Dec 17, 2023 · Dec 16, 2023 · Dec 17, 2023
diff --git a/graphblas/core/automethods.py b/graphblas/core/automethods.py
    return self._get_value("to_edgelist")


 def to_values(self):
    return self._get_value("to_values")


 def value(self):
    return self._get_value("value")

        "ss",
        "to_coo",
        "to_dense",
        "to_values",
    }
    vector = {
        "_as_matrix",
diff --git a/graphblas/core/infix.py b/graphblas/core/infix.py
    to_coo = wrapdoc(Vector.to_coo)(property(automethods.to_coo))
    to_dense = wrapdoc(Vector.to_dense)(property(automethods.to_dense))
    to_dict = wrapdoc(Vector.to_dict)(property(automethods.to_dict))
    to_values = wrapdoc(Vector.to_values)(property(automethods.to_values))
    vxm = wrapdoc(Vector.vxm)(property(automethods.vxm))
    wait = wrapdoc(Vector.wait)(property(automethods.wait))
    # These raise exceptions
    to_dense = wrapdoc(Matrix.to_dense)(property(automethods.to_dense))
    to_dicts = wrapdoc(Matrix.to_dicts)(property(automethods.to_dicts))
    to_edgelist = wrapdoc(Matrix.to_edgelist)(property(automethods.to_edgelist))
    to_values = wrapdoc(Matrix.to_values)(property(automethods.to_values))
    wait = wrapdoc(Matrix.wait)(property(automethods.wait))
    # These raise exceptions
    __array__ = Matrix.__array__
diff --git a/graphblas/core/matrix.py b/graphblas/core/matrix.py
 import itertools
 import warnings
 from collections.abc import Sequence

 import numpy as np
        self._nrows = nrows.value
        self._ncols = ncols.value

    def to_values(self, dtype=None, *, rows=True, columns=True, values=True, sort=True):
        """Extract the indices and values as a 3-tuple of numpy arrays
        corresponding to the COO format of the Matrix.

        .. deprecated:: 2022.11.0
            ``Matrix.to_values`` will be removed in a future release.
            Use ``Matrix.to_coo`` instead. Will be removed in version 2023.9.0 or later

        Parameters
        ----------
        dtype :
            Requested dtype for the output values array.
        rows : bool, default=True
            Whether to return rows; will return ``None`` for rows if ``False``
        columns : bool, default=True
            Whether to return columns; will return ``None`` for columns if ``False``
        values : bool, default=True
            Whether to return values; will return ``None`` for values if ``False``
        sort : bool, default=True
            Whether to require sorted indices.
            If internally stored rowwise, the sorting will be first by rows, then by column.
            If internally stored columnwise, the sorting will be first by column, then by row.

        Returns
        -------
        np.ndarray[dtype=uint64] : Rows
        np.ndarray[dtype=uint64] : Columns
        np.ndarray : Values
        """
        warnings.warn(
            "`Matrix.to_values(...)` is deprecated; please use `Matrix.to_coo(...)` instead.",
            DeprecationWarning,
            stacklevel=2,
        )
        return self.to_coo(dtype, rows=rows, columns=columns, values=values, sort=sort)

    def to_coo(self, dtype=None, *, rows=True, columns=True, values=True, sort=True):
        """Extract the indices and values as a 3-tuple of numpy arrays
        corresponding to the COO format of the Matrix.
            "Indices should get a single element, which will be extracted as a Python scalar."
        )

    @classmethod
    def from_values(
        cls,
        rows,
        columns,
        values,
        dtype=None,
        *,
        nrows=None,
        ncols=None,
        dup_op=None,
        name=None,
    ):
        """Create a new Matrix from row and column indices and values.

        .. deprecated:: 2022.11.0
            ``Matrix.from_values`` will be removed in a future release.
            Use ``Matrix.from_coo`` instead. Will be removed in version 2023.9.0 or later

        Parameters
        ----------
        rows : list or np.ndarray
            Row indices.
        columns : list or np.ndarray
            Column indices.
        values : list or np.ndarray or scalar
            List of values. If a scalar is provided, all values will be set to this single value.
        dtype :
            Data type of the Matrix. If not provided, the values will be inspected
            to choose an appropriate dtype.
        nrows : int, optional
            Number of rows in the Matrix. If not provided, ``nrows`` is computed
            from the maximum row index found in ``rows``.
        ncols : int, optional
            Number of columns in the Matrix. If not provided, ``ncols`` is computed
            from the maximum column index found in ``columns``.
        dup_op : :class:`~graphblas.core.operator.BinaryOp`, optional
            Function used to combine values if duplicate indices are found.
            Leaving ``dup_op=None`` will raise an error if duplicates are found.
        name : str, optional
            Name to give the Matrix.

