def test_eog_plot():
eog_signal = nk.data("eog_100hz")
signals, info = nk.eog_process(eog_signal, sampling_rate=100)
# Plot
nk.eog_plot(signals, peaks=info, sampling_rate=100)
fig = plt.gcf()
assert len(fig.axes) == 3
titles = ["Raw and Cleaned Signal", "Blink Rate", "Individual Blinks"]
legends = [["Raw", "Cleaned", "Blinks"], ["Rate", "Mean"], ["Median"]]
ylabels = ["Amplitude (mV)", "Blinks per minute"]
for (ax, title, legend, ylabel) in zip(fig.get_axes(), titles, legends,
ylabels):
assert ax.get_title() == title
subplot = ax.get_legend_handles_labels()
assert subplot[1] == legend
assert ax.get_ylabel() == ylabel
assert fig.get_axes()[1].get_xlabel() == "Time (seconds)"
np.testing.assert_array_equal(fig.axes[0].get_xticks(),
fig.axes[1].get_xticks())
plt.close(fig)
with pytest.raises(ValueError, match=r'NeuroKit error: eog_plot.*'):
nk.eog_plot(None)
def test_eog_process():
eog_signal = nk.data("eog_200hz")["vEOG"]
signals, info = nk.eog_process(eog_signal, sampling_rate=200)
# Extract blinks, test across dataframe and dict
blinks = np.where(signals["EOG_Blinks"] == 1)[0]
assert np.all(blinks == info["EOG_Blinks"])
def test_eog_eventrelated():
eog = nk.data('eog_200hz')['vEOG']
eog_signals, info = nk.eog_process(eog, sampling_rate=200)
epochs = nk.epochs_create(eog_signals,
events=[5000, 10000, 15000],
epochs_start=-0.1,
epochs_end=1.9)
eog_eventrelated = nk.eog_eventrelated(epochs)
# Test rate features
assert np.alltrue(
np.array(eog_eventrelated["EOG_Rate_Min"]) < np.array(
eog_eventrelated["EOG_Rate_Mean"]))
assert np.alltrue(
np.array(eog_eventrelated["EOG_Rate_Mean"]) < np.array(
eog_eventrelated["EOG_Rate_Max"]))
# Test blink presence
assert np.alltrue(
np.array(eog_eventrelated["EOG_Blinks_Presence"]) == np.array(
[1, 0, 0]))
# Test warning on missing columns
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `EOG_Blinks`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["EOG_Blinks"]
nk.eog_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `EOG_Rate`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["EOG_Rate"]
nk.eog_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
def test_eog_eventrelated():
eog = nk.data('eog_200hz')['vEOG']
eog_signals, info = nk.eog_process(eog, sampling_rate=200)
epochs = nk.epochs_create(eog_signals,
events=[5000, 10000, 15000],
epochs_start=-0.1,
epochs_end=1.9)
eog_eventrelated = nk.eog_eventrelated(epochs)
# Test rate features
assert np.alltrue(
np.array(eog_eventrelated["EOG_Rate_Min"]) < np.array(
eog_eventrelated["EOG_Rate_Mean"]))
assert np.alltrue(
np.array(eog_eventrelated["EOG_Rate_Mean"]) < np.array(
eog_eventrelated["EOG_Rate_Max"]))
# Test blink presence
assert np.alltrue(
np.array(eog_eventrelated["EOG_Blinks_Presence"]) == np.array(
[1, 0, 0]))
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 in np.array(epochs_dict.columns.values, dtype=str))
assert epochs_dict.shape[0] == len(epochs) # Number of rows
nk.ppg_plot(signals, sampling_rate=250)
# Save it
plot = nk.ppg_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_ppg.png", dpi=300, h_pad=3)
# =============================================================================
# Electrooculography (EOG)
# =============================================================================
# Import EOG data
eog_signal = nk.data("eog_100hz")
# Process it
signals, info = nk.eog_process(eog_signal, sampling_rate=100)
# Plot
plot = nk.eog_plot(signals, peaks=info, sampling_rate=100)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_eog.png", dpi=300, h_pad=3)
# =============================================================================
# Signal Processing
# =============================================================================
# Generate original signal
original = nk.signal_simulate(duration=6, frequency=1)
# Distort the signal (add noise, linear trend, artifacts etc.)
distorted = nk.signal_distort(original,