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Fix sparse storage of multidimensional tally results #4030

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Fix sparse storage of multidimensional tally results #4030

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32 changes: 19 additions & 13 deletions openmc/tallies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -479,10 +479,12 @@ def _read_results(self):

# Convert NumPy arrays to SciPy sparse LIL matrices
if self.sparse:
self._sum = lil_array(self._sum.flatten(), self._sum.shape)
self._sum_sq = lil_array(self._sum_sq.flatten(), self._sum_sq.shape)
self._sum_third = lil_array(self._sum_third.flatten(), self._sum_third.shape)
self._sum_fourth = lil_array(self._sum_fourth.flatten(), self._sum_fourth.shape)
self._sum = lil_array(self._sum.reshape(1, -1))
self._sum_sq = lil_array(self._sum_sq.reshape(1, -1))
if self._sum_third is not None:
self._sum_third = lil_array(self._sum_third.reshape(1, -1))
if self._sum_fourth is not None:
self._sum_fourth = lil_array(self._sum_fourth.reshape(1, -1))

# Read simulation time (needed for figure of merit)
self._simulation_time = f["runtime"]["simulation"][()]
Expand Down Expand Up @@ -578,7 +580,7 @@ def mean(self):

# Convert NumPy array to SciPy sparse LIL matrix
if self.sparse:
self._mean = lil_array(self._mean.flatten(), self._mean.shape)
self._mean = lil_array(self._mean.reshape(1, -1))

if self.sparse:
return np.reshape(self._mean.toarray(), self.shape)
Expand All @@ -599,7 +601,7 @@ def std_dev(self):

# Convert NumPy array to SciPy sparse LIL matrix
if self.sparse:
self._std_dev = lil_array(self._std_dev.flatten(), self._std_dev.shape)
self._std_dev = lil_array(self._std_dev.reshape(1, -1))

self.with_batch_statistics = True

Expand Down Expand Up @@ -630,7 +632,7 @@ def vov(self):
self._vov[mask] = numerator[mask]/denominator[mask] - 1.0/n

if self.sparse:
self._vov = lil_array(self._vov.flatten(), self._vov.shape)
self._vov = lil_array(self._vov.reshape(1, -1))

if self.sparse:
return np.reshape(self._vov.toarray(), self.shape)
Expand Down Expand Up @@ -1005,17 +1007,19 @@ def sparse(self, sparse):
# Convert NumPy arrays to SciPy sparse LIL matrices
if sparse and not self.sparse:
if self._sum is not None:
self._sum = lil_array(self._sum.flatten(), self._sum.shape)
self._sum = lil_array(self._sum.reshape(1, -1))
if self._sum_sq is not None:
self._sum_sq = lil_array(self._sum_sq.flatten(), self._sum_sq.shape)
self._sum_sq = lil_array(self._sum_sq.reshape(1, -1))
if self._sum_third is not None:
self._sum_third = lil_array(self._sum_third.flatten(), self._sum_third.shape)
self._sum_third = lil_array(self._sum_third.reshape(1, -1))
if self._sum_fourth is not None:
self._sum_fourth = lil_array(self._sum_fourth.flatten(), self._sum_fourth.shape)
self._sum_fourth = lil_array(self._sum_fourth.reshape(1, -1))
if self._mean is not None:
self._mean = lil_array(self._mean.flatten(), self._mean.shape)
self._mean = lil_array(self._mean.reshape(1, -1))
if self._std_dev is not None:
self._std_dev = lil_array(self._std_dev.flatten(), self._std_dev.shape)
self._std_dev = lil_array(self._std_dev.reshape(1, -1))
if self._vov is not None:
self._vov = lil_array(self._vov.reshape(1, -1))

self._sparse = True

Expand All @@ -1033,6 +1037,8 @@ def sparse(self, sparse):
self._mean = np.reshape(self._mean.toarray(), self.shape)
if self._std_dev is not None:
self._std_dev = np.reshape(self._std_dev.toarray(), self.shape)
if self._vov is not None:
self._vov = np.reshape(self._vov.toarray(), self.shape)
self._sparse = False

def remove_score(self, score):
Expand Down
34 changes: 34 additions & 0 deletions tests/unit_tests/test_tallies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -184,6 +184,40 @@ def _tally_from_data(x, *, higher_moments=True, normality=True):
t._sum_fourth = np.array([[[np.sum(x**4)]]], dtype=float)
return t


def test_sparse_tally_data_roundtrip():
tally = _tally_from_data(np.arange(1.0, 21.0))
expected_sum = tally._sum.copy()
expected_sum_sq = tally._sum_sq.copy()
expected_sum_third = tally._sum_third.copy()
expected_sum_fourth = tally._sum_fourth.copy()
expected_mean = expected_sum / tally.num_realizations
expected_std_dev = np.sqrt(
(expected_sum_sq / tally.num_realizations - expected_mean**2)
/ (tally.num_realizations - 1)
)

tally.sparse = True

assert tally.sum == pytest.approx(expected_sum)
assert tally.sum_sq == pytest.approx(expected_sum_sq)
assert tally.sum_third == pytest.approx(expected_sum_third)
assert tally.sum_fourth == pytest.approx(expected_sum_fourth)
assert tally.mean == pytest.approx(expected_mean)
assert tally.std_dev == pytest.approx(expected_std_dev)
expected_vov = tally.vov.copy()

tally.sparse = False

assert tally.sum == pytest.approx(expected_sum)
assert tally.sum_sq == pytest.approx(expected_sum_sq)
assert tally.sum_third == pytest.approx(expected_sum_third)
assert tally.sum_fourth == pytest.approx(expected_sum_fourth)
assert tally.mean == pytest.approx(expected_mean)
assert tally.std_dev == pytest.approx(expected_std_dev)
assert tally.vov == pytest.approx(expected_vov)


@pytest.mark.parametrize(
"x, skew_true, kurt_true",
[ # Rademacher distribution
Expand Down
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