Repository: python-graphblas/python-graphblas

Issue:
Add pad/shift/translate
There is already `.resize`, which can add or trim an object on the right or the bottom.  It might be handy to have a more general operation that can both resize objects and shift values (or only shift).

For example, such a function could be used to translate a small local chunk in `dask-grblas` to its proper place in the global matrix.  This might be handy to implement https://github.com/metagraph-dev/dask-grblas/issues/19.

We should also consider how this this would look as a C API and Python API.  Some questions:
- Should this mutate an existing object like `.resize` or create a new object?
- What should the Python API be?
  - Should we try to mimic `numpy.pad`?
    - `pad(2)` to add a length 2 border around everything
    - `pad((1, 2))` to pad with width 1 on the left and width 2 on the right
    - Should we allow negative such as `pad(-2)` to undo `pad(2)` or `pad((1, -1))` to shift everything to the right by 1 but keep the shape the same?
- Possible names: `pad`, `shift`, `translate`, etc.
- If we "shift" all values by N, is there any value in having different boundary conditions such as drop or wraparound?

I think there are several possible variations here, and I don't yet have a strong opinion.  So, let's discuss!

This function may come in handy:
https://github.com/numpy/numpy/blob/a8f9711493adee93fa3d61e7ef1bee11d7055a85/numpy/lib/arraypad.py#L454

Also, thanks to @ParticularMiner for first asking about this in a side channel.

Discussion:

Patch:
diff --git a/graphblas/__init__.py b/graphblas/__init__.py
index 6b39bfe..34a64dd 100644
--- a/graphblas/__init__.py
+++ b/graphblas/__init__.py
@@ -186,7 +186,7 @@ class _GraphblasModuleFinder(_MetaPathFinder):
         if fullname in _NEEDS_OPERATOR and "operator" not in globals():
             _load("operator")
             name = fullname[7:]  # Trim "graphblas."
-            if name in globals():
+            if name in globals():  # pragma: no cover
                 # Make sure we execute the module only once
                 module = globals()[name]
                 spec = module.__spec__
@@ -195,14 +195,14 @@ class _GraphblasModuleFinder(_MetaPathFinder):
 
 
 class _SkipLoad(_Loader):
-    def __init__(self, module, orig_loader):
+    def __init__(self, module, orig_loader):  # pragma: no cover
         self.module = module
         self.orig_loader = orig_loader
 
-    def create_module(self, spec):
+    def create_module(self, spec):  # pragma: no cover
         return self.module
 
-    def exec_module(self, module):
+    def exec_module(self, module):  # pragma: no cover
         # Don't execute the module, but restore the original loader
         module.__spec__.loader = self.orig_loader
 
diff --git a/graphblas/_automethods.py b/graphblas/_automethods.py
index fc3c95c..793ae24 100644
--- a/graphblas/_automethods.py
+++ b/graphblas/_automethods.py
@@ -230,6 +230,10 @@ def reduce_scalar(self):
     return self._get_value("reduce_scalar")
 
 
+def reposition(self):
+    return self._get_value("reposition")
+
+
 def select(self):
     return self._get_value("select")
 
@@ -353,6 +357,7 @@ if __name__ == "__main__":
         "ewise_add",
         "ewise_mult",
         "ewise_union",
+        "reposition",
         "select",
         "ss",
         "to_values",
diff --git a/graphblas/base.py b/graphblas/base.py
index 37df02a..359dbf7 100644
--- a/graphblas/base.py
+++ b/graphblas/base.py
@@ -423,10 +423,14 @@ class BaseType:
 
         if input_mask is not None:
             raise TypeError("`input_mask` argument may only be used for extract")
-        if expr.op is not None and expr.op.opclass == "Aggregator":
+        elif expr.op is not None and expr.op.opclass == "Aggregator":
             updater = self(mask=mask, accum=accum, replace=replace)
             expr.op._new(updater, expr)
             return
+        elif expr.cfunc_name is None:  # Custom recipe
+            updater = self(mask=mask, accum=accum, replace=replace)
+            expr.args[-2](updater, *expr.args[-1])
+            return
 
         # Normalize mask and separate out complement and structural flags
         if mask is None:
@@ -540,9 +544,9 @@ class BaseExpression:
         self.bt = bt
         self.op = op
         if expr_repr is None:
-            if len(args) == 1:
+            if len(args) == 1 or cfunc_name is None and len(args) == 3:
                 expr_repr = "{0.name}.{method_name}({op})"
-            elif len(args) == 2:
+            elif len(args) == 2 or cfunc_name is None and len(args) == 4:
                 expr_repr = "{0.name}.{method_name}({1.name}, op={op})"
             else:  # pragma: no cover
                 raise ValueError(f"No default expr_repr for len(args) == {len(args)}")
@@ -573,6 +577,8 @@ class BaseExpression:
         if self.op is not None and self.op.opclass == "Aggregator":
             updater = output(mask=mask)
             self.op._new(updater, self)
+        elif self.cfunc_name is None:  # Custom recipe
+            self.args[-2](output(mask=mask), *self.args[-1])
         elif mask is None:
             output.update(self)
         else:
@@ -583,13 +589,15 @@ class BaseExpression:
     dup = new
 
     def _format_expr(self):
-        return self.expr_repr.format(*self.args, method_name=self.method_name, op=self.op)
+        args = self.args[:-2] if self.cfunc_name is None else self.args
+        return self.expr_repr.format(*args, method_name=self.method_name, op=self.op)
 
     def _format_expr_html(self):
         expr_repr = self.expr_repr.replace(".name", "._name_html").replace(
             "._expr_name", "._expr_name_html"
         )
-        return expr_repr.format(*self.args, method_name=self.method_name, op=self.op)
+        args = self.args[:-2] if self.cfunc_name is None else self.args
+        return expr_repr.format(*args, method_name=self.method_name, op=self.op)
 
     _expect_type = _expect_type
     _expect_op = _expect_op
diff --git a/graphblas/formatting.py b/graphblas/formatting.py
index be1cb6c..2979b0d 100644
--- a/graphblas/formatting.py
+++ b/graphblas/formatting.py
@@ -109,7 +109,7 @@ def _update_matrix_dataframe(df, matrix, rows, row_offset, columns, column_offse
         if type(matrix) is TransposedMatrix:
             parent = matrix._matrix
             submatrix = Matrix(parent.dtype, parent._nrows, parent._ncols, name="")
-            if parent._nvals > 0:
+            if parent._nvals > 0:  # pragma: no branch
                 # Get val to support iso-valued matrices
                 val = parent.reduce_scalar(monoid.any, allow_empty=False).new(name="")
                 submatrix(parent.S)[columns, rows] = val
@@ -257,7 +257,7 @@ def _get_matrix_dataframe(matrix, max_rows, min_rows, max_columns, *, mask=None)
         if min(nonzero._nvals, num_rows) > 2 * df.count().sum():
             rows, cols, vals = nonzero.ss.head(num_rows, sort=True)
             if mask.complement:
-                if not vals.flags.writeable:
+                if not vals.flags.writeable:  # pragma: no branch
                     vals = vals.copy()
                 vals[:] = 0
             df = pd.DataFrame({"row": rows, "col": cols, "val": vals})
@@ -307,7 +307,7 @@ def _get_vector_dataframe(vector, max_rows, min_rows, max_columns, *, mask=None)
         if min(nonzero._nvals, num_rows) > 2 * df.count().sum():
             indices, vals = nonzero.ss.head(num_rows, sort=True)
             if mask.complement:
-                if not vals.flags.writeable:
+                if not vals.flags.writeable:  # pragma: no branch
                     vals = vals.copy()
                 vals[:] = 0
             df = pd.DataFrame({"index": indices, "val": vals})
diff --git a/graphblas/infix.py b/graphblas/infix.py
index 06ae6c1..1c1003a 100644
--- a/graphblas/infix.py
+++ b/graphblas/infix.py
@@ -89,6 +89,7 @@ class VectorInfixExpr(InfixExprBase):
     nvals = wrapdoc(Vector.nvals)(property(_automethods.nvals))
     outer = wrapdoc(Vector.outer)(property(_automethods.outer))
     reduce = wrapdoc(Vector.reduce)(property(_automethods.reduce))
+    reposition = wrapdoc(Vector.reposition)(property(_automethods.reposition))
     select = wrapdoc(Vector.select)(property(_automethods.select))
     ss = wrapdoc(Vector.ss)(property(_automethods.ss))
     to_pygraphblas = wrapdoc(Vector.to_pygraphblas)(property(_automethods.to_pygraphblas))
@@ -206,6 +207,7 @@ class MatrixInfixExpr(InfixExprBase):
     reduce_columnwise = wrapdoc(Matrix.reduce_columnwise)(property(_automethods.reduce_columnwise))
     reduce_rowwise = wrapdoc(Matrix.reduce_rowwise)(property(_automethods.reduce_rowwise))
     reduce_scalar = wrapdoc(Matrix.reduce_scalar)(property(_automethods.reduce_scalar))
+    reposition = wrapdoc(Matrix.reposition)(property(_automethods.reposition))
     select = wrapdoc(Matrix.select)(property(_automethods.select))
     ss = wrapdoc(Matrix.ss)(property(_automethods.ss))
     to_pygraphblas = wrapdoc(Matrix.to_pygraphblas)(property(_automethods.to_pygraphblas))
diff --git a/graphblas/matrix.py b/graphblas/matrix.py
index d24ae62..922e4ad 100644
--- a/graphblas/matrix.py
+++ b/graphblas/matrix.py
@@ -26,6 +26,29 @@ from .vector import Vector, VectorExpression
 ffi_new = ffi.new
 
 
+# Custom recipes
+def _m_add_v(updater, left, right, op):
+    full = Vector(right.dtype, left._nrows, name="v_full")
+    full[:] = 0
+    temp = full.outer(right, binary.second).new(name="M_temp", mask=updater.kwargs.get("mask"))
+    updater << left.ewise_add(temp, op, require_monoid=False)
+
+
+def _m_mult_v(updater, left, right, op):
+    updater << left.mxm(right.diag(name="M_temp"), get_semiring(monoid.any, op))
+
+
+def _m_union_v(updater, left, right, left_default, right_default, op):
+    full = Vector(right.dtype, left._nrows, name="v_full")
+    full[:] = 0
+    temp = full.outer(right, binary.second).new(name="M_temp", mask=updater.kwargs.get("mask"))
+    updater << left.ewise_union(temp, op, left_default=left_default, right_default=right_default)
+
+
+def _reposition(updater, indices, chunk):
+    updater[indices] = chunk
+
+
 class Matrix(BaseType):
     """
     GraphBLAS Sparse Matrix
@@ -477,17 +500,20 @@ class Matrix(BaseType):
             self._expect_op(op, ("BinaryOp", "Monoid"), within=method_name, argname="op")
         if other.ndim == 1:
             # Broadcast rowwise from the right
-            # Can we do `C(M.S) << plus(A | v)` -> `C(M.S) << plus(any_second(M @ v.diag()) | A)`?
             if self._ncols != other._size:
-                # Check this before we compute a possibly large matrix below
                 raise DimensionMismatch(
                     "Dimensions not compatible for broadcasting Vector from the right "
                     f"to rows of Matrix in {method_name}.  Matrix.ncols (={self._ncols}) "
                     f"must equal Vector.size (={other._size})."
                 )
-            full = Vector(other.dtype, self._nrows, name="v_full")
-            full[:] = 0
-            other = full.outer(other, binary.second).new(name="M_temp")
+            return MatrixExpression(
+                method_name,
+                None,
+                [self, other, _m_add_v, (self, other, op)],  # [*expr_args, func, args]
+                nrows=self._nrows,
+                ncols=self._ncols,
+                op=op,
+            )
         expr = MatrixExpression(
             method_name,
             f"GrB_Matrix_eWiseAdd_{op.opclass}",
@@ -515,7 +541,21 @@ class Matrix(BaseType):
         # Per the spec, op may be a semiring, but this is weird, so don't.
         self._expect_op(op, ("BinaryOp", "Monoid"), within=method_name, argname="op")
         if other.ndim == 1:
-            return self.mxm(other.diag(name="M_temp"), get_semiring(monoid.any, op))
+            # Broadcast rowwise from the right
+            if self._ncols != other._size:
+                raise DimensionMismatch(
+                    "Dimensions not compatible for broadcasting Vector from the right "
+                    f"to rows of Matrix in {method_name}.  Matrix.ncols (={self._ncols}) "
+                    f"must equal Vector.size (={other._size})."
+                )
+            return MatrixExpression(
+                method_name,
+                None,
+                [self, other, _m_mult_v, (self, other, op)],  # [*expr_args, func, args]
+                nrows=self._nrows,
+                ncols=self._ncols,
+                op=op,
+            )
         expr = MatrixExpression(
             method_name,
             f"GrB_Matrix_eWiseMult_{op.opclass}",
@@ -584,19 +624,24 @@ class Matrix(BaseType):
         self._expect_op(op, ("BinaryOp", "Monoid"), within=method_name, argname="op")
         if op.opclass == "Monoid":
             op = op.binaryop
+        expr_repr = "{0.name}.{method_name}({2.name}, {op}, {1._expr_name}, {3._expr_name})"
         if other.ndim == 1:
             # Broadcast rowwise from the right
-            # Can we do `C(M.S) << plus(A | v)` -> `C(M.S) << plus(any_second(M @ v.diag()) | A)`?
             if self._ncols != other._size:
-                # Check this before we compute a possibly large matrix below
                 raise DimensionMismatch(
                     "Dimensions not compatible for broadcasting Vector from the right "
                     f"to rows of Matrix in {method_name}.  Matrix.ncols (={self._ncols}) "
                     f"must equal Vector.size (={other._size})."
                 )
-            full = Vector(other.dtype, self._nrows, name="v_full")
-            full[:] = 0
-            other = full.outer(other, binary.second).new(name="M_temp")
+            return MatrixExpression(
+                method_name,
+                None,
+                [self, left, other, right, _m_union_v, (self, other, left, right, op)],
+                expr_repr=expr_repr,
+                nrows=self._nrows,
+                ncols=self._ncols,
+                op=op,
+            )
         expr = MatrixExpression(
             method_name,
             "GxB_Matrix_eWiseUnion",
@@ -604,7 +649,7 @@ class Matrix(BaseType):
             op=op,
             at=self._is_transposed,
             bt=other._is_transposed,
-            expr_repr="{0.name}.{method_name}({2.name}, {op}, {1._expr_name}, {3._expr_name})",
+            expr_repr=expr_repr,
         )
         if self.shape != other.shape:
             expr.new(name="")  # incompatible shape; raise now
@@ -917,6 +962,67 @@ class Matrix(BaseType):
             is_cscalar=not allow_empty,
         )
 
