def UP_UDR(driver):
# Uses the UDR method for UP
methd = 'UDR'
method = 5
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
node,weight = params.params(method, nodes, inpt, stvars)
# ---------------------- Model ---------------------------
# set_printoptions(precision=4)
# set_printoptions(suppress=True)
x = kron(mu, ones((inpt * nodes[0], 1)))
for ii in range(0,inpt):
k = ii * nodes[ii]
l = (ii+1) * nodes[ii]
x[k:l, ii] = node[ii,:]
# G_mu = run_model(driver, mu)
values = [mu]
G_s = zeros((inpt,max(nodes),otpt))
for k,inputname in enumerate(driver._json_tree['Configurations']['Configuration']['PCCInputArguments']['StochasticInputs']['InputDistributions']):
if krig == 1:
load('dmodel')
for j in range(0,nodes[k]):
var = k * nodes[k] + j
X = x[var, :]
G_s[k, j] = predictor(X, dmodel)
else:
for j in range(0,nodes[k]):
var = k * nodes[k] + j
X = x[var, :]
# print 'Running simulation on node',j,'of input',inputname['Name']
# G_s[k, j] = run_model(driver, X)
values.append(X)
out = iter(run_list(driver, values))
G_mu = out.next()
for k,inputname in enumerate(driver._json_tree['Configurations']['Configuration']['PCCInputArguments']['StochasticInputs']['InputDistributions']):
for j in range(0,nodes[k]):
G_s[k, j] = out.next()
G_mean = zeros(otpt)
G_kurt = zeros(otpt)
G_skew = zeros(otpt)
G_sigma = zeros(otpt)
covar_m = zeros((otpt,otpt))
gs = zeros(otpt)
gk = zeros(otpt)
moms = []
for l in range(0,otpt):
moms.append(newmoment(inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l]))
G_mean[l] = moment(1, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l])
for l in range(0,otpt):
moms.append(newmoment(inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l]))
G_sigma[l] = moment(2, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l])
gs[l] = moment(3, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l])
G_skew[l] = moment(3, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l]) / G_sigma[l] ** 1.5
gk[l] = moment(4, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l])
G_kurt[l] = moment(4, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l], G_mean[l]) / G_sigma[l] ** 2
for j in range(l,otpt):
covar_m[l, j] = moment2(1, inpt, nodes[0], weight, G_s[:, :, l], l, G_s[:, :, j], j, G_mu, G_mean)
covar_m[j, l] = covar_m[l, j]
CovarianceMatrix = covar_m.transpose()
Moments = {'Mean': G_mean, 'Variance': diag(CovarianceMatrix), 'Skewness': G_skew, 'Kurtosis': G_kurt}
# ---------------------- Analyze ---------------------------
# Calculate the PCC for the FFNI method
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
dtype = [0]*otpt
Inv1 = [0]*otpt
Inv2 = [0]*otpt
m1 = [0]*otpt
m2 = [0]*otpt
a1 = [0]*otpt
a2 = [0]*otpt
alph = [0]*otpt
beta = [0]*otpt
lo = [0]*otpt
hi = [0]*otpt
C_Y_pdf = [0]*otpt
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(0,otpt):
PCC[k],dtype[k],Inv1[k],m1[k],m2[k],a1[k],a2[k],alph[k],beta[k],lo[k],hi[k] = pearscdf.pearscdf(limstate[k], Moments['Mean'][k], sqrt(CovarianceMatrix[k, k]), Moments['Skewness'][k], Moments['Kurtosis'][k], methd, k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(dtype[k],limstate[k],Moments['Mean'][k],Moments['Variance'][k],numbins)
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore') #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
Distribution = {'PearsonType': dtype, 'm1': m1, 'm2': m2, 'a1': a1, 'a2': a2, 'Complexity': C_Y_pdf}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix=where(isnan(CorrelationMatrix), None, CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt)-inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {'Moments': Moments, 'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix, 'Distribution': Distribution, 'Plotting': Plotting, 'PCC': PCC}
return Results
def SA_FAST(driver):
# First order indicies for a given model computed with Fourier Amplitude Sensitivity Test (FAST).
# R. I. Cukier, C. M. Fortuin, Kurt E. Shuler, A. G. Petschek and J. H. Schaibly.
# Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients.
# I-III Theory/Applications/Analysis The Journal of Chemical Physics
#
# Input:
# inpt : no. of input factors
#
# Output:
# SI[] : sensitivity indices
# Other used variables/constants:
# OM[] : frequencies of parameters
# S[] : search curve
# X[] : coordinates of sample points
# Y[] : output of model
# OMAX : maximum frequency
# N : number of sample points
# AC[],BC[]: fourier coefficients
# V : total variance
# VI : partial variances
# ---------------------- Setup ---------------------------
methd = 'FAST'
method = '9'
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
#
MI = 4#: maximum number of fourier coefficients that may be retained in
# calculating the partial variances without interferences between the assigned frequencies
#
# Frequency assignment to input factors.
OM = SETFREQ(inpt)
# Computation of the maximum frequency
# OMAX and the no. of sample points N.
OMAX = int(OM[inpt-1])
N = 2 * MI * OMAX + 1
# Setting the relation between the scalar variable S and the coordinates
# {X(1),X(2),...X(inpt)} of each sample point.
S = pi / 2.0 * (2 * arange(1,N+1) - N-1) / N
ANGLE = matrix(OM).T * matrix(S)
X = 0.5 + arcsin(sin(ANGLE.T)) / pi
# Transform distributions from standard uniform to general.
for j in range(inpt):
if stvars[j].dist == 'NORM':
X[:,j] = norm.ppf(uniform.cdf(X[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
X[:,j] = lognorm.ppf(uniform.cdf(X[:, j], 0, 1), stvars[j].param[1], 0, exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
X[:,j] = beta.ppf(uniform.cdf(X[:, j], 0, 1), stvars[j].param[0], stvars[j].param[1], stvars[j].param[2], stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
X[:,j] = uniform.ppf(uniform.cdf(X[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
# Do the N model evaluations.
Y = zeros((N, otpt))
if krig == 1:
load("dmodel")
Y = predictor(X, dmodel)
else:
values = []
for p in range(N):
# print 'Running simulation on test',p+1,'of',N
# Y[p] = run_model(driver, array(X[p])[0])
values.append(array(X[p])[0])
Y = run_list(driver, values)
# Computation of Fourier coefficients.
AC = zeros((N, otpt))# initially zero
BC = zeros((N, otpt))# initially zero
# q = int(N / 2)-1
q = (N-1)/2
for j in range(2,N+1,2): # j is even
# print "Y[q]",Y[q]
# print "matrix(cos(pi * j * arange(1,q+) / N))",matrix(cos(pi * j * arange(1,q+1) / N))
# print "matrix(Y[q + arange(0,q)] + Y[q - arange(0,q)])",matrix(Y[q + arange(1,q+1)] + Y[q - arange(1,q+1)])
AC[j-1] = 1.0 / N * matrix(Y[q] + matrix(cos(pi * j * arange(1,q+1) / N)) * matrix(Y[q + arange(1,q+1)] + Y[q - arange(1,q+1)]))
for j in range(1,N+1,2): # j is odd
BC[j-1] = 1.0 / N * matrix(sin(pi * j * arange(1,q+1) / N)) * matrix(Y[q + arange(1,q+1)] - Y[q - arange(1,q+1)])
# Computation of the general variance V in the frequency domain.
V = 2 * (matrix(AC).T * matrix(AC) + matrix(BC).T * matrix(BC))
# Computation of the partial variances and sensitivity indices.
# Si=zeros(inpt,otpt);
Si = zeros((otpt,otpt,inpt));
for i in range(inpt):
Vi = zeros((otpt, otpt))
for j in range(1,MI+1):
idx = j * OM[i]-1
Vi = Vi + AC[idx].T * AC[idx] + BC[idx].T * BC[idx]
Vi = 2. * Vi
Si[:, :, i] = Vi / V
if lrflag == 1:
SRC, stat = SRC_regress.SRC_regress(X, Y, otpt, N)
# ---------------------- Analyze ---------------------------
Sti = []# appears right after the call to this method in the original PCC_Computation.m
# if plotf == 1:
# piecharts(inpt, otpt, Si, Sti, method, output)
if simple == 1:
Si_t = zeros((inpt,otpt))
for p in range(inpt):
Si_t[p] = diag(Si[:, :, p])
Si = Si_t.T
Results = {'FirstOrderSensitivity': Si}
if lrflag == 1:
Results.update({'SRC': SRC, 'R^2': stat})
return Results
def SA_FAST(problem, driver):
# First order indicies for a given model computed with Fourier Amplitude Sensitivity Test (FAST).
# R. I. Cukier, C. M. Fortuin, Kurt E. Shuler, A. G. Petschek and J. H. Schaibly.
# Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients.
