# spectral_connectivity_parameters
con_method = data_con['con_method']
epoch_window_length = data_con['epoch_window_length']
# sampling frequency
sfreq = data_con['sfreq'] # When starting from raw MEG
# (.fif) data, can be directly extracted from the file info
frequency_node = get_frequency_band(freq_band_names, freq_bands)
###############################################################################
# Then we create a node to pass input filenames to DataGrabber from nipype
subject_ids = ['sub-0003'] # 'sub-0004', 'sub-0006'
infosource = create_iterator(['subject_id', 'freq_band_name'],
[subject_ids, freq_band_names])
###############################################################################
# and a node to grab data. The template_args in this node iterate upon
# the values in the infosource node
template_path = '*%s_task-rest_run-01_meg_0_60_raw_filt_dsamp_ica_ROI_ts.npy'
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['ts_file']),
name='datasource')
datasource.inputs.base_directory = data_path
datasource.inputs.template = template_path
datasource.inputs.template_args = dict(ts_file=[['subject_id']])
reject = params["preprocessing"]['reject']
###############################################################################
# Then, we create our workflow and specify the `base_dir` which tells
# nipype the directory in which to store the outputs.
# workflow directory within the `base_dir`
preproc_pipeline_name = 'preprocessing_workflow'
main_workflow = pe.Workflow(name=preproc_pipeline_name)
main_workflow.base_dir = data_path
###############################################################################
# Then we create a node to pass input filenames to DataGrabber from nipype
infosource = create_iterator(['subject_id', 'session_id'],
[subject_ids, session_ids])
###############################################################################
# and a node to grab data. The template_args in this node iterate upon
# the values in the infosource node
template_path = '*%s/%s/meg/%s*rest*0_60*raw.fif'
template_args = [['subject_id', 'session_id', 'subject_id']]
datasource = create_datagrabber(data_path, template_path, template_args)
###############################################################################
# Ephypype creates for us a pipeline which can be connected to these
# nodes we created. The preprocessing pipeline is implemented by the function
# :func:`ephypype.pipelines.preproc_meeg.create_pipeline_preproc_meeg`, thus to
# instantiate this pipeline node, we import it and pass our
# parameters to it.
###############################################################################
# Then, we create our workflow and specify the `base_dir` which tells
# nipype the directory in which to store the outputs.
# workflow directory within the `base_dir`
src_reconstruction_pipeline_name = 'source_reconstruction_' + \
inv_method + '_' + parc.replace('.', '')
main_workflow = pe.Workflow(name=src_reconstruction_pipeline_name)
main_workflow.base_dir = data_path
###############################################################################
# Then we create a node to pass input filenames to DataGrabber from nipype
infosource = create_iterator(['subject_id'], [subject_ids])
###############################################################################
# and a node to grab data. The template_args in this node iterate upon
# the values in the infosource node
template_path = '*%s/MEG/%s_sss_filt_ica-raw.fif'
template_args = [['subject_id', 'subject_id']]
infields = ['subject_id']
datasource = create_datagrabber(data_path,
template_path,
template_args,
infields=infields)
###############################################################################
# Ephypype creates for us a pipeline which can be connected to these
def create_main_workflow_FS_segmentation():
# Check envoiroment variables
if not os.environ.get('FREESURFER_HOME'):
raise RuntimeError('FREESURFER_HOME environment variable not set')
if not os.environ.get('SUBJECTS_DIR'):
os.environ["SUBJECTS_DIR"] = subjects_dir
if not op.exists(subjects_dir):
os.mkdir(subjects_dir)
print('SUBJECTS_DIR %s ' % os.environ["SUBJECTS_DIR"])
main_workflow = pe.Workflow(name=MAIN_WF_name)
main_workflow.base_dir = subjects_dir
# (1) we create a node to pass input filenames to DataGrabber from nipype
# iterate over subjects
infosource = create_iterator(['subject_id'], [subject_ids])
# # and a node to grab data. The template_args in this node iterate upon
# the values in the infosource node
# Here we define an input field for datagrabber called subject_id.
# This is then used to set the template (see %s in the template).
# we look for .nii files
template_path = '%s/anatomy/highres001.nii.gz'
template_args = [['subject_id']]
infields = ['subject_id']
datasource = create_datagrabber(data_path,
template_path,
template_args,
infields=infields)
# (2) ReconAll Node to generate surfaces and parcellations of structural
# data from anatomical images of a subject.
recon_all = pe.Node(interface=ReconAll(),
infields=['T1_files'],
name='recon_all')
recon_all.inputs.subjects_dir = subjects_dir
recon_all.inputs.directive = 'all'
# reconall_workflow will be a node of the main workflow
reconall_workflow = pe.Workflow(name=FS_WF_name)
reconall_workflow.base_dir = wf_path
reconall_workflow.connect(infosource, 'subject_id', recon_all,
'subject_id')
reconall_workflow.connect(infosource, 'subject_id', datasource,
'subject_id')
reconall_workflow.connect(datasource, 'raw_file', recon_all, 'T1_files')
# (3) BEM generation by make_watershed_bem of MNE Python package
bem_generation = pe.Node(interface=Function(
input_names=['subjects_dir', 'sbj_id'],
output_names=['sbj_id'],
function=_create_bem_sol),
name='call_mne_watershed_bem')
bem_generation.inputs.subjects_dir = subjects_dir
main_workflow.connect(reconall_workflow, 'recon_all.subject_id',
bem_generation, 'sbj_id')
return main_workflow
sfreq = 1200
###############################################################################
# Then, we create our workflow and specify the `base_dir` which tells
# nipype the directory in which to store the outputs.
# workflow directory within the `base_dir`
correl_analysis_name = 'spectral_connectivity_' + con_method
main_workflow = pe.Workflow(name=correl_analysis_name)
main_workflow.base_dir = data_path
###############################################################################
# Then we create a node to pass input filenames to DataGrabber from nipype
infosource = create_iterator(['subject_id', 'session_id', 'run_id', 'cond_id', 'freq_band_name'],
[subject_ids, session_ids, run_ids, cond_ids, freq_band_names])
###############################################################################
# and a node to grab data. The template_args in this node iterate upon
# the values in the infosource node
sources_fp = '/scratch/hyruuk/saflow_data/saflow_bids/source_reconstruction_MNE_aparca2009s/inv_sol_pipeline/'
template_path = sources_fp + '_run_id_%s_session_id_%s_subject_id_%s/inv_solution/%s_%s_task-gradCPT_%s_meg_%s_-epo_stc.hdf5'
template_args = [['run_id', 'session_id', 'subject_id', 'subject_id', 'session_id', 'run_id', 'cond_id']]
datasource = pe.Node(
interface=nio.DataGrabber(infields=['subject_id', 'session_id', 'run_id', 'cond_id'], outfields=['ts_file']),
name='datasource')
datasource.inputs.base_directory = data_path
datasource.inputs.template = template_path