+    # Unofficial methods
+    def reposition(self, row_offset, column_offset, *, nrows=None, ncols=None):
+        """Reposition values by adding `row_offset` and `column_offset` to the indices.
+
+        Positive offset moves values to the right (or down), negative to the left (or up).
+        Values repositioned outside of the new Matrix are dropped (i.e., they don't wrap around).
+
+        This is not a standard GraphBLAS method.  This is implemented with an extract and assign.
+
+        Parameters
+        ----------
+        row_offset : int
+        column_offset : int
+        nrows : int, optional
+            The nrows of the new Matrix.  If not specified, same nrows as input Matrix.
+        ncols : int, optional
+            The ncols of the new Matrix.  If not specified, same ncols as input Matrix.
+
+        """
+        if nrows is None:
+            nrows = self._nrows
+        else:
+            nrows = int(nrows)
+        if ncols is None:
+            ncols = self._ncols
+        else:
+            ncols = int(ncols)
+        row_offset = int(row_offset)
+        if row_offset < 0:
+            row_start = -row_offset
+            row_stop = row_start + nrows
+        else:
+            row_start = 0
+            row_stop = max(0, nrows - row_offset)
+        col_offset = int(column_offset)
+        if col_offset < 0:
+            col_start = -col_offset
+            col_stop = col_start + ncols
+        else:
+            col_start = 0
+            col_stop = max(0, ncols - col_offset)
+        if self._is_transposed:
+            chunk = (
+                self._matrix[col_start:col_stop, row_start:row_stop].new(name="M_repositioning").T
+            )
+        else:
+            chunk = self[row_start:row_stop, col_start:col_stop].new(name="M_repositioning")
+        indices = (
+            slice(row_start + row_offset, row_start + row_offset + chunk._nrows),
+            slice(col_start + col_offset, col_start + col_offset + chunk._ncols),
+        )
+        return MatrixExpression(
+            "reposition",
+            None,
+            [self, _reposition, (indices, chunk)],  # [*expr_args, func, args]
+            expr_repr="{2.name}.reposition(%d, %d)" % (row_offset, column_offset),
+            nrows=nrows,
+            ncols=ncols,
+            dtype=self.dtype,
+        )
+
     ##################################
     # Extract and Assign index methods
     ##################################
@@ -1474,6 +1580,7 @@ class MatrixExpression(BaseExpression):
     reduce_columnwise = wrapdoc(Matrix.reduce_columnwise)(property(_automethods.reduce_columnwise))
     reduce_rowwise = wrapdoc(Matrix.reduce_rowwise)(property(_automethods.reduce_rowwise))
     reduce_scalar = wrapdoc(Matrix.reduce_scalar)(property(_automethods.reduce_scalar))
+    reposition = wrapdoc(Matrix.reposition)(property(_automethods.reposition))
     select = wrapdoc(Matrix.select)(property(_automethods.select))
     ss = wrapdoc(Matrix.ss)(property(_automethods.ss))
     to_pygraphblas = wrapdoc(Matrix.to_pygraphblas)(property(_automethods.to_pygraphblas))
@@ -1553,6 +1660,7 @@ class MatrixIndexExpr(AmbiguousAssignOrExtract):
     reduce_columnwise = wrapdoc(Matrix.reduce_columnwise)(property(_automethods.reduce_columnwise))
     reduce_rowwise = wrapdoc(Matrix.reduce_rowwise)(property(_automethods.reduce_rowwise))
     reduce_scalar = wrapdoc(Matrix.reduce_scalar)(property(_automethods.reduce_scalar))
+    reposition = wrapdoc(Matrix.reposition)(property(_automethods.reposition))
     select = wrapdoc(Matrix.select)(property(_automethods.select))
     ss = wrapdoc(Matrix.ss)(property(_automethods.ss))
     to_pygraphblas = wrapdoc(Matrix.to_pygraphblas)(property(_automethods.to_pygraphblas))
@@ -1660,6 +1768,7 @@ class TransposedMatrix:
     reduce_rowwise = Matrix.reduce_rowwise
     reduce_columnwise = Matrix.reduce_columnwise
     reduce_scalar = Matrix.reduce_scalar
+    reposition = Matrix.reposition
 
     # Operator sugar
     __or__ = Matrix.__or__
diff --git a/graphblas/vector.py b/graphblas/vector.py
index 7ecbe92..eb91abd 100644
--- a/graphblas/vector.py
+++ b/graphblas/vector.py
@@ -25,6 +25,29 @@ from .utils import (
 ffi_new = ffi.new
 
 
+# Custom recipes
+def _v_add_m(updater, left, right, op):
+    full = Vector(left.dtype, right._ncols, name="v_full")
+    full[:] = 0
+    temp = left.outer(full, binary.first).new(name="M_temp", mask=updater.kwargs.get("mask"))
+    updater << temp.ewise_add(right, op, require_monoid=False)
+
+
+def _v_mult_m(updater, left, right, op):
+    updater << left.diag(name="M_temp").mxm(right, get_semiring(monoid.any, op))
+
+
+def _v_union_m(updater, left, right, left_default, right_default, op):
+    full = Vector(left.dtype, right._ncols, name="v_full")
+    full[:] = 0
+    temp = left.outer(full, binary.first).new(name="M_temp", mask=updater.kwargs.get("mask"))
+    updater << temp.ewise_union(right, op, left_default=left_default, right_default=right_default)
+
+
+def _reposition(updater, indices, chunk):
+    updater[indices] = chunk
+
+
 class Vector(BaseType):
     """
     GraphBLAS Sparse Vector
@@ -422,7 +445,7 @@ class Vector(BaseType):
         performing any operation. In the case of `gt`, the non-empty value is cast to a boolean.
         For these reasons, users are required to be explicit when choosing this surprising behavior.
         """
-        from .matrix import Matrix, TransposedMatrix
+        from .matrix import Matrix, MatrixExpression, TransposedMatrix
 
         method_name = "ewise_add"
         other = self._expect_type(
@@ -443,7 +466,6 @@ class Vector(BaseType):
             self._expect_op(op, ("BinaryOp", "Monoid"), within=method_name, argname="op")
         if other.ndim == 2:
             # Broadcast columnwise from the left
-            # Can we do `C(M.S) << plus(v | A)` -> `C(M.S) << plus(any_first(v.diag() @ M) | A)`?
             if other._nrows != self._size:
                 # Check this before we compute a possibly large matrix below
                 raise DimensionMismatch(
@@ -451,10 +473,14 @@ class Vector(BaseType):
                     f"to columns of Matrix in {method_name}.  Matrix.nrows (={other._nrows}) "
                     f"must equal Vector.size (={self._size})."
                 )
-            full = Vector(self.dtype, other._ncols, name="v_full")
-            full[:] = 0
-            temp = self.outer(full, binary.first).new(name="M_temp")
-            return temp.ewise_add(other, op, require_monoid=False)
+            return MatrixExpression(
+                method_name,
+                None,
+                [self, other, _v_add_m, (self, other, op)],
+                nrows=other._nrows,
+                ncols=other._ncols,
+                op=op,
+            )
         expr = VectorExpression(
             method_name,
             f"GrB_Vector_eWiseAdd_{op.opclass}",
@@ -472,7 +498,7 @@ class Vector(BaseType):
         Result will contain the intersection of indices from both Vectors
         Default op is binary.times
         """
-        from .matrix import Matrix, TransposedMatrix
+        from .matrix import Matrix, MatrixExpression, TransposedMatrix
 
         method_name = "ewise_mult"
         other = self._expect_type(
@@ -482,7 +508,21 @@ class Vector(BaseType):
         # Per the spec, op may be a semiring, but this is weird, so don't.
         self._expect_op(op, ("BinaryOp", "Monoid"), within=method_name, argname="op")
         if other.ndim == 2:
-            return self.diag(name="M_temp").mxm(other, get_semiring(monoid.any, op))
+            # Broadcast columnwise from the left
+            if other._nrows != self._size:
+                raise DimensionMismatch(
+                    "Dimensions not compatible for broadcasting Vector from the left "
+                    f"to columns of Matrix in {method_name}.  Matrix.nrows (={other._nrows}) "
+                    f"must equal Vector.size (={self._size})."
+                )
+            return MatrixExpression(
+                method_name,
+                None,
+                [self, other, _v_mult_m, (self, other, op)],
+                nrows=other._nrows,
+                ncols=other._ncols,
+                op=op,
+            )
         expr = VectorExpression(
             method_name,
             f"GrB_Vector_eWiseMult_{op.opclass}",
@@ -506,7 +546,7 @@ class Vector(BaseType):
         ``op`` should be a BinaryOp or Monoid.
         """
         # SS, SuiteSparse-specific: eWiseUnion
-        from .matrix import Matrix, TransposedMatrix
+        from .matrix import Matrix, MatrixExpression, TransposedMatrix
 
         method_name = "ewise_union"
         other = self._expect_type(
@@ -551,26 +591,30 @@ class Vector(BaseType):
         self._expect_op(op, ("BinaryOp", "Monoid"), within=method_name, argname="op")
         if op.opclass == "Monoid":
             op = op.binaryop
+        expr_repr = "{0.name}.{method_name}({2.name}, {op}, {1._expr_name}, {3._expr_name})"
         if other.ndim == 2:
             # Broadcast columnwise from the left
-            # Can we do `C(M.S) << plus(v | A)` -> `C(M.S) << plus(any_first(v.diag() @ M) | A)`?
             if other._nrows != self._size:
-                # Check this before we compute a possibly large matrix below
                 raise DimensionMismatch(
                     "Dimensions not compatible for broadcasting Vector from the left "
                     f"to columns of Matrix in {method_name}.  Matrix.nrows (={other._nrows}) "
                     f"must equal Vector.size (={self._size})."
                 )
-            full = Vector(self.dtype, other._ncols, name="v_full")
-            full[:] = 0
-            temp = self.outer(full, binary.first).new(name="M_temp")
-            return temp.ewise_union(other, op, left_default, right_default)
+            return MatrixExpression(
+                method_name,
+                None,
+                [self, left, other, right, _v_union_m, (self, other, left, right, op)],
+                expr_repr=expr_repr,
+                nrows=other._nrows,
+                ncols=other._ncols,
+                op=op,
+            )
         expr = VectorExpression(
             method_name,
             "GxB_Vector_eWiseUnion",
             [self, left, other, right],
             op=op,
-            expr_repr="{0.name}.{method_name}({2.name}, {op}, {1._expr_name}, {3._expr_name})",
+            expr_repr=expr_repr,
         )
         if self._size != other._size:
             expr.new(name="")  # incompatible shape; raise now
@@ -837,6 +881,43 @@ class Vector(BaseType):
         )
         return expr
 