# I-III Theory/Applications/Analysis The Journal of Chemical Physics
#
# Input:
# inpt : no. of input factors
#
# Output:
# SI[] : sensitivity indices
# Other used variables/constants:
# OM[] : frequencies of parameters
# S[] : search curve
# X[] : coordinates of sample points
# Y[] : output of model
# OMAX : maximum frequency
# N : number of sample points
# AC[],BC[]: fourier coefficients
# V : total variance
# VI : partial variances
# ---------------------- Setup ---------------------------
methd = 'FAST'
method = '9'
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate = driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
#
MI = 4 #: maximum number of fourier coefficients that may be retained in
# calculating the partial variances without interferences between the assigned frequencies
#
# Frequency assignment to input factors.
OM = SETFREQ(inpt)
# Computation of the maximum frequency
# OMAX and the no. of sample points N.
OMAX = int(OM[inpt - 1])
N = 2 * MI * OMAX + 1
# Setting the relation between the scalar variable S and the coordinates
# {X(1),X(2),...X(inpt)} of each sample point.
S = pi / 2.0 * (2 * arange(1, N + 1) - N - 1) / N
ANGLE = matrix(OM).T * matrix(S)
X = 0.5 + arcsin(sin(ANGLE.T)) / pi
# Transform distributions from standard uniform to general.
for j in range(inpt):
if stvars[j].dist == 'NORM':
X[:, j] = norm.ppf(uniform.cdf(X[:, j], 0, 1), stvars[j].param[0],
stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
X[:, j] = lognorm.ppf(uniform.cdf(X[:, j], 0,
1), stvars[j].param[1], 0,
exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
X[:, j] = beta.ppf(uniform.cdf(X[:, j], 0, 1), stvars[j].param[0],
stvars[j].param[1], stvars[j].param[2],
stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
X[:, j] = uniform.ppf(uniform.cdf(X[:, j], 0, 1),
stvars[j].param[0], stvars[j].param[1])
# Do the N model evaluations.
Y = zeros((N, otpt))
if krig == 1:
load("dmodel")
Y = predictor(X, dmodel)
else:
values = []
for p in range(N):
# print 'Running simulation on test',p+1,'of',N
# Y[p] = run_model(driver, array(X[p])[0])
values.append(array(X[p])[0])
Y = run_list(problem, driver, values)
# Computation of Fourier coefficients.
AC = zeros((N, otpt)) # initially zero
BC = zeros((N, otpt)) # initially zero
# q = int(N / 2)-1
q = (N - 1) / 2
for j in range(2, N + 1, 2): # j is even
# print "Y[q]",Y[q]
# print "matrix(cos(pi * j * arange(1,q+) / N))",matrix(cos(pi * j * arange(1,q+1) / N))
# print "matrix(Y[q + arange(0,q)] + Y[q - arange(0,q)])",matrix(Y[q + arange(1,q+1)] + Y[q - arange(1,q+1)])
AC[j - 1] = 1.0 / N * matrix(
Y[q] + matrix(cos(pi * j * arange(1, q + 1) / N)) *
matrix(Y[q + arange(1, q + 1)] + Y[q - arange(1, q + 1)]))
for j in range(1, N + 1, 2): # j is odd
BC[j - 1] = 1.0 / N * matrix(sin(
pi * j * arange(1, q + 1) / N)) * matrix(Y[q + arange(1, q + 1)] -
Y[q - arange(1, q + 1)])
# Computation of the general variance V in the frequency domain.
V = 2 * (matrix(AC).T * matrix(AC) + matrix(BC).T * matrix(BC))
# Computation of the partial variances and sensitivity indices.
# Si=zeros(inpt,otpt);
Si = zeros((otpt, otpt, inpt))
for i in range(inpt):
Vi = zeros((otpt, otpt))
for j in range(1, MI + 1):
idx = j * OM[i] - 1
Vi = Vi + AC[idx].T * AC[idx] + BC[idx].T * BC[idx]
Vi = 2. * Vi
Si[:, :, i] = Vi / V
if lrflag == 1:
SRC, stat = SRC_regress.SRC_regress(X, Y, otpt, N)
# ---------------------- Analyze ---------------------------
Sti = [
] # appears right after the call to this method in the original PCC_Computation.m
# if plotf == 1:
# piecharts(inpt, otpt, Si, Sti, method, output)
if simple == 1:
Si_t = zeros((inpt, otpt))
for p in range(inpt):
Si_t[p] = diag(Si[:, :, p])
Si = Si_t.T
Results = {'FirstOrderSensitivity': Si}
if lrflag == 1:
Results.update({'SRC': SRC, 'R^2': stat})
return Results
def SA_EFAST(driver):
#[SI,STI] = EFAST(K,WANTEDN)
# First order and total effect indices for a given model computed with
# Extended Fourier Amplitude Sensitivity Test (EFAST).
# Andrea Saltelli, Stefano Tarantola and Karen Chan. 1999
# A quantitative model-independent method for global sensitivity analysis of model output.
# Technometrics 41:39-56
#
# Input:
# inpt : no. of input factors
# WANTEDN : wanted no. of sample points
#
# Output:
# SI[] : first order sensitivity indices
# STI[] : total effect sensitivity indices
# Other used variables/constants:
# OM[] : vector of inpt frequencies
# OMI : frequency for the group of interest
# OMCI[] : set of freq. used for the compl. group
# X[] : parameter combination rank matrix
# AC[],BC[]: fourier coefficients
# FI[] : random phase shift
# V : total output variance (for each curve)
# VI : partial var. of par. i (for each curve)
# VCI : part. var. of the compl. set of par...
# AV : total variance in the time domain
# AVI : partial variance of par. i
# AVCI : part. var. of the compl. set of par.
# Y[] : model output
# N : no. of runs on each curve
# ---------------------- Setup ---------------------------
methd = 'EFAST'
method = '10'
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
NR = 1#: no. of search curves
MI = 4#: maximum number of fourier coefficients that may be retained in calculating
# the partial variances without interferences between the assigned frequencies
#
# Computation of the frequency for the group of interest OMi and the no. of sample points N.
OMi = int(floor((nEFAST / NR - 1) / (2 * MI) / inpt))
N = 2 * MI * OMi + 1
total_sims = N*NR*inpt
sim = 0
if (N * NR < 65):
logging.error('sample size must be >= 65 per factor.')
raise ValueError,'sample size must be >= 65 per factor.'
# Algorithm for selecting the set of frequencies. OMci(i), i=1:inpt-1, contains
# the set of frequencies to be used by the complementary group.
OMci = SETFREQ(N - 1, OMi / 2 / MI)
# Loop over the inpt input factors.
Si = zeros((otpt,otpt,inpt));
Sti = zeros((otpt,otpt,inpt));
for i in range(inpt):
# Initialize AV,AVi,AVci to zero.
AV = 0
AVi = 0
AVci = 0
# Loop over the NR search curves.
for L in range(NR):
# Setting the vector of frequencies OM for the inpt factors.
cj = 1
OM = zeros(inpt)
for j in range(inpt):
if (j == i):
# For the factor of interest.
OM[i] = OMi
else:
# For the complementary group.
OM[j] = OMci[cj]
cj = cj + 1
# Setting the relation between the scalar variable S and the coordinates
# {X(1),X(2),...X(inpt)} of each sample point.
FI = zeros(inpt)
for j in range(inpt):
FI[j] = random.random() * 2 * pi # random phase shift
S_VEC = pi * (2 * arange(1,N+1) - N - 1) / N
OM_VEC = OM[range(inpt)]
FI_MAT = transpose(array([FI]*N))
ANGLE = matrix(OM_VEC).T*matrix(S_VEC) + matrix(FI_MAT)
X = 0.5 + arcsin(sin(ANGLE.T)) / pi
# Transform distributions from standard uniform to general.
for j in range(inpt):
if stvars[j].dist == 'NORM':
X[:,j] = norm.ppf(uniform.cdf(X[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
X[:,j] = lognorm.ppf(uniform.cdf(X[:, j], 0, 1), stvars[j].param[1], 0, exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
X[:,j] = beta.ppf(uniform.cdf(X[:, j], 0, 1), stvars[j].param[0], stvars[j].param[1], stvars[j].param[2], stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
X[:,j] = uniform.ppf(uniform.cdf(X[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
# Do the N model evaluations.
Y = zeros((N, otpt))
if krig == 1:
load("dmodel")
Y = predictor(X, dmodel)
else:
values = []
for p in range(N):
# sim += 1
# print 'Running simulation on test',sim,'of',total_sims
# Y[p] = run_model(driver, array(X[p])[0])
values.append(array(X[p])[0])
Y = run_list(driver, values)
# Subtract the average value.
Y = Y - kron(mean(Y,0), ones((N, 1)))
# Fourier coeff. at [1:OMi/2].
NQ = int(N / 2)-1
N0 = NQ + 1
COMPL = 0
Y_VECP = Y[N0+1:] + Y[NQ::-1]
Y_VECM = Y[N0+1:] - Y[NQ::-1]
# AC = zeros((int(ceil(OMi / 2)), otpt))
# BC = zeros((int(ceil(OMi / 2)), otpt))
AC = zeros((OMi * MI, otpt))
BC = zeros((OMi * MI, otpt))
for j in range(int(ceil(OMi / 2))+1):
ANGLE = (j+1) * 2 * arange(1,NQ+2) * pi / N
C_VEC = cos(ANGLE)
S_VEC = sin(ANGLE)
AC[j] = (Y[N0] +matrix(C_VEC)*matrix(Y_VECP)) / N
BC[j] = matrix(S_VEC) * matrix(Y_VECM) / N
COMPL = COMPL + matrix(AC[j]).T * matrix(AC[j]) + matrix(BC[j]).T * matrix(BC[j])
# Computation of V_{(ci)}.
Vci = 2 * COMPL
AVci = AVci + Vci
# Fourier coeff. at [P*OMi, for P=1:MI].
COMPL = 0
# Do these need to be recomputed at all?
# Y_VECP = Y[N0 + range(NQ)] + Y[N0 - range(NQ)]
# Y_VECM = Y[N0 + range(NQ)] - Y[N0 - range(NQ)]
for j in range(OMi, OMi * MI + 1, OMi):
ANGLE = j * 2 * arange(1,NQ+2) * pi / N
C_VEC = cos(ANGLE)
S_VEC = sin(ANGLE)
AC[j-1] = (Y[N0] + matrix(C_VEC)*matrix(Y_VECP)) / N
BC[j-1] = matrix(S_VEC) * matrix(Y_VECM) / N
COMPL = COMPL + matrix(AC[j-1]).T * matrix(AC[j-1]) + matrix(BC[j-1]).T * matrix(BC[j-1])
# Computation of V_i.
Vi = 2 * COMPL
AVi = AVi + Vi
# Computation of the total variance in the time domain.
AV = AV + matrix(Y).T * matrix(Y) / N
# Computation of sensitivity indicies.
AV = AV / NR
AVi = AVi / NR
AVci = AVci / NR
Si[:, :, i] = AVi / AV
Sti[:, :, i] = 1 - AVci / AV
if lrflag == 1:
SRC, stat = SRC_regress.SRC_regress(X, Y, otpt, N)
# ---------------------- Analyze ---------------------------
# if plotf == 1:
# piecharts(inpt, otpt, Si, Sti, methd, output)
if simple == 1:
Si_t = zeros((inpt,otpt))
for p in range(inpt):
Si_t[p] = diag(Si[:, :, p])
Si = Si_t.T
if simple == 1:
Sti_t = zeros((inpt,otpt))
for p in range(inpt):
Sti_t[p] = diag(Sti[:, :, p])
Sti = Sti_t.T
Results = {'FirstOrderSensitivity': Si, 'TotalEffectSensitivity': Sti}
if lrflag == 1:
Results.update({'SRC': SRC, 'R^2': stat})
return Results
def UP_PCE(driver):
