def test_basic_assign(app_inst: ArrayApplication):
from_arr: np.ndarray = np.arange(5)
block_shape = 3,
slice_params = list(
get_slices(size=10, index_multiplier=2, basic_step=True))
pbar = tqdm.tqdm(total=len(slice_params))
for slice_sel in slice_params:
pbar.set_description(str(slice_sel))
pbar.update(1)
from_ba = app_inst.array(from_arr, block_shape=block_shape)
from_bav = ArrayView.from_block_array(from_ba)
to_arr: np.ndarray = np.zeros(5)
to_ba = app_inst.array(to_arr, block_shape=block_shape)
to_bav = ArrayView.from_block_array(to_ba)
to_bav[slice_sel] = from_bav[slice_sel]
to_arr[slice_sel] = from_arr[slice_sel]
from_res = (from_arr, from_bav.create().get())
assert np.allclose(*from_res), str(from_res)
to_res = (to_arr, to_bav.create().get())
assert np.allclose(*to_res), str(to_res)
def __getitem__(self, item):
if not isinstance(item, tuple):
ss = (item,)
else:
ss = item
# We need to fetch any block arrays.
tmp = []
for entry in ss:
if isinstance(entry, BlockArray):
tmp.append(entry.get())
else:
tmp.append(entry)
ss = tmp
is_handled_advanced = True
if len(ss) > 1:
# Check if all entries are full slices except the last entry.
for entry in ss[:-1]:
is_handled_advanced = is_handled_advanced and (isinstance(entry, slice)
and entry.start is None
and entry.stop is None)
if is_handled_advanced and selection.is_advanced_selection((ss[-1],)):
# Treat this as a shuffle.
return self._advanced_single_array_subscript(sel=(ss[-1],), axis=len(ss)-1)
av: ArrayView = ArrayView.from_block_array(self)
# TODO (hme): We don't have to create, but do so for now until we need to optimize.
return av[item].create(BlockArray)
def test_assignment(left_item, left_mode, right_item, right_mode):
lshape, lblock_shape, laccessor = left_item
laccessor = tuple(left_mode[i](*laccessor[i]) for i in range(num_axes))
rshape, rblock_shape, raccessor = right_item
raccessor = tuple(right_mode[i](*raccessor[i]) for i in range(num_axes))
if not is_broadcastable(lshape, laccessor, rshape, raccessor):
return False
npA = np.zeros(np.product(lshape)).reshape(*lshape)
npB = np.random.random_sample(np.product(rshape)).reshape(*rshape)
baA = app_inst.array(npA, block_shape=lblock_shape)
baB = app_inst.array(npB, block_shape=rblock_shape)
bavA = ArrayView.from_block_array(baA)
bavB = ArrayView.from_block_array(baB)
assert np.allclose(npA[laccessor], bavA[laccessor].create().get())
assert np.allclose(npB[raccessor], bavB[raccessor].create().get())
npA[laccessor] = npB[raccessor]
bavA[laccessor] = bavB[raccessor]
assert np.allclose(npA, bavA.create().get())
assert np.allclose(npB, bavB.create().get())
return True
def test_basic_select(app_inst: ArrayApplication):
arr: np.ndarray = np.arange(5)
block_shape = 3,
slice_params = list(get_slices(size=10, index_multiplier=2, basic_step=True))
pbar = tqdm.tqdm(total=len(slice_params))
for slice_sel in slice_params:
pbar.set_description(str(slice_sel))
pbar.update(1)
ba = app_inst.array(arr, block_shape=block_shape)
bav = ArrayView.from_block_array(ba)
res = (arr[slice_sel], bav[slice_sel].create().get())
assert np.allclose(*res), str(res)
def __setitem__(self, key, value):
av: ArrayView = ArrayView.from_block_array(self)
av[key] = value
def __getitem__(self, item):
av: ArrayView = ArrayView.from_block_array(self)
# TODO (hme): We don't have to create, but do so for now until we need to optimize.
return av[item].create(BlockArray)