def dipole_fit():
mu.add_mmvt_code_root_to_path()
from src.preproc import meg
importlib.reload(meg)
subject = mu.get_user()
args = meg.read_cmd_args(
dict(subject=subject, mri_subject=subject, atlas=mu.get_atlas()))
meg.init(subject, args)
dipoles_times = [(bpy.context.scene.meg_dipole_fit_tmin,
bpy.context.scene.meg_dipole_fit_tmax)]
dipoloes_title = mask_roi = MEGPanel.current_cluster['intersects'][0][
'name']
meg.dipoles_fit(dipoles_times,
dipoloes_title,
None,
mu.get_real_fname('meg_noise_cov_fname'),
mu.get_real_fname('meg_evoked_fname'),
mu.get_real_fname('head_to_mri_trans_mat_fname'),
5.,
bpy.context.scene.meg_dipole_fit_use_meg,
bpy.context.scene.meg_dipole_fit_use_eeg,
mask_roi=mask_roi,
do_plot=False,
n_jobs=4)
def morph_stc(args):
args = meg.read_cmd_args(
dict(subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
data_per_task=True,
contrast='interference',
cleaning_method='nTSSS'))
morph_to_subject = 'ab' # 'fsaverage5'
fname_format, fname_format_cond, conditions = meg.init(
args.subject[0], args, args.mri_subject[0])
meg.morph_stc(conditions, morph_to_subject, args.inverse_method[0],
args.n_jobs)
def calc_msit_stcs_diff(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
task='MSIT',
data_per_task=True,
contrast='interference',
cleaning_method='nTSSS'))
smooth = False
fname_format, fname_format_cond, conditions = meg.init(args.subject[0], args, args.mri_subject[0])
stc_template_name = meg.STC_HEMI_SMOOTH if smooth else meg.STC_HEMI
stc_fnames = [stc_template_name.format(cond=cond, method=args.inverse_method[0], hemi='lh') for cond in conditions.keys()]
output_fname = stc_template_name.format(cond='diff', method=args.inverse_method[0], hemi='lh')
meg.calc_stc_diff(*stc_fnames, output_fname)
def calc_mne_python_sample_data_stcs_diff(args):
args = meg.read_cmd_args(dict(
subject=args.subject,
mri_subject=args.mri_subject,
contrast = 'audvis',
fname_format = '{subject}_audvis-{ana_type}.{file_type}',
fname_format_cond = '{subject}_audvis_{cond}-{ana_type}.{file_type}',
conditions = ['LA', 'RA']
))
smooth = False
fname_format, fname_format_cond, conditions = meg.init(args.subject[0], args, args.mri_subject[0])
stc_template_name = meg.STC_HEMI_SMOOTH if smooth else meg.STC_HEMI
stc_fnames = [stc_template_name.format(cond=cond, method=args.inverse_method[0], hemi='lh') for cond in conditions.keys()]
output_fname = stc_template_name.format(cond='diff', method=args.inverse_method[0], hemi='lh')
meg.calc_stc_diff(*stc_fnames, output_fname)
def analyze(subject):
flags = {}
args = meg.read_cmd_args(
dict(
subject=subject,
task='tapping',
conditions='left',
# atlas='laus250',
inverse_method='MNE',
t_min=-2,
t_max=2,
noise_t_min=-2.5,
noise_t_max=-1.5,
bad_channels=[],
stim_channels='STIM',
pick_ori='normal',
reject=False,
overwrite_epochs=True,
overwrite_inv=True,
overwrite_noise_cov=True,
overwrite_ica=True))
fname_format, fname_format_cond, conditions = meg.init(subject, args)
conditions['left'] = 4
args.conditions = conditions
if op.isfile(meg.RAW):
raw = mne.io.read_raw_fif(meg.RAW, preload=True)
else:
raw = mne.io.read_raw_ctf(op.join(MEG_DIR, subject, 'raw',
'DC_leftIndex_day1.ds'),
preload=True)
meg.remove_artifacts(raw,
remove_from_raw=True,
overwrite_ica=args.overwrite_ica,
do_plot=False)
# print(raw.info['sfreq'])
# if not op.isfile(meg.RAW):
# raw.save(meg.RAW)
# flags, evoked, epochs = meg.calc_evokes_wrapper(subject, conditions, args, flags, raw=raw)
# if evoked is not None:
# fig = evoked[0].plot_joint(times=[-0.5, 0.05, 0.150, 0.250, 0.6])
# plt.show()
# flags = meg.calc_fwd_inv_wrapper(subject, conditions, args, flags)
# flags, stcs_conds, _ = meg.calc_stc_per_condition_wrapper(subject, conditions, args.inverse_method, args, flags)
# flags = meg.calc_labels_avg_per_condition_wrapper(subject, conditions, args.atlas, args.inverse_method, stcs_conds, args, flags)
dipoles_times = [(0.25, 0.35)]
dipoles_names = ['peak_left_motor']
stc_fname = op.join(meg_sub_fol, '{}-{}.stc'.format(subject, task))
hcp_params = dict(hcp_path=HCP_DIR, subject=subject, data_type=task)
args = meg.read_cmd_args(
dict(
subject=subject,
task='task',
conditions='face',
# atlas='laus250',
inverse_method='MNE',
t_min=-1.5,
t_max=2.5,
reject=False,
overwrite_epochs=False))
fname_format, fname_format_cond, conditions = meg.init(subject, args)
print('epo: {}, evo: {}'.format(meg.EPO, meg.EVO))
##############################################################################
# We first reprocess the data from scratch
#
# That is, almost from scratch. We're relying on the ICA solutions and
# data annotations.
#
# In order to arrive at the final ERF we need to pool over two runs.
# for each run we need to read the raw data, all annotations, apply
# the reference sensor compensation, the ICA, bandpass filter, baseline
# correction and decimation (downsampling)
# these values are looked up from the HCP manual
l_freq=1,
h_freq=15,
extract_mode='mean_flip',
bad_channels=[],
stim_channels='STIM',
pick_ori='normal',
reject=False,
overwrite_epochs=False,
overwrite_inv=False,
overwrite_noise_cov=False,
overwrite_ica=False,
do_plot_ica=False,
fwd_usingEEG=False)))
subject = args.subject[0]
meg.init(subject, args)
conditions = dict(left=4, right=8)
raw_files_template = op.join(MEG_DIR, args.subject[0], 'raw',
'DC_{cond}Index_day?.ds')
sessions = sorted([
f[-4] for f in glob.glob(
raw_files_template.format(cond=list(conditions.keys())[0]))
])
motor_rois = ['precentral_{}'.format(ind)
for ind in range(1, 20)] # ['precentral_11', 'precentral_5']
sfreq = 625
eog_channel = 'MZF01-1410' # Doesn't give good results, so we'll use manuualy pick ICA componenets
for inverse_method in args.inverse_method:
# analyze(subject, raw_files_template, inverse_method, conditions, sessions, args)
# dipole_fit(conditions, True, (int(args.l_freq), int(args.h_freq)), extract_mode=args.extract_mode)
# plot_motor_response(subject, args.atlas, motor_rois, sfreq, sessions, args)
