EEG_ProcessingTools/NetworkBasedBandDecomConnCalc.py at main · hamidm317/EEG_ProcessingTools · GitHub

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import numpy as np
import pickle
import scipy.stats as sps

import Modules.GRConnPy as GRC
import Modules.GRUniPy as GRU
from Utils import Local
from Utils import Constants
from Utils import SciPlot as SP

########################################################### Define Parameters ###########################################################

group_labels = Constants.LocalDataConstants.Labels['groups']
data_labels = Constants.LocalDataConstants.Labels['data_block']

# ConKers = [kernel for kernel in Constants.DC_Constants.Properties.keys()]
ConKers = ['PLI', 'dPLI']

overlap_ratio = Constants.LocalDataConstants.DefaulValues['overlap_ratio']
win_length = Constants.LocalDataConstants.DefaulValues['window_length']

confile_dir = Constants.LocalDataConstants.directories['n_confile_dir']

# NOIs = [Network for Network in Constants.LocalDataConstants.NetworksOfInterest.keys()][1:] # Networks Of Interest!
NOIs = ['ZeroAxis', 'Frontal', 'OcciTemporal']
# Bands = Constants.LocalDataConstants.names['freq_bands']
Bands = ['Delta', 'Theta', 'Alpha', 'Beta', 'Gamma']

Fs = 500

st = -0.2
ft = 0.6
sp = int((st + 0.4) * Fs)
fp = int((ft + 0.4) * Fs)

NB = 2
TB = Constants.LocalDataConstants.DefaulValues['trial_in_block']

########################################################### Load Available Data ###########################################################

BehavioralData, _ = Local.ExperimentDataLoader()
SOI = Local.AvailableSubjects()

################################################## Divide Subject into DEP and CTRL Groups ###########################################################

Sub_G = [[], []] # first element is CTRL Group Members and the Second one the DEP Group

for i, sub_i in enumerate(SOI[0]):

    if BehavioralData['BDI'][sub_i] < 10:

        Sub_G[0].append([i, sub_i])

    else:

        Sub_G[1].append([i, sub_i])

########################################################## Generate Connectivity Data ###########################################################

event_numbers = [3] # Stim Onset

specs = {

    'orders_matrix': 12,
    'overlap_ratio': overlap_ratio,
    'window_length': win_length,
    'start time': st,
    'end time': ft
}

for event in event_numbers:

    raw_data, data_lengths = Local.ClusteredEEGLoader(event = event)

    Data_ERP = [np.mean(sps.zscore(raw_data[sub_i][SP.DeterminedBlockSampling(Length = int(data_lengths[sub_i]), NumBlock = 2, NumSample_inBlock = 10), :, :], axis = -1), axis = 1)[:, :, sp : fp] for sub_i in range(len(SOI[1]))]
    BandDecompERP = np.array([[GRU.FrequencyBandExt(Data_ERP[sub_i][Trial], Band = 'All') for Trial in range(2)] for sub_i in range(len(SOI[1]))])

    event_name = Constants.LocalDataConstants.names['events'][event]
    print("The Event is " + event_name)

    SaveFileDir = Local.HandleDir(confile_dir + '\\' + event_name)

    for NOI in NOIs:

        SaveFileDir = Local.HandleDir(confile_dir + '\\' + event_name + '\\' + NOI)

        for kernel in ConKers:

            SaveFileDir = Local.HandleDir(confile_dir + '\\' + event_name + '\\' + NOI + '\\' + kernel)

            for Band_i, Band in enumerate(Bands):

                SaveFileDir = Local.HandleDir(confile_dir + '\\' + event_name + '\\' + NOI + '\\' + kernel + '\\' + Band)

                tConDataDict = {}

                for i, sub_i in enumerate(SOI[0]):

                    print("subject " + str(i))

                    # dl = int(data_lengths[i])
                    # data = sps.zscore(raw_data[i][:, :, sp : fp], axis = -1)
                    # Samples = SP.DeterminedBlockSampling(dl, NumBlock = NB, NumSample_inBlock = TB)
                    # divData = np.mean(data[Samples], axis = 1)

                    divData = BandDecompERP[i, :, Band_i, :, :]

                    sub_Data = GRC.DynamicConnectivityMeasure(divData, kernel = kernel, Band = 'All', overlap_ratio = specs['overlap_ratio'], window_length = specs['window_length'], orders_matrix = specs['orders_matrix'], inc_channels = Constants.LocalDataConstants.NetworksOfInterest[NOI])

                    tConDataDict[str(SOI[1][i])] = sub_Data

                SaveFileName, version_number = Local.HandleFileName(SaveFileDir, specs)

                with open(SaveFileDir + "\\" + SaveFileName, 'wb') as f:

                    pickle.dump(tConDataDict, f, protocol=pickle.HIGHEST_PROTOCOL)

                print("Version " + str(version_number) + " of File Saved")

                SaveFileDir = Local.HandleDir(confile_dir + '\\' + event_name)