Equivalent of skimage.util.view_as_windows for dask arrays.
Example:
>>> import numpy as np
>>> import dask.array
>>> from dask_windows import view_as_windows
>>> x = dask.array.random.randint(0, 255, (100_000, 100_000), chunks=(10_000, 10_000), dtype=np.uint8)
>>> w = view_as_windows(x, window_shape=50, step=3)
>>> w
dask.array<_block_windows, shape=(9951, 9951, 50, 50), dtype=uint8, chunksize=(1000, 1000, 50, 50), chunktype=numpy.ndarray>
>>> print(f"{w.nbytes // (1024 ** 3)} GB")
2584 GB
>>> w[5000, 5000, ...].compute()
array([[181, 155, 63, ..., 152, 154, 52],
[ 45, 92, 200, ..., 63, 241, 253],
[215, 246, 95, ..., 243, 9, 101],
...,
[224, 97, 134, ..., 92, 247, 189],
[112, 59, 44, ..., 45, 96, 237],
[232, 13, 242, ..., 153, 35, 106]], dtype=uint8)
This is superceded by dask.array.overlap.sliding_window_view and slicing:
>>> from dask.array.overlap import sliding_window_view
>>> w2 = sliding_window_view(x, window_shape=[50, 50])[::3, ::3, ...]
>>> w2
dask.array<getitem, shape=(33317, 33317, 50, 50), dtype=uint8, chunksize=(3334, 3334, 50, 50), chunktype=numpy.ndarray>
>>> w2[5000, 5000, ...].compute()
array([[181, 155, 63, ..., 152, 154, 52],
[ 45, 92, 200, ..., 63, 241, 253],
[215, 246, 95, ..., 243, 9, 101],
...,
[224, 97, 134, ..., 92, 247, 189],
[112, 59, 44, ..., 45, 96, 237],
[232, 13, 242, ..., 153, 35, 106]], dtype=uint8)