# Uses the PCE method for UP
# This routine has been updated as part of refactoring code before the port
# from MATLAB to Python/NumPy/SciPy. Sections of PCC_Computation that apply
# this method have been moved here.
# ---------------------- Setup ---------------------------
methd = "PCE"
method = 6
inpt = len(driver.inputs)
krig = driver.krig
limstate = driver.limstate
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
stvars = driver.stvars
numbins = driver.numbins
# current settings for these two vars
ii = 0
jj = 0
# ---------------------- Model ---------------------------
mu_g = zeros(inpt)
sigma_g = ones(inpt)
node_t = zeros((inpt, nodes[0]))
weight_t = zeros((inpt, nodes[0]))
for i in range(inpt):
node_t[i], weight_t[i] = gaussquad.gaussquad(nodes[i], "NORM", mu_g[i], sigma_g[i])
x = []
for i in range(inpt):
x.append(symbols("x" + str(i)))
x = array(x)
j = fullfact(nodes)
pts = shape(j)[0]
node = zeros((pts, inpt))
wj = zeros((pts, inpt))
for y in range(pts):
for i in range(inpt):
node[y][i] = node_t[i][j[y][i]]
wj[y][i] = weight_t[i][j[y][i]]
weight = prod(wj, 1)
P = zeros(order)
P[0] = 1
for p in range(1, order):
term2 = 0
for s in range(1, p + 1):
term1 = 1
for r in range(s):
term1 = term1 * (inpt + r)
term2 = term2 + (1.0 / int(scipy.misc.factorial(s))) * term1
if p == 1:
P[p] = term2
else:
P[p] = term2 - sum(P[range(1, p + 1)])
G_s = zeros((pts, otpt))
if krig == 1:
t = strcat("SS_K", num2str(ii), num2str(jj))
load(char(t))
for j in range(pts):
# Rosenblatt Transformation
T_L = Dist.Dist(stvars, node[j], inpt)
G_s[j] = predictor(T_L, dmodel)
else:
values = []
for j in range(pts):
# Rosenblatt Transformation
# print 'Running simulation',j+1,'of',pts
T_L = Dist.Dist(stvars, node[j], inpt)
# G_s[j] = run_model(driver, T_L)
values.append(T_L)
G_s = run_list(driver, values)
indx = 0
bn = zeros((sum(P), otpt))
bd = zeros(sum(P))
for k in range(order):
vec = xvector.xvector(k, inpt)
for j in range(int(P[k])):
for i in range(pts):
L = node[i]
if k == 0:
bn[indx] = bn[indx] + weight[i] * G_s[i]
bd[indx] = bd[indx] + weight[i]
else:
h, h_sym = hermite.hermite(k, vec[j], L, x)
bn[indx] += weight[i] * G_s[i] * h
bd[indx] += weight[i] * (h ** 2)
indx += 1
b = zeros((sum(P), otpt))
for l in range(otpt):
b[:, l] = bn[:, l] / bd
indx = 0
U_sum = 0
for k in range(order):
vec = xvector.xvector(k, inpt)
for j in range(int(P[k])):
if k == 0:
U_sum = b[0]
else:
h, h_sym = hermite.hermite(k, vec[j], L, x)
U_sum = U_sum + b[indx] * N(h_sym)
indx += 1
U = U_sum
U_s = zeros((pts, otpt))
G_mean = zeros(otpt)
G_kurt = zeros(otpt)
G_skew = zeros(otpt)
covar_m = zeros((otpt, otpt))
for i in range(pts):
for k in range(otpt):
U_s[i][k] = U[k].subs(dict(zip(x, node[i])))
for k in range(otpt):
# G_mean[k] = sum(matrix(weight) * matrix(U_s[:, k]).transpose())
G_mean[k] = sum(weight * U_s[:, k])
for k in range(otpt):
for j in range(k, otpt):
covar_m[k, j] = sum(weight * (U_s[:, k] - G_mean[k]) * (G_s[:, j] - G_mean[j]))
covar_m[j, k] = covar_m[k, j]
G_skew[k] = sum(weight * (U_s[:, k] - G_mean[k]) ** 3) / covar_m[k, k] ** 1.5
G_kurt[k] = sum(weight * (U_s[:, k] - G_mean[k]) ** 4) / covar_m[k, k] ** 2
CovarianceMatrix = covar_m.transpose()
Moments = {"Mean": G_mean, "Variance": diag(CovarianceMatrix), "Skewness": G_skew, "Kurtosis": G_kurt}
# ---------------------- Analyze ---------------------------
if any(Moments["Variance"] == 0):
print "Warning: One or more outputs does not vary over given parameter variation."
# Calculate the PCC for the FFNI method
if otpt > 1:
PCC = [0] * (otpt + 1)
else:
PCC = [0] * otpt
dtype = [0] * otpt
Inv1 = [0] * otpt
Inv2 = [0] * otpt
m1 = [0] * otpt
m2 = [0] * otpt
a1 = [0] * otpt
a2 = [0] * otpt
alph = [0] * otpt
beta = [0] * otpt
lo = [0] * otpt
hi = [0] * otpt
C_Y_pdf = [0] * otpt
if any(Moments["Variance"] == 0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(otpt):
PCC[k], dtype[k], Inv1[k], m1[k], m2[k], a1[k], a2[k], alph[k], beta[k], lo[k], hi[k] = pearscdf.pearscdf(
limstate[k],
Moments["Mean"][k],
sqrt(CovarianceMatrix[k, k]),
Moments["Skewness"][k],
Moments["Kurtosis"][k],
methd,
k,
output,
)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(
dtype[k], limstate[k], Moments["Mean"][k], Moments["Variance"][k], numbins
)
sigma_mat = matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid="ignore") # ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix = CovarianceMatrix / multiply(sigma_mat, sigma_mat.transpose())
Distribution = {"PearsonType": dtype, "m1": m1, "m2": m2, "a1": a1, "a2": a2, "Complexity": C_Y_pdf}
Plotting = {"alpha": alph, "beta": beta, "lo": lo, "hi": hi}
CorrelationMatrix = where(isnan(CorrelationMatrix), None, CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt) - inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {
"Moments": Moments,
"CorrelationMatrix": CorrelationMatrix,
"CovarianceMatrix": CovarianceMatrix,
"Distribution": Distribution,
"Plotting": Plotting,
"PCC": PCC,
}
return Results
def UP_FFNI(problem, driver):
# Uses the FFNI method for UP
# ---------------------- Setup ---------------------------
methd = 'FFNI'
method = 4
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
node,w = params.params(method, nodes, inpt, stvars) # Do I need to transpose these matrices?
#[quadpts] = params(method, nodes, inpt, stvars)
# ---------------------- Model ---------------------------
# Create full factorial experiment from individual nodes and weights
j = fullfact(nodes)
pts = shape(j)[0]
x=zeros((pts,inpt))
wj=zeros((pts,inpt))
for y in range(pts):
for i in range(inpt):
x[y][i] = node[i][j[y][i]]
wj[y][i] = w[i][j[y][i]]
weight = prod(wj, 1)
if krig == 1:
load("dmodel")
G_s = predictor(x, dmodel)
else:
# G_s = zeros((pts, otpt))
# for i in range(pts):
# print 'Running simulation',i+1,'of',pts
# G_s[i] = run_model(driver, x[i])
# G_s[i] = modelica.RunModelica(x[i], modelname, properties)
G_s = run_list(problem, driver, x)
G_mean = zeros(otpt)
G_kurt = zeros(otpt)
G_skew = zeros(otpt)
covar_m = zeros((otpt,otpt))
for k in range(otpt):
G_mean[k] = sum(weight * G_s[:, k])
for k in range(otpt):
for j in range(otpt):
covar_m[k, j] = sum(weight * (G_s[:, k] - G_mean[k]) * (G_s[:, j] - G_mean[j]))
covar_m[j, k] = covar_m[k, j]
G_skew[k] = sum(weight * (G_s[:, k] - G_mean[k]) ** 3) / covar_m[k, k] ** 1.5
G_kurt[k] = sum(weight * (G_s[:, k] - G_mean[k]) ** 4) / covar_m[k, k] ** 2
CovarianceMatrix = covar_m.transpose()
Moments = {'Mean': G_mean, 'Variance': diag(CovarianceMatrix), 'Skewness': G_skew, 'Kurtosis': G_kurt}
# ---------------------- Analyze ---------------------------
# Calculate the PCC for the FFNI method
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
dtype = [0]*otpt
Inv1 = [0]*otpt
Inv2 = [0]*otpt
m1 = [0]*otpt
m2 = [0]*otpt
a1 = [0]*otpt
a2 = [0]*otpt
alph = [0]*otpt
beta = [0]*otpt
lo = [0]*otpt
hi = [0]*otpt
C_Y_pdf = [0]*otpt
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(0,otpt):
PCC[k],dtype[k],Inv1[k],m1[k],m2[k],a1[k],a2[k],alph[k],beta[k],lo[k],hi[k] = pearscdf.pearscdf(limstate[k], Moments['Mean'][k], sqrt(CovarianceMatrix[k, k]), Moments['Skewness'][k], Moments['Kurtosis'][k], methd, k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(dtype[k],limstate[k],Moments['Mean'][k],Moments['Variance'][k],numbins)
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore') #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
Distribution = {'PearsonType': dtype, 'm1': m1, 'm2': m2, 'a1': a1, 'a2': a2, 'Complexity': C_Y_pdf}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix=where(isnan(CorrelationMatrix), None, CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt)-inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {'Moments': Moments, 'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix, 'Distribution': Distribution, 'Plotting': Plotting, 'PCC': PCC}
return Results
def UP_MCS(problem, driver):
# Uses the MCS method for UP
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate = driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
#*****************RANDOM DRAWS FROM INPUT DISTRIBUTIONS********************
value = asarray(LHS.LHS(inpt, nMCS))
for j in range(inpt):
if stvars[j].dist == 'NORM':
value[:, j] = norm.ppf(uniform.cdf(value[:, j], 0, 1),
stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
value[:, j] = lognorm.ppf(uniform.cdf(value[:, j], 0, 1),
stvars[j].param[1], 0,
exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
value[:, j] = beta.ppf(uniform.cdf(value[:, j], 0,
1), stvars[j].param[0],
stvars[j].param[1], stvars[j].param[2],
stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
value[:, j] = uniform.ppf(uniform.cdf(value[:, j], 0, 1),
stvars[j].param[0], stvars[j].param[1])
# ---------------------- Model ---------------------------
out = zeros((nMCS, otpt))
if krig == 1:
load("dmodel")
out = predictor(value, dmodel)
else:
# for i in range(nMCS):
# print 'Running simulation', i+1, 'of', nMCS, 'with inputs', value[i]
# out[i] = run_model(driver, value[i])
out = run_list(problem, driver, value)
limstate = asarray(limstate)
limstate1 = asarray(kron(limstate[:, 0],
ones(nMCS))).reshape(otpt, nMCS).transpose()
limstate2 = asarray(kron(limstate[:, 1],
ones(nMCS))).reshape(otpt, nMCS).transpose()
B = logical_and(greater_equal(out, limstate1), less_equal(out, limstate2))
PCC = sum(B, 0) / nMCS
B_t = B[sum(B, 1) == otpt]
if otpt > 1 and not 0 in PCC[0:otpt]:
PCC = append(PCC, len(B_t) / nMCS)
#Moments
CovarianceMatrix = matrix(cov(out, None, 0)) #.transpose()
Moments = {
'Mean': mean(out, 0),
'Variance': diag(CovarianceMatrix),
'Skewness': skew(out),
'Kurtosis': kurtosis(out, fisher=False)
}
# combine the display of the correlation matrix with setting a var that will be needed below
sigma_mat = matrix(sqrt(diag(CovarianceMatrix)))
CorrelationMatrix = CovarianceMatrix / multiply(sigma_mat,
sigma_mat.transpose())
# ---------------------- Analyze ---------------------------
if any(Moments['Variance'] == 0):
print "Warning: One or more outputs does not vary over given parameter variation."
