[Numpy-discussion] help on fast slicing on a grid
Fri Jan 30 22:41:57 CST 2009
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.
In : out> Out: array([ 1.+3.j, -5.+9.j])> > In : error> Out: 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?
Frank> Date: Fri, 30 Jan 2009 14:52:27 -0600> From: email@example.com> To: firstname.lastname@example.org> Subject: Re: [Numpy-discussion] help on fast slicing on a grid> > On Fri, Jan 30, 2009 at 12:58, frank wang <email@example.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 : Out=cnstl> > ---------------------------------------------------------------------------> > 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 : cnstl.dtype> > Out: 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 : out> Out: array([ 1.+3.j, -5.+9.j])> > In : error> Out: 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> Numpyfirstname.lastname@example.org> http://projects.scipy.org/mailman/listinfo/numpy-discussion
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