def get_Functional_connectivity_windowed(inputfile, outputfile):
print("\nCalculating Functional Connectivity:")
print("inputfile: {0}".format(inputfile))
print("outputfile: {0}".format(outputfile))
with open(inputfile, 'rb') as f:
data, fs = pickle.load(f) #un-pickles files
data_array = np.array(data)
fs = float(fs)
moving_window = (
1 / 5) #window moved over 1/5th of a second when this value = (1/5)
window_size = 1 #1 = 1s econds
totalSecs = np.floor(np.size(data_array, 0) / fs / moving_window)
totalSecs = int(totalSecs)
alphatheta = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
beta = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
broadband = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
highgamma = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
lowgamma = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
startInd_non_round = 0
for w in range(0, totalSecs):
printProgressBar(w + 1,
totalSecs,
prefix="Progress:",
suffix=". {0}/{1}. {2} sec. Mov Win: {3} sec.".format(
w + 1, totalSecs,
np.round(totalSecs * moving_window, 2),
moving_window))
startInd_non_round = startInd_non_round + fs * moving_window
startInd_round = int(np.round(startInd_non_round))
endInd = int((startInd_round + fs * window_size) -
1) #calculating over 1 second windows
window = data_array[startInd_round:endInd, :]
broad = broadband_conn(window, int(fs), avgref=True)
adj_alphatheta, adj_beta, adj_lowgamma, adj_highgamma = multiband_conn(
window, int(fs), avgref=True)
alphatheta[:, :, w] = adj_alphatheta
beta[:, :, w] = adj_beta
broadband[:, :, w] = broad
highgamma[:, :, w] = adj_highgamma
lowgamma[:, :, w] = adj_lowgamma
print("Saving to: {0}".format(outputfile))
electrode_row_and_column_names = data.columns.values
order_of_matrices_in_pickle_file = pd.DataFrame(
["broadband", "alphatheta", "beta", "lowgamma", "highgamma"],
columns=["Order of matrices in pickle file"])
with open(outputfile, 'wb') as f:
pickle.dump([
broadband, alphatheta, beta, lowgamma, highgamma,
electrode_row_and_column_names, order_of_matrices_in_pickle_file
], f)
#np.savez(outputfile, broadband=broadband, alphatheta=alphatheta, beta=beta, lowgamma=lowgamma, highgamma=highgamma)
print("...done\n\n")
def get_Functional_connectivity(inputfile, outputfile):
print("\nCalculating Functional Connectivity:")
print("inputfile: {0}".format(inputfile))
print("outputfile: {0}".format(outputfile))
with open(inputfile, 'rb') as f:
data, fs = pickle.load(f) #un-pickles files
data_array = np.array(data)
fs = float(fs)
totalSecs = np.floor(np.size(data_array, 0) / fs)
totalSecs = int(totalSecs)
alphatheta = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
beta = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
broadband = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
highgamma = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
lowgamma = np.zeros((np.size(data_array, 1), np.size(data_array,
1), totalSecs))
for t in range(0, totalSecs):
printProgressBar(
t + 1,
totalSecs,
prefix="Progress:",
suffix=
"done. Calculating {0} of {1} functional connectivity matrices".
