def showOtherSchemes(self):
if self.otherRadio.isChecked():
self.schemesList.clear()
self.schemesList.addItem(SamplingMethods.getFullName(SamplingMethods.MC))
self.schemesList.addItem(SamplingMethods.getFullName(SamplingMethods.LPTAU))
self.schemesList.addItem(SamplingMethods.getFullName(SamplingMethods.LH))
self.schemesList.addItem(SamplingMethods.getFullName(SamplingMethods.OA))
def showParamScreenSchemes(self):
if self.paramScreenRadio.isChecked():
self.schemesList.clear()
self.schemesList.addItem(
SamplingMethods.getFullName(SamplingMethods.MOAT))
self.schemesList.addItem(
SamplingMethods.getFullName(SamplingMethods.GMOAT))
self.schemesList.addItem(
SamplingMethods.getFullName(SamplingMethods.LSA))
def showAdaptiveRefineSchemes(self):
if self.adaptiveRefineRadio.isChecked():
foundLibs = LocalExecutionModule.getPsuadeInstalledModules();
foundMETIS = foundLibs['METIS']
self.schemesList.clear()
self.schemesList.addItem(SamplingMethods.getFullName(SamplingMethods.METIS))
if not foundMETIS:
item = self.schemesList.item(0)
text = item.text()
item.setText(text + ' (Not installed)')
flags = item.flags()
item.setFlags(flags & ~Qt.ItemIsEnabled)
def createPsuadeInFile(data, filename, includePDF=True):
outf = open(filename, 'w')
outf.write('PSUADE\n')
#Write inputs
outf.write('INPUT\n')
outf.write(' dimension = %d\n' % data.getNumInputs())
names = data.getInputNames()
mins = data.getInputMins()
maxs = data.getInputMaxs()
indices = list(range(data.getNumInputs()))
for i, name, minimum, maximum in zip(indices, names, mins, maxs):
outf.write(' variable %d %s = %e %e\n' %
(i + 1, name, minimum, maximum))
distributions = data.getInputDistributions()
for i, dist in zip(indices, distributions):
distType = dist.getDistributionType()
distParams = dist.getParameterValues()
if distType == Distribution.SAMPLE:
outf.write(' PDF %d %c' %
(i + 1, Distribution.getPsuadeName(distType)))
if distParams[0] is not None:
filename = distParams[0]
if platform.system() == 'Windows':
import win32api
filename = win32api.GetShortPathName(filename)
outf.write(' %s' % filename)
if distParams[1] is not None:
outf.write(' %d' % distParams[1])
outf.write('\n')
elif includePDF: # write out PDF info for all non-uniform PDF types
if distType != Distribution.UNIFORM:
outf.write(' PDF %d %c' %
(i + 1, Distribution.getPsuadeName(distType)))
if distParams[0] is not None:
outf.write(' %e' % distParams[0])
if distParams[1] is not None:
outf.write(' %e' % distParams[1])
outf.write('\n')
outf.write('END\n')
#Write outputs
outf.write('OUTPUT\n')
if data.getNumOutputs() == 0:
outf.write(' dimension = 1\n')
names = ['ghostOuput']
indices = list(range(1))
for i, name in zip(indices, names):
outf.write(' variable %d %s\n' % (i + 1, name))
else:
outf.write(' dimension = %d\n' % data.getNumOutputs())
names = data.getOutputNames()
indices = list(range(data.getNumOutputs()))
for i, name in zip(indices, names):
outf.write(' variable %d %s\n' % (i + 1, name))
outf.write('END\n')
#Write Method
outf.write('METHOD\n')
outf.write(' sampling = %s\n' %
SamplingMethods.getPsuadeName(data.getSampleMethod()))
outf.write(' num_samples = %d\n' % data.getNumSamples())
if data.getSampleMethod() != SamplingMethods.GMOAT:
outf.write(' randomize\n')
outf.write('END\n')
