[Numpy-discussion] NumPy-Discussion Digest, Vol 66, Issue 61

Matthieu Rigal rigal@rapideye....
Tue Mar 20 07:13:15 CDT 2012


Hi Richard,

Thanks for your answer and the related help !

In fact, I was hoping to have a less memory and more speed solution. Something 
equivalent to a "raster calculator" for numpy. Wouldn't it make sense to have 
some optimized function to work on more than 2 arrays for numpy anyway ?

At the end, I am rather interested by more speed.

I tried first a code-sparing version :
array = numpy.asarray([(aBlueChannel < 1.0),(aNirChannel > aBlueChannel * 
1.0),(aNirChannel < aBlueChannel * 1.8)]).all()

But this one is at the end more than 2 times slower than :
array1 = numpy.empty([3,6566,6682], dtype=numpy.bool)
numpy.less(aBlueChannel, 1.0, out=array1[0])
numpy.greater(aNirChannel, (aBlueChannel * 1.0), out=array1[1])
numpy.less(aNirChannel, (aBlueChannel * 1.8), out=array1[2])
array = array1.all()

(and this solution is about 30% faster than the original one)

I could find another way which was fine for me too:
array = (aBlueChannel < 1.0) * (aNirChannel > (aBlueChannel * 1.0)) * 
(aNirChannel < (aBlueChannel * 1.8))

But this one is only 5-10% faster than the original solution, even if probably 
using less memory than the 2 previous ones. (same was possible with operator 
+, but slower than operator *)

Regards,
Matthieu Rigal


On Monday 19 March 2012 18:00:02 numpy-discussion-request@scipy.org wrote:
> Message: 2
> Date: Mon, 19 Mar 2012 13:20:23 +0000
> From: Richard Hattersley <rhattersley@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=oSrXUAW0WF8B49hqcXQ@mail.gmail.com
> > Content-Type: text/plain; charset=ISO-8859-1
> 
> 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 will let you avoid
> one of the intermediate arrays.
> 
> More speed?:
> Perhaps putting all three boolean temporary results into a single
> boolean array (using the "out" parameter of numpy.greater, etc) and
> using numpy.all might benefit from logical short-circuiting.
> 
> And watch out for divide-by-zero from "aNirChannel/aBlueChannel".
> 
> Regards,
> Richard Hattersley
> 

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