def writeSample(fname, data):
xdat = data.getInputData()
RSAnalyzer.writeRSsample(fname, xdat, row=True)
return fname
def writescript(vartypes,
fnameOUU,
outputsAsConstraint,
phi=None,
x3sample=None,
x4sample=None,
useRS=False,
useBobyqa=False,
useEnsOptDriver=None):
M1 = vartypes.count(1)
M2 = vartypes.count(2)
M3 = vartypes.count(3)
M4 = vartypes.count(4)
nInputs = M1 + M2 + M3 + M4
# write script
#f = open('ouu.in','w')
f = tempfile.SpooledTemporaryFile()
if platform.system() == 'Windows':
import win32api
fnameOUU = win32api.GetShortPathName(fnameOUU)
f.write('run %s\n' % fnameOUU)
f.write('y\n') # ready to proceed
# ... partition variables
f.write('%d\n' % M1) # number of design opt variables
if M1 == nInputs:
f.write('quit\n')
f.seek(0)
return f
# ________ M1 < nInputs ________
f.write('%d\n' % M2) # number of operating opt variables
if M1 + M2 == nInputs:
for i in xrange(nInputs):
f.write('%d\n' % vartypes[i])
if useBobyqa:
f.write('n\n') # use BOBYQA means 'no' to use own driver
else:
f.write('y\n') # use own driver as optimizer
f.write('quit\n')
f.seek(0)
return f
# ________ M1+M2 < nInputs ________
f.write('%d\n' % M3) # number of discrete UQ variables
for i in xrange(nInputs):
f.write('%d\n' % vartypes[i])
# ... set objective function w.r.t. to uncertainty
ftype = phi['type']
f.write('%d\n' % ftype) # optimization objective w.r.t. UQ variables
if ftype == 2:
beta = max(0, phi['beta']) # beta >= 0
f.write('%f\n' % beta)
elif ftype == 3:
alpha = phi['alpha']
alpha = min(max(alpha, 0.5), 1.0) # 0.05 <= alpha <= 1.0
f.write('%f\n' % alpha)
if outputsAsConstraint.count(True) > 0:
f.write('1\n')
# ... get sample for discrete UQ variables
# The file format should be:
# line 1: <nSamples> <nInputs>
# line 2: <sample 1 input 1> <input 2> ... <probability>
# line 3: <sample 2 input 1> <input 2> ... <probability>
if M3 > 0:
if platform.system() == 'Windows':
import win32api
x3sample['file'] = win32api.GetShortPathName(x3sample['file'])
f.write('%s\n' %
x3sample['file']) # sample file for discrete UQ variables
# ... get sample for continuous UQ variables
# The file format should be:
# line 1: <nSamples> <nInputs>
# line 2: <sample 1 input 1> <input 2> ...
# line 3: <sample 2 input 1> <input 2> ...
# .....
# line N: <sample N input 1> <input 2> ...
if M4 > 0:
loadcs = 'file' in x4sample
if loadcs:
if platform.system() == 'Windows':
import win32api
x4sample['file'] = win32api.GetShortPathName(
x4sample['file'])
f.write('1\n') # load samples for continuous UQ vars
f.write('%s\n' %
x4sample['file']) # sample file for continuous UQ vars
else:
f.write('2\n') # generate samples for continuous UQ variables
# ... apply response surface
if useRS:
f.write('y\n') # use response surface
if loadcs:
Nrs = x4sample['nsamplesRS']
f.write(
'%d\n' % Nrs
) # number of points to build RS (range: [M4+1,N] where N=samplesize)
randsample = True # set to False to pass in subsample based on convex hull analysis
# set to True to use psuade's built-in random sampler
if randsample:
f.write('2\n') # 2 to generate random sample
else:
f.write('1\n') # 1 to upload subsample file
x, y = RSAnalyzer.readRSsample(x4sample['file'])
xsub = OUU.subsample(x, Nrs)
dname = OUU.dname
if platform.system() == 'Windows':
import win32api
dname = win32api.GetShortPathName(dname)
x4subsample = Common.getLocalFileName(
dname, x4sample['file'], '.subsample')
RSAnalyzer.writeRSsample(x4subsample, xsub)
f.write(
'%s\n' % x4subsample
) # subsample file containing subset of original points
else:
f.write('n\n') # do not use response surface
# ... # create samples for continuous UQ variables
if not loadcs:
Nmin = M4 + 1
if x4sample['method'] == SamplingMethods.LH:
f.write('1\n') # sampling scheme: Latin Hypercube
nSamples = x4sample['nsamples']
nSamples = min(max(nSamples, Nmin), 1000)
f.write('%d\n' %
nSamples) # number of samples (range: [M4+1,1000])
elif x4sample['method'] == SamplingMethods.FACT:
f.write('2\n') # sampling scheme: Factorial
nlevels = x4sample['nlevels']
nlevels = min(max(nlevels, 3), 100)
f.write('%d\n' % nlevels
) # number of levels per variable (range: [3,100])
elif x4sample['method'] == SamplingMethods.LPTAU:
f.write('3\n') # sampling scheme: Quasi Monte Carlo
nSamples = x4sample['nsamples']
nSamples = min(max(nSamples, Nmin), 1000)
f.write('%d\n' %
nSamples) # number of samples (range: [M4+1,1000])
# ... choose optimizer
if M2 > 0:
#if useBobyqa:
# f.write('n\n') # use BOBYQA
#else:
# f.write('y\n') # use own driver as optimizer
f.write('y\n') # use own driver as optimizer
# ... choose ensemble optimization driver
if M3 + M4 > 0: #and not useBobyqa:
if useEnsOptDriver:
f.write('y\n') # use ensemble driver
else:
f.write('n\n')
# ... choose mode to run simulations for computing statistics
if M3 + M4 > 0:
f.write('n\n') # do not use asynchronous mode (not tested)
f.write('quit\n')
f.seek(0)
#for line in f:
# print line.strip()
#f.seek(0)
return f