def finishOUU(self, out, error):
if error:
return None
# clean up
if os.path.exists(OUU.hfile):
hfile_ = OUU.dname + os.path.sep + OUU.hfile
os.rename(OUU.hfile, hfile_)
hfile = hfile_
for f in os.listdir('.'):
if 'psuadeOpt' in f:
os.remove(f)
# save output for debugging
f = open('ouu.out', 'w')
f.write(out)
f.close()
# grab optimization results
if self.ignoreResults:
self.ignoreResults = False
else:
self.results = OUU.getPsuadeResults(out)
if self.results == None:
error = 'OUU: Optimization error.'
Common.showError(error, out)
return None
if self.endFunction is not None:
self.endFunction()
def checkDists(self, tabIndex):
if tabIndex == 0 or self.ignoreDistributionCheck:
self.ignoreDistributionCheck = False
return
showMessage = False
if self.distTable.getNumVariables() == 0:
showMessage = True
message = "All inputs are fixed! One needs to be variable."
else:
valid, error = self.distTable.checkValidInputs()
if not valid:
showMessage = True
message = (
"Distribution settings not correct or entirely filled out! %s"
% error)
else:
rowsToWarnAboutMass = []
for row in range(self.distTable.rowCount()):
for col in [3, 4]:
item = self.distTable.item(row, col)
if col == 3:
minVal = float(item.text())
else:
maxVal = float(item.text())
#### Get distribution parameters
for col in [6, 7]:
cellTable = self.distTable.cellWidget(row, col)
if isinstance(cellTable, QComboBox):
continue
item = None
if cellTable is not None:
item = cellTable.item(0, 1)
if item is not None and item.text():
if col == 6:
distParam1 = float(item.text())
else:
distParam2 = float(item.text())
else:
if col == 6:
distParam1 = None
else:
distParam2 = None
#### Check mass and warn if below 50%
# Only collect those that are not fixed to generate inputs
combobox = self.distTable.cellWidget(row, 5)
dist = combobox.currentIndex()
# Create file for psuade input
if dist not in [Distribution.UNIFORM, Distribution.SAMPLE]:
f = tempfile.SpooledTemporaryFile()
for i in range(2):
f.write(b"cdf_lookup\n")
distNum = dist
if dist == Distribution.BETA:
distNum = 4
elif dist == Distribution.WEIBULL:
distNum = 5
elif dist == Distribution.GAMMA:
distNum = 6
elif dist == Distribution.EXPONENTIAL:
distNum = 7
f.write(b"%d\n" %
distNum) # Number of distribution
f.write(b"%f\n" % distParam1) # Parameter 1
if distParam2 is not None:
f.write(b"%f\n" % distParam2) # Parameter 2
if i == 0:
val = minVal
else:
val = maxVal
f.write(b"%f\n" % val) # Min or max value
f.write(b"quit\n")
f.seek(0)
# invoke psuade
psuadePath = LocalExecutionModule.getPsuadePath()
if psuadePath is None:
return
p = subprocess.Popen(
psuadePath,
stdin=f,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
)
f.close()
# process error
out, error = p.communicate()
if error:
Common.showError(error, out)
return None
# parse output
lines = out.splitlines()
vals = []
for line in lines:
if "Cumulative probability = " in line.decode(
"utf-8"):
words = line.split()
vals.append(float(words[-1]))
mass = vals[1] - vals[0]
if mass < 0.5:
rowsToWarnAboutMass.append(row)
if len(rowsToWarnAboutMass) > 0:
self.samplingTabs.setCurrentIndex(0)
for row in rowsToWarnAboutMass:
msgbox = QMessageBox()
msgbox.setWindowTitle("UQ/Opt GUI Warning")
msgbox.setText(
"Regarding input " +
self.model.getInputNames()[row] +
": Min/max range is narrow for its distribution. "
+
"This could cause sample generation to take more time. Continue?"
