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