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Adding support for viewing distribcell tallies #87

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merged 5 commits into from
Apr 27, 2022
Merged

Adding support for viewing distribcell tallies #87

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90a4358
Adding support for plotting distribcel tallies.
pshriwise Apr 26, 2022
293203f
Apply suggestions from @paulromano
pshriwise Apr 27, 2022
913d6e4
Cleaning up a bit
pshriwise Apr 27, 2022
126c049
Apply suggestions from code review
pshriwise Apr 27, 2022
0d6badb
Background values are NaN for distribcell filters. Setting dtype of d…
pshriwise Apr 27, 2022
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73 changes: 66 additions & 7 deletions openmc_plotter/plotmodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@
_SPATIAL_FILTERS = (openmc.UniverseFilter,
openmc.MaterialFilter,
openmc.CellFilter,
openmc.DistribcellFilter,
openmc.MeshFilter)

_PRODUCTIONS = ('delayed-nu-fission', 'prompt-nu-fission', 'nu-fission',
Expand Down Expand Up @@ -280,6 +281,19 @@ def storeCurrent(self):
self.previousViews.append(copy.deepcopy(self.currentView))

def create_tally_image(self, view=None):
"""
Parameters
----------
view :
View used to set bounds of the tally data

Returns
-------
tuple
image data (numpy.ndarray), data extents (optional),
data_min_value (float), data_max_value (float),
data label (str)
"""
if view is None:
view = self.currentView

Expand Down Expand Up @@ -346,20 +360,44 @@ def create_tally_image(self, view=None):
nuclides,
view)
return image + (units_out,)
elif tally.contains_filter(openmc.DistribcellFilter):
if tally_value == 'rel_err':
mean_data = self._create_distribcell_image(tally,
'mean',
scores,
nuclides,
view)
std_dev_data = self._create_distribcell_image(tally,
'std_dev',
scores,
nuclides,
view)
image_data = 100 * np.divide(std_dev_data[0],
mean_data[0],
out=np.zeros_like(mean_data[0]),
where=mean_data != 0)
data_min = np.min(image_data)
data_max = np.max(image_data)
return image_data, None, data_min, data_max, '% error'
else:
image = self._create_distribcell_image(tally,
tally_value,
scores,
nuclides,
view)
return image + (units_out,)
else:
# same as above, get the std. dev. data
# and mean date to produce the relative error data
if tally_value == 'rel_err':
mean_data = self._create_tally_domain_image(tally,
'mean',
scores,
nuclides,
view)
nuclides)
std_dev_data = self._create_tally_domain_image(tally,
'std_dev',
scores,
nuclides,
view)
nuclides)
image_data = 100 * np.divide(std_dev_data[0],
mean_data[0],
out=np.zeros_like(mean_data[0]),
Expand All @@ -377,8 +415,7 @@ def create_tally_image(self, view=None):
image = self._create_tally_domain_image(tally,
tally_value,
scores,
nuclides,
view)
nuclides)
return image + (units_out,)

def _create_tally_domain_image(self, tally, tally_value, scores, nuclides, view=None):
Expand Down Expand Up @@ -467,10 +504,32 @@ def _do_op(array, tally_value, ax=0):

return image_data, None, data_min, data_max

def _create_distribcell_image(self, tally, tally_value, scores, nuclides):
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sp = self.statepoint
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dfilter = tally.find_filter(openmc.DistribcellFilter)

data = tally.get_values(scores=scores, nuclides=nuclides, value=tally_value)
data = data.flatten()

cell_id = dfilter.bins[0]
# create a mask for ids that match the cell
image_data = np.zeros_like(self.ids)
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cell_id_mask = self.cell_ids == cell_id
for i, v in enumerate(data):
instance_mask = self.instances == i
image_data[cell_id_mask & instance_mask] = v

data_min = np.min(data)
data_max = np.max(data)
image_data = np.ma.masked_where(image_data < 0.0, image_data)

return image_data, None, data_min, data_max

def _create_tally_mesh_image(self, tally, tally_value, scores, nuclides, view=None):
# some variables used throughout
if view is None:
cv = self.currentView
view = self.currentView

sp = self.statepoint
mesh_filter = tally.find_filter(openmc.MeshFilter)
Expand Down