[Numpy-discussion] 2 greatest values, in a 3-d array, along one axis

eat e.antero.tammi@gmail....
Fri Aug 3 14:09:13 CDT 2012


Hi,

On Fri, Aug 3, 2012 at 7:02 PM, Angus McMorland <amcmorl@gmail.com> wrote:

> On 3 August 2012 11:18, Jim Vickroy <jim.vickroy@noaa.gov> wrote:
>
>> Hello everyone,
>>
>> I'm trying to determine the 2 greatest values, in a 3-d array, along one
>> axis.
>>
>> Here is an approach:
>>
>> # ------------------------------------------------------
>> # procedure to determine greatest 2 values for 3rd dimension of 3-d
>> array ...
>> import numpy, numpy.ma
>> xcnt, ycnt, zcnt   = 2,3,4 # actual case is (1024, 1024, 8)
>> p0                 = numpy.empty ((xcnt,ycnt,zcnt))
>> for z in range (zcnt) : p0[:,:,z] = z*z
>> zaxis              = 2                                            # max
>> values to be determined for 3rd axis
>> p0max              = numpy.max (p0, axis=zaxis)                   # max
>> values for zaxis
>> maxindices         = numpy.argmax (p0, axis=zaxis)                #
>> indices of max values
>> p1                 = p0.copy()                                    # work
>> array to scan for 2nd highest values
>> j, i               = numpy.meshgrid (numpy.arange (ycnt), numpy.arange
>> (xcnt))
>> p1[i,j,maxindices] = numpy.NaN                                    # flag
>> all max values
>> p1                 = numpy.ma.masked_where (numpy.isnan (p1), p1) # hide
>> all max values
>> p1max              = numpy.max (p1, axis=zaxis)                   # 2nd
>> highest values for zaxis
>> # additional code to analyze p0max and p1max goes here
>> # ------------------------------------------------------
>>
>> I would appreciate feedback on a simpler approach -- e.g., one that does
>> not require masked arrays and or use of magic values like NaN.
>>
>> Thanks,
>> -- jv
>>
>
> Here's a way that only uses argsort and fancy indexing:
>
> >>>a = np.random.randint(10, size=(3,3,3))
> >>>print a
>
> [[[0 3 8]
>   [4 2 8]
>   [8 6 3]]
>
>  [[0 6 7]
>   [0 3 9]
>   [0 9 1]]
>
>  [[7 9 7]
>   [5 2 9]
>   [9 3 3]]]
>
> >>>am = a.argsort(axis=2)
> >>>maxs = a[np.arange(a.shape[0])[:,None], np.arange(a.shape[1])[None],
> am[:,:,-1]]
> >>>print maxs
>
> [[8 8 8]
>  [7 9 9]
>  [9 9 9]]
>
> >>>seconds = a[np.arange(a.shape[0])[:,None], np.arange(a.shape[1])[None],
> am[:,:,-2]]
> >>>print seconds
>
> [[3 4 6]
>  [6 3 1]
>  [7 5 3]]
>
> And to double check:
>
> >>>i, j = 0, 1
> >>>l = a[i, j,:]
> >>>print l
>
> [4 2 8]
>
>  >>>print np.max(a[i,j,:]), maxs[i,j]
>
> 8 8
>
> >>>print l[np.argsort(l)][-2], second[i,j]
>
> 4 4
>
> Good luck.
>
Here the np.indicies function may help a little bit, like:
In []: a= randint(10, size= (3, 2, 4))
In []: a
Out[]:
array([[[1, 9, 6, 6],
        [0, 3, 4, 2]],
       [[4, 2, 4, 4],
        [5, 9, 4, 4]],
       [[6, 1, 4, 3],
        [5, 4, 5, 5]]])

In []: ndx= indices(a.shape)
In []: # largest
In []: a[a.argsort(0), ndx[1], ndx[2]][-1]
Out[]:
array([[6, 9, 6, 6],
       [5, 9, 5, 5]])

In []: # second largest
In []: a[a.argsort(0), ndx[1], ndx[2]][-2]
Out[]:
array([[4, 2, 4, 4],
       [5, 4, 4, 4]])


My 2 cents,
-eat

>
> Angus.
> --
> AJC McMorland
> Post-doctoral research fellow
> Neurobiology, University of Pittsburgh
>
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