[Numpy-discussion] Nasty bug using pre-initialized arrays
Stuart Brorson
sdb@cloud9....
Fri Jan 4 15:08:54 CST 2008
NumPy gurus --
I just discovered this today. It looks like a bug to me. Please
flame me mercilessly if I am wrong! :-)
Sometimes you need to initialize an array using zeros() before doing
an assignment to it in a loop. If you assign a complex value to the
initialized array, the imaginary part of the array is dropped. Does
NumPy do a silent type-cast which causes this behavior? Is this
typecast a feature?
Below I attach a session log showing the bug. Note that I have boiled
down my complex code to this simple case for ease of comprehension. [1]
I will also input this bug into the tracking system.
By the way, this is NumPy 1.0.4:
In [39]: numpy.__version__
Out[39]: '1.0.4'
Cheers,
Stuart Brorson
Interactive Supercomputing, inc.
135 Beaver Street | Waltham | MA | 02452 | USA
http://www.interactivesupercomputing.com/
---------------------- <session log> --------------------
In [29]: A = numpy.random.rand(4) + 1j*numpy.random.rand(4)
In [30]: B = numpy.zeros((4))
In [31]:
In [31]: for i in range(4):
....: B[i] = A[i]
....:
In [32]: A
Out[32]:
array([ 0.12150180+0.00577893j, 0.39792515+0.03607227j,
0.61933379+0.04506978j, 0.56751678+0.24576083j])
In [33]: B
Out[33]: array([ 0.1215018 , 0.39792515, 0.61933379, 0.56751678])
----------------------- </session log> -------------------
[1] Yes, I know that I should use vectorized code as often as
possible, and that this example is not vectorized. This is a simple
example illustrating the problem. Moreover, many times the
computation you wish to perform can't easily be vectorized, leaving
the nasty old for loop as the only choice......
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