from sklearn.preprocessing import LabelEncoder
from braindecode.datautil.signal_target import SignalAndTarget
exclude = [7]
all_epochs = list()
# We start by exploring the frequence content of our epochs.
for subject_id in range(1, 6):
if subject_id in exclude:
continue
subject = 'S%02d' % subject_id
data_path = os.path.join('/home/claire/DATA/Data_Face_House/' + subject +
'/EEG/Evoked_Lowpass')
fname_in = os.path.join(data_path, '%s-epo.fif' % subject)
epochs = mne.read_epochs(fname_in)
epochs.interpolate_bads()
all_epochs.append(epochs)
epochs_train = epochs = mne.concatenate_epochs(all_epochs)
mne.epochs.combine_event_ids(epochs_train, ['stim/face', 'stim/house'],
{'stim': 100},
copy=False)
mne.epochs.combine_event_ids(epochs_train, ['imag/face', 'imag/house'],
{'imag': 200},
copy=False)
# Load Test subject
all_epochs = list()
for subject_id in range(7, 11):
def SourceReconstructionv2(condnames, ListSubj, modality, Method,
covmatsource):
# condnames = (('Qt_all','Qs_all'))
# ListSubj = [('jm100109')]
# modality = 'MEG'
# method = 'MNE'
import mne
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.beamformer import lcmv
import os
os.chdir('/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/SCRIPTS/MNE_PYTHON')
os.environ['SUBJECTS_DIR'] = '/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/mri'
os.environ['MNE_ROOT'] = '/neurospin/local/mne'
wdir = "/neurospin/meg/meg_tmp/MTT_MEG_Baptiste/MEG/"
for c in range(len(condnames)):
for i in range(len(ListSubj)):
# which modality?
if modality == 'MEG':
megtag = True
eegtag = False
fname_fwd = (wdir + ListSubj[i] +
"/mne_python/run3_ico-5_megonly_-fwd.fif")
elif modality == 'EEG':
megtag = False
eegtag = True
fname_fwd = (wdir + ListSubj[i] +
"/mne_python/run3_ico-5_eegonly_-fwd.fif")
elif modality == 'MEEG':
megtag = True
eegtag = True
fname_fwd = (wdir + ListSubj[i] +
"/mne_python/run3_ico-5_meeg_-fwd.fif")
# load noise covariance matrice
fname_noisecov = (wdir + ListSubj[i] + "/mne_python/COVMATS/" +
modality + "_noisecov_" + covmatsource + "_" +
ListSubj[i] + "-cov.fif")
NOISE_COV1 = mne.read_cov(fname_noisecov)
# load MEEG epochs, then pick
fname_epochs = (wdir + ListSubj[i] +
"/mne_python/EPOCHS/MEEG_epochs_" + condnames[c] +
'_' + ListSubj[i] + "-epo.fif")
epochs = mne.read_epochs(fname_epochs)
picks = mne.pick_types(epochs.info,
meg=megtag,
eeg=eegtag,
stim=False,
eog=False)
# compute evoked
evokedcond1 = epochs.average(picks=picks)
forward = mne.read_forward_solution(fname_fwd, surf_ori=True)
