[Numpy-discussion] Raveling, reshape order keyword unnecessarily confuses index and memory ordering
josef.pktd@gmai...
josef.pktd@gmai...
Sat Mar 30 16:20:19 CDT 2013
On Sat, Mar 30, 2013 at 4:57 PM, <josef.pktd@gmail.com> wrote:
> On Sat, Mar 30, 2013 at 3:51 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
>> Hi,
>>
>> On Sat, Mar 30, 2013 at 4:14 AM, <josef.pktd@gmail.com> wrote:
>>> On Fri, Mar 29, 2013 at 10:08 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
>>>>
>>>> Hi,
>>>>
>>>> We were teaching today, and found ourselves getting very confused
>>>> about ravel and shape in numpy.
>>>>
>>>> Summary
>>>> --------------
>>>>
>>>> There are two separate ideas needed to understand ordering in ravel and reshape:
>>>>
>>>> Idea 1): ravel / reshape can proceed from the last axis to the first,
>>>> or the first to the last. This is "ravel index ordering"
>>>> Idea 2) The physical layout of the array (on disk or in memory) can be
>>>> "C" or "F" contiguous or neither.
>>>> This is "memory ordering"
>>>>
>>>> The index ordering is usually (but see below) orthogonal to the memory ordering.
>>>>
>>>> The 'ravel' and 'reshape' commands use "C" and "F" in the sense of
>>>> index ordering, and this mixes the two ideas and is confusing.
>>>>
>>>> What the current situation looks like
>>>> ----------------------------------------------------
>>>>
>>>> Specifically, we've been rolling this around 4 experienced numpy users
>>>> and we all predicted at least one of the results below wrongly.
>>>>
>>>> This was what we knew, or should have known:
>>>>
>>>> In [2]: import numpy as np
>>>>
>>>> In [3]: arr = np.arange(10).reshape((2, 5))
>>>>
>>>> In [5]: arr.ravel()
>>>> Out[5]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>>
>>>> So, the 'ravel' operation unravels over the last axis (1) first,
>>>> followed by axis 0.
>>>>
>>>> So far so good (even if the opposite to MATLAB, Octave).
>>>>
>>>> Then we found the 'order' flag to ravel:
>>>>
>>>> In [10]: arr.flags
>>>> Out[10]:
>>>> C_CONTIGUOUS : True
>>>> F_CONTIGUOUS : False
>>>> OWNDATA : False
>>>> WRITEABLE : True
>>>> ALIGNED : True
>>>> UPDATEIFCOPY : False
>>>>
>>>> In [11]: arr.ravel('C')
>>>> Out[11]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>>
>>>> But we soon got confused. How about this?
>>>>
>>>> In [12]: arr_F = np.array(arr, order='F')
>>>>
>>>> In [13]: arr_F.flags
>>>> Out[13]:
>>>> C_CONTIGUOUS : False
>>>> F_CONTIGUOUS : True
>>>> OWNDATA : True
>>>> WRITEABLE : True
>>>> ALIGNED : True
>>>> UPDATEIFCOPY : False
>>>>
>>>> In [16]: arr_F
>>>> Out[16]:
>>>> array([[0, 1, 2, 3, 4],
>>>> [5, 6, 7, 8, 9]])
>>>>
>>>> In [17]: arr_F.ravel('C')
>>>> Out[17]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>>
>>>> Right - so the flag 'C' to ravel, has got nothing to do with *memory*
>>>> ordering, but is to do with *index* ordering.
>>>>
>>>> And in fact, we can ask for memory ordering specifically:
>>>>
>>>> In [22]: arr.ravel('K')
>>>> Out[22]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>>
>>>> In [23]: arr_F.ravel('K')
>>>> Out[23]: array([0, 5, 1, 6, 2, 7, 3, 8, 4, 9])
>>>>
>>>> In [24]: arr.ravel('A')
>>>> Out[24]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>>>
>>>> In [25]: arr_F.ravel('A')
>>>> Out[25]: array([0, 5, 1, 6, 2, 7, 3, 8, 4, 9])
>>>>
>>>> There are some confusions to get into with the 'order' flag to reshape
>>>> as well, of the same type.
>>>>
>>>> Ravel and reshape use the tems 'C' and 'F" in the sense of index ordering.
>>>>
>>>> This is very confusing. We think the index ordering and memory
>>>> ordering ideas need to be separated, and specifically, we should avoid
>>>> using "C" and "F" to refer to index ordering.
>>>>
>>>> Proposal
>>>> -------------
>>>>
>>>> * Deprecate the use of "C" and "F" meaning backwards and forwards
>>>> index ordering for ravel, reshape
>>>> * Prefer "Z" and "N", being graphical representations of unraveling in
>>>> 2 dimensions, axis1 first and axis0 first respectively (excellent
>>>> naming idea by Paul Ivanov)
>>>>
>>>> What do y'all think?
>>>>
>>>> Cheers,
>>>>
>>>> Matthew
>>>> Paul Ivanov
>>>> JB Poline
>>>> _______________________________________________
>>>> NumPy-Discussion mailing list
>>>> NumPy-Discussion@scipy.org
>>>> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>>
>>> I always thought "F" and "C" are easy to understand, I always thought about
>>> the content and never about the memory when using it.
>>
>> I can only say that 4 out of 4 experienced numpy developers found
>> themselves unable to predict the behavior of these functions before
>> they saw the output.
>>
>> The problem is always that explaining something makes it clearer for a
>> moment, but, for those who do not have the explanation or who have
>> forgotten it, at least among us here, the outputs were generating
>> groans and / or high fives as we incorrectly or correctly guessed what
>> was going to happen.
>>
>> I think the only way to find out whether this really is confusing or
>> not, is to put someone in front of these functions without any
>> explanation and ask them to predict what is going to come out of the
>> various inputs and flags. Or to try and teach it, which was the
>> problem we were having.
>
> changing the names doesn't make it easier to understand.
> I think the confusion is because the new A and K refer to existing memory
>
>
> ``ravel`` is just stacking columns ('F') or stacking rows ('C'), I
> don't remember having seen any weird cases.
example from our statistics use:
rows are observations/time periods, columns are variables/individuals
using "F" or "C", we can stack either by time-periods (observations)
or individuals (cross-section units)
that's easy to understand.
"A" and "K" are pretty useless for us, because we don't know which
stacking we would get (we don't try to control the memory layout)
The only reason to use "A" or "K", in my opinion, is to use the
existing memory efficiently. Since the order in the array is
unpredictable, it only makes sense if we don't care about it, for
example when we only have elementwise operations.
Josef
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