Hi there,
I was trying to visualize the pin-power distribution of an assembly containing two different fuel types in the same picture. But I am getting errors.
Let me share the tally and loop block part of the code
import openmc as mc
# Instantiate the Tally
tally_c = mc.Tally(name='distribcell tally 1')
tally_c.filters = [mc.DistribcellFilter(fuelc)]
tally_c.scores = ['fission']
# 2nd distribcell tally
tally_2 = mc.Tally(name='distribcell tally 2')
tally_2.filters = [mc.DistribcellFilter(fuel2)]
tally_2.scores = ['fission']
# Export all tallies to a "tallies.xml" file
tallies_file = mc.Tallies([tally_c, tally_2])
tallies_file.export_to_xml()
#Reading Statepoint to get data
with mc.StatePoint('statepoint.10.h5') as sp:
tally1 = sp.get_tally(name='distribcell tally 1')
df1 = tally1.get_pandas_dataframe()
tally2 = sp.get_tally(name='distribcell tally 2')
df2 = tally2.get_pandas_dataframe()
# let's merge two dataframe object into one
concat = pd.concat([df1, df2])
ce_fission_rates = concat[concat['score'] == 'fission']
# Normalize
m = (ce_fission_rates[['mean']]/ce_fission_rates[['mean']].mean())
# create another column with norm value
df3 = ce_fission_rates.assign(Normalized = m)
fis_value = df3.Normalized.ravel()
#setting up resolution and min, max value
resolution = (600, 600)
img = np.full(resolution, np.nan)
xmin, xmax = -l, l
ymin, ymax = -l, l
#reading library memory to sort data according to Cell ID
with mc.lib.run_in_memory():
for row, y in enumerate(np.linspace(ymin, ymax, resolution[0])):
for col, x in enumerate(np.linspace(xmin, xmax, resolution[1])):
try:
# For each (x, y, z) point, determine the cell and distribcell index
cell, distribcell_index = openmc.lib.find_cell((x, y, 0.))
except mc.exceptions.GeometryError:
# If a point appears outside the geometry, you'll get a GeometryError exception.
# These lines catch the exception and continue on
continue
if cell.id == fuelc.id and cell.id == fuel2.id:
# When the cell ID matches, we set the corresponding pixel in the image using the
# distribcell index. Note that we're taking advantage of the fact that the i-th element
# in the flux array corresponds to the i-th distribcell instance.
print(distribcell_index)
img[row, col] = fis_value[distribcell_index]
# Visualizing data
ce_fission_rates[ce_fission_rates == 0.] = np.nan
options = {
'origin': 'lower',
'extent': (xmin, xmax, ymin, ymax),
'vmin': 0.50,
'vmax': 2.00,
'cmap': 'jet',
}
plt.imshow(img, **options)
plt.title('Continuous-Energy Fission Rates')
plt.colorbar()
plt.show()
plt.savefig('ref.png', format='png', dpi=1200, transparent= False)
Can anyone help me solve this?
Thank you
