Is 'a' a regular numpy array or something fancier? -=- Olivier
2011/12/6 questions anon <questions.a...@gmail.com> > 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 >> > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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