How to use the neurokit2.data function in neurokit2

def test_eog_intervalrelated():

    eog = nk.data('eog_200hz')['vEOG']
    eog_signals, info = nk.eog_process(eog, sampling_rate=200)

    columns = ['EOG_Peaks_N', 'EOG_Rate_Mean']

    # Test with signal dataframe
    features = nk.eog_intervalrelated(eog_signals)

    assert all(elem in np.array(features.columns.values, dtype=str) for elem
               in columns)
    assert features.shape[0] == 1  # Number of rows

    # Test with dict
    epochs = nk.epochs_create(eog_signals, events=[5000, 10000, 15000], epochs_start=-0.1, epochs_end=1.9)
    epochs_dict = nk.eog_intervalrelated(epochs)

    assert all(elem in columns for elem

def test_eog_findpeaks():

    eog_signal = nk.data('eog_100hz')
    eog_cleaned = nk.eog_clean(eog_signal, sampling_rate=100)

    # Test with Neurokit
    nk_peaks = nk.eog_findpeaks(eog_cleaned, sampling_rate=100, method="neurokit", threshold=0.33, show=False)
    assert nk_peaks.size == 19

    # Test with MNE
    mne_peaks = nk.eog_findpeaks(eog_cleaned, method="mne")
    assert mne_peaks.size == 44

    # Test with brainstorm
    brainstorm_peaks = nk.eog_findpeaks(eog_cleaned, method="brainstorm")
    assert brainstorm_peaks.size == 28

    blinker_peaks = nk.eog_findpeaks(eog_cleaned, method="blinker", sampling_rate=100)
    assert blinker_peaks.size == 14

def test_rsp_intervalrelated():

    data = nk.data("bio_resting_5min_100hz")
    df, info = nk.rsp_process(data["RSP"], sampling_rate=100)

    # Test with signal dataframe
    features_df = nk.rsp_intervalrelated(df)

    assert features_df.shape[0] == 1  # Number of rows

    # Test with dict
    epochs = nk.epochs_create(df, events=[0, 15000], sampling_rate=100, epochs_end=150)
    features_dict = nk.rsp_intervalrelated(epochs)

    assert features_dict.shape[0] == 2  # Number of rows

def test_data():

    dataset = "bio_eventrelated_100hz"

    data = nk.data(dataset)
    assert len(data.columns) == 4
    assert data.size == 15000 * 4
    assert all(elem in ["ECG", "EDA", "Photosensor", "RSP"] for elem in np.array(data.columns.values, dtype=str))

    dataset2 = "bio_eventrelated_100hz.csv"

    data2 = nk.data(dataset2)
    assert len(data.columns) == len(data2.columns)
    assert data2.size == data.size
    assert all(elem in np.array(data.columns.values, dtype=str) for elem in np.array(data2.columns.values, dtype=str))

data = nk.data("bio_eventrelated_100hz")
    df, info = nk.bio_process(
        ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], keep=data["Photosensor"], sampling_rate=100
    )
    events = nk.events_find(
        data["Photosensor"], threshold_keep="below", event_conditions=["Negative", "Neutral", "Neutral", "Negative"]
    )
    epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-0.1, epochs_end=1.9)
    event_related = nk.bio_analyze(epochs)

    assert len(event_related) == len(epochs)
    labels = [int(i) for i in event_related["Label"]]
    assert labels == list(np.arange(1, len(epochs) + 1))

    # Example with interval-related analysis
    data = nk.data("bio_resting_8min_100hz")
    df, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)
    interval_related = nk.bio_analyze(df)

    assert len(interval_related) == 1

def test_bio_analyze():

    # Example with event-related analysis
    data = nk.data("bio_eventrelated_100hz")
    df, info = nk.bio_process(
        ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], keep=data["Photosensor"], sampling_rate=100
    )
    events = nk.events_find(
        data["Photosensor"], threshold_keep="below", event_conditions=["Negative", "Neutral", "Neutral", "Negative"]
    )
    epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-0.1, epochs_end=1.9)
    event_related = nk.bio_analyze(epochs)

    assert len(event_related) == len(epochs)
    labels = [int(i) for i in event_related["Label"]]
    assert labels == list(np.arange(1, len(epochs) + 1))

    # Example with interval-related analysis
    data = nk.data("bio_resting_8min_100hz")
    df, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)

matplotlib.use('Agg')
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib.cm
import neurokit2 as nk




# =============================================================================
# Quick Example
# =============================================================================


# Download example data
data = nk.data("bio_eventrelated_100hz")

# Preprocess the data (filter, find peaks, etc.)
processed_data, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)

# Compute relevant features
results = nk.bio_analyze(processed_data, sampling_rate=100)

# =============================================================================
# Simulate physiological signals
# =============================================================================

# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)

# 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
ecg_signal = nk.data(dataset="ecg_3000hz")['ECG']

# Extract R-peaks locations
_, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=3000)

# Delineate
signal, waves = nk.ecg_delineate(ecg_signal, rpeaks, sampling_rate=3000, method="dwt", show=True, show_type='all')

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


# =============================================================================
# Complexity
# =============================================================================