def draw_coi(self):
"""
Draws the COI on the spectrum.
Also redraws the ridge!
"""
ax_spec = self.wCanvas.fig.axes[1] # the spectrum axis
# COI desired?
if bool(self.cb_coi.checkState()):
# draw on the spectrum
pl.draw_COI(ax_spec, self.tvec)
else:
ax_spec.lines = [] # remove coi, and ridge?!
if self._has_ridge:
self.draw_ridge() # re-draw ridge
# refresh the canvas
self.wCanvas.draw()
def draw_coi(self):
'''
Draws the COI on the spectrum.
Also redraws the ridge!
'''
ax_spec = self.wCanvas.fig.axes[1] # the spectrum axis
# COI desired?
if bool(self.cb_coi.checkState()):
# compute slope of COI
coi_m = core.Morlet_COI()
# draw on the spectrum
pl.draw_COI(ax_spec, self.tvec, coi_m)
else:
ax_spec.lines = [] # remove coi, and ridge?!
if self._has_ridge:
self.draw_ridge() # re-draw ridge
# refresh the canvas
self.wCanvas.draw()
def compute_spectrum(self,
raw_signal,
sinc_detrend=True,
norm_amplitude=False,
do_plot=True,
draw_coi=False):
"""
Computes the Wavelet spectrum for a given *signal* for the given *periods*
signal : a sequence, the time-series to be analyzed
sinc_detrend : boolean, if True sinc-filter detrending
will be done with the set T_cut_off parameter
norm_amplitude : boolean, if True sliding window amplitude envelope
estimation is performed, and normalisation with
1/envelope
do_plot : boolean, set to False if no plot is desired,
good for for batch processing
draw_coi: boolean, set to True if cone of influence
shall be drawn on the wavelet power spectrum
After a successful analysis, the analyser instance updates
self.wlet
self.modulus
with the results.
"""
if sinc_detrend:
detrended = self.sinc_detrend(raw_signal)
ana_signal = detrended
else:
ana_signal = raw_signal
# only after potential detrending!
if norm_amplitude:
ana_signal = self.normalize_amplitude(ana_signal)
self.ana_signal = ana_signal
modulus, wlet = core.compute_spectrum(ana_signal, self.dt,
self.periods)
if do_plot:
tvec = np.arange(len(ana_signal)) * self.dt
axs = pl.mk_signal_modulus_ax(self.time_unit_label)
pl.plot_signal_modulus(
axs,
time_vector=tvec,
signal=ana_signal,
modulus=modulus,
periods=self.periods,
p_max=self.p_max,
)
fig = ppl.gcf()
fig.tight_layout()
self.ax_spec = axs[1]
if draw_coi:
coi_m = core.Morlet_COI()
pl.draw_COI(axs[1], time_vector=tvec)
self.wlet = wlet
self.modulus = modulus
self._has_spec = True
def compute_spectrum(self,
raw_signal,
T_c=None,
window_size=None,
do_plot=True,
draw_coi=False):
"""
Computes the Wavelet spectrum for a given *raw_signal*.
Parameters
----------
signal : a sequence, the time-series to be analyzed
T_c : float, optional
Cut off period for the sinc-filter detrending, all periods
larger than that one are removed from the signal. If not given,
no sinc-detending will be done.
window_size : float, optional
Length of the sliding window for amplitude
envelope estimation in real time units, e.g. 17 minutes.
If not given no amplitude normalization will be done.
do_plot : boolean, set to False if no plot is desired,
good for batch processing
draw_coi: boolean, set to True if cone of influence
shall be drawn on the wavelet power spectrum
Returns
-------
modulus : 2d ndarray
the real Wavelet power spectrum normalized by signal
variance, has shape len(periods) x len(signal). Is set to None
before any analysis is done.
transform : 2d ndarray
the complex results of the Wavelet transform with
shape len(periods) x len(signal). Is set to None
before any analysis is done.
After a successful analysis, the analyzer instance updates
self.transform
self.modulus
with the results. If do_plot was set to True, the attributes
self.ax_spec_signal
self.ax_spec
allow to access the created subplots directly.
"""
if T_c:
detrended = self.sinc_detrend(raw_signal, T_c)
ana_signal = detrended
else:
ana_signal = raw_signal
# only after potential detrending!
if window_size:
ana_signal = self.normalize_amplitude(ana_signal, window_size)
self.ana_signal = ana_signal
modulus, transform = core.compute_spectrum(ana_signal, self.dt, self.periods)
if do_plot:
tvec = np.arange(len(ana_signal)) * self.dt
axs = pl.mk_signal_modulus_ax(self.time_unit_label)
pl.plot_signal_modulus(
axs,
time_vector=tvec,
signal=ana_signal,
modulus=modulus,
periods=self.periods,
p_max=self.p_max,
)
fig = ppl.gcf()
fig.tight_layout()
# some space for a title
fig.subplots_adjust(top=0.95)
self.ax_spec_signal = axs[0]
self.ax_spec = axs[1]
if draw_coi:
pl.draw_COI(axs[1], time_vector=tvec)
self.transform = transform
self.modulus = modulus
# return also directly
return modulus, transform