# [Numpy-discussion] Unexpected reorganization of internal data

Tue Jan 31 02:29:23 CST 2012

```Hi,

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
that

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],
[r21,r22,r23],
[r31,r32,r33]])

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,

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