How to use the neurokit2.eda_simulate function in neurokit2

def test_eda_eventrelated():

    eda = nk.eda_simulate(duration=15, scr_number=3)
    eda_signals, info = nk.eda_process(eda, sampling_rate=1000)
    epochs = nk.epochs_create(
        eda_signals, events=[5000, 10000, 15000], sampling_rate=1000, epochs_start=-0.1, epochs_end=1.9
    )
    eda_eventrelated = nk.eda_eventrelated(epochs)

    no_activation = np.where(eda_eventrelated["EDA_SCR"] == 0)[0][0]
    assert int(pd.DataFrame(eda_eventrelated.values[no_activation]).isna().sum()) == 4

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

def test_eda_peaks():

    sampling_rate = 1000
    eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate, scr_number=6, noise=0, drift=0.01, random_state=42)
    eda_phasic = nk.eda_phasic(nk.standardize(eda), method="highpass")["EDA_Phasic"].values

    signals, info = nk.eda_peaks(eda_phasic, method="gamboa2008")
    onsets, peaks, amplitudes = biosppy.eda.basic_scr(eda_phasic, sampling_rate=1000)
    assert np.allclose((info["SCR_Peaks"] - peaks).mean(), 0, atol=1e-5)

    signals, info = nk.eda_peaks(eda_phasic, method="kim2004")
    onsets, peaks, amplitudes = biosppy.eda.kbk_scr(eda_phasic, sampling_rate=1000)
    assert np.allclose((info["SCR_Peaks"] - peaks).mean(), 0, atol=1)

def test_bio_process():

    sampling_rate = 1000

    # Create data
    ecg = nk.ecg_simulate(duration=30, sampling_rate=sampling_rate)
    rsp = nk.rsp_simulate(duration=30, sampling_rate=sampling_rate)
    eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate, scr_number=3)
    emg = nk.emg_simulate(duration=30, sampling_rate=sampling_rate, burst_number=3)

    bio_df, bio_info = nk.bio_process(ecg=ecg, rsp=rsp, eda=eda, emg=emg, sampling_rate=sampling_rate)

    # SCR components
    scr = [val for key, val in bio_info.items() if "SCR" in key]
    assert all(len(elem) == len(scr[0]) for elem in scr)
    assert all(bio_info["SCR_Onsets"] < bio_info["SCR_Peaks"])
    assert all(bio_info["SCR_Peaks"] < bio_info["SCR_Recovery"])

    # RSP
    assert all(bio_info["RSP_Peaks"] > bio_info["RSP_Troughs"])
    assert len(bio_info["RSP_Peaks"]) == len(bio_info["RSP_Troughs"])

    # EMG
    assert all(bio_info["EMG_Offsets"] > bio_info["EMG_Onsets"])

def test_eda_process():

    eda = nk.eda_simulate(duration=30, scr_number=5, drift=0.1, noise=0, sampling_rate=250)
    signals, info = nk.eda_process(eda, sampling_rate=250)

    assert signals.shape == (7500, 11)
    assert (
        np.array(
            [
                "EDA_Raw",
                "EDA_Clean",
                "EDA_Tonic",
                "EDA_Phasic",
                "SCR_Onsets",
                "SCR_Peaks",
                "SCR_Height",
                "SCR_Amplitude",
                "SCR_RiseTime",
                "SCR_Recovery",

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_eda_clean():

    sampling_rate = 1000
    eda = nk.eda_simulate(
        duration=30, sampling_rate=sampling_rate, scr_number=6, noise=0.01, drift=0.01, random_state=42
    )

    clean = nk.eda_clean(eda, sampling_rate=sampling_rate)
    assert len(clean) == len(eda)

    # Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/master/biosppy/signals/eda.py)

    eda_biosppy = nk.eda_clean(eda, sampling_rate=sampling_rate, method="biosppy")
    original, _, _ = biosppy.tools.filter_signal(
        signal=eda, ftype="butter", band="lowpass", order=4, frequency=5, sampling_rate=sampling_rate
    )

    original, _ = biosppy.tools.smoother(signal=original, kernel="boxzen", size=int(0.75 * sampling_rate), mirror=True)

    #    pd.DataFrame({"our":eda_biosppy, "biosppy":original}).plot()

def test_eda_plot():

    sampling_rate = 1000
    eda = nk.eda_simulate(duration=30, sampling_rate=sampling_rate, scr_number=6, noise=0, drift=0.01, random_state=42)
    eda_summary, _ = nk.eda_process(eda, sampling_rate=sampling_rate)

    # Plot data over samples.
    nk.eda_plot(eda_summary)
    # This will identify the latest figure.
    fig = plt.gcf()
    assert len(fig.axes) == 3
    titles = ["Raw and Cleaned Signal", "Skin Conductance Response (SCR)", "Skin Conductance Level (SCL)"]
    for (ax, title) in zip(fig.get_axes(), titles):
        assert ax.get_title() == title
    assert fig.get_axes()[2].get_xlabel() == "Samples"
    np.testing.assert_array_equal(fig.axes[0].get_xticks(), fig.axes[1].get_xticks(), fig.axes[2].get_xticks())
    plt.close(fig)

    # Plot data over seconds.
    nk.eda_plot(eda_summary, sampling_rate=sampling_rate)

"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, layout=(5, 1), color=['#f44336', "#E91E63", "#2196F3", "#9C27B0", "#FF9800"])
fig = plt.gcf()
fig.set_size_inches(10, 6, forward=True)
[ax.legend(loc=1) for ax in plt.gcf().axes]
fig.savefig("README_simulation.png", dpi=300, h_pad=3)

# =============================================================================
# Electrodermal Activity (EDA) processing
# =============================================================================

# Generate 10 seconds of EDA signal (recorded at 250 samples / second) with 2 SCR peaks
eda = nk.eda_simulate(duration=10, sampling_rate=250, scr_number=2, drift=0.1)

# Process it
signals, info = nk.eda_process(eda, sampling_rate=250)

# Visualise the processing
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
# =============================================================================

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, layout=(5, 1), color=['#f44336', "#E91E63", "#2196F3", "#9C27B0", "#FF9800"])
fig = plt.gcf()
fig.set_size_inches(10, 6, forward=True)
[ax.legend(loc=1) for ax in plt.gcf().axes]
fig.savefig("README_simulation.png", dpi=300, h_pad=3)

# =============================================================================
# Electrodermal Activity (EDA) processing
# =============================================================================

# Generate 10 seconds of EDA signal (recorded at 250 samples / second) with 2 SCR peaks
eda = nk.eda_simulate(duration=10, sampling_rate=250, scr_number=2, drift=0.1)

# Process it
signals, info = nk.eda_process(eda, sampling_rate=250)