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# Reversed in inner loop is used to avoid sequencial data being overwritten.
# E.g. if 0 is renamed to 1, then the next loop all 1's will be renamed to 2
# Add group status
Group_status = np.array(["CTRL"]*len(microstate_time_df["Subject_ID"]))
Group_status[np.array([i in cases for i in microstate_time_df["Subject_ID"]])] = "PTSD"
# Add to dataframe
microstate_time_df.insert(2, "Group_status", Group_status)
# Save df
microstate_time_df.to_pickle(os.path.join(Feature_savepath,"microstate_time_df.pkl"))
# Transition data - mean
# Get index for groups
PTSD_idx = np.array([i in cases for i in Subject_id])
CTRL_idx = np.array([not i in cases for i in Subject_id])
n_groups = 2
microstate_transition_data_mean = np.zeros((n_groups,n_eye_status,n_maps,n_maps))
microstate_transition_data_mean[0,:,:,:] = np.mean(microstate_transition_data[PTSD_idx,:,:,:], axis=0)
microstate_transition_data_mean[1,:,:,:] = np.mean(microstate_transition_data[CTRL_idx,:,:,:], axis=0)
# Convert entropy data to Pandas dataframe
# The dimensions will each be a column with numbers and the last column will be the actual values
arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, microstate_entropy_data.shape), indexing="ij"))) + [microstate_entropy_data.ravel()])
microstate_entropy_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Value"])
# Change from numerical coding to actual values
eye_status = list(final_epochs[0].event_id.keys())
index_values = [Subject_id,eye_status]
for col in range(len(index_values)):
col_name = microstate_entropy_df.columns[col]
for shape in reversed(range(microstate_entropy_data.shape[col])): # notice this is the shape of original numpy array. Not shape of DF
microstate_entropy_df.loc[microstate_entropy_df.iloc[:,col] == shape,col_name]\
= index_values[col][shape]
# Reversed in inner loop is used to avoid sequencial data being overwritten.
# E.g. if 0 is renamed to 1, then the next loop all 1's will be renamed to 2
# Add group status
Group_status = np.array(["CTRL"]*len(microstate_entropy_df["Subject_ID"]))
Group_status[np.array([i in cases for i in microstate_entropy_df["Subject_ID"]])] = "PTSD"
# Add to dataframe
microstate_entropy_df.insert(2, "Group_status", Group_status)
# Add dummy variable for re-using plot code
dummy_variable = ["Entropy"]*len(Group_status)
microstate_entropy_df.insert(3, "Measurement", dummy_variable)
# Save df
microstate_entropy_df.to_pickle(os.path.join(Feature_savepath,"microstate_entropy_df.pkl"))
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# # %% Long-range temporal correlations (LRTC)
# """
# See Hardstone et al, 2012
# Hurst exponent estimation steps:
# 1. Preprocess
# 2. Band-pass filter for frequency band of interest
# 3. Hilbert transform to obtain amplitude envelope
# 4. Perform DFA
# 4.1 Compute cumulative sum of time series to create signal profile
# 4.2 Define set of window sizes (see below)
# 4.3 Remove the linear trend using least-squares for each window
# 4.4 Calculate standard deviation for each window and take the mean
# 4.5 Plot fluctuation function (Standard deviation) as function
# for all window sizes, on double logarithmic scale
# 4.6 The DFA exponent alpha correspond to Hurst exponent
# f(L) = sd = L^alpha (with alpha as linear coefficient in log plot)
# If 0 < alpha < 0.5: The process exhibits anti-correlations
# If 0.5 < alpha < 1: The process exhibits positive correlations
# If alpha = 0.5: The process is indistinguishable from a random process
# If 1.0 < alpha < 2.0: The process is non-stationary. H = alpha - 1
# Window sizes should be equally spaced on a logarithmic scale
# Sizes should be at least 4 samples and up to 10% of total signal length
# Filters can influence neighboring samples, thus filters should be tested
# on white noise to estimate window sizes that are unaffected by filters
# filter_length=str(2*1/fmin)+"s" # cannot be used with default transition bandwidth
# """
# # From simulations with white noise I determined window size thresholds for the 5 frequency bands:
# thresholds = [7,7,7,6.5,6.5]
# # And their corresponding log step sizes
# with open("LRTC_log_win_sizes.pkl", "rb") as filehandle:
# log_win_sizes = pickle.load(filehandle)
# # Variables for the the different conditions
# # Sampling frequency
# sfreq = final_epochs[0].info["sfreq"]
# # Channels
# ch_names = final_epochs[0].info["ch_names"]
# n_channels = len(ch_names)
# # Frequency
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# # Eye status
# eye_status = list(final_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# ### Estimating Hurst exponent for the data
# # The data should be re-referenced to common average (Already done)
# # Data are transformed to numpy arrays
# # Then divided into EO and EC and further into each of the 5 trials
# # So DFA is estimated for each trial separately, which was concluded from simulations
# gaps_trials_idx = np.load("Gaps_trials_idx.npy") # re-used from microstate analysis
# n_trials = 5
# H_data = []
# for i in range(n_subjects):
# # Transform data to correct shape
# temp_arr = final_epochs[i].get_data() # get data
# arr_shape = temp_arr.shape # get shape
# temp_arr = temp_arr.swapaxes(1,2) # swap ch and time axis
# temp_arr = temp_arr.reshape(arr_shape[0]*arr_shape[2],arr_shape[1]) # reshape by combining epochs and times
# # Get indices for eyes open and closed
# EC_index = final_epochs[i].events[:,2] == 1
# EO_index = final_epochs[i].events[:,2] == 2
# # Repeat with 4s * sample frequency to correct for concatenation of times and epochs
# EC_index = np.repeat(EC_index,4*sfreq)
# EO_index = np.repeat(EO_index,4*sfreq)
# # Divide into eye status
# EC_data = temp_arr[EC_index]
# EO_data = temp_arr[EO_index]
# # Divide into trials
# EC_gap_idx = np.array([0]+list(gaps_trials_idx[i,0])+[len(EC_data)])
# EO_gap_idx = np.array([0]+list(gaps_trials_idx[i,1])+[len(EO_data)])
# EC_trial_data = []
# EO_trial_data = []
# for t in range(n_trials):
# EC_trial_data.append(EC_data[EC_gap_idx[t]:EC_gap_idx[t+1]])
# EO_trial_data.append(EO_data[EO_gap_idx[t]:EO_gap_idx[t+1]])
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# # Save data
