[Numpy-discussion] 2 greatest values, in a 3-d array, along one axis
Daπid
davidmenhur at gmail.com
Fri Aug 3 11:41:03 EDT 2012
Here goes a 1D simple implementation. It shouldn't be difficult to
generalize to more dimensions, as all the functions support axis
argument:
>>> a=np.array([1, 2, 3, 5, 2])
>>> a.max() # This is the maximum value
5
>>> mask=np.zeros_like(a)
>>> mask[np.argmax(a)]=1
>>> a=np.ma.masked_array(a, mask=mask)
>>> a.max() # Second maximum value
3
I am using a masked array, so the structure of the array remains (ie,
you can still use it in multi-dimensional arrays). I could have
deleted de value, but then that wouldn't be useful for your case.
On Fri, Aug 3, 2012 at 4:18 PM, Jim Vickroy <jim.vickroy at 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
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