Stefan van der Walt wrote: > One last case, which confuses me still (probably because it is > 04:16am): > > In [41]: x = N.array([[0,1,2],[3,4,5]],order='F') > > In [42]: x > Out[42]: > array([[0, 1, 2], > [3, 4, 5]]) > > I assume the data is now stored in memory as > > [0 3 1 4 2 5] (column-wise) > > If I now do > > x.reshape((3,2),order='C') > > i.e. take that block of memory, assume it is in 'C' order, and make > its shape (3,2), I expect > > [[0 3] > [1 4] > [2 5]] > > but get > > [[1 2] > [3 4] > [5 6]] > > I'm obviously missing something trivial -- I'll try again tomorrow. >
I think I see what is going on and where people are getting tripped up. You have to remember, that to NumPy it doesn't semantically matter what the "ordering" of the array is. There is no guarantee that C- order or Fortran-order is *ever* preserved through an operation. Because, in fact the general memory model of the array has no defined "order". It's defined by the strides array. It just so happens that two special-cases are tracked so that we can call out to compiled routines that expect contiguous arrays more easily. So, your mistake is trying to think that the "block" of memory is [0, 3, 1, 4, 2, 5] when you pass the Fortran-order array to the reshape method. While this is true, and it means that you will save a copy if you passed this off to a Fortran routine, the reshape command does not use this information in determining how to "think-about" the input array. In fact, the reshape method does not allow any way to specify the order of the "input" array (self) separately from the order of the output array. The order argument indicates the defined order of both input and output. You might think that the order of self should be used as the order of the input array. The problem with this is, again, that a general array does not have a defined "order". What should be used as the assumed "order" for an un-strided array? You're left with an unresolved question. To avoid two input order arguments, I just let order indicate the order for both the input and the output arrays. We could provide both, but this seems a bit over-done as the same could be accomplished by separately raveling the input to 1-d and then specifying the order argument on reshape. Please continue to question. All the code needs as much review as it can get. Best regards, -Travis ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
