Here is the 3D implementation: http://pastebin.com/ERVLWhbS
I was only able to do it using a double nested loop, but I am sure someone more clever than me can do a slicing trick to overcome it. Otherwise, I hope this is fast enough for your purpose. David. On Fri, Aug 3, 2012 at 4:41 PM, Daπid <davidmen...@gmail.com> wrote: > 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.vick...@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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
