for idim in chdimlist:
for line in fileinput.input("surf_rxn.in.xml", inplace = 1):
print line.replace('PAR_'+str(idim), str(chstart[ii])),
ii=ii+1
print "Running the parameter inference"
os.system('./SurfRxnInfer.x')
#
# Import data from MCMC file
#
# load chain file
# it expects first column is line id, then the last
# two columns are alpha and current log posterior
print "Loading in chain file",chainfile
all_samples, vnames = file_utils.extract_all_vars(chainfile,n_burnin,0,1)
n_all_vars = len(vnames)
n_cols = len(all_samples[0,:])
# Extract all MCMC chain variables in separate array
d0 = all_samples[:,1:1+n_all_vars]
samfile="insamples.dat"
np.savetxt(samfile,d0)
# Find PC coefficients corresponding to the chain samples
print "Running KDE-Rosenblatt transformation to build input PCE"
os.system(pcequad+' -o' + str(pcord)+' -f ' + samfile + ' -x ' + pctype + ' -w' + str(bw) + ' > pcequad.log')
# Prepare the xml file for forward propagation
for line in fileinput.input("surf_rxn.in.xml", inplace = 1):
print line.replace('uncertain', 'det'),
for line in fileinput.input("surf_rxn.in.xml", inplace = 1):
from pylab import *
import file_utils
rc('legend',loc='upper left', fontsize=12)
rc('lines', linewidth=4, color='r')
rc('axes',linewidth=3,grid=True,labelsize=22)
rc('xtick',labelsize=20)
rc('ytick',labelsize=20)
chainfile="chain.dat"; # chain file
nskip=5000; # skip first 'nskip' states
nthin=10; # pick every 'nthin' state
all_samples, vnames = file_utils.extract_all_vars(chainfile,nskip,0,1)
n_all_vars = len(vnames)
n_cols = len(all_samples[0,:])
chain = all_samples[:,0:1+n_all_vars]
for i in range(n_all_vars):
fig = plt.figure(figsize=(10,7))
ax=fig.add_axes([0.10,0.15,0.85,0.75])
plt.plot(chain[:,0],chain[:,i+1],color='black',linewidth=2)
ax.set_xlabel("MCMC step",fontsize=22)
ax.set_ylabel(vnames[i],fontsize=22)
plt.savefig('chain_'+vnames[i]+'.eps')
plt.clf()
sys.exit(1)
if (np_kde < 1):
print "The number of KDE points per dimension needs to be >= 1"
print help_string
sys.exit(1)
#
# Import data from MCMC file
#
# Section 1
# load chain file
# it expects first column is line id, then the last
# two columns are alpha and current log posterior
print "Loading in data file", samples_file_name
all_samples, vnames = file_utils.extract_all_vars(samples_file_name, n_burnin,
debug, stride)
n_all_vars = len(vnames)
n_cols = len(all_samples[0, :])
# Extract all MCMC chain variables in separate array
d0 = all_samples[:, 1:1 + n_all_vars]
if (debug > 0):
print d0.shape
# Some settings to connect with code Cosmin gave me
nthin = 1 # take only every nthin state (for faster kde)
nskip = 0 # entries to skip
#npdf = 100 # no of grid points for 1D pdf's
istart = 0 # number of column with first MCMC variable
#cend = 0 # number of extra columns at the end.
cend = 0 # remove columns at end so code runs faster for debugging
from pylab import *
import file_utils
rc('legend',loc='upper left', fontsize=12)
rc('lines', linewidth=4, color='r')
rc('axes',linewidth=3,grid=True,labelsize=22)
rc('xtick',labelsize=20)
rc('ytick',labelsize=20)
chainfile="chain.dat"; # chain file
nskip=5000; # skip first 'nskip' states
nthin=10; # pick every 'nthin' state
all_samples, vnames = file_utils.extract_all_vars(chainfile,nskip,0,1)
n_all_vars = len(vnames)
n_cols = len(all_samples[0,:])
chain = all_samples[:,0:1+n_all_vars]
for i in range(n_all_vars):
fig = plt.figure(figsize=(10,7))
ax=fig.add_axes([0.10,0.15,0.85,0.75])
plt.plot(chain[:,0],chain[:,i+1],color='black',linewidth=2)
ax.set_xlabel("MCMC step",fontsize=22)
ax.set_ylabel(vnames[i],fontsize=22)
plt.savefig('chain_'+vnames[i]+'.eps')
plt.clf()
for idim in chdimlist:
for line in fileinput.input("surf_rxn.in.xml", inplace=1):
print line.replace('PAR_' + str(idim), str(chstart[ii])),
ii = ii + 1
print "Running the parameter inference"
os.system('./SurfRxnInfer.x')
#
# Import data from MCMC file
#
# load chain file
# it expects first column is line id, then the last
# two columns are alpha and current log posterior
print "Loading in chain file", chainfile
all_samples, vnames = file_utils.extract_all_vars(chainfile, n_burnin, 0, 1)
n_all_vars = len(vnames)
n_cols = len(all_samples[0, :])
# Extract all MCMC chain variables in separate array
d0 = all_samples[:, 1:1 + n_all_vars]
samfile = "insamples.dat"
np.savetxt(samfile, d0)
# Find PC coefficients corresponding to the chain samples
print "Running KDE-Rosenblatt transformation to build input PCE"
os.system(pcequad + ' -o' + str(pcord) + ' -f ' + samfile + ' -x ' + pctype +
' -w' + str(bw) + ' > pcequad.log')
# Prepare the xml file for forward propagation
for line in fileinput.input("surf_rxn.in.xml", inplace=1):
print line.replace('uncertain', 'det'),
print help_string
sys.exit(1)
# Base name of file for outputting results
out_file_base = samples_file_name + ".nb" + str(n_burnin) + ".s" + str(stride)
#
# Import data from MCMC file
#
# Section 1
# load chain file
# it expects first column is line id, then the last
# two columns are alpha and current log posterior
#d0 = npy.genfromtxt(chainfile)
print "Loading in data file",samples_file_name
all_samples, vnames = file_utils.extract_all_vars(samples_file_name,n_burnin,debug,stride)
n_all_vars = len(vnames)
n_cols = len(all_samples[0,:])
# Extract all MCMC chain variables in separate array
d0 = all_samples[:,1:1+n_all_vars]
if (debug > 0):
print d0.shape
# Some settings to connect with code Cosmin gave me
nthin = 1 # take only every nthin state (for faster kde)
nskip = 0 # entries to skip
#npdf = 100 # no of grid points for 1D pdf's
istart = 0 # number of column with first MCMC variable
#cend = 0 # number of extra columns at the end.
cend = 0 # remove columns at end so code runs faster for debugging
