.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_paper/preprocessing_info.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_paper_preprocessing_info.py: Preprocessing routine ===================== Preprocessing includes: * Selection of proper time-interval * Band pass filtering in [2, 50]Hz * Mark bad segments * Bad channels interpolation * Independent component analysis * Additional automatic removal of bad segments .. GENERATED FROM PYTHON SOURCE LINES 13-548 .. code-block:: default import mne import pandas as pd import os.path as op from os import makedirs import numpy as np from mne_bids import BIDSPath, read_raw_bids from autoreject import get_rejection_threshold def preprocessing(data_root, datatype, subject, session, task, suffix, data_path): bids_path = BIDSPath(subject=subject, task=task, suffix=suffix, session=session, datatype=datatype, root=data_root) elec_path = BIDSPath(subject=subject, task=task, suffix='electrodes', session=session, datatype=datatype, root=data_root) # Load data for one subject and remove unused channels extra_params = {'preload': True} raw = read_raw_bids(bids_path=bids_path, extra_params=extra_params, verbose=False) raw.drop_channels(['TP9', 'TP10', 'FT9', 'FT10', 'X', 'Y', 'Z']) elec_df = pd.read_csv(elec_path, sep='\t', header=0, index_col=None) ch_names = elec_df['name'].tolist()[:-3] ch_coords = (np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]]).dot( (elec_df[['x', 'y', 'z']].to_numpy(dtype=float)[:-3, :]*10**(-3)).T)).T ch_pos = dict(zip(ch_names, ch_coords)) # Define EEG channel montage montage = mne.channels.make_dig_montage(ch_pos, coord_frame='head') raw.set_montage(montage) ch_type = {} for ch_name in raw.info['ch_names']: if ch_name == 'VEOG': ch_type[ch_name] = 'eog' elif ch_name == 'X': ch_type[ch_name] = 'misc' elif ch_name == 'Y': ch_type[ch_name] = 'misc' elif ch_name == 'Z': ch_type[ch_name] = 'misc' else: ch_type[ch_name] = 'eeg' raw.set_channel_types(ch_type) # Extract time-intervals corresponding to eye-closed (EC) and task conditions tmin_EC = None tmin_task1 = None for ann in raw.annotations: if ('Eyes Closed' in ann['description']) & (tmin_EC is None): tmin_EC = ann['onset'] elif ('Eyes Closed' in ann['description']) & (tmin_EC is not None): tmax_EC = ann['onset'] elif ('Tone' in ann['description']) & (tmin_task1 is None): tmin_task1 = ann['onset'] tmax_task1 = tmin_task1 elif ('Tone' in ann['description']) & (tmin_task1 is not None): if ann['onset'] - tmax_task1 < 6: tmax_task1 = ann['onset'] # Manual selection for two subjects if subject == '033': tmin_EC = 402 tmax_EC = 460 if subject == '019': tmin_EC = 355 tmax_EC = 414 raw_EC = raw.copy().crop(tmin=tmin_EC, tmax=tmax_EC) raw_task = raw.copy().crop(tmin=tmin_task1, tmax=tmax_task1) # Band-pass filter between 2 and 50 hz raw_EC.filter(2, 50, method='fir', fir_design='firwin', filter_length='auto', fir_window='hamming', picks='all') raw_task.filter(2, 50, method='fir', fir_design='firwin', filter_length='auto', fir_window='hamming', picks='all') raw_EC = raw_EC.pick_types(eeg=True, exclude=raw_EC.info['bads']) raw_task = raw_task.pick_types(eeg=True, exclude=raw_task.info['bads']) # Visually inspect data and mark bad channels duration = 1 overlap = 0 eve_EC = mne.make_fixed_length_events(raw_EC, id=1, start=0, stop=None, duration=duration, first_samp=False, overlap=overlap) eve_EC[:, 0] = raw_EC.first_samp + eve_EC[:, 0] eve_task = mne.make_fixed_length_events(raw_task, id=1, start=0, stop=None, duration=duration, first_samp=False, overlap=overlap) eve_task[:, 0] = raw_task.first_samp + eve_task[:, 0] epo_EC = mne.Epochs(raw_EC, eve_EC, preload=True, baseline=None, tmin=0, tmax=duration, proj=False, reject=None, flat=None, detrend=None, reject_by_annotation=False) epo_task = mne.Epochs(raw_task, eve_task, preload=True, baseline=None, tmin=0, tmax=duration, proj=False, reject=None, flat=None, detrend=None, reject_by_annotation=True) if subject in drop_idxs['ses'+session]['EC'].keys(): drop_idx = drop_idxs['ses'+session]['EC'][subject] else: drop_idx = [] epo_EC.drop(drop_idx) data_epo_EC = epo_EC.get_data() data_raw_EC = np.zeros((data_epo_EC.shape[1], data_epo_EC.shape[0]*data_epo_EC.shape[2])) for i_ch in range(data_epo_EC.shape[1]): data_raw_EC[i_ch, :] = np.reshape(data_epo_EC[:, i_ch, :].squeeze(), (1, data_epo_EC.shape[0]*data_epo_EC.shape[2])) raw_EC = mne.io.RawArray(data_raw_EC, raw_EC.info) if subject in drop_idxs['ses'+session]['task'].keys(): drop_idx = drop_idxs['ses'+session]['task'][subject] else: drop_idx = [] epo_task.drop(drop_idx) data_epo_task = epo_task.get_data() data_raw_task = np.zeros((data_epo_task.shape[1], data_epo_task.shape[0]*data_epo_task.shape[2])) for i_ch in range(data_epo_task.shape[1]): data_raw_task[i_ch, :] = np.reshape(data_epo_task[:, i_ch, :].squeeze(), (1, data_epo_task.shape[0]*data_epo_task.shape[2])) raw_task = mne.io.RawArray(data_raw_task, raw_task.info) # Manually mark bad channels raw_EC.info['bads'] = bad_chs['ses'+session]['EC'][subject] raw_task.info['bads'] = bad_chs['ses'+session]['task'][subject] raw_EC.annotations.delete(np.arange(len(raw_EC.annotations))) raw_task.annotations.delete(np.arange(len(raw_task.annotations))) # Interpolate bad channels raw_EC.interpolate_bads() raw_task.interpolate_bads() # Rereferencing raw_EC.set_eeg_reference(ref_channels='average', ch_type='eeg') raw_task.set_eeg_reference(ref_channels='average', ch_type='eeg') # Independent component analysis (ICA) of EC ica = mne.preprocessing.ICA(n_components=0.99, method='picard', random_state=42) picks = mne.pick_types(raw_EC.info, meg=False, eeg=True, eog=True, stim=False, exclude='bads') ica.fit(raw_EC, picks=picks) ica.exclude = bad_ICA_EC['ses'+session][subject] ica.apply(raw_EC) # ICA of task ica = mne.preprocessing.ICA(n_components=0.99, method='picard', random_state=42) picks = mne.pick_types(raw_task.info, meg=False, eeg=True, eog=True, stim=False, exclude='bads') ica.fit(raw_task, picks=picks) ica.exclude = bad_ICA_task['ses'+session][subject] ica.apply(raw_task) # Automatically reject bad epochs duration = 1 