[Numpy-discussion] Raveling, reshape order keyword unnecessarily confuses index and memory ordering

josef.pktd at gmail.com josef.pktd at gmail.com
Thu Apr 4 15:54:22 EDT 2013

On Thu, Apr 4, 2013 at 3:40 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
> On Thu, Apr 4, 2013 at 11:45 AM, Matthew Brett <matthew.brett at gmail.com> wrote:
>> Hi,
>> On Thu, Apr 4, 2013 at 9:21 AM, Chris Barker - NOAA Federal
>> <chris.barker at noaa.gov> wrote:
>>> On Wed, Apr 3, 2013 at 6:13 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
>>>>> We all agree that 'order' is used with two different and orthogonal
>>>>> meanings in numpy.
>> Brief thank you for your helpful and thoughtful discussion.
>>> well, not entirely orthogonal -- they are the some concept, used in
>>> different contexts,
>> Here's a further clarification, in the hope that it is helpful:
>> Input and output index orderings are orthogonal - I can read the data
>> with C index ordering and return an array that is index ordered
>> any-old-how.
>> F and C are used in the sense of F contiguous and C contiguous - where
>> contiguous is not the same concept as index ordering.
>> So I think it's hard to say these concepts are not orthogonal, simply
>> in the technical sense that order='F" could mean:
>> * read my data using F-style index ordering
>> * return my data in an array using F-style index ordering
>> * (related to above) return my data in F-contiguous memory layout
> Sorry this is not well-put and should increase confusion rather than
> decrease it.  I'll try again if I may.
> What do we mean by 'Fortran' 'order'.
> Two things :
> * np.array(a, order='F') - Fortran contiguous : the array memory is
> contiguous, the strides vector is strictly increasing
> * np.ravel(a, order='F') - first-to-last index ordering used to
> recover values from the array
> They are related in the sense that Fortran contiguous layout in memory
> means that returning the elements as stored in memory gives the same
> answer as first to last index ordering.  They are different in the
> sense that first-to-last index ordering applies to any memory layout -
> is orthogonal to memory layout.   In particular 'contiguous' has no
> meaning for first-to-last or last-to-first index ordering.
> So - to restate in other words - this :
> np.reshape(a, (3, 4), order='F')
> could reasonably mean one of two orthogonal things
> 1) Retrieve data from the array using first-to-last indexing, return
> any memory layout you like
> 2) Retrieve data from the array using the default last-to-first index
> ordering, and return memory in F-contiguous layout

no to interpretation 2)
reshape and ravel (in contrast to flatten) just return a view (if possible)
(with possible some strange strides)

numpy.reshape(a, newshape, order='C')
Gives a new shape to an array without changing its data

functions that return views versus functions that create new arrays


> Cheers,
> Matthew
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