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Support for CellInstanceFilter #88

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merged 2 commits into from
Apr 28, 2022
Merged

Support for CellInstanceFilter #88

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54c4b73
Implement support for CellInstanceFilter tally plotting
paulromano Apr 27, 2022
258a503
Combine separate functions for distribcell / cell instance plotting
paulromano Apr 27, 2022
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7 changes: 5 additions & 2 deletions openmc_plotter/docks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -406,8 +406,11 @@ def _createFilterTree(self, spatial_filters):
continue

def _bin_sort_val(bin):
if isinstance(bin, Iterable) and all([isinstance(val, float) for val in bin]):
return np.sum(bin)
if isinstance(bin, Iterable):
if all([isinstance(val, float) for val in bin]):
return np.sum(bin)
else:
return tuple(bin)
else:
return bin

Expand Down
57 changes: 32 additions & 25 deletions openmc_plotter/plotmodel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@
openmc.MaterialFilter,
openmc.CellFilter,
openmc.DistribcellFilter,
openmc.CellInstanceFilter,
openmc.MeshFilter)

_PRODUCTIONS = ('delayed-nu-fission', 'prompt-nu-fission', 'nu-fission',
Expand Down Expand Up @@ -330,6 +331,9 @@ def create_tally_image(self, view=None):

units_out = list(units)[0]

contains_distribcell = tally.contains_filter(openmc.DistribcellFilter)
contains_cellinstance = tally.contains_filter(openmc.CellInstanceFilter)

if tally.contains_filter(openmc.MeshFilter):
if tally_value == 'rel_err':
# get both the std. dev. data and mean data
Expand Down Expand Up @@ -360,28 +364,23 @@ def create_tally_image(self, view=None):
nuclides,
view)
return image + (units_out,)
elif tally.contains_filter(openmc.DistribcellFilter):
elif contains_distribcell or contains_cellinstance:
if tally_value == 'rel_err':
mean_data = self._create_distribcell_image(tally,
'mean',
scores,
nuclides)
std_dev_data = self._create_distribcell_image(tally,
'std_dev',
scores,
nuclides)
image_data = 100 * np.divide(std_dev_data[0],
mean_data[0],
out=np.zeros_like(mean_data[0]),
where=mean_data != 0)
mean_data = self._create_distribcell_image(
tally, 'mean', scores, nuclides, contains_cellinstance)
std_dev_data = self._create_distribcell_image(
tally, 'std_dev', scores, nuclides)
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)
image = self._create_distribcell_image(
tally, tally_value, scores, nuclides, contains_cellinstance)
return image + (units_out,)
else:
# same as above, get the std. dev. data
Expand Down Expand Up @@ -501,19 +500,27 @@ 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):
dfilter = tally.find_filter(openmc.DistribcellFilter)

def _create_distribcell_image(self, tally, tally_value, scores, nuclides, cellinstance=False):
# Get flattened array of tally results
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
# Create an empty array of appropriate shape for image
image_data = np.full_like(self.ids, np.nan, dtype=float)

cell_id_mask = self.cell_ids == cell_id
for i, v in enumerate(data):
instance_mask = self.instances == i
# Determine appropriate set of bins depending on filter type
if cellinstance:
f = tally.find_filter(openmc.CellInstanceFilter)
bins = f.bins
else:
f = tally.find_filter(openmc.DistribcellFilter)
bins = [(f.bins[0], i) for i in range(data.size)]

# Iterate over tally bins, setting any pixels that have matching (cell
# ID, instance) each time
for v, (cell_id, instance) in zip(data, bins):
cell_id_mask = (self.cell_ids == cell_id)
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I'm not sure how much of a performance hit this might be, but having this in the loop is going to cause a repeated calculation of this mask for DistribCellFilter's. The same is true for CellInstanceFilter's that contain instances of the same cell.

I suppose that since we're typically working with around 100k pixels in the image data this shouldn't be too bad. Mainly noting it here in case we run into a performance issue with these filter types in the future.

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Yup, I'm well aware that this is performance-shitty code. But, so far on the models I've played with it doesn't seem to be a big deal. It would be easy enough to optimize if we find later on that it is causing significant lags on larger models. I can go ahead and add a comment in the code about this since I'm sure we'll probably forget.

instance_mask = (self.instances == instance)
image_data[cell_id_mask & instance_mask] = v

data_min = np.min(data)
Expand Down