# H_data.append([EC_trial_data,EO_trial_data]) # output [subject][eye][trial][time,ch]
# # Calculate H for each subject, eye status, trial, freq and channel
# H_arr = np.zeros((n_subjects,n_eye_status,n_trials,n_channels,n_freq_bands))
# w_len = [len(ele) for ele in log_win_sizes]
# DFA_arr = np.empty((n_subjects,n_eye_status,n_trials,n_channels,n_freq_bands,2,np.max(w_len)))
# DFA_arr[:] = np.nan
# # Get current time
# c_time1 = time.localtime()
# c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
# print("Started",c_time1)
# # Nolds are already using all cores so multiprocessing with make it slower
# # Warning occurs when R2 is estimated during detrending - but R2 is not used
# warnings.simplefilter("ignore")
# for i in range(n_subjects):
# # Pre-allocate memory
# DFA_temp = np.empty((n_eye_status,n_trials,n_channels,n_freq_bands,2,np.max(w_len)))
# DFA_temp[:] = np.nan
# H_temp = np.empty((n_eye_status,n_trials,n_channels,n_freq_bands))
# for e in range(n_eye_status):
# for trial in range(n_trials):
# for c in range(n_channels):
# # Get the data
# signal = H_data[i][e][trial][:,c]
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# counter = 0 # prepare counter
# for fmin, fmax in Freq_Bands.values():
# # Filter for each freq band
# signal_filtered = mne.filter.filter_data(signal, sfreq=sfreq, verbose=0,
# l_freq=fmin, h_freq=fmax)
# # Hilbert transform
# analytic_signal = scipy.signal.hilbert(signal_filtered)
# # Get Amplitude envelope
# # np.abs is the same as np.linalg.norm, i.e. the length for complex input which is the amplitude
# ampltude_envelope = np.abs(analytic_signal)
# # Perform DFA using predefined window sizes from simulation
# a, dfa_data = nolds.dfa(ampltude_envelope,
# nvals=np.exp(log_win_sizes[counter]).astype("int"),
# debug_data=True)
# # Save DFA results
# DFA_temp[e,trial,c,counter,:,0:w_len[counter]] = dfa_data[0:2]
# H_temp[e,trial,c,counter] = a
# # Update counter
# counter += 1
# # Print run status
# print("Finished {} out of {}".format(i+1,n_subjects))
# # Save the results
# H_arr[i] = H_temp
# DFA_arr[i] = DFA_temp
# warnings.simplefilter("default")
# # Get current time
# c_time2 = time.localtime()
# c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
# print("Started", c_time1, "\nCurrent Time",c_time2)
# # Save the DFA analysis data
# np.save(Feature_savepath+"DFA_arr.npy", DFA_arr)
# np.save(Feature_savepath+"H_arr.npy", H_arr)
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# # Load
# DFA_arr = np.load(Feature_savepath+"DFA_arr.npy")
# H_arr = np.load(Feature_savepath+"H_arr.npy")
# # Average the Hurst Exponent across trials
# H_arr = np.mean(H_arr, axis=2)
# # Convert to Pandas dataframe (Hurst exponent)
# # The dimensions will each be a column with numbers and the last column will be the actual values
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, H_arr.shape), indexing="ij"))) + [H_arr.ravel()])
# H_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "Channel", "Freq_band", "Value"])
# # Change from numerical coding to actual values
# eye_status = list(final_epochs[0].event_id.keys())
# ch_name = final_epochs[0].info["ch_names"]
# index_values = [Subject_id,eye_status,ch_name,list(Freq_Bands.keys())]
# for col in range(len(index_values)):
# col_name = H_data_df.columns[col]
# for shape in range(H_arr.shape[col]): # notice this is the shape of original numpy array. Not shape of DF
# H_data_df.loc[H_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# # Add group status
# Group_status = np.array(["CTRL"]*len(H_data_df["Subject_ID"]))
# Group_status[np.array([i in cases for i in H_data_df["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# H_data_df.insert(2, "Group_status", Group_status)
# # Fix Freq_band categorical order
# H_data_df["Freq_band"] = H_data_df["Freq_band"].astype("category").\
# cat.reorder_categories(list(Freq_Bands.keys()), ordered=True)
# # Global Hurst exponent
# H_data_df_global = H_data_df.groupby(["Subject_ID", "Eye_status", "Freq_band"]).mean().reset_index() # by default pandas mean skip nan
# # Add group status (cannot use group_by as each subject only have 1 group, not both)
# Group_status = np.array(["CTRL"]*len(H_data_df_global["Subject_ID"]))
# Group_status[np.array([i in cases for i in H_data_df_global["Subject_ID"]])] = "PTSD"
# # Add to dataframe
# H_data_df_global.insert(2, "Group_status", Group_status)
# # Add dummy variable for re-using plot code
# dummy_variable = ["Global Hurst Exponent"]*H_data_df_global.shape[0]
# H_data_df_global.insert(3, "Measurement", dummy_variable )
# # Save the data
# H_data_df.to_pickle(os.path.join(Feature_savepath,"H_data_df.pkl"))
