def onFitfunctionChanged(self, row, newfitname ):
"""Swap out the fitfunction on the current analysis"""
if self.fitfunction:
self.currentEvaluation.fitfunctionCache[self.fitfunction.name] = StoredFitFunction.fromFitfunction( self.fitfunction )
self.currentEvaluation.fitfunctionName = newfitname
if newfitname in self.currentEvaluation.fitfunctionCache:
self.currentEvaluation.fitfunction = self.currentEvaluation.fitfunctionCache[newfitname]
self.setFitfunction( self.currentEvaluation.fitfunction.fitfunction() )
else:
self.setFitfunction( fitFunctionMap[newfitname]() )
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(self.fitfunction)
self.pushTableModel.setPushVariables(self.currentEvaluation.pushVariables, self.fitfunction)
def onActiveAnalysisChanged(self, selected, deselected=None):
if deselected and self.fitfunction:
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(
self.fitfunction)
if selected.row() >= 0:
self.currentEvaluation = self.analysisDefinition[
selected.row()] if self.analysisDefinition else None
self.currentEvaluationIndex = selected.row(
) if self.analysisDefinition else None
self.currentEvaluationLabel.setText(
self.currentEvaluation.name if self.currentEvaluation else "")
if self.currentEvaluation and self.currentEvaluation.fitfunction:
self.setFitfunction(
self.currentEvaluation.fitfunction.fitfunction())
else:
self.setFitfunction(None)
self.pushTableModel.setPushVariables(
self.currentEvaluation.pushVariables
if self.currentEvaluation else None, self.fitfunction)
else:
self.currentEvaluation = None
self.currentEvaluationIndex = None
self.currentEvaluationLabel.setText("")
self.setFitfunction(None)
self.pushTableModel.setPushVariables(None, self.fitfunction)
self.setButtonEnabledState()
def saveable(self):
name = str(self.analysisNameComboBox.currentText())
definition = StoredFitFunction.fromFitfunction(self.fitfunction)
definition.name = name
return name != '' and (
name not in self.analysisDefinitions
or not (self.analysisDefinitions[name] == definition))
def onLoadFitFunction(self, name=None):
name = str(name) if name is not None else self.currentAnalysisName
if name in self.analysisDefinitions:
if StoredFitFunction.fromFitfunction(
self.fitfunction) != self.analysisDefinitions[name]:
self.setFitfunction(
self.analysisDefinitions[name].fitfunction())
def saveable(self):
if self.currentEvaluation is not None and self.fitfunction is not None:
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(
self.fitfunction)
return self.currentAnalysisName != '' and (
self.currentAnalysisName not in self.analysisDefinitionDict
or not (self.analysisDefinitionDict[self.currentAnalysisName]
== self.analysisDefinition))
def fit(self, evaluation):
if self.currentEvaluation is not None and evaluation == self.currentEvaluation:
plot = self.plottedTraceDict.get(evaluation.evaluation)
self.fitfunction.evaluate(self.globalDict)
if plot is not None:
sigma = None
if plot.hasHeightColumn:
sigma = plot.height
elif plot.hasTopColumn and plot.hasBottomColumn:
sigma = abs(plot.top + plot.bottom)
self.fitfunction.leastsq(plot.x, plot.y, sigma=sigma)
plot.fitFunction = copy.deepcopy(self.fitfunction)
plot.plot(-2)
evaluation.fitfunction = StoredFitFunction.fromFitfunction(
self.fitfunction)
self.fitfunctionTableModel.fitDataChanged()
self.fitResultsTableModel.fitDataChanged()
replacements = self.fitfunction.replacementDict()
replacements.update(self.globalDict)
evaluation.updatePushVariables(replacements)
self.pushTableModel.fitDataChanged()
else:
fitfunction = evaluation.fitfunction.fitfunction()
fitfunction.evaluate(self.globalDict)
plot = self.plottedTraceDict.get(evaluation.evaluation)
if plot is not None:
sigma = None
if plot.hasHeightColumn:
sigma = plot.height
elif plot.hasTopColumn and plot.hasBottomColumn:
sigma = abs(plot.top + plot.bottom)
fitfunction.leastsq(plot.x, plot.y, sigma=sigma)
plot.fitFunction = fitfunction
plot.plot(-2)
evaluation.fitfunction = StoredFitFunction.fromFitfunction(
fitfunction)
self.fitfunctionTableModel.fitDataChanged()
self.fitResultsTableModel.fitDataChanged()
replacements = fitfunction.replacementDict()
replacements.update(self.globalDict)
evaluation.updatePushVariables(replacements)
names = evaluation.fitfunction.fitfunction().parameterNames
vals = evaluation.fitfunction.fitfunction().parameters
return dict(list(zip(
names,
vals))) #Return a dictionary of fit parameters and fitted values
def autoSave(self):
if self.parameters.autoSave:
if self.currentEvaluation is not None and self.fitfunction is not None:
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction( self.fitfunction )
self.onSave()
self.saveButton.setEnabled( False )
else:
self.saveButton.setEnabled( self.saveable() )
def autoSave(self):
if self.parameters.autoSave:
if self.currentEvaluation is not None and self.fitfunction is not None:
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(
self.fitfunction)
self.onSave()
self.saveButton.setEnabled(False)
else:
self.saveButton.setEnabled(self.saveable())
def onFitfunctionChanged(self, row, newfitname):
"""Swap out the fitfunction on the current analysis"""
if self.fitfunction:
self.currentEvaluation.fitfunctionCache[
self.fitfunction.name] = StoredFitFunction.fromFitfunction(
self.fitfunction)
self.currentEvaluation.fitfunctionName = newfitname
if newfitname in self.currentEvaluation.fitfunctionCache:
self.currentEvaluation.fitfunction = self.currentEvaluation.fitfunctionCache[
newfitname]
self.setFitfunction(
self.currentEvaluation.fitfunction.fitfunction())
else:
self.setFitfunction(fitFunctionMap[newfitname]())
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(
self.fitfunction)
self.pushTableModel.setPushVariables(
self.currentEvaluation.pushVariables, self.fitfunction)
def fit(self, evaluation):
if self.currentEvaluation is not None and evaluation == self.currentEvaluation:
plot = self.plottedTraceDict.get( evaluation.evaluation )
self.fitfunction.evaluate( self.globalDict )
if plot is not None:
sigma = None
if plot.hasHeightColumn:
sigma = plot.height
elif plot.hasTopColumn and plot.hasBottomColumn:
sigma = abs(plot.top + plot.bottom)
self.fitfunction.leastsq(plot.x, plot.y, sigma=sigma)
plot.fitFunction = copy.deepcopy(self.fitfunction)
plot.plot(-2)
evaluation.fitfunction = StoredFitFunction.fromFitfunction(self.fitfunction)
self.fitfunctionTableModel.fitDataChanged()
self.fitResultsTableModel.fitDataChanged()
replacements = self.fitfunction.replacementDict()
replacements.update( self.globalDict )
evaluation.updatePushVariables( replacements )
self.pushTableModel.fitDataChanged()
else:
fitfunction = evaluation.fitfunction.fitfunction()
fitfunction.evaluate( self.globalDict )
plot = self.plottedTraceDict.get( evaluation.evaluation )
if plot is not None:
sigma = None
if plot.hasHeightColumn:
sigma = plot.height
elif plot.hasTopColumn and plot.hasBottomColumn:
sigma = abs(plot.top + plot.bottom)
fitfunction.leastsq(plot.x, plot.y, sigma=sigma)
plot.fitFunction = fitfunction
plot.plot(-2)
evaluation.fitfunction = StoredFitFunction.fromFitfunction(fitfunction)
self.fitfunctionTableModel.fitDataChanged()
self.fitResultsTableModel.fitDataChanged()
replacements = fitfunction.replacementDict()
replacements.update( self.globalDict )
