def test_signal_findpeaks():
signal1 = np.cos(np.linspace(start=0, stop=30, num=1000))
info1 = nk.signal_findpeaks(signal1)
signal2 = np.concatenate([np.arange(0, 20, 0.1), np.arange(17, 30, 0.1), np.arange(30, 10, -0.1)])
info2 = nk.signal_findpeaks(signal2)
assert len(info1["Peaks"]) > len(info2["Peaks"])
def test_ecg_simulate():
ecg1 = nk.ecg_simulate(duration=20, length=5000)
assert len(ecg1) == 5000
ecg2 = nk.ecg_simulate(duration=20, length=5000, heart_rate=500)
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).plot()
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).hist()
assert len(nk.signal_findpeaks(ecg1)[0]) < len(nk.signal_findpeaks(ecg2)[0])
def test_eda_simulate():
eda1 = nk.eda_simulate(duration=10, length=None, scr_number=1, random_state=333)
assert len(nk.signal_findpeaks(eda1, height_min=0.6)["Peaks"]) == 1
eda2 = nk.eda_simulate(duration=10, length=None, scr_number=5, random_state=333)
assert len(nk.signal_findpeaks(eda2, height_min=0.6)["Peaks"]) == 5
# pd.DataFrame({"EDA1": eda1, "EDA2": eda2}).plot()
assert len(nk.signal_findpeaks(eda2, height_min=0.6)["Peaks"]) > len(nk.signal_findpeaks(eda1, height_min=0.6)["Peaks"])
def test_eda_simulate():
eda1 = nk.eda_simulate(duration=10, length=None, n_peaks=1)
assert len(nk.signal_findpeaks(eda1, height_min=0.6)[0]) == 1
eda2 = nk.eda_simulate(duration=10, length=None, n_peaks=5)
assert len(nk.signal_findpeaks(eda2, height_min=0.6)[0]) == 5
# pd.DataFrame({"EDA1": eda1, "EDA2": eda2}).plot()
assert len(nk.signal_findpeaks(eda2, height_min=0.6)[0]) > len(
nk.signal_findpeaks(eda1, height_min=0.6)[0])
def test_emg_simulate():
emg1 = nk.emg_simulate(duration=20, length=5000, n_bursts=1)
assert len(emg1) == 5000
emg2 = nk.emg_simulate(duration=20, length=5000, n_bursts=15)
assert scipy.stats.median_absolute_deviation(emg1) < scipy.stats.median_absolute_deviation(emg2)
emg3 = nk.emg_simulate(duration=20, length=5000, n_bursts=1, duration_bursts=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_ecg_simulate():
ecg1 = nk.ecg_simulate(duration=20, length=5000, method="simple", noise=0)
assert len(ecg1) == 5000
ecg2 = nk.ecg_simulate(duration=20, length=5000, heart_rate=500)
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).plot()
# pd.DataFrame({"ECG1":ecg1, "ECG2": ecg2}).hist()
assert (len(nk.signal_findpeaks(ecg1, height_min=0.6)["Peaks"]) < len(
nk.signal_findpeaks(ecg2, height_min=0.6)["Peaks"]))
ecg3 = nk.ecg_simulate(duration=10, length=5000)
# pd.DataFrame({"ECG1":ecg1, "ECG3": ecg3}).plot()
assert (len(nk.signal_findpeaks(ecg2, height_min=0.6)["Peaks"]) > len(
nk.signal_findpeaks(ecg3, height_min=0.6)["Peaks"]))
def test_rsp_simulate():
rsp1 = nk.rsp_simulate(duration=20, length=3000)
assert len(rsp1) == 3000
rsp2 = nk.rsp_simulate(duration=20, length=3000, respiratory_rate=80)
# pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).plot()
# pd.DataFrame({"RSP1":rsp1, "RSP2":rsp2}).hist()
assert (len(nk.signal_findpeaks(rsp1, height_min=0.2)[0]) <
len(nk.signal_findpeaks(rsp2, height_min=0.2)[0]))
rsp3 = nk.rsp_simulate(duration=20, length=3000, method="sinusoidal")
rsp4 = nk.rsp_simulate(duration=20, length=3000, method="breathmetrics")
# pd.DataFrame({"RSP3":rsp3, "RSP4":rsp4}).plot()
assert (len(nk.signal_findpeaks(rsp3, height_min=0.2)[0]) >
len(nk.signal_findpeaks(rsp4, height_min=0.2)[0]))
def test_signal_rate(): # since singal_rate wraps signal_period, the latter is tested as well
