[Numpy-discussion] Unexpected reorganization of internal data

Mads Ipsen madsipsen@gmail....
Tue Jan 31 02:29:23 CST 2012


I am confused. Here's the reason:

The following structure is a representation of N points in 3D space:

U = numpy.array([[x1,y1,z1], [x1,y1,z1],...,[xn,yn,zn]])

So the array U has shape (N,3). This order makes sense to me since U[i] 
will give you the i'th point in the set. Now, I want to pass this array 
to a C++ function that does some stuff with the points. Here's how I do 

void Foo::doStuff(int n, PyObject * numpy_data)
     // Get pointer to data
     double * const positions = (double *) PyArray_DATA(numpy_data);

     // Print positions
     for (int i=0; i<n; ++i)
     float x = static_cast<float>(positions[3*i+0])
     float y = static_cast<float>(positions[3*i+1])
     float z = static_cast<float>(positions[3*i+2])

     printf("Pos[%d] = %f %f %f\n", x, y, z);

When I call this routine, using a swig wrapped Python interface to the 
C++ class, everything prints out nice.

Now, I want to apply a rotation to all the positions. So I set up some 
rotation matrix R like this:

R = numpy.array([[r11,r12,r13],

To apply the matrix to the data in one crunch, I do

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

Now when I call my C++ function from the Python side, all the data in V 
is printed, but it has been transposed. So apparently the internal data 
structure handled by numpy has been reorganized, even though I called 
transpose() twice, which I would expect to cancel out each other.

However, if I do:

V = numpy.array(U.transpose()).transpose()

and call the C++ routine, everything is perfectly fine, ie. the data 
structure is as expected.

What went wrong?

Best regards,


| Mads Ipsen                                          |
| Gåsebæksvej 7, 4. tv |                              |
| DK-2500 Valby        | phone:          +45-29716388 |
| Denmark              | email:  mads.ipsen@gmail.com |

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