evaluation.updatePushVariables( replacements )
names = evaluation.fitfunction.fitfunction().parameterNames
vals = evaluation.fitfunction.fitfunction().parameters
return dict(list(zip(names, vals))) #Return a dictionary of fit parameters and fitted values
def onSaveAnalysis(self):
name = str(self.analysisNameComboBox.currentText())
if name:
definition = StoredFitFunction.fromFitfunction(self.fitfunction)
definition.name = name
isNew = name not in self.analysisDefinitions
self.analysisDefinitions[name] = definition
if self.analysisNameComboBox.findText(name)<0:
self.analysisNameComboBox.addItem(name)
if isNew:
self.analysisNamesChanged.emit( list(self.analysisDefinitions.keys()) )
self.saveButton.setEnabled( False )
def onSaveAnalysis(self):
name = str(self.analysisNameComboBox.currentText())
if name:
definition = StoredFitFunction.fromFitfunction(self.fitfunction)
definition.name = name
isNew = name not in self.analysisDefinitions
self.analysisDefinitions[name] = definition
if self.analysisNameComboBox.findText(name) < 0:
self.analysisNameComboBox.addItem(name)
if isNew:
self.analysisNamesChanged.emit(
list(self.analysisDefinitions.keys()))
self.saveButton.setEnabled(False)
def onActiveAnalysisChanged(self, selected, deselected=None):
if deselected and self.fitfunction:
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(self.fitfunction)
if selected.row()>=0:
self.currentEvaluation = self.analysisDefinition[selected.row()] if self.analysisDefinition else None
self.currentEvaluationIndex = selected.row() if self.analysisDefinition else None
self.currentEvaluationLabel.setText( self.currentEvaluation.name if self.currentEvaluation else "" )
if self.currentEvaluation and self.currentEvaluation.fitfunction:
self.setFitfunction( self.currentEvaluation.fitfunction.fitfunction() )
else:
self.setFitfunction( None )
self.pushTableModel.setPushVariables(self.currentEvaluation.pushVariables if self.currentEvaluation else None, self.fitfunction)
else:
self.currentEvaluation = None
self.currentEvaluationIndex = None
self.currentEvaluationLabel.setText( "" )
self.setFitfunction( None )
self.pushTableModel.setPushVariables( None, self.fitfunction)
self.setButtonEnabledState()
def onLoadAnalysis(self, name=None):
name = str(name) if name is not None else str(self.analysisNameComboBox.currentText())
if name in self.analysisDefinitions:
if StoredFitFunction.fromFitfunction(self.fitfunction) != self.analysisDefinitions[name]:
self.setFitfunction( self.analysisDefinitions[name].fitfunction() )
def saveable(self):
name = str(self.analysisNameComboBox.currentText())
definition = StoredFitFunction.fromFitfunction(self.fitfunction)
definition.name = name
return name != '' and ( name not in self.analysisDefinitions or not (self.analysisDefinitions[name] == definition) )
def onUseSmartStart(self, state):
if self.fitfunction is not None:
self.fitfunction.useSmartStartValues = state==QtCore.Qt.Checked
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(self.fitfunction)
self.autoSave()
def saveable(self):
if self.currentEvaluation is not None and self.fitfunction is not None:
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction( self.fitfunction )
return self.currentAnalysisName != '' and ( self.currentAnalysisName not in self.analysisDefinitionDict or not (self.analysisDefinitionDict[self.currentAnalysisName] == self.analysisDefinition))
def onUseSmartStart(self, state):
if self.fitfunction is not None:
self.fitfunction.useSmartStartValues = state == QtCore.Qt.Checked
self.currentEvaluation.fitfunction = StoredFitFunction.fromFitfunction(
self.fitfunction)
self.autoSave()