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

Jim Vickroy jim.vickroy@noaa....
Fri Aug 3 13:18:56 CDT 2012


Thanks for each of the improved solutions.  The one using argsort took a 
little while for me to understand.  I have a long way to go to fully 
utilize fancy indexing!  -- jv


On 8/3/2012 10:02 AM, Angus McMorland wrote:
> On 3 August 2012 11:18, Jim Vickroy <jim.vickroy@noaa.gov 
> <mailto: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 <http://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.
>
> Angus.
> -- 
> AJC McMorland
> Post-doctoral research fellow
> Neurobiology, University of Pittsburgh
>
>
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