def test_multidimensional_list(self):
a = np.arange(30).reshape(5, 2, 3).tolist()
dci = DataChunkIterator(a)
self.assertTupleEqual(dci.maxshape, (5, 2, 3))
self.assertEqual(dci.dtype, np.dtype(int))
count = 0
for chunk in dci:
self.assertTupleEqual(chunk.data.shape, (1, 2, 3))
count += 1
self.assertEqual(count, 5)
self.assertTupleEqual(dci.recommended_data_shape(), (5, 2, 3))
self.assertIsNone(dci.recommended_chunk_shape())
def test_standard_iterator_unbuffered(self):
dci = DataChunkIterator(data=range(10), buffer_size=1)
self.assertEqual(dci.dtype, np.dtype(int))
self.assertTupleEqual(dci.maxshape, (10,))
self.assertTupleEqual(dci.recommended_data_shape(), (10,)) # Test before and after iteration
count = 0
for chunk in dci:
self.assertEqual(chunk.data.shape[0], 1)
count += 1
self.assertEqual(count, 10)
self.assertTupleEqual(dci.recommended_data_shape(), (10,)) # Test before and after iteration
self.assertIsNone(dci.recommended_chunk_shape())
def test_none_iter(self):
dci = DataChunkIterator(None)
self.assertIsNone(dci.maxshape)
self.assertIsNone(dci.dtype)
count = 0
for chunk in dci:
self.assertEqual(chunk.data, None)
self.assertEqual(chunk.selection, None)
count += 1
self.assertEqual(count, 0)
self.assertIsNone(dci.recommended_data_shape())
self.assertIsNone(dci.recommended_chunk_shape())
def test_numpy_iter_unmatched_buffer_size(self):
a = np.arange(10)
dci = DataChunkIterator(data=a, buffer_size=3)
self.assertTupleEqual(dci.maxshape, a.shape)
self.assertEqual(dci.dtype, a.dtype)
count = 0
for chunk in dci:
if count < 3:
self.assertEqual(chunk.data.shape[0], 3)
else:
self.assertEqual(chunk.data.shape[0], 1)
count += 1
self.assertEqual(count, 4)
self.assertTupleEqual(dci.recommended_data_shape(), a.shape)
self.assertIsNone(dci.recommended_chunk_shape())