def __init__(self, sf, npts, f=(2, 200, 10, 5), pha=[8, 13], time=None,
baseline=None, norm=None, **powArgs):
# Define objects:
self._normBck = norm
self._baseline = baseline
self._powObj = power(
sf, npts, f=f, baseline=baseline, norm=0, time=time, **powArgs)
self._phaObj = phase(sf, npts, f=pha)
self._sigObj = sigfilt(sf, npts, f=pha)
def compute_PSD(epochs, sf, epochs_length, f=None):
if f == None:
f = [[4, 8], [8, 12], [12, 20], [20, 30], [30, 60], [60, 90],
[90, 120]]
# Choose MEG channels
data = epochs.get_data(
) # On sort les data de l'objet MNE pour les avoir dans une matrice (un numpy array pour être précis)
data = data.swapaxes(0, 1).swapaxes(
1, 2
) # On réarange l'ordre des dimensions pour que ça correspond à ce qui est requis par Brainpipe
objet_PSD = feature.power(
sf=int(sf),
npts=int(sf * epochs_length),
width=int(sf),
step=int(sf / 4),
f=f,
method='hilbert1'
) # La fonction Brainpipe pour créer un objet de calcul des PSD
psds = objet_PSD.get(data)[0] # Ici on calcule la PSD !
return psds
# Fetch time series data from any file whatsoever
# Must be at least 30 seconds at 250Hz
raw = read_raw_fif('/home/hyruuk/GitHub/eegsynth/datafiles/SA04_01_preprocessed.fif.gz', preload=True)
raw_rs = raw.copy().resample(250)
sf = raw_rs.info['sfreq']
data = np.asarray(raw_rs.get_data()[1:3,7500:15000])
# 2.
# Compute hilbert using brainpipe
f = [ [2, 4], [4, 8], [8, 12], [12, 30], [30, 60] ]
npts = data.shape[1]
print(data.shape)
power_obj = power(sf, npts, f=f, baseline=None, norm=None, method='hilbert', window=None, width=None, step=None, split=None, time=None)
start = time.time()
power_vals = power_obj.get(data.T)
stop = time.time()
print(stop-start)
print(power_vals[0].shape)
print(power_vals[1])
print('Done')
powow = power_vals[0].squeeze()
dtp = powow[2,:,0]
t = np.arange(0,len(dtp),1)
plt.plot(t, dtp)
plt.show()