def Compute_electrode_welch(single_electrode):
all_psd = []
global FREQS
for epoch in single_electrode:
all_psd.append(my_welch(epoch, fs = 500)[1][0,:])
freqs = my_welch(epoch, fs = 500)[0]
all_psd = np.array(all_psd)
FREQS = freqs
return all_psd
def Compute_electrode_welch(single_electrode):
all_psd = []
global FREQS
for epoch in single_electrode:
all_psd.append(my_welch(epoch, fs=500)[1][0, :])
freqs = my_welch(epoch, fs=500)[0]
all_psd = np.array(all_psd)
FREQS = freqs
return all_psd
def Compute_electrode_welch(single_electrode):
"""
Take the average from a single electrode from all trials. Changed from independent analysis version where
the average was taken only before plotting in a downstream function Plot_electrode_welch
"""
all_psd = []
global FREQS
for epoch in single_electrode:
freqs, power = my_welch(epoch, fs = 500, nperseg =256, nfft = 256, noverlap = 128)
#power[0,:], changes a shape so then the np.array easilly converts it so that each row is a single trial fft
all_psd.append(power[0,:])
all_psd = np.array(all_psd)
FREQS = freqs
return np.mean(all_psd, axis = 0)
def Compute_electrode_welch(single_electrode):
"""
Take the average from a single electrode from all trials. Changed from independent analysis version where
the average was taken only before plotting in a downstream function Plot_electrode_welch
"""
all_psd = []
global FREQS
for epoch in single_electrode:
freqs, power = my_welch(epoch, fs = 500, nperseg =256, nfft = 256, noverlap = 128)
#why is it [0,:]?, to change the shape?
all_psd.append(power[0,:])
all_psd = np.array(all_psd)
FREQS = freqs[3:36]
return np.mean(all_psd, axis = 0)[3:36]
def Compute_electrode_welch(single_electrode):
"""
Take the average from a single electrode from all trials. Changed from independent analysis version where
the average was taken only before plotting in a downstream function Plot_electrode_welch
"""
all_psd = []
global FREQS
for epoch in single_electrode:
freqs, power = my_welch(epoch,
fs=500,
nperseg=256,
nfft=256,
noverlap=128)
#why is it [0,:]?, to change the shape?
all_psd.append(power[0, :])
all_psd = np.array(all_psd)
FREQS = freqs[3:36]
return np.mean(all_psd, axis=0)[3:36]