# H_data_df_global.to_pickle(os.path.join(Feature_savepath,"H_data_global_df.pkl"))
# # %% Source localization of sensor data
# # Using non-interpolated channels
# # Even interpolated channels during preprocessing and visual inspection
# # are dropped
# # Prepare epochs for estimation of source connectivity
# source_epochs = [0]*n_subjects
# for i in range(n_subjects):
# source_epochs[i] = final_epochs[i].copy()
# ### Make forward solutions
# # A forward solution is first made for all individuals with no dropped channels
# # Afterwards individual forward solutions are made for subjects with bad
# # channels that were interpolated in preprocessing and these are dropped
# # First forward operator is computed using a template MRI for each dataset
# fs_dir = "/home/glia/MNE-fsaverage-data/fsaverage"
# subjects_dir = os.path.dirname(fs_dir)
# trans = "fsaverage"
# src = os.path.join(fs_dir, "bem", "fsaverage-ico-5-src.fif")
# bem = os.path.join(fs_dir, "bem", "fsaverage-5120-5120-5120-bem-sol.fif")
# # Read the template sourcespace
# sourcespace = mne.read_source_spaces(src)
# temp_idx = 0 # Index with subject that had no bad channels
# subject_eeg = source_epochs[temp_idx].copy()
# subject_eeg.set_eeg_reference(projection=True) # needed for inverse modelling
# # Make forward solution
# fwd = mne.make_forward_solution(subject_eeg.info, trans=trans, src=src,
# bem=bem, eeg=True, mindist=5.0, n_jobs=1)
# # Save forward operator
# fname_fwd = "./Source_fwd/fsaverage-fwd.fif"
# mne.write_forward_solution(fname_fwd, fwd, overwrite=True)
# # A specific forward solution is also made for each subject with bad channels
# with open("./Preprocessing/bad_ch.pkl", "rb") as file:
# bad_ch = pickle.load(file)
# All_bad_ch = bad_ch
# All_drop_epochs = dropped_epochs_df
# All_dropped_ch = []
# Bad_ch_idx = [idx for idx, item in enumerate(All_bad_ch) if item != 0]
# Bad_ch_subjects = All_drop_epochs["Subject_ID"][Bad_ch_idx]
# # For each subject with bad channels, drop the channels and make forward operator
# for n in range(len(Bad_ch_subjects)):
# Subject = Bad_ch_subjects.iloc[n]
# try:
# Subject_idx = Subject_id.index(Subject)
# # Get unique bad channels
# Bad_ch0 = All_bad_ch[Bad_ch_idx[n]]
# Bad_ch1 = []
# for i2 in range(len(Bad_ch0)):
# if type(Bad_ch0[i2]) == list:
# for i3 in range(len(Bad_ch0[i2])):
# Bad_ch1.append(Bad_ch0[i2][i3])
# elif type(Bad_ch0[i2]) == str:
# Bad_ch1.append(Bad_ch0[i2])
# Bad_ch1 = np.unique(Bad_ch1)
# # Drop the bad channels
# source_epochs[Subject_idx].drop_channels(Bad_ch1)
# # Save the overview of dropped channels
# All_dropped_ch.append([Subject,Subject_idx,Bad_ch1])
# # Make forward operator
# subject_eeg = source_epochs[Subject_idx].copy()
# subject_eeg.set_eeg_reference(projection=True) # needed for inverse modelling
# # Make forward solution
# fwd = mne.make_forward_solution(subject_eeg.info, trans=trans, src=src,
# bem=bem, eeg=True, mindist=5.0, n_jobs=1)
# # Save forward operator
# fname_fwd = "./Source_fwd/fsaverage_{}-fwd.fif".format(Subject)
# mne.write_forward_solution(fname_fwd, fwd, overwrite=True)
# except:
# print(Subject,"was already dropped")
# with open("./Preprocessing/All_datasets_bad_ch.pkl", "wb") as filehandle:
# pickle.dump(All_dropped_ch, filehandle)
# # %% Load forward operators
# # Re-use for all subjects without dropped channels
# fname_fwd = "./Source_fwd/fsaverage-fwd.fif"
# fwd = mne.read_forward_solution(fname_fwd)
# fwd_list = [fwd]*n_subjects
# # Use specific forward solutions for subjects with dropped channels
# with open("./Preprocessing/All_datasets_bad_ch.pkl", "rb") as file:
# All_dropped_ch = pickle.load(file)
# for i in range(len(All_dropped_ch)):
# Subject = All_dropped_ch[i][0]
# Subject_idx = All_dropped_ch[i][1]
# fname_fwd = "./Source_fwd/fsaverage_{}-fwd.fif".format(Subject)
# fwd = mne.read_forward_solution(fname_fwd)
# fwd_list[Subject_idx] = fwd
# # Check the correct number of channels are present in fwd
# random_point = int(np.random.randint(0,len(All_dropped_ch)-1,1))
# assert len(fwds[All_dropped_ch[random_point][1]].ch_names) == source_epochs[All_dropped_ch[random_point][1]].info["nchan"]
# # %% Make parcellation
# # After mapping to source space, I end up with 20484 vertices
# # but I wanted to map to fewer sources and not many more
# # Thus I need to perform parcellation
# # Get labels for FreeSurfer "aparc" cortical parcellation (example with 74 labels/hemi - Destriuex)
# labels_aparc = mne.read_labels_from_annot("fsaverage", parc="aparc.a2009s",
# subjects_dir=subjects_dir)
# labels_aparc = labels_aparc[:-2] # remove unknowns
# labels_aparc_names = [label.name for label in labels_aparc]
# # Manually adding the 31 ROIs (14-lh/rh + 3 in midline) from Toll et al, 2020
# # Making fuction to take subset of a label
# def label_subset(label, subset, name="ROI_name"):
# label_subset = mne.Label(label.vertices[subset], label.pos[subset,:],
# label.values[subset], label.hemi,
# name = "{}-{}".format(name,label.hemi),
# subject = label.subject, color = None)
# return label_subset
# ### Visual area 1 (V1 and somatosensory cortex BA1-3)
# label_filenames = ["lh.V1.label", "rh.V1.label",
# "lh.BA1.label", "rh.BA1.label",
# "lh.BA2.label", "rh.BA2.label",
# "lh.BA3a.label", "rh.BA3a.label",
# "lh.BA3b.label", "rh.BA3b.label"]
# labels0 = [0]*len(label_filenames)
# for i, filename in enumerate(label_filenames):
# labels0[i] = mne.read_label(os.path.join(fs_dir, "label", filename), subject="fsaverage")
# # Add V1 to final label variable
# labels = labels0[:2]
# # Rename to remove redundant hemi information
# labels[0].name = "V1-{}".format(labels[0].hemi)
# labels[1].name = "V1-{}".format(labels[1].hemi)
# # Assign a color
# labels[0].color = matplotlib.colors.to_rgba("salmon")
# labels[1].color = matplotlib.colors.to_rgba("salmon")
# # Combine Brodmann Areas for SMC. Only use vertices ones to avoid duplication error
# SMC_labels = labels0[2:]
# for hem in range(2):
# SMC_p1 = SMC_labels[hem]
# for i in range(1,len(SMC_labels)//2):
# SMC_p2 = SMC_labels[hem+2*i]
# p2_idx = np.isin(SMC_p2.vertices, SMC_p1.vertices, invert=True)
# SMC_p21 = label_subset(SMC_p2, p2_idx, "SMC")
# SMC_p1 = SMC_p1.