+    def reposition(self, offset, *, size=None):
+        """Reposition the values by adding `offset` to the indices.
+
+        Positive offset moves values to the right, negative to the left.
+        Values repositioned outside of the new Vector are dropped (i.e., they don't wrap around).
+
+        This is not a standard GraphBLAS method.  This is implemented with an extract and assign.
+
+        Parameters
+        ----------
+        offset : int
+        size : int, optional
+            The size of the new Vector.  If not specified, same size as input Vector.
+
+        """
+        if size is None:
+            size = self._size
+        else:
+            size = int(size)
+        offset = int(offset)
+        if offset < 0:
+            start = -offset
+            stop = start + size
+        else:
+            start = 0
+            stop = max(0, size - offset)
+        chunk = self[start:stop].new(name="v_repositioning")
+        indices = slice(start + offset, start + offset + chunk._size)
+        return VectorExpression(
+            "reposition",
+            None,
+            [self, _reposition, (indices, chunk)],  # [*expr_args, func, args]
+            expr_repr="{0.name}.reposition(%d)" % offset,
+            size=size,
+            dtype=self.dtype,
+        )
+
     ##################################
     # Extract and Assign index methods
     ##################################
@@ -1112,6 +1193,7 @@ class VectorExpression(BaseExpression):
     nvals = wrapdoc(Vector.nvals)(property(_automethods.nvals))
     outer = wrapdoc(Vector.outer)(property(_automethods.outer))
     reduce = wrapdoc(Vector.reduce)(property(_automethods.reduce))
+    reposition = wrapdoc(Vector.reposition)(property(_automethods.reposition))
     select = wrapdoc(Vector.select)(property(_automethods.select))
     ss = wrapdoc(Vector.ss)(property(_automethods.ss))
     to_pygraphblas = wrapdoc(Vector.to_pygraphblas)(property(_automethods.to_pygraphblas))
@@ -1183,6 +1265,7 @@ class VectorIndexExpr(AmbiguousAssignOrExtract):
     nvals = wrapdoc(Vector.nvals)(property(_automethods.nvals))
     outer = wrapdoc(Vector.outer)(property(_automethods.outer))
     reduce = wrapdoc(Vector.reduce)(property(_automethods.reduce))
+    reposition = wrapdoc(Vector.reposition)(property(_automethods.reposition))
     select = wrapdoc(Vector.select)(property(_automethods.select))
     ss = wrapdoc(Vector.ss)(property(_automethods.ss))
     to_pygraphblas = wrapdoc(Vector.to_pygraphblas)(property(_automethods.to_pygraphblas))
diff --git a/notebooks/Connected Components -- FastSV.ipynb b/notebooks/Connected Components -- FastSV.ipynb
index 2e4abe2..dcb0c8b 100644
--- a/notebooks/Connected Components -- FastSV.ipynb
+++ b/notebooks/Connected Components -- FastSV.ipynb
@@ -103,9 +103,20 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_20958/1209887720.py:2: DeprecationWarning: \n",
+      "\n",
+      "The scipy.sparse array containers will be used instead of matrices\n",
+      "in Networkx 3.0. Use `from_scipy_sparse_array` instead.\n",
+      "  G = nx.convert_matrix.from_scipy_sparse_matrix(A)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -234,7 +245,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>A</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Matrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
@@ -245,7 +256,7 @@
        "    <td>22</td>\n",
        "    <td>12</td>\n",
        "    <td>12</td>\n",
-       "    <td>INT32</td>\n",
+       "    <td>INT64</td>\n",
        "    <td>bitmapr (iso)</td>\n",
        "  </tr>\n",
        "</table>\n",
@@ -468,9 +479,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"A\"            nvals  nrows  ncols  dtype         format\n",
-       "graphblas.Matrix     22     12     12  INT32  bitmapr (iso)\n",
-       "--------------------------------------------------------\n",
+       "\"A\"        nvals  nrows  ncols  dtype         format\n",
+       "gb.Matrix     22     12     12  INT64  bitmapr (iso)\n",
+       "----------------------------------------------------\n",
        "   0  1  2  3  4  5  6  7  8  9  10 11\n",
        "0      1  1  1                        \n",
        "1   1     1                           \n",
@@ -515,7 +526,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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8zPySJTzxxBOBhQxXAc8FPo9tEzsL/BvwoqC/x9yclSNXkUJXREpTqKT39tsDf0DryD5/ELgWC8kthJiJRqM8PTkJa9YEXt43ge8CV2Oz6Rjwr0En9fZWvf2jGt6ISHFSKdsOtmeP/ViWW7stomPXCmxJwf+ntstF5ucZ+clPuO3lL+dPfv1rlvisv94MPFzM4B0d1vaxyjTTFZHwii3p9bEe+BxwBvuRaxh4Y9BJ6TT09HCwvR08r+TPzsvzYMOGyo6Zh2a6IhJOrqS3nAqzBT6G7Sq4AStguBPYFnBO/AUv4LsjI/aHZz/bwr/Y8uJ8EgnYvNlJY3PNdEUkWCVKehdpw8pyzwK/Be7GwregxV//c93IAraPBYpGbZzBwfLGCUmhKyLBKlXSW47FX//jcWtA3tVVevBGo3b+yEiogotKUOiKiL8QJb1XLHpEsZ0JFZNIwNatl3/97+6G0VFYvdreU+yYq1fb+bpzhIjUjRAlvecWPH6HbQl7W6U+P+jrf3e37Q/etMkKGzo68r8vJ5Gw923eDMePOw1cUOiKSJAQJb0LfR1rqfjKSnx22K//8Tjs3HmxGXlfnx3r7ITly+05HrfjuaWSHTucLSksFPF8tl309/d7o6OjDi9HROpKOm2BVcQe3FuBPwe2l/vZiYTNcEu9cWSlbnxZgkgkctjzvP58r2nLmIgUNj5us8GQoZvEmoWX1Qo8kYD5efv6PzhY+mw0FrMKszqj0BWRwmZni9oZ8GXgFVjfg1A6OixUMxnre9Dba9vCNmxwsme2FhS6IlJYe3tR28S+DPxz2Dd3dsK3v21FDo6//tdS8/8NRaR0PT2hb03+U+wODaF3LczNwate1RJBu5B2L4hIYbFYqI5eYK0T7wCWhR3bQUeveqTQFRF/69cH730Fvgh8JeyYjjp61SOFroj427ixYTt61SOFroj46+qCLVuKL7MtpFBJb4tQ6IpIsAbt6FWPFLoiEqxBO3rVI4WuiITTgB296pFCV0TCa7COXvVIoSsixWmgjl71SF3GRKR8NezoVY/UZUxEqqtOO3rVIy0viIg4pNAVEXFIoSsi4pDWdCtNPyiIiA+lQSVMTNgdU/fvh1OnbGtMNGrNn1Opi93wN25s2XpzETFaXihHKgXbtsGqVVZLfuKEHTt/Hqam7Hluzo7nate3bSvqJn8i0lwUuqVKJmHtWhgetqWEoFtUz8zY+4aH7bxk0sVVikidUeiWIpmE/n4YG4Pp6eLOnZ628/r7FbwiLUihW6xUCgYGYHKyqBv2XSKTsfMHBkLff0pEmoNCt1i5WvJSAzcnk7lYuy4iLUOhW4yJCdiz549LCvcA/cBS4N2L3voD4PlAAvhL4PF8401Pw+7dNusVkZag0C3Gvn0Qifzxj88BPgosvtPTU9hdUT8BTGLB/PZCY0YisHdvxS9VROqTQrcY+/dfskvhDmAd8MxFb/smcBPwNqAd2A4cBX6Zb8yZGThwoOKXKiL1SaEbVjpthQ8hHAdetODPncCa7PG8Tp608UWk6Sl0wxofD92E+Rxw5aJjVwJPFzqhrc3GF5Gmp9ANa3Y29A35rgCmFh2bApYVOiEatfFFpOkpdIOk0/b1P5kMvQRwE7aGm3MeOJU9nlcmY81xRKTpqeFNPvka2CxZcln1WTr7yGQfs9i/0DcD/wh8A3gD8C/AC7EtZHnNzVk3MhFpegrdhVIpK1bYs8e2cuV2KhRoULMDWFja8FVgENut8A3gA8A7gJcBh/w+t7dX7R9FWoT+S89JJq0s9/Tp0Our27OPfAYosEVssY4Oa/soIi1BoQsXG9iU00+hVJ4HGxaXV4hIs1LoVqKBTakSCdi8WY3NRVqIdi9UqoFNsaJRa2o+OOj2c0Wkplp7pptrYON6j2w0arPbkZHQBRci0hxae6a7qIFNPieAW7GKsl7gP8r9zEQCVq+G0VHo7i53NBFpMK0duosa2CyWBt4EvBHrFvYlbAvYr0r5rETCCiA2b4bjxxW4Ii2qdZcXQjSw+SXwv8BmIILNeG8BvoK1bfS1bBnMz1vhQ+5uwBs26EczkRbXGqGbTltDmdlZm2329FxsYONzZ16vwLFjQZ/X0QGHDsENN9hnqfBBRLKaNw3ylfJGo7ZLIZWC664L7KXwfOBZwC5stvtD4BHsThC+2tps+aC3twJ/ERFpJs0XumFLecfGAodqA74FfBD4DHYHiDux2/P4UgMbESmguUK3hFLeIC/EZrc5fwa8K+gkNbARkQKaJ3SrVMr738ANwDxwL/AbLr8J5WXUwEZECmiOLWNVLOX9CnAttrb7A+D7BCwvqIGNiPhojulYFUt5d2UfoamBjYj4aPyZbq6Ud0GD8XuwH72WculSQAp4K9CD7bt9uNLXkkjA1q3aiysiBTV+6OYp5X0O8FEg33zzFViz8WsqfR1qYCMiITT+8kKeUt47ss+jwJMLjseBTdl/DneLyZDUwEZEQmrsmW6IUt6qUwMbESlCY4durpS3FtTARkRK0NjLC7Oz9tW+GpYutTXaJ5+8tIRYDWxEpAyNHbrt7dW740MsBg89dLE5zsJmOSp8EJESNXZ69PTYzHORdPaRyT5msb9oDLjAxe5hqexrS7EtZJfIlfLGYmpcIyIV09hrurEYrFlz2eEdQAfwaWx7WEf2GMCN2T+fBl6b/efH842tUl4RqYLGDl2wtdWOjksObcdmswsf27Ovjed5rWfxmCrlFZEqafzQ3bjRSm8rSaW8IlIljR+6XV2wZYtt4aoElfKKSBU1fuiCld6uXFn+9jGV8opIlTVH6MbjVoLb1VV68KqUV0QcaI7QBasIGx21ktxilxpUyisijjRP6IIF5rFjsGmTFTIs2tVwGZXyiohjzRW6YEsNO3daU/OhIejrs2OdnbB8uT3H43Y81/x8xw4tKYiIExHPZ7tVf3+/Nzo66vByqiSdVimviDgTiUQOe57Xn++11kgelfKKSJ1ovuUFEZE6ptAVEXFIoSsi4pBCV0TEIYWuiIhDvlvGIpHI7ynQblZERAq63vO8q/O94Bu6IiJSWVpeEBFxSKErIuKQQldExCGFroiIQwpdERGH/h+bgKIr3K8eMQAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -686,7 +697,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>parents</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -753,9 +764,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"parents\"      nvals  size   dtype  format\n",
-       "graphblas.Vector      0    12  UINT32  sparse\n",
-       "------------------------------------------\n",
+       "\"parents\"  nvals  size   dtype  format\n",
+       "gb.Vector      0    12  UINT32  sparse\n",
+       "--------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9  10 11\n",
        "                                    "
       ]
@@ -870,7 +881,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>modified?</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -937,9 +948,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"modified?\"    nvals  size  dtype  format\n",
-       "graphblas.Vector      0    12   BOOL  sparse\n",
-       "-----------------------------------------\n",
+       "\"modified?\"  nvals  size  dtype  format\n",
+       "gb.Vector        0    12   BOOL  sparse\n",
+       "---------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9  10 11\n",
        "                                    "
       ]
@@ -1072,7 +1083,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>parents</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -1139,9 +1150,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"parents\"      nvals  size   dtype  format\n",
-       "graphblas.Vector     12    12  UINT32    full\n",
-       "------------------------------------------\n",
+       "\"parents\"  nvals  size   dtype  format\n",
+       "gb.Vector     12    12  UINT32    full\n",
+       "--------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9   10  11\n",
        "  0  1  2  3  4  5  6  7  8  9  10  11"
       ]
@@ -1276,7 +1287,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>Minimum grandparent</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -1344,7 +1355,7 @@
       ],
       "text/plain": [
        "\"Minimum grandparent\"  nvals  size   dtype  format\n",
-       "graphblas.Vector             12    12  UINT32    full\n",
+       "gb.Vector                 12    12  UINT32    full\n",
        "--------------------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9  10 11\n",
        "  0  0  0  0  2  2  6  6  6  9  9  9"
@@ -1620,7 +1631,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>modified?</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -1687,9 +1698,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"modified?\"    nvals  size  dtype  format\n",
-       "graphblas.Vector     12    12   BOOL    full\n",
-       "-----------------------------------------\n",
+       "\"modified?\"  nvals  size  dtype  format\n",
+       "gb.Vector       12    12   BOOL    full\n",
+       "---------------------------------------\n",
        "     0     1     2     3     4     5      6     7     8      9     10    11\n",
        "  False  True  True  True  True  True  False  True  True  False  True  True"
       ]
@@ -1724,7 +1735,7 @@
        "<div class=\"gb-scalar\"><tt>changed?</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Scalar</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Scalar</pre></td>\n",
        "    <td><pre>value</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
        "  </tr>\n",
@@ -1737,8 +1748,8 @@
        "</div>"
       ],
       "text/plain": [
-       "\"changed?\"     value  dtype\n",
-       "graphblas.Scalar   True   BOOL"
+       "\"changed?\"  value  dtype\n",
+       "gb.Scalar    True   BOOL"
       ]
      },
      "execution_count": 23,
@@ -1952,7 +1963,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>parents</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -1961,7 +1972,7 @@
        "  <tr>\n",
        "    <td>12</td>\n",
        "    <td>12</td>\n",
-       "    <td>INT32</td>\n",
+       "    <td>INT64</td>\n",
        "    <td>full</td>\n",
        "  </tr>\n",
        "</table>\n",
@@ -2019,9 +2030,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"parents\"      nvals  size  dtype  format\n",
-       "graphblas.Vector     12    12  INT32    full\n",
-       "-----------------------------------------\n",
+       "\"parents\"  nvals  size  dtype  format\n",
+       "gb.Vector     12    12  INT64    full\n",
+       "-------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9  10 11\n",
        "  0  0  0  0  0  0  6  6  6  9  9  9"
       ]
@@ -2063,7 +2074,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2145,7 +2156,7 @@
     {
      "data": {
       "text/plain": [
-       "array([10,  3,  2, 11,  4,  6,  7,  5,  8,  0,  1,  9])"
+       "array([ 6,  1,  7,  4,  0,  9, 10,  3,  8,  5,  2, 11])"
       ]
      },
      "execution_count": 30,
@@ -2323,7 +2334,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>P</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Matrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
@@ -2334,7 +2345,7 @@
        "    <td>12</td>\n",
        "    <td>12</td>\n",
        "    <td>12</td>\n",
-       "    <td>INT32</td>\n",
+       "    <td>INT64</td>\n",
        "    <td>csr (iso)</td>\n",
        "  </tr>\n",
        "</table>\n",
@@ -2380,19 +2391,19 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2406,12 +2417,12 @@
        "      <th>2</th>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2423,6 +2434,7 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2430,15 +2442,14 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2455,10 +2466,10 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "    </tr>\n",
@@ -2471,10 +2482,10 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2482,9 +2493,9 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2509,12 +2520,12 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2525,8 +2536,8 @@
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
@@ -2548,31 +2559,31 @@
        "      <td></td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>1</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"P\"            nvals  nrows  ncols  dtype     format\n",
-       "graphblas.Matrix     12     12     12  INT32  csr (iso)\n",
-       "----------------------------------------------------\n",
+       "\"P\"        nvals  nrows  ncols  dtype     format\n",
+       "gb.Matrix     12     12     12  INT64  csr (iso)\n",
+       "------------------------------------------------\n",
        "   0  1  2  3  4  5  6  7  8  9  10 11\n",
-       "0                                 1   \n",
-       "1            1                        \n",
-       "2         1                           \n",
-       "3                                    1\n",
-       "4               1                     \n",
-       "5                     1               \n",
-       "6                        1            \n",
-       "7                  1                  \n",
+       "0                     1               \n",
+       "1      1                              \n",
+       "2                        1            \n",
+       "3               1                     \n",
+       "4   1                                 \n",
+       "5                              1      \n",
+       "6                                 1   \n",
+       "7            1                        \n",
        "8                           1         \n",
-       "9   1                                 \n",
-       "10     1                              \n",
-       "11                             1      "
+       "9                  1                  \n",
+       "10        1                           \n",
+       "11                                   1"
       ]
      },
      "execution_count": 32,
@@ -2641,9 +2652,20 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_20958/873994875.py:2: DeprecationWarning: \n",
+      "\n",
+      "The scipy.sparse array containers will be used instead of matrices\n",
+      "in Networkx 3.0. Use `from_scipy_sparse_array` instead.\n",