)
msgbox.setIcon(QMessageBox.Warning)
msgbox.setStandardButtons(QMessageBox.Yes
| QMessageBox.No)
msgbox.setDefaultButton(QMessageBox.Yes)
ret = msgbox.exec_()
if ret != QMessageBox.Yes:
self.distTable.selectRow(row)
return
self.ignoreDistributionCheck = True
self.samplingTabs.setCurrentIndex(1)
if showMessage:
self.samplingTabs.setCurrentIndex(0)
msgbox = QMessageBox()
msgbox.setWindowTitle("UQ/Opt GUI Warning")
msgbox.setText(message)
msgbox.setIcon(QMessageBox.Warning)
msgbox.exec_()
return
def writeOUUdata(outfile, outputs, constraints, derivatives, data, xtable,
**kwargs):
# Charles TODO: Handle y is now a list of inputs
# Charles TODO: Handle derivatives
# defaults
rseed = None
driver = data.getDriverName()
if driver is None:
driver = 'NONE'
optdriver = data.getOptDriverName()
if optdriver is None:
optdriver = 'NONE'
ensoptdriver = data.getEnsembleOptDriverName()
if ensoptdriver is None:
ensoptdriver = 'NONE'
auxdriver = data.getAuxDriverName()
if auxdriver is None:
auxdriver = 'NONE'
inputLB = None
inputUB = None
inputDefaults = None
distributions = None
init_input = None
# process keyworded arguments
for key in kwargs:
k = key.lower()
if k == 'randseed':
rseed = kwargs[key]
elif k == 'driver':
driver = kwargs[key]
elif k == 'optdriver':
optdriver = kwargs[key]
elif k == 'ensoptdriver':
ensoptdriver = kwargs[key]
elif k == 'auxdriver':
auxdriver = kwargs[key]
elif k == 'inputlowerbounds':
inputLB = kwargs[key]
elif k == 'inputupperbounds':
inputUB = kwargs[key]
elif k == 'inputpdf':
distributions = kwargs[key]
elif k == 'init_input':
init_input = kwargs[key]
inputTypes = data.getInputTypes()
nInputs = data.getNumInputs()
inputNames = data.getInputNames()
variableInputIndices = []
for e in xtable:
if e['type'] != u'Fixed':
variableInputIndices.append(inputNames.index(e['name']))
nVariableInputs = len(variableInputIndices)
nOutputs = len(outputs)
nSamples = num_fmin = 1 # number of random restarts
nConstraints = constraints.count(True)
nDerivatives = derivatives.count(True)
totalOutputs = nOutputs + nConstraints + nDerivatives
f = open(outfile, 'w')
if init_input:
f.write('PSUADE_IO\n')
f.write('%d %d %d\n' % (nVariableInputs, totalOutputs, nSamples))
f.write("1 0\n") # assume initial point has not been run
for x in init_input:
f.write(' % .16e\n' % x)
for i in xrange(totalOutputs):
f.write(' 9.9999999999999997e+34\n')
f.write("PSUADE_IO\n")
# TO DO: merge with RSAnalyzer.writeRSdata()
f.write('PSUADE\n')