__add__(SMC_p21)
# SMC_p1.name = SMC_p21.name
# # Assign a color
# SMC_p1.color = matplotlib.colors.to_rgba("orange")
# labels.append(SMC_p1)
# ### Inferior frontal junction
# # Located at junction between inferior frontal and inferior precentral sulcus
# label_aparc_names0 = ["S_front_inf","S_precentral-inf-part"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# pos1 = temp_labels[0].pos
# pos2 = temp_labels[2].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.001))[0]) # q chosen to correspond to around 10% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[1]) # q chosen to correspond to around 10% of ROI
# IFJ_label_p1 = label_subset(temp_labels[0], l1_idx, "IFJ")
# IFJ_label_p2 = label_subset(temp_labels[2], l2_idx, "IFJ")
# # Combine the 2 parts
# IFJ_label = IFJ_label_p1.__add__(IFJ_label_p2)
# IFJ_label.name = IFJ_label_p1.name
# # Assign a color
# IFJ_label.color = matplotlib.colors.to_rgba("chartreuse")
# # Append to final list
# labels.append(IFJ_label)
# # Do the same for the right hemisphere
# pos1 = temp_labels[1].pos
# pos2 = temp_labels[3].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.00075))[0]) # q chosen to correspond to around 10% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[1]) # q chosen to correspond to around 10% of ROI
# IFJ_label_p1 = label_subset(temp_labels[1], l1_idx, "IFJ")
# IFJ_label_p2 = label_subset(temp_labels[3], l2_idx, "IFJ")
# # Combine the 2 parts
# IFJ_label = IFJ_label_p1.__add__(IFJ_label_p2)
# IFJ_label.name = IFJ_label_p1.name
# # Assign a color
# IFJ_label.color = matplotlib.colors.to_rgba("chartreuse")
# # Append to final list
# labels.append(IFJ_label)
# ### Intraparietal sulcus
# label_aparc_names0 = ["S_intrapariet_and_P_trans"]
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[0])]
# for i in range(len(labels_aparc_idx)):
# labels.append(labels_aparc[labels_aparc_idx[i]].copy())
# labels[-1].name = "IPS-{}".format(labels[-1].hemi)
# ### Frontal eye field as intersection between middle frontal gyrus and precentral gyrus
# label_aparc_names0 = ["G_front_middle","G_precentral"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# # Take 10% of middle frontal gyrus closest to precentral gyrus (most posterior)
# temp_label0 = temp_labels[0]
# G_fm_y = temp_label0.pos[:,1]
# thres_G_fm_y = np.sort(G_fm_y)[len(G_fm_y)//10]
# idx_p1 = np.where(G_fm_y<thres_G_fm_y)[0]
# FEF_label_p1 = label_subset(temp_label0, idx_p1, "FEF")
# # Take 10% closest for precentral gyrus (most anterior)
# temp_label0 = temp_labels[2]
# # I cannot only use y (anterior/posterior) but also need to restrict z-position
# G_pre_cen_z = temp_label0.pos[:,2]
# thres_G_pre_cen_z = 0.04 # visually inspected threshold
# G_pre_cen_y = temp_label0.pos[:,1]
# thres_G_pre_cen_y = np.sort(G_pre_cen_y[G_pre_cen_z>thres_G_pre_cen_z])[-len(G_pre_cen_y)//10] # notice - for anterior
# idx_p2 = np.where((G_pre_cen_y>thres_G_pre_cen_y) & (G_pre_cen_z>thres_G_pre_cen_z))[0]
# FEF_label_p2 = label_subset(temp_label0, idx_p2, "FEF")
# # Combine the 2 parts
# FEF_label = FEF_label_p1.__add__(FEF_label_p2)
# FEF_label.name = FEF_label_p1.name
# # Assign a color
# FEF_label.color = matplotlib.colors.to_rgba("aqua")
# # Append to final list
# labels.append(FEF_label)
# # Do the same for the right hemisphere
# temp_label0 = temp_labels[1]
# G_fm_y = temp_label0.pos[:,1]
# thres_G_fm_y = np.sort(G_fm_y)[len(G_fm_y)//10]
# idx_p1 = np.where(G_fm_y<thres_G_fm_y)[0]
# FEF_label_p1 = label_subset(temp_label0, idx_p1, "FEF")
# temp_label0 = temp_labels[3]
# G_pre_cen_z = temp_label0.pos[:,2]
# thres_G_pre_cen_z = 0.04 # visually inspected threshold
# G_pre_cen_y = temp_label0.pos[:,1]
# thres_G_pre_cen_y = np.sort(G_pre_cen_y[G_pre_cen_z>thres_G_pre_cen_z])[-len(G_pre_cen_y)//10] # notice - for anterior
# idx_p2 = np.where((G_pre_cen_y>thres_G_pre_cen_y) & (G_pre_cen_z>thres_G_pre_cen_z))[0]
# FEF_label_p2 = label_subset(temp_label0, idx_p2, "FEF")
# # Combine the 2 parts
# FEF_label = FEF_label_p1.__add__(FEF_label_p2)
# FEF_label.name = FEF_label_p1.name
# # Assign a color
# FEF_label.color = matplotlib.colors.to_rgba("aqua")
# # Append to final list
# labels.append(FEF_label)
# ### Supplementary eye fields
# # Located at caudal end of frontal gyrus and upper part of paracentral sulcus
# label_aparc_names0 = ["G_and_S_paracentral","G_front_sup"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# pos1 = temp_labels[0].pos
# pos2 = temp_labels[2].