On 15.01.2013 20:50, Sturla Molden wrote:
You might want to look at this first:
https://github.com/numpy/numpy/issues/1811
Yes it is possible to compute the median faster by doing quickselect
instead of quicksort. Best case O(n) for quickselect, O(n log n) for
quicksort. But adding selection
Hi,
On Tue, Jan 15, 2013 at 1:50 PM, Mads Ipsen wrote:
> Hi,
>
> I simply can't understand this. I'm trying to use argsort to produce
> indices that can be used to sort an array:
>
> from numpy import *
>
> indices = array([[4,3],[1,12],[23,7],[11,6],[8,9]])
> args = argsort(indices, axis
You might want to look at this first:
https://github.com/numpy/numpy/issues/1811
Yes it is possible to compute the median faster by doing quickselect
instead of quicksort. Best case O(n) for quickselect, O(n log n) for
quicksort. But adding selection and partial sorting to NumPy is a bigger
is
On Jan 15, 2013, at 8:31 PM, Jerome Caron wrote:
> Dear all,
> I am new to the Numpy-discussion list.
> I would like to follow up some possibly useful information about calculating
> median.
> The message below was posted today on the AstroPy mailing list.
> Kind regards
> Jerome Caron
>
> #-
Dear all,
I am new to the Numpy-discussion list.
I would like to follow up some possibly useful information about calculating
median.
The message below was posted today on the AstroPy mailing list.
Kind regards
Jerome Caron
#
I think the calculation of medi
I ended coding the dtype reduction, it's not foolproof but it might be useful
for others as well.
Nicolas
import numpy as np
def dtype_reduce(dtype, level=0, depth=0):
"""
Try to reduce dtype up to a given level when it is possible
dtype = [ ('vertex', [('x', 'f4'), ('y', 'f4
On Tue, Jan 15, 2013 at 3:44 PM, Charles R Harris
wrote:
> Fancy indexing is a funny creature and not easy to understand in more than
> one dimension. What is happening is that each index is replaced by the
> corresponding row of a and the result is of shape (5,2,2). To do what you
> want to do:
>
On Tue, Jan 15, 2013 at 4:50 AM, Mads Ipsen wrote:
> Hi,
>
> I simply can't understand this. I'm trying to use argsort to produce
> indices that can be used to sort an array:
>
> from numpy import *
>
> indices = array([[4,3],[1,12],[23,7],[11,6],[8,9]])
> args = argsort(indices, axis=0)
>
Hi,
I simply can't understand this. I'm trying to use argsort to produce
indices that can be used to sort an array:
from numpy import *
indices = array([[4,3],[1,12],[23,7],[11,6],[8,9]])
args = argsort(indices, axis=0)
print indices[args]
gives:
[[[ 1 12]
[ 4 3]]
[[ 4 3]
[1