inverse_operator1 = make_inverse_operator(evokedcond1.info,
forward,
NOISE_COV1,
loose=0.2,
depth=0.8)
if Method == 'LCMV':
fname_datacov = (wdir + ListSubj[i] + "/mne_python/COVMATS/" +
modality + "datacov_" + condnames[c] + "_" +
ListSubj[i] + "-cov.fif")
DATA_COV1 = mne.read_cov(fname_datacov)
###############################
# dSPM/ MNE/ sLORETA solution #
###############################
if Method == 'dSPM' or Method == 'MNE' or Method == 'sLORETA':
snr = 3.0
lambda2 = 1.0 / snr**2
# MEG source reconstruction
stccond1 = apply_inverse(evokedcond1,
inverse_operator1,
lambda2,
method=Method,
pick_ori=None)
stccond1.save(wdir + ListSubj[i] + "/mne_python/STCS/" + modality +
"_" + ListSubj[i] + "_" + condnames[c] +
"_pick_oriNone_" + Method + "_ico-5-fwd.fif")
stccond1norm = apply_inverse(evokedcond1,
inverse_operator1,
lambda2,
method=Method,
pick_ori="normal")
stccond1norm.save(wdir + ListSubj[i] + "/mne_python/STCS/" +
modality + "_" + ListSubj[i] + "_" +
condnames[c] + "_pick_orinormal_" + Method +
"_ico-5-fwd.fif")
# morphing to fsaverage
stc_fsaverage_cond1 = mne.morph_data(ListSubj[i],
'fsaverage',
stccond1,
smooth=20)
stc_fsaverage_cond1.save(wdir + ListSubj[i] + "/mne_python/STCS/" +
modality + "_" + ListSubj[i] + "_" +
condnames[c] + "_pick_oriNone_" + Method +
"_ico-5-fwd-fsaverage.fif")
stc_fsaverage_cond1norm = mne.morph_data(ListSubj[i],
'fsaverage',
stccond1norm,
smooth=20)
stc_fsaverage_cond1norm.save(wdir + ListSubj[i] +
"/mne_python/STCS/" + modality + "_" +
ListSubj[i] + "_" + condnames[c] +
"_pick_orinormal_" + Method +
"_ico-5-fwd-fsaverage.fif")
###################
# LCMV Beamformer #
###################
elif Method == 'LCMV':
# MEG source reconstruction
stccond1 = lcmv(evokedcond1,
forward,
NOISE_COV1,
DATA_COV1,
reg=0.01,
pick_ori=None)
stccond1.save(wdir + ListSubj[i] + "/mne_python/STCS/" + modality +
"_" + ListSubj[i] + "_" + condnames[c] +
"_pick_oriNone_" + Method + "_ico-5-fwd.fif")
stccond1norm = lcmv(evokedcond1,
forward,
NOISE_COV1,
DATA_COV1,
reg=0.01,
pick_ori="normal")
stccond1norm.save(wdir + ListSubj[i] + "/mne_python/STCS/" +
modality + "_" + ListSubj[i] + "_" +
condnames[c] + "_pick_orinormal_" + Method +
"_ico-5-fwd.fif")
# morphing to fsaverage
stc_fsaverage_cond1 = mne.morph_data(ListSubj[i],
'fsaverage',
stccond1,
smooth=20)
stc_fsaverage_cond1.save(wdir + ListSubj[i] + "/mne_python/STCS/" +
modality + "_" + ListSubj[i] + "_" +
condnames[c] + "_pick_oriNone_" + Method +
"_ico-5-fwd-fsaverage.fif")