overlap = 0 eve_EC = mne.make_fixed_length_events(raw_EC, id=1, start=0, stop=None, duration=duration, first_samp=False, overlap=overlap) eve_EC[:, 0] = raw_EC.first_samp + eve_EC[:, 0] eve_task = mne.make_fixed_length_events(raw_task, id=1, start=0, stop=None, duration=duration, first_samp=False, overlap=overlap) eve_task[:, 0] = raw_task.first_samp + eve_task[:, 0] epo_EC = mne.Epochs(raw_EC, eve_EC, preload=True, baseline=None, tmin=0, tmax=duration, proj=False, reject=None, flat=None, detrend=None, reject_by_annotation=False) epo_task = mne.Epochs(raw_task, eve_task, preload=True, baseline=None, tmin=0, tmax=duration, proj=False, reject=None, flat=None, detrend=None, reject_by_annotation=True) reject_EC = get_rejection_threshold(epo_EC, ch_types='eeg') epo_EC_clean = epo_EC.drop_bad(reject=reject_EC) data_EC_clean = epo_EC_clean.get_data() reject_task = get_rejection_threshold(epo_task, ch_types='eeg') epo_task_clean = epo_task.drop_bad(reject=reject_task) data_task_clean = epo_task_clean.get_data() data_raw_EC = np.zeros((data_EC_clean.shape[1], data_EC_clean.shape[0]*data_EC_clean.shape[2])) for i_ch in range(data_EC_clean.shape[1]): data_raw_EC[i_ch, :] = np.reshape(data_EC_clean[:, i_ch, :].squeeze(), (1, data_EC_clean.shape[0]*data_EC_clean.shape[2])) data_raw_task = np.zeros((data_task_clean.shape[1], data_task_clean.shape[0]*data_task_clean.shape[2])) for i_ch in range(data_task_clean.shape[1]): data_raw_task[i_ch, :] = np.reshape(data_task_clean[:, i_ch, :].squeeze(), (1, data_task_clean.shape[0]*data_task_clean.shape[2])) raw_EC = mne.io.RawArray(data_raw_EC, raw_EC.info) raw_task = mne.io.RawArray(data_raw_task, raw_task.info) # Saving EC and task preprocessed data f_path = op.join(data_path, 'preproc_files', 'sub'+subject, 'ses'+session) if not op.isdir(f_path): makedirs(f_path) f_name_EC = op.join(f_path, 'sub-'+subject+'_ses-'+session+'_task-eyeclose_raw.fif') f_name_task = op.join(f_path, 'sub-'+subject+'_ses-'+session+'_task-memory_raw.fif') raw_EC.save(f_name_EC, overwrite=True) raw_task.save(f_name_task, overwrite=True) return drop_idxs = {'ses01': {'EC': {'003': [39], '031': [0], '032': [0, 1, 2, 32, 33, 56], '036': [2, 3, 4], '038': [33, 34], '041': [0, 1], '046': [21, 24]}, 'task': {'031': [0, 1, 2, 3, 24], '034': [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 95, 96, 97, 98, 99, 100, 101], '036': [0, 1], '038': [21, 22, 60, 61, 78, 79, 111, 112], '041': [0], '046': [6, 7, 8, 9, 10, 21, 22, 27, 28, 32, 45, 51, 58, 59, 60, 62, 63, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 92, 94, 95, 96, 97, 108, 114, 115], '047': [0, 1, 2, 3, 4, 5, 6, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115]}}, 'ses02': {'EC': {}, 'task': {'013': [47, 48, 49], '016': [23, 24, 27, 28, 33, 34, 35, 59, 60, 61]}} } bad_chs = {'ses02': {'EC': {'001': [], '002': [], '004': ['T8', 'Cz'], '006': ['AF7'], '007': [], '008': ['CP2'], '009': ['CP2', 'CP1'], '010': ['CP2'], '011': [], '012': [], '013': ['CP2', 'CP1'], '014': ['Cz'], '015': [], '016': ['Pz', 