#Write Application
outf.write('APPLICATION\n')
driverString = data.getDriverName()
if driverString is None:
driverString = 'NONE'
elif platform.system() == 'Windows':
if os.path.exists(driverString):
import win32api
driverString = win32api.GetShortPathName(driverString)
else:
driverString = 'NONE'
driverString = 'NONE'
outf.write(' driver = %s\n' % driverString)
outf.write(' save_frequency = 1\n')
outf.write('END\n')
#Write Analysis
outf.write('ANALYSIS\n')
outf.write(' diagnostics 2\n')
outf.write('END\n')
outf.write('END\n')
outf.close()
def generateSamples(self):
# self.setModal(False)
# Gather all info into SampleData object
if isinstance(self.model, Model):
model = copy.deepcopy(self.model)
# runData = SampleData(self.model)
else:
model = copy.deepcopy(self.model.model)
# runData = copy.deepcopy(self.model)
# Gather distributions from distribution table
types = []
modelTypes = self.model.getInputTypes()
defaults = []
modelDefaults = self.model.getInputDefaults()
mins = []
modelMins = self.model.getInputMins()
maxs = []
modelMaxs = self.model.getInputMaxs()
dists = []
selectedInputs = []
# Set sampling scheme to selected or monte carlo if adaptive
if self.chooseSchemeRadio.isChecked():
scheme = self.schemesList.currentItem().text()
else:
scheme = "Monte Carlo"
# First get parameters for the model
row = 0
for inputNum in range(self.model.getNumInputs()):
if modelTypes[inputNum] == Model.VARIABLE:
# Type
combobox = self.distTable.cellWidget(row, 1)
if combobox is None:
text = self.distTable.item(row, 1).text()
else:
text = combobox.currentText()
if text == "Fixed":
value = Model.FIXED
else:
value = Model.VARIABLE
types.append(value)
if value == Model.VARIABLE:
selectedInputs.append(inputNum)
# Defaults
item = self.distTable.item(row, 2)
if item is None or len(item.text()) == 0:
defaults.append(None)
else:
defaults.append(float(item.text()))
# Mins
item = self.distTable.item(row, 3)
mins.append(float(item.text()))
# Maxs
item = self.distTable.item(row, 4)
maxs.append(float(item.text()))
row += 1
else: # Fixed
types.append(Model.FIXED)
defaults.append(modelDefaults[inputNum])
mins.append(modelMins[inputNum])
maxs.append(modelMaxs[inputNum])
# Update model
model.setInputTypes(types)
model.setInputDefaults(defaults)
model.setInputMins(mins)
model.setInputMaxs(maxs)
# Create SampleData object
runData = SampleData(model, self.session)
runData.setModelName(
self.session.flowsheet.results.incrimentSetName("UQ_Ensemble"))
runData.setFromFile(False)
# Now get distributions for the SampleData object
numSampleFromFile = 0
row = 0
for inputNum in range(self.model.getNumInputs()):
if modelTypes[inputNum] == Model.VARIABLE:
# Only collect those that are not fixed to generate inputs
combobox = self.distTable.cellWidget(row, 5)
dist = combobox.currentIndex()
# Check non-uniform distribution and non-Monte Carlo scheme
if (
False
): # dist != Distribution.UNIFORM and SamplingMethods.getEnumValue(scheme) != SamplingMethods.MC:
msgbox = QMessageBox()
msgbox.setWindowTitle("UQ/Opt GUI Warning")
msgbox.setText(
"Non-Uniform distributions are not compatible with any "
+
"sampling scheme other than Monte Carlo! Please change "
+
"all distributions back to uniform or select Monte Carlo "
+ "sampling scheme.")
