def test_rescale(arr, ndv, dst_dtype):
if dst_dtype == np.__dict__['uint16']:
assert np.array_equal(
_rescale(arr, ndv, dst_dtype),
np.concatenate(
[
(arr).astype(dst_dtype),
_simple_mask(
arr.astype(dst_dtype),
(ndv, ndv, ndv)
).reshape(1, arr.shape[1], arr.shape[2])
]
)
)
else:
assert np.array_equal(
_rescale(arr, ndv, dst_dtype),
np.concatenate(
[
(arr / 257.0).astype(dst_dtype),
_simple_mask(
arr.astype(dst_dtype),
(ndv, ndv, ndv)
).reshape(1, arr.shape[1], arr.shape[2])
]
)
)
def test_simple_mask(data, ndv):
'''Exact nodata masking'''
nd = np.iinfo(data.dtype).max
assert np.array_equal(
_simple_mask(data, ndv),
np.invert(np.all(np.dstack(data) == ndv, axis=2)).astype(data.dtype) *
nd)
def test_simple_mask(data, ndv):
'''Exact nodata masking'''
nd = np.iinfo(data.dtype).max
assert np.array_equal(
_simple_mask(data, ndv),
np.invert(
np.all(
np.dstack(data) == ndv, axis=2
)
).astype(data.dtype) * nd)
def test_rescale(arr, ndv, dst_dtype):
if dst_dtype == np.__dict__['uint16']:
assert np.array_equal(
_rescale(arr, ndv, dst_dtype),
np.concatenate([
(arr).astype(dst_dtype),
_simple_mask(arr.astype(dst_dtype),
(ndv, ndv, ndv)).reshape(1, arr.shape[1],
arr.shape[2])
]))
else:
assert np.array_equal(
_rescale(arr, ndv, dst_dtype),
np.concatenate([
(arr / 257.0).astype(dst_dtype),
_simple_mask(arr.astype(dst_dtype),
(ndv, ndv, ndv)).reshape(1, arr.shape[1],
arr.shape[2])
]))
