def wrapped_callback(*args, **kwargs):
try:
return callback(args[0])
except:
# catch all errors
error_string = traceback.format_exc()
alert("Error!", error_string)
return None
def predict_signals(self):
def _compute_signals(μ, σ):
μ_rad, σ_rad = np.deg2rad(μ), np.deg2rad(σ)
return np.array(
[
nP_SHG(μ_rad, σ_rad),
nS_SHG(μ_rad, σ_rad),
nP_TPF(μ_rad, σ_rad),
nS_TPF(μ_rad, σ_rad),
]
)
ref_angle = self.referenceTiltSpinBox.value()
ref_dist = self.referenceDistributionSpinBox.value()
arg_angle = self.targetTiltSpinBox.value()
arg_dist = self.targetDistributionSpinBox.value()
if (ref_angle, ref_dist) == (arg_angle, arg_dist):
alert("Warning", "Reference and final values are the same")
else:
ref_intensities = _compute_signals(ref_angle, ref_dist)
arg_intensities = _compute_signals(arg_angle, arg_dist)
# compute relative signal changes as a percentage
signal_changes = 100.0 * (
(arg_intensities - ref_intensities) / ref_intensities
)
bar_labels = ["P-SHG", "S-SHG", "P-FL", "S-FL"]
xpos = np.arange(1, 5)
# clear the axis
self.predictedSignalWidget.canvas.axes.clear()
# do the actual plotting
bars1 = self.predictedSignalWidget.canvas.axes.bar(xpos, signal_changes)
autolabel(bars1, self.predictedSignalWidget.canvas.axes)
self.predictedSignalWidget.canvas.axes.grid(b=False, axis="x")
self.predictedSignalWidget.canvas.axes.set_xticks(xpos)
self.predictedSignalWidget.canvas.axes.set_xticklabels(bar_labels)
self.predictedSignalWidget.canvas.axes.set_xlabel("Channels")
self.predictedSignalWidget.canvas.axes.set_ylabel(r"% signal change")
self.predictedSignalWidget.canvas.refresh()
def visualize_4ch(self):
# self.visualchecksWidget
# get raw data
data = self.tableWidget.getVisibleData()
if data.empty:
alert("Warning!", "No data is loaded")
return False
else:
columnsOK, missing = checkcolumns(data.columns)
fraclabeled = data["frac_labeled"].unique().tolist()
if not columnsOK:
alert("Warning!", f"Columns {missing} are missing")
return False
if len(fraclabeled) < 3:
alert("Warning!", f"You need at least 2 frac_labeled")
return False
# do checks on columns
if columnsOK:
# create a shorthand for the internal axes
ax = self.visualchecksWidget.canvas.axes
# last axis for linearity check
self.visualchecksWidget.lasttwinax.clear()
# clear previous plots (if any)
for axes in ax.flat:
axes.cla()
overview(
data,
fighandles=(self.visualchecksWidget.canvas.figure, ax),
twin_ax=self.visualchecksWidget.lasttwinax,
)
self.visualchecksWidget.canvas.refresh()
else:
warnstr = f"Columns : {missing} missing from data table."
alert("Warning!", warnstr)
def savefile(self):
data = self.tableWidget._data_model.df
if data.empty:
alert("Warning", "Data table is empty")
else:
options = QFileDialog.Options()
options |= QFileDialog.DontUseNativeDialog
fileName, fileType = QFileDialog.getSaveFileName(
self,
"Save data table",
"",
"CSV files (*.csv);;Excel file (*.xlsx)",
options=options,
)
extension = ".csv" if "CSV" in fileType else ".xlsx"
targetfile = Path(fileName).with_suffix(extension)
if extension == ".csv":
data.to_csv(targetfile, index=False)
elif extension == ".xlsx":
data.to_excel(targetfile, index=False)
def fit_phase_difference(self):
data = self.tableWidget.getVisibleData()
if self.currentfilepath is not None:
exptname = self.currentfilepath.stem
else:
exptname = "DataNotLoaded"
if data.empty:
alert("Warning!", "No data is loaded")
return False
else:
columnsOK, missing = checkcolumns(data.columns)
fraclabeled = data["frac_labeled"].unique().tolist()
if not columnsOK:
alert("Warning!", f"Columns {missing} are missing")
return False
if len(fraclabeled) < 3:
alert("Warning!", f"You need at least 2 frac_labeled")
return False
if columnsOK:
self.experiment = AMPSexperiment(data, name=exptname)
self.experiment.fit_phases()
fitmsg = "P-polarization:\n"
fitmsg += self.experiment.Pphases.optres.message
fitmsg += "\nS-Polarization:\n"
fitmsg += self.experiment.Sphases.optres.message
alert("Phase determination", fitmsg)
# extract parameters and put into GUI
Pphase = self.experiment.pshg_phase
Pphase_sigma = self.experiment.Pphases.optres.params["delphi"].stderr
Pphase_error = (
np.rad2deg(Pphase_sigma) if Pphase_sigma is not None else np.nan
)
self.Pphase_sigma_label.setText(f"±{Pphase_error:.2f}")
Sphase = self.experiment.sshg_phase
Sphase_sigma = self.experiment.Sphases.optres.params["delphi"].stderr
Sphase_error = (
np.rad2deg(Sphase_sigma) if Sphase_sigma is not None else np.nan
)
self.Sphase_sigma_label.setText(f"±{Sphase_error:.2f}")
if Pphase > np.pi / 2.0:
# destructive interference
self.PinflectionSpinBox.setValue(self.experiment.p_inflection)
else:
# constructive has no inflection point
self.PinflectionSpinBox.setValue(0.0)
if Sphase > np.pi / 2.0:
# destructive intereference
self.SinflectionSpinBox.setValue(self.experiment.s_inflection)
else:
self.SinflectionSpinBox.setValue(0.0)
Pbg = self.experiment.pshg_bg
Sbg = self.experiment.sshg_bg
self.phasePspinBox.setValue(np.rad2deg(Pphase))
self.backgroundPspinBox.setValue(Pbg)
self.phaseSspinBox.setValue(np.rad2deg(Sphase))
self.backgroundSspinBox.setValue(Sbg)
# create a shorthand for the internal axes
ax = self.visualchecksWidget.canvas.axes
# last axis for linearity check
self.visualchecksWidget.lasttwinax.clear()
# clear previous plots (if any)
for axes in ax.flat:
axes.cla()
overview(
data,
fighandles=(self.visualchecksWidget.canvas.figure, ax),
twin_ax=self.visualchecksWidget.lasttwinax,
experiment=self.experiment,
)
self.visualchecksWidget.canvas.refresh()