format(t + 1, totalSecs))
startInd = int(t * fs)
endInd = int(((t + 1) * fs) - 1) #calculating over 1 second windows
window = data_array[startInd:endInd, :]
broad = broadband_conn(window, int(fs), avgref=True)
adj_alphatheta, adj_beta, adj_lowgamma, adj_highgamma = multiband_conn(
window, int(fs), avgref=True)
alphatheta[:, :, t] = adj_alphatheta
beta[:, :, t] = adj_beta
broadband[:, :, t] = broad
highgamma[:, :, t] = adj_highgamma
lowgamma[:, :, t] = adj_lowgamma
print("Saving to: {0}".format(outputfile))
electrode_row_and_column_names = data.columns.values
order_of_matrices_in_pickle_file = pd.DataFrame(
["broadband", "alphatheta", "beta", "lowgamma", "highgamma"],
columns=["Order of matrices in pickle file"])
with open(outputfile, 'wb') as f:
pickle.dump([
broadband, alphatheta, beta, lowgamma, highgamma,
electrode_row_and_column_names, order_of_matrices_in_pickle_file
], f)
#np.savez(outputfile, broadband=broadband, alphatheta=alphatheta, beta=beta, lowgamma=lowgamma, highgamma=highgamma)
print("...done\n\n")
def get_Functional_connectivity(inputfile,outputfile,windowlength):
print("\nCalculating Functional Connectivity:")
print("inputfile: {0}".format(inputfile))
print("outputfile: {0}".format(outputfile))
with open(inputfile, 'rb') as f: data, fs = pickle.load(f)#un-pickles files
data_array = np.array(data)
fs = float(fs)
totalSecs = np.floor(np.size(data_array,0)/fs)
totalSecs = int(totalSecs)
alphatheta = np.zeros((np.size(data_array,1),np.size(data_array,1),totalSecs))
beta = np.zeros((np.size(data_array,1),np.size(data_array,1),totalSecs))
broadband = np.zeros((np.size(data_array,1),np.size(data_array,1),totalSecs))
highgamma = np.zeros((np.size(data_array,1),np.size(data_array,1),totalSecs))
lowgamma = np.zeros((np.size(data_array,1),np.size(data_array,1),totalSecs))
windows = []
for t in range(0,totalSecs,windowlength):
#printProgressBar(t+1, totalSecs, prefix = "Progress:", suffix = "done. Calculating {0} of {1} functional connectivity matrices".format(t+1,totalSecs ) )
startInd = int(t*fs)
endInd = int(((t+windowlength)*fs) - 1) # calculating over windowlength
windows.append((data_array[startInd:endInd,:],int(fs)))
if __name__ == 'functional_connectivity':
print('begin multiprocessing')
try:
mp.set_start_method('spawn')
except RuntimeError:
pass
num_workers = mp.cpu_count()
print('num cores = ' + str(num_workers))
try:
with Pool(processes=num_workers) as pool:
broad = pool.starmap(broadband_conn,windows)
print('broadband calculations complete')
processed_bands = pool.starmap(multiband_conn,windows)
print('other band calculations complete')
except:
print('error encountered, continuing with multiprocessing disabled...')
try:
broad = [broadband_conn(x[0],x[1]) for x in windows]
print('broadband calculations complete')
processed_bands = [multiband_conn(x[0],x[1]) for x in windows]
print('other band calculations complete')
except ValueError:
print('Encountered ValueError: input data most likely contains NaNs. Skipping...\n')
# calculations did not finish
return True
for t in range(0,len(range(0,totalSecs,windowlength))):
alphatheta[:,:,t] = processed_bands[t][0]
beta[:,:,t] = processed_bands[t][1]
highgamma[:,:,t] = processed_bands[t][3]
lowgamma[:,:,t] = processed_bands[t][2]
#printProgressBar(t+1, totalSecs, prefix = "Progress:", suffix = "done. Calculating {0} of {1} functional connectivity matrices".format(t+1,totalSecs ) )
#startInd = int(t*fs)
#endInd = int(((t+1)*fs) - 1) #calculating over 1 second windows
#window = data_array[startInd:endInd,:]
#broad = broadband_conn(window,int(fs),avgref=True)
broadband[:,:,t] = broad[t]
print("Saving to: {0}".format(outputfile))
electrode_row_and_column_names = data.columns.values
order_of_matrices_in_pickle_file = pd.DataFrame(["broadband", "alphatheta", "beta", "lowgamma" , "highgamma" ], columns=["Order of matrices in pickle file"])
with open(outputfile, 'wb') as f: pickle.dump([broadband, alphatheta, beta, lowgamma, highgamma, electrode_row_and_column_names, order_of_matrices_in_pickle_file], f)
#np.savez(outputfile, broadband=broadband, alphatheta=alphatheta, beta=beta, lowgamma=lowgamma, highgamma=highgamma)
print("...done\n\n")
return False