# ... input ...
numFixed = nInputs - nVariableInputs
f.write('INPUT\n')
if numFixed > 0:
f.write(' num_fixed %d\n' % numFixed)
f.write(' dimension = %d\n' % nVariableInputs)
if inputLB is None:
inputLB = data.getInputMins()
if inputUB is None:
inputUB = data.getInputMaxs()
if inputDefaults is None:
inputDefaults = data.getInputDefaults()
indices = range(nInputs)
variableIndex = 1
fixedIndex = 1
for i, name, inType, lb, ub, default in zip(indices, inputNames, \
inputTypes, inputLB, inputUB, inputDefaults):
if i in variableInputIndices: #inType == Model.VARIABLE:
f.write(' variable %d %s = % .16e % .16e\n' % \
(variableIndex, name, lb, ub))
variableIndex = variableIndex + 1
else:
f.write(' fixed %d %s = % .16e\n' %
(fixedIndex, name, default))
fixedIndex = fixedIndex + 1
# inject discrete variables in psuade
opttypes = []
cnt = 0
for e in xtable:
cnt = cnt + 1
t = e['type']
if t == u'Opt: Primary Discrete (Z1d)':
opttypes.append(cnt)
nn = len(opttypes)
for ii in range(nn):
jj = opttypes[ii]
f.write(' discrete %d\n' % (jj))
if distributions is None:
distributions = SampleData.getInputDistributions(data)
for i, inType, dist in zip(indices, inputTypes, distributions):
if i in variableInputIndices: #inType == Model.VARIABLE:
distType = dist.getDistributionType()
distParams = dist.getParameterValues()
if distType != Distribution.UNIFORM:
f.write(' PDF %d %c' % (i+1, \
Distribution.getPsuadeName(distType)))
if distType == Distribution.SAMPLE:
error = 'OUU: In function writeOUUdata(), '
error = error + 'SAMPLE distribution is not supported.'
Common.showError(error)
return None
else:
if distParams[0] is not None:
f.write(' % .16e' % distParams[0])
if distParams[1] is not None:
f.write(' % .16e' % distParams[1])
f.write('\n')
f.write('END\n')
# ... output ...
outActive = nOutputs
nConstrs = 0
nDerivs = 0
for ii in range(len(constraints)):
if constraints[ii]:
outActive = outActive + 1
nConstrs = nConstrs + 1
for ii in range(len(derivatives)):
if derivatives[ii]:
outActive = outActive + 1
nDerivs = nDerivs + 1
if (nOutputs != 1):
error = 'OUU: In function writeOUUdata(), '
error = error + 'multi-objective optimization not supported.'
Common.showError(error)
return None
else:
if ((nConstrs > 0) and (nDerivs > 0)):
error = 'OUU: In function writeOUUdata(), '
error = error + 'LBFGS does not support inequality constraints.'
Common.showError(error)
return None
elif ((nDerivs > 0) and (nDerivs != nVariableInputs)):
error = 'OUU: In function writeOUUdata(), '
error = error + 'Number of derivatives not correct'
Common.showError(error)
return None
f.write('OUTPUT\n')
f.write(' dimension = %d\n' % (outActive))
outputNames = SampleData.getOutputNames(data)
for ii in range(nOutputs):
ind = outputs[ii]
f.write(' variable %d %s\n' % (ii + 1, outputNames[ind - 1]))
print(' variable %d %s\n' % (ii + 1, outputNames[ind - 1]))
outActive = nOutputs + 1
for ii in range(len(constraints)):
if constraints[ii]:
f.write(' variable %d %s\n' % (outActive, outputNames[ii]))
print(' variable %d %s\n' % (outActive, outputNames[ii]))
outActive = outActive + 1
for ii in range(len(derivatives)):
if derivatives[ii]:
f.write(' variable %d %s\n' % (outActive, outputNames[ii]))
print(' variable %d %s\n' % (outActive, outputNames[ii]))
outActive = outActive + 1
f.write('END\n')
# ... method ...
f.write('METHOD\n')
f.write(' sampling = MC\n') # OUU uses this to create
f.write(' num_samples = 1\n') # initial guess
if rseed is not None:
f.write('random_seed = %d\n' % rseed) # random seed
f.write('END\n')
# ... application ...
f.write('APPLICATION\n')
if platform.system() == 'Windows':
import win32api
if driver != 'NONE' and driver != 'PSUADE_LOCAL':
driver = win32api.GetShortPathName(driver)
if optdriver != 'NONE' and optdriver != 'PSUADE_LOCAL':
optdriver = win32api.GetShortPathName(optdriver)
if ensoptdriver != 'NONE' and ensoptdriver != 'PSUADE_LOCAL':
ensoptdriver = win32api.GetShortPathName(ensoptdriver)
if auxdriver != 'NONE' and auxdriver != 'PSUADE_LOCAL':
auxdriver = win32api.GetShortPathName(auxdriver)
f.write(' driver = %s\n' % driver)
f.write(' opt_driver = %s\n' % optdriver)
f.write(' ensemble_opt_driver = %s\n' % ensoptdriver)
f.write(' aux_opt_driver = %s\n' % auxdriver)
f.write(' launch_interval = 0\n')
f.write('END\n')
