def test_mean_non_consistent_shape(shapes):
sa = StackedArray([da.random.random(shape) for shape in shapes])
for axis in [
None, -1, *list(list(range(x)) for x in range(len(sa.shape)))
]:
note(f"shapes: {shapes}, axis: {axis}")
np.testing.assert_array_almost_equal(sa.mean(axis=axis),
sa.array.mean(axis=axis))
def test_reshape():
sa = StackedArray([da.random.random(size=(4, 4)) for _ in range(7)])
for new_shape in [(16, ), (2, 8), (4, 4), (8, 2)]:
sa1 = sa.reshape(new_shape)
assert sa1.shape == new_shape
np.testing.assert_array_almost_equal(sa1.mean(), sa.mean(), decimal=12)
np.testing.assert_array_almost_equal(sa1.std(), sa.std(), decimal=12)
def test_persist():
def delay_array(x):
# slow operation takes about 2 seconds on my computer
d = da.mean(da.random.random(1e9))
return d + x - d
arrays = [delay_array(da.random.random(size=(4, ))) for _ in range(2)]
sa = StackedArray(arrays)
sa_persist = sa.persist()
start = time.time()
sa_persist.mean().compute()
persist_took = time.time() - start
start_mean = time.time()
sa.mean().compute()
mean_took = time.time() - start_mean
assert persist_took <= mean_took / 10
def test_fallback_methods():
sa = StackedArray([da.random.random(size=(4, 4)) for x in range(7)])
assert sa.shape == sa.array.shape
assert sa.chunks == sa.array.chunks
for axis in [None, 0, 1]:
np.testing.assert_array_almost_equal(sa.std(axis=axis),
sa.array.std(axis=axis),
decimal=12)
np.testing.assert_array_almost_equal(sa.mean(axis=axis),
sa.array.mean(axis=axis),
decimal=12)
np.testing.assert_array_almost_equal(sa.max(axis=axis),
sa.array.max(axis=axis),
decimal=12)
np.testing.assert_array_almost_equal(sa.min(axis=axis),
sa.array.min(axis=axis),
decimal=12)
def test_mean_consistent_shape():
sa = StackedArray([da.random.random(size=(10, 10, 10)) for _ in range(7)])
for axis in [None, 0, 1, 2, (0, 1), -1, (1, 2), (0, 1, 2)]:
np.testing.assert_array_almost_equal(sa.mean(axis=axis),
sa.array.mean(axis=axis))