        Returns
        -------
        Matrix
        """
        warnings.warn(
            "`Matrix.from_values(...)` is deprecated; please use `Matrix.from_coo(...)` instead.",
            DeprecationWarning,
            stacklevel=2,
        )
        return cls.from_coo(
            rows, columns, values, dtype, nrows=nrows, ncols=ncols, dup_op=dup_op, name=name
        )

    @classmethod
    def from_coo(
        cls,
    to_dense = wrapdoc(Matrix.to_dense)(property(automethods.to_dense))
    to_dicts = wrapdoc(Matrix.to_dicts)(property(automethods.to_dicts))
    to_edgelist = wrapdoc(Matrix.to_edgelist)(property(automethods.to_edgelist))
    to_values = wrapdoc(Matrix.to_values)(property(automethods.to_values))
    wait = wrapdoc(Matrix.wait)(property(automethods.wait))
    # These raise exceptions
    __array__ = Matrix.__array__
    to_dense = wrapdoc(Matrix.to_dense)(property(automethods.to_dense))
    to_dicts = wrapdoc(Matrix.to_dicts)(property(automethods.to_dicts))
    to_edgelist = wrapdoc(Matrix.to_edgelist)(property(automethods.to_edgelist))
    to_values = wrapdoc(Matrix.to_values)(property(automethods.to_values))
    wait = wrapdoc(Matrix.wait)(property(automethods.wait))
    # These raise exceptions
    __array__ = Matrix.__array__
        )
        return cols, rows, vals

    @wrapdoc(Matrix.to_values)
    def to_values(self, dtype=None, *, rows=True, columns=True, values=True, sort=True):
        rows, cols, vals = self._matrix.to_values(
            dtype, rows=rows, columns=columns, values=values, sort=sort
        )
        return cols, rows, vals

    @wrapdoc(Matrix.diag)
    def diag(self, k=0, dtype=None, *, name=None, **opts):
        return self._matrix.diag(-k, dtype, name=name, **opts)
diff --git a/graphblas/core/vector.py b/graphblas/core/vector.py
 import itertools
 import warnings

 import numpy as np

        call("GrB_Vector_resize", [self, size])
        self._size = size.value

    def to_values(self, dtype=None, *, indices=True, values=True, sort=True):
        """Extract the indices and values as a 2-tuple of numpy arrays.

        .. deprecated:: 2022.11.0
            ``Vector.to_values`` will be removed in a future release.
            Use ``Vector.to_coo`` instead. Will be removed in version 2023.9.0 or later

        Parameters
        ----------
        dtype :
            Requested dtype for the output values array.
        indices :bool, default=True
            Whether to return indices; will return ``None`` for indices if ``False``
        values : bool, default=True
            Whether to return values; will return ``None`` for values if ``False``
        sort : bool, default=True
            Whether to require sorted indices.

        Returns
        -------
        np.ndarray[dtype=uint64] : Indices
        np.ndarray : Values
        """
        warnings.warn(
            "`Vector.to_values(...)` is deprecated; please use `Vector.to_coo(...)` instead.",
            DeprecationWarning,
            stacklevel=2,
        )
        return self.to_coo(dtype, indices=indices, values=values, sort=sort)

    def to_coo(self, dtype=None, *, indices=True, values=True, sort=True):
        """Extract the indices and values as a 2-tuple of numpy arrays.

            "A single index should be given, and the result will be a Python scalar."
        )

    @classmethod
    def from_values(cls, indices, values, dtype=None, *, size=None, dup_op=None, name=None):
        """Create a new Vector from indices and values.

        .. deprecated:: 2022.11.0
            ``Vector.from_values`` will be removed in a future release.
            Use ``Vector.from_coo`` instead. Will be removed in version 2023.9.0 or later

        Parameters
        ----------
        indices : list or np.ndarray
            Vector indices.
        values : list or np.ndarray or scalar
            List of values. If a scalar is provided, all values will be set to this single value.
        dtype :
            Data type of the Vector. If not provided, the values will be inspected
            to choose an appropriate dtype.
        size : int, optional
            Size of the Vector. If not provided, ``size`` is computed from
            the maximum index found in ``indices``.
        dup_op : BinaryOp, optional
            Function used to combine values if duplicate indices are found.
            Leaving ``dup_op=None`` will raise an error if duplicates are found.
        name : str, optional
            Name to give the Vector.

        Returns
        -------
        Vector
        """
        warnings.warn(
            "`Vector.from_values(...)` is deprecated; please use `Vector.from_coo(...)` instead.",
            DeprecationWarning,
            stacklevel=2,
        )
        return cls.from_coo(indices, values, dtype, size=size, dup_op=dup_op, name=name)

    @classmethod
    def from_coo(cls, indices, values=1.0, dtype=None, *, size=None, dup_op=None, name=None):
        """Create a new Vector from indices and values.
    to_coo = wrapdoc(Vector.to_coo)(property(automethods.to_coo))
    to_dense = wrapdoc(Vector.to_dense)(property(automethods.to_dense))
    to_dict = wrapdoc(Vector.to_dict)(property(automethods.to_dict))
    to_values = wrapdoc(Vector.to_values)(property(automethods.to_values))
    vxm = wrapdoc(Vector.vxm)(property(automethods.vxm))
    wait = wrapdoc(Vector.wait)(property(automethods.wait))
    # These raise exceptions
    to_coo = wrapdoc(Vector.to_coo)(property(automethods.to_coo))
    to_dense = wrapdoc(Vector.to_dense)(property(automethods.to_dense))
    to_dict = wrapdoc(Vector.to_dict)(property(automethods.to_dict))
    to_values = wrapdoc(Vector.to_values)(property(automethods.to_values))
    vxm = wrapdoc(Vector.vxm)(property(automethods.vxm))
    wait = wrapdoc(Vector.wait)(property(automethods.wait))
    # These raise exceptions
diff --git a/graphblas/tests/test_matrix.py b/graphblas/tests/test_matrix.py
        "from_dicts",
        "from_edgelist",
        "from_scalar",
        "from_values",
        "resize",
        "setdiag",
        "update",
        "from_dicts",
        "from_edgelist",
        "from_scalar",
        "from_values",
        "resize",
        "setdiag",
    }
        A.ss.compactify("bad_how")


 def test_deprecated(A):
    with pytest.warns(DeprecationWarning):
        A.to_values()
    with pytest.warns(DeprecationWarning):
        A.T.to_values()
    with pytest.warns(DeprecationWarning):
        A.from_values([1], [2], [3])


 def test_ndim(A):
    assert A.ndim == 2
    assert A.ewise_mult(A).ndim == 2
diff --git a/graphblas/tests/test_vector.py b/graphblas/tests/test_vector.py
        "from_dict",
        "from_pairs",
        "from_scalar",
        "from_values",
        "resize",
        "update",
    }
        "from_dict",
        "from_pairs",
        "from_scalar",
        "from_values",
        "resize",
    }
    ignore = {"__sizeof__", "_ewise_add", "_ewise_mult", "_ewise_union", "_inner", "_vxm"}
    assert x2.name == "split_1"


 def test_deprecated(v):
    with pytest.warns(DeprecationWarning):
        v.to_values()
    with pytest.warns(DeprecationWarning):
        Vector.from_values([1], [2])


 def test_ndim(A, v):
    assert v.ndim == 1
    assert v.ewise_mult(v).ndim == 1
Original file line number	Diff line number	Diff line change
Expand Up		@@ -281,10 +281,6 @@ def to_edgelist(self):
		return self._get_value("to_edgelist")


		def to_values(self):
		return self._get_value("to_values")


		def value(self):
		return self._get_value("value")

Expand DownExpand Up		@@ -398,7 +394,6 @@ def _main():
		"ss",
		"to_coo",
		"to_dense",
		"to_values",
		}
		vector = {
		"_as_matrix",
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -236,7 +236,6 @@ def dup(self, dtype=None, , clear=False, mask=None, name=None, *opts):
		to_coo = wrapdoc(Vector.to_coo)(property(automethods.to_coo))
		to_dense = wrapdoc(Vector.to_dense)(property(automethods.to_dense))
		to_dict = wrapdoc(Vector.to_dict)(property(automethods.to_dict))
		to_values = wrapdoc(Vector.to_values)(property(automethods.to_values))
		vxm = wrapdoc(Vector.vxm)(property(automethods.vxm))
		wait = wrapdoc(Vector.wait)(property(automethods.wait))
		# These raise exceptions
Expand DownExpand Up		@@ -396,7 +395,6 @@ def dup(self, dtype=None, , clear=False, mask=None, name=None, *opts):
		to_dense = wrapdoc(Matrix.to_dense)(property(automethods.to_dense))
		to_dicts = wrapdoc(Matrix.to_dicts)(property(automethods.to_dicts))
		to_edgelist = wrapdoc(Matrix.to_edgelist)(property(automethods.to_edgelist))
		to_values = wrapdoc(Matrix.to_values)(property(automethods.to_values))
		wait = wrapdoc(Matrix.wait)(property(automethods.wait))
		# These raise exceptions
		__array__ = Matrix.__array__
Expand Down
Original file line number	Diff line number	Diff line change
		@@ -1,5 +1,4 @@
		import itertools
		import warnings
		from collections.abc import Sequence

		import numpy as np
Expand DownExpand Up		@@ -515,42 +514,6 @@ def resize(self, nrows, ncols):
		self._nrows = nrows.value
		self._ncols = ncols.value

		def to_values(self, dtype=None, *, rows=True, columns=True, values=True, sort=True):
		"""Extract the indices and values as a 3-tuple of numpy arrays
		corresponding to the COO format of the Matrix.

		.. deprecated:: 2022.11.0
		``Matrix.to_values`` will be removed in a future release.
		Use ``Matrix.to_coo`` instead. Will be removed in version 2023.9.0 or later

		Parameters
		----------
		dtype :
		Requested dtype for the output values array.
		rows : bool, default=True
		Whether to return rows; will return ``None`` for rows if ``False``
		columns : bool, default=True
		Whether to return columns; will return ``None`` for columns if ``False``
		values : bool, default=True
		Whether to return values; will return ``None`` for values if ``False``
		sort : bool, default=True
		Whether to require sorted indices.
		If internally stored rowwise, the sorting will be first by rows, then by column.
		If internally stored columnwise, the sorting will be first by column, then by row.

		Returns
		-------
		np.ndarray[dtype=uint64] : Rows
		np.ndarray[dtype=uint64] : Columns
		np.ndarray : Values
		"""
		warnings.warn(
		"`Matrix.to_values(...)` is deprecated; please use `Matrix.to_coo(...)` instead.",
		DeprecationWarning,
		stacklevel=2,
		)
		return self.to_coo(dtype, rows=rows, columns=columns, values=values, sort=sort)

		def to_coo(self, dtype=None, *, rows=True, columns=True, values=True, sort=True):
		"""Extract the indices and values as a 3-tuple of numpy arrays
		corresponding to the COO format of the Matrix.
Expand DownExpand Up		@@ -837,61 +800,6 @@ def get(self, row, col, default=None):
		"Indices should get a single element, which will be extracted as a Python scalar."
		)

		@classmethod
		def from_values(
		cls,
		rows,
		columns,
		values,
		dtype=None,
		*,
		nrows=None,
		ncols=None,
		dup_op=None,
		name=None,
		):
		"""Create a new Matrix from row and column indices and values.

		.. deprecated:: 2022.11.0
		``Matrix.from_values`` will be removed in a future release.
		Use ``Matrix.from_coo`` instead. Will be removed in version 2023.9.0 or later

		Parameters
		----------
		rows : list or np.ndarray
		Row indices.
		columns : list or np.ndarray
		Column indices.
		values : list or np.ndarray or scalar
		List of values. If a scalar is provided, all values will be set to this single value.
		dtype :
		Data type of the Matrix. If not provided, the values will be inspected
		to choose an appropriate dtype.
		nrows : int, optional
		Number of rows in the Matrix. If not provided, ``nrows`` is computed
		from the maximum row index found in ``rows``.
		ncols : int, optional
		Number of columns in the Matrix. If not provided, ``ncols`` is computed
		from the maximum column index found in ``columns``.
		dup_op : :class:`~graphblas.core.operator.BinaryOp`, optional
		Function used to combine values if duplicate indices are found.
		Leaving ``dup_op=None`` will raise an error if duplicates are found.
		name : str, optional
		Name to give the Matrix.

