On Tue, Dec 6, 2011 at 9:36 PM, Olivier Delalleau <shish@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.anon@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 <shish@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 <njs@pobox.com>
I think you want np.maximum(a, b, out=a)
- Nathaniel
On Dec 6, 2011 9:04 PM, "questions anon" <questions.anon@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.pktd@gmail.com> wrote:
On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau <shish@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.anon@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
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