# ... analysis ...
f.write('ANALYSIS\n')
if (nDerivs > 0):
f.write(' optimization method = ouu_lbfgs\n')
else:
f.write(' optimization method = ouu\n')
f.write(' optimization num_local_minima = 1\n')
f.write(' optimization max_feval = 1000000\n')
f.write(' optimization fmin = 0.0\n')
f.write(' optimization tolerance = 1.000000e-06\n')
f.write(' optimization num_fmin = %d\n' % num_fmin)
f.write(' optimization print_level = 3\n')
#f.write(' analyzer output_id = %d\n' % y)
f.write(' analyzer output_id = 1\n')
f.write(' opt_expert\n')
f.write(' printlevel 0\n')
f.write('END\n')
f.write('END\n')
f.close()
return outfile
def ouu(self,
fname,
y,
outputsAsConstraint,
outputsAsDerivative,
xtable,
phi,
x3sample=None,
x4sample=None,
useRS=False,
useBobyqa=True,
driver=None,
optDriver=None,
auxDriver=None,
ensOptDriver=None,
plotSignal=None,
endFunction=None):
# Function to execute after inference has finished.
# Function would enable button again and such things.
self.endFunction = endFunction
# read data, assumes data already have fixed variables written to file
data = LocalExecutionModule.readSampleFromPsuadeFile(fname)
if optDriver == None and ensOptDriver == None and \
data.getOptDriverName() == None and \
data.getEnsembleOptDriverName() == None:
Common.showError('Model file does not have any drivers set!', \
showDeveloperHelpMessage = False)
self.hadError = True
if endFunction is not None:
endFunction()
return
if driver != None:
data.setDriverName(driver)
if optDriver != None:
data.setOptDriverName(optDriver)
if auxDriver != None:
data.setAuxDriverName(auxDriver)
if ensOptDriver != None:
data.setEnsembleOptDriverName(ensOptDriver)
else:
ensOptDriver = data.getEnsembleOptDriverName()
# Remove file that tells OUU to stop
if os.path.exists(OUU.stopFile):
os.remove(OUU.stopFile)
# process input table
dname = OUU.dname
deleteFiles = True
if x3sample is not None:
deleteFiles = not x3sample['file'].startswith(dname)
#Common.initFolder(dname, deleteFiles = deleteFiles)
if platform.system() == 'Windows':
import win32api
dname = win32api.GetShortPathName(dname)
fnameOUU = Common.getLocalFileName(dname, fname, '.ouudat')
p = RSAnalyzer.parsePrior(data, xtable)
if p is not None:
inputLB = p['inputLB']
inputUB = p['inputUB']
dist = p['dist']
init_input = []
vartypes = []
for e in xtable:
t = e['type']
if t == u'Opt: Primary Continuous (Z1)':
vartypes.append(1)
elif t == u'Opt: Primary Continuous (Z1c)':
vartypes.append(1)
elif t == u'Opt: Primary Discrete (Z1d)':
vartypes.append(1)
elif t == u'Opt: Recourse (Z2)':
vartypes.append(2)
elif t == u'UQ: Discrete (Z3)':
vartypes.append(3)
elif t == u'UQ: Continuous (Z4)':
vartypes.append(4)
if t != u'Fixed':
init_input.append(e['value'])
M1 = vartypes.count(1)
M2 = vartypes.count(2)
M3 = vartypes.count(3)
M4 = vartypes.count(4)
# check arguments
if M1 < 1:
error = 'OUU: In function ouu(), number of Z1 (design opt) '
error = error + 'must be at least 1.'
if M3 > 0:
if x3sample == None:
error = 'OUU: In function ouu(), "x3sample" is undefined.'