C_Y = [0] * otpt
for k in range(0, otpt):
if Moments['Variance'][k] != 0:
C_Y[k] = estimate_complexity.with_samples(out[:, k], nMCS)
sigma_mat = matrix(sqrt(diag(CovarianceMatrix)))
seterr(
invalid='ignore'
) # ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix = CovarianceMatrix / multiply(sigma_mat,
sigma_mat.transpose())
Distribution = {'Complexity': C_Y}
CorrelationMatrix = where(isnan(CorrelationMatrix), None,
CorrelationMatrix)
Results = {
'Moments': Moments,
'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix,
'Distribution': Distribution,
'PCC': PCC
}
return Results
def UP_FFNI(driver):
# Uses the FFNI method for UP
# ---------------------- Setup ---------------------------
methd = 'FFNI'
method = 4
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
node,w = params.params(method, nodes, inpt, stvars) # Do I need to transpose these matrices?
#[quadpts] = params(method, nodes, inpt, stvars)
# ---------------------- Model ---------------------------
# Create full factorial experiment from individual nodes and weights
j = fullfact(nodes)
pts = shape(j)[0]
x=zeros((pts,inpt))
wj=zeros((pts,inpt))
for y in range(pts):
for i in range(inpt):
x[y][i] = node[i][j[y][i]]
wj[y][i] = w[i][j[y][i]]
weight = prod(wj, 1)
if krig == 1:
load("dmodel")
G_s = predictor(x, dmodel)
else:
# G_s = zeros((pts, otpt))
# for i in range(pts):
# print 'Running simulation',i+1,'of',pts
# G_s[i] = run_model(driver, x[i])
# G_s[i] = modelica.RunModelica(x[i], modelname, properties)
G_s = run_list(driver, x)
G_mean = zeros(otpt)
G_kurt = zeros(otpt)
G_skew = zeros(otpt)
covar_m = zeros((otpt,otpt))
for k in range(otpt):
G_mean[k] = sum(weight * G_s[:, k])
for k in range(otpt):
for j in range(otpt):
covar_m[k, j] = sum(weight * (G_s[:, k] - G_mean[k]) * (G_s[:, j] - G_mean[j]))
covar_m[j, k] = covar_m[k, j]
G_skew[k] = sum(weight * (G_s[:, k] - G_mean[k]) ** 3) / covar_m[k, k] ** 1.5
G_kurt[k] = sum(weight * (G_s[:, k] - G_mean[k]) ** 4) / covar_m[k, k] ** 2
CovarianceMatrix = covar_m.transpose()
Moments = {'Mean': G_mean, 'Variance': diag(CovarianceMatrix), 'Skewness': G_skew, 'Kurtosis': G_kurt}
# ---------------------- Analyze ---------------------------
# Calculate the PCC for the FFNI method
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
dtype = [0]*otpt
Inv1 = [0]*otpt
Inv2 = [0]*otpt
m1 = [0]*otpt
m2 = [0]*otpt
a1 = [0]*otpt
a2 = [0]*otpt
alph = [0]*otpt
beta = [0]*otpt
lo = [0]*otpt
hi = [0]*otpt
C_Y_pdf = [0]*otpt
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(0,otpt):
PCC[k],dtype[k],Inv1[k],m1[k],m2[k],a1[k],a2[k],alph[k],beta[k],lo[k],hi[k] = pearscdf.pearscdf(limstate[k], Moments['Mean'][k], sqrt(CovarianceMatrix[k, k]), Moments['Skewness'][k], Moments['Kurtosis'][k], methd, k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(dtype[k],limstate[k],Moments['Mean'][k],Moments['Variance'][k],numbins)
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore') #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
Distribution = {'PearsonType': dtype, 'm1': m1, 'm2': m2, 'a1': a1, 'a2': a2, 'Complexity': C_Y_pdf}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix=where(isnan(CorrelationMatrix), None, CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt)-inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {'Moments': Moments, 'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix, 'Distribution': Distribution, 'Plotting': Plotting, 'PCC': PCC}
return Results
def UP_PCE(problem, driver):
# Uses the PCE method for UP
# This routine has been updated as part of refactoring code before the port
# from MATLAB to Python/NumPy/SciPy. Sections of PCC_Computation that apply
# this method have been moved here.
# ---------------------- Setup ---------------------------
methd = 'PCE'
method = 6
inpt = len(driver.inputs)
krig = driver.krig
limstate= driver.limstate
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
stvars = driver.stvars
numbins = driver.numbins
#current settings for these two vars
ii = 0
jj = 0
# ---------------------- Model ---------------------------
mu_g = zeros(inpt)
sigma_g = ones(inpt)
node_t = zeros((inpt,nodes[0]))
weight_t = zeros((inpt,nodes[0]))
for i in range(inpt):
node_t[i], weight_t[i] = gaussquad.gaussquad(nodes[i], 'NORM', mu_g[i], sigma_g[i])
x=[]
for i in range(inpt):
x.append(symbols('x'+str(i)))
x=array(x)
j=fullfact(nodes);
pts = shape(j)[0]
node=zeros((pts,inpt))
wj=zeros((pts,inpt))
for y in range(pts):
for i in range(inpt):
node[y][i] = node_t[i][j[y][i]]
wj[y][i] = weight_t[i][j[y][i]]
weight=prod(wj,1);
P = zeros(order)
P[0] = 1
for p in range(1,order):
term2 = 0
for s in range(1,p+1):
term1 = 1
for r in range(s):
term1 = term1 * (inpt + r)
term2 = term2 + (1.0 / int(scipy.misc.factorial(s))) * term1
if p == 1:
P[p] = term2
else:
P[p] = term2 - sum(P[range(1,p+1)])
G_s = zeros((pts, otpt))
if krig == 1:
t = strcat('SS_K', num2str(ii), num2str(jj))
load(char(t))
for j in range(pts):
#Rosenblatt Transformation
T_L = Dist.Dist(stvars, node[j], inpt)
G_s[j] = predictor(T_L, dmodel)
else:
values = []
for j in range(pts):
#Rosenblatt Transformation
# print 'Running simulation',j+1,'of',pts
T_L = Dist.Dist(stvars, node[j], inpt)
# G_s[j] = run_model(driver, T_L)
values.append(T_L)
G_s = run_list(problem, driver, values)
indx = 0
bn = zeros((sum(P), otpt))
bd = zeros(sum(P))
for k in range(order):
vec = xvector.xvector(k, inpt)
for j in range(int(P[k])):
for i in range(pts):
L=node[i]
if k == 0:
bn[indx] = bn[indx] + weight[i] * G_s[i]
bd[indx] = bd[indx] + weight[i]
else:
h, h_sym = hermite.hermite(k, vec[j], L, x)
bn[indx] += weight[i] * G_s[i] * h
bd[indx] += weight[i] * (h ** 2)
indx+=1
b = zeros((sum(P),otpt))
for l in range(otpt):
b[:, l] = bn[:, l] / bd
indx = 0
U_sum = 0
for k in range(order):
vec = xvector.xvector(k, inpt)
for j in range(int(P[k])):
if k == 0:
U_sum = b[0]
else:
h, h_sym = hermite.hermite(k, vec[j], L, x)
U_sum = U_sum + b[indx] * N(h_sym)
indx+=1
U = U_sum
U_s = zeros((pts,otpt))
G_mean = zeros(otpt)
G_kurt = zeros(otpt)
G_skew = zeros(otpt)
covar_m = zeros((otpt,otpt))
for i in range(pts):
for k in range(otpt):
U_s[i][k] = U[k].subs(dict(zip(x, node[i])))
for k in range(otpt):
# G_mean[k] = sum(matrix(weight) * matrix(U_s[:, k]).transpose())
G_mean[k] = sum(weight * U_s[:, k])
for k in range(otpt):
for j in range(k,otpt):
covar_m[k, j] = sum(weight * (U_s[:, k] - G_mean[k]) * (G_s[:, j] - G_mean[j]))
covar_m[j, k] = covar_m[k, j]
G_skew[k] = sum(weight * (U_s[:, k] - G_mean[k]) ** 3) / covar_m[k, k] ** 1.5
G_kurt[k] = sum(weight * (U_s[:, k] - G_mean[k]) ** 4) / covar_m[k, k] ** 2
CovarianceMatrix = covar_m.transpose()
Moments = {'Mean': G_mean, 'Variance': diag(CovarianceMatrix), 'Skewness': G_skew, 'Kurtosis': G_kurt}
# ---------------------- Analyze ---------------------------
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
# Calculate the PCC for the FFNI method
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
dtype = [0]*otpt
Inv1 = [0]*otpt
Inv2 = [0]*otpt
m1 = [0]*otpt
m2 = [0]*otpt
a1 = [0]*otpt
a2 = [0]*otpt
alph = [0]*otpt
beta = [0]*otpt
lo = [0]*otpt
hi = [0]*otpt
C_Y_pdf = [0]*otpt
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(otpt):
PCC[k],dtype[k],Inv1[k],m1[k],m2[k],a1[k],a2[k],alph[k],beta[k],lo[k],hi[k] =\
pearscdf.pearscdf(limstate[k], Moments['Mean'][k], sqrt(CovarianceMatrix[k, k]), Moments['Skewness'][k], Moments['Kurtosis'][k], methd, k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(dtype[k],limstate[k],Moments['Mean'][k],Moments['Variance'][k],numbins)
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore') #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
Distribution = {'PearsonType': dtype, 'm1': m1, 'm2': m2, 'a1': a1, 'a2': a2, 'Complexity': C_Y_pdf}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix=where(isnan(CorrelationMatrix), None, CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt)-inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {'Moments': Moments, 'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix, 'Distribution': Distribution, 'Plotting': Plotting, 'PCC': PCC}
return Results
def UP_MCS(driver):
