def test_events_plot():
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
events = nk.events_find(signal)
data = nk.events_plot(events, signal, show=False)
assert len(data["Event_Onset"]) == 1000
# Different events
events1 = events["onset"]
events2 = np.linspace(0, len(signal), 8)
nk.events_plot([events1, events2], signal)
fig = plt.gcf()
for ax in fig.get_axes():
handles, labels = ax.get_legend_handles_labels()
assert len(handles) == len(events1) + len(events2) + 1
assert len(labels) == len(handles)
plt.close(fig)
# Different conditions
events = nk.events_find(signal, event_conditions=["A", "B", "A", "B"])
nk.events_plot(events, signal)
fig = plt.gcf()
for ax in fig.get_axes():
handles, labels = ax.get_legend_handles_labels()
assert len(handles) == len(events) + 1
assert len(labels) == len(handles)
plt.close(fig)
def helper_plot(attribute, ecg_characteristics, test_data):
peaks = [ecg_characteristics[attribute], test_data[attribute]]
print("0: computed\n1: data")
ecg = test_data["ecg"]
nk.events_plot(peaks, ecg)
plt.title(attribute)
plt.show()
def Get_R_Peaks(ecg_signal):
# Extract R-peak locations from ecg signal file
print(
"======== Retrieving R Peaks of QRS complex ========")
_, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=3000)
#Print to console
for key in rpeaks:
print(key, ' : ', rpeaks[key])
print()
# Visualize R-peaks in ECG signal **for Jupyter Notebook
plot_Rpeak_Signal = nk.events_plot(rpeaks['ECG_R_Peaks'], ecg_signal)
plot_Rpeak_Signal.savefig("rpeaks_signal", dpi=300)
# Visual R-peak locations for first 5 5 R-peaks **for Jupyter Notebook
plot_Rpeak_Head = nk.events_plot(rpeaks['ECG_R_Peaks'][:5],
ecg_signal[:20000])
plot_Rpeak_Head.savefig("rpeaks_head", dpi=300)
return rpeaks
def Get_TPQS_Peaks(ecg_signal, rpeaks):
# Delineate ECG signal to get TPQS peaks
print(
"======= Using DWT to retrieve TPQS Peaks and On/Offsets ========")
dwt_sig, waves_dwt_peak = nk.ecg_delineate(ecg_signal,
rpeaks,
sampling_rate=3000,
method="dwt",
show=True,
show_type="all")
#Print to console
for key in waves_dwt_peak:
print(key, ' : ', waves_dwt_peak[key])
def_sig, waves_peak = nk.ecg_delineate(ecg_signal,
rpeaks,
sampling_rate=3000,
show_type="peaks")
# Visualize the T-peaks, P-peaks, Q-peaks and S-peaks **for JupyterNotebook
plot_TPQS_Signal = nk.events_plot([
waves_peak['ECG_T_Peaks'], waves_peak['ECG_P_Peaks'],
waves_peak['ECG_Q_Peaks'], waves_peak['ECG_S_Peaks']
], ecg_signal)
plot_TPQS_Signal.savefig("TPQS_signal", dpi=300)
# Zooming into the first 3 R-peaks, with focus on
# T_peaks, P-peaks, Q-peaks and S-peaks **for JupyterNotebook
plot_TPQS_Head = nk.events_plot([
waves_peak['ECG_T_Peaks'][:3], waves_peak['ECG_P_Peaks'][:3],
waves_peak['ECG_Q_Peaks'][:3], waves_peak['ECG_S_Peaks'][:3]
], ecg_signal[:12500])
plot_TPQS_Head.savefig("TPQS_head", dpi=300)
# Delineate the ECG signal and visualizing all peaks of ECG complexes
_, waves_peak = nk.ecg_delineate(ecg_signal,
rpeaks,
sampling_rate=3000,
show=True,
show_type='peaks')
# Delineate the ECG signal and visualizing all P-peaks boundaries
signal_peak, waves_peak = nk.ecg_delineate(ecg_signal,
rpeaks,
sampling_rate=3000,
show=True,
show_type='bounds_P')
# Delineate the ECG signal and visualizing all T-peaks boundaries
signal_peaj, waves_peak = nk.ecg_delineate(ecg_signal,
rpeaks,
sampling_rate=3000,
show=True,
show_type='bounds_T')
#Print to Console
for key in waves_peak:
if (key == 'ECG_Q_Peaks' or key == 'ECG_S_Peaks'):
print(key, ' : ', waves_peak[key])
print()
return waves_dwt_peak, waves_peak
def test_events_plot():
signal = np.cos(np.linspace(start=0, stop=20, num=1000))
events = nk.events_find(signal)
data = nk.events_plot(events, signal, show=False)
assert len(data['Event_Onset']) == 1000