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[0]) # q chosen to correspond to around 15% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.005))[1]) # q chosen to correspond to around 10% of ROI
# # Notice that superior frontal gyrus is around 4 times bigger than paracentral
# len(l1_idx)/pos1.shape[0]
# len(l2_idx)/pos2.shape[0]
# # Only use upper part
# z_threshold = 0.06 # visually inspected
# l1_idx = l1_idx[pos1[l1_idx,2] > z_threshold]
# l2_idx = l2_idx[pos2[l2_idx,2] > z_threshold]
# SEF_label_p1 = label_subset(temp_labels[0], l1_idx, "SEF")
# SEF_label_p2 = label_subset(temp_labels[2], l2_idx, "SEF")
# # Combine the 2 parts
# SEF_label = SEF_label_p1.__add__(SEF_label_p2)
# SEF_label.name = SEF_label_p1.name
# # Assign a color
# SEF_label.color = matplotlib.colors.to_rgba("royalblue")
# # Append to final list
# labels.append(SEF_label)
# # Do the same for the right hemisphere
# pos1 = temp_labels[1].pos
# pos2 = temp_labels[3].pos
# distm = scipy.spatial.distance.cdist(pos1,pos2)
# # Find the closest points between the 2 ROIs
# l1_idx = np.unique(np.where(distm<np.quantile(distm, 0.0005))[0]) # q chosen to correspond to around 15% of ROI
# l2_idx = np.unique(np.where(distm<np.quantile(distm, 0.005))[1]) # q chosen to correspond to around 10% of ROI
# # Notice that superior frontal gyrus is around 4 times bigger than paracentral
# len(l1_idx)/pos1.shape[0]
# len(l2_idx)/pos2.shape[0]
# # Only use upper part
# z_threshold = 0.06 # visually inspected
# l1_idx = l1_idx[pos1[l1_idx,2] > z_threshold]
# l2_idx = l2_idx[pos2[l2_idx,2] > z_threshold]
# SEF_label_p1 = label_subset(temp_labels[1], l1_idx, "SEF")
# SEF_label_p2 = label_subset(temp_labels[3], l2_idx, "SEF")
# # Combine the 2 parts
# SEF_label = SEF_label_p1.__add__(SEF_label_p2)
# SEF_label.name = SEF_label_p1.name
# # Assign a color
# SEF_label.color = matplotlib.colors.to_rgba("royalblue")
# # Append to final list
# labels.append(SEF_label)
# ### Posterior cingulate cortex
# label_aparc_names0 = ["G_cingul-Post-dorsal", "G_cingul-Post-ventral"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# labels0 = []
# for hem in range(2):
# PCC_p1 = temp_labels[hem]
# for i in range(1,len(temp_labels)//2):
# PCC_p2 = temp_labels[hem+2*i]
# PCC_p1 = PCC_p1.__add__(PCC_p2)
# PCC_p1.name = "PCC-{}".format(PCC_p1.hemi)
# labels0.append(PCC_p1)
# # Combine the 2 hemisphere in 1 label
# labels.append(labels0[0].__add__(labels0[1]))
# ### Medial prefrontal cortex
# # From their schematic it looks like rostral 1/4 of superior frontal gyrus
# label_aparc_names0 = ["G_front_sup"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels0 = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels0 = temp_labels0.split(4, subjects_dir=subjects_dir)[3]
# temp_labels0.name = "mPFC-{}".format(temp_labels0.hemi)
# temp_labels.append(temp_labels0)
# # Combine the 2 hemisphere in 1 label
# labels.append(temp_labels[0].__add__(temp_labels[1]))
# ### Angular gyrus
# label_aparc_names0 = ["G_pariet_inf-Angular"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels.name = "ANG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Posterior middle frontal gyrus
# label_aparc_names0 = ["G_front_middle"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels = temp_labels.split(2, subjects_dir=subjects_dir)[0]
# temp_labels.name = "PMFG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Inferior parietal lobule
# # From their parcellation figure seems to be rostral angular gyrus and posterior supramarginal gyrus
# label_aparc_names0 = ["G_pariet_inf-Angular","G_pariet_inf-Supramar"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# # Split angular in 2 and get rostral part
# temp_labels[0] = temp_labels[0].split(2, subjects_dir=subjects_dir)[1]
# temp_labels[1] = temp_labels[1].split(2, subjects_dir=subjects_dir)[1]
# # Split supramarginal in 2 and get posterior part
# temp_labels[2] = temp_labels[2].split(2, subjects_dir=subjects_dir)[0]
# temp_labels[3] = temp_labels[3].split(2, subjects_dir=subjects_dir)[0]
# for hem in range(2):
# PCC_p1 = temp_labels[hem]
# for i in range(1,len(temp_labels)//2):
# PCC_p2 = temp_labels[hem+2*i]
# PCC_p1 = PCC_p1.__add__(PCC_p2)
# PCC_p1.name = "IPL-{}".format(PCC_p1.hemi)
# labels.append(PCC_p1)
# ### Orbital gyrus
# # From their figure it seems to correspond to orbital part of inferior frontal gyrus
# label_aparc_names0 = ["G_front_inf-Orbital"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels.name = "ORB-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Middle temporal gyrus
# # From their figure it seems to only be 1/4 of MTG at the 2nd to last caudal part
# label_aparc_names0 = ["G_temporal_middle"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels = temp_labels.split(4, subjects_dir=subjects_dir)[1]
# temp_labels.name = "MTG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Anterior middle frontal gyrus
# label_aparc_names0 = ["G_front_middle"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels = temp_labels.split(2, subjects_dir=subjects_dir)[1]
# temp_labels.name = "AMFG-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# ### Insula
# label_aparc_names0 = ["G_Ins_lg_and_S_cent_ins","G_insular_short"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# for hem in range(2):
# PCC_p1 = temp_labels[hem]
# for i in range(1,len(temp_labels)//2):
# PCC_p2 = temp_labels[hem+2*i]
# PCC_p1 = PCC_p1.__add__(PCC_p2)
# PCC_p1.name = "INS-{}".format(PCC_p1.hemi)
# labels.append(PCC_p1)
# ### (Dorsal) Anterior Cingulate Cortex
# label_aparc_names0 = ["G_and_S_cingul-Ant"]
# temp_labels = []
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels.append(labels_aparc[labels_aparc_idx[i2]].copy())
