Maybe this is not the intended use of where, but it seems to work:
>>> from numpy import * # No complaining now
>>> a = arange(12)
>>> a.shape = (4,3)
>>> a
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>>> b = array([6,7,8])
>>> row = all( equal(a,b), 1 )
>>> where(row==True)
(array([2]),)
On Aug 15, 1:38 pm, "Matthieu Brucher" <[EMAIL PROTECTED]>
wrote:
> The where function ?
>
> Matthieu
>
> 2007/8/15, mark <[EMAIL PROTECTED]>:
>
>
>
> > Oops, 'find' is in pylab (matplotlib).
> > I guess in numpy you have to use 'where', which does almost the same,
> > but it returns a Tuple.
> > Is there a function that is more like the find in matplotlib?
> > Mark
>
> > On Aug 15, 12:26 pm, Andy Cheesman <[EMAIL PROTECTED]>
> > wrote:
> > > Thanks for the speedy response but where can I locate the find function
> > > as it isn't in numpy.
>
> > > Andy
>
> > > mark wrote:
> > > > I think you can create an array with a true value in the right spot as
> > > > folows:
>
> > > > row = all( equal(a,b), 1 )
>
> > > > Then you can either find the row (but you already knew that one, as it
> > > > is b)
>
> > > > a[row]
>
> > > > or the row index
>
> > > > find(row==True)
>
> > > > Mark
>
> > > > On Aug 15, 11:53 am, Andy Cheesman <[EMAIL PROTECTED]>
> > > > wrote:
> > > >> Dear nice people
>
> > > >> I'm trying to match a row (b) within a large numpy array (a). My most
> > > >> successful attempt is below
>
> > > >> hit = equal(b, a)
> > > >> total_hits = add.reduce(hit, 1)
> > > >> max_hit = argmax(total_hits, 0)
> > > >> answer = a[max_hit]
>
> > > >> where ...
> > > >> a = array([[ 0, 1, 2, 3],
> > > >> [ 4, 5, 6, 7],
> > > >> [ 8, 9, 10, 11],
> > > >> [12, 13, 14, 15]])
>
> > > >> b = array([8, 9, 10, 11])
>
> > > >> I was wondering if people could suggest a possible more efficient
> > route
> > > >> as there seems to be numerous steps.
>
> > > >> Thanks
> > > >> Andy
> > > >> _______________________________________________
> > > >> Numpy-discussion mailing list
> > > >> [EMAIL PROTECTED]
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>
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