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)"
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,
sampling_rate=250,
heart_rate=70,
random_state=333)