def draw_ridge(self):
""" makes also the ridge_data !! """
if not self._has_ridge:
msgBox = QMessageBox()
msgBox.setWindowTitle("No Ridge")
msgBox.setText("Do a ridge detection first!")
msgBox.exec()
ridge_data = core.eval_ridge(
self.ridge,
self.wlet,
self.signal,
self.periods,
self.tvec,
power_thresh=self.power_thresh,
smoothing_wsize=self.rsmoothing,
)
# plot the ridge
ax_spec = self.wCanvas.fig.axes[1] # the spectrum
# already has a plotted ridge
if ax_spec.lines:
ax_spec.lines = [] # remove old ridge line
self.cb_coi.setCheckState(0) # remove COI
pl.draw_Wavelet_ridge(ax_spec, ridge_data, marker_size=1.5)
# refresh the canvas
self.wCanvas.draw()
self.ridge_data = ridge_data
def draw_ridge(self):
''' makes also the ridge_data !! '''
if not self._has_ridge:
self.e = MessageWindow('Run a ridge detection first!', 'No Ridge')
return
ridge_data = core.eval_ridge(self.ridge,
self.wlet,
self.signal,
self.periods,
self.tvec,
power_thresh=self.power_thresh,
smoothing_wsize=self.rsmoothing)
# plot the ridge
ax_spec = self.wCanvas.fig.axes[1] # the spectrum
# already has a plotted ridge
if ax_spec.lines:
ax_spec.lines = [] # remove old ridge line
self.cb_coi.setCheckState(0) # remove COI
pl.draw_Wavelet_ridge(ax_spec, ridge_data, marker_size=1.5)
# refresh the canvas
self.wCanvas.draw()
self.ridge_data = ridge_data
def get_maxRidge(self, power_thresh=0, smoothing_wsize=None):
"""
Computes the ridge as consecutive maxima of the modulus.
Returns the ridge_data dictionary (see core.eval_ridge)!
"""
if not self._has_spec:
print("Need to compute a wavelet spectrum first!")
return
# for easy integration
modulus = self.modulus
Nt = modulus.shape[1] # number of time points
tvec = np.arange(Nt) * self.dt
# ================ridge detection=====================================
# just pick the consecutive modulus
# (squared complex wavelet transform) maxima as the ridge
# has to be odd
if smoothing_wsize % 2 == 0:
smoothing_wsize = smoothing_wsize + 1
ridge_y = core.get_maxRidge_ys(modulus)
rd = core.eval_ridge(
ridge_y,
self.wlet,
self.ana_signal,
self.periods,
tvec=tvec,
power_thresh=power_thresh,
smoothing_wsize=smoothing_wsize,
)
self.ridge_data = rd
self._has_ridge = True
# return also directly
return rd
def get_sign_maxRidge(self,
empirical_background,
confidence=core.chi2_95,
smoothing_wsize=None):
"""
Computes and evaluates the ridge as consecutive
maxima of the modulus, thresholded by a significance test for
the given background spectrum. Confidence defaults to 95% interval.
Returns the ridge_data DataFrame, see also `core.eval_ridge`!
Additionally the analyser instance updates
self.ridge_data
with the results.
Parameters
----------
empirical_background : 1d sequence, Fourier estimate of the background.
Must hold the powers
at exactly the periods used for
the wavelet analysis!
confidence : float, the Chi-squared value at the desired
confidence level. Defaults to the 95% confidence interval.
smoothing_wsize : int, optional
Savitkzy-Golay smoothing window size
for ridge smoothing
Returns
-------
A DataFrame with the following columns:
time : the t-values of the ridge, can have gaps if thresholding!
periods : the instantaneous periods
frequencies : the instantaneous frequencies
phase : the instantaneous phases
power : the Wavelet Power normalized to white noise (<P(WN)> = 1)
amplitude : the estimated amplitudes of the signal
"""
if self.transform is None:
logger.warning("Need to compute a wavelet spectrum first!")
return
# for easy integration
modulus = self.modulus
Nt = modulus.shape[1] # number of time points
tvec = np.arange(Nt) * self.dt
# has to be odd
if smoothing_wsize and smoothing_wsize % 2 == 0:
smoothing_wsize = smoothing_wsize + 1
# ================ridge detection=====================================
ridge_ys = core.get_maxRidge_ys(modulus)
rd = core.eval_ridge(
ridge_ys,
self.transform,
self.ana_signal,
self.periods,
tvec=tvec,
power_thresh=0, # we need the complete ridge here!
smoothing_wsize=smoothing_wsize
)
spectrum_bool = core.get_significant_regions(self.modulus,
empirical_background)
# boolean mask for the ridge
ridge_bool = spectrum_bool[ridge_ys, rd.index]
# the significant parts of the ridge
sign_ridge = rd[ridge_bool]
# attach additional data, total length and sampling interval
sign_ridge.Nt = rd.Nt
sign_ridge.dt = rd.dt
self.ridge_data = sign_ridge
# return also directly
return sign_ridge
def get_maxRidge(self, power_thresh=0, smoothing_wsize=None):
"""
Computes and evaluates the ridge as consecutive
maxima of the modulus.
Returns the ridge_data DataFrame, see also `core.eval_ridge`!
Additionally the analyser instance updates
self.ridge_data
with the results.
Parameters
----------
power_thresh : float, threshold for the ridge.
smoothing_wsize : int, optional
Savitkzy-Golay smoothing window size
for ridge smoothing
Returns
-------
A DataFrame with the following columns:
time : the t-values of the ridge, can have gaps if thresholding!
periods : the instantaneous periods
frequencies : the instantaneous frequencies
phase : the instantaneous phases
power : the Wavelet Power normalized to white noise (<P(WN)> = 1)
amplitude : the estimated amplitudes of the signal
"""
if self.transform is None:
logger.warning("Need to compute a wavelet spectrum first!")
return
# for easy integration
modulus = self.modulus
Nt = modulus.shape[1] # number of time points
tvec = np.arange(Nt) * self.dt
# has to be odd
if smoothing_wsize and smoothing_wsize % 2 == 0:
smoothing_wsize = smoothing_wsize + 1
# ================ridge detection=====================================
ridge_ys = core.get_maxRidge_ys(modulus)
rd = core.eval_ridge(
ridge_ys,
self.transform,
self.ana_signal,
self.periods,
tvec=tvec,
power_thresh=power_thresh,
smoothing_wsize=smoothing_wsize
)
self.ridge_data = rd
# return also directly
return rd