# Uses the MCS method for UP
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
#*****************RANDOM DRAWS FROM INPUT DISTRIBUTIONS********************
value = asarray(LHS.LHS(inpt, nMCS))
for j in range(inpt):
if stvars[j].dist == 'NORM':
value[:,j] = norm.ppf(uniform.cdf(value[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
value[:,j] = lognorm.ppf(uniform.cdf(value[:, j], 0, 1), stvars[j].param[1], 0, exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
value[:,j] = beta.ppf(uniform.cdf(value[:, j], 0, 1), stvars[j].param[0], stvars[j].param[1], stvars[j].param[2], stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
value[:,j] = uniform.ppf(uniform.cdf(value[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
# ---------------------- Model ---------------------------
out = zeros((nMCS, otpt))
if krig == 1:
load("dmodel")
out = predictor(value, dmodel)
else:
# for i in range(nMCS):
# print 'Running simulation',i+1,'of',nMCS,'with inputs',value[i]
# out[i] = run_model(driver, value[i])
out = run_list(driver, value)
limstate = asarray(limstate)
limstate1 = asarray(kron(limstate[:, 0], ones(nMCS))).reshape(otpt,nMCS).transpose()
limstate2 = asarray(kron(limstate[:, 1], ones(nMCS))).reshape(otpt,nMCS).transpose()
B = logical_and(greater_equal(out,limstate1),less_equal(out,limstate2))
PCC = sum(B,0) / nMCS
B_t = B[sum(B,1) == otpt]
if otpt > 1 and not 0 in PCC[0:otpt]:
PCC = append(PCC,len(B_t) / nMCS)
#Moments
CovarianceMatrix = matrix(cov(out,None,0))#.transpose()
Moments = {'Mean': mean(out,0), 'Variance': diag(CovarianceMatrix), 'Skewness': skew(out), 'Kurtosis': kurtosis(out,fisher=False)}
# combine the display of the correlation matrix with setting a var that will be needed below
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
# ---------------------- Analyze ---------------------------
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
C_Y = [0]*otpt
for k in range(0,otpt):
if Moments['Variance'][k]!=0:
C_Y[k] = estimate_complexity.with_samples(out[:,k],nMCS)
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore') #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
Distribution = {'Complexity': C_Y}
CorrelationMatrix=where(isnan(CorrelationMatrix), None, CorrelationMatrix)
Results = {'Moments': Moments, 'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix, 'Distribution': Distribution, 'PCC': PCC}
return Results
def UP_TS(problem, driver):
# Uses the TS method for UP
# ---------------------- Setup ---------------------------
methd = 'TS'
method = '2'
delta = driver.TSdelta
mu = [inp.get_I_mu() for inp in driver.inputs]
sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate = driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
rho = identity(inpt)
# ---------------------- Model ---------------------------
if krig == 1:
load("dmodel")
G_mean = predictor(mu, dmodel).cT
G_k = lambda x: predictor(x, dmodel)
F1 = taylorseries.taylorseries(G_k, mu, delta * sigma, inpt, otpt)
else:
# G_mean = run_model(driver, mu)
# G = lambda x: run_model(driver, x)
values = [mu]
values.extend(
taylorseries.pretaylorseries(mu, delta * array(sigma), inpt))
out = iter(run_list(problem, driver, values))
G_mean = out.next()
G = lambda x: out.next()
F1 = taylorseries.taylorseries(G, mu, delta * array(sigma), inpt, otpt)
print 'Taylor Series:\n', F1
covar_m = zeros((otpt, otpt))
for j in range(otpt):
for k in range(j, otpt):
for l in range(inpt):
for m in range(inpt):
covar_m[j, k] = covar_m[j, k] + F1[l, j] * F1[
m, k] * sigma[l] * sigma[m] * rho[l, m]
covar_m[k, j] = covar_m[j, k]
CovarianceMatrix = covar_m.transpose()
print 'Covariance Matrix:\n', CovarianceMatrix
Moments = {
'Mean': G_mean,
'Variance': diag(CovarianceMatrix),
'Skewness': zeros((otpt, 1)),
'Kurtosis': 3 * ones((otpt, 1))
}
if otpt > 1:
PCC = [0] * (otpt + 1)
else:
PCC = [0] * otpt
dtype = [0] * otpt
Inv1 = [0] * otpt
Inv2 = [0] * otpt
m1 = [0] * otpt
m2 = [0] * otpt
a1 = [0] * otpt
a2 = [0] * otpt
alph = [0] * otpt
beta = [0] * otpt
lo = [0] * otpt
hi = [0] * otpt
C_Y_pdf = [0] * otpt
# ---------------------- Analyze ---------------------------
if any(Moments['Variance'] == 0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(0, otpt):
PCC[k], dtype[k], Inv1[k], m1[k], m2[k], a1[k], a2[k], alph[k], beta[
k], lo[k], hi[k] = pearscdf.pearscdf(limstate[k],
Moments['Mean'][k],
sqrt(CovarianceMatrix[k, k]),
Moments['Skewness'][k],
Moments['Kurtosis'][k], methd,
k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(
dtype[k], limstate[k], Moments['Mean'][k],
Moments['Variance'][k], numbins)
sigma_mat = matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore'
) #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix = CovarianceMatrix / multiply(sigma_mat,
sigma_mat.transpose())
Distribution = {
'PearsonType': dtype,
'm1': m1,
'm2': m2,
'a1': a1,
'a2': a2,
'Complexity': C_Y_pdf
}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix = where(isnan(CorrelationMatrix), None,
CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt) - inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {
'Moments': Moments,
'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix,
'Distribution': Distribution,
'Plotting': Plotting,
'PCC': PCC
}
return Results
def SA_SOBOL(driver):
# Uses the Sobel Method for SA.
# Input:
# inpt : no. of input factors
# N: number of Sobel samples
#
# Output:
# SI[] : sensitivity indices
# STI[] : total effect sensitivity indices
# Other used variables/constants:
# V : total variance
# VI : partial variances
# ---------------------- Setup ---------------------------
methd = 'SOBOL'
method = '7'
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
value = asarray(LHS.LHS(2*inpt, nSOBOL))
for j in range(inpt):
if stvars[j].dist == 'NORM':
value[:,j] = norm.ppf(uniform.cdf(value[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
value[:,j+inpt] = norm.ppf(uniform.cdf(value[:,j+inpt], 0, 1), stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
value[:,j] = lognorm.ppf(uniform.cdf(value[:, j], 0, 1), stvars[j].param[1], 0, exp(stvars[j].param[0]))
value[:,j+inpt] = lognorm.ppf(uniform.cdf(value[:, j+inpt], 0, 1), stvars[j].param[1], 0, exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
value[:,j] = beta.ppf(uniform.cdf(value[:, j], 0, 1), stvars[j].param[0], stvars[j].param[1], stvars[j].param[2], stvars[j].param[3] - stvars[j].param[2])
value[:,j+inpt] = beta.ppf(uniform.cdf(value[:, j+inpt], 0, 1), stvars[j].param[0], stvars[j].param[1], stvars[j].param[2], stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
value[:,j] = uniform.ppf(uniform.cdf(value[:,j], 0, 1), stvars[j].param[0], stvars[j].param[1])
value[:,j+inpt] = uniform.ppf(uniform.cdf(value[:,j+inpt], 0, 1), stvars[j].param[0], stvars[j].param[1])
values = []
XMA = value[0:nSOBOL, 0:inpt]
XMB = value[0:nSOBOL, inpt:2 * inpt]
YXMA = zeros((nSOBOL, otpt))
YXMB = zeros((nSOBOL, otpt))
if krig == 1:
load("dmodel")
YXMA = predictor(XMA, dmodel)
YXMB = predictor(XMB, dmodel)
else:
values.extend(list(XMA))
values.extend(list(XMB))
YXMC = zeros((inpt, nSOBOL, otpt))
for i in range(inpt):
XMC = deepcopy(XMB)
XMC[:, i] = deepcopy(XMA[:, i])
if krig == 1:
YXMC[i] = predictor(XMC, dmodel)
else:
values.extend(list(XMC))
if krig != 1:
out = iter(run_list(driver, values))
for i in range(nSOBOL):
YXMA[i] = out.next()
for i in range(nSOBOL):
YXMB[i] = out.next()
for i in range(inpt):
for j in range(nSOBOL):
YXMC[i, j] = out.next()
f0 = mean(YXMA,0)
if otpt==1:
V = cov(YXMA,None,0,1)
else: #multiple outputs
V = diag(cov(YXMA,None,0,1))
Vi = zeros((otpt, inpt))
Vci = zeros((otpt, inpt))
for i in range(inpt):
for p in range(otpt):
Vi[p,i] = 1.0/nSOBOL*sum(YXMA[:,p]*YXMC[i,:,p])-f0[p]**2;
Vci[p,i]= 1.0/nSOBOL*sum(YXMB[:,p]*YXMC[i,:,p])-f0[p]**2;
Si = zeros((otpt,inpt));
Sti = zeros((otpt,inpt));
for j in range(inpt):
Si[:, j] = Vi[:, j] / V
Sti[:, j] = 1 - Vci[:, j] / V
if lrflag == 1:
SRC, stat = SRC_regress.SRC_regress(XMA, YXMA, otpt, nSOBOL)
# ---------------------- Analyze ---------------------------
Results = {'FirstOrderSensitivity': Si, 'TotalEffectSensitivity': Sti}
if lrflag == 1:
Results.update({'SRC': SRC, 'R^2': stat})
return Results
def UP_MPP(driver):
