def run_fourier_ana(self):
if not np.any(self.raw_signal):
self.NoSignalSelected = MessageWindow(
"Please select a signal first!", "No Signal")
return False
# shift new analyser windows
self.w_position += 20
if self.cb_use_detrended2.isChecked():
trend = self.calc_trend()
signal = self.raw_signal - trend
else:
signal = self.raw_signal
if self.cb_use_envelope2.isChecked():
L = self.get_L(self.L_edit)
signal = pyboat.normalize_with_envelope(signal, L, self.dt)
# periods or frequencies?
if self.cb_FourierT.isChecked():
show_T = False
else:
show_T = True
self.anaWindows[self.w_position] = FourierAnalyzer(
signal=signal,
dt=self.dt,
signal_id=self.signal_id,
position=self.w_position,
time_unit=self.time_unit,
show_T=show_T,
)
def run_fourier_ana(self):
if not np.any(self.raw_signal):
self.NoSignalSelected = MessageWindow(
"Please select a signal first!", "No Signal")
return False
# shift new analyser windows
self.w_position += 20
if self.cb_use_detrended2.isChecked() and not self.T_c:
self.NoTrend = MessageWindow(
"Detrending not set, can not use detrended signal!",
"No Trend")
return
elif self.cb_use_detrended2.isChecked():
trend = self.calc_trend()
signal = self.raw_signal - trend
else:
signal = self.raw_signal
if self.cb_use_envelope2.isChecked() and not self.L:
self.NoTrend = MessageWindow(
"Envelope parameter not set,\n" +
"specify a sliding window size!",
"No Envelope",
)
return
elif self.cb_use_envelope2.isChecked():
signal = pyboat.normalize_with_envelope(signal, self.L)
# periods or frequencies?
if self.cb_FourierT.isChecked():
show_T = False
else:
show_T = True
self.anaWindows[self.w_position] = FourierAnalyzer(
signal=signal,
dt=self.dt,
signal_id="Synthetic Signal",
position=self.w_position,
time_unit=self.time_unit,
show_T=show_T,
)
def run_wavelet_ana(self):
""" run the Wavelet Analysis """
if not np.any(self.raw_signal):
msgBox = QMessageBox()
msgBox.setWindowTitle("No Signal")
msgBox.setText("Please select a signal first!")
msgBox.exec()
return False
wlet_pars = self.set_wlet_pars() # Error handling done there
if not wlet_pars:
if self.debug:
print("Wavelet parameters could not be set!")
return False
if self.cb_use_detrended.isChecked():
trend = self.calc_trend()
signal = self.raw_signal - trend
else:
signal = self.raw_signal
if self.cb_use_envelope.isChecked():
window_size = self.get_wsize(self.wsize_edit)
signal = pyboat.normalize_with_envelope(signal,
window_size,
dt=self.dt)
self.w_position += 20
self.anaWindows[self.w_position] = WaveletAnalyzer(
signal=signal,
dt=self.dt,
Tmin=wlet_pars['Tmin'],
Tmax=wlet_pars['Tmax'],
pow_max=wlet_pars['pow_max'],
step_num=wlet_pars['step_num'],
position=self.w_position,
signal_id=self.signal_id,
time_unit=self.time_unit,
DEBUG=self.debug,
)
def run_wavelet_ana(self):
""" run the Wavelet Analysis on the synthetic signal """
if not np.any(self.raw_signal):
self.NoSignalSelected = MessageWindow(
"Please create a signal first!", "No Signal")
return False
wlet_pars = self.set_wlet_pars() # Error handling done there
if not wlet_pars:
if self.debug:
print("Wavelet parameters could not be set!")
return False
if self.cb_use_detrended.isChecked():
trend = self.calc_trend()
signal = self.raw_signal - trend
else:
signal = self.raw_signal
if self.cb_use_envelope.isChecked():
L = self.get_L(self.L_edit)
signal = pyboat.normalize_with_envelope(signal, L, dt=self.dt)
self.w_position += 20
self.anaWindows[self.w_position] = WaveletAnalyzer(
signal=signal,
dt=self.dt,
T_min=wlet_pars['T_min'],
T_max=wlet_pars['T_max'],
p_max=wlet_pars['p_max'],
step_num=wlet_pars['step_num'],
position=self.w_position,
signal_id='Synthetic Signal',
time_unit=self.time_unit,
DEBUG=self.debug,
)
def run_fourier_ana(self):
if not np.any(self.raw_signal):
msgBox = QMessageBox()
msgBox.setWindowTitle("No Signal")
msgBox.setText("Please select a signal first!")
msgBox.exec()
return False
# shift new analyser windows
self.w_position += 20
if self.cb_use_detrended2.isChecked():
trend = self.calc_trend()
signal = self.raw_signal - trend
else:
signal = self.raw_signal
if self.cb_use_envelope2.isChecked():
window_size = self.get_wsize(self.wsize_edit)
signal = pyboat.normalize_with_envelope(signal, window_size,
self.dt)
# periods or frequencies?
if self.cb_FourierT.isChecked():
show_T = False
else:
show_T = True
self.anaWindows[self.w_position] = FourierAnalyzer(
signal=signal,
dt=self.dt,
signal_id=self.signal_id,
position=self.w_position,
time_unit=self.time_unit,
show_T=show_T,
)
def run_wavelet_ana(self):
""" run the Wavelet Analysis """
if not np.any(self.raw_signal):
self.NoSignalSelected = MessageWindow(
"Please select a signal first!", "No Signal")
return False
succ = self.set_wlet_pars() # Error handling done there
if not succ:
if self.debug:
print("Wavelet parameters could not be set!")
return False
# move to set_wlet_pars?!
if self.step_num_value > 1000:
choice = QMessageBox.question(
self,
"Too much periods?: ",
"High number of periods: Do you want to continue?",
QMessageBox.Yes | QMessageBox.No,
)
if choice == QMessageBox.Yes:
pass
else:
return
# detrend for the analysis?
if self.cb_use_detrended.isChecked() and not self.T_c:
self.NoTrend = MessageWindow(
"Detrending parameter not set,\n" +
"specify a cut-off period!",
"No Trend",
)
return
elif self.cb_use_detrended.isChecked():
trend = self.calc_trend()
signal = self.raw_signal - trend
else:
signal = self.raw_signal
# amplitude normalization is downstram of detrending!
if self.cb_use_envelope.isChecked() and not self.L:
self.NoTrend = MessageWindow(
"Envelope parameter not set,\n" +
"specify a sliding window size!",
"No Envelope",
)
return
elif self.cb_use_envelope.isChecked():
signal = pyboat.normalize_with_envelope(signal, self.L)
self.w_position += 20
self.anaWindows[self.w_position] = WaveletAnalyzer(
signal=signal,
dt=self.dt,
T_min=self.T_min_value,
T_max=self.T_max_value,
position=self.w_position,
signal_id="Synthetic Signal",
step_num=self.step_num_value,
p_max=self.p_max_value,
time_unit=self.time_unit,
DEBUG=self.debug,
)
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
