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

Daπid davidmenhur@gmail....
Fri Aug 3 10:41:03 CDT 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
>>> 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@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
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
```