def test_rsp_rrv():
rsp90 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=90,
random_state=42)
rsp110 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=110,
random_state=42)
cleaned90 = nk.rsp_clean(rsp90, sampling_rate=1000)
_, peaks90 = nk.rsp_peaks(cleaned90)
rsp_rate90 = nk.signal_rate(peaks90, desired_length=len(rsp90))
cleaned110 = nk.rsp_clean(rsp110, sampling_rate=1000)
_, peaks110 = nk.rsp_peaks(cleaned110)
rsp_rate110 = nk.signal_rate(peaks110, desired_length=len(rsp110))
rsp90_rrv = nk.rsp_rrv(rsp_rate90, peaks90)
rsp110_rrv = nk.rsp_rrv(rsp_rate110, peaks110)
assert np.array(rsp90_rrv["RRV_SDBB"]) < np.array(rsp110_rrv["RRV_SDBB"])
assert np.array(rsp90_rrv["RRV_RMSSD"]) < np.array(rsp110_rrv["RRV_RMSSD"])
assert np.array(rsp90_rrv["RRV_SDSD"]) < np.array(rsp110_rrv["RRV_SDSD"])
# assert np.array(rsp90_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN20"]) == np.array(rsp90_rrv["RRV_pNN20"]) == 0
# assert np.array(rsp90_rrv["RRV_TINN"]) < np.array(rsp110_rrv["RRV_TINN"])
# assert np.array(rsp90_rrv["RRV_HTI"]) > np.array(rsp110_rrv["RRV_HTI"])
assert np.array(rsp90_rrv["RRV_HF"]) < np.array(rsp110_rrv["RRV_HF"])
assert np.isnan(rsp90_rrv["RRV_LF"][0])
assert np.isnan(rsp110_rrv["RRV_LF"][0])
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, )
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 signal_process(pulses, sampling_rate):
"""
数据的初步处理分析 寻找峰值 初步时域分析
:param pulses: 标准化的数据
:param sampling_rate: 采样率
:return:
"""
# 数据检查 是否是1维格式
pulses = nk.as_vector(pulses)
# 数据去噪 低通滤波去噪 bessel
cleaned_pulses = nk.signal_filter(pulses,
sampling_rate=sampling_rate,
lowcut=0.5,
highcut=8,
order=3,
method="bessel")
# common_plot(None, cleaned_pulses, title="Cleand Data", sampling_rate=sampling_rate)
feature_points = find_feature_points(cleaned_pulses, sampling_rate)
# 寻找其他特征点
peaks = feature_points["Peaks"]
# 计算心率
rates = nk.signal_rate(peaks,
sampling_rate=sampling_rate,
desired_length=None)
feature_points["Heart Rates"] = rates
feature_points["HR_Time"] = np.mean(rates)
print(np.round(np.mean(rates), 2))
return feature_points
pass
def test_rsp_rrv():
rsp90 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=90,
random_state=42)
rsp110 = nk.rsp_simulate(duration=60,
sampling_rate=1000,
respiratory_rate=110,
random_state=42)
cleaned90 = nk.rsp_clean(rsp90, sampling_rate=1000)
_, peaks90 = nk.rsp_peaks(cleaned90)
rsp_rate90 = nk.signal_rate(peaks90, desired_length=len(rsp90))
cleaned110 = nk.rsp_clean(rsp110, sampling_rate=1000)
_, peaks110 = nk.rsp_peaks(cleaned110)
rsp_rate110 = nk.signal_rate(peaks110, desired_length=len(rsp110))
rsp90_rrv = nk.rsp_rrv(rsp_rate90, peaks90)
rsp110_rrv = nk.rsp_rrv(rsp_rate110, peaks110)
assert np.array(rsp90_rrv["RRV_SDBB"]) < np.array(rsp110_rrv["RRV_SDBB"])
assert np.array(rsp90_rrv["RRV_RMSSD"]) < np.array(rsp110_rrv["RRV_RMSSD"])
assert np.array(rsp90_rrv["RRV_SDSD"]) < np.array(rsp110_rrv["RRV_SDSD"])
# assert np.array(rsp90_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN50"]) == np.array(rsp110_rrv["RRV_pNN20"]) == np.array(rsp90_rrv["RRV_pNN20"]) == 0
# assert np.array(rsp90_rrv["RRV_TINN"]) < np.array(rsp110_rrv["RRV_TINN"])
# assert np.array(rsp90_rrv["RRV_HTI"]) > np.array(rsp110_rrv["RRV_HTI"])
assert np.array(rsp90_rrv["RRV_HF"]) < np.array(rsp110_rrv["RRV_HF"])
assert np.isnan(rsp90_rrv["RRV_LF"][0])
assert np.isnan(rsp110_rrv["RRV_LF"][0])
# Test warning on too short duration
with pytest.warns(nk.misc.NeuroKitWarning,
match=r"The duration of recording is too short.*"):
short_rsp90 = nk.rsp_simulate(duration=10,
sampling_rate=1000,
respiratory_rate=90,
random_state=42)
short_cleaned90 = nk.rsp_clean(short_rsp90, sampling_rate=1000)
_, short_peaks90 = nk.rsp_peaks(short_cleaned90)
short_rsp_rate90 = nk.signal_rate(short_peaks90,
desired_length=len(short_rsp90))
nk.rsp_rrv(short_rsp_rate90, short_peaks90)
