If there are no objections, I'll file this ticket in the trac site:
<snip>
Title:
Return type inconsistency in recarray
Description:
The sub-arrays of rank-0 recarrays are returned as scalars rather than
rank-0 ndarrays.
Example:
>>> import numpy as N
>>> dt = N.dtype([('x','f8'),('y','f8')])
>>> rarr = N.zeros((), dtype = dt).view(N.recarray)
>>> rarr
recarray((0.0, 0.0),
dtype=[('x', '<f8'), ('y', '<f8')])
>>> # oddly, rarr.x is not an array
>>> rarr.x
0.0
>>> # This makes it impossible to fill values in the usual manner
>>> rarr.x[...] = 2.0
TypeError: object does not support item assignment
>>> # A workaround is to reshape to non-zero rank
>>> rarr.shape = [1]
>>> # Now rarr.x returns a ndarray of the same rank as rarr
>>> rarr.x
array([ 0.])
>>> # Value-setting now works as expected.
>>> rarr.x[...] = 2.0
>>> rarr
recarray([(2.0, 0.0)],
dtype=[('x', '<f8'), ('y', '<f8')])
This is a problem for any function that takes record arrays of arbitrary
rank, as it necessitates special-case checks for ndim == 0.
</snip>
-- Matt
Francesc Altet wrote:
> Yeah, I agree. In fact, one of the main reasons to keep rank-0 arrays
> around is to provide generality enough to address to these kind of
> problems in an elegant way.
>
> Cheers,
>
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
