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tests for vectorized getitem_with_mask #1

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tests for vectorized getitem_with_mask #1

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35 changes: 35 additions & 0 deletions xarray/tests/test_variable.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,6 +112,37 @@ def test_getitem_with_mask(self):
assert_identical(v._getitem_with_mask([0, -1, 1], fill_value=-99),
self.cls(['x'], [0, -99, 1]))

def test_getitem_with_mask_fancy(self):
def one_test(key):
# key should be a dict
v = self.cls(['x', 'y', 'z'], np.random.randn(4, 5, 6))
actual = v._getitem_with_mask(key)
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This will need a .compute() after it. Currently the tests uses dask fail, because dask does not support assignment.


valid_indices = (~np.isnan(actual.values)).nonzero()
for indices in zip(*valid_indices):
# all the not-nan key should be nonnegative.
assert all(key[d][index] >= 0 for d, index in
zip(actual.dims, indices) if d in key.keys())
actual[indices] = np.nan
assert np.isnan(actual.values).all()

# orthogonal indexing
one_test({'x': [0, 1], 'y': [0, 1, 2], 'z': [3]})
one_test({'x': [0, 1]})
one_test({'x': [0, 1, -1], 'y': [0, 1, 2], 'z': [3]})
# vectorized indexing
one_test({'x': Variable('a', [0, 1, -1]),
'y': Variable('a', [0, 1, 2])})
one_test({'x': Variable('a', [0, 1, -1]),
'y': [-1, 2],
'z': Variable('a', [0, 1, 2])})
# advanced indexing
one_test({'x': Variable(('a', 'b'), [[0, 1, -1], [3, 1, 2]]),
'y': Variable('b', [0, -1, 2])})
one_test({'x': Variable(('a', 'b'), [[0, 1, -1], [3, 1, 2]]),
'y': Variable('b', [0, -1, 2]),
'z': Variable('a', [2, 1])})

def test_getitem_with_mask_size_zero(self):
v = self.cls(['x'], [])
assert_identical(v._getitem_with_mask(-1), Variable((), np.nan))
Expand Down Expand Up @@ -1615,6 +1646,10 @@ def test_getitem_fancy(self):
def test_getitem_uint(self):
super(TestIndexVariable, self).test_getitem_fancy()

@pytest.mark.xfail
def test_getitem_with_mask_fancy(self):
super(TestIndexVariable, self).test_getitem_with_mask_fancy()


class TestAsCompatibleData(TestCase):
def test_unchanged_types(self):
Expand Down