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.vick...@noaa.gov
<mailto: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 <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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