A followup on the previous thread on scalar speed. operations with numpy scalars I can *maybe* understand this

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np.array(2)[()] * [0.5, 1] [0.5, 1, 0.5, 1]

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np.array(2.+0.1j)[()] * [0.5, 1] __main__:1: ComplexWarning: Casting complex values to real discards

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import numpy as np np.array(2.+0.1j)[()] * [0.5, 1] __main__:1: ComplexWarning: Casting complex values to real discards

but don't understand this the imaginary part [0.5, 1, 0.5, 1] The difference in behavior compared to the other operators, +,-, /,**, looks, at least, like an inconsistency to me. Python 2.6.5 (r265:79096, Mar 19 2010, 21:48:26) [MSC v.1500 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. the imaginary part [0.5, 1, 0.5, 1]

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np.array(2.+0.1j)[()] ** [0.5, 1] array([ 1.41465516+0.0353443j, 2.00000000+0.1j ]) np.array(2.+0.1j)[()] + [0.5, 1] array([ 2.5+0.1j, 3.0+0.1j]) np.array(2.+0.1j)[()] / [0.5, 1] array([ 4.+0.2j, 2.+0.1j])

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np.array(2)[()] * [0.5, 1] [0.5, 1, 0.5, 1] np.array(2)[()] / [0.5, 1] array([ 4., 2.]) np.array(2)[()] ** [0.5, 1] array([ 1.41421356, 2. ]) np.array(2)[()] - [0.5, 1] array([ 1.5, 1. ]) np.__version__ '1.5.1'

or

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np.array(-2.+0.1j)[()] * [0.5, 1] [] np.multiply(np.array(-2.+0.1j)[()], [0.5, 1]) array([-1.+0.05j, -2.+0.1j ])

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np.array([-2.+0.1j])[0] * [0.5, 1] [] np.multiply(np.array([-2.+0.1j])[0], [0.5, 1]) array([-1.+0.05j, -2.+0.1j ])

Josef defensive programming = don't use python, use numpy arrays, or at least remember which kind of animals you have