# temp_labels[-1].name = "ACC-{}".format(temp_labels[-1].hemi)
# # Combine the 2 hemisphere in 1 label
# labels.append(temp_labels[0].__add__(temp_labels[1]))
# ### Supramarginal Gyrus
# label_aparc_names0 = ["G_pariet_inf-Supramar"]
# for i in range(len(label_aparc_names0)):
# labels_aparc_idx = [labels_aparc_names.index(l) for l in labels_aparc_names if l.startswith(label_aparc_names0[i])]
# for i2 in range(len(labels_aparc_idx)):
# temp_labels = labels_aparc[labels_aparc_idx[i2]].copy()
# temp_labels.name = "SUP-{}".format(temp_labels.hemi)
# labels.append(temp_labels)
# print("{} ROIs have been defined".format(len(labels)))
# # # Visualize positions
# # fig = plt.figure()
# # ax = fig.add_subplot(111, projection="3d")
# # for i in range(0,3):
# # temp_pos = temp_labels[i].pos
# # ax.scatter(temp_pos[:,0],temp_pos[:,1],temp_pos[:,2], marker="o", alpha=0.1)
# # # Add to plot
# # ax.scatter(labels[-1].pos[:,0],labels[-1].pos[:,1],labels[-1].pos[:,2], marker="o")
# # # Visualize the labels
# # # temp_l = labels_aparc[labels_aparc_idx[0]]
# # temp_l = labels[-2]
# # l_stc = stc[100].in_label(temp_l)
# # l_stc.vertices
# # l_stc.plot(**surfer_kwargs)
# # Save the annotation file
# with open("custom_aparc2009_Li_et_al_2022.pkl", "wb") as file:
# pickle.dump(labels, file)
# # %% Calculate orthogonalized power envelope connectivity in source space
# # In non-interpolated channels
# # Updated 22/1 - 2021 to use delta = 1/81 and assumption
# # about non-correlated and equal variance noise covariance matrix for channels
# # Load
# with open("custom_aparc2009_Li_et_al_2022.pkl", "rb") as file:
# labels = pickle.load(file)
# label_names = [label.name for label in labels]
# # Define function to estimate PEC
# def PEC_estimation(x, freq_bands, sfreq=200):
# """
# This function takes a source timeseries signal x and performs:
# 1. Bandpass filtering
# 2. Hilbert transform to yield analytical signal
# 3. Compute all to all connectivity by iteratively computing for each pair
# a. Orthogonalization
# b. Computing power envelopes by squaring the signals |x|^2
# c. Log-transform to enhance normality
# d. Pearson's correlation between each pair
# e. Fisher's r-to-z transform to enhance normality
# The code has been optimized by inspiration from MNE-Python's function:
# mne.connectivity.enelope_correlation.
# In MNE-python version < 0.22 there was a bug, but after the fix in 0.22
# the mne function is equivalent to my implementation, although they don't
# use epsilon but gives same result with a RuntimeWarning about log(0)
# IMPORTANT NOTE:
# Filtering introduce artifacts for first and last timepoint
# The values are very low, more than 1e-12 less than the others
# If they are not removed, then they will heavily influence Pearson's
# correlation as it is outlier sensitive
# Inputs:
# x - The signal in source space as np.array with shape (ROIs,Timepoints)
# freq_bands - The frequency bands of interest as a dictionary e.g.
# {"alpha": [8.0, 13.0], "beta": [13.0, 30.0]}
# sfreq - The sampling frequency in Hertz
# Output:
# The pairwise connectivity matrix
# """
# n_roi, n_timepoints = x.shape
# n_freq_bands = len(freq_bands)
# epsilon = 1e-100 # small value to prevent log(0) errors
# # Filter the signal in the different freq bands
# PEC_con0 = np.zeros((n_roi,n_roi,n_freq_bands))
# for fname, frange in freq_bands.items():
# fmin, fmax = [float(interval) for interval in frange]
# signal_filtered = mne.filter.filter_data(x, sfreq, fmin, fmax,
# fir_design="firwin", verbose=0)
# # Filtering on finite signals will yield very low values for first
# # and last timepoint, which can create outliers. E.g. 1e-29 compared to 1e-14
# # Outlier sensitive methods, like Pearson's correlation, is therefore
# # heavily affected and this systematic error is removed by removing
# # the first and last timepoint
# signal_filtered = signal_filtered[:,1:-1]
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# # Hilbert transform
