def test_condensed_indices_scalar_and_list(self):
"""Test input where i is a scalar and j is a list."""
for fixed_i in range(self.n):
# pick out the cases with i as the first index
tests = zip(self.test_cases, self.expected)
j_i = []
expected_i = []
for (i, j), expected in tests:
if i == fixed_i:
j_i.append(j)
expected_i.append(expected)
# test with scalar input
actual = phdlib.condensed_indices(self.n, fixed_i, j_i)
np.testing.assert_equal(actual, expected_i)
# now test with length-1 list
actual = phdlib.condensed_indices(self.n, [fixed_i], j_i)
np.testing.assert_equal(actual, expected_i)
def test_condensed_indices_scalars(self):
for case, expected in zip(self.test_cases, self.expected):
actual = phdlib.condensed_indices(self.n, *case)
self.assertEqual(actual, expected)
self.assertIsInstance(actual, np.integer)
def test_condensed_indices_lists(self):
i, j = zip(*self.test_cases)
actual = phdlib.condensed_indices(self.n, i, j)
np.testing.assert_equal(actual, self.expected)
def test_condensed_indices_checks(self):
# don't allow on-diagonal indices or out-of-bounds indices
with self.assertRaises(ValueError):
phdlib.condensed_indices(self.n, 0, 0, check=True)
phdlib.condensed_indices(self.n, -1, 0, check=True)
phdlib.condensed_indices(self.n, 42, 0, check=True)
