Hi, If I create a structured array with vector columns:
array = np.array(zip([[1,2],[1,2],[1,3]]),dtype=[('a',float,2)])
array.dtype[0]
then examine the type of the column, I get: dtype(('float64',(2,))) Then, if I try and view the numerical type, I see:
array.dtype[0].type <type 'numpy.void'>
array.dtype[0].subdtype[0]
I have to basically do dtype('float64') to get what I need. I seem to remember that this used not to be the case, and that even for vector columns, one could access array.dtype[0].type to get the numerical type. Is this a bug, or deliberate? Thanks, Thomas
Thomas Robitaille wrote:
I seem to remember that this used not to be the case, and that even for vector columns, one could access array.dtype[0].type to get the numerical type. Is this a bug, or deliberate?
I submitted a bug report: http://projects.scipy.org/numpy/ticket/1557 Cheers, Tom -- View this message in context: http://old.nabble.com/dtype.type-for-structured-arrays-tp29256618p29276859.h... Sent from the Numpy-discussion mailing list archive at Nabble.com.
On Jul 24, 2010, at 2:42 PM, Thomas Robitaille wrote:
Hi,
If I create a structured array with vector columns:
array = np.array(zip([[1,2],[1,2],[1,3]]),dtype=[('a',float,2)])
then examine the type of the column, I get:
array.dtype[0] dtype(('float64',(2,)))
Then, if I try and view the numerical type, I see:
array.dtype[0].type <type 'numpy.void'>
I have to basically do
array.dtype[0].subdtype[0] dtype('float64')
to get what I need. I seem to remember that this used not to be the case, and that even for vector columns, one could access array.dtype[0].type to get the numerical type. Is this a bug, or deliberate?
This looks the same as I remember it. The dtype is a structured array with the filed name 'a' having an element which is a vector of floats. As a result, the first dtype[0] extracts the vector of floats dtype. This must be type "void" because it is a vector of floats. To get to the underlying type, you have to do what you did. I don't see how it would have worked another way in the past. -Travis
Thanks,
Thomas _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
--- Travis Oliphant Enthought, Inc. oliphant@enthought.com 1-512-536-1057 http://www.enthought.com
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