I think this does what you want: def dim_weight(x): weights = x[0] volumes = x[1]*x[2]*x[3] return np.where(volumes>5184, volumes / 194.0, weights) Best, Ken On 4/17/11 1:00 PM, Laszlo Nagy <gandalf@shopzeus.com> wrote:
Message: 1 Date: Sat, 16 Apr 2011 21:08:55 +0200 From: Laszlo Nagy<gandalf@shopzeus.com> Subject: [Numpy-discussion] Beginner's question To: Discussion of Numerical Python<numpy-discussion@scipy.org> Message-ID:<4DA9E947.7060209@shopzeus.com> Content-Type: text/plain; charset=ISO-8859-2; format=flowed
Hi All,
I have this example program:
import numpy as np import numpy.random as rnd
def dim_weight(X): weights = X[0] volumes = X[1]*X[2]*X[3] res = np.empty(len(volumes), dtype=np.double) for i,v in enumerate(volumes): if v>5184: res[i] = v/194.0 else: res[i] = weights[i] return res
# TEST N = 10 X = rnd.randint( 1,25, (4,N)) print dim_weight(X)
I want to implement the dim_weight() function effectively. But I'm not sure how to construct a numpy expression that does the same think for me.
I got to this point:
def dim_weight(X): weights = X[0] volumes = X[1]*X[2]*X[3] dweights = volumes/194.0 return ( weights[weights>5184] , dweights[weights<=5184] ) # ???
I don't know how to preserve the order of the elements and return the result in one array. Maybe I need to do create a matrix multiply matrix? But don't know how.
Thanks,
Laszlo