'POz'], '017': [], '018': [], '019': [], '020': ['CP2', 'CP1'], '021': [], '022': [], '023': ['CP2', 'CP1'], '024': ['T8', 'CP2', 'CP1'], '025': [], '026': [], '027': ['Cz']}, 'task': {'001': ['T8', 'FT7', 'F4'], '002': [], '004': ['T8', 'Cz'], '005': [], '006': [], '007': [], '008': ['CP2'], '009': ['CP2', 'CP1'], '010': ['CP2'], '011': [], '012': [], '013': ['CP2', 'CP1'], '014': ['Cz'], '015': [], '016': ['Pz', 'POz'], '017': [], '018': [], '019': [], '020': ['CP2', 'CP1'], '021': [], '022': [], '023': ['CP2', 'CP1'], '024': ['T8', 'CP2', 'CP1'], '025': [], '026': [], '027': ['Cz']}}, 'ses01': {'EC': {'001': ['TP7', 'FT8'], '002': ['P7', 'TP7'], '003': [], '004': ['FCz', 'Cz'], '005': [], '006': ['CP2', 'CP1'], '007': ['FT7', 'T7'], '008': [], '009': [], '010': [], '011': [], '012': [], '013': ['F2'], '014': ['AF8', 'F6', 'F8'], '015': [], '016': ['Cz'], '017': [], '018': [], '019': [], '020': [], '021': ['F1', 'Cz'], '022': ['F1'], '023': [], '024': ['T8', 'CP1', 'CP2'], '025': [], '026': ['T7'], '027': [], '028': [], '029': [], '030': ['CP1'], '031': [], '032': [], '033': [], '034': ['T8', 'T7'], '035': ['F1'], '036': ['F8'], '037': ['CP5', 'CP6'], '038': [], '039': [], '040': ['Cz', 'C2', 'FCz'], '041': [], '042': [], '043': [], '044': [], '045': [], '046': [], '047': [], '048': ['P6'], '049': [], '050': []}, 'task': {'001': ['TP7', 'FT8', 'T8'], '002': ['P7', 'TP7'], '003': [], '004': ['FCz', 'Cz', 'T7'], '005': [], '006': ['CP2', 'CP1'], '007': ['FT7', 'T7'], '008': [], '009': [], '010': [], '011': [], '012': [], '013': [], '014': [], '015': [], '016': ['Cz'], '017': [], '018': [], '019': [], '020': ['F5'], '021': ['F1', 'Cz'], '022': ['F1'], '023': [], '024': ['CP1', 'CP2'], '025': [], '026': [], '027': [], '028': [], '029': [], '030': ['CP1', 'FC6', 'FT8'], '031': [], '032': ['AF7'], '033': [], '034': ['T8', 'T7'], '035': ['F1'], '036': [], '037': ['CP6'], '038': [], '039': [], '040': ['Cz', 'C2', 'FCz'], '041': [], '042': [], '043': [], '044': [], '045': [], '046': [], '047': [], '048': ['P6'], '049': [], '050': []}}} bad_ICA_EC = {'ses01': {'001': [2, 5, 6, 7, 9, 19, 20, 21], '002': [1, 4, 10, 12, 14, 16, 20, 21, 24, 28, 30, 32, 33, 37, 40], '003': [0, 16, 24, 25, 27, 36, 37], '004': [0, 9, 14, 16, 17, 19, 28, 29], '005': [], '006': [15, 16, 18, 19, 21, 26, 27, 28, 29, 31, 32], '007': [3, 4, 15, 16, 19, 21, 24, 30], '008': [7], '009': [0, 11, 12, 13, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27], '010': [0, 11, 13, 16, 17, 20, 21, 23, 25], '011': [22, 23, 24, 25, 26, 27, 28, 29, 30, 31], '012': [26, 27, 28, 33, 34, 35, 37, 38, 38, 40], '013': [1, 2, 8, 13, 18, 19, 24, 25, 33], '014': [3, 5, 17, 21, 27, 28, 31, 33, 34], '015': [16, 17, 18, 19, 20, 23, 25, 26], '016': [13, 14, 18, 24], '017': [6, 8, 9, 11, 12, 13, 14, 15, 16, 17], '018': [], '019': [11, 16, 17, 18, 20, 22, 23, 24, 25, 30], '020': [17, 20, 22, 24, 25, 27, 28, 29, 30, 31, 32, 33], '021': [3, 10, 11, 14, 15, 17], '022': [9, 15, 16, 21, 28], '023': [4, 5, 6, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27], '024': [4, 