Common.showError(error)
return None
if M4 > 0:
if x4sample == None:
error = 'OUU: In function ouu(), "x4sample" is undefined.'
Common.showError(error)
return None
loadcs = 'file' in x4sample
if loadcs:
N = 0 # number of samples in x4sample['file']
### TO DO for Jeremy: check sample size in GUI
with open(x4sample['file']) as f:
header = f.readline()
header = header.split()
N = int(header[0])
Nmin = M4 + 1 # minimum number of samples
if N < Nmin:
error = 'OUU: In function ouu(), "x4sample file" requires '
error = error + 'at least %d samples.' % Nmin
Common.showError(error)
return None
if useRS:
Nrs = 'nsamplesRS' in x4sample
if not Nrs:
error = 'OUU: In function ouu(), "x4sample nsamplesRS" is '
error = error + 'required for setting up response surface.'
Common.showError(error)
return None
Nrs = x4sample['nsamplesRS']
Nrs = min(max(Nrs, Nmin),
N) ### TO DO for Jeremy: check in GUI
# TO DO: remove randSeed
ouuFile = OUU.writeOUUdata(fnameOUU,
y,
outputsAsConstraint,
outputsAsDerivative,
data,
xtable,
randSeed=41491431,
inputLowerBounds=inputLB,
inputUpperBounds=inputUB,
inputPDF=dist,
useEnsOptDriver=(ensOptDriver != None),
init_input=init_input)
if (ouuFile == None):
return None
# write script
f = OUU.writescript(vartypes,
fnameOUU,
outputsAsConstraint,
phi,
x3sample,
x4sample,
useRS,
useBobyqa,
useEnsOptDriver=(ensOptDriver != None))
# delete previous history file
if os.path.exists(OUU.hfile):
os.remove(OUU.hfile)
self.textDialog = Common.textDialog()
self.thread = psuadeThread(self, f, self.finishOUU, self.textDialog,
plotSignal)
self.thread.start()
def compress(fname):
N = 0 # number of samples in x3sample['file']
with open(fname) as f: ### TO DO for Jeremy: check sample size in GUI
header = f.readline()
header = header.split()
N = int(header[0])
nInputs = int(header[1])
Nmin = 100 # psuade minimum for genhistogram
if N < Nmin:
warn = 'OUU: In function compress(), "x3sample file" requires '
warn = warn + 'at least %d samples.' % Nmin
Common.showError(warn)
return {N: fname} # return original sample file
outfiles = {}
nbins_max = 20
nscenarios_max = 1501
for nbins in xrange(2, nbins_max):
# write script to invoke scenario compression
f = tempfile.SpooledTemporaryFile()
if platform.system() == 'Windows':
import win32api
fname = win32api.GetShortPathName(fname)
f.write('read_std %s\n' % fname)
f.write('genhistogram\n')
for x in xrange(nInputs):
f.write('%d\n' % nbins)
f.write('quit\n')
f.seek(0)
# invoke psuade
out, error = Common.invokePsuade(f)
f.close()
if error:
return None
# check output file
sfile = 'psuade_pdfhist_sample'
if os.path.exists(sfile):
Ns = 0 # number of samples in psuade_pdfhist_sample
with open(sfile) as f:
header = f.readline()
header = header.split()
Ns = int(header[0])
sfile_ = Common.getLocalFileName(OUU.dname, fname,
'.compressed' + str(Ns))
if os.path.exists(sfile_):
os.remove(sfile_)
os.rename(sfile, sfile_)
sfile = sfile_
else:
error = 'OUU: %s does not exist.' % sfile
Common.showError(error, out)
return None
# append scenario file to data structure
if len(outfiles) > 1 and Ns > min(N, nscenarios_max):
return outfiles
else:
outfiles[Ns] = (sfile, nbins)
return outfiles