def _wt_func(signal: ndarray, params: TFParams, return_opt: bool):
impl = params.get_item("implementation") or "python"
result = pymodalib.wavelet_transform(
signal=signal,
fs=params.fs,
fmin=params.get_item("fmin"),
fmax=params.get_item("fmax"),
resolution=params.get_item("f0"),
cut_edges=params.get_item("CutEdges") == "on",
wavelet=params.get_item("Wavelet"),
padding=params.get_item("Padding"),
preprocess=params.get_item("Preprocess") == "on",
rel_tolerance=params.get_item("RelTol"),
return_opt=return_opt,
implementation=impl,
)
try:
wt, freq, opt = result
except ValueError:
wt, freq = result
opt = {}
return wt, freq, opt
def _wt_surrogate_calc(wt_signal: ndarray, surrogate: ndarray,
params: PCParams) -> ndarray:
"""
Calculates the phase coherence between a signal and a surrogate.
:param wt_signal: the wavelet transform of the signal
:param surrogate: the values of the surrogate (not the wavelet transform)
:param params: the params object with parameters to pass to the wavelet transform function
:return: [1D array] the wavelet phase coherence between the signal and the surrogate
"""
wt_surrogate, _ = pymodalib.wavelet_transform(surrogate,
params.fs,
**params.get(),
Display="off")
surr_avg, _ = wphcoh(wt_signal, wt_surrogate)
return surr_avg
import platform
import numpy
import numpy as np
import pymodalib
os.chdir(os.path.abspath(os.path.dirname(__file__)))
if __name__ == "__main__":
signal = np.load("../1signal_10Hz.npy")
fs = 10
times = np.arange(0, signal.size / fs, 1 / fs)
wt, freq = pymodalib.wavelet_transform(signal,
fs,
cut_edges=True,
implementation="matlab")
# Save results to a data file.
numpy.savez("output",
wt=wt,
freq=freq,
times=times,
implementation="MATLAB")
if platform.system() != "Linux":
# Plot the result by importing the plotting script.
import plot_wavelet
# Prevents Pycharm from cleaning up the import statement.
dummy_variable = plot_wavelet
os.chdir(os.path.abspath(os.path.dirname(__file__)))
if __name__ == "__main__":
signal = np.load("../1signal_10Hz.npy")
fs = 10
# Time values associated with the signal.
times = np.arange(0, signal.size / fs, 1 / fs)
# Frequency interval (0.07Hz-0.33Hz).
fmin, fmax = 0.07, 0.33
# Note: your IDE may incorrectly display an error on this statement.
wt, freq, wopt = pymodalib.wavelet_transform(
signal, fs, fmin=fmin, fmax=fmax, return_opt=True, implementation="matlab",
)
iamp, iphi, ifreq = pymodalib.ridge_extraction(wt, freq, fs, wopt=wopt)
fig, ax = plt.subplots()
wt_amp = np.abs(wt)
mesh1, mesh2 = np.meshgrid(times, freq)
# Plot wavelet transform.
ax.contourf(mesh1, mesh2, wt_amp)
# Plot frequency ridge.
ax.plot(times, ifreq, color="red")