Numpy in place permutation
I have a fairly large one dimension numpy array for which I would like to apply some sort of slice-in-place sorting and also get the permutation vector for other processing.
However, the ndarray.sort () method (which is inplace) does not return this vector, and I can use the ndarray.argsort () method to get the permutation vector and use it to permute the slice. However, I cannot figure out how to do this locally.
Vslice = V[istart:istop] # This is a view of the slice
iperm = Vslice.argsort()
V[istart:istop] = Vslice[iperm] # Not an inplace operation...
Ancillary question: why does the following code not modify V as we are working on the V representation?
Vslice = Vslice[iperm]
Regards!
Francois
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To answer the question why the view assignment doesn't change the original:
You need to change Vslice = Vslice[iperm]
to Vslice[:] = Vslice[iperm]
, otherwise you are assigning a new value Vslice
instead of changing the values inside Vslice
:
>>> a = np.arange(10, 0, -1)
>>> a
array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
>>> b = a[2:-2]
>>> b
array([8, 7, 6, 5, 4, 3])
>>> i = b.argsort()
>>> b[:] = b[i] # change the values inside the view
>>> a # note `a` has been sorted in [2:-2] slice
array([10, 9, 3, 4, 5, 6, 7, 8, 2, 1])
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