+      "  G_perm = nx.convert_matrix.from_scipy_sparse_matrix(A_sci)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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ZERgMXSIHTFYJopsG3TaTDTFazVXfi9GqcdlkdbmfzWrBF2V/R3V1NaxW19v6U08emRFIQdeRxglEKBho1SIkN2F06oIBKlFAWmIUqi62AQB+kByLE/WXXO5nsdqw6jf/hWePf42GhgYkJycjNTXV4WvAgAHQ6Xz/JJsnIzO8EQojM4JFUIQuh6lQsNEn6Nx+XDZabNh+tA5P3ZWJ5z46jJtvjMVdN/fFA29/4XI/lSYC+3Z8DLVKhNlsRm1tLaqrqztfX375JTZu3Ijq6mrU1NQgLi6uM4AdBXN8fDwEd8MsruHJyAy7tMQobF88Dp8eqcPS//na6XbBOjIj2AR0whtOIELBbNLKEpx0MvTLLk6nwa8fGIbcjCQ0tVnwn9uOOR2na5fRJwY7l473qAySJKG+vv6qUL72BcBpTTk1NRV9+/aFKF59v3jyu9n997zRiFSrcKbZ6DJ0vf3dwllQTnjDCUQo2E3P0qOguMJlZaDFaMGj68s9PmakRsSDI/Ueby+KIpKTk5GcnIycnJzrfi7LMpqbm68K4dOnT+Orr77q/LqlpQX9+/fvrC33H5CKStNIAO6ruvcOS0ar0YIDDU1ITYx2u719ZAabBJ0LSOjah6l0pdf0ymEqW5/IZfCS38zI7o+Vuyp8ekxZBh4a1d9nxxMEAQkJCUhISMDw4cMdbmM0GnH69OnOED5SVQ9ZsgKixuH2djFaNZbelYlZhWV42MMy20dmpCW5D+ieSvG3Iw5ToVARHxWB/Nx06DS+uU10GhUWjB2oeL+ETqfDkCFDMHnyZCxYsAD/tmgxojzonHv6rkz8z1c1ONfS7vH/SxQEn3XOhSvFQ5fDVCiULM7LRN/YSKi87Ki6lkoQ0C9Wi8V5GT4qWdd5MjLj5uRY3DE4Ce/tq/Tq2JIsQ8v+FpcUbV7wZJiKPl6HFffdgqwBCTBbbfj0SB1e++u3sEnXXyQcpkL+FqEW8UF+Tpebw4COwI2L0mB9fo7bBy6U4MnIjJyBvaBP0OGL5+4EAERFqKESBWT0ycWPVu91up/FJkGfwCY/VxQNXY8mELnvFly8bMLo/9iF2EgN1s0fjZ/lpOL9L6ocbs9hKuRvKfE6bH0it7Pj16vHZa0mJERrsOWJiUHT/6BWiUhNjHY5euFPfz+NrYfOdX69YOxA6BN0+MWWIy6PnZYYzU40NxQ9O+4mEAGA/glR+OvhczBZJZy/bEJJxXlk9olxun0wTiBC4SclXocdS8Zj3h3p0KpFRLpp59VpRGjVIu4fmoDTbz8Ka0uDQiX1zPQsvcvfod3Scf/ZX21mK0xWCY0Gs9N9vB2Z0VMpVtP1ZAIRACjaV4l7h92I0lMXEafTYEJmH/xm53GX+3CYCikhQi3i51NuwoKxAzse5imvRZWDh3nSEqPx4Eg9HhrV8TBP8vmnMHPmTJSUlECtDornkbwemVHgQd+Jr0dmhCvFrgD7BCLXzs95rdLKRswYPQBHXpkCtUrE5vIabP+23uU+HKZCSoqPisBj4wbhsXGDPHps/emnn8auXbvw6quvYsWKFQEq9dXsIzPW7Kv0enYxR3QaFeazb8UjilUNPZlARBA6nn7ZdqQON7+yHcNX7ECcToPnp97kcj8OU6FAUatEpCVFY0i/G5CW5Lg9UxRFrF27Fu+99x52794dgFI6Fo4jM0KBYqHryTCVeJ0GKfE6/PeXVTDbJDS3WbCpvBYTh/RxuR+HqVCw69evH4qKijBnzhxcuHAh0MUB8M+RGXFRmi4Hb7CNzAgFip0lT4apNLVZcLqxDbNzUqESBcRGqvFAlh7f1bW63I/DVCgUTJkyBTNmzMC8efPgas4TJdlHZvTvpfP6IRCdRoUBvXR8MtRLioWufZiKO/+6vhzjM3vjwC/uwufPTIRVkrHir9+63IfDVChUvP766zh37hxWr14d6KJ06urIjPm56dixdDwD10uKzjL2h5Lv3U4g4q1IjYinJmXiUY7TpRBx8uRJjBkzBjt37nQ6X0KgNLeZnY7MaDdbEGUz4MkfZXeOzCDHXM0ypmjoNreZcfsbxT7t9NKqRZQty+MFQCHlgw8+wIoVK1BeXo7o6OAcdXPtyIyGyu8w95E5+PZb1588yXXoKvqZPFwmECHqrlmzZiEnJwdPPvlkoIvi1LUjM0ZmjcC5c+dQV1cX6KKFNMUbQjlMhajD6tWrsWfPHmzYsCHQRfGISqXCuHHjUFJSEuiihDTFQ5fDVIg6xMTEYMOGDVi0aBFOnToV6OJ4ZOLEiUE11jgUBSSxOEyFqENWVhZeeOEFzJw5ExaLxf0OATZhwgSGbjcFrJrIYSpEHZYsWYLExES89NJLgS6KW8OGDcOFCxdw9qzrdeDIuYB+NrdPIFK2LA9LJ2Uio08MVJAhSlbEaNWIilBBoxKQ0ScGSydlomxZHp6ZPIRNChRWBEFAUVER1q1bh507dwa6OC6Joojx48ezttsNAV0N2JHf/f5tlB09iRdfWu50AhGicFRcXIw5c+bg4MGD6NPH9aPvgfTb3/4Whw4dQmFhYaCLErSCZsiYJ9qNbegdCZcTiBCFo7y8PDzyyCN45JFHIEnBO4ETO9O6J+gSra2tDVFRUYEuBlFAvPrqq2hubkZBQUGgi+LU0KFDcenSJZw+fTrQRQlJDF2iIKLRaPCnP/0Jb7zxBsrLywNdHIcEQcCECRPw+eefB7ooIYmhSxRk0tPTsXr1asyYMQOXLl0KdHEc4tCxrmPoEgWhhx9+GOPHj8fChQsDXRSH2K7bdQxdoiC1atUqfPXVV1i3bp3Dn1ttEqouGHC87hKqLnSsE6iUm266CSaTCZWVlYr9P8NFcKySdwWGLlGH6OhobNiwAZMmTUJOTg4yMjLQZPjH1IsHalHtYFHM1MRoTM/SY0a2f6detLfr7t69G+np6X77/4Qj1nSJgthtt92GV155BTNmzsYvPz2KnF8Wo6C4AicbLsNik9FmtuGyyYo2sw0Wm4yTDZdRUFyB298oxq+3H4PZj2sHsomhaxi6REHuvlnzcGnsk3j3/76HySq5XQSg3SLBZJWwZl8lJheU4Eyz0S/lsodusCw9FCqCJnTt7VOtYgxapAhF26eIgtWZZiP+5Xf7YI2Mh01QebWv0SKhptGIe1fv9UvwDh48GABQceJkwNqWQ1FAHwN21D7VZjAgUqeDTYZi7VPB7toZ/PlodM9gtkqYXFCCmkYjbN2oTaoEAQN66bBj6XifzVtiv3dXbS2DWRMLbYQ6IG3LwSpoluuxM1slrCquQOHeSggCXH5citSIkGUgPzcdi/MyEdFDlloPlg4TCpxfbz+GNfsqYfTBmoI6jQrzc9PxzOQh3ToO713PBFXonmk2YlZhKepb2726mHQaEX1jI/FBfk5YT+vIi5qAjjfdnF8G13qCvHc9FzShe6a5o32ppc3SpY9L9hUjwnUCc17UZOfJytlHl0+56utIjQrrSquxfOtRh9tHakQsnZSJx7qwcjbvXe8ExSxjZquEWYWlXf6jAYBNltHSZsHswlJYwqyx3n5R1zQavf446e8OE1Le5gO1bkcpDF2+vfM16vVdaLfY8Onhc063b7dI2Fxe63VZeO/6lmKhu6q4AvWt7d3qEAA6/nh1rSasKj7ho5IFHi9qupLVJqH6osGrfe65tR8uGsz4e1Wjy+2qLno/uoD3rm8pErpNBjMK917dITBnTCo+XngHjq+YijenD7tq+x8OSkTx0vH47tWp+NDBR2ajxYZ395xCc5tZieL7HS9qulJtk9HrUQYPZOnx5wPua7EalYjaJs8/DTm6d525d1gydi0dj29fnYKSZyYgOy3hum3C7d7tCkVCd+P+Gly78G99qwmrd5/Epv1XXygJURr8YfZI/NfO4xi+Yge+OdOM1T8dcd0xBaHjuKHOm4vaE7yoQ5/JKkH0YqXsG+MicXt6IjZ7ELoCgHaLzeNjO7p3HckdnITnpt6En28+hKHLt+OhP36J041tjssQJvduVyky94Kj9qntR+sAALemxCE5LrLz+1OH9sOJ+sv49EjHzwt2ncDBl+7CoN7R+P78Pz9y2dunutIpEEw8vahXPjQcdwxKhC5ChfOXTXin5JTTC9d+UYf6uemptGoRkhefeu7P0mN/VaNHNdjLBgOyRw5Hv2gVUlJScOONNzp9RUVFedS2DABLJ2Xirb+dxMGaZgAdlSpnwuXe7Sq/h6637VOZfW/Ad+daO782WmyovtiGjD43XBW6wD/bp0L5QQFPL+rff34Sz330Dcw2CYN6R2PDghwcPduCI2dbr9u2p1/UoU6foPOqXf7+rBS8/fn3Hm0bEanD6e++RkPdOZw9e7bzVVNTg7Kysqu+p43UIX5BEaByHROi0FF52vVdPT5/ZgK0ahE7vq3Hf3z6ndMhb+Fw73aV30PX3j5lsXn2kSYqQo1Gw9XvkpfaLYjRXl9UQZbw3oa/IPkGNTQaDdTqjv9e+29PvhZF5f/43rwhnWi43PlvWe54pSZGOwxdoGdf1KFOrRKRmhiNk1f8zZ3JGpCAfrGRLkctXKl/fCQS4mKREBeLIUOcPyghyzK+qTyHh4u+RrvVda07KUaLCLWIu2/phwff+RJWm4R3fzYKi+7MwJs7jjvcx962nJYU7VG5w4nfQ9fb9qk2sxUxkZqrvhcTqcZlk/W6bW0WC/6y9ROoDQ2wWq2wWCydryu/dvUz+0sQhC4FtrfhfuXXlwUdIA2Cp03rK358C6Zn6aGLUOHImRbsPt7gdNuefFGHg+lZerfjdDu2S8G2o3UwmN1XagTJiiMfv4uflr+LuXPnYtKkSVCpHM/nIAgCIqNugFqlAqzX33tXsrcRr/2yCucvdVSYCvdWYtGdg52GrigIPn3wI5T4PXS9bZ+qqL+EB7L0nV/rNCqk9orGiYbrly3R6nR4e/VbPgkWm83WpcDuati3tbXhorUdsloCBM9C96UtR/DKx0eQNSABOQMTXU7b15Mv6nAwI7s/Vu6qcLvdC3854vExIyIiUPynAny2ZTNefPFFzJ8/H3PmzMHcuXORmZl53fae3rut7VacbTbCm8E3kixD20OfoPR76Dprn1KJAtSiAJUoQBQFaNUirJKM7d/WY9k9P8DUof2w+3gDFudl4Fhd63XtuQBgsUnQJ/jm6RaVSgWVSoXIyEj3G/tI1QUD9v12D6we1FLsJBnYX92En4xIweycVLz/RZWT7XruRR0O4qMikJ+b7vO5Fwbq+2LhwoVYuHAhDh8+jLVr12LcuHEYNGgQ5s6di4cffhixsbEAvGtb3lRei0d+mIaSivOw2CTMuyMdxcecfxLz5b0bavx+V9rbp661aOJgHF9xN/5twmDcP0KP4yvuxqKJg9FoMOPx9eX4+eQhOPTyZAzvH49FHx50eOy0xOiQbrP0tsPkSipRQGov5/MO9+SLOlwszstE39hIqLxonnNEJQjoF6vF4ryMq75/66234s0330RNTQ2ef/55fPbZZxgwYABmz56N4uJiiAIc3ruO/PZvJ/BNbQt2Pz0BxU+Nx9FzLfjd7pNOtw/1e7c7FJl7wZPnyL0VqRHx1KRMPBriPfSTVpa47TBJjI7oeGDkWAPaLTbkDk7CH2aPxOINX2Pnd/UO98noE4OdS8f7o8ikIKXnPDh//jw+/PBDFBUVobGxESNnPYejYhpMNt9NVB4u964rAZ97YUZ2f6/aezwhy8BDo/r79qABMD1Lj0iN6z+DDGD27akofT4Ph16ejBfu+QFe++u3TgM3UiPiwZF6hz+j0JISr8PWJ3LRv5cOOjfXybV0GhUG9NJ5NclM79698eSTT+LgwYPYsmUL+rWdQrvJ+ZjbrgiXe7erFHk4wl/tU+Ewj6wnHSaNBjMefrfU42P29Is63KTE67BjyfjOKT8BGSYXw7h0GhGSDMzPTcfivIwuT1w+bNgwDEnvj//9dBt0w++BJHY/LsLp3u0qxRpV/N0+Farsb0je1mKc0WlUWDB2YI++qMNRhFrEz6fchLJlebgzyQhteyM0KgFRESrEaNWIilBBoxKQ0ScGSydlomxZHp6ZPKTLgfv9999jwoQJ2LRpE/721jMY0DuW966PKLYEe4RaxAf5OT5pn1qfn+OzZUeCweK8THxy+JxPlmXhRR3e4qMiYCjfgiVjxmDBo7N8voyTJEl4++23sXz5crzwwgt48sknoVKp8EFfPe9dHwmhlSNU6Berxfownaibk0STJyRJQnJyMkpLS5Genu7TY1dWVmLevHkwmUwoKiq67ok13rueC3hH2pXs7VPz7kiHVi267UTSaURo1SLm56Zjx9LxYftHU7rDhELTwYMHkZCQ4NPAlWUZf/jDHzB69GhMmzYNe/bscfiIsLf3LqwmqCDhwVt7hfW9662Argbc3PaPxRfLa1HlYPHFtMRoPDhSj4dG9ZzFF71ZI83eYbJg7MBudZhQ6Hj99ddx4cIFrFy50ifHq66uRn5+PlpaWrB27Vr84Ac/8Gg/T+7dnD4yzn3xv/jfDeuRlZWFuXPn4ic/+QmiopyPL/elQK6iHTRrpLnCZcavxjckciQ3Nxcvv/wyJk+e3K3jyLKM9957D8uWLcPTTz+NZ555Bmp117p43N277e3t2LJlC95//32UlZVh+vTpmDt3LsaMGQOhm51z1wqWVbRDInTJOb4hEQA0NTUhNTUVDQ0N3Xpcvba2Fvn5+Th//jzWrl2LW265xYeldO3MmTNYt24dioqKAABz587Fz372M+j13RtXHmyraAdVmy55T60SkZYUjSH9bkBaUs99fLKn27VrF8aOHdvlwJVlGUVFRcjKykJubi5KS0sVDVwASElJwfPPP49jx47h/fffR1VVFYYNG4apU6diw4YNMBq9X1j1TLMRkwtKsGZfJUxWye2Tr+0WCSarhDX7KjG5oETxxVx59xKFiM8++wxTp07t0r5nz57Fj370I7z11lvYuXMnfvGLX0Cj0bjf0U8EQcCYMWPwzjvvoLa2FnPmzMGaNWug1+vx+OOPo6ysDK4+hduF4iraDF2iECDLMrZt24a7777b6/3WrVuH4cOHIzs7G2VlZbjtttv8VMquiYqKwsyZM7Fjxw4cPHgQer0es2fPxtChQ/GrX/0KZ8+edbhfqK6izdAlCkJWm4SqCwYcr7uEqgsGHPz6EKKjozF48GCPj1FXV4f77rsPv/71r7Ft2zYsX74cERHB3ek6YMAAvPjii6ioqMAf//hHHD9+HEOHDsW0adOwadMmmK6YByJUV9FmRxpRkHDV895utiBKasMT94xy2/MuyzI2bNiAJUuWYMGCBXjppZeg1WoV/E18y2Aw4M9//jOKiorwzTffYMaMGXjgp3Pwr9su+nSifq1aRNmyPJ+MauDoBaIg5sue94aGBjz++OOdHVXZ2dn+Lr6iqqqqsHbtWhSV1gK33AOonQfkoN4xWPHjobglJQ6NBjPe+PQ7bP/W8cx8QMe5XTop0ycLunL0AlGQ8mXP+6ZNmzBs2DBkZGSgvLw87AIXANLS0vDKK69g8F2zXAauShTw7pyRKD7WgOGv7cCyPx/GyoeHI93F0l72VbT9TbEJb4joat2Zb+PKnvf/nnkzVix7CocOHcKWLVtw++23+6nEwcFqk1Dd2OZym0G9o9H3hki8t7cSAPDlqYudy1z9ZqfzqVSVWEWbNV2iAPBVz3uTwYR73twG/YBUHDx4MOwDFwBqm4xuH3kXcP2TbgKAIX1vcLmffRVtf2LoEgWAr3reZQjQ9eqH5LvmQ6frGRPKmKwSRDePD39//jIuGsx4bNxAqEUBYzOScHt6InQax0vO2ymxijZDl0hhTQYzCve6XkVlzphUfLzwDhxfMRVvTh/m8ngmG/DunlNobjP7uqhByZOl4a2SjEfX7cedQ/rgqxcnYUHuQHxy+BzOtbS73E+JVbTZpkuksI37a+Bunpf6VhNW7z6JcRm93U+hCEAQOo7ri573YOfpKtrH6i5dtczVR//6Q3x0wHVHmRKraLOmS6SwzQdq3Y5S2H60Dju+rUeTh7VXpXreg4FaJXq0NPxN/W7onPd3wdiB6HOD1u05UmJpeNZ0iRRktUmovmjwy7GV6HkPFtOz9CgornD55vWTESmYkT0AalHAV1WNmL2mDGYXNWSlVtFm6BIpyN7zbrHZfH5se897mouxqOHCk1W03/jsGN747JjHx1RqFe3wf0skCiKe9Lx3lRI978EilFfRZugSKciTnveuUqLnPZgszstE39jIkFsavuf8hYiCgKc97ypRgFYtQiUKEK/4tytK9LwHkwi1iA/ycxAXpely8AZiaXiGLpGCPO15XzRxMI6vuBv/NmEw7h+hx/EVd2PRRNfTOirR8x5sQnEVbXakESnMk573guITKPBiflelet6DkX1peG9X0Z6fmx6QVbQZukQK86Tn3VtK9bwHqwi1iJ9PuQkLxg4M+lW0GbpECrP3vK/Z5/pRYE/pNCrMz00PWIgEk/ioCDw2bhAeGzcoaFfRDnwJiHqgUO15DyXBuop2cJSCqIcJ1Z536j7+pYgCJBR73qn7GLpEAWTveZ93R3rn5Cyu6DQitGoR83PTsWPpeAZuCGJHGlGAhVLPO3UfVwMmCkLB2vNOnnG1GjBrukRByN7zTuGHb51ERApi6BIRKYihS0SkIIYuEZGCGLpERApyOWRMEITzAKqVKw4RUVhIlWW5t6MfuAxdIiLyLTYvEBEpiKFLRKQghi4RkYIYukRECmLoEhEp6P8BimV0efqi5n0AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2760,7 +2782,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>parents</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -2769,7 +2791,7 @@
        "  <tr>\n",
        "    <td>12</td>\n",
        "    <td>12</td>\n",
-       "    <td>INT32</td>\n",
+       "    <td>INT64</td>\n",
        "    <td>full</td>\n",
        "  </tr>\n",
        "</table>\n",
@@ -2812,14 +2834,14 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2827,11 +2849,11 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"parents\"      nvals  size  dtype  format\n",
-       "graphblas.Vector     12    12  INT32    full\n",
-       "-----------------------------------------\n",
+       "\"parents\"  nvals  size  dtype  format\n",
+       "gb.Vector     12    12  INT64    full\n",
+       "-------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9  10 11\n",
-       "  0  0  2  2  2  5  2  5  5  0  2  2"
+       "  0  0  2  3  0  2  0  0  3  0  3  2"
       ]
      },
      "execution_count": 35,
@@ -2973,7 +2995,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 7,  1, 10,  4, 11,  0,  8,  5,  6,  3,  9,  2])"
+       "array([ 8,  1,  7,  2,  9, 11,  5,  3,  4,  0,  6, 10])"
       ]
      },
      "execution_count": 40,
@@ -3042,9 +3064,20 @@
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_20958/2236542675.py:2: DeprecationWarning: \n",
+      "\n",
+      "The scipy.sparse array containers will be used instead of matrices\n",
+      "in Networkx 3.0. Use `from_scipy_sparse_array` instead.\n",
+      "  G_perm2 = nx.convert_matrix.from_scipy_sparse_matrix(AAA_sci)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3153,7 +3186,7 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>parents</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
@@ -3162,7 +3195,7 @@
        "  <tr>\n",
        "    <td>12</td>\n",
        "    <td>12</td>\n",
-       "    <td>INT32</td>\n",
+       "    <td>INT64</td>\n",
        "    <td>full</td>\n",
        "  </tr>\n",
        "</table>\n",
@@ -3203,28 +3236,28 @@
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
        "      <td>2</td>\n",
+       "      <td>0</td>\n",
        "      <td>2</td>\n",
+       "      <td>0</td>\n",
        "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"parents\"      nvals  size  dtype  format\n",
-       "graphblas.Vector     12    12  INT32    full\n",
-       "-----------------------------------------\n",
+       "\"parents\"  nvals  size  dtype  format\n",
+       "gb.Vector     12    12  INT64    full\n",
+       "-------------------------------------\n",
        " 0  1  2  3  4  5  6  7  8  9  10 11\n",
-       "  0  1  2  1  2  0  0  1  2  2  2  2"
+       "  0  0  2  0  2  0  2  7  0  0  7  7"
       ]
      },
      "execution_count": 44,
@@ -3300,7 +3333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Operators.ipynb b/notebooks/Operators.ipynb
index c2b77ff..170bf96 100644
--- a/notebooks/Operators.ipynb
+++ b/notebooks/Operators.ipynb
@@ -233,19 +233,19 @@
     {
      "data": {
       "text/plain": [
-       "{'FC32': 'FC32',\n",
-       " 'FC64': 'FC64',\n",
-       " 'BOOL': 'BOOL',\n",
-       " 'FP32': 'FP32',\n",
-       " 'FP64': 'FP64',\n",
-       " 'INT16': 'INT16',\n",
-       " 'INT32': 'INT32',\n",
-       " 'INT64': 'INT64',\n",
-       " 'INT8': 'INT8',\n",
-       " 'UINT16': 'UINT16',\n",
-       " 'UINT32': 'UINT32',\n",
-       " 'UINT64': 'UINT64',\n",
-       " 'UINT8': 'UINT8'}"
+       "{FC32: FC32,\n",
+       " FC64: FC64,\n",
+       " BOOL: BOOL,\n",
+       " FP32: FP32,\n",
+       " FP64: FP64,\n",
+       " INT16: INT16,\n",
+       " INT32: INT32,\n",
+       " INT64: INT64,\n",
+       " INT8: INT8,\n",
+       " UINT16: UINT16,\n",
+       " UINT32: UINT32,\n",
+       " UINT64: UINT64,\n",
+       " UINT8: UINT8}"
       ]
      },
      "execution_count": 11,
@@ -329,7 +329,7 @@
     {
      "data": {
       "text/plain": [
-       "'INT64'"
+       "INT64"
       ]
      },
      "execution_count": 14,
@@ -350,7 +350,7 @@
     {
      "data": {
       "text/plain": [
-       "'INT64'"
+       "INT64"
       ]
      },
      "execution_count": 15,
@@ -434,7 +434,7 @@
     {
      "data": {
       "text/plain": [
-       "<cdata 'struct GB_BinaryOp_opaque *' 0x16bb90ab8>"
+       "<cdata 'struct GB_BinaryOp_opaque *' 0x7f3ca37e84c0>"
       ]
      },
      "execution_count": 19,
@@ -578,19 +578,19 @@
     {
      "data": {
       "text/plain": [
-       "{'FC32': 'FC32',\n",
-       " 'FC64': 'FC64',\n",
-       " 'FP32': 'FP32',\n",
-       " 'FP64': 'FP64',\n",
-       " 'BOOL': 'FP32',\n",
-       " 'INT8': 'FP32',\n",
-       " 'INT16': 'FP32',\n",
-       " 'UINT8': 'FP32',\n",
-       " 'UINT16': 'FP32',\n",
-       " 'INT32': 'FP64',\n",
-       " 'INT64': 'FP64',\n",
-       " 'UINT32': 'FP64',\n",
-       " 'UINT64': 'FP64'}"
+       "{FC32: FC32,\n",
+       " FC64: FC64,\n",
+       " FP32: FP32,\n",
+       " FP64: FP64,\n",
+       " BOOL: FP32,\n",
+       " INT8: FP32,\n",
+       " INT16: FP32,\n",
+       " UINT8: FP32,\n",
+       " UINT16: FP32,\n",
+       " INT32: FP64,\n",
+       " INT64: FP64,\n",
+       " UINT32: FP64,\n",
+       " UINT64: FP64}"
       ]
      },
      "execution_count": 25,
@@ -700,18 +700,18 @@
     {
      "data": {
       "text/plain": [
-       "{'FC32': 'FC32',\n",
-       " 'FC64': 'FC64',\n",
-       " 'FP32': 'FP32',\n",
-       " 'FP64': 'FP64',\n",
-       " 'INT16': 'INT16',\n",
-       " 'INT32': 'INT32',\n",
-       " 'INT64': 'INT64',\n",
-       " 'INT8': 'INT8',\n",
-       " 'UINT16': 'UINT16',\n",
-       " 'UINT32': 'UINT32',\n",
-       " 'UINT64': 'UINT64',\n",
-       " 'UINT8': 'UINT8'}"
+       "{FC32: FC32,\n",
+       " FC64: FC64,\n",
+       " FP32: FP32,\n",
+       " FP64: FP64,\n",
+       " INT16: INT16,\n",
+       " INT32: INT32,\n",
+       " INT64: INT64,\n",
+       " INT8: INT8,\n",
+       " UINT16: UINT16,\n",
+       " UINT32: UINT32,\n",
+       " UINT64: UINT64,\n",
+       " UINT8: UINT8}"
       ]
      },
      "execution_count": 30,
@@ -732,18 +732,18 @@
     {
      "data": {
       "text/plain": [
-       "{'FC32': 0j,\n",
-       " 'FC64': 0j,\n",
-       " 'FP32': 0.0,\n",
-       " 'FP64': 0.0,\n",
-       " 'INT16': 0,\n",
-       " 'INT32': 0,\n",
-       " 'INT64': 0,\n",
-       " 'INT8': 0,\n",
-       " 'UINT16': 0,\n",
-       " 'UINT32': 0,\n",
-       " 'UINT64': 0,\n",
-       " 'UINT8': 0}"
+       "{FC32: 0j,\n",
+       " FC64: 0j,\n",
+       " FP32: 0.0,\n",
+       " FP64: 0.0,\n",
+       " INT16: 0,\n",
+       " INT32: 0,\n",
+       " INT64: 0,\n",
+       " INT8: 0,\n",
+       " UINT16: 0,\n",
+       " UINT32: 0,\n",
+       " UINT64: 0,\n",
+       " UINT8: 0}"
       ]
      },
      "execution_count": 31,
@@ -910,16 +910,16 @@
     {
      "data": {
       "text/plain": [
-       "{'FP32': 'FP32',\n",
-       " 'FP64': 'FP64',\n",
-       " 'INT16': 'INT16',\n",
-       " 'INT32': 'INT32',\n",
-       " 'INT64': 'INT64',\n",
-       " 'INT8': 'INT8',\n",
-       " 'UINT16': 'UINT16',\n",
-       " 'UINT32': 'UINT32',\n",
-       " 'UINT64': 'UINT64',\n",
-       " 'UINT8': 'UINT8'}"
+       "{FP32: FP32,\n",
+       " FP64: FP64,\n",
+       " INT16: INT16,\n",
+       " INT32: INT32,\n",
+       " INT64: INT64,\n",
+       " INT8: INT8,\n",
+       " UINT16: UINT16,\n",
+       " UINT32: UINT32,\n",
+       " UINT64: UINT64,\n",
+       " UINT8: UINT8}"
       ]
      },
      "execution_count": 37,
@@ -1127,7 +1127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/repr_demo.ipynb b/notebooks/repr_demo.ipynb
index dedd704..5a85828 100644
--- a/notebooks/repr_demo.ipynb
+++ b/notebooks/repr_demo.ipynb
@@ -107,15 +107,17 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>v<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>3</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmap</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -158,9 +160,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"v_0\"          nvals  size  dtype\n",
-       "graphblas.Vector      3     5  INT64\n",
-       "---------------------------------\n",
+       "\"v_0\"      nvals  size  dtype  format\n",
+       "gb.Vector      3     5  INT64  bitmap\n",
+       "-------------------------------------\n",
        "  0 1  2 3  4\n",
        "  0    1    2"
       ]
@@ -265,15 +267,17 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>v<sub>huge</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>2</td>\n",
        "    <td>9007199254740992</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>sparse</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -348,9 +352,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"v_huge\"       nvals              size  dtype\n",
-       "graphblas.Vector      2  9007199254740992  INT64\n",
-       "---------------------------------------------\n",
+       "\"v_huge\"   nvals              size  dtype  format\n",
+       "gb.Vector      2  9007199254740992  INT64  sparse\n",
+       "-------------------------------------------------\n",
        " 0                1                2                3                 \\\n",
        "                0                                                      \n",
        "\n",
@@ -466,17 +470,19 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Matrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -543,9 +549,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"M_0\"          nvals  nrows  ncols  dtype\n",
-       "graphblas.Matrix      4      4      5  INT64\n",
-       "-----------------------------------------\n",
+       "\"M_0\"      nvals  nrows  ncols  dtype   format\n",
+       "gb.Matrix      4      4      5  INT64  bitmapr\n",
+       "----------------------------------------------\n",
        "   0  1  2 3  4\n",
        "0              \n",
        "1  0     2     \n",
@@ -646,17 +652,19 @@
        "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub>.T</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.TransposedMatrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.TransposedMatrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>4</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapc</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -725,9 +733,9 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"M_0.T\"                  nvals  nrows  ncols  dtype\n",
-       "graphblas.TransposedMatrix      4      5      4  INT64\n",
-       "---------------------------------------------------\n",
+       "\"M_0.T\"              nvals  nrows  ncols  dtype   format\n",
+       "gb.TransposedMatrix      4      5      4  INT64  bitmapc\n",