stc_fsaverage_cond1norm = mne.morph_data(ListSubj[i],
'fsaverage',
stccond1norm,
smooth=20)
stc_fsaverage_cond1norm.save(wdir + ListSubj[i] +
"/mne_python/STCS/" + modality + "_" +
ListSubj[i] + "_" + condnames[c] +
"_pick_orinormal_" + Method +
"_ico-5-fwd-fsaverage.fif")
base_dir = "C:/Users/kimca/Documents/MEG_analyses/NEMO/"
proc_dir = base_dir + "proc/"
subjs = [
"nc_NEM_10", "nc_NEM_11", "nc_NEM_12", "nc_NEM_14", "nc_NEM_15",
"nc_NEM_16", "nc_NEM_17", "nc_NEM_18", "nc_NEM_19", "nc_NEM_20",
"nc_NEM_21", "nc_NEM_22", "nc_NEM_23", "nc_NEM_24", "nc_NEM_26",
"nc_NEM_27", "nc_NEM_28", "nc_NEM_29", "nc_NEM_30", "nc_NEM_31",
"nc_NEM_32", "nc_NEM_33", "nc_NEM_34", "nc_NEM_35", "nc_NEM_36",
"nc_NEM_37"
]
subjs = ["nc_NEM_23"]
for subj in subjs:
#load resting state measurement of a subjects
rest = mne.read_epochs(proc_dir + subj + "_1_ica-epo.fif")
# produce a layout that we use for sensor plotting; same for all conditions
layout = mne.find_layout(rest.info)
mag_names = [rest.ch_names[p] for p in mne.pick_types(rest.info, meg=True)]
layout.names = mag_names
#load tone baseline (run 2) of a subject
tonbas = mne.read_epochs(proc_dir + subj + "_2_ica-epo.fif")
#load run 3 and 4 of a subject
epo_a = mne.read_epochs(proc_dir + subj + "_3_ica-epo.fif")
epo_b = mne.read_epochs(proc_dir + subj + "_4_ica-epo.fif")
#interpolate bad n_channels
rest.interpolate_bads()
tonbas.interpolate_bads()
epo_a.interpolate_bads()
epo_b.interpolate_bads()
#override coil positions of block b with those of block a for concatenation (sensor level only!!)
from my_settings import (epochs_folder, tf_folder, subjects_dir, mne_folder)
import mne
import sys
import numpy as np
import pandas as pd
from mne.minimum_norm import read_inverse_operator, source_induced_power
subject = sys.argv[1]
epochs = mne.read_epochs(epochs_folder + "%s_target-epo.fif" % subject)
inv = read_inverse_operator(mne_folder + "%s-inv.fif" % subject)
labels = mne.read_labels_from_annot(
subject,
parc='PALS_B12_Lobes',
# regexp="Bro",
subjects_dir=subjects_dir)
labels_selc = labels[9], labels[10]
frequencies = np.arange(8, 13, 1) # define frequencies of interest
n_cycles = frequencies / 3. # different number of cycle per frequency
method = "dSPM"
sides = ["left", "right"]
conditions = ["ctl", "ent"]
cor = ["correct", "incorrect"]
phase = ["in_phase", "out_phase"]
congrunet = ["cong", "incong"]
import matplotlib.pyplot as plt
import scipy
import os
path="C:\\Users\\kathr\\OneDrive\\Documents\\GitHub\\Bachelor-Project"
os.chdir(path)
