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"""
Interface of the sorting function of the dpnp
Notes
-----
This module is a face or public interface file for the library
it contains:
- Interface functions
- documentation for the functions
- The functions parameters check
"""
import dpctl.tensor as dpt
import numpy
from numpy.core.numeric import normalize_axis_index
import dpnp
# pylint: disable=no-name-in-module
from .dpnp_algo import (
dpnp_partition,
)
from .dpnp_array import dpnp_array
from .dpnp_utils import (
call_origin,
map_dtype_to_device,
)
__all__ = ["argsort", "partition", "sort", "sort_complex"]
[docs]
def argsort(a, axis=-1, kind=None, order=None):
"""
Returns the indices that would sort an array.
For full documentation refer to :obj:`numpy.argsort`.
Parameters
----------
a : {dpnp.ndarray, usm_ndarray}
Array to be sorted.
axis : int or None, optional
Axis along which to sort. If ``None``, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {None, "stable"}, optional
Default is ``None``, which is equivalent to `"stable"`.
Unlike in NumPy any other options are not accepted here.
Returns
-------
out : dpnp.ndarray
Array of indices that sort `a` along the specified `axis`.
If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
More generally, ``dpnp.take_along_axis(a, index_array, axis=axis)``
always yields the sorted `a`, irrespective of dimensionality.
The return array has default array index data type.
Notes
-----
For zero-dimensional arrays, if `axis=None`, output is a one-dimensional
array with a single zero element. Otherwise, an ``AxisError`` is raised.
Limitations
-----------
Parameters `order` is only supported with its default value.
Parameters `kind` can only be ``None`` or ``"stable"`` which
are equivalent.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.ndarray.argsort` : Equivalent method.
:obj:`dpnp.sort` : Return a sorted copy of an array.
:obj:`dpnp.lexsort` : Indirect stable sort with multiple keys.
:obj:`dpnp.argpartition` : Indirect partial sort.
:obj:`dpnp.take_along_axis` : Apply ``index_array`` from obj:`dpnp.argsort`
to an array as if by calling sort.
Examples
--------
>>> import dpnp as np
>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])
>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
[2, 2]])
>>> ind = np.argsort(x, axis=0) # sorts along first axis
>>> ind
array([[0, 1],
[1, 0]])
>>> np.take_along_axis(x, ind, axis=0) # same as np.sort(x, axis=0)
array([[0, 2],
[2, 3]])
>>> ind = np.argsort(x, axis=1) # sorts along last axis
>>> ind
array([[0, 1],
[0, 1]])
>>> np.take_along_axis(x, ind, axis=1) # same as np.sort(x, axis=1)
array([[0, 3],
[2, 2]])
"""
if order is not None:
raise NotImplementedError(
"order keyword argument is only supported with its default value."
)
if kind is not None and kind != "stable":
raise NotImplementedError(
"kind keyword argument can only be None or 'stable'."
)
dpnp.check_supported_arrays_type(a)
if axis is None:
a = a.flatten()
axis = -1
axis = normalize_axis_index(axis, ndim=a.ndim)
return dpnp_array._create_from_usm_ndarray(
dpt.argsort(dpnp.get_usm_ndarray(a), axis=axis)
)
[docs]
def partition(x1, kth, axis=-1, kind="introselect", order=None):
"""
Return a partitioned copy of an array.
For full documentation refer to :obj:`numpy.partition`.
Limitations
-----------
Input array is supported as :obj:`dpnp.ndarray`.
Input `kth` is supported as :obj:`int`.
Parameters `axis`, `kind` and `order` are supported only with default
values.
"""
x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
if x1_desc:
if not isinstance(kth, int):
pass
elif x1_desc.ndim == 0:
pass
elif kth >= x1_desc.shape[x1_desc.ndim - 1] or x1_desc.ndim + kth < 0:
pass
elif axis != -1:
pass
elif kind != "introselect":
pass
elif order is not None:
pass
else:
return dpnp_partition(x1_desc, kth, axis, kind, order).get_pyobj()
return call_origin(numpy.partition, x1, kth, axis, kind, order)
[docs]
def sort(a, axis=-1, kind=None, order=None):
"""
Return a sorted copy of an array.
For full documentation refer to :obj:`numpy.sort`.
Parameters
----------
a : {dpnp.ndarray, usm_ndarray}
Array to be sorted.
axis : int or None, optional
Axis along which to sort. If ``None``, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {None, "stable"}, optional
Default is ``None``, which is equivalent to `"stable"`.
Unlike in NumPy any other options are not accepted here.
Returns
-------
out : dpnp.ndarray
Sorted array with the same type and shape as `a`.
Notes
-----
For zero-dimensional arrays, if `axis=None`, output is the input array
returned as a one-dimensional array. Otherwise, an ``AxisError`` is raised.
Limitations
-----------
Parameters `order` is only supported with its default value.
Parameters `kind` can only be ``None`` or ``"stable"`` which
are equivalent.
Otherwise ``NotImplementedError`` exception will be raised.
See Also
--------
:obj:`dpnp.ndarray.sort` : Sort an array in-place.
:obj:`dpnp.argsort` : Return the indices that would sort an array.
:obj:`dpnp.lexsort` : Indirect stable sort on multiple keys.
:obj:`dpnp.searchsorted` : Find elements in a sorted array.
:obj:`dpnp.partition` : Partial sort.
Examples
--------
>>> import dpnp as np
>>> a = np.array([[1,4],[3,1]])
>>> np.sort(a) # sort along the last axis
array([[1, 4],
[1, 3]])
>>> np.sort(a, axis=None) # sort the flattened array
array([1, 1, 3, 4])
>>> np.sort(a, axis=0) # sort along the first axis
array([[1, 1],
[3, 4]])
"""
if order is not None:
raise NotImplementedError(
"order keyword argument is only supported with its default value."
)
if kind is not None and kind != "stable":
raise NotImplementedError(
"kind keyword argument can only be None or 'stable'."
)
dpnp.check_supported_arrays_type(a)
if axis is None:
a = a.flatten()
axis = -1
axis = normalize_axis_index(axis, ndim=a.ndim)
return dpnp_array._create_from_usm_ndarray(
dpt.sort(dpnp.get_usm_ndarray(a), axis=axis)
)
[docs]
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
For full documentation refer to :obj:`numpy.sort_complex`.
Parameters
----------
a : {dpnp.ndarray, usm_ndarray}
Input array.
Returns
-------
out : dpnp.ndarray of complex dtype
Always returns a sorted complex array.
Examples
--------
>>> import dpnp as np
>>> a = np.array([5, 3, 6, 2, 1])
>>> np.sort_complex(a)
array([1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> a = np.array([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
>>> np.sort_complex(a)
array([1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = dpnp.sort(a)
if not dpnp.issubsctype(b.dtype, dpnp.complexfloating):
if b.dtype.char in "bhBH":
return b.astype(dpnp.complex64)
return b.astype(map_dtype_to_device(dpnp.complex128, b.sycl_device))
return b