def __init__(self,
signal,
freq,
file=False,
filename=None,
show_plot=False):
self._y = signal
self._freq = freq
self._derivative = self._get_derivative(signal)
if file:
self.data = nk.eda_process(signal, sampling_rate=freq)
if show_plot:
nk.plot_events_in_signal(nk.z_score(self.data["df"]),
self.data["EDA"]["SCR_Peaks_Indexes"])
print(self.data["EDA"]["SCR_Peaks_Indexes"])
plt.show()
self.save_to_file(filename, self.data)
else:
self.data = self.read_from_file(filename)
def test_create_epochs(df):
df = pd.DataFrame({
"Trigger":
pd.Series(
scipy.signal.square(1 * np.pi * 5 *
np.linspace(0, 1, 2000, endpoint=False))),
"Signal":
pd.Series(np.sin(20 * np.pi * np.linspace(0, 1, 2000, endpoint=False)))
* np.random.normal(0, 1, 2000),
"Signal2":
pd.Series(np.sin(60 * np.pi * np.linspace(0, 1, 2000, endpoint=False)))
* np.random.normal(0, 2, 2000)
})
df.plot()
events = nk.find_events(df["Trigger"], cut="lower")
assert len(events) == 2
fig = nk.plot_events_in_signal(df, events["onsets"])
epochs = nk.create_epochs(df, events["onsets"], duration=0.1)
assert len(epochs) == 2
import neurokit as nk
df = nk.read_acqknowledge("abnormal_ECG.acq")
events = nk.find_events(df["Photosensor"], cut="lower")
df = nk.create_epochs(df, events["onsets"], duration=events["durations"])[0]
#df["Photosensor"].plot()
df = nk.ecg_process(df['ECG, X, RSPEC-R'])
ecg = df["ECG"]
df = df["df"]
#rpeaks = nk.ecg_find_peaks(df['ECG_Filtered'])
nk.plot_events_in_signal(df['ECG_Filtered'], ecg["R_Peaks"])
#df['ECG, X, RSPEC-R'].iloc[104000:140000].plot()
threshold = 0.5*np.std(decg_power)
threshold = np.median(thresholds)
max_power = np.median(max_powers)
decg_power[decg_power < threshold] = 0
decg_power = decg_power/max_power
decg_power[decg_power > 1.0] = 1.0
square_decg_power = decg_power**2
# shannon_energy = -square_decg_power*np.log(square_decg_power) # This errors
# shannon_energy[np.where(np.isfinite(shannon_energy) == False)] = 0.0
shannon_energy = -square_decg_power*np.log(square_decg_power.clip(min=1e-6))
shannon_energy[np.where(shannon_energy <= 0)] = 0.0
mean_window_len = int(sampling_rate*0.125+1)
lp_energy = np.convolve(shannon_energy, [1.0/mean_window_len]*mean_window_len, mode='same')
#lp_energy = scipy.signal.filtfilt(*lowpass2, x=shannon_energy)
lp_energy = scipy.ndimage.gaussian_filter1d(lp_energy, sampling_rate/8.0)
lp_energy_diff = np.diff(lp_energy)
rpeaks = (lp_energy_diff[:-1] > 0) & (lp_energy_diff[1:] < 0)
rpeaks = np.flatnonzero(rpeaks)
rpeaks -= 1
return(rpeaks)
rpeaks = segmenter_pekkanen(df["ECG_Raw"], 1000)
nk.plot_events_in_signal(df["ECG_Raw"], rpeaks)