thanks again my only problem though is that the out=a in the loop does not seem to replace my a= outside the loop so my final a is whatever I started with for a. Not sure what I am doing wrong whether it is something with the loop or with the command.
On Wed, Dec 7, 2011 at 1:44 PM, <josef.p...@gmail.com> wrote: > On Tue, Dec 6, 2011 at 9:36 PM, Olivier Delalleau <sh...@keba.be> wrote: > > The "out=a" keyword will ensure your first array will keep being > updated. So > > you can do something like: > > > > a = my_list_of_arrays[0] > > for b in my_list_of_arrays[1:]: > > numpy.maximum(a, b, out=a) > > I didn't think of the out argument which makes it more efficient, but > in my example I used Python's reduce which takes an iterable and not > one huge array. > > Josef > > > > > > -=- Olivier > > > > 2011/12/6 questions anon <questions.a...@gmail.com> > >> > >> thanks for all of your help, that does look appropriate but I am not > sure > >> how to loop it over thousands of files. > >> I need to keep the first array to compare with but replace any greater > >> values as I loop through each array comparing back to the same array. > does > >> that make sense? > >> > >> > >> On Wed, Dec 7, 2011 at 1:12 PM, Olivier Delalleau <sh...@keba.be> > wrote: > >>> > >>> Thanks, I didn't know you could specify the out array :) > >>> > >>> (to the OP: my initial suggestion, although probably not very > efficient, > >>> seems to work with 2D arrays too, so I have no idea why it didn't work > for > >>> you -- but Nathaniel's one seems to be the ideal one anyway). > >>> > >>> -=- Olivier > >>> > >>> > >>> 2011/12/6 Nathaniel Smith <n...@pobox.com> > >>>> > >>>> I think you want > >>>> np.maximum(a, b, out=a) > >>>> > >>>> - Nathaniel > >>>> > >>>> On Dec 6, 2011 9:04 PM, "questions anon" <questions.a...@gmail.com> > >>>> wrote: > >>>>> > >>>>> thanks for responding Josef but that is not really what I am looking > >>>>> for, I have a multidimensional array and if the next array has any > values > >>>>> greater than what is in my first array I want to replace them. The > data are > >>>>> contained in netcdf files. > >>>>> I can achieve what I want if I combine all of my arrays using numpy > >>>>> concatenate and then using the command numpy.max(myarray, axis=0) but > >>>>> because I have so many arrays I end up with a memory error so I need > to find > >>>>> a way to get the maximum while looping. > >>>>> > >>>>> > >>>>> > >>>>> On Wed, Dec 7, 2011 at 12:36 PM, <josef.p...@gmail.com> wrote: > >>>>>> > >>>>>> On Tue, Dec 6, 2011 at 7:55 PM, 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 > >>>>>> > >>>>>> if I understand correctly it's a minimum.reduce > >>>>>> > >>>>>> numpy > >>>>>> > >>>>>> >>> a = np.concatenate((np.arange(5)[::-1], > >>>>>> >>> np.arange(5)))*np.ones((4,3,1)) > >>>>>> >>> np.minimum.reduce(a, axis=2) > >>>>>> array([[ 0., 0., 0.], > >>>>>> [ 0., 0., 0.], > >>>>>> [ 0., 0., 0.], > >>>>>> [ 0., 0., 0.]]) > >>>>>> >>> a.T.shape > >>>>>> (10, 3, 4) > >>>>>> > >>>>>> python with iterable > >>>>>> > >>>>>> >>> reduce(np.maximum, a.T) > >>>>>> array([[ 4., 4., 4., 4.], > >>>>>> [ 4., 4., 4., 4.], > >>>>>> [ 4., 4., 4., 4.]]) > >>>>>> >>> reduce(np.minimum, a.T) > >>>>>> array([[ 0., 0., 0., 0.], > >>>>>> [ 0., 0., 0., 0.], > >>>>>> [ 0., 0., 0., 0.]]) > >>>>>> > >>>>>> Josef > >>>>>> > >>>>>> >> > >>>>>> >> _______________________________________________ > >>>>>> >> 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 > >>>>> > >>>>> > >>>>> > >>>>> _______________________________________________ > >>>>> 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 > >>> > >> > >> > >> _______________________________________________ > >> 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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