# analytic_signal = scipy.signal.hilbert(signal_filtered)
# # I will use x and y to keep track of orthogonalization
# x0 = analytic_signal
# # Get power envelope
# x0_mag = np.abs(x0)
# # Get scaled conjugate used for orthogonalization estimation
# x0_conj_scaled = x0.conj()
# x0_conj_scaled /= x0_mag
# # Take square power envelope
# PEx = np.square(x0_mag)
# # Take log transform
# lnPEx = np.log(PEx+epsilon)
# # Remove mean for Pearson correlation calculation
# lnPEx_nomean = lnPEx - np.mean(lnPEx, axis=-1, keepdims=True) # normalize each roi timeseries
# # Get std for Pearson correlation calculation
# lnPEx_std = np.std(lnPEx, axis=-1)
# lnPEx_std[lnPEx_std == 0] = 1 # Prevent std = 0 problems
# # Prepare con matrix
# con0 = np.zeros((n_roi,n_roi))
# for roi_r, y0 in enumerate(x0): # for each y0
# # Calculate orthogonalized signal y with respect to x for all x
# # Using y_ort = imag(y*x_conj/|x|)
# # I checked the formula in temp_v3 and it works as intended
# # I want to orthogonalize element wise for each timepoint
# y0_ort = (y0*x0_conj_scaled).imag
# # Here y0_ort.shape = (n_roi, n_timepoints)
# # So y is current roi and the first axis gives each x it is orthogonalized to
# # Take the abs to get power envelope
# y0_ort = np.abs(y0_ort)
# # Prevent log(0) error when calculating y_ort on y
# y0_ort[roi_r] = 1. # this will be 0 zero after mean subtraction
# # Take square power envelope
# PEy = np.square(y0_ort) # squared power envelope
# # Take log transform
# lnPEy = np.log(PEy+epsilon)
# # Remove mean for pearson correlation calculation
# lnPEy_nomean = lnPEy - np.mean(lnPEy, axis=-1, keepdims=True)
# # Get std for Pearson correlation calculation
# lnPEy_std = np.std(lnPEy, axis=-1)
# lnPEy_std[lnPEy_std == 0] = 1.
# # Pearson correlation is expectation of X_nomean * Y_nomean for each time-series divided with standard deviations
# PEC = np.mean(lnPEx_nomean*lnPEy_nomean, axis=-1)
# PEC /= lnPEx_std
# PEC /= lnPEy_std
# con0[roi_r] = PEC
# # The con0 connectivity matrix should be read as correlation between
# # orthogonalized y (row number) and x (column number)
# # It is not symmetrical, as cor(roi2_ort, roi1) is not cor(roi1_ort, roi2)
# # To make it symmetrical the average of the absolute correlation
# # of the 2 possibilities for each pair are taken
# con0 = np.abs(con0)
# con0 = (con0.T+con0)/2.
# # Fisher's z transform - which is equivalent to arctanh
# con0 = np.arctanh(con0)
# # The diagonal is not 0 as I wanted to avoid numerical errors with log(0)
# # and used a small epsilon value. Thus the diagonal is explicitly set to 0
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# # Save to array
# PEC_con0[:,:,list(freq_bands.keys()).index(fname)] = con0
# return PEC_con0
# # Prepare variables
# Freq_Bands = {"delta": [1.25, 4.0],
# "theta": [4.0, 8.0],
# "alpha": [8.0, 13.0],
# "beta": [13.0, 30.0],
# "gamma": [30.0, 49.0]}
# n_freq_bands = len(Freq_Bands)
# n_roi = len(labels)
# # Get current time
# c_time1 = time.localtime()
# c_time1 = time.strftime("%a %d %b %Y %H:%M:%S", c_time1)
# print(c_time1)
# # PEC analysis
# PEC_data_list = [0]*n_subjects
# STCs_list = [0]*n_subjects
# # Using inverse operator as generator interferes with concurrent processes
# # If I run it for multiple subjects I run out of ram
# # Thus concurrent processes are used inside the for loop
# def PEC_analysis(input_args): # iterable epoch number and corresponding ts
# i2, ts = input_args
# # Estimate PEC
# PEC_con0 = PEC_estimation(ts, Freq_Bands, sfreq)
# print("Finished {} out of {} epochs".format(i2+1,n_epochs))
# return i2, PEC_con0, ts
# for i in range(n_subjects):
# n_epochs, n_ch, n_timepoints = source_epochs[i].get_data().shape
# # Use different forward solutions depending on number of channels
# cur_subject_id = Subject_id[i]
# fwd = fwds[i]
# # Using assumption about equal variance and no correlations I make a diagonal matrix
# # Using the default option for 0.2µV std for EEG data
# noise_cov = mne.make_ad_hoc_cov(source_epochs[i].info, None)
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# # Make inverse operator
