def test_apply_mask_job(default_k2is, lt_ctx):
mask = np.ones((1860, 2048))
tileshape = Shape(
(16, 930, 16),
sig_dims=2,
)
tiling_scheme = TilingScheme.make_for_shape(
tileshape=tileshape,
dataset_shape=default_k2is.shape,
)
job = ApplyMasksJob(
dataset=default_k2is,
mask_factories=[lambda: mask],
tiling_scheme=tiling_scheme,
)
out = job.get_result_buffer()
executor = InlineJobExecutor()
for tiles in executor.run_job(job):
for tile in tiles:
tile.reduce_into_result(out)
results = lt_ctx.run(job)
assert results[0].shape == (34 * 35, )
# there should be _something_ in each result pixel
for px in results[0].reshape((-1, )):
assert not np.isclose(px, 0)
def test_apply_mask_on_raw_job(default_blo, lt_ctx):
mask = np.ones((144, 144))
job = ApplyMasksJob(dataset=default_blo, mask_factories=[lambda: mask])
out = job.get_result_buffer()
executor = InlineJobExecutor()
for tiles in executor.run_job(job):
for tile in tiles:
tile.reduce_into_result(out)
results = lt_ctx.run(job)
assert results[0].shape == (90 * 121, )
def test_apply_mask_on_empad_job(default_empad, lt_ctx):
mask = np.ones((128, 128))
job = ApplyMasksJob(dataset=default_empad, mask_factories=[lambda: mask])
out = job.get_result_buffer()
executor = InlineJobExecutor()
for tiles in executor.run_job(job):
for tile in tiles:
tile.reduce_into_result(out)
results = lt_ctx.run(job)
assert results[0].shape == (4 * 4,)
assert np.count_nonzero(results[0]) > 0
def test_apply_mask_on_raw_job(default_raw, lt_ctx):
mask = np.ones((128, 128))
job = ApplyMasksJob(dataset=default_raw, mask_factories=[lambda: mask])
out = job.get_result_buffer()
executor = InlineJobExecutor()
for tiles in executor.run_job(job):
for tile in tiles:
tile.reduce_into_result(out)
results = lt_ctx.run(job)
# FIXME: should the result here be 1D or 2D?
# currently, for inherently 4D datasets it is 2D, and for 3D datasets
# it is 1D. make this consistent?
assert results[0].shape == (16, 16)