How to use the neurokit2.ecg_peaks function in neurokit2

def test_ecg_delineate():

    sampling_rate = 1000

    # test with simulated signals
    ecg = nk.ecg_simulate(duration=20, sampling_rate=sampling_rate, random_state=42)
    _, rpeaks = nk.ecg_peaks(ecg, sampling_rate=sampling_rate)
    number_rpeaks = len(rpeaks["ECG_R_Peaks"])

    # Method 1: derivative
    _, waves_derivative = nk.ecg_delineate(ecg, rpeaks, sampling_rate=sampling_rate)
    assert len(waves_derivative["ECG_P_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_Q_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_S_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_T_Peaks"]) == number_rpeaks
    assert len(waves_derivative["ECG_P_Onsets"]) == number_rpeaks
    assert len(waves_derivative["ECG_T_Offsets"]) == number_rpeaks

    # Method 2: CWT
    _, waves_cwt = nk.ecg_delineate(ecg, rpeaks, sampling_rate=sampling_rate, method="cwt")
    assert np.allclose(len(waves_cwt["ECG_P_Peaks"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_T_Peaks"]), 22, atol=1)
    assert np.allclose(len(waves_cwt["ECG_R_Onsets"]), 23, atol=1)

def test_ecg_peaks():

    sampling_rate = 1000
    noise = 0.15

    ecg = nk.ecg_simulate(duration=120, sampling_rate=sampling_rate, noise=noise, random_state=42)
    ecg_cleaned_nk = nk.ecg_clean(ecg, sampling_rate=sampling_rate, method="neurokit")

    # Test without request to correct artifacts.
    signals, info = nk.ecg_peaks(ecg_cleaned_nk, correct_artifacts=False, method="neurokit")

    assert signals.shape == (120000, 1)
    assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64), 139, atol=1)

    # Test with request to correct artifacts.
    signals, info = nk.ecg_peaks(ecg_cleaned_nk, correct_artifacts=True, method="neurokit")

    assert signals.shape == (120000, 1)
    assert np.allclose(signals["ECG_R_Peaks"].values.sum(dtype=np.int64), 139, atol=1)

def rollingz(ecg, sampling_rate):
    ecg = nk.standardize(ecg, window=sampling_rate*2)
    signal, info = nk.ecg_peaks(ecg, sampling_rate=sampling_rate, method="neurokit")
    return info["ECG_R_Peaks"]

# Save plot
fig = plt.gcf()
fig.set_size_inches(10, 6)
fig.savefig("README_signalprocessing.png", dpi=300, h_pad=3)


# =============================================================================
# Heart Rate Variability
# =============================================================================

# Download data
data = nk.data("bio_resting_8min_100hz")

# Find peaks
peaks, info = nk.ecg_peaks(data["ECG"], sampling_rate=100)

# Compute HRV indices
hrv = nk.hrv(peaks, sampling_rate=100, show=True)
hrv

# Save plot
fig = plt.gcf()
fig.set_size_inches(10*1.5, 6*1.5, forward=True)
fig.savefig("README_hrv.png", dpi=300, h_pad=3)


# =============================================================================
# ECG Delineation
# =============================================================================

# Download data

import numpy as np
import pandas as pd

import neurokit2 as nk

sampling_rate = 1000

for heartrate in [80]:
    # Simulate signal
    ecg = nk.ecg_simulate(duration=60,
                          sampling_rate=sampling_rate,
                          heartrate=heartrate,
                          noise=0)

    # Segment
    _, rpeaks = nk.ecg_peaks(ecg, sampling_rate=sampling_rate)
#    _, waves = nk.ecg_delineator(ecg, rpeaks=rpeaks["ECG_R_Peaks"])

def tarvainen(ecg, sampling_rate):
    ecg = nk.signal_detrend(ecg, method="tarvainen2002")
    signal, info = nk.ecg_peaks(ecg, sampling_rate=sampling_rate, method="neurokit")
    return info["ECG_R_Peaks"]

def locreg(ecg, sampling_rate):
    ecg = nk.signal_detrend(ecg,
                            method="locreg",
                            window=0.5*sampling_rate,
                            stepsize=0.02*sampling_rate)
    signal, info = nk.ecg_peaks(ecg, sampling_rate=sampling_rate, method="neurokit")
    return info["ECG_R_Peaks"]

def polylength(ecg, sampling_rate):
    length = len(ecg) / sampling_rate
    ecg = nk.signal_detrend(ecg, method="polynomial", order=int(length / 2))
    signal, info = nk.ecg_peaks(ecg, sampling_rate=sampling_rate, method="neurokit")
    return info["ECG_R_Peaks"]