CODEWITHSUNDEEP
pythonnumpy
user2667066
user2667066

Reputation: 2109

How to compare equality of memory representation of numpy arrays (e.g. including dtype)

How can I compare whether two numpy arrays are exactly identical in memory, so that e.g.

np.array([0,1]) == np.array([0,1])

is True, but

np.array([0,1]) == np.array([[0,1]])
np.array([0,1], dtype=np.int32) == np.array([0,1], dtype=np.int64)

are both False. np.array_equal doesn't have a compare_dtypes option. I guess there might be other ways for the memory representation of an array to differ too (e.g. endian-ness)

Upvotes: 2

Views: 451

Answers (3)

user2667066
user2667066

Reputation: 2109

Currently I'm using pickle.dumps:

import pickle
a1 = np.array([0,1], dtype=np.int32)
a2 = np.array([0,1], dtype=np.int64)
pickle.dumps(a1) == pickle.dumps(a2)

But it seems a bit of a hack.

Upvotes: 0

Paul Panzer
Paul Panzer

Reputation: 53079

Depending on which aspects you want to cover the minimum would be comparing x.dtype (this includes endianness) x.shape and x.strides.

You may also want to look at some flags. For example, x.flags.aligned may be considered part of the memory layout in a broad sense as may be x.flags.writeable (and perhaps x.flags.owndata).

The C/F_CONTIGUOUS flags, on the other hand, are redundant once you know shape and strides, and finally, there are UPDATEIFCOPY and WRITEBACKIFCOPY which I don't understand well enough to comment on.

Upvotes: 1

yatu
yatu

Reputation: 88276

You could use itemsize to compare them in terms of their length in bytes:

a1 = np.array([0,1], dtype=np.int32)
a2 = np.array([0,1], dtype=np.int64)
a1.itemsize == a2.itemsize
# False

If you want to compare both their size and content you could examine the raw contents of data memory with ndarray.tobytes:

a1.tobytes() == a2.tobytes()
# False

Upvotes: 1

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