		Returns
		-------
		Matrix
		"""
		warnings.warn(
		"`Matrix.from_values(...)` is deprecated; please use `Matrix.from_coo(...)` instead.",
		DeprecationWarning,
		stacklevel=2,
		)
		return cls.from_coo(
		rows, columns, values, dtype, nrows=nrows, ncols=ncols, dup_op=dup_op, name=name
		)

		@classmethod
		def from_coo(
		cls,
Expand DownExpand Up		@@ -3751,7 +3659,6 @@ def dup(self, dtype=None, , clear=False, mask=None, name=None, *opts):
		to_dense = wrapdoc(Matrix.to_dense)(property(automethods.to_dense))
		to_dicts = wrapdoc(Matrix.to_dicts)(property(automethods.to_dicts))
		to_edgelist = wrapdoc(Matrix.to_edgelist)(property(automethods.to_edgelist))
		to_values = wrapdoc(Matrix.to_values)(property(automethods.to_values))
		wait = wrapdoc(Matrix.wait)(property(automethods.wait))
		# These raise exceptions
		__array__ = Matrix.__array__
Expand DownExpand Up		@@ -3852,7 +3759,6 @@ def dup(self, dtype=None, , clear=False, mask=None, name=None, *opts):
		to_dense = wrapdoc(Matrix.to_dense)(property(automethods.to_dense))
		to_dicts = wrapdoc(Matrix.to_dicts)(property(automethods.to_dicts))
		to_edgelist = wrapdoc(Matrix.to_edgelist)(property(automethods.to_edgelist))
		to_values = wrapdoc(Matrix.to_values)(property(automethods.to_values))
		wait = wrapdoc(Matrix.wait)(property(automethods.wait))
		# These raise exceptions
		__array__ = Matrix.__array__
Expand DownExpand Up		@@ -3927,13 +3833,6 @@ def to_coo(self, dtype=None, *, rows=True, columns=True, values=True, sort=True)
		)
		return cols, rows, vals

		@wrapdoc(Matrix.to_values)
		def to_values(self, dtype=None, *, rows=True, columns=True, values=True, sort=True):
		rows, cols, vals = self._matrix.to_values(
		dtype, rows=rows, columns=columns, values=values, sort=sort
		)
		return cols, rows, vals

		@wrapdoc(Matrix.diag)
		def diag(self, k=0, dtype=None, , name=None, *opts):
		return self._matrix.diag(-k, dtype, name=name, **opts)
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2952,7 +2952,6 @@ def test_expr_is_like_matrix(A):
		"from_dicts",
		"from_edgelist",
		"from_scalar",
		"from_values",
		"resize",
		"setdiag",
		"update",
Expand DownExpand Up		@@ -3018,7 +3017,6 @@ def test_index_expr_is_like_matrix(A):
		"from_dicts",
		"from_edgelist",
		"from_scalar",
		"from_values",
		"resize",
		"setdiag",
		}
Expand DownExpand Up		@@ -3557,15 +3555,6 @@ def compare(A, expected, isequal=True, **kwargs):
		A.ss.compactify("bad_how")


		def test_deprecated(A):
		with pytest.warns(DeprecationWarning):
		A.to_values()
		with pytest.warns(DeprecationWarning):
		A.T.to_values()
		with pytest.warns(DeprecationWarning):
		A.from_values([1], [2], [3])


		def test_ndim(A):
		assert A.ndim == 2
		assert A.ewise_mult(A).ndim == 2
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -1675,7 +1675,6 @@ def test_expr_is_like_vector(v):
		"from_dict",
		"from_pairs",
		"from_scalar",
		"from_values",
		"resize",
		"update",
		}
Expand DownExpand Up		@@ -1725,7 +1724,6 @@ def test_index_expr_is_like_vector(v):
		"from_dict",
		"from_pairs",
		"from_scalar",
		"from_values",
		"resize",
		}
		ignore = {"__sizeof__", "_ewise_add", "_ewise_mult", "_ewise_union", "_inner", "_vxm"}
Expand DownExpand Up		@@ -2012,13 +2010,6 @@ def test_ss_split(v):
		assert x2.name == "split_1"


		def test_deprecated(v):
		with pytest.warns(DeprecationWarning):
		v.to_values()
		with pytest.warns(DeprecationWarning):
		Vector.from_values([1], [2])


		def test_ndim(A, v):
		assert v.ndim == 1
		assert v.ewise_mult(v).ndim == 1
Expand Down