# Compute the average of the given species
if (qoi=='ave'):
ave=np.average(sol[-tail:,1:],axis=0)
ncol=ave.shape[0]
npc=ncol/3
np.savetxt("pccf.dat",ave[npc*(spid-1):npc*spid])
elif (qoi=='tf'):
sp_tf=np.array(sol[-1,1:])
ncol=sp_tf.shape[0]
npc=ncol/3
np.savetxt("pccf.dat",sp_tf[npc*(spid-1):npc*spid])
# Find (in a very unpleasant way) the stochastic dimension
for dim in range(1,7):
npcc=get_npc(dim,ord)
if (npc==npcc):
break
pcerv=uqtkbin+"/pce_rv"
os.system(pcerv+" -w'PC' -f'pccf.dat' -x" + pctype + " -d1 -n" + str(nsam) +" -p"+str(dim)+" -o"+str(ord))
spls=np.genfromtxt("rvar.dat")
#xlin=np.linspace(spls.min(),spls.max(),npts) ;
xlin=np.linspace(lims[0],lims[1],npts) ;
kernlin=stats.kde.gaussian_kde(spls);
pdflin1=kernlin(xlin);
plt.plot(xlin,pdflin1,linewidth=2,label=method)
ax.set_xlim(lims)
if qoi == 'ave':
f_handle = file("pcfile", "a")
npy.savetxt(f_handle, stdmat, fmt="%.12e", delimiter=" ", newline="\n")
f_handle.close()
print "Mean of model parameters:"
print nomdim
print "Std.dev. of model parameters:"
print stdmat
#-check number of PC terms
inord=1
pccf = npy.genfromtxt('pcfile')
if len(pccf.shape) == 1:
pccf=npy.array(npy.transpose([pccf]))
indim_par = pccf.shape[1]
npc = pccf.shape[0]
npcc = get_npc(dim,inord)
if npc != npcc:
print "forUQ_BB(): The number of input PC coefficients does not match to the given dimension and order -> Abort !\n"
quit()
print "-----------------------------------------------------------------------------------------------------"
print " ____ _ "
print " | _ \ _ _ _ __ _ __ (_)_ __ __ _ _ "
print " | |_) | | | | '_ \| '_ \| | '_ \ / _` | (_)"
print " | _ <| |_| | | | | | | | | | | | (_| | _ "
print " |_| \_\\\__,_|_| |_|_| |_|_|_| |_|\__, | (_)"
print " |___/ "
print "-----------------------------------------------------------------------------------------------------"
#--------------------------------------------------------------------------------------------------
# Construct a PCE expansion of model output as a function of input chaos germ
#--------------------------------------------------------------------------------------------------
# Compute the average of the given species
if (qoi=='ave'):
ave=np.average(sol[-tail:,1:],axis=0)
ncol=ave.shape[0]
npc=ncol/3
np.savetxt("pccf.dat",ave[npc*(spid-1):npc*spid])
elif (qoi=='tf'):
sp_tf=np.array(sol[-1,1:])
ncol=sp_tf.shape[0]
npc=ncol/3
np.savetxt("pccf.dat",sp_tf[npc*(spid-1):npc*spid])
# Find (in a very unpleasant way) the stochastic dimension
for dim in range(1,7):
npcc=get_npc(dim,ord)
if (npc==npcc):
break
pcerv=uqtkbin+"/pce_rv"
os.system(pcerv+" -w'PC' -f'pccf.dat' -x" + pctype + " -d1 -n" + str(nsam) +" -p"+str(dim)+" -q"+str(ord))
spls=np.genfromtxt("rvar.dat")
xlin=np.linspace(spls.min(),spls.max(),100) ;
kernlin=stats.kde.gaussian_kde(spls);
pdflin1=kernlin(xlin);
plt.plot(xlin,pdflin1,linewidth=2,label=method)