I filed issue 9714 trying to get some feedback on what to do with
updateifcopy semantics and user-exposed nditer.
For those who are unfamiliar with the issue see below for a short
summary, issue 7054 for a lengthy discussion, or pull request 9639
(which is still not merged).
As I mention in the issue, I am willing to put in the work to make the
magical update done in the last line of this snippet more explicit:
a = arange(24, dtype='<i4').reshape(2, 3, 4)
i = nditer(a, ['buffered'], order='F', casting='unsafe',
op_dtypes='>f8', buffersize=5)
j = i.copy()
i = None # <<<< HERE
but need some direction from the community. Possible solutions:
1. nditer is rarely used, just deprecate updateifcopy use on iterands
and raise an exception
2. make nditer into a context manager, so the code would become explicit
a = arange(24, dtype='<i4').reshape(2, 3, 4)
with nditer(a, ['buffered'], order='F', casting='unsafe',
op_dtypes='>f8', buffersize=5) as i:
j = i.copy()
3. something else?
Any opinions?
Matti
-------------------------
what are updateifcopy semantics? When a temporary copy or work buffer is
required, NumPy can (ab)use the base attribute of an ndarray by
- creating a copy of the data from the base array
- mark the base array read-only
Then when the temporary buffer is "no longer needed"
- the data is copied back
- the original base array is marked read-write
The trigger for the "no longer needed" decision before pull request 9639
is in the dealloc function.
That is not generally a place to do useful work, especially on PyPy
which can call dealloc much later.
Pull request 9639 adds an explicit PyArray_ResolveWritebackIfCopy api
function, and recommends calling it explicitly before dealloc.
The only place this change is visible to the python-level user is in
nditer.
C-API users will need to adapt their code to use the new API function,
with a deprecation cycle that is backwardly compatible on CPython.
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