def test_dense_stream(lazy):
arr = np.random.randint(0, 65535, size=(2, 3, 4, 5)).astype("uint16")
stream = array_to_stream(arr)
if lazy:
arrs = stream_to_sparse_COO_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
arrs = arrs.compute()
assert (arrs == arr).all()
else:
arrs = stream_to_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
assert (arrs == arr).all()
def test_empty_stream(lazy):
arr = np.zeros((2, 3, 4, 5), dtype="uint16")
stream = array_to_stream(arr)
if lazy:
arrs = stream_to_sparse_COO_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
arrs = arrs.compute()
assert not arrs.any()
else:
arrs = stream_to_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
assert not arrs.any()
def test_empty_stream(lazy):
arr = np.zeros((2, 3, 4, 5), dtype="uint16")
stream = array_to_stream(arr)
if lazy:
arrs = da.from_array(stream_to_sparse_COO_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2), chunks=(1, 1, 2, 5))
arrs = arrs.compute()
assert not arrs.any()
else:
arrs = stream_to_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
assert not arrs.any()
def test_dense_stream(lazy):
arr = np.random.randint(0, 65535, size=(2, 3, 4, 5)).astype("uint16")
stream = array_to_stream(arr)
if lazy:
arrs = da.from_array(stream_to_sparse_COO_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2), chunks=(1, 1, 2, 5))
arrs = arrs.compute()
assert (arrs == arr).all()
else:
arrs = stream_to_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
assert (arrs == arr).all()
def test_sparse_stream(lazy):
arr = np.zeros((2, 3, 4, 5), dtype="uint16")
arr[0, 0, 0, 0] = 1
arr[-1, -1, -1, -1] = 2
arr[1, 1, 3, 3] = 3
stream = array_to_stream(arr)
if lazy:
arrs = stream_to_sparse_COO_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
arrs = arrs.compute()
assert (arrs == arr).all()
else:
arrs = stream_to_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
assert (arrs == arr).all()
def test_sparse_stream(lazy):
arr = np.zeros((2, 3, 4, 5), dtype="uint16")
arr[0, 0, 0, 0] = 1
arr[-1, -1, -1, -1] = 2
arr[1, 1, 3, 3] = 3
stream = array_to_stream(arr)
if lazy:
arrs = da.from_array(stream_to_sparse_COO_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2), chunks=(1, 1, 2, 5))
arrs = arrs.compute()
assert (arrs == arr).all()
else:
arrs = stream_to_array(
stream, spatial_shape=(3, 4), sum_frames=False, channels=5,
last_frame=2)
assert (arrs == arr).all()
def test_empty_stream(lazy):
arr = np.zeros((2, 3, 4, 5), dtype="uint16")
stream = array_to_stream(arr)
if lazy:
if not sparse_installed:
pytest.skip("The sparse package is not installed")
arrs = da.from_array(stream_to_sparse_COO_array(stream,
spatial_shape=(3, 4),
sum_frames=False,
channels=5,
last_frame=2),
chunks=(1, 1, 2, 5))
arrs = arrs.compute()
assert not arrs.any()
else:
arrs = stream_to_array(stream,
spatial_shape=(3, 4),
sum_frames=False,
channels=5,
last_frame=2)
assert not arrs.any()
def test_dense_stream(lazy):
arr = np.random.randint(0, 65535, size=(2, 3, 4, 5)).astype("uint16")
stream = array_to_stream(arr)
if lazy:
if not sparse_installed:
pytest.skip("The sparse package is not installed")
arrs = da.from_array(stream_to_sparse_COO_array(stream,
spatial_shape=(3, 4),
sum_frames=False,
channels=5,
last_frame=2),
chunks=(1, 1, 2, 5))
arrs = arrs.compute()
assert (arrs == arr).all()
else:
arrs = stream_to_array(stream,
spatial_shape=(3, 4),
sum_frames=False,
channels=5,
last_frame=2)
assert (arrs == arr).all()
def stream_to_array(self, stream_data, spectrum_image=None):
"""Convert stream to array.
Parameters
----------
stream_data: array
spectrum_image: array or None
If array, the data from the stream are added to the array.
Otherwise it creates a new array and returns it.
"""
spectrum_image = stream_readers.stream_to_array(
stream=stream_data,
spatial_shape=self.reader.spatial_shape,
channels=self.bin_count,
first_frame=self.reader.first_frame,
last_frame=self.reader.last_frame,
rebin_energy=self.reader.rebin_energy,
sum_frames=self.reader.sum_frames,
spectrum_image=spectrum_image,
dtype=self.reader.SI_data_dtype,
)
return spectrum_image