6, 12, 14, 15, 16, 19, 21, 22, 23, 29], '025': [13, 21, 22, 24], '026': [0, 7], '027': [12, 15, 17, 25], '028': [0, 2, 28], '029': [0, 4, 6, 8, 13, 16, 26, 31], '030': [0, 1, 2, 3, 5, 6, 10, 11, 14, 15, 16, 17, 21, 23, 30, 31], '031': [4, 15, 18, 28, 31, 32, 33], '032': [0, 16, 19, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 34], '033': [0, 1, 4, 11, 26, 32, 34, 37], '034': [3, 15, 28], '035': [7, 10], '036': [0, 1, 2, 3, 4, 16, 25, 33, 36, 37], '037': [0, 1, 3, 4, 5, 6, 10, 12, 20, 30, 43], '038': [0, 1, 6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28], '039': [4, 10, 18, 19], '040': [0, 4, 9, 15, 19, 29], '041': [6, 9, 10, 12, 15, 16], '042': [1, 3, 21, 25, 31, 37], '043': [0, 6, 35], '044': [0, 4, 35], '045': [0, 1, 3, 4, 11, 12, 14, 15, 16, 17], '046': [0, 1, 2, 3, 4, 5, 6, 8, 11, 12, 13, 15, 16, 18, 20, 22, 23], '047': [4, 15, 16, 17, 24, 25, 26, 29, 30, 31, 32, 34], '048': [0, 1, 2, 3, 18, 27, 28, 35, 36, 37, 38, 39], '049': [0, 1], '050': [0, 2, 14, 15, 31, 34, 36]}, 'ses02': {'001': [5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23, 24, 25, 26, 31, 34], '002': [0, 20, 22, 24, 35, 38, 39], '004': [14, 19, 20, 21, 22], '006': [0, 5, 16, 18, 21, 22, 23], '007': [4, 7, 14, 16], '008': [2, 10], '009': [0, 5, 6, 12, 14, 21, 23, 24, 25, 26], '010': [0, 8, 12, 13, 17, 19, 21, 23, 24, 25, 26], '011': [10, 12, 17, 18, 19, 24, 25, 27, 32, 34, 41], '012': [0, 2, 31, 33, 34, 35], '013': [19, 20, 21, 26], '014': [0, 5, 18, 20, 23, 24, 26, 28, 29, 33], '015': [1, 14, 18, 20, 21, 23, 24, 25, 26, 28], '016': [14, 15, 18, 19, 24, 25], '017': [8, 9, 11, 14, 15, 18, 19, 22, 26, 27], '018': [6, 10, 21, 22, 25, 27, 28, 38], '019': [0, 7, 15, 18, 19, 20, 21, 23, 25, 26, 27], '020': [1, 5, 13, 17, 18, 23, 24, 25, 26, 27, 30, 31, 33], '021': [12, 14, 15, 16, 19, 20, 21, 22, 23, 25, 26], '022': [0, 2, 8, 12, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28], '023': [13, 14, 16, 21, 23, 24, 25, 26, 27, 28], '024': [7, 10, 20, 21, 22, 23, 24, 27], '025': [10, 11, 13, 17, 18, 20, 21, 22, 23], '026': [5, 12, 14, 19, 22, 26], '027': [13, 14, 19, 20, 21]}, } bad_ICA_task = {'ses01': {'001': [0, 2, 3, 4, 9, 12, 20, 27, 28], '002': [1, 2, 5, 6, 27, 34, 38, 40, 41, 42], '003': [0, 1, 11, 12, 14, 15], '004': [3, 11, 12, 13, 20, 23, 25, 26, 27], '005': [], '006': [0, 12, 13, 14, 16, 17, 18, 20, 21, 22], '007': [2, 5, 11, 12, 13, 17, 18, 19, 21], '008': [0, 5, 8, 26, 27, 31], '009': [0, 11, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], '010': [0, 5, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23], '011': [0, 8, 9, 11, 13, 15, 16, 20, 21, 22, 25, 26, 28, 29], '012': [0, 11, 12, 16, 18, 20, 22, 23, 24, 25, 29, 30], '013': [0, 4, 6, 11, 13, 14, 15, 16, 17, 24, 25, 29, 32], '014': [0, 5, 12, 19, 20, 22, 23, 24, 26, 30, 31, 33], '015': [0, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23], '016': [0, 9, 11, 12, 16, 17, 19, 23], '017': [0, 6, 9, 10, 11, 13, 14, 15, 18, 21, 22, 24, 25, 26, 27, 28], '018': [], '019': [0, 3, 9, 13, 14, 15, 18, 19, 23, 24, 25, 26], '020': [0, 