How to use the neurokit2.signal_findpeaks function in neurokit2

def test_signal_findpeaks():

    signal1 = np.cos(np.linspace(start=0, stop=30, num=1000))
    info1 = nk.signal_findpeaks(signal1)

    signal2 = np.concatenate([np.arange(0, 20, 0.1), np.arange(17, 30, 0.1), np.arange(30, 10, -0.1)])
    info2 = nk.signal_findpeaks(signal2)
    assert len(info1["Peaks"]) > len(info2["Peaks"])

def test_rsp_simulate():
    rsp1 = nk.rsp_simulate(duration=20, length=3000, random_state=42)
    assert len(rsp1) == 3000

    rsp2 = nk.rsp_simulate(duration=20, length=3000, respiratory_rate=80, random_state=42)
    #    pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).plot()
    #    pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).hist()
    assert len(nk.signal_findpeaks(rsp1, height_min=0.2)["Peaks"]) < len(
        nk.signal_findpeaks(rsp2, height_min=0.2)["Peaks"]
    )

    rsp3 = nk.rsp_simulate(duration=20, length=3000, method="sinusoidal", random_state=42)
    rsp4 = nk.rsp_simulate(duration=20, length=3000, method="breathmetrics", random_state=42)
    #    pd.DataFrame({"RSP3":rsp3, "RSP4":rsp4}).plot()
    assert len(nk.signal_findpeaks(rsp3, height_min=0.2)["Peaks"]) > len(
        nk.signal_findpeaks(rsp4, height_min=0.2)["Peaks"]
    )

def test_eda_simulate():

    eda1 = nk.eda_simulate(duration=10, length=None, scr_number=1, random_state=333)
    assert len(nk.signal_findpeaks(eda1, height_min=0.6)["Peaks"]) == 1

    eda2 = nk.eda_simulate(duration=10, length=None, scr_number=5, random_state=333)
    assert len(nk.signal_findpeaks(eda2, height_min=0.6)["Peaks"]) == 5
    #   pd.DataFrame({"EDA1": eda1, "EDA2": eda2}).plot()

    assert len(nk.signal_findpeaks(eda2, height_min=0.6)["Peaks"]) > len(
        nk.signal_findpeaks(eda1, height_min=0.6)["Peaks"]
    )

def test_signal_rate():  # since singal_rate wraps signal_period, the latter is tested as well

    # Test with array.
    duration = 10
    sampling_rate = 1000
    signal = nk.signal_simulate(duration=duration, sampling_rate=sampling_rate, frequency=1)
    info = nk.signal_findpeaks(signal)
    rate = nk.signal_rate(peaks=info["Peaks"], sampling_rate=1000, desired_length=len(signal))
    assert rate.shape[0] == duration * sampling_rate

    # Test with dictionary.produced from signal_findpeaks.
    assert info[list(info.keys())[0]].shape == (info["Peaks"].shape[0],)

    # Test with DataFrame.
    duration = 120
    sampling_rate = 1000
    rsp = nk.rsp_simulate(
        duration=duration, sampling_rate=sampling_rate, respiratory_rate=15, method="sinuosoidal", noise=0
    )
    rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=sampling_rate)
    signals, info = nk.rsp_peaks(rsp_cleaned)
    rate = nk.signal_rate(signals, sampling_rate=sampling_rate, desired_length=duration * sampling_rate)
    assert rate.shape == (signals.shape[0],)

def test_emg_simulate():

    emg1 = nk.emg_simulate(duration=20, length=5000, burst_number=1)
    assert len(emg1) == 5000

    emg2 = nk.emg_simulate(duration=20, length=5000, burst_number=15)
    assert scipy.stats.median_absolute_deviation(emg1) < scipy.stats.median_absolute_deviation(emg2)

    emg3 = nk.emg_simulate(duration=20, length=5000, burst_number=1, burst_duration=2.0)
    #    pd.DataFrame({"EMG1":emg1, "EMG3": emg3}).plot()
    assert len(nk.signal_findpeaks(emg3, height_min=1.0)["Peaks"]) > len(
        nk.signal_findpeaks(emg1, height_min=1.0)["Peaks"]
    )

