If you want to do it in just one line (the third line below), this seems to work - unless you have zillions of types in the structured array it should be plenty fast, too:

...

...

import numpy as np A = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)]) array([A[n] for n in A.dtype.names],dtype=float).T array([[1., 2.], [3., 4.]])

You may or may not want the transpose depending on which way you meant to have the matrix aligned... On Wed, May 12, 2010 at 10:40 PM, Eric Firing <efiring@hawaii.edu> wrote:

On 05/12/2010 12:37 PM, Gregory, Matthew wrote:

...

Apologies for what is likely a simple question and I hope it hasn't been asked before ...

Given a recarray with a dtype consisting of more than one type, e.g.

   >>>  import numpy as n    >>>  a = n.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])    >>>  b = a.view(n.recarray)    >>>  b    rec.array([(1.0, 2), (3.0, 4)],          dtype=[('x', '<f8'), ('y', '<i4')])

Is there a simple way to convert 'b' to a floating-point ndarray, casting the integer field to a floating-point?  I've tried the naïve:

   >>>  c = b.view(dtype='float').reshape(b.size,-1)

but that fails with:

   ValueError: new type not compatible with array.

I understand why this would fail (as it is a view and not a copy), but I'm lost on a method to do this conversion simply.

It may not be as simple as you would like, but the following works efficiently:

import numpy as np a = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)]) b = np.empty((a.shape[0], 2), dtype=np.float) b[:,0] = a['x'] b[:,1] = a['y']

Eric

...

thanks, matt

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