5, 9, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33], '021': [0, 7, 8, 10, 11, 12, 15, 16], '022': [0, 2, 4, 6, 7, 8, 11, 12, 15, 17, 23, 25], '023': [0, 4, 5, 6, 7, 8, 10, 11, 12, 13], '024': [0, 1, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16], '025': [20, 26], '026': [0], '027': [0, 12, 13, 23, 28, 30], '028': [0, 1, 2, 6, 7, 11, 13, 14, 15, 16, 18, 20, 23, 26], '029': [0, 1, 4, 5, 9, 10, 12, 14, 15], '030': [0, 1, 2, 5, 7, 9, 10, 11, 12, 13, 16, 19, 20, 22, 23, 24, 25], '031': [0, 11, 12, 17, 20], '032': [0, 1, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 20, 27], '033': [0, 1, 5, 6, 7, 8, 9, 10, 12, 14, 16], '034': [0, 1, 4, 5, 6, 11, 13, 14, 18, 19, 20, 21, 22, 23, 28], '035': [0, 7], '036': [0, 1, 2, 3, 4, 8, 30, 31, 32], '037': [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17], '038': [0, 1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], '039': [0, 1, 3, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], '040': [0, 1, 2, 7, 11, 12, 13, 15, 16, 17, 18, 22, 25, 26, 27], '041': [0, 7, 11, 12, 15, 16, 19, 22, 25, 26, 27], '042': [0, 4, 6, 11, 16, 20, 21, 22, 29], '043': [0, 1, 5, 7, 9], '044': [0, 1, 4, 12, 14, 19], '045': [0, 2, 3, 7, 9, 12, 14, 19], '046': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 22, 23, 25, 26, 30], '047': [0, 1, 4, 8, 13, 36, 39], '048': [0, 2, 3, 7, 8, 10, 12, 18, 19, 20, 21, 22, 23, 24, 26, 30, 31], '049': [0, 1, 2, 3, 10, 12], '050': [0, 1, 2, 3, 8, 12, 14, 15]}, 'ses02': {'001': [0, 1, 2, 3, 5, 7, 11, 12, 13, 14, 15, 19, 20, 21, 22, 25, 26], '002': [0, 4, 6, 11, 12, 13, 20, 21, 24, 25, 28, 33, 35, 37, 38], '004': [1, 11, 13, 14, 21, 23], '006': [0, 5, 10, 13, 14, 17, 18, 19, 23, 24], '007': [0, 1, 3, 8, 9, 11, 25, 31, 36], '008': [0, 4, 13, 22], '009': [0, 10, 12, 14, 15, 16, 17, 18], '010': [0, 9, 10, 13, 14, 15, 16, 19, 20, 21, 22], '011': [3, 4, 5, 6, 9, 10, 12, 13, 14, 15, 16, 21, 23, 26], '012': [1, 6, 15, 19, 21, 23, 25, 31, 36], '013': [2, 3, 5, 9, 11, 12, 13, 15, 16, 18, 19, 20, 22, 23, 28, 29, 30], '014': [0, 1, 2, 11, 13, 14, 17, 19, 20, 21, 22, 24, 25], '015': [0, 6, 9, 13, 14, 15, 16, 17, 18], '016': [0, 2, 8, 10, 11, 12, 13, 14, 15, 17, 18, 19], '017': [0, 5, 7, 8, 9, 12, 13, 14, 15, 16, 17, 19, 20, 21, 24, 26], '018': [2, 17, 26, 29, 30, 33, 35, 36, 37, 39, 40], '019': [8, 12, 15, 16, 18, 21, 22, 23, 25, 26], '020': [0, 7, 13, 14, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32], '021': [0, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16], '022': [0, 1, 2, 4, 5, 7, 9, 10, 13, 14, 15, 17, 18, 20, 21, 22, 23], '023': [0, 2, 3, 9, 10, 11, 12], '024': [0, 1, 3, 9, 10, 11, 12, 13], '025': [0, 10, 14, 16, 17, 18, 19], '026': [0, 3, 5, 6, 17], '027': [1, 4, 14, 16, 17, 18, 19, 20, 21, 22, 23]}, } .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_auto_paper_preprocessing_info.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: preprocessing_info.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: preprocessing_info.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_