[Numpy-discussion] loop through values in a array and find maximum as looping
Olivier Delalleau
shish at keba.be
Tue Dec 6 21:07:04 EST 2011
If you need to do them one after the other, numpy.maximum(a, b) will do it
(it won't work in-place on 'a' though, it'll make a new copy).
-=- Olivier
2011/12/6 questions anon <questions.anon at gmail.com>
> 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 at gmail.com> wrote:
>
>> On Tue, Dec 6, 2011 at 7:55 PM, Olivier Delalleau <shish at 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 at 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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>> >
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>
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