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 >
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