+       "--------------------------------------------------------\n",
        "  0  1 2  3\n",
        "0    0     \n",
        "1         1\n",
@@ -839,17 +847,19 @@
        "  <tr>\n",
        "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>StructuralMask\n",
        "of\n",
-       "graphblas.Matrix</pre></td>\n",
+       "gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -916,10 +926,10 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"M_0.S\"           nvals  nrows  ncols  dtype\n",
-       "StructuralMask  \n",
-       "of graphblas.Matrix      4      4      5  INT64\n",
-       "--------------------------------------------\n",
+       "\"M_0.S\"         nvals  nrows  ncols  dtype   format\n",
+       "StructuralMask\n",
+       "of gb.Matrix        4      4      5  INT64  bitmapr\n",
+       "---------------------------------------------------\n",
        "   0  1  2 3  4\n",
        "0              \n",
        "1  1     1     \n",
@@ -1021,17 +1031,19 @@
        "  <tr>\n",
        "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>ComplementedValueMask\n",
        "of\n",
-       "graphblas.Matrix</pre></td>\n",
+       "gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -1098,10 +1110,10 @@
        "</div></details></div>"
       ],
       "text/plain": [
-       "\"~M_0.V\"               nvals  nrows  ncols  dtype\n",
+       "\"~M_0.V\"               nvals  nrows  ncols  dtype   format\n",
        "ComplementedValueMask\n",
-       "of graphblas.Matrix           4      4      5  INT64\n",
-       "-------------------------------------------------\n",
+       "of gb.Matrix               4      4      5  INT64  bitmapr\n",
+       "----------------------------------------------------------\n",
        "   0  1  2 3  4\n",
        "0              \n",
        "1  1     0     \n",
@@ -1133,7 +1145,7 @@
     {
      "data": {
       "text/html": [
-       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>graphblas.VectorExpression:</tt></b><div>\n",
+       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>gb.VectorExpression:</tt></b><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
        "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>M<sub>0</sub>.mxv(v<sub>0</sub>, op=semiring.plus_times[INT64])</pre></td>\n",
@@ -1218,20 +1230,22 @@
        "    padding-bottom: 0px;\n",
        "}\n",
        "</style>\n",
-       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
+       "<details class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Matrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -1367,18 +1381,20 @@
        "    padding-bottom: 0px;\n",
        "}\n",
        "</style>\n",
-       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>v<sub>0</sub></tt><div>\n",
+       "<details class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>v<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>3</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmap</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -1418,12 +1434,141 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "</div></details></div><hr><div>\n",
+       "<style>\n",
+       "table.gb-info-table {\n",
+       "    border: 1px solid black;\n",
+       "    max-width: 100%;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       "td.gb-info-name-cell {\n",
+       "    line-height: 100%;\n",
+       "}\n",
+       "\n",
+       "details.gb-arg-details {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "    margin-left: 10px;\n",
+       "}\n",
+       "\n",
+       "summary.gb-arg-summary {\n",
+       "    display: list-item;\n",
+       "    outline: none;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "    margin-left: -10px;\n",
+       "}\n",
+       "\n",
+       "details.gb-expr-details {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       "summary.gb-expr-summary {\n",
+       "    display: list-item;\n",
+       "    outline: none;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       "blockquote.gb-expr-blockquote {\n",
+       "    margin-top: 5px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "    margin-left: 15px;\n",
+       "}\n",
+       "\n",
+       ".gb-scalar {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       "/* modify pandas dataframe */\n",
+       "table.dataframe {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "</style>\n",
+       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>Result</tt><div>\n",
+       "<table class=\"gb-info-table\">\n",
+       "  <tr>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
+       "    <td><pre>nvals</pre></td>\n",
+       "    <td><pre>size</pre></td>\n",
+       "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <td>2</td>\n",
+       "    <td>4</td>\n",
+       "    <td>INT64</td>\n",
+       "    <td>bitmap</td>\n",
+       "  </tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "</summary><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td></td>\n",
+       "      <td>2</td>\n",
+       "      <td></td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
        "</div></details></div></blockquote></details><em>Do <code>expr.new()</code> or <code>other << expr</code> to calculate the expression.</em></div>"
       ],
       "text/plain": [
-       "graphblas.VectorExpression                      size  dtype\n",
+       "gb.VectorExpression                          size  dtype\n",
        "M_0.mxv(v_0, op=semiring.plus_times[INT64])     4  INT64\n",
        "\n",
+       "\"Result\"   nvals  size  dtype  format\n",
+       "gb.Vector      2     4  INT64  bitmap\n",
+       "-------------------------------------\n",
+       " 0  1 2  3\n",
+       "    2    6\n",
+       "\n",
        "Do expr.new() or other << expr to calculate the expression."
       ]
      },
@@ -1444,7 +1589,7 @@
     {
      "data": {
       "text/html": [
-       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>graphblas.MatrixExpression:</tt></b><div>\n",
+       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>gb.MatrixExpression:</tt></b><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
        "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>M<sub>0</sub>.mxm(M<sub>0</sub>.T, op=semiring.min_plus[INT64])</pre></td>\n",
@@ -1531,20 +1676,22 @@
        "    padding-bottom: 0px;\n",
        "}\n",
        "</style>\n",
-       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
+       "<details class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Matrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -1680,20 +1827,22 @@
        "    padding-bottom: 0px;\n",
        "}\n",
        "</style>\n",
-       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub>.T</tt><div>\n",
+       "<details class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub>.T</tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.TransposedMatrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.TransposedMatrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>4</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapc</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -1759,12 +1908,167 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "</div></details></div><hr><div>\n",
+       "<style>\n",
+       "table.gb-info-table {\n",
+       "    border: 1px solid black;\n",
+       "    max-width: 100%;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       "td.gb-info-name-cell {\n",
+       "    line-height: 100%;\n",
+       "}\n",
+       "\n",
+       "details.gb-arg-details {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "    margin-left: 10px;\n",
+       "}\n",
+       "\n",
+       "summary.gb-arg-summary {\n",
+       "    display: list-item;\n",
+       "    outline: none;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "    margin-left: -10px;\n",
+       "}\n",
+       "\n",
+       "details.gb-expr-details {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       "summary.gb-expr-summary {\n",
+       "    display: list-item;\n",
+       "    outline: none;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       "blockquote.gb-expr-blockquote {\n",
+       "    margin-top: 5px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "    margin-left: 15px;\n",
+       "}\n",
+       "\n",
+       ".gb-scalar {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       "/* modify pandas dataframe */\n",
+       "table.dataframe {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "</style>\n",
+       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>Result</tt><div>\n",
+       "<table class=\"gb-info-table\">\n",
+       "  <tr>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
+       "    <td><pre>nvals</pre></td>\n",
+       "    <td><pre>nrows</pre></td>\n",
+       "    <td><pre>ncols</pre></td>\n",
+       "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <td>2</td>\n",
+       "    <td>4</td>\n",
+       "    <td>4</td>\n",
+       "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
+       "  </tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "</summary><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td></td>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
        "</div></details></div></blockquote></details><em>Do <code>expr.new()</code> or <code>other << expr</code> to calculate the expression.</em></div>"
       ],
       "text/plain": [
-       "graphblas.MatrixExpression                      nrows  ncols  dtype\n",
+       "gb.MatrixExpression                          nrows  ncols  dtype\n",
        "M_0.mxm(M_0.T, op=semiring.min_plus[INT64])      4      4  INT64\n",
        "\n",
+       "\"Result\"   nvals  nrows  ncols  dtype   format\n",
+       "gb.Matrix      2      4      4  INT64  bitmapr\n",
+       "----------------------------------------------\n",
+       "  0  1 2  3\n",
+       "0          \n",
+       "1    0     \n",
+       "2          \n",
+       "3         2\n",
+       "\n",
        "Do expr.new() or other << expr to calculate the expression."
       ]
      },
@@ -1785,7 +2089,7 @@
     {
      "data": {
       "text/html": [
-       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>graphblas.VectorExpression:</tt></b><div>\n",
+       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>gb.VectorExpression:</tt></b><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
        "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>v<sub>0</sub>.apply(binary.times[INT64], right=10)</pre></td>\n",
@@ -1870,18 +2174,20 @@
        "    padding-bottom: 0px;\n",
        "}\n",
        "</style>\n",
-       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>v<sub>0</sub></tt><div>\n",
+       "<details class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>v<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Vector</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>size</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>3</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmap</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -1921,10 +2227,10 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div></details></div><div class=\"gb-scalar\"><tt>10</tt><div>\n",
+       "</div></details></div><div class=\"gb-scalar\"><tt></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Scalar</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Scalar</pre></td>\n",
        "    <td><pre>value</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
        "  </tr>\n",
@@ -1934,12 +2240,143 @@
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
-       "</div></blockquote></details><em>Do <code>expr.new()</code> or <code>other << expr</code> to calculate the expression.</em></div>"
+       "</div><hr><div>\n",
+       "<style>\n",
+       "table.gb-info-table {\n",
+       "    border: 1px solid black;\n",
+       "    max-width: 100%;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       "td.gb-info-name-cell {\n",
+       "    line-height: 100%;\n",
+       "}\n",
+       "\n",
+       "details.gb-arg-details {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "    margin-left: 10px;\n",
+       "}\n",
+       "\n",
+       "summary.gb-arg-summary {\n",
+       "    display: list-item;\n",
+       "    outline: none;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "    margin-left: -10px;\n",
+       "}\n",
+       "\n",
+       "details.gb-expr-details {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       "summary.gb-expr-summary {\n",
+       "    display: list-item;\n",
+       "    outline: none;\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       "blockquote.gb-expr-blockquote {\n",
+       "    margin-top: 5px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "    margin-left: 15px;\n",
+       "}\n",
+       "\n",
+       ".gb-scalar {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       "/* modify pandas dataframe */\n",
+       "table.dataframe {\n",
+       "    margin-top: 0px;\n",
+       "    margin-bottom: 0px;\n",
+       "    padding-top: 0px;\n",
+       "    padding-bottom: 0px;\n",
+       "}\n",
+       "</style>\n",
+       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>Result</tt><div>\n",
+       "<table class=\"gb-info-table\">\n",
+       "  <tr>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Vector</pre></td>\n",
+       "    <td><pre>nvals</pre></td>\n",
+       "    <td><pre>size</pre></td>\n",
+       "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <td>3</td>\n",
+       "    <td>5</td>\n",
+       "    <td>INT64</td>\n",
+       "    <td>bitmap</td>\n",
+       "  </tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "</summary><div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <td>0</td>\n",
+       "      <td></td>\n",
+       "      <td>10</td>\n",
+       "      <td></td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div></details></div></blockquote></details><em>Do <code>expr.new()</code> or <code>other << expr</code> to calculate the expression.</em></div>"
       ],
       "text/plain": [
-       "graphblas.VectorExpression                   size  dtype\n",
+       "gb.VectorExpression                       size  dtype\n",
        "v_0.apply(binary.times[INT64], right=10)     5  INT64\n",
        "\n",
+       "\"Result\"   nvals  size  dtype  format\n",
+       "gb.Vector      3     5  INT64  bitmap\n",
+       "-------------------------------------\n",
+       "  0 1   2 3   4\n",
+       "  0    10    20\n",
+       "\n",
        "Do expr.new() or other << expr to calculate the expression."
       ]
      },
@@ -1960,7 +2397,7 @@
     {
      "data": {
       "text/html": [
-       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>graphblas.ScalarExpression:</tt></b><div>\n",
+       "<div><details class=\"gb-expr-details\"><summary class=\"gb-expr-summary\"><b><tt>gb.ScalarExpression:</tt></b><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
        "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>M<sub>0</sub>.reduce_scalar(monoid.plus[INT64])</pre></td>\n",
@@ -2043,20 +2480,22 @@
        "    padding-bottom: 0px;\n",
        "}\n",
        "</style>\n",
-       "<details open class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
+       "<details class=\"gb-arg-details\"><summary class=\"gb-arg-summary\"><tt>M<sub>0</sub></tt><div>\n",
        "<table class=\"gb-info-table\">\n",
        "  <tr>\n",
-       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>graphblas.Matrix</pre></td>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Matrix</pre></td>\n",
        "    <td><pre>nvals</pre></td>\n",
        "    <td><pre>nrows</pre></td>\n",
        "    <td><pre>ncols</pre></td>\n",
        "    <td><pre>dtype</pre></td>\n",
+       "    <td><pre>format</pre></td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>4</td>\n",
        "    <td>4</td>\n",
        "    <td>5</td>\n",
        "    <td>INT64</td>\n",
+       "    <td>bitmapr</td>\n",
        "  </tr>\n",
        "</table>\n",
        "</div>\n",
@@ -2120,12 +2559,28 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "</div></details></div></blockquote></details><em>Do <code>expr.new()</code> or <code>other << expr</code> to calculate the expression.</em></div>"
+       "</div></details></div><hr><div class=\"gb-scalar\"><tt>Result</tt><div>\n",
+       "<table class=\"gb-info-table\">\n",
+       "  <tr>\n",
+       "    <td rowspan=\"2\" class=\"gb-info-name-cell\"><pre>gb.Scalar</pre></td>\n",
+       "    <td><pre>value</pre></td>\n",
+       "    <td><pre>dtype</pre></td>\n",
+       "  </tr>\n",
+       "  <tr>\n",
+       "    <td>6</td>\n",
+       "    <td>INT64</td>\n",
+       "  </tr>\n",
+       "</table>\n",
+       "</div>\n",
+       "</div></blockquote></details><em>Do <code>expr.new()</code> or <code>other << expr</code> to calculate the expression.</em></div>"
       ],
       "text/plain": [
-       "graphblas.ScalarExpression                dtype\n",
+       "gb.ScalarExpression                    dtype\n",
        "M_0.reduce_scalar(monoid.plus[INT64])  INT64\n",
        "\n",
+       "\"Result\"   value  dtype\n",
+       "gb.Scalar      6  INT64\n",
+       "\n",
        "Do expr.new() or other << expr to calculate the expression."
       ]
      },
@@ -2148,7 +2603,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2162,7 +2617,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/setup.cfg b/setup.cfg
index a9809b6..ad7d5fc 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -3,6 +3,7 @@ test=pytest
 
