Thanks for the correction. I will learn the ravel() function since I do not know it. Moving from Matlab world into python is tricky sometime.
 
Your output
In [22]: out
> Out[22]: array([ 1.+3.j, -5.+9.j])
>
> In [23]: error
> Out[23]: array([ 0.4-0.3j, 0.1-0.7j])

are correct answer.
 
However, if my data set is large, this solution takes long time to run. Are there any python/numpy magic to speed it up?
 
Thanks
 
Frank

> Date: Fri, 30 Jan 2009 14:52:27 -0600
> From: robert.kern@gmail.com
> To: numpy-discussion@scipy.org
> Subject: Re: [Numpy-discussion] help on fast slicing on a grid
>
> On Fri, Jan 30, 2009 at 12:58, frank wang <f.yw@hotmail.com> wrote:
> > I have created a test example for the question using for loop and hope
> > someone can help me to get fast solution. My data set is about 2000000 data.
> >
> > However, I have the problem to run the code, the Out[i]=cnstl[j] line gives
> > me error says:
> >
> > In [107]: Out[0]=cnstl[0]
> > ---------------------------------------------------------------------------
> > TypeError Traceback (most recent call last)
> > C:\Frank_share\qamslicer.py in <module>()
> > ----> 1
> > 2
> > 3
> > 4
> > 5
> > TypeError: can't convert complex to float; use abs(z)
> > In [108]: cnstl.dtype
> > Out[108]: dtype('complex128')
> >
> > I do not know why that my data is complex128 already. Can anyone help to
> > figure why?
>
> It's an odd error message, certainly. The root of the problem is that
> you are attempting to put a (1,)-shaped array into a scalar. You don't
> want to do that.
>
> By the way, you don't want to use the variable name Out in IPython.
> It's already used to capture the output.
>
> > Thanks
> >
> > Frank
> >
> > from numpy import *
> > a = arange(-15,16,2)
> > cnstl=a.reshape(16,1)+1j*a
> > cnstl=cnstl.reshape(256,1)
>
> Change that line to
>
> cnstl = cnstl.ravel()
>
> > X = array([1.4 + 1j*2.7, -4.9 + 1j*8.3])
> > Out = array(X)
> > error =array(X)
> > for i in xrange(2):
> > for j in xrange(256):
> > a0 = real(X[i]) < (real(cnstl[j])+1)
> > a1 = real(X[i]) > (real(cnstl[j])-1)
> > a2 = imag(X[i]) > (imag(cnstl[j])-1)
> > a3 = imag(X[i]) < (imag(cnstl[j])+1)
> > if (a0 & a1 & a2 &a3):
> > Out[i] = cnstl[j]
> > error[i] = X[i] - cnstl[j]
>
> After reindenting this correctly, I get the following results:
>
> In [22]: out
> Out[22]: array([ 1.+3.j, -5.+9.j])
>
> In [23]: error
> Out[23]: array([ 0.4-0.3j, 0.1-0.7j])
>
>
> Are those correct?
>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
> -- Umberto Eco
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion@scipy.org
> http://projects.scipy.org/mailman/listinfo/numpy-discussion

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