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 draw_Ridge(self):
if not self._has_ridge:
print("Can't draw ridge, need to do a ridge detection first!")
return
if not self.ax_spec:
print("Can't draw ridge, plot the spectrum first!")
return
pl.draw_Wavelet_ridge(self.ax_spec, self.ridge_data)
def draw_Ridge(self, marker_size=1.5):
if self.ridge_data is None:
logger.warning("Can't draw ridge, need to do a ridge detection first!")
return
if self.transform is None:
logger.warning("Can't draw ridge, plot the spectrum first!")
return
if self.ax_spec is None:
logger.warning("No spectrum plotted, can't draw ridge!")
pl.draw_Wavelet_ridge(self.ax_spec, self.ridge_data, marker_size)
# pl.draw_detrended(ax, tvec, signal) pl.draw_trend(ax, tvec, trend) ppl.legend(ncol=2) ppl.tight_layout() # --- compute spectrum on the original signal --- modulus, wlet = pyboat.compute_spectrum(signal, dt, periods) # plot spectrum and ridge ax_sig, ax_spec = pl.mk_signal_modulus_ax(time_unit) pl.plot_signal_modulus((ax_sig, ax_spec), tvec, signal, modulus, periods) # --- compute spectrum on the detrended signal --- modulus, wlet = pyboat.compute_spectrum(detr_signal, dt, periods) # get maximum ridge ridge_ys = pyboat.get_maxRidge_ys(modulus) # evaluate along the ridge ridge_results = pyboat.eval_ridge(ridge_ys, wlet, signal, periods, tvec) # plot spectrum and ridge ax_sig2, ax_spec2 = pl.mk_signal_modulus_ax(time_unit) pl.plot_signal_modulus((ax_sig2, ax_spec2), tvec, signal, modulus, periods) pl.draw_Wavelet_ridge(ax_spec2, ridge_results) ppl.tight_layout() # plot readout pl.plot_readout(ridge_results)
def do_the_loop(self):
'''
Uses the explicitly parsed self.wlet_pars
to control signal analysis settings.
Takes general analysis Parameters
self.parentDV.dt
self.parentDV.time_unit
and the DataFrame
self.parentDV.df
from the parent DataViewer.
Reads additional settings from this Batch Process Window.
'''
if self.export_options.isChecked():
OutPath = self.get_OutPath()
if OutPath is None:
return
periods = np.linspace(self.wlet_pars['T_min'], self.wlet_pars['T_max'],
self.wlet_pars['step_num'])
# retrieve batch settings
power_thresh = self.get_thresh()
rsmooth = self.get_ridge_smooth()
ridge_results = {}
for i, signal_id in enumerate(self.parentDV.df):
# log to terminal
print(f"processing {signal_id}..")
# sets parentDV.raw_signal and parentDV.tvec
succ = self.parentDV.vector_prep(signal_id)
# ui silently passes over..
if not succ:
print(f"Can't process signal {signal_id}..")
continue
# detrend?!
if self.parentDV.cb_use_detrended.isChecked():
trend = self.parentDV.calc_trend()
signal = self.parentDV.raw_signal - trend
else:
signal = self.parentDV.raw_signal
# amplitude normalization?
if self.parentDV.cb_use_envelope.isChecked():
if self.debug:
print('Calculating envelope with L=', self.wlet_pars['L'])
signal = pyboat.normalize_with_envelope(
signal, self.wlet_pars['L'], self.parentDV.dt)
# compute the spectrum
modulus, wlet = pyboat.compute_spectrum(signal, self.parentDV.dt,
periods)
# get maximum ridge
ridge = pyboat.get_maxRidge_ys(modulus)
# generate time vector
tvec = np.arange(len(signal)) * self.parentDV.dt
# evaluate along the ridge
ridge_data = pyboat.eval_ridge(ridge,
wlet,
signal,
periods,
tvec,
power_thresh,
smoothing_wsize=rsmooth)
ridge_results[signal_id] = (ridge_data)
# -- Save out individual results --
if self.cb_specs.isChecked():
# plot spectrum and ridge
ax_sig, ax_spec = pl.mk_signal_modulus_ax(
self.parentDV.time_unit)
pl.plot_signal_modulus((ax_sig, ax_spec),
tvec,
signal,
modulus,
periods,
p_max=self.wlet_pars['p_max'])
pl.draw_Wavelet_ridge(ax_spec, ridge_data)
plt.tight_layout()
fname = f'{OutPath}/{signal_id}_wspec.png'
if self.debug:
print(f'Plotting and saving {signal_id} to {fname}')
plt.savefig(fname)
plt.close()
if self.cb_readout_plots.isChecked():
pl.plot_readout(ridge_data)
fname = f'{OutPath}/{signal_id}_readout.png'
if self.debug:
print(f'Plotting and saving {signal_id} to {fname}')
plt.savefig(fname)
plt.close()
if self.cb_readout.isChecked():
fname = f'{OutPath}/{signal_id}_readout.csv'
if self.debug:
print(f'Saving ridge reatout to {fname}')
ridge_data.to_csv(fname,
sep=',',
float_format='%.3f',
index=False)
self.progress.setValue(i)
return ridge_results
def do_the_loop(self):
'''
Uses the explicitly parsed self.wlet_pars
to control signal analysis settings.
