Odtnhj

I was trying to generate an array of trigrams (i.e. continuous-three-letter combinations) from a super long char array:

# data is actually load from a source file
a = np.random.randint(0, 256, 2**28, 'B').view('c')

Since making copy is not efficient (and it creates problems like cache miss), I directly generated the trigram using stride tricks:

tri = np.lib.stride_tricks.as_strided(a, (len(a)-2,3), a.strides*2)

This generates a trigram list with shape (2**28-2, 3) where each row is a trigram. Now I want to convert the trigram to a list of string (i.e. S3) so that numpy displays it more "reasonably" (instead of individual chars).

tri = tri.view('S3')

It gives the exception:

ValueError: To change to a dtype of a different size, the array must be C-contiguous

I understand generally data should be contiguous in order to create a meaningful view, but this data is contiguous at "where it should be": each three elements are contiguous.

So I'm wondering how to view contiguous part in non-contiguous np.ndarray as dtype of bigger size? A more "standard" way would be better, while hackish ways are also welcome. It seems that I can set shape and stride freely with np.lib.stride_tricks.as_strided, but I can't force the dtype to be something, which is the problem here.

EDIT

Non-contiguous array can be made by simple slicing. For example:

np.empty((8, 4), 'uint32')[:, :2].view('uint64')

will throw the same exception above (while from a memory point of view I should be able to do this). This case is much more common than my example above.

If you have access to a contiguous array from which your non-contiguous one is derived, it should typically be possible to work around this limitation.

For example your trigrams can be obtained like so:

>>> a = np.random.randint(0, 256, 2**28, 'B').view('c')
>>> a
array([b')', b'xf2', b'xf7', ..., b'xf4', b'xf1', b'z'], dtype='|S1')
>>> np.lib.stride_tricks.as_strided(a[:0].view('S3'), ((2**28)-2,), (1,))
array([b')xf2xf7', b'xf2xf7x14', b'xf7x14x1b', ...,
 b'xc9x14xf4', b'x14xf4xf1', b'xf4xf1z'], dtype='|S3')

In fact, this example demonstrates that all we need is a contiguous "stub" at the memory buffer's base for view casting, since afterwards, because as_strided does not do many checks we are essentially free to do whatever we like.

It seems we can always get such a stub by slicing to a size 0 array. For your second example:

>>> X = np.empty((8, 4), 'uint32')[:, :2]
>>> np.lib.stride_tricks.as_strided(X[:0].view(np.uint64), (8, 1), X.strides)
array([[140133325248280],
 [ 32],
 [ 32083728],
 [ 31978800],
 [ 0],
 [ 29686448],
 [ 32],
 [ 32362720]], dtype=uint64)