def test_signal_interpolate():
x_axis = np.linspace(start=10, stop=30, num=10)
signal = np.cos(x_axis)
interpolated = nk.signal_interpolate(signal, desired_length=1000)
assert len(interpolated) == 1000
new_x = np.linspace(start=0, stop=40, num=1000)
interpolated = nk.signal_interpolate(signal,
desired_length=1000,
x_axis=x_axis,
new_x=new_x)
assert len(interpolated) == 1000
assert interpolated[0] == signal[0]
def test_signal_interpolate():
x_axis = np.linspace(start=10, stop=30, num=10)
signal = np.cos(x_axis)
interpolated = nk.signal_interpolate(x_axis, signal, x_new=np.arange(1000))
assert len(interpolated) == 1000
assert interpolated[0] == signal[0]
assert interpolated[-1] == signal[-1]
def _hrv_get_rri(peaks=None, sampling_rate=1000, interpolate=False, **kwargs):
rri = np.diff(peaks) / sampling_rate * 1000
if interpolate is False:
return rri
else:
# Minimum sampling rate for interpolation
if sampling_rate < 10:
sampling_rate = 10
# Compute length of interpolated heart period signal at requested sampling rate.
desired_length = int(np.rint(peaks[-1]))
rri = nk.signal_interpolate(
peaks[
1:], # Skip first peak since it has no corresponding element in heart_period
rri,
x_new=np.arange(desired_length),
**kwargs)
return rri, sampling_rate
for database in np.unique(file["Database"]):
print(str(database))
data = file[file["Database"] == database]
for participant in np.unique(data["Participant"]):
data_participant = data[data["Participant"] == participant]
sampling_rate = np.unique(data_participant["Sampling_Rate"])[0]
rpeaks = data_participant["Rpeaks"].values
# Interpolate
rri = np.diff(rpeaks) / sampling_rate * 1000
desired_length = int(
np.rint(rpeaks[-1] / sampling_rate * sampling_rate))
rri = nk.signal_interpolate(rpeaks[1:],
rri,
x_new=np.arange(desired_length))
# Get PSD
psd = nk.signal_psd(rri, sampling_rate=sampling_rate)
#
# results = nk.hrv_frequency(rpeaks, sampling_rate=sampling_rate)
# results["Participant"] = participant
# results["Database"] = database
# results["Recording_Length"] = rpeaks[-1] / sampling_rate / 60
#
# all_results = pd.concat([all_results, results], axis=0)
#
#all_results.to_csv("data.csv", index=False)