Robert Kern-2 wrote:
<snip>
Downcasting data is a necessary operation sometimes. We explicitly made a choice a long time ago to allow this.
Robert Kern
This might be the time to recognize that that was a bad choice and reverse it. It is not clear to me why downcasting from complex to real sometimes raises an exception, and sometimes doesn't. Here are two examples where the behavior of NumPy is inexplicably different: Example #1: IPython 0.10 [on Py 2.5.4] [~]|1> z= zeros(3) [~]|2> z[0]= 1+1J TypeError: can't convert complex to float; use abs(z) Example #2: ### START OF CODE ### from numpy import * q = ones(2,dtype=complex)*(1 + 1J) r = zeros(2,dtype=float) r[:] = q print 'q = ',q print 'r = ',r ### END OF CODE ### [~]|9> run err q = [ 1.+1.j 1.+1.j] r = [ 1. 1.] [~]|10> In example #1, when we attempt to assign a complex value to a single element of a float array, an exception is triggered. In example #2, when we assign the entire array (actually, a slice), the imaginary part is silently discarded and we get a wrong result. At a minimum, this inconsistency needs to be cleared up. My preference would be that the programmer should have to explicitly downcast from complex to float, and that if he/she fails to do this, that an exception be triggered. Dr. Phillip M. Feldman -- View this message in context: http://old.nabble.com/Assigning-complex-values-to-a-real-array-tp22383353p26... Sent from the Numpy-discussion mailing list archive at Nabble.com.