# Uses the MPP method for UP
# This routine has been updated as part of refactoring code before the port
# from MATLAB to Python/NumPy/SciPy. Sections of PCC_Computation that apply
# this method have been moved here.
# ---------------------- Setup ---------------------------
methd = 'MPP'
method = '3'
delta = driver.MPPdelta
mu = [inp.get_I_mu() for inp in driver.inputs]
sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
G_temp = lambda x: run_list(driver, x)
Tinv = lambda x: Dist.Dist(stvars, x, inpt)
#USING THE HASOFER-LIND ALGORITHM
alpha = zeros((otpt, inpt))
Xl = zeros((otpt, inpt))
Xu = zeros((otpt, inpt))
beta1 = zeros(otpt)
beta2 = zeros(otpt)
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
for k in range(otpt):
print 'Testing output',k+1,'of',otpt
if krig == 1:
load("dmodel")
if limstate[k][1] == inf:
cdist = 1
G = lambda x: limstate[k][0]- predictor(x, dmodel)
elif limstate[k][0] == -inf:
cdist = 2
G = lambda x: predictor(x, dmodel) - limstate[k][1]
else:
cdist = 3
G1 = lambda x: limstate[k][0] - predictor(x, dmodel)
G2 = lambda x: predictor(x, dmodel) - limstate[k][2]
else:
if limstate[k][1] == inf:
cdist = 1
G = lambda x: limstate[k][0] - G_temp(x)
elif limstate[k][0] == -inf:
cdist = 2
G = lambda x: G_temp(x) - limstate[k][1]
else:
cdist = 3
G1 = lambda x: limstate[k][0] - G_temp(x)
G2 = lambda x: G_temp(x) - limstate[k][1]
I_sigma=ones((inpt));
if cdist == 1 or cdist == 2:
u = zeros((inpt))
diff = 1.0
while diff > .005:
uk, alp = Hasofer.Hasofer(G, u, Tinv, k, delta, I_sigma, inpt, otpt)
diff = abs(linalg.norm(uk) - linalg.norm(u))
u = uk
print 'X =', Tinv(u)
beta1[k] = linalg.norm(u)
beta2[k] = inf
if cdist == 1:
Xl[k] = Tinv(u)
Xu[k] = ones((inpt)) * inf
alpha[k] = alp
else:
Xl[k] = ones((inpt)) * -inf
Xu[k] = Tinv(u)
alpha[k] = -alp
PCC[k] = norm.cdf(beta1[k])
alpha[k] = alp
else:
u = zeros((inpt))
diff = 1.0
try:
while diff > .005:
uk, alp = Hasofer.Hasofer(G1, u, Tinv, k, delta, I_sigma, inpt, otpt)
diff = abs(linalg.norm(uk) - linalg.norm(u))
u = uk
print 'X =', Tinv(u)
beta1[k] = linalg.norm(u)
Xl[k] = Tinv(u)
except ValueError:
beta1[k] = inf
Xl[k] = nan
u = zeros((inpt))
diff = 1.0
try:
while diff > .005:
uk, alp = Hasofer.Hasofer(G2, u, Tinv, k, delta, I_sigma, inpt, otpt)
diff = abs(linalg.norm(uk) - linalg.norm(u))
u = uk
print 'X =', Tinv(u)
beta2[k] = linalg.norm(u)
Xu[k] = Tinv(u)
alpha[k] = -alp
except ValueError:
beta2[k] = inf
Xu[k] = nan
alpha[k] = nan
PCC[k] = norm.cdf(beta2[k]) - norm.cdf(-beta1[k])
corr_mat = ones((otpt,otpt))
for j in range(otpt):
for k in range(j,otpt):
corr_mat[j, k] = matrix(alpha[j]) * matrix(alpha[k]).transpose()
corr_mat[k, j] = corr_mat[j, k]
# ---------------------- Analyze ---------------------------
# There's not much to analyze; that already happened. Just print out the results
if otpt > 1 and not 0 in PCC[0:otpt]:
PCC[otpt] = mvstdnormcdf(-beta1, beta2, corr_mat)
# requested feature: replace all "NaNs" with "None"
Xu=where(isnan(Xu), None, Xu)
Xl=where(isnan(Xl), None, Xl)
corr_mat=where(isnan(corr_mat), None, corr_mat)
Results = {'MPPUpperBound':Xu ,'MPPLowerBound':Xl,'CorrelationMatrix': corr_mat, 'PCC': PCC}
return Results
def SA_EFAST(problem, driver):
#[SI,STI] = EFAST(K,WANTEDN)
# First order and total effect indices for a given model computed with
# Extended Fourier Amplitude Sensitivity Test (EFAST).
# Andrea Saltelli, Stefano Tarantola and Karen Chan. 1999
# A quantitative model-independent method for global sensitivity analysis of model output.
# Technometrics 41:39-56
#
# Input:
# inpt : no. of input factors
# WANTEDN : wanted no. of sample points
#
# Output:
# SI[] : first order sensitivity indices
# STI[] : total effect sensitivity indices
# Other used variables/constants:
# OM[] : vector of inpt frequencies
# OMI : frequency for the group of interest
# OMCI[] : set of freq. used for the compl. group
# X[] : parameter combination rank matrix
# AC[],BC[]: fourier coefficients
# FI[] : random phase shift
# V : total output variance (for each curve)
# VI : partial var. of par. i (for each curve)
# VCI : part. var. of the compl. set of par...
# AV : total variance in the time domain
# AVI : partial variance of par. i
# AVCI : part. var. of the compl. set of par.
# Y[] : model output
# N : no. of runs on each curve
# ---------------------- Setup ---------------------------
methd = 'EFAST'
method = '10'
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate = driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
NR = 1 #: no. of search curves
MI = 4 #: maximum number of fourier coefficients that may be retained in calculating
# the partial variances without interferences between the assigned frequencies
#
# Computation of the frequency for the group of interest OMi and the no. of sample points N.
OMi = int(floor((nEFAST / NR - 1) / (2 * MI) / inpt))
N = 2 * MI * OMi + 1
total_sims = N * NR * inpt
sim = 0
if (N * NR < 65):
logging.error('sample size must be >= 65 per factor.')
raise ValueError, 'sample size must be >= 65 per factor.'
# Algorithm for selecting the set of frequencies. OMci(i), i=1:inpt-1, contains
# the set of frequencies to be used by the complementary group.
OMci = SETFREQ(N - 1, OMi / 2 / MI)
# Loop over the inpt input factors.
Si = zeros((otpt, otpt, inpt))
Sti = zeros((otpt, otpt, inpt))
for i in range(inpt):
# Initialize AV,AVi,AVci to zero.
AV = 0
AVi = 0
AVci = 0
# Loop over the NR search curves.
for L in range(NR):
# Setting the vector of frequencies OM for the inpt factors.
cj = 1
OM = zeros(inpt)
for j in range(inpt):
if (j == i):
# For the factor of interest.
OM[i] = OMi
else:
# For the complementary group.
OM[j] = OMci[cj]
cj = cj + 1
# Setting the relation between the scalar variable S and the coordinates
# {X(1),X(2),...X(inpt)} of each sample point.
FI = zeros(inpt)
for j in range(inpt):
FI[j] = random.random() * 2 * pi # random phase shift
S_VEC = pi * (2 * arange(1, N + 1) - N - 1) / N
OM_VEC = OM[range(inpt)]
FI_MAT = transpose(array([FI] * N))
ANGLE = matrix(OM_VEC).T * matrix(S_VEC) + matrix(FI_MAT)
X = 0.5 + arcsin(sin(ANGLE.T)) / pi
# Transform distributions from standard uniform to general.
for j in range(inpt):
if stvars[j].dist == 'NORM':
X[:, j] = norm.ppf(uniform.cdf(X[:, j], 0, 1),
stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
X[:, j] = lognorm.ppf(uniform.cdf(X[:, j], 0, 1),
stvars[j].param[1], 0,
exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
X[:, j] = beta.ppf(uniform.cdf(X[:, j], 0,
1), stvars[j].param[0],
stvars[j].param[1], stvars[j].param[2],
stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
X[:, j] = uniform.ppf(uniform.cdf(X[:, j], 0,
1), stvars[j].param[0],
stvars[j].param[1])
# Do the N model evaluations.
Y = zeros((N, otpt))
if krig == 1:
load("dmodel")
Y = predictor(X, dmodel)
else:
values = []
for p in range(N):
# sim += 1
# print 'Running simulation on test',sim,'of',total_sims
# Y[p] = run_model(driver, array(X[p])[0])
values.append(array(X[p])[0])
Y = run_list(problem, driver, values)
# Subtract the average value.
Y = Y - kron(mean(Y, 0), ones((N, 1)))