Takes general analysis Parameters
self.parentDV.dt
self.parentDV.time_unit
and the DataFrame
self.parentDV.df
from the parent DataViewer.
Reads additional settings from this Batch Process Window.
'''
EmptyRidge = 0
if self.export_options.isChecked():
OutPath = self.get_OutPath()
if OutPath is None:
return
periods = np.linspace(self.wlet_pars['T_min'], self.wlet_pars['T_max'],
self.wlet_pars['step_num'])
# retrieve batch settings
power_thresh = self.get_thresh()
rsmooth = self.get_ridge_smooth()
# results get stored here
ridge_results = {}
df_fouriers = pd.DataFrame(index=periods)
df_fouriers.index.name = 'period'
for i, signal_id in enumerate(self.parentDV.df):
# log to terminal
print(f"processing {signal_id}..")
# sets parentDV.raw_signal and parentDV.tvec
succ = self.parentDV.vector_prep(signal_id)
# ui silently passes over..
if not succ:
print(f"Can't process signal {signal_id}..")
continue
# detrend?!
if self.parentDV.cb_use_detrended.isChecked():
trend = self.parentDV.calc_trend()
signal = self.parentDV.raw_signal - trend
else:
signal = self.parentDV.raw_signal
# amplitude normalization?
if self.parentDV.cb_use_envelope.isChecked():
if self.debug:
print('Calculating envelope with L=', self.wlet_pars['L'])
signal = pyboat.normalize_with_envelope(
signal, self.wlet_pars['L'], self.parentDV.dt)
# compute the spectrum
modulus, wlet = pyboat.compute_spectrum(signal, self.parentDV.dt,
periods)
# get maximum ridge
ridge = pyboat.get_maxRidge_ys(modulus)
# generate time vector
tvec = np.arange(len(signal)) * self.parentDV.dt
# evaluate along the ridge
ridge_data = pyboat.eval_ridge(ridge,
wlet,
signal,
periods,
tvec,
power_thresh,
smoothing_wsize=rsmooth)
# from ridge thresholding..
if ridge_data.empty:
EmptyRidge += 1
else:
ridge_results[signal_id] = ridge_data
# time average the spectrum, all have shape len(periods)!
averaged_Wspec = np.mean(modulus, axis=1)
df_fouriers[signal_id] = averaged_Wspec
# -- Save out individual results --
if self.cb_specs.isChecked():
# plot spectrum and ridge
ax_sig, ax_spec = pl.mk_signal_modulus_ax(
self.parentDV.time_unit)
pl.plot_signal_modulus((ax_sig, ax_spec),
tvec,
signal,
modulus,
periods,
p_max=self.wlet_pars['p_max'])
pl.draw_Wavelet_ridge(ax_spec, ridge_data)
plt.tight_layout()
fname = os.path.join(OutPath, f'{signal_id}_wspec.png')
if self.debug:
print(
f'Plotting and saving spectrum {signal_id} to {fname}')
plt.savefig(fname, dpi=DPI)
plt.close()
if self.cb_specs_noridge.isChecked():
# plot spectrum without ridge
ax_sig, ax_spec = pl.mk_signal_modulus_ax(
self.parentDV.time_unit)
pl.plot_signal_modulus((ax_sig, ax_spec),
tvec,
signal,
modulus,
periods,
p_max=self.wlet_pars['p_max'])
plt.tight_layout()
fname = os.path.join(OutPath, f'{signal_id}_wspecNR.png')
if self.debug:
print(
f'Plotting and saving spectrum {signal_id} to {fname}')
plt.savefig(fname, dpi=DPI)
plt.close()
if self.cb_readout_plots.isChecked() and not ridge_data.empty:
pl.plot_readout(ridge_data)
fname = os.path.join(OutPath, f'{signal_id}_readout.png')
if self.debug:
print(f'Plotting and saving {signal_id} to {fname}')
plt.savefig(fname, dpi=DPI)
plt.close()
if self.cb_readout.isChecked() and not ridge_data.empty:
fname = os.path.join(OutPath, f'{signal_id}_readout.csv')
if self.debug:
print(f'Saving ridge reatout to {fname}')
ridge_data.to_csv(fname,
sep=',',
float_format='%.3f',
index=False)
self.progress.setValue(i)
if EmptyRidge > 0:
self.NoRidges = MessageWindow(
f'{EmptyRidge} ridge readouts entirely below threshold..',
'Discarded ridges')
return ridge_results, df_fouriers
def do_the_loop(self):
"""
Uses the explicitly parsed self.wlet_pars
to control signal analysis settings.
Takes general analysis Parameters
self.parentDV.dt
self.parentDV.time_unit
and the DataFrame
self.parentDV.df
from the parent DataViewer.
Reads additional settings from this Batch Process Window.
