sort¶
Sorting functions

xarray_extras.sort.
topk
(a, k, dim, split_every=None)¶ Extract the k largest elements from a on the given dimension, and return them sorted from largest to smallest. If k is negative, extract the k smallest elements instead, and return them sorted from smallest to largest.
This assumes that
k
is small. All results will be returned in a single chunk along the given axis.

xarray_extras.sort.
argtopk
(a, k, dim, split_every=None)¶ Extract the indexes of the k largest elements from a on the given dimension, and return them sorted from largest to smallest. If k is negative, extract the k smallest elements instead, and return them sorted from smallest to largest.
This assumes that
k
is small. All results will be returned in a single chunk along the given axis.

xarray_extras.sort.
take_along_dim
(a, ind, dim)¶ Use the output of
argtopk()
to pick points from a.Parameters:  a – any xarray object
 ind – array of ints, as returned by
argtopk()
 dim – dimension along which argtopk was executed
An example that uses all of the above functions is source attribution. Given a generic function \(y = f(x_{0}, x_{1}, ..., x_{i})\), which is embarassingly parallel along a given dimension, one wants to find:
 the top k elements of y along the dimension
 the elements of all x’s that generated the top k elements of y
>>> from xarray import DataArray
>>> from xarray_extras.sort import *
>>> x = DataArray([[5, 3, 2, 8, 1],
>>> [0, 7, 1, 3, 2]], dims=['x', 's'])
>>> y = x.sum('x') # y = f(x), embarassingly parallel among dimension 's'
>>> y
<xarray.DataArray (s: 5)>
array([ 5, 10, 3, 11, 3])
Dimensions without coordinates: s
>>> top_y = topk(y, 3, 's')
>>> top_y
<xarray.DataArray (s: 3)>
array([11, 10, 5])
Dimensions without coordinates: s
>>> top_x = take_along_dim(x, argtopk(y, 3, 's'), 's')
>>> top_x
<xarray.DataArray (x: 2, s: 3)>
array([[8, 3, 5],
[3, 7, 0]])
Dimensions without coordinates: x, s