# Test with array.
duration = 10
sampling_rate = 1000
signal = nk.signal_simulate(duration=duration, sampling_rate=sampling_rate, frequency=1)
info = nk.signal_findpeaks(signal)
rate = nk.signal_rate(peaks=info["Peaks"], sampling_rate=1000, desired_length=len(signal))
assert rate.shape[0] == duration * sampling_rate
# Test with dictionary.produced from signal_findpeaks.
assert info[list(info.keys())[0]].shape == (info["Peaks"].shape[0],)
# Test with DataFrame.
duration = 120
sampling_rate = 1000
rsp = nk.rsp_simulate(
duration=duration, sampling_rate=sampling_rate, respiratory_rate=15, method="sinuosoidal", noise=0
)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=sampling_rate)
signals, info = nk.rsp_peaks(rsp_cleaned)
rate = nk.signal_rate(signals, sampling_rate=sampling_rate, desired_length=duration * sampling_rate)
assert rate.shape == (signals.shape[0],)
# Test with dictionary.produced from rsp_findpeaks.
rate = nk.signal_rate(info, sampling_rate=sampling_rate, desired_length=duration * sampling_rate)
assert rate.shape == (duration * sampling_rate,)
def test_signal_rate():
# Test with array.
signal = nk.signal_simulate(duration=10, sampling_rate=1000, frequency=1)
info = nk.signal_findpeaks(signal)
rate = nk.signal_rate(peaks=info["Peaks"],
sampling_rate=1000,
desired_length=None)
assert rate.shape[0] == len(info["Peaks"])
# Test with dictionary.produced from signal_findpeaks.
assert info[list(info.keys())[0]].shape == (info["Peaks"].shape[0], )
# Test with DataFrame.
rsp = nk.rsp_simulate(duration=120,
sampling_rate=1000,
respiratory_rate=15,
method="sinuosoidal",
noise=0)
rsp_cleaned = nk.rsp_clean(rsp, sampling_rate=1000)
signals, info = nk.rsp_peaks(rsp_cleaned)
rate = nk.signal_rate(signals, sampling_rate=1000)
assert rate.shape == (signals.shape[0], )
# Test with dictionary.produced from rsp_findpeaks.
test_length = 30
rate = nk.signal_rate(info, sampling_rate=1000, desired_length=test_length)
assert rate.shape == (test_length, )
params_scr = pd.DataFrame(
columns=["time_peak", "rise", "decay1", "decay2", "Participant", "Task"])
for i in range(4):
print("Task: " + str(i))
data = pd.read_csv("../../data/eogdb/eogdb_task" + str(i + 1) + ".csv")
for j, participant in enumerate(np.unique(data["Participant"])[1:3]):
print(" - " + str(j + 1))
segment = data[data["Participant"] == participant]
signal = segment["vEOG"]
cleaned = nk.eog_clean(signal, sampling_rate=200, method='neurokit')
blinks = nk.signal_findpeaks(cleaned, relative_height_min=1.5)["Peaks"]
events = nk.epochs_create(cleaned,
blinks,
sampling_rate=200,
epochs_start=-0.4,
epochs_end=0.6)
events = nk.epochs_to_array(events) # Convert to 2D array
x = np.linspace(0, 100, num=len(events))
p_gamma = np.full((events.shape[1], 3), np.nan)
p_bateman = np.full((events.shape[1], 3), np.nan)
p_scr = np.full((events.shape[1], 4), np.nan)
for i in range(events.shape[1]):