# %% Cluster permutation test - define neighbors for pairs of electrodes
# To use the MNE function I need each group data in the format 3D array (n_obs, n_freq, n_connectivity_combination)
# And the 2 groups should be as elements in a list
# I should test each connectivity measurement separately
# First I have to define the "neighbors" for the connectivity combinations
# Definition: For the connection ch1 - ch2
# All spatial neighbors to ch1 and all spatial neighbors to ch2 and their connections are also considered neighbors!
epochs_a_s = mne.read_epochs('epochs_a_short_10.fif')
connectivity, ch_names = mne.channels.find_ch_adjacency(epochs_a_s[0].info, ch_type="eeg")
n_channels = len(ch_names)
ch_names = np.array(ch_names)
ch_names_new = []
connectivity_names_new = []
for i in range(n_channels):
for j in range(n_channels):
# Avoid connections between same electrode
# Connectivity is symmetric, thus I will also avoid repeats, e.g. Fp1-Fp2, Fp2-Fp1
if i >= j:
continue
else:
# Get the label for the connection
# spacing between channnels (DC offset)
scale = 1e-5
# loop through the participants
for p, pid in enumerate(pids):
# loop through the blocks
for b, block in enumerate(blocks):
# loop through the conditions
for c, condition in enumerate(conditions):
# construct the filename
dfile = "./EPODATA/%d_%s_%s-epo.fif" % (pid, block, condition)
# read data into mne
epo = mne.read_epochs(dfile, proj=False, add_eeg_ref=False)
# get data
epo_data = epo.get_data()
t = epo.times
# loop through the trials
for m in range(n_trials):
# get the trial data
trial_data = epo_data[m, :72]
for ch in range(72):
# compute range
rng = trial_data[ch].max() - trial_data[ch].min()
fout.write("%d %s %s %d %d %.4e\n" % (pid, block, condition, m, ch, rng))
def Extract_data_multisubject(root_dir,N_S_list, datatype='EEG'):
"""
Load all blocks for a list of subject and stack the results in X
"""
import mne
import numpy as np
import gc
N_B_arr = [1,2,3]
tmp_list_X = []
tmp_list_Y = []
rows = []
total_elem = len(N_S_list)*3 # assume 3 sessions per subject
S = 0
for N_S in N_S_list:
print("Iteration ", S)
print("Subject ", N_S)
for N_B in N_B_arr:
# name correction if N_Subj is less than 10
if N_S<10:
Num_s='sub-0'+str(N_S)
else:
Num_s='sub-'+str(N_S)
base_file_name = root_dir + '/derivatives/' + Num_s + '/ses-0'+ str(N_B) + '/' +Num_s+'_ses-0'+str(N_B)
events_file_name = base_file_name+'_events.dat'
data_tmp_Y = np.load(events_file_name,allow_pickle=True)
tmp_list_Y.append(data_tmp_Y)
print("Inner iteration " , N_B)
if datatype=="EEG" or datatype=="eeg":
# load data and events
eeg_file_name = base_file_name+'_eeg-epo.fif'
data_tmp_X = mne.read_epochs(eeg_file_name,verbose='WARNING')._data
rows.append(data_tmp_X.shape[0])
if S == 0 and N_B == 1: # assume same number of channels, time steps, and column labels in every subject and session
chann=data_tmp_X.shape[1]
steps=data_tmp_X.shape[2]
columns=data_tmp_Y.shape[1]
tmp_list_X.append(data_tmp_X)
elif datatype=="EXG" or datatype=="exg":
exg_file_name = base_file_name+'_exg-epo.fif'
data_tmp_X = mne.read_epochs(exg_file_name,verbose='WARNING')._data
rows.append(data_tmp_X.shape[0])
if S == 0 and N_B == 1:
chann=data_tmp_X.shape[1]
steps=data_tmp_X.shape[2]
columns=data_tmp_Y.shape[1]
tmp_list_X.append(data_tmp_X)
elif datatype=="Baseline" or datatype=="baseline":
baseline_file_name = base_file_name+'_baseline-epo.fif'
data_tmp_X = mne.read_epochs(baseline_file_name,verbose='WARNING')._data
rows.append(data_tmp_X.shape[0])
if S == 0 and N_B == 1:
chann=data_tmp_X.shape[1]
steps=data_tmp_X.shape[2]
columns=data_tmp_Y.shape[1]
tmp_list_X.append(data_tmp_X)
else:
print("Invalid Datatype")
return None, None
S += 1
X = np.empty((sum(rows), chann, steps))
Y = np.empty((sum(rows), columns))
offset = 0
offset_Y = 0
# put elements of list into numpy array
for i in range(total_elem):
print("Saving element {} into array ".format(i))
X[offset:offset+rows[i],:,:] = tmp_list_X[0]
if datatype=="EEG" or datatype=="eeg" or datatype=="EXG" or datatype=="exg":
Y[offset:offset+rows[i],:] = tmp_list_Y[0] # only build Y for the datatypes that uses it
offset+=rows[i]
del tmp_list_X[0]
del tmp_list_Y[0]
gc.collect()
print("X shape", X.shape)
print("Y shape", Y.shape)
if datatype=="EEG" or datatype=="eeg" or datatype=="EXG" or datatype=="exg":
# for eeg and exg types, there is a predefined label that is returned
return X,Y
else:
# for baseline datatypes, there's no such label (rest phase)
return X
