Add implementation of dpnp.argwhere

dpnp/dpnp_iface_searching.py

@@ -45,7 +45,7 @@
 from .dpnp_array import dpnp_array
 from .dpnp_utils.dpnp_utils_reduction import dpnp_wrap_reduction_call
 
-__all__ = ["argmax", "argmin", "searchsorted", "where"]
+__all__ = ["argmax", "argmin", "argwhere", "searchsorted", "where"]
 
 
 def _get_search_res_dt(a, _dtype, out):
@@ -244,6 +244,62 @@ def argmin(a, axis=None, out=None, *, keepdims=False):
     )
 
 
+def argwhere(a):
+    """
+    Find the indices of array elements that are non-zero, grouped by element.
+
+    For full documentation refer to :obj:`numpy.argwhere`.
+
+    Parameters
+    ----------
+    a : {dpnp.ndarray, usm_ndarray}
+        Input array.
+
+    Returns
+    -------
+    out : dpnp.ndarray
+        Indices of elements that are non-zero. Indices are grouped by element.
+        This array will have shape ``(N, a.ndim)`` where ``N`` is the number of
+        non-zero items.
+
+    See Also
+    --------
+    :obj:`dpnp.where` : Returns elements chosen from input arrays depending on
+                        a condition.
+    :obj:`dpnp.nonzero` : Return the indices of the elements that are non-zero.
+
+    Notes
+    -----
+    ``dpnp.argwhere(a)`` is almost the same as
+    ``dpnp.transpose(dpnp.nonzero(a))``, but produces a result of the correct
+    shape for a 0D array.
+    The output of :obj:`dpnp.argwhere` is not suitable for indexing arrays.
+    For this purpose use :obj:`dpnp.nonzero` instead.
+
+    Examples
+    --------
+    >>> import dpnp as np
+    >>> x = np.arange(6).reshape(2, 3)
+    >>> x
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> np.argwhere(x > 1)
+    array([[0, 2],
+           [1, 0],
+           [1, 1],
+           [1, 2]])
+
+    """
+
+    dpnp.check_supported_arrays_type(a)
+    if a.ndim == 0:
+        # nonzero does not behave well on 0d, so promote to 1d
+        a = dpnp.atleast_1d(a)
+        # and then remove the added dimension
+        return dpnp.argwhere(a)[:, :0]
+    return dpnp.stack(dpnp.nonzero(a)).T
+
+
 def searchsorted(a, v, side="left", sorter=None):
     """
     Find indices where elements should be inserted to maintain order.

tests/test_search.py

@@ -1,7 +1,12 @@
 import dpctl.tensor as dpt
 import numpy
 import pytest
-from numpy.testing import assert_allclose, assert_array_equal, assert_raises
+from numpy.testing import (
+    assert_allclose,
+    assert_array_equal,
+    assert_equal,
+    assert_raises,
+)
 
 import dpnp
 
@@ -99,6 +104,64 @@ def test_ndarray(self, axis, keepdims):
         assert_dtype_allclose(dpnp_res, np_res)
 
 
+class TestArgwhere:
+    @pytest.mark.parametrize("dt", get_all_dtypes(no_none=True))
+    def test_basic(self, dt):
+        a = numpy.array([4, 0, 2, 1, 3], dtype=dt)
+        ia = dpnp.array(a)
+
+        result = dpnp.argwhere(ia)
+        expected = numpy.argwhere(a)
+        assert_equal(result, expected)
+
+    @pytest.mark.parametrize("ndim", [0, 1, 2])
+    def test_ndim(self, ndim):
+        # get an nd array with multiple elements in every dimension
+        a = numpy.empty((2,) * ndim)
+
+        # none
+        a[...] = False
+        ia = dpnp.array(a)
+
+        result = dpnp.argwhere(ia)
+        expected = numpy.argwhere(a)
+        assert_equal(result, expected)
+
+        # only one
+        a[...] = False
+        a.flat[0] = True
+        ia = dpnp.array(a)
+
+        result = dpnp.argwhere(ia)
+        expected = numpy.argwhere(a)
+        assert_equal(result, expected)
+
+        # all but one
+        a[...] = True
+        a.flat[0] = False
+        ia = dpnp.array(a)
+
+        result = dpnp.argwhere(ia)
+        expected = numpy.argwhere(a)
+        assert_equal(result, expected)
+
+        # all
+        a[...] = True
+        ia = dpnp.array(a)
+
+        result = dpnp.argwhere(ia)
+        expected = numpy.argwhere(a)
+        assert_equal(result, expected)
+
+    def test_2d(self):
+        a = numpy.arange(6).reshape((2, 3))
+        ia = dpnp.array(a)
+
+        result = dpnp.argwhere(ia > 1)
+        expected = numpy.argwhere(a > 1)
+        assert_array_equal(result, expected)
+
+
 class TestWhere:
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
     def test_basic(self, dtype):

tests/test_sycl_queue.py

@@ -437,6 +437,7 @@ def test_meshgrid(device):
         pytest.param("argmax", [1.0, 2.0, 4.0, 7.0]),
         pytest.param("argmin", [1.0, 2.0, 4.0, 7.0]),
         pytest.param("argsort", [2.0, 1.0, 7.0, 4.0]),
+        pytest.param("argwhere", [[0, 3], [1, 4], [2, 5]]),
         pytest.param("cbrt", [1.0, 8.0, 27.0]),
         pytest.param("ceil", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]),
         pytest.param("conjugate", [[1.0 + 1.0j, 0.0], [0.0, 1.0 + 1.0j]]),

tests/test_usm_type.py

@@ -557,6 +557,7 @@ def test_norm(usm_type, ord, axis):
         pytest.param("argmax", [1.0, 2.0, 4.0, 7.0]),
         pytest.param("argmin", [1.0, 2.0, 4.0, 7.0]),
         pytest.param("argsort", [2.0, 1.0, 7.0, 4.0]),
+        pytest.param("argwhere", [[0, 3], [1, 4], [2, 5]]),
         pytest.param("cbrt", [1, 8, 27]),
         pytest.param("ceil", [-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]),
         pytest.param("conjugate", [[1.0 + 1.0j, 0.0], [0.0, 1.0 + 1.0j]]),

tests/third_party/cupy/sorting_tests/test_search.py

@@ -399,7 +399,6 @@ def test_flatnonzero(self, xp, dtype):
     {"array": numpy.empty((0, 2, 0))},
     _ids=False,  # Do not generate ids from randomly generated params
 )
-@pytest.mark.skip("argwhere isn't implemented yet")
 class TestArgwhere:
     @testing.for_all_dtypes()
     @testing.numpy_cupy_array_equal()
@@ -412,7 +411,6 @@ def test_argwhere(self, xp, dtype):
     {"value": 0},
     {"value": 3},
 )
-@pytest.mark.skip("argwhere isn't implemented yet")
 @testing.with_requires("numpy>=1.18")
 class TestArgwhereZeroDimension:
     @testing.for_all_dtypes()