# Fourier coeff. at [1:OMi/2].
NQ = int(N / 2) - 1
N0 = NQ + 1
COMPL = 0
Y_VECP = Y[N0 + 1:] + Y[NQ::-1]
Y_VECM = Y[N0 + 1:] - Y[NQ::-1]
# AC = zeros((int(ceil(OMi / 2)), otpt))
# BC = zeros((int(ceil(OMi / 2)), otpt))
AC = zeros((OMi * MI, otpt))
BC = zeros((OMi * MI, otpt))
for j in range(int(ceil(OMi / 2)) + 1):
ANGLE = (j + 1) * 2 * arange(1, NQ + 2) * pi / N
C_VEC = cos(ANGLE)
S_VEC = sin(ANGLE)
AC[j] = (Y[N0] + matrix(C_VEC) * matrix(Y_VECP)) / N
BC[j] = matrix(S_VEC) * matrix(Y_VECM) / N
COMPL = COMPL + matrix(AC[j]).T * matrix(AC[j]) + matrix(
BC[j]).T * matrix(BC[j])
# Computation of V_{(ci)}.
Vci = 2 * COMPL
AVci = AVci + Vci
# Fourier coeff. at [P*OMi, for P=1:MI].
COMPL = 0
# Do these need to be recomputed at all?
# Y_VECP = Y[N0 + range(NQ)] + Y[N0 - range(NQ)]
# Y_VECM = Y[N0 + range(NQ)] - Y[N0 - range(NQ)]
for j in range(OMi, OMi * MI + 1, OMi):
ANGLE = j * 2 * arange(1, NQ + 2) * pi / N
C_VEC = cos(ANGLE)
S_VEC = sin(ANGLE)
AC[j - 1] = (Y[N0] + matrix(C_VEC) * matrix(Y_VECP)) / N
BC[j - 1] = matrix(S_VEC) * matrix(Y_VECM) / N
COMPL = COMPL + matrix(AC[j - 1]).T * matrix(
AC[j - 1]) + matrix(BC[j - 1]).T * matrix(BC[j - 1])
# Computation of V_i.
Vi = 2 * COMPL
AVi = AVi + Vi
# Computation of the total variance in the time domain.
AV = AV + matrix(Y).T * matrix(Y) / N
# Computation of sensitivity indicies.
AV = AV / NR
AVi = AVi / NR
AVci = AVci / NR
Si[:, :, i] = AVi / AV
Sti[:, :, i] = 1 - AVci / AV
if lrflag == 1:
SRC, stat = SRC_regress.SRC_regress(X, Y, otpt, N)
# ---------------------- Analyze ---------------------------
# if plotf == 1:
# piecharts(inpt, otpt, Si, Sti, methd, output)
if simple == 1:
Si_t = zeros((inpt, otpt))
for p in range(inpt):
Si_t[p] = diag(Si[:, :, p])
Si = Si_t.T
if simple == 1:
Sti_t = zeros((inpt, otpt))
for p in range(inpt):
Sti_t[p] = diag(Sti[:, :, p])
Sti = Sti_t.T
Results = {'FirstOrderSensitivity': Si, 'TotalEffectSensitivity': Sti}
if lrflag == 1:
Results.update({'SRC': SRC, 'R^2': stat})
return Results
def UP_TS(driver):
# Uses the TS method for UP
# ---------------------- Setup ---------------------------
methd = 'TS'
method = '2'
delta = driver.TSdelta
mu = [inp.get_I_mu() for inp in driver.inputs]
sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
rho = identity(inpt)
# ---------------------- Model ---------------------------
if krig == 1:
load("dmodel")
G_mean = predictor(mu, dmodel).cT
G_k = lambda x: predictor(x, dmodel)
F1 = taylorseries.taylorseries(G_k, mu, delta*sigma, inpt, otpt)
else:
# G_mean = run_model(driver, mu)
# G = lambda x: run_model(driver, x)
values = [mu]
values.extend(taylorseries.pretaylorseries(mu, delta*array(sigma), inpt))
out = iter(run_list(driver, values))
G_mean = out.next()
G = lambda x: out.next()
F1 = taylorseries.taylorseries(G, mu, delta*array(sigma), inpt, otpt)
print 'Taylor Series:\n',F1
covar_m = zeros((otpt, otpt))
for j in range(otpt):
for k in range(j,otpt):
for l in range(inpt):
for m in range(inpt):
covar_m[j, k] = covar_m[j, k] + F1[l, j] * F1[m, k] * sigma[l] * sigma[m] * rho[l, m]
covar_m[k, j] = covar_m[j, k]
CovarianceMatrix = covar_m.transpose()
print 'Covariance Matrix:\n',CovarianceMatrix
Moments = {'Mean': G_mean, 'Variance': diag(CovarianceMatrix), 'Skewness': zeros((otpt, 1)), 'Kurtosis': 3 * ones((otpt, 1))}
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
dtype = [0]*otpt
Inv1 = [0]*otpt
Inv2 = [0]*otpt
m1 = [0]*otpt
m2 = [0]*otpt
a1 = [0]*otpt
a2 = [0]*otpt
alph = [0]*otpt
beta = [0]*otpt
lo = [0]*otpt
hi = [0]*otpt
C_Y_pdf = [0]*otpt
# ---------------------- Analyze ---------------------------
if any(Moments['Variance']==0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(0,otpt):
PCC[k],dtype[k],Inv1[k],m1[k],m2[k],a1[k],a2[k],alph[k],beta[k],lo[k],hi[k] = pearscdf.pearscdf(limstate[k], Moments['Mean'][k], sqrt(CovarianceMatrix[k, k]), Moments['Skewness'][k], Moments['Kurtosis'][k], methd, k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(dtype[k],limstate[k],Moments['Mean'][k],Moments['Variance'][k],numbins)
sigma_mat=matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore') #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix= CovarianceMatrix/multiply(sigma_mat,sigma_mat.transpose())
Distribution = {'PearsonType': dtype, 'm1': m1, 'm2': m2, 'a1': a1, 'a2': a2, 'Complexity': C_Y_pdf}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix=where(isnan(CorrelationMatrix), None, CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt)-inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {'Moments': Moments, 'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix, 'Distribution': Distribution, 'Plotting': Plotting, 'PCC': PCC}
return Results
def SA_SOBOL(problem, driver):
# Uses the Sobel Method for SA.
# Input:
# inpt : no. of input factors
# N: number of Sobel samples
#
# Output:
# SI[] : sensitivity indices
# STI[] : total effect sensitivity indices
# Other used variables/constants:
# V : total variance
# VI : partial variances
# ---------------------- Setup ---------------------------
methd = 'SOBOL'
method = '7'
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate = driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
value = asarray(LHS.LHS(2 * inpt, nSOBOL))
for j in range(inpt):
if stvars[j].dist == 'NORM':
value[:, j] = norm.ppf(uniform.cdf(value[:, j], 0, 1),
stvars[j].param[0], stvars[j].param[1])
value[:,
j + inpt] = norm.ppf(uniform.cdf(value[:, j + inpt], 0, 1),
stvars[j].param[0], stvars[j].param[1])
elif stvars[j].dist == 'LNORM':
value[:, j] = lognorm.ppf(uniform.cdf(value[:, j], 0, 1),
stvars[j].param[1], 0,
exp(stvars[j].param[0]))
value[:, j + inpt] = lognorm.ppf(
uniform.cdf(value[:, j + inpt], 0, 1), stvars[j].param[1], 0,
exp(stvars[j].param[0]))
elif stvars[j].dist == 'BETA':
value[:, j] = beta.ppf(uniform.cdf(value[:, j], 0,
1), stvars[j].param[0],
stvars[j].param[1], stvars[j].param[2],
stvars[j].param[3] - stvars[j].param[2])
value[:,
j + inpt] = beta.ppf(uniform.cdf(value[:, j + inpt], 0,
1), stvars[j].param[0],
stvars[j].param[1], stvars[j].param[2],
stvars[j].param[3] - stvars[j].param[2])
elif stvars[j].dist == 'UNIF':
value[:, j] = uniform.ppf(uniform.cdf(value[:, j], 0, 1),
stvars[j].param[0], stvars[j].param[1])
value[:, j + inpt] = uniform.ppf(
uniform.cdf(value[:, j + inpt], 0, 1), stvars[j].param[0],
stvars[j].param[1])
values = []
XMA = value[0:nSOBOL, 0:inpt]
XMB = value[0:nSOBOL, inpt:2 * inpt]
YXMA = zeros((nSOBOL, otpt))
YXMB = zeros((nSOBOL, otpt))
if krig == 1:
load("dmodel")
YXMA = predictor(XMA, dmodel)
YXMB = predictor(XMB, dmodel)
else:
values.extend(list(XMA))
values.extend(list(XMB))
YXMC = zeros((inpt, nSOBOL, otpt))
for i in range(inpt):
XMC = deepcopy(XMB)
XMC[:, i] = deepcopy(XMA[:, i])
if krig == 1:
YXMC[i] = predictor(XMC, dmodel)
else:
values.extend(list(XMC))
if krig != 1:
out = iter(run_list(problem, driver, values))
for i in range(nSOBOL):
YXMA[i] = out.next()
for i in range(nSOBOL):
YXMB[i] = out.next()
for i in range(inpt):
for j in range(nSOBOL):
YXMC[i, j] = out.next()
f0 = mean(YXMA, 0)
if otpt == 1:
V = cov(YXMA, None, 0, 1)
else: #multiple outputs
V = diag(cov(YXMA, None, 0, 1))
Vi = zeros((otpt, inpt))
Vci = zeros((otpt, inpt))
for i in range(inpt):
for p in range(otpt):
Vi[p,
i] = 1.0 / nSOBOL * sum(YXMA[:, p] * YXMC[i, :, p]) - f0[p]**2
Vci[p,
i] = 1.0 / nSOBOL * sum(YXMB[:, p] * YXMC[i, :, p]) - f0[p]**2
Si = zeros((otpt, inpt))
Sti = zeros((otpt, inpt))
for j in range(inpt):
Si[:, j] = Vi[:, j] / V
Sti[:, j] = 1 - Vci[:, j] / V
if lrflag == 1:
SRC, stat = SRC_regress.SRC_regress(XMA, YXMA, otpt, nSOBOL)
# ---------------------- Analyze ---------------------------
Results = {'FirstOrderSensitivity': Si, 'TotalEffectSensitivity': Sti}
if lrflag == 1:
Results.update({'SRC': SRC, 'R^2': stat})
return Results
def UP_MPP(problem, driver):
