[Numpy-discussion] Using logical function on more than 2 arrays, availability of a "between" function ?

Matthieu Rigal rigal@rapideye....
Tue Mar 20 08:05:22 CDT 2012

```Auto-answer, sorry,

Well, actually I made a mistake lower... that you may have noticed...
On the faster (your) solution, even with a cleaner use of the out parameter,
the fact that the all has then to be used with parameter axis=0 takes more
time and makes it actually slower than the initial solution...

So I may go for the "multiplier" solution.

Regards,
Matthieu

On Tuesday 20 March 2012 13:13:15 you wrote:
> 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
>

RapidEye AG
Molkenmarkt 30
14776 Brandenburg an der Havel
Germany

Follow us on Twitter! www.twitter.com/rapideye_ag

Head Office/Sitz der Gesellschaft: Brandenburg an der Havel
Management Board/Vorstand: Ryan Johnson
Chairman of Supervisory Board/Vorsitzender des Aufsichtsrates:
Robert Johnson
Commercial Register/Handelsregister Potsdam HRB 24742 P
Tax Number/Steuernummer: 048/100/00053
VAT-Ident-Number/Ust.-ID: DE 199331235
DIN EN ISO 9001 certified

```

More information about the NumPy-Discussion mailing list