On Sat, Mar 30, 2013 at 3:51 PM, Matthew Brett
Hi,
On Sat, Mar 30, 2013 at 4:14 AM,
wrote: On Fri, Mar 29, 2013 at 10:08 PM, Matthew Brett
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. ------------ I always thought of "order" in array creation is the way we want to have the memory layout of the *target* array and has nothing to do with existing memory layout (creating view or copy as needed). reshape, and ravel are *views* if possible, memory might just be some weird strides (and can be ignored unless you want to do some memory optimization, keeping track of the memory is difficult. I don't think I will start to use A and K after upgrading numpy.)
a1 = np.ones((10,4))
not contiguous
arr2 = a1[:, 2:4] arr2.flags C_CONTIGUOUS : False F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
stack columns (needs to make a copy)
arr3 = arr2.ravel('F') arr3.flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
stack columns or rows with reshape (I have no idea what it did with the memory)
arr2.reshape(-1,1).flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
arr2.reshape(-1,1, order='F').flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
arr2.reshape(-1, order='F').flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False
------------------- one case where I do pay attention to memory layout is column slicing
arr = np.ones((10, 5), order='F') for i in range(1, 5): print arr[:, :i+2].ravel('C').flags['OWNDATA'] ??? for i in range(1,5): print arr[:, :i+2].ravel('F').flags['OWNDATA'] ???
Josef
Cheers,
Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion