Add COO.nonzero method. (#148)

docs/generated/sparse.COO.nonzero.rst

@@ -0,0 +1,6 @@
+COO.nonzero
+===========
+
+.. currentmodule:: sparse
+
+.. automethod:: COO.nonzero

docs/generated/sparse.COO.rst

@@ -61,6 +61,7 @@ COO
       COO.dot
       COO.reshape
       COO.transpose
+      COO.nonzero
 
    .. rubric:: Utility functions
    .. autosummary::

sparse/coo/core.py

@@ -1437,6 +1437,27 @@ def maybe_densify(self, max_size=1000, min_density=0.25):
             raise ValueError("Operation would require converting "
                              "large sparse array to dense")
 
+    def nonzero(self):
+        """
+        Get the indices where this array is nonzero.
+
+        Returns
+        -------
+        idx : tuple[numpy.ndarray]
+            The indices where this array is nonzero.
+
+        See Also
+        --------
+        :obj:`numpy.ndarray.nonzero` : NumPy equivalent function
+
+        Examples
+        --------
+        >>> s = COO.from_numpy(np.eye(5))
+        >>> s.nonzero()
+        (array([0, 1, 2, 3, 4], dtype=uint8), array([0, 1, 2, 3, 4], dtype=uint8))
+        """
+        return tuple(self.coords)
+
 
 def _keepdims(original, new, axis):
     shape = list(original.shape)

sparse/tests/test_coo.py

@@ -1374,3 +1374,24 @@ def test_two_arg_where():
 
     with pytest.raises(ValueError):
         sparse.where(cs, xs)
+
+
+def test_nonzero():
+    s = sparse.random((2, 3, 4), density=0.5)
+    x = s.todense()
+
+    expected = x.nonzero()
+    actual = s.nonzero()
+
+    assert isinstance(actual, tuple)
+    assert len(expected) == len(actual)
+
+    for e, a in zip(expected, actual):
+        assert_eq(e, a, compare_dtype=False)
+
+
+def test_argwhere():
+    s = sparse.random((2, 3, 4), density=0.5)
+    x = s.todense()
+
+    assert_eq(np.argwhere(s), np.argwhere(x), compare_dtype=False)