def test_emg_eventrelated():
emg = nk.emg_simulate(duration=20, sampling_rate=1000, burst_number=3)
emg_signals, info = nk.emg_process(emg, sampling_rate=1000)
epochs = nk.epochs_create(emg_signals,
events=[3000, 6000, 9000],
sampling_rate=1000,
epochs_start=-0.1,
epochs_end=1.9)
emg_eventrelated = nk.emg_eventrelated(epochs)
# Test amplitude features
no_activation = np.where(emg_eventrelated["EMG_Activation"] == 0)[0][0]
assert int(
pd.DataFrame(emg_eventrelated.values[no_activation]).isna().sum()) == 4
assert np.alltrue(
np.nansum(np.array(emg_eventrelated["EMG_Amplitude_Mean"])) <
np.nansum(np.array(emg_eventrelated["EMG_Amplitude_Max"])))
assert len(emg_eventrelated["Label"]) == 3
assert len(emg_eventrelated.columns) == 7
assert all(elem in [
"EMG_Activation", "EMG_Amplitude_Mean", "EMG_Amplitude_Max",
"EMG_Amplitude_Max_Time", "EMG_Bursts", "Label", "Event_Onset"
] for elem in np.array(emg_eventrelated.columns.values, dtype=str))
def test_emg_plot():
sampling_rate = 1000
emg = nk.emg_simulate(duration=10, sampling_rate=1000, burst_number=3)
emg_summary, _ = nk.emg_process(emg, sampling_rate=sampling_rate)
# Plot data over samples.
nk.emg_plot(emg_summary)
# This will identify the latest figure.
fig = plt.gcf()
assert len(fig.axes) == 2
titles = ["Raw and Cleaned Signal", "Muscle Activation"]
for (ax, title) in zip(fig.get_axes(), titles):
assert ax.get_title() == title
assert fig.get_axes()[1].get_xlabel() == "Samples"
np.testing.assert_array_equal(fig.axes[0].get_xticks(),
fig.axes[1].get_xticks())
plt.close(fig)
# Plot data over time.
nk.emg_plot(emg_summary, sampling_rate=sampling_rate)
# This will identify the latest figure.
fig = plt.gcf()
assert fig.get_axes()[1].get_xlabel() == "Time (seconds)"
def test_emg_eventrelated():
emg = nk.emg_simulate(duration=20, sampling_rate=1000, burst_number=3)
emg_signals, info = nk.emg_process(emg, sampling_rate=1000)
epochs = nk.epochs_create(emg_signals,
events=[3000, 6000, 9000],
sampling_rate=1000,
epochs_start=-0.1,
epochs_end=1.9)
emg_eventrelated = nk.emg_eventrelated(epochs)
# Test amplitude features
no_activation = np.where(emg_eventrelated["EMG_Activation"] == 0)[0][0]
assert int(
pd.DataFrame(emg_eventrelated.values[no_activation]).isna().sum()) == 4
assert np.alltrue(
np.nansum(np.array(emg_eventrelated["EMG_Amplitude_Mean"])) <
np.nansum(np.array(emg_eventrelated["EMG_Amplitude_Max"])))
assert len(emg_eventrelated["Label"]) == 3
# Test warning on missing columns
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `EMG_Onsets`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["EMG_Onsets"]
nk.emg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an `EMG_Activity`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["EMG_Activity"]
nk.emg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
with pytest.warns(nk.misc.NeuroKitWarning,
match=r".*does not have an.*`EMG_Amplitude`.*"):
first_epoch_key = list(epochs.keys())[0]
first_epoch_copy = epochs[first_epoch_key].copy()
del first_epoch_copy["EMG_Amplitude"]
nk.emg_eventrelated({**epochs, first_epoch_key: first_epoch_copy})
def test_emg_intervalrelated():
emg = nk.emg_simulate(duration=40, sampling_rate=1000, burst_number=3)
emg_signals, info = nk.emg_process(emg, sampling_rate=1000)
columns = ['EMG_Activation_N', 'EMG_Amplitude_Mean']
# Test with signal dataframe
features_df = nk.emg_intervalrelated(emg_signals)
assert all(elem in columns
for elem in np.array(features_df.columns.values, dtype=str))
assert features_df.shape[0] == 1 # Number of rows
# Test with dict
epochs = nk.epochs_create(emg_signals,
events=[0, 20000],
sampling_rate=1000,
epochs_end=20)
features_dict = nk.emg_intervalrelated(epochs)
assert all(elem in columns
for elem in np.array(features_dict.columns.values, dtype=str))
assert features_dict.shape[0] == 2 # Number of rows
nk.rsp_plot(signals, sampling_rate=250)
# Save it
plot = nk.rsp_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_rsp.png", dpi=300, h_pad=3)
# =============================================================================
# Electromyography (EMG) processing
# =============================================================================
# Generate 10 seconds of EMG signal (recorded at 250 samples / second)
emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
# Process it
signals, _ = nk.emg_process(emg, sampling_rate=250)
# Visualise the processing
nk.emg_plot(signals, sampling_rate=250)
# Save it
plot = nk.emg_plot(signals, sampling_rate=250)
plot.set_size_inches(10, 6, forward=True)
plot.savefig("README_emg.png", dpi=300, h_pad=3)
# =============================================================================
# Photoplethysmography (PPG/BVP)
# =============================================================================
# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
ppg = nk.ppg_simulate(duration=15,
def my_emg_process(emg_signal, sampling_rate):
return nk.emg_process(emg_signal,
sample_rating=sampling_rate) # TODO update