"""
EmptyRidge = 0
if self.export_options.isChecked():
OutPath = self.get_OutPath()
if OutPath is None:
return
periods = np.linspace(self.wlet_pars["Tmin"], self.wlet_pars["Tmax"],
self.wlet_pars["step_num"])
# retrieve batch settings
power_thresh = self.get_thresh()
rsmooth = self.get_ridge_smooth()
# results get stored here
ridge_results = {}
df_fouriers = pd.DataFrame(index=periods)
df_fouriers.index.name = "period"
for i, signal_id in enumerate(self.parentDV.df):
# log to terminal
print(f"processing {signal_id}..")
# sets parentDV.raw_signal and parentDV.tvec
succ = self.parentDV.vector_prep(signal_id)
# ui silently passes over..
if not succ:
print(f"Warning, can't process signal {signal_id}..")
continue
# detrend?!
if self.parentDV.cb_use_detrended.isChecked():
trend = self.parentDV.calc_trend()
signal = self.parentDV.raw_signal - trend
else:
signal = self.parentDV.raw_signal
# amplitude normalization?
if self.parentDV.cb_use_envelope.isChecked():
if self.debug:
print("Calculating envelope with L=",
self.wlet_pars["window_size"])
signal = pyboat.normalize_with_envelope(
signal, self.wlet_pars["window_size"], self.parentDV.dt)
# compute the spectrum
modulus, wlet = pyboat.compute_spectrum(signal, self.parentDV.dt,
periods)
# get maximum ridge
ridge = pyboat.get_maxRidge_ys(modulus)
# generate time vector
tvec = np.arange(len(signal)) * self.parentDV.dt
# evaluate along the ridge
ridge_data = pyboat.eval_ridge(
ridge,
wlet,
signal,
periods,
tvec,
power_thresh,
smoothing_wsize=rsmooth,
)
# from ridge thresholding..
if ridge_data.empty:
EmptyRidge += 1
else:
ridge_results[signal_id] = ridge_data
# time average the spectrum, all have shape len(periods)!
averaged_Wspec = np.mean(modulus, axis=1)
df_fouriers[signal_id] = averaged_Wspec
# -- Save out individual results --
settings = QSettings()
float_format = settings.value('float_format', '%.3f')
graphics_format = settings.value('graphics_format', 'png')
exbox_checked = self.export_options.isChecked()
if exbox_checked and self.cb_filtered_sigs.isChecked():
signal_df = pd.DataFrame()
signal_df['signal'] = signal
signal_df.index = tvec
signal_df.index.name = 'time'
fname = os.path.join(OutPath, f"{signal_id}_filtered.csv")
if self.debug:
print(f"Saving filtered signal to {fname}")
signal_df.to_csv(fname,
sep=",",
float_format=float_format,
index=True,
header=True)
if exbox_checked and self.cb_specs.isChecked():
# plot spectrum and ridge
ax_sig, ax_spec = pl.mk_signal_modulus_ax(
self.parentDV.time_unit)
pl.plot_signal_modulus(
(ax_sig, ax_spec),
tvec,
signal,
modulus,
periods,
p_max=self.wlet_pars["pow_max"],
)
pl.draw_Wavelet_ridge(ax_spec, ridge_data)
plt.tight_layout()
fname = os.path.join(OutPath,
f"{signal_id}_wspec.{graphics_format}")
if self.debug:
print(
f"Plotting and saving spectrum {signal_id} to {fname}")
plt.savefig(fname, dpi=DPI)
plt.close()
if exbox_checked and self.cb_specs_noridge.isChecked():
# plot spectrum without ridge
ax_sig, ax_spec = pl.mk_signal_modulus_ax(
self.parentDV.time_unit)
pl.plot_signal_modulus(
(ax_sig, ax_spec),
tvec,
signal,
modulus,
periods,
p_max=self.wlet_pars["pow_max"],
)
plt.tight_layout()
fname = os.path.join(OutPath,
f"{signal_id}_wspecNR.{graphics_format}")
if self.debug:
print(
f"Plotting and saving spectrum {signal_id} to {fname}")
plt.savefig(fname, dpi=DPI)
plt.close()
if exbox_checked and self.cb_readout_plots.isChecked(
) and not ridge_data.empty:
pl.plot_readout(ridge_data)
fname = os.path.join(OutPath,
f"{signal_id}_readout.{graphics_format}")
if self.debug:
print(f"Plotting and saving {signal_id} to {fname}")
plt.savefig(fname, dpi=DPI)
plt.close()
if exbox_checked and self.cb_readout.isChecked(
) and not ridge_data.empty:
fname = os.path.join(OutPath, f"{signal_id}_readout.csv")
if self.debug:
print(f"Saving ridge readout to {fname}")
ridge_data.to_csv(fname,
sep=",",
float_format=float_format,
index=False)
self.progress.setValue(i)
if EmptyRidge > 0:
msg = f"{EmptyRidge} ridge readouts entirely below threshold.."
msgBox = QMessageBox()
msgBox.setWindowTitle("Discarded Ridges")
msgBox.setText(msg)
msgBox.exec()
return ridge_results, df_fouriers