Hi,
I have an off topic but somehow related question :
Le 19/03/2012 12:04, Matthieu Rigal a écrit :
array = numpy.logical_and(numpy.logical_and(aBlueChannel 1.0, aNirChannel
(aBlueChannel * 1.0)), aNirChannel (aBlueChannel * 1.8))
Is there any significant difference between :
z =
On 03/25/2012 06:55 AM, Pierre Haessig wrote:
Hi,
I have an off topic but somehow related question :
Le 19/03/2012 12:04, Matthieu Rigal a écrit :
array = numpy.logical_and(numpy.logical_and(aBlueChannel 1.0, aNirChannel
(aBlueChannel * 1.0)), aNirChannel (aBlueChannel * 1.8))
Is there
Hi Eric,
Thanks for the hints !
Le 25/03/2012 20:33, Eric Firing a écrit :
Using the bitwise operators in place of logical operators is a hack to
get around limitations of the language; but, if done carefully, it is a
useful one.
What is the rationale behind not overloading __and__ other
On 03/25/2012 12:22 PM, Pierre Haessig wrote:
Hi Eric,
Thanks for the hints !
Le 25/03/2012 20:33, Eric Firing a écrit :
Using the bitwise operators in place of logical operators is a hack to
get around limitations of the language; but, if done carefully, it is a
useful one.
What is the
Mar 2012 13:20:23 +
From: Richard Hattersley rhatters...@gmail.com
Subject: Re: [Numpy-discussion] Using logical function on more than 2
arrays, availability of a between function ?
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID:
CAP=RS9
Hattersley rhatters...@gmail.com
Subject: Re: [Numpy-discussion] Using logical function on more than 2
arrays, availability of a between function ?
To: Discussion of Numerical Python numpy-discussion@scipy.org
Message-ID:
CAP=RS9=UBOc6Kmtmnne7W093t19w=T=osrxuaw0wf8b49hq...@mail.gmail.com
Dear Numpy fellows,
I have actually a double question, which only aims to answer a single one :
how to get the following line being processed more efficiently :
array = numpy.logical_and(numpy.logical_and(aBlueChannel 1.0, aNirChannel
(aBlueChannel * 1.0)), aNirChannel (aBlueChannel * 1.8))
What do you mean by efficient? Are you trying to get it execute
faster? Or using less memory? Or have more concise source code?
Less memory:
- numpy.vectorize would let you get to the end result without any
intermediate arrays but will be slow.
- Using the out parameter of numpy.logical_and