[Numpy-discussion] wrong casting of augmented assignment statements

[Sebastian Walter]

Hello,
I have a question about the augmented assignment statements *=, +=, etc.
Apparently, the casting of types is not working correctly. Is this
known resp. intended behavior of numpy?
(I'm using numpy.__version__ = '1.4.0.dev7039' on this machine but I
remember a recent checkout of numpy yielded the same result).

The problem is best explained at some examples:

wrong casting from float to int::

            In [1]: import numpy

            In [2]: x = numpy.ones(2,dtype=int)

            In [3]: y = 1.3 * numpy.ones(2,dtype=float)

            In [4]: z = x * y

            In [5]: z
            Out[5]: array([ 1.3,  1.3])

            In [6]: x *= y

            In [7]: x
            Out[7]: array([1, 1])

            In [8]: x.dtype
            Out[8]: dtype('int32')

 wrong casting from float to object::

            In [1]: import numpy

            In [2]: import adolc

            In [3]: x = adolc.adouble(numpy.array([1,2,3],dtype=float))

            In [4]: y = numpy.array([4,5,6],dtype=float)

            In [5]: x
            Out[5]: array([1(a), 2(a), 3(a)], dtype=object)

            In [6]: y
            Out[6]: array([ 4.,  5.,  6.])

            In [7]: x * y
            Out[7]: array([4(a), 10(a), 18(a)], dtype=object)

            In [8]: y *= x

            In [9]: y

            Out[9]: array([ 4.,  5.,  6.])


        It is inconsistent to the Python behavior::

            In [9]: a = 1

            In [10]: b = 1.3

            In [11]: c = a * b

            In [12]: c
            Out[12]: 1.3

            In [13]: a *= b

            In [14]: a
            Out[14]: 1.3


I would expect that numpy should at least raise an exception in the
case of casting object to float.
Any thoughts?

regards,
Sebastian