I have some questions about how np.lib.stride_tricks.as_strided differs from just using np.ndarray and if the as_strided can be intercepted by sub-classes to np.ndarray.
Given an array:
a = np.arange(10, dtype=np.int8)
Is there any difference (effectively) between using as_strided and creating the ndarray with the appropriate strides directly?
np.lib.stride_tricks.as_strided(a, (9,2), (1,1))
np.ndarray((9,2), np.int8, buffer=a.data, strides=(1,1))
as_strided just seems to be more implicit about what it is doing.
Can either function be intercepted by a sub-classes for np.ndarray? If a in the example above was a custom subclass of ndarray will there be some function that gets called before returning the strided view? I know that setting subok for as_strided will preserve any subclass, but is this actually doing anything else than casting the array?