On 31/01/2012 18:23, Chris Barker wrote:

On Tue, Jan 31, 2012 at 6:14 AM, Malcolm Reynolds wrote:

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Not exactly an answer to your question, but I can highly recommend using Boost.python, PyUblas and Ublas for your C++ vectors and matrices. It gives you a really good interface on the C++ side to numpy arrays and matrices, which can be passed in both directions over the language threshold with no copying. or use Cython...

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If I had to guess I'd say sometimes when transposing numpy simply sets a flag internally to avoid copying the data, but in some cases (such as perhaps when multiplication needs to take place) the data has to be placed in a new object. good guess:

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V = numpy.dot(R, U.transpose()).transpose()

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a array([[1, 2], [3, 4], [5, 6]]) a.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

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b = a.transpose() b.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

so the transpose() simple re-arranges the strides to Fortran order, rather than changing anything in memory.

np.dot() produces a new array, so it is C-contiguous, then you transpose it, so you get a fortran-ordered array.

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Now when I call my C++ function from the Python side, all the data in V is printed, but it has been transposed. as mentioned, if you are working with arrays in C++ (or fortran, orC, or...) and need to count on the ordering of the data, you need to check it in your extension code. There are utilities for this.

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However, if I do: V = numpy.array(U.transpose()).transpose() right:

In [7]: a.flags Out[7]: C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

In [8]: a.transpose().flags Out[8]: C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

In [9]: np.array( a.transpose() ).flags Out[9]: C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

so the np.array call doesn't re-arrange the order if it doesn't need to. If you want to force it, you can specify the order:

In [10]: np.array( a.transpose(), order='C' ).flags Out[10]: C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False

(note: this does surprise me a bit, as it is making a copy, but there you go -- if order matters, specify it)

In general, numpy does a lot of things for the sake of efficiency -- avoiding copies when it can, for instance -- this give efficiency and flexibility, but you do need to be careful, particularly when interfacing with the binary data directly.

-Chris

Thanks for all the answers to my question. Helped a lot. Mads -- +-----------------------------------------------------+ | Mads Ipsen | +----------------------+------------------------------+ | Gåsebæksvej 7, 4. tv | | | DK-2500 Valby | phone: +45-29716388 | | Denmark | email: mads.ipsen@gmail.com | +----------------------+------------------------------+