def test_rsp_simulate():
    rsp1 = nk.rsp_simulate(duration=20, length=3000, random_state=42)
    assert len(rsp1) == 3000

    rsp2 = nk.rsp_simulate(duration=20, length=3000, respiratory_rate=80, random_state=42)
    #    pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).plot()
    #    pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).hist()
    assert len(nk.signal_findpeaks(rsp1, height_min=0.2)["Peaks"]) < len(
        nk.signal_findpeaks(rsp2, height_min=0.2)["Peaks"]
    )

    rsp3 = nk.rsp_simulate(duration=20, length=3000, method="sinusoidal", random_state=42)
    rsp4 = nk.rsp_simulate(duration=20, length=3000, method="breathmetrics", random_state=42)
    #    pd.DataFrame({"RSP3":rsp3, "RSP4":rsp4}).plot()
    assert len(nk.signal_findpeaks(rsp3, height_min=0.2)["Peaks"]) > len(
        nk.signal_findpeaks(rsp4, height_min=0.2)["Peaks"]
    )

def test_rsp_simulate():
    rsp1 = nk.rsp_simulate(duration=20, length=3000, random_state=42)
    assert len(rsp1) == 3000

    rsp2 = nk.rsp_simulate(duration=20, length=3000, respiratory_rate=80, random_state=42)
    #    pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).plot()
    #    pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).hist()
    assert len(nk.signal_findpeaks(rsp1, height_min=0.2)["Peaks"]) < len(
        nk.signal_findpeaks(rsp2, height_min=0.2)["Peaks"]
    )

    rsp3 = nk.rsp_simulate(duration=20, length=3000, method="sinusoidal", random_state=42)
    rsp4 = nk.rsp_simulate(duration=20, length=3000, method="breathmetrics", random_state=42)
    #    pd.DataFrame({"RSP3":rsp3, "RSP4":rsp4}).plot()
    assert len(nk.signal_findpeaks(rsp3, height_min=0.2)["Peaks"]) > len(
        nk.signal_findpeaks(rsp4, height_min=0.2)["Peaks"]
    )

def test_eda_simulate():

    eda1 = nk.eda_simulate(duration=10, length=None, scr_number=1, random_state=333)
    assert len(nk.signal_findpeaks(eda1, height_min=0.6)["Peaks"]) == 1

    eda2 = nk.eda_simulate(duration=10, length=None, scr_number=5, random_state=333)
    assert len(nk.signal_findpeaks(eda2, height_min=0.6)["Peaks"]) == 5
    #   pd.DataFrame({"EDA1": eda1, "EDA2": eda2}).plot()

    assert len(nk.signal_findpeaks(eda2, height_min=0.6)["Peaks"]) > len(
        nk.signal_findpeaks(eda1, height_min=0.6)["Peaks"]
    )

data_rmse = pd.DataFrame(columns=["RMSE", "Index", "Participant", "Task", "Function"])
for i in range(4):
    data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv")

    for j, participant in enumerate(np.unique(data["Participant"])[1:3]):
        segment = data[data["Participant"] == participant]
        signal = segment["vEOG"]

        cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit')
        blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"]

        events = nk.epochs_create(cleaned, blinks, sampling_rate=200, epochs_start=-0.4, epochs_end=0.6)
        events = nk.epochs_to_array(events)  # Convert to 2D array

        # Rescale
        for i in range(events.shape[1]):
            events[:, i] = nk.rescale(events[:, i], to=[0, 1])  # Reshape to 0-1 scale

        # RMSE - Gamma
        rmse = pd.DataFrame({"RMSE": [nk.fit_rmse(events[:, i], template_gamma) for i in range(events.shape[1])],
                             "Index": range(events.shape[1]),
                             "Participant": [participant]*events.shape[1],
                             "Task": [data["Task"][0]]*events.shape[1],
                             "Function": ["Gamma"] * events.shape[1]})
        rmse["Index"] = rmse["Participant"] + "_" + rmse["Task"] + "_" + rmse["Index"].astype(str)
        data_rmse = pd.concat([data_rmse, rmse], axis=0)