# # Using default depth parameter = 0.8 and free orientation (loose = 1)
# inverse_operator = mne.minimum_norm.make_inverse_operator(source_epochs[i].info,
# fwd, noise_cov,
# loose = 1, depth = 0.8,
# verbose = 0)
# src_inv = inverse_operator["src"]
# # Compute inverse solution and retrieve time series for each label
# # Preallocate memory
# label_ts = np.full((n_epochs,len(labels),n_timepoints),np.nan)
# # Define regularization
# snr = 9 # Zhang et al, 2020 used delta = 1/81, which is inverse SNR and correspond to lambda2
# # A for loop is used for each label due to memory issues when doing all labels at the same time
# for l in range(len(labels)):
# stc = mne.minimum_norm.apply_inverse_epochs(source_epochs[i],inverse_operator,
# lambda2 = 1/(snr**2),
# label = labels[l],
# pick_ori = "vector",
# return_generator=False,
# method = "MNE", verbose = 0)
# # Use PCA to reduce the 3 orthogonal directions to 1 principal direction with max power
# # There can be ambiguity about the orientation, thus the one that
# # is pointing most "normal", i.e. closest 90 degrees to the skull is used
# stc_pca = [0]*len(stc)
# for ep in range(n_epochs):
# stc_pca[ep], pca_dir = stc[ep].project(directions="pca", src=src_inv)
# # Get mean time series for the whole label
# temp_label_ts = mne.extract_label_time_course(stc_pca, labels[l], src_inv, mode="mean_flip",
# return_generator=False, verbose=0)
# # Save to array
# label_ts[:,l,:] = np.squeeze(np.array(temp_label_ts))
# print("Finished estimating STC for {} out of {} ROIs".format(l+1,len(labels)))
# # Free up memory
# del stc
# # Prepare variables
# sfreq=source_epochs[i].info["sfreq"]
# n_epochs = len(source_epochs[i])
# # Estimate the pairwise PEC for each epoch
# PEC_con_subject = np.zeros((n_epochs,n_roi,n_roi,n_freq_bands))
# stcs0 = np.zeros((n_epochs,n_roi,int(sfreq)*4)) # 4s epochs
# # Make list of arguments to pass into PEC_analysis using the helper func
# args = []
# for i2 in range(n_epochs):
# args.append((i2,label_ts[i2]))
# with concurrent.futures.ProcessPoolExecutor(max_workers=16) as executor:
# for i2, PEC_result, stc_result in executor.map(PEC_analysis, args): # Function and arguments
# PEC_con_subject[i2] = PEC_result
# stcs0[i2] = stc_result
# # Save to list
# PEC_data_list[i] = PEC_con_subject # [subject](epoch,ch,ch,freq)
# STCs_list[i] = stcs0 # [subject][epoch,roi,timepoint]
# # Print progress
# print("Finished {} out of {} subjects".format(i+1,n_subjects))
# # Get current time
# c_time2 = time.localtime()
# c_time2 = time.strftime("%a %d %b %Y %H:%M:%S", c_time2)
# print("Started", c_time1, "\nFinished",c_time2)
# with open(Feature_savepath+"PEC_each_epoch_drop_interpol_ch_fix_snr.pkl", "wb") as file:
# pickle.dump(PEC_data_list, file)
# with open(Feature_savepath+"STCs_each_epoch_drop_interpol_ch_fix_snr.pkl", "wb") as file:
# pickle.dump(STCs_list, file)
# # # # Load
# # with open(Feature_savepath+"PEC_each_epoch_drop_interpol_ch_fix_snr.pkl", "rb") as file:
# # PEC_data_list = pickle.load(file)
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# # with open(Feature_savepath+"STCs_each_epoch_drop_interpol_ch_fix_snr.pkl", "rb") as file:
# # STCs_list = pickle.load(file)
# # Average over eye status
# eye_status = list(source_epochs[0].event_id.keys())
# n_eye_status = len(eye_status)
# pec_data = np.zeros((n_subjects,n_eye_status,n_roi,n_roi,n_freq_bands))
# for i in range(n_subjects):
# # Get indices for eyes open and closed
# EC_index = source_epochs[i].events[:,2] == 1
# EO_index = source_epochs[i].events[:,2] == 2
# # Average over the indices and save to array
# pec_data[i,0] = np.mean(PEC_data_list[i][EC_index], axis=0)
# pec_data[i,1] = np.mean(PEC_data_list[i][EO_index], axis=0)
# # Only use the lower diagonal as the diagonal should be 0 (or very small due to numerical errors)
# # And it is symmetric
# for f in range(n_freq_bands):
# pec_data[i,0,:,:,f] = np.tril(pec_data[i,0,:,:,f],k=-1)
# pec_data[i,1,:,:,f] = np.tril(pec_data[i,1,:,:,f],k=-1)
# # Also save as dataframe format for feature selection
# # Convert to Pandas dataframe
# # The dimensions will each be a column with numbers and the last column will be the actual values
# arr = np.column_stack(list(map(np.ravel, np.meshgrid(*map(np.arange, pec_data.shape), indexing="ij"))) + [pec_data.ravel()])
# pec_data_df = pd.DataFrame(arr, columns = ["Subject_ID", "Eye_status", "chx", "chy", "Freq_band", "Value"])
# # Change from numerical coding to actual values
# eye_status = list(source_epochs[0].event_id.keys())
# freq_bands_name = list(Freq_Bands.keys())
# label_names = [label.name for label in labels]
# index_values = [Subject_id,eye_status,label_names,label_names,freq_bands_name]
# for col in range(len(index_values)):
# col_name = pec_data_df.columns[col]
# for shape in range(pec_data.shape[col]): # notice not dataframe but the array
# pec_data_df.loc[pec_data_df.iloc[:,col] == shape,col_name]\
# = index_values[col][shape]
# # Add group status