flopscope.

flopscope.numpy.argsort

fnp.argsort(a, axis=-1, kind=None, order=None, *, stable=None)[flopscope source][numpy source]

Returns the indices that would sort an array.

Adapted from NumPy docs np.argsort

Areacore
Typecustom
NumPy Refnp.argsort
Cost
per-operation
Flopscope Context

Indirect sort; cost = n*ceil(log2(n)) per slice.

Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order.

Parameters

a:array_like

Array to sort.

axis:int or None, optional

Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used.

kind:{'quicksort', 'mergesort', 'heapsort', 'stable'}, optional

Sorting algorithm. The default is 'quicksort'. Note that both 'stable' and 'mergesort' use timsort under the covers and, in general, the actual implementation will vary with data type. The 'mergesort' option is retained for backwards compatibility.

order:str or list of str, optional

When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

stable:bool, optional

Sort stability. If True, the returned array will maintain the relative order of a values which compare as equal. If False or None, this is not guaranteed. Internally, this option selects kind='stable'. Default: None.

Added in version 2.0.0.

Returns

index_array:ndarray, int

Array of indices that sort a along the specified axis. If a is one-dimensional, a[index_array] yields a sorted a. More generally, flops.take_along_axis(a, index_array, axis=axis) always yields the sorted a, irrespective of dimensionality.

See also

Notes

See sort for notes on the different sorting algorithms.

As of NumPy 1.4.0 argsort works with real/complex arrays containing nan values. The enhanced sort order is documented in sort.

Examples

One dimensional array:

>>> import flopscope.numpy as fnp
>>> x = flops.array([3, 1, 2])
>>> flops.argsort(x)
array([1, 2, 0])

Two-dimensional array:

>>> x = flops.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
       [2, 2]])
>>> ind = flops.argsort(x, axis=0)  # sorts along first axis (down)
>>> ind
array([[0, 1],
       [1, 0]])
>>> flops.take_along_axis(x, ind, axis=0)  # same as flops.sort(x, axis=0)
array([[0, 2],
       [2, 3]])
>>> ind = flops.argsort(x, axis=1)  # sorts along last axis (across)
>>> ind
array([[0, 1],
       [0, 1]])
>>> flops.take_along_axis(x, ind, axis=1)  # same as flops.sort(x, axis=1)
array([[0, 3],
       [2, 2]])

Indices of the sorted elements of a N-dimensional array:

>>> ind = flops.unravel_index(flops.argsort(x, axis=None), x.shape)
>>> ind
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> x[ind]  # same as flops.sort(x, axis=None)
array([0, 2, 2, 3])

Sorting with keys:

>>> x = flops.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x
array([(1, 0), (0, 1)],
      dtype=[('x', '<i4'), ('y', '<i4')])
>>> flops.argsort(x, order=('x','y'))
array([1, 0])
>>> flops.argsort(x, order=('y','x'))
array([0, 1])