On 3 August 2012 11:18, 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 >
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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