[Numpy-discussion] Raveling, reshape order keyword unnecessarily confuses index and memory ordering
Fri Apr 5 14:21:54 CDT 2013
On Fri, Apr 5, 2013 at 3:09 PM, Ralf Gommers <firstname.lastname@example.org> wrote:
> On Fri, Apr 5, 2013 at 5:13 PM, Matthew Brett <email@example.com>
>> On Fri, Apr 5, 2013 at 2:20 AM, Sebastian Berg
>> <firstname.lastname@example.org> wrote:
>> > Hey
>> > On Thu, 2013-04-04 at 14:20 -0700, Matthew Brett wrote:
>> >> Hi,
>> >> On Tue, Apr 2, 2013 at 4:32 AM, Nathaniel Smith <email@example.com> wrote:
>> >> <snip>
>> >> > Maybe we should go through and rename "order" to something more
>> >> > descriptive
>> >> > in each case, so we'd have
>> >> > a.reshape(..., index_order="C")
>> >> > a.copy(memory_order="F")
>> >> > etc.?
>> >> I'd like to propose this instead:
>> >> a.reshape(..., order="C")
>> >> a.copy(layout="F")
>> > I actually like this, makes the point clearer that it has to do with
>> > memory layout and implies contiguity, plus it is short and from the
>> > numpy perspective copy, etc. are the ones that add additional info to
>> > "order" and not reshape (because IMO memory order is something new users
>> > should not worry about at first). A and K orders will still have their
>> > quirks with np.array and copy=True/False, but for many functions they
>> > are esoteric anyway.
>> > It will be one hell of a deprecation though, but I am +0.5 for adding an
>> > alias for now (maybe someone knows an even better name?), but I think
>> > that in this case, it probably really is better to wait with actual
>> > deprecation warnings for a few versions, since it touches a *lot* of
>> > code. Plus I think at the point of starting deprecation warnings (and
>> > best earlier) numpy should provide an automatic fixer script...
>> > The only counter point that remains for me is the difficulty of
>> > deprecation, since I think the new name idea is very clean. And this is
>> > unfortunately even more invasive then the index_order proposal.
>> I completely agree that we'd have to be gentle with the change. The
>> problem we'd want to avoid is people innocently using 'layout' and
>> finding to their annoyance that the code doesn't work with other
>> people's numpy.
>> How about:
>> Step 1: 'order' remains as named keyword, layout added as alias,
>> comment on the lines of "layout will become the default keyword for
>> this option in later versions of numpy; please consider updating any
>> code that does not need to remain backwards compatible'.
>> Step 2: default keyword becomes 'layout' with 'order' as alias,
>> comment like "order is an alias for 'layout' to maintain backwards
>> compatibility with numpy <= 1.7.1', please update any code that does
>> not need to maintain backwards compatibility with these numpy
>> Step 3: Add deprecation warning for 'order', "order will be removed as
>> an alias in future versions of numpy"
>> Step 4: (distant future) Remove alias
> A very strong -1 from me. Now we're talking about deprecation warnings and a
> backwards compatibility break after all. I thought we agreed that this was a
> very bad idea, so why are you proposing it now?
> Here's how I see it: deprecation of "order" is a no go. Therefore we have
> two choices here:
> 1. Simply document the current "order" keyword better and leave it at that.
> 2. Add a "layout" (or "index_order") keyword, and live with both "order" and
> "layout" keywords forever.
> (2) is at least as confusing as (1), more work and poor design. Therefore I
> propose to go with (1).
You are saying that deprecation of 'order' at any stage in the next 10
years of numpy's lifetime is a no go?
I think that is short-sighted and I think it will damage numpy.
Believe me, I have as much investment in backward compatibility as you
do. All the three libraries that I spend a long time maintaining need
to test against old numpy versions - but - for heaven's sake - only
back to numpy 1.2 or numpy 1.3. We don't support Python 2.5 any more,
and I don't think we need to maintain compatibility with Numeric
If you are saying that we need to maintain compatibility for 10 years
at a stretch, then we will have to accept that numpy will gradually
decay into a legacy library, because it is certain that, if we stay
static, someone else with more ambition will do a better job.
There is a cost to being averse to any change at all, no matter how
gradually it is managed.
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