def do_maxRidge_detection(self):
ridge_y = core.get_maxRidge_ys(self.modulus)
self.ridge = ridge_y
if not np.any(ridge_y):
self.e = MessageWindow("No ridge found..check spectrum!",
"Ridge detection error")
return
self._has_ridge = True
self.draw_ridge() # ridge_data made here
def do_maxRidge_detection(self):
ridge_y = core.get_maxRidge_ys(self.modulus)
self.ridge = ridge_y
if not np.any(ridge_y):
msgBox = QMessageBox()
msgBox.setWindowTitle("Ridge detection error")
msgBox.setText("No ridge found..check spectrum!")
msgBox.exec()
return
self._has_ridge = True
self.draw_ridge() # ridge_data made here
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
def transform_stack(movie, dt, Tmin, Tmax, nT, T_c = None, win_size = None):
'''
Analyzes a 3-dimensional array
with shape (NFrames, ydim, xdim) along
its 1st axis 'pixel-by-pixel'.
Returns four arrays with the same shape as the input
array holding the results of the transform for each pixel.
For high spatial resolution input this might take a very
long time as ydim \times xdim transformations have to be calculated!
Parallel execution is recommended (see `run_parallel` below).
Parameters
----------
movie : ndarray with ndim = 3, transform is done along 1st axis
dt : float, sampling interval
Tmin : float, smallest period
Tmax : float, largest period
nT : int, number of periods/transforms
T_c : float, sinc cut off period, defaults to None to disable
sinc-detrending (not recommended!)
win_size : float, amplitude normalization sliding window size.
Default is None which disables normalization.
Returns
-------
results : dictionary, with keys holding the output movies
'phase' : 32bit ndarray, holding the instantaneous phases
'period' : 32bit ndarray, holding the instantaneous periods
'power' : 32bit ndarray, holding the wavelet powers
'amplitude' : 32bit ndarray, holding the instantaneous amplitudes
'''
if Tmin < 2 * dt:
logger.warning('Warning, Nyquist limit is 2 times the sampling interval!')
logger.info('..setting Tmin to {:.2f}'.format( 2 * dt ))
Tmin = 2 * dt
if Tmax > dt * movie.shape[0]:
logger.warning('Warning: Very large periods chosen!')
logger.info('..setting Tmax to {:.2f}'.format( dt * Nt ))
Tmax = dt * Nt
# the periods to scan for
periods = np.linspace(Tmin, Tmax, nT)
# create output arrays, needs 32bit for Fiji FloatProcessor :/
period_movie = np.zeros(movie.shape, dtype=np.float32)
phase_movie = np.zeros(movie.shape, dtype=np.float32)
power_movie = np.zeros(movie.shape, dtype=np.float32)
amplitude_movie = np.zeros(movie.shape, dtype=np.float32)
ydim, xdim = movie.shape[1:] # F, Y, X ordering
Npixels = ydim * xdim
logger.info(f'Computing the transforms for {Npixels} pixels')
sys.stdout.flush()
# loop over pixel coordinates
for x in range(xdim):
for y in range(ydim):
# show progress
if Npixels < 10:
logger.info(f"Processed {(ydim*x + y)/Npixels * 100 :.1f}%..")
sys.stdout.flush()
elif (ydim*x + y)%(int(Npixels/5)) == 0 and x != 0:
logger.info(f"Processed {(ydim*x + y)/Npixels * 100 :.1f}%..")
input_vec = movie[:, y, x] # the time_series at pixel (x,y)
signal = input_vec
# detrending
if T_c is not None:
trend = pbcore.sinc_smooth(signal, T_c, dt)
signal = signal - trend
# amplitude normalization?
if win_size is not None:
signal = pbcore.normalize_with_envelope(signal, win_size, dt)
sigma = np.std(signal)
Nt = len(signal)
modulus, wlet = pbcore.compute_spectrum(signal, dt, periods)
ridge_ys = pbcore.get_maxRidge_ys(modulus)
ridge_periods = periods[ridge_ys]
powers = modulus[ridge_ys, np.arange(Nt)]
phases = np.angle(wlet[ridge_ys, np.arange(Nt)])
# map to [0, 2pi]
phases = phases % (2 * np.pi)
amplitudes = pbcore.power_to_amplitude(ridge_periods,
powers, sigma, dt)
phase_movie[:, y, x] = phases
period_movie[:, y, x] = ridge_periods
power_movie[:, y, x] = powers
amplitude_movie[:, y, x] = amplitudes
results = {'phase' : phase_movie, 'period' : period_movie,
'power' : power_movie, 'amplitude' : amplitude_movie}
return results