How to use the neurokit2.ecg_process function in neurokit2

def test_hrv_frequency():
    # Test frequency domain
    ecg1 = nk.ecg_simulate(duration=60, sampling_rate=2000, heart_rate=70, random_state=42)
    _, peaks1 = nk.ecg_process(ecg1, sampling_rate=2000)
    hrv1 = nk.hrv_frequency(peaks1, sampling_rate=2000)

    ecg2 = nk.signal_resample(ecg1, sampling_rate=2000, desired_sampling_rate=500)
    _, peaks2 = nk.ecg_process(ecg2, sampling_rate=500)
    hrv2 = nk.hrv_frequency(peaks2, sampling_rate=500)

    assert np.allclose(hrv1["HRV_HF"] - hrv2["HRV_HF"], 0, atol=1.5)
    assert np.isnan(hrv1["HRV_LF"][0])
    assert np.isnan(hrv2["HRV_LF"][0])
    assert np.isnan(hrv1["HRV_VLF"][0])
    assert np.isnan(hrv2["HRV_LF"][0])

def test_hrv_frequency():
    # Test frequency domain
    ecg1 = nk.ecg_simulate(duration=60, sampling_rate=2000, heart_rate=70, random_state=42)
    _, peaks1 = nk.ecg_process(ecg1, sampling_rate=2000)
    hrv1 = nk.hrv_frequency(peaks1, sampling_rate=2000)

    ecg2 = nk.signal_resample(ecg1, sampling_rate=2000, desired_sampling_rate=500)
    _, peaks2 = nk.ecg_process(ecg2, sampling_rate=500)
    hrv2 = nk.hrv_frequency(peaks2, sampling_rate=500)

    assert np.allclose(hrv1["HRV_HF"] - hrv2["HRV_HF"], 0, atol=1.5)
    assert np.isnan(hrv1["HRV_LF"][0])
    assert np.isnan(hrv2["HRV_LF"][0])
    assert np.isnan(hrv1["HRV_VLF"][0])
    assert np.isnan(hrv2["HRV_LF"][0])

def test_ecg_intervalrelated():

    data = nk.data("bio_resting_5min_100hz")
    df, info = nk.ecg_process(data["ECG"], sampling_rate=100)

    columns = ['ECG_Rate_Mean', 'HRV_RMSSD', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDSD',
       'HRV_CVNN', 'HRV_CVSD', 'HRV_MedianNN', 'HRV_MadNN', 'HRV_MCVNN',
       'HRV_IQRNN', 'HRV_pNN50', 'HRV_pNN20', 'HRV_TINN', 'HRV_HTI',
       'HRV_ULF', 'HRV_VLF', 'HRV_LF', 'HRV_HF', 'HRV_VHF', 'HRV_LFHF',
       'HRV_LFn', 'HRV_HFn', 'HRV_LnHF', 'HRV_SD1', 'HRV_SD2',
       'HRV_SD1SD2', 'HRV_S', 'HRV_CSI', 'HRV_CVI', 'HRV_CSI_Modified',
       'HRV_PIP', 'HRV_IALS', 'HRV_PSS', 'HRV_PAS', 'HRV_ApEn',
       'HRV_SampEn', 'HRV_GI', 'HRV_SI', 'HRV_AI', 'HRV_PI',
       'HRV_C1d', 'HRV_C1a', 'HRV_SD1d',
       'HRV_SD1a', 'HRV_C2d',
       'HRV_C2a', 'HRV_SD2d', 'HRV_SD2a',
       'HRV_Cd', 'HRV_Ca', 'HRV_SDNNd',
       'HRV_SDNNa']

    # Test with signal dataframe

def test_ecg_eventrelated():

    ecg, info = nk.ecg_process(nk.ecg_simulate(duration=20))
    epochs = nk.epochs_create(ecg, events=[5000, 10000, 15000], epochs_start=-0.1, epochs_end=1.9)
    ecg_eventrelated = nk.ecg_eventrelated(epochs)

    # Test rate features
    assert np.alltrue(np.array(ecg_eventrelated["ECG_Rate_Min"]) < np.array(ecg_eventrelated["ECG_Rate_Mean"]))

    assert np.alltrue(np.array(ecg_eventrelated["ECG_Rate_Mean"]) < np.array(ecg_eventrelated["ECG_Rate_Max"]))

    assert len(ecg_eventrelated["Label"]) == 3

def test_ecg_process():

    sampling_rate = 1000
    noise = 0.05

    ecg = nk.ecg_simulate(sampling_rate=sampling_rate, noise=noise)
    signals, info = nk.ecg_process(ecg, sampling_rate=sampling_rate, method="neurokit")

def test_hrv_time():
    ecg_slow = nk.ecg_simulate(duration=60, sampling_rate=1000, heart_rate=70, random_state=42)
    ecg_fast = nk.ecg_simulate(duration=60, sampling_rate=1000, heart_rate=110, random_state=42)

    _, peaks_slow = nk.ecg_process(ecg_slow, sampling_rate=1000)
    _, peaks_fast = nk.ecg_process(ecg_fast, sampling_rate=1000)

    hrv_slow = nk.hrv_time(peaks_slow, sampling_rate=1000)
    hrv_fast = nk.hrv_time(peaks_fast, sampling_rate=1000)

    assert np.all(hrv_fast["HRV_RMSSD"] < hrv_slow["HRV_RMSSD"])
    assert np.all(hrv_fast["HRV_MeanNN"] < hrv_slow["HRV_MeanNN"])
    assert np.all(hrv_fast["HRV_SDNN"] < hrv_slow["HRV_SDNN"])
    assert np.all(hrv_fast["HRV_CVNN"] < hrv_slow["HRV_CVNN"])
    assert np.all(hrv_fast["HRV_CVSD"] < hrv_slow["HRV_CVSD"])
    assert np.all(hrv_fast["HRV_MedianNN"] < hrv_slow["HRV_MedianNN"])
    assert np.all(hrv_fast["HRV_MadNN"] < hrv_slow["HRV_MadNN"])
    assert np.all(hrv_fast["HRV_MCVNN"] < hrv_slow["HRV_MCVNN"])
    assert np.all(hrv_fast["HRV_pNN50"] == hrv_slow["HRV_pNN50"])
    assert np.all(hrv_fast["HRV_pNN20"] < hrv_slow["HRV_pNN20"])
    assert np.all(hrv_fast["HRV_TINN"] < hrv_slow["HRV_TINN"])

nk.eda_plot(signals, sampling_rate=None)

# Save it
plot = nk.eda_plot(signals, sampling_rate=None)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_eda.png", dpi=300, h_pad=3)

# =============================================================================
# Cardiac activity (ECG) processing
# =============================================================================

# Generate 15 seconds of ECG signal (recorded at 250 samples / second)
ecg = nk.ecg_simulate(duration=15, sampling_rate=250, heart_rate=70, random_state=333)

# Process it
signals, info = nk.ecg_process(ecg, sampling_rate=250)

# Visualise the processing
nk.ecg_plot(signals, sampling_rate=250)

# Save it
plot = nk.ecg_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_ecg.png", dpi=300, h_pad=3)

# =============================================================================
# Respiration (RSP) processing
# =============================================================================

# Generate one minute of RSP signal (recorded at 250 samples / second)
rsp = nk.rsp_simulate(duration=60, sampling_rate=250, respiratory_rate=15)

# Load the NeuroKit package
import neurokit2 as nk

# Simulate 30 seconds of ECG Signal (recorded at 250 samples / second)
ecg_signal = nk.ecg_simulate(duration=30, sampling_rate=250)

# Automatically process the (raw) ECG signal
signals, info = nk.ecg_process(ecg_signal, sampling_rate=250)

# Extract clean ECG and R-peasks location
rpeaks = info["ECG_R_Peaks"]
cleaned_ecg = signals["ECG_Clean"]

# Visualize R-peaks in ECG signal
nk.events_plot(rpeaks, cleaned_ecg)

# Segment the signal around the R-peaks
epochs = nk.epochs_create(cleaned_ecg, events=rpeaks, sampling_rate=250, epochs_start=-0.4, epochs_duration=1)

# Plotting all the heart beats
nk.epochs_plot(epochs)

nk.ecg_plot(signals)