def generate(time: int, rate: int):
ecg1 = nk.ecg_simulate(duration=time, sampling_rate= rate, method = "ecgsyn") # duration - ile sekund #sampling rate - ile punktów na sekunde
ecg2 = nk.ecg_simulate(duration=time, sampling_rate= rate, method = "ecgsyn")
ecg3 = nk.ecg_simulate(duration=time, sampling_rate= rate, method="ecgsyn")
ecg4 = nk.ecg_simulate(duration=time, sampling_rate= rate, method="daubechies")
emg1 = nk.emg_simulate(duration=time, sampling_rate= rate, random_state=1, burst_number=3)
emg2 = nk.emg_simulate(duration=time, sampling_rate= rate, random_state=2, burst_number=3)
emg3 = nk.emg_simulate(duration=time, sampling_rate= rate, random_state=3, burst_number=3)
emg4 = nk.emg_simulate(duration=time, sampling_rate= rate, random_state=4, burst_number=3)
# sa jeszcze analogicznie fajne wykresy dla ppg, rsp i eda
nk.signal_plot(ecg1, sampling_rate=rate)
nk.signal_plot(ecg2, sampling_rate=rate)
data = pd.DataFrame({'ecg1': ecg1,
'ecg2': ecg2,
'emg1': emg1,
})
return data
def test_signal_plot():
# Test with array
signal = nk.signal_simulate(duration=10, sampling_rate=1000)
nk.signal_plot(signal, sampling_rate=1000)
fig = plt.gcf()
for ax in fig.get_axes():
handles, labels = ax.get_legend_handles_labels()
assert labels == ['Signal']
assert len(labels) == len(handles) == len([signal])
assert ax.get_xlabel() == 'Time (s)'
plt.close(fig)
# Test with dataframe
data = pd.DataFrame({
"Signal2":
np.cos(np.linspace(start=0, stop=20, num=1000)),
"Signal3":
np.sin(np.linspace(start=0, stop=20, num=1000)),
"Signal4":
nk.signal_binarize(np.cos(np.linspace(start=0, stop=40, num=1000)))
})
nk.signal_plot(data, sampling_rate=None)
fig = plt.gcf()
for ax in fig.get_axes():
handles, labels = ax.get_legend_handles_labels()
assert labels == list(data.columns.values)
assert len(labels) == len(handles) == len(data.columns)
assert ax.get_xlabel() == 'Samples'
plt.close(fig)
# Test with list
signal = nk.signal_binarize(nk.signal_simulate(duration=10))
phase = nk.signal_phase(signal, method="percents")
nk.signal_plot([signal, phase])
fig = plt.gcf()
for ax in fig.get_axes():
handles, labels = ax.get_legend_handles_labels()
assert labels == ['Signal1', 'Signal2']
assert len(labels) == len(handles) == len([signal, phase])
assert ax.get_xlabel() == 'Samples'
plt.close(fig)
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)
emg = nk.emg_simulate(duration=10, burst_number=2)
# Visualise biosignals
data = pd.DataFrame({
"ECG": ecg,
"PPG": ppg,
"RSP": rsp,
"EDA": eda,
"EMG": emg
})
nk.signal_plot(data, subplots=True)
# Save it
data = pd.DataFrame({
"ECG":
nk.ecg_simulate(duration=10, heart_rate=70, noise=0),
"PPG":
nk.ppg_simulate(duration=10, heart_rate=70, powerline_amplitude=0),
"RSP":
nk.rsp_simulate(duration=10, respiratory_rate=15, noise=0),
"EDA":
nk.eda_simulate(duration=10, scr_number=3, noise=0),
"EMG":
nk.emg_simulate(duration=10, burst_number=2, noise=0)
})
plot = data.plot(subplots=True,
samples = 8 * (normal1 + normal2) #facteur 8 seulement pour scaler entre 0 et 3.3
# La valeur en Voltage d'une personne qui a une systolique normal (120), est autour de 2.8 , pour une systolique eleve(140), on est autour de 3.2 , une sytolique basse (100) est autour de 2.3
#la valeur en Voltage d'une personne qui a une diastolique normal (80) est autour de 1.8 , pour une diastolique eleve(90), on est atour de 2.10, une diastolique basse (60) est autour de 1.4
#prolongement du signal de pression selon le temps de signal voulu
for i in range(0,int(t)):
for y in samples:
ech_array.append(y)
elif "re" in signaltype:
t_sin = np.arange(t*f)
ech_array = 1.65 * np.sin(respiration * (2 * np.pi * t_sin) / f) + 1.65 # le plus 1 est pour ne pas avoir de valeur negative
elif "pe" in signaltype:
ech_array = genPespiration(pespiration,step,f,t)
else :
ValueError("The f**k is that signal type ?? give me a valid one , you gave me {}".format(signaltype))
# # Visualize
ecg = pd.DataFrame({"{}".format(signaltype): ech_array})
nk.signal_plot(ecg)
#
plt.show()
generateVHDLsamples(ech_array,signaltype)
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)
emg = nk.emg_simulate(duration=10, burst_number=2)
# Visualise biosignals
data = pd.DataFrame({
"ECG": ecg,
"PPG": ppg,
"RSP": rsp,
"EDA": eda,
"EMG": emg
})
nk.signal_plot(data, subplots=True)
# Save it
data = pd.DataFrame({
"ECG":
nk.ecg_simulate(duration=10, heart_rate=70, noise=0),
"PPG":
nk.ppg_simulate(duration=10, heart_rate=70, powerline_amplitude=0),
"RSP":
nk.rsp_simulate(duration=10, respiratory_rate=15, noise=0),
"EDA":
nk.eda_simulate(duration=10, scr_number=3, noise=0),
"EMG":
nk.emg_simulate(duration=10, burst_number=2, noise=0)
})
plot = data.plot(subplots=True,
# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)
emg = nk.emg_simulate(duration=10, burst_number=2)
# Visualise biosignals
data = pd.DataFrame({
"ECG": ecg,
"PPG": ppg,
"RSP": rsp,
"EDA": eda,
"EMG": emg
})
nk.signal_plot(data, subplots=True)
# Save it
data = pd.DataFrame({
"ECG":
nk.ecg_simulate(duration=10, heart_rate=70, noise=0),
"PPG":
nk.ppg_simulate(duration=10, heart_rate=70, powerline_amplitude=0),
"RSP":
nk.rsp_simulate(duration=10, respiratory_rate=15, noise=0),
"EDA":
nk.eda_simulate(duration=10, scr_number=3, noise=0),
"EMG":
nk.emg_simulate(duration=10, burst_number=2, noise=0)
})
plot = data.plot(subplots=True,