[Numpy-discussion] loop through values in a array and find maximum as looping
Olivier Delalleau
shish at keba.be
Tue Dec 6 21:12:47 EST 2011
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 at pobox.com>
> I think you want
> np.maximum(a, b, out=a)
>
> - Nathaniel
> On Dec 6, 2011 9:04 PM, "questions anon" <questions.anon at 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 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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