- Each element of the array is a coordinate (x, y).
- Each coordinate has two labels.
Goal: sum the elements that have the same two labels.
How can this be faster?
>>> import numpy
>>> from scipy import ndimage
>>>
>>> lable1 = numpy.array([0,0,1,1,2,2])
>>> kinds_of_lable1 = 3
>>>
>>> lable2 = numpy.array([0,1,0,0,1,1])
>>> kinds_of_lable2 = 2
>>>
>>> data = numpy.array( [ [1, 2], [3,8], [4,5], [2,9], [1, 3], [7, 2] ] )
>>> data_T = data.view().T
>>>
>>> ### processing ####
... lable1_and_2 = lable1*kinds_of_lable2 + lable2
>>>
>>> result = numpy.empty( (kinds_of_lable1 * kinds_of_lable2, 2) )
>>> result_T = result.view().T
>>>
>>> result_T[0] = ndimage.measurements.sum( position.T[0], labels = lable1_and_2, index = range(kinds_of_lable1 * kinds_of_lable2) )
>>> result_T[1] = ndimage.measurements.sum( position.T[1], labels = lable1_and_2, index = range(kinds_of_lable1 * kinds_of_lable2) )
>>>
>>> ### output ###
... print(result)
[[ 3. 4.]
[ 1. 2.]
[ 8. 15.]
[ 0. 0.]
[ 0. 0.]
[ 1. 6.]]