# Uses the MPP method for UP
# This routine has been updated as part of refactoring code before the port
# from MATLAB to Python/NumPy/SciPy. Sections of PCC_Computation that apply
# this method have been moved here.
# ---------------------- Setup ---------------------------
methd = 'MPP'
method = '3'
delta = driver.MPPdelta
mu = [inp.get_I_mu() for inp in driver.inputs]
sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate= driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
# ---------------------- Model ---------------------------
G_temp = lambda x: run_list(problem, driver, x)
Tinv = lambda x: Dist.Dist(stvars, x, inpt)
#USING THE HASOFER-LIND ALGORITHM
alpha = zeros((otpt, inpt))
Xl = zeros((otpt, inpt))
Xu = zeros((otpt, inpt))
beta1 = zeros(otpt)
beta2 = zeros(otpt)
if otpt>1:
PCC = [0]*(otpt+1)
else:
PCC = [0]*otpt
for k in range(otpt):
print 'Testing output',k+1,'of',otpt
if krig == 1:
load("dmodel")
if limstate[k][1] == inf:
cdist = 1
G = lambda x: limstate[k][0]- predictor(x, dmodel)
elif limstate[k][0] == -inf:
cdist = 2
G = lambda x: predictor(x, dmodel) - limstate[k][1]
else:
cdist = 3
G1 = lambda x: limstate[k][0] - predictor(x, dmodel)
G2 = lambda x: predictor(x, dmodel) - limstate[k][2]
else:
if limstate[k][1] == inf:
cdist = 1
G = lambda x: limstate[k][0] - G_temp(x)
elif limstate[k][0] == -inf:
cdist = 2
G = lambda x: G_temp(x) - limstate[k][1]
else:
cdist = 3
G1 = lambda x: limstate[k][0] - G_temp(x)
G2 = lambda x: G_temp(x) - limstate[k][1]
I_sigma=ones((inpt));
if cdist == 1 or cdist == 2:
u = zeros((inpt))
diff = 1.0
while diff > .005:
uk, alp = Hasofer.Hasofer(G, u, Tinv, k, delta, I_sigma, inpt, otpt)
diff = abs(linalg.norm(uk) - linalg.norm(u))
u = uk
print 'X =', Tinv(u)
beta1[k] = linalg.norm(u)
beta2[k] = inf
if cdist == 1:
Xl[k] = Tinv(u)
Xu[k] = ones((inpt)) * inf
alpha[k] = alp
else:
Xl[k] = ones((inpt)) * -inf
Xu[k] = Tinv(u)
alpha[k] = -alp
PCC[k] = norm.cdf(beta1[k])
alpha[k] = alp
else:
u = zeros((inpt))
diff = 1.0
try:
while diff > .005:
uk, alp = Hasofer.Hasofer(G1, u, Tinv, k, delta, I_sigma, inpt, otpt)
diff = abs(linalg.norm(uk) - linalg.norm(u))
u = uk
print 'X =', Tinv(u)
beta1[k] = linalg.norm(u)
Xl[k] = Tinv(u)
except ValueError:
beta1[k] = inf
Xl[k] = nan
u = zeros((inpt))
diff = 1.0
try:
while diff > .005:
uk, alp = Hasofer.Hasofer(G2, u, Tinv, k, delta, I_sigma, inpt, otpt)
diff = abs(linalg.norm(uk) - linalg.norm(u))
u = uk
print 'X =', Tinv(u)
beta2[k] = linalg.norm(u)
Xu[k] = Tinv(u)
alpha[k] = -alp
except ValueError:
beta2[k] = inf
Xu[k] = nan
alpha[k] = nan
PCC[k] = norm.cdf(beta2[k]) - norm.cdf(-beta1[k])
corr_mat = ones((otpt,otpt))
for j in range(otpt):
for k in range(j,otpt):
corr_mat[j, k] = matrix(alpha[j]) * matrix(alpha[k]).transpose()
corr_mat[k, j] = corr_mat[j, k]
# ---------------------- Analyze ---------------------------
# There's not much to analyze; that already happened. Just print out the results
if otpt > 1 and not 0 in PCC[0:otpt]:
PCC[otpt] = mvstdnormcdf(-beta1, beta2, corr_mat)
# requested feature: replace all "NaNs" with "None"
Xu=where(isnan(Xu), None, Xu)
Xl=where(isnan(Xl), None, Xl)
corr_mat=where(isnan(corr_mat), None, corr_mat)
Results = {'MPPUpperBound':Xu ,'MPPLowerBound':Xl,'CorrelationMatrix': corr_mat, 'PCC': PCC}
return Results
def UP_UDR(problem, driver):
# Uses the UDR method for UP
methd = 'UDR'
method = 5
mu = [inp.get_I_mu() for inp in driver.inputs]
I_sigma = [inp.get_I_sigma() for inp in driver.inputs]
inpt = len(driver.inputs)
input = driver.inputNames
krig = driver.krig
limstate = driver.limstate
lrflag = driver.lrflag
n_meta = driver.n_meta
nEFAST = driver.nEFAST
nSOBOL = driver.nSOBOL
nMCS = driver.nMCS
numbins = driver.numbins
nodes = driver.nodes
order = driver.order
otpt = len(driver.outputNames)
output = driver.outputNames
p = driver.p
plotf = 0
r = driver.r
simple = driver.simple
stvars = driver.stvars
node, weight = params.params(method, nodes, inpt, stvars)
# ---------------------- Model ---------------------------
# set_printoptions(precision=4)
# set_printoptions(suppress=True)
x = kron(mu, ones((inpt * nodes[0], 1)))
for ii in range(0, inpt):
k = ii * nodes[ii]
l = (ii + 1) * nodes[ii]
x[k:l, ii] = node[ii, :]
# G_mu = run_model(driver, mu)
values = [mu]
G_s = zeros((inpt, max(nodes), otpt))
for k, inputname in enumerate(
driver._json_tree['Configurations']['Configuration']
['PCCInputArguments']['StochasticInputs']['InputDistributions']):
if krig == 1:
load('dmodel')
for j in range(0, nodes[k]):
var = k * nodes[k] + j
X = x[var, :]
G_s[k, j] = predictor(X, dmodel)
else:
for j in range(0, nodes[k]):
var = k * nodes[k] + j
X = x[var, :]
# print 'Running simulation on node',j,'of input',inputname['Name']
# G_s[k, j] = run_model(driver, X)
values.append(X)
out = iter(run_list(problem, driver, values))
G_mu = out.next()
for k, inputname in enumerate(
driver._json_tree['Configurations']['Configuration']
['PCCInputArguments']['StochasticInputs']['InputDistributions']):
for j in range(0, nodes[k]):
G_s[k, j] = out.next()
G_mean = zeros(otpt)
G_kurt = zeros(otpt)
G_skew = zeros(otpt)
G_sigma = zeros(otpt)
covar_m = zeros((otpt, otpt))
gs = zeros(otpt)
gk = zeros(otpt)
moms = []
for l in range(0, otpt):
moms.append(
newmoment(inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l]))
G_mean[l] = moment(1, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l])
for l in range(0, otpt):
moms.append(
newmoment(inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l]))
G_sigma[l] = moment(2, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l])
gs[l] = moment(3, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l])
G_skew[l] = moment(3, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l]) / G_sigma[l]**1.5
gk[l] = moment(4, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l])
G_kurt[l] = moment(4, inpt, nodes[0], weight, G_s[:, :, l], G_mu[l],
G_mean[l]) / G_sigma[l]**2
for j in range(l, otpt):
covar_m[l, j] = moment2(1, inpt, nodes[0], weight, G_s[:, :, l], l,
G_s[:, :, j], j, G_mu, G_mean)
covar_m[j, l] = covar_m[l, j]
CovarianceMatrix = covar_m.transpose()
Moments = {
'Mean': G_mean,
'Variance': diag(CovarianceMatrix),
'Skewness': G_skew,
'Kurtosis': G_kurt
}
# ---------------------- Analyze ---------------------------
# Calculate the PCC for the FFNI method
if otpt > 1:
PCC = [0] * (otpt + 1)
else:
PCC = [0] * otpt
dtype = [0] * otpt
Inv1 = [0] * otpt
Inv2 = [0] * otpt
m1 = [0] * otpt
m2 = [0] * otpt
a1 = [0] * otpt
a2 = [0] * otpt
alph = [0] * otpt
beta = [0] * otpt
lo = [0] * otpt
hi = [0] * otpt
C_Y_pdf = [0] * otpt
if any(Moments['Variance'] == 0):
print "Warning: One or more outputs does not vary over given parameter variation."
for k in range(0, otpt):
PCC[k], dtype[k], Inv1[k], m1[k], m2[k], a1[k], a2[k], alph[k], beta[
k], lo[k], hi[k] = pearscdf.pearscdf(limstate[k],
Moments['Mean'][k],
sqrt(CovarianceMatrix[k, k]),
Moments['Skewness'][k],
Moments['Kurtosis'][k], methd,
k, output)
if dtype[k] != None:
if iscomplex(a1[k]):
a1[k] = [a1[k].real, a1[k].imag]
if iscomplex(a2[k]):
a2[k] = [a2[k].real, a2[k].imag]
C_Y_pdf[k] = estimate_complexity.with_distribution(
dtype[k], limstate[k], Moments['Mean'][k],
Moments['Variance'][k], numbins)
sigma_mat = matrix(sqrt(diag(CovarianceMatrix)))
seterr(invalid='ignore'
) #ignore problems with divide-by-zero, just give us 'nan' as usual
CorrelationMatrix = CovarianceMatrix / multiply(sigma_mat,
sigma_mat.transpose())
Distribution = {
'PearsonType': dtype,
'm1': m1,
'm2': m2,
'a1': a1,
'a2': a2,
'Complexity': C_Y_pdf
}
Plotting = {'alpha': alph, 'beta': beta, 'lo': lo, 'hi': hi}
CorrelationMatrix = where(isnan(CorrelationMatrix), None,
CorrelationMatrix)
if otpt > 1 and not 0 in PCC[0:otpt]:
lower = zeros(otpt) - inf
PCC[otpt] = mvstdnormcdf(lower, Inv1, CorrelationMatrix)
Results = {
'Moments': Moments,
'CorrelationMatrix': CorrelationMatrix,
'CovarianceMatrix': CovarianceMatrix,
'Distribution': Distribution,
'Plotting': Plotting,
'PCC': PCC
}
return Results