Travis Oliphant wrote: > David Cournapeau wrote: > >> Hi, >> >> >> What are the rules concerning storage with numpy ? >> > The rule is that a numpy array has "strides" which specify how many > bytes to skip to get to the next element in the array. That's the > internal model. There are no hard and fast rules about storage order. > Internally, C-order is as good as Fortran-order (except the array > iterator gives special preference to C-order and all functions for which > the order can be specified (like zeros) default to C-order). > Ok, this is again a bad habit from matlab to think in C or F order... > Thus, the storage order is whatever the strides say it is. Now, there > are flags that keep track of whether or not the strides agree with the 2 > recognized special cases of "Fortran-order" (first-index varies the > fastest) or "C-order" (last-index varies the fastest). But, this is > only for convenience. Very few functions actually require a > specification of storage order. Those that allow it default to "C-order". > > You can't think of a NumPy array has having a particular storage order > unless you explicitly request it. One of the most common ways that > Fortran-order arrays show up, for example is when a C-order array is > transposed. A transpose operation does nothing except flip the strides > (and therefore the flags) of the array. This is what is happening in > concatenate (using axis=1) to give you a Fortran-order array. > Bascially, code equivalent to the following is being run: > concatenate([X1.T, X2.T]).T > > In the second example, you explicitly create the array (and therefore > the strides) as C-order and then fill it (so it doesn't change on you). > The first example used array calculations which don't guarantee the > storage order. > > This is all seamless to the user until you have to interface with > extension code. Ideally, you write compiled code that deals with > strided arrays. If you can't, then you request an array of the required > storage-order. > > By the way, for interfacing with ctypes, check out the > ctypeslib.ndpointer class-creation function for flag checking and the > require function for automatic conversion of an array to specific > requirements. > > I tried to to that at first, but I couldn't make the examples of numpy works; after having tried at home with beta versions, it looks like it is a ctype version problem, as it works with ctype 1.0 + numpy 1.0rc1.
Thanks for the explanations, this answers most questions I had in mind for numpy internal layout compared to matlab which I am used to. I think this should be in the wiki somewhere; do you mind if I use your email as a basis for the tentative numpy tutorial (memory layout section, maybe) ? David ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
