On Sun, 2018-12-30 at 16:03 +0100, Matthias Geier wrote:
On Sat, Dec 29, 2018 at 6:00 PM Sebastian Berg wrote:
On Sat, 2018-12-29 at 17:16 +0100, Matthias Geier wrote:
Hi Sebastian.
I don't have an opinion (yet) about this matter, but I have a question:
On Thu, Dec 27, 2018 at 12:30 AM Sebastian Berg wrote:
[...]
new_arr = arr.reshape(new_shape) assert np.may_share_memory(arr, new_arr)
# Which is sometimes -- but should not be -- written as: arr.shape = new_shape # unnecessary container modification
[...]
Why is this discouraged?
Why do you call this "unnecessary container modification"?
I've used this idiom in the past for exactly those cases where I wanted to make sure no copy is made.
And if we are not supposed to assign to arr.shape, why is it allowed in the first place?
Well, this may be a matter of taste, but say you have an object that stores an array:
class MyObject: def __init__(self): self.myarr = some_array
Now, lets say I do:
def some_func(arr): # Do something with the array: arr.shape = -1
myobject = MyObject() some_func(myobject)
then myobject will suddenly have the wrong shape stored. In most cases this is harmless, but I truly believe this is exactly why we have views and why they are so awesome. The content of arrays is mutable, but the array object itself should not be muted normally.
Thanks for the example! I don't understand its point, though. Also, it's not working since MyObject doesn't have a .shape attribute.
The example should have called `some_func(myobject.arr)`. The thing is that if you have more references to the same array around, you change all their shapes. And if those other references are there for a reason, that is not what you want. That does not matter much in most cases, but it could change the shape of an array in a completely different place then intended. Creating a new view is cheap, so I think such things should be avoided. I admit, most code will effectively do: arr = input_arr[...] # create a new view arr.shape = ... so that there is no danger. But conceptually, I do not think there should be a danger of magically changing the shape of a stored array in a different part of the code. Does that make some sense? Maybe shorter example: arr = np.arange(10) arr2 = arr arr2.shape = (5, 2) print(arr.shape) # also (5, 2) so the arr container (shape, dtype) is changed/muted. I think we expect that for content here, but not for the shape. - Sebastian
There may be some corner cases, but a lot of the "than why is it allowed" questions are answered with: for history reasons.
OK, that's a good point.
By the way, on error the `arr.shape = ...` code currently creates the copy temporarily.
That's interesting and it should probably be fixed.
But it is not reason enough for me not to use it. I find it important that is doesn't make a copy in the success case, I don't care very much for the error case.
Would you mind elaborating on the real reasons why I shouldn't use it?
cheers, Matthias
- Sebastian
cheers, Matthias _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
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