# Get filename
filename = argv[1]
# Determine axial level
axial_level = int(argv[2]) if len(argv) > 2 else 1
score = int(argv[3]) if len(argv) > 3 else 1
# Create StatePoint object
sp = StatePoint(filename)
# Read number of realizations for global tallies
sp.n_realizations = sp._get_int()[0]
# Read global tallies
n_global_tallies = sp._get_int()[0]
sp.global_tallies = np.array(sp._get_double(2 * n_global_tallies))
sp.global_tallies.shape = (n_global_tallies, 2)
# Flag indicating if tallies are present
tallies_present = sp._get_int()[0]
# Check if tallies are present
if not tallies_present:
raise Exception("No tally data in state point!")
# Loop over all tallies
print("Reading data...")
for t in sp.tallies:
# Calculate t-value for 95% two-sided CI
n = t.n_realizations
t_value = scipy.stats.t.ppf(0.975, n - 1)
# Create lists for tallies
mean = np.zeros((nx,ny))
error = np.zeros((nx,ny))
criteria = np.zeros((nx,ny))
# Determine starting position for data
start = sp1._f.tell()
for x in range(nx):
for y in range(ny):
# Seek to position of data
sp1._f.seek(start + x*ny*nz*ns*16 + y*nz*ns*16)
# Read sum and sum-squared
s, s2 = sp1._get_double(2)
s /= n
mean[x,y] = s
if s != 0.0:
error[x,y] = t_value*sqrt((s2/n - s*s)/(n-1))/s
# Make figure
plt.imshow(error, interpolation="nearest", vmin=0.0, vmax=0.03)
cb = plt.colorbar()
[t.set_fontsize(16) for t in cb.ax.get_yticklabels()]
plt.xlim((0,nx))
plt.ylim((0,ny))
plt.xticks(range(1), (''))
plt.yticks(range(1), (''))
plt.savefig('opr-without-ufs.pdf', bbox_inches='tight')
plt.close()
# Get filename
filename = argv[1]
# Determine axial level
axial_level = int(argv[2]) if len(argv) > 2 else 1
score = int(argv[3]) if len(argv) > 3 else 1
# Create StatePoint object
sp = StatePoint(filename)
# Read number of realizations for global tallies
sp.n_realizations = sp._get_int()[0]
# Read global tallies
n_global_tallies = sp._get_int()[0]
sp.global_tallies = np.array(sp._get_double(2*n_global_tallies))
sp.global_tallies.shape = (n_global_tallies, 2)
# Flag indicating if tallies are present
tallies_present = sp._get_int()[0]
# Check if tallies are present
if not tallies_present:
raise Exception("No tally data in state point!")
# Loop over all tallies
print("Reading data...")
for t in sp.tallies:
# Calculate t-value for 95% two-sided CI
n = t.n_realizations
t_value = scipy.stats.t.ppf(0.975, n - 1)
# Create lists for tallies
mean = np.zeros((nx, ny))
error = np.zeros((nx, ny))
criteria = np.zeros((nx, ny))
# Determine starting position for data
start = sp1._f.tell()
for x in range(nx):
for y in range(ny):
# Seek to position of data
sp1._f.seek(start + x * ny * nz * ns * 16 + y * nz * ns * 16)
# Read sum and sum-squared
s, s2 = sp1._get_double(2)
s /= n
mean[x, y] = s
if s != 0.0:
error[x, y] = t_value * sqrt((s2 / n - s * s) / (n - 1)) / s
# Make figure
plt.imshow(error, interpolation="nearest", vmin=0.0, vmax=0.03)
cb = plt.colorbar()
[t.set_fontsize(16) for t in cb.ax.get_yticklabels()]
plt.xlim((0, nx))
plt.ylim((0, ny))
plt.xticks(range(1), (''))
plt.yticks(range(1), (''))
plt.savefig('opr-without-ufs.pdf', bbox_inches='tight')
plt.close()
