As the subject says, numpy.concatenate doesn't seem to obey -- or even
check -- the axis flag when concatenating 1D arrays:
----------------------- <session log> -----------------
In [30]: A = numpy.array([1, 2, 3, 4])
In [31]: D = numpy.array([6, 7, 8, 9])
In [32]: numpy.concatenate((A, D))
Out[32]: array([1, 2, 3, 4, 6, 7, 8, 9])
In [33]: numpy.concatenate((A, D), axis=0)
Out[33]: array([1, 2, 3, 4, 6, 7, 8, 9])
In [34]: numpy.concatenate((A, D), axis=1)
Out[34]: array([1, 2, 3, 4, 6, 7, 8, 9])
In [35]: numpy.concatenate((A, D), axis=2)
Out[35]: array([1, 2, 3, 4, 6, 7, 8, 9])
----------------------- </session log> -----------------
However, if you create the same arrays as 2D (1xn) arrays, then numpy
checks, and does the right thing:
---------------------- <session log> -------------------
In [36]: A = numpy.array([[1, 2, 3, 4]])
In [37]: D = numpy.array([[6, 7, 8, 9]])
In [38]: A.shape
Out[38]: (1, 4)
In [39]: numpy.concatenate((A, D))
Out[39]:
array([[1, 2, 3, 4],
[6, 7, 8, 9]])
In [40]: numpy.concatenate((A, D), axis=0)
Out[40]:
array([[1, 2, 3, 4],
[6, 7, 8, 9]])
In [41]: numpy.concatenate((A, D), axis=1)
Out[41]: array([[1, 2, 3, 4, 6, 7, 8, 9]])
In [42]: numpy.concatenate((A, D), axis=2)
---------------------------------------------------------------------------
<type 'exceptions.ValueError'> Traceback (most recent call
last)
/fs/home/sdb/<ipython console> in <module>()
<type 'exceptions.ValueError'>: bad axis1 argument to swapaxes
----------------------- </session log> -----------------
Question: Is is a bug or a feature?
I'd at least think that numpy would check the axis arg in the 1D case,
and issue an error if the user tried to do something impossible
(e.g. axis=2).
Whether numpy would promote a 1D to a 2D array is a different
question, and I am agnostic about that one.
Cheers,
Stuart Brorson
Interactive Supercomputing, inc.
135 Beaver Street | Waltham | MA | 02452 | USA
http://www.interactivesupercomputing.com/
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