 [flake8]
 max-line-length = 100
+inline-quotes = "
 exclude =
     versioneer.py,
     graphblas/tests/test_formatting.py,


Test patch:
diff --git a/graphblas/tests/test_matrix.py b/graphblas/tests/test_matrix.py
index 084a565..5f9cf30 100644
--- a/graphblas/tests/test_matrix.py
+++ b/graphblas/tests/test_matrix.py
@@ -2602,7 +2602,12 @@ def test_expr_is_like_matrix(A):
         "resize",
         "update",
     }
-    assert attrs - expr_attrs == expected
+    assert attrs - expr_attrs == expected, (
+        "If you see this message, you probably added a method to Matrix.  You may need to "
+        "add an entry to `matrix` or `matrix_vector` set in `graphblas._automethods.py` "
+        "and then run `python -m graphblas._automethods`.  If you're messing with infix "
+        "methods, then you may need to run `python -m graphblas._infixmethods`."
+    )
     assert attrs - infix_attrs == expected
     # TransposedMatrix is used differently than other expressions,
     # so maybe it shouldn't support everything.
@@ -2638,7 +2643,12 @@ def test_index_expr_is_like_matrix(A):
         "from_values",
         "resize",
     }
-    assert attrs - expr_attrs == expected
+    assert attrs - expr_attrs == expected, (
+        "If you see this message, you probably added a method to Matrix.  You may need to "
+        "add an entry to `matrix` or `matrix_vector` set in `graphblas._automethods.py` "
+        "and then run `python -m graphblas._automethods`.  If you're messing with infix "
+        "methods, then you may need to run `python -m graphblas._infixmethods`."
+    )
 
 
 def test_flatten(A):
@@ -3284,3 +3294,50 @@ def test_udt():
     info = A.ss.export("coor")
     result = A.ss.import_any(**info)
     assert result.isequal(A)
+
+
+def test_reposition(A):
+    rows, cols, values = A.to_values()
+    rows = rows.astype(int)
+    cols = cols.astype(int)
+
+    def get_expected(row_offset, col_offset, nrows, ncols, is_transposed):
+        r = rows
+        c = cols
+        if is_transposed:
+            r, c = c, r
+        r = r + row_offset
+        c = c + col_offset
+        mask = (r >= 0) & (r < nrows) & (c >= 0) & (c < ncols)
+        return Matrix.from_values(r[mask], c[mask], values[mask], nrows=nrows, ncols=ncols)
+
+    for row_offset in range(-A.nrows - 2, A.nrows + 3, 3):
+        for col_offset in range(-A.ncols - 2, A.ncols + 3, 3):
+            for M in [A, A.T]:
+                result = M.reposition(row_offset, col_offset).new()
+                expected = get_expected(row_offset, col_offset, M.nrows, M.ncols, M._is_transposed)
+                assert result.isequal(expected)
+                result = M.reposition(row_offset, col_offset, nrows=3, ncols=10).new()
+                expected = get_expected(row_offset, col_offset, 3, 10, M._is_transposed)
+                assert result.isequal(expected)
+                result = M.reposition(row_offset, col_offset, nrows=10, ncols=3).new()
+                expected = get_expected(row_offset, col_offset, 10, 3, M._is_transposed)
+                assert result.isequal(expected)
+
+    result = A.reposition(3, 1).new(mask=A.S)
+    expected = Matrix.from_values([3, 4, 6], [2, 5, 3], [2, 8, 3], nrows=A.nrows, ncols=A.ncols)
+    assert result.isequal(expected)
+
+    result(A.S, binary.plus) << A.reposition(3, 1)
+    expected *= 2
+    assert result.isequal(expected)
+
+    result = A.T.reposition(-1, 1).new(mask=A.S)
+    expected = Matrix.from_values(
+        [0, 1, 1, 3, 4, 5], [1, 4, 6, 2, 5, 2], [2, 3, 1, 8, 7, 4], nrows=A.ncols, ncols=A.nrows
+    )
+    assert result.isequal(expected)
+
+    result(A.S, binary.plus) << A.T.reposition(-1, 1)
+    expected *= 2
+    assert result.isequal(expected)
diff --git a/graphblas/tests/test_scalar.py b/graphblas/tests/test_scalar.py
index 1baa1f7..2c4ef88 100644
--- a/graphblas/tests/test_scalar.py
+++ b/graphblas/tests/test_scalar.py
@@ -328,7 +328,12 @@ def test_expr_is_like_scalar(s):
     }
     if s.is_cscalar:
         expected.add("_empty")
-    assert attrs - expr_attrs == expected
+    assert attrs - expr_attrs == expected, (
+        "If you see this message, you probably added a method to Scalar.  You may need to "
+        "add an entry to `scalar` set in `graphblas._automethods.py` "
+        "and then run `python -m graphblas._automethods`.  If you're messing with infix "
+        "methods, then you may need to run `python -m graphblas._infixmethods`."
+    )
     assert attrs - infix_attrs == expected
 
 
@@ -354,7 +359,12 @@ def test_index_expr_is_like_scalar(s):
     }
     if s.is_cscalar:
         expected.add("_empty")
-    assert attrs - expr_attrs == expected
+    assert attrs - expr_attrs == expected, (
+        "If you see this message, you probably added a method to Scalar.  You may need to "
+        "add an entry to `scalar` set in `graphblas._automethods.py` "
+        "and then run `python -m graphblas._automethods`.  If you're messing with infix "
+        "methods, then you may need to run `python -m graphblas._infixmethods`."
+    )
 
 
 def test_ndim(s):
diff --git a/graphblas/tests/test_vector.py b/graphblas/tests/test_vector.py
index 54a4678..e360caa 100644
--- a/graphblas/tests/test_vector.py
+++ b/graphblas/tests/test_vector.py
@@ -1405,7 +1405,12 @@ def test_expr_is_like_vector(v):
         "resize",
         "update",
     }
-    assert attrs - expr_attrs == expected
+    assert attrs - expr_attrs == expected, (
+        "If you see this message, you probably added a method to Vector.  You may need to "
+        "add an entry to `vector` or `matrix_vector` set in `graphblas._automethods.py` "
+        "and then run `python -m graphblas._automethods`.  If you're messing with infix "
+        "methods, then you may need to run `python -m graphblas._infixmethods`."
+    )
     assert attrs - infix_attrs == expected
 
 
@@ -1434,7 +1439,12 @@ def test_index_expr_is_like_vector(v):
         "from_values",
         "resize",
     }
-    assert attrs - expr_attrs == expected
+    assert attrs - expr_attrs == expected, (
+        "If you see this message, you probably added a method to Vector.  You may need to "
+        "add an entry to `vector` or `matrix_vector` set in `graphblas._automethods.py` "
+        "and then run `python -m graphblas._automethods`.  If you're messing with infix "
+        "methods, then you may need to run `python -m graphblas._infixmethods`."
+    )
 
 
 def test_random(v):
@@ -1892,6 +1902,10 @@ def test_broadcasting(A, v):
     assert result.isequal(expected)
     result = binary.plus(v | A).new()
     assert result.isequal(expected)
+    # use mask
+    result(A.S, replace=True) << binary.plus(v | A)
+    expected(A.S, replace=True) << expected
+    assert result.isequal(expected)
 
     expected = semiring.any_plus(v.diag() @ A).new()
     result = v.ewise_mult(A, monoid.plus).new()
@@ -1914,6 +1928,10 @@ def test_broadcasting(A, v):
     result = A.dup()
     result += v
     assert result.isequal(expected)
+    # use mask
+    result(A.S, replace=True) << binary.plus(A | v)
+    expected(A.S, replace=True) << expected
+    assert result.isequal(expected)
 
     expected = semiring.any_plus(A @ v.diag()).new()
     result = A.ewise_mult(v, monoid.plus).new()
@@ -1959,3 +1977,24 @@ def test_ewise_union_infix():
             op(v & w, 5, left_default=10, right_default=20)
         with pytest.raises(TypeError):
             op(v, w, left_default=10, right_default=20)
+
+
+def test_reposition(v):
+    indices, values = v.to_values()
+    indices = indices.astype(int)
+
+    def get_expected(offset, size):
+        ind = indices + offset
+        mask = (ind >= 0) & (ind < size)
+        return Vector.from_values(ind[mask], values[mask], size=size)
+
+    for offset in range(-v.size - 2, v.size + 3):
+        result = v.reposition(offset).new()
+        expected = get_expected(offset, v.size)
+        assert result.isequal(expected)
+        result = v.reposition(offset, size=3).new()
+        expected = get_expected(offset, 3)
+        assert result.isequal(expected)
+        result = v.reposition(offset, size=10).new()
+        expected = get_expected(offset, 10)
+        assert result.isequal(expected)