Weird, it worked for me (with a and b two 1d numpy arrays). Anyway, Josef's solution is probably much more efficient (especially if you can put all your arrays into a single tensor).
-=- Olivier 2011/12/6 questions anon <questions.a...@gmail.com> > Hi Olivier, > No that does not seem to do anything > am I missing another step whereever b is greater than a replace b with a? > thanks > > > On Wed, Dec 7, 2011 at 11:55 AM, Olivier Delalleau <sh...@keba.be> wrote: > >> It may not be the most efficient way to do this, but you can do: >> mask = b > a >> a[mask] = b[mask] >> >> -=- Olivier >> >> 2011/12/6 questions anon <questions.a...@gmail.com> >> >>> I would like to produce an array with the maximum values out of many >>> (10000s) of arrays. >>> I need to loop through many multidimentional arrays and if a value is >>> larger (in the same place as the previous array) then I would like that >>> value to replace it. >>> >>> e.g. >>> a=[1,1,2,2 >>> 11,2,2 >>> 1,1,2,2] >>> b=[1,1,3,2 >>> 2,1,0,0 >>> 1,1,2,0] >>> >>> where b>a replace with value in b, so the new a should be : >>> >>> a=[1,1,3,2] >>> 2,1,2,2 >>> 1,1,2,2] >>> >>> and then keep looping through many arrays and replace whenever value is >>> larger. >>> >>> I have tried numpy.putmask but that results in >>> TypeError: putmask() argument 1 must be numpy.ndarray, not list >>> Any other ideas? Thanks >>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@scipy.org >>> http://mail.scipy.org/mailman/listinfo/numpy-discussion >>> >>> >> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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