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tests_emg.py
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tests_emg.py
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import biosppy
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
import scipy.stats
import neurokit2 as nk
# =============================================================================
# EMG
# =============================================================================
def test_emg_simulate():
emg1 = nk.emg_simulate(duration=20, length=5000, burst_number=1)
assert len(emg1) == 5000
emg2 = nk.emg_simulate(duration=20, length=5000, burst_number=15)
assert scipy.stats.median_absolute_deviation(emg1) < scipy.stats.median_absolute_deviation(emg2)
emg3 = nk.emg_simulate(duration=20, length=5000, burst_number=1, burst_duration=2.0)
# pd.DataFrame({"EMG1":emg1, "EMG3": emg3}).plot()
assert len(nk.signal_findpeaks(emg3, height_min=1.0)["Peaks"]) > len(
nk.signal_findpeaks(emg1, height_min=1.0)["Peaks"]
)
def test_emg_activation():
emg = nk.emg_simulate(duration=10, burst_number=3)
cleaned = nk.emg_clean(emg)
emg_amplitude = nk.emg_amplitude(cleaned)
activity_signal, info = nk.emg_activation(emg_amplitude)
assert set(activity_signal.columns.to_list()) == set(list(info.keys()))
assert len(info["EMG_Onsets"]) == len(info["EMG_Offsets"])
for i, j in zip(info["EMG_Onsets"], info["EMG_Offsets"]):
assert i < j
def test_emg_clean():
sampling_rate = 1000
emg = nk.emg_simulate(duration=20, sampling_rate=sampling_rate)
emg_cleaned = nk.emg_clean(emg, sampling_rate=sampling_rate)
assert emg.size == emg_cleaned.size
# Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/emg.py)
original, _, _ = biosppy.tools.filter_signal(
signal=emg, ftype="butter", band="highpass", order=4, frequency=100, sampling_rate=sampling_rate
)
emg_cleaned_biosppy = nk.signal_detrend(original, order=0)
assert np.allclose((emg_cleaned - emg_cleaned_biosppy).mean(), 0, atol=1e-6)
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