def test_transforms(self):
# Add transform at initialisation
indexer = DaskLazyIndexer(self.data_dask, transforms=[lambda x: 0 * x])
np.testing.assert_array_equal(indexer[:], np.zeros_like(indexer))
# Add transform before first use of object
indexer = DaskLazyIndexer(self.data_dask)
indexer.add_transform(lambda x: 0 * x)
np.testing.assert_array_equal(indexer[:], np.zeros_like(indexer))
# Add transform after first use of object
indexer = DaskLazyIndexer(self.data_dask)
indexer.dataset
indexer.add_transform(lambda x: 0 * x)
np.testing.assert_array_equal(indexer[:], np.zeros_like(indexer))
def test_str_repr(self):
def transform1(x):
return x
transform2 = lambda x: x # noqa: E731
class Transform3: # noqa: E306
def __call__(self, x):
return x
transform3 = Transform3()
transform4 = partial(transform1)
transforms = [transform1, transform2, transform3, transform4]
indexer = DaskLazyIndexer(self.data_dask, transforms=transforms)
expected = 'x | transform1 | <lambda> | Transform3 | transform1'
expected += f' -> {indexer.shape} {indexer.dtype}'
assert_equal(str(indexer), expected)
# Simply exercise repr - no need to check result
repr(indexer)
def _test_with(self, stage1=(), stage2=()):
npy1 = numpy_oindex(self.data, stage1)
npy2 = numpy_oindex(npy1, stage2)
indexer = DaskLazyIndexer(self.data_dask, stage1)
np.testing.assert_array_equal(indexer[stage2], npy2)
def test_stage1_multiple_boolean_indices(self):
stage1 = tuple([True] * d for d in self.data.shape)
indexer = DaskLazyIndexer(self.data_dask, stage1)
np.testing.assert_array_equal(indexer[:], self.data)
def test_stage1_slices(self):
stage1 = np.s_[5:, :, 1::2]
indexer = DaskLazyIndexer(self.data_dask, stage1)
np.testing.assert_array_equal(indexer[:], self.data[stage1])