On Fri, Mar 29, 2013 at 10:08 PM, Matthew Brett <matthew.br...@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
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> 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.
In my numpy htmlhelp for version 1.5, I don't have a K or A option
>>> np.__version__
'1.5.1'
>>> np.arange(5).ravel("K")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: order not understood
>>> np.arange(5).ravel("A")
array([0, 1, 2, 3, 4])
>>>
the C, F in ravel have their twins in reshape
>>> arr = np.arange(10).reshape(2,5, order="C").copy()
>>> arr
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> arr.ravel()
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> arr = np.arange(10).reshape(2,5, order="F").copy()
>>> arr
array([[0, 2, 4, 6, 8],
[1, 3, 5, 7, 9]])
>>> arrarr.ravel("F")
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
For example we use it when we get raveled arrays from R,
and F for column order and C for row order indexing are pretty
obvious names when coming from another package (Matlab, R, Gauss)
Josef
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