msgbox.setIcon(QMessageBox.Warning)
msgbox.exec_()
return
if dist == Distribution.SAMPLE:
numSampleFromFile += 1
dists += [self.distTable.getDistribution(row)]
row += 1
else: # Fixed
dist = Distribution(Distribution.UNIFORM)
dists = dists + [dist]
runData.setInputDistributions(dists)
numSamples = int(self.numSamplesBox.value())
runData.setNumSamples(numSamples)
runData.setSampleMethod(scheme)
# Check number of samples
scheme = runData.getSampleMethod()
newNumSamples = SamplingMethods.validateSampleSize(
scheme, len(selectedInputs), numSamples)
if scheme == SamplingMethods.LSA:
if newNumSamples != numSamples:
msgbox = QMessageBox()
msgbox.setWindowTitle("UQ/Opt GUI Warning")
msgbox.setText(
"%s scheme with %d variable inputs requires %d samples! Do you want to proceed?"
% (
SamplingMethods.getPsuadeName(scheme),
len(selectedInputs),
newNumSamples,
))
msgbox.setIcon(QMessageBox.Question)
msgbox.setStandardButtons(QMessageBox.Yes | QMessageBox.No)
msgbox.setDefaultButton(QMessageBox.Yes)
response = msgbox.exec_()
if response == QMessageBox.Yes:
runData.setNumSamples(newNumSamples)
else:
return
elif scheme == SamplingMethods.MOAT or scheme == SamplingMethods.GMOAT:
if type(newNumSamples) is tuple:
msgbox = QMessageBox()
msgbox.setWindowTitle("UQ/Opt GUI Warning")
msgbox.setText(
"%s scheme with %d variable inputs cannot have %d samples! How do you want to proceed?"
% (
SamplingMethods.getFullName(scheme),
len(selectedInputs),
numSamples,
))
msgbox.setIcon(QMessageBox.Question)
firstValButton = msgbox.addButton(
"Change to %d samples" % newNumSamples[0],
QMessageBox.AcceptRole)
secondValButton = msgbox.addButton(
"Change to %d samples" % newNumSamples[1],
QMessageBox.AcceptRole)
cancelButton = msgbox.addButton(QMessageBox.Cancel)
msgbox.exec_()
if msgbox.clickedButton() == firstValButton:
runData.setNumSamples(int(newNumSamples[0]))
elif msgbox.clickedButton() == secondValButton:
runData.setNumSamples(int(newNumSamples[1]))
else:
return
# Visual indications of processing
QApplication.setOverrideCursor(QCursor(Qt.WaitCursor))
self.generateStatusText.setText("Generating...")
self.generateStatusText.repaint()
# Generate samples for the variable inputs
selectedRunData = ExperimentalDesign.generateSamples(
runData, selectedInputs, self.model.getSelectedOutputs())
if selectedRunData is None:
QApplication.restoreOverrideCursor()
self.generateStatusText.setText("")
return
selectedInputData = selectedRunData.getInputData()
# Add fixed inputs back in
## print runData.getNumSamples()
fullInputData = [0] * runData.getNumSamples()
for row in range(runData.getNumSamples()):
rowData = []
selectedIndex = 0
for col in range(runData.getNumInputs()):
if col in selectedInputs:
rowData.append(selectedInputData[row][selectedIndex])
selectedIndex = selectedIndex + 1
else:
rowData.append(defaults[col])
fullInputData[row] = rowData
runData.setInputData(fullInputData)
runData.setRunState([0] * runData.getNumSamples())
self.runData = runData
# Handle archive of METIS file
if self.runData.getSampleMethod() == SamplingMethods.METIS:
if self.currentArchiveData is not None:
# Common.removeArchive(self.currentArchive)
self.currentArchiveData.removeArchiveFolder()
pass
# Common.archiveFile('psuadeMetisInfo', self.runData.getID())
self.runData.archiveFile("psuadeMetisInfo")
self.currentArchiveData = self.runData
# Restore cursor
QApplication.restoreOverrideCursor()
self.generateStatusText.setText("Done!")
self.samplesGenerated = True
self.previewButton.setEnabled(True)
self.doneButton.setEnabled(True)
