@@ -33,7 +33,7 @@ jobs:
 conda install -c conda-forge python-graphblas scipy pandas \
 pytest-cov pytest-randomly black flake8-comprehensions flake8-bugbear
 # matplotlib lxml pygraphviz pydot sympy # Extra networkx deps we don't need yet
-pip install git+https://.com/jim22k/networkx.git@nx-sparse --no-deps
+pip install git+https://.com/networkx/networkx.git@main --no-deps
 pip install -e . --no-deps
 - name: Style checks
 run: |

@@ -2,6 +2,8 @@
 #
 # Before first use: `pre-commit install`
 # To run: `pre-commit run --all-files`
+# To update: `pre-commit autoupdate`
+# - &flake8_dependencies below needs updated manually
 fail_fast: true
 repos:
 - repo: https://.com/pre-commit/pre-commit-hooks
@@ -26,7 +28,7 @@ repos:
 - id: isort
 language_version: python3
 - repo: https://.com/asottile/pyupgrade
-rev: v3.1.0
+rev: v3.2.2
 hooks:
 - id: pyupgrade
 args: [--py38-plus]
@@ -45,9 +47,10 @@ repos:
 hooks:
 - id: flake8
 additional_dependencies: &flake8_dependencies
+# These versions need updated manually
 - flake8==5.0.4
-- flake8-comprehensions==3.10.0
-- flake8-bugbear==22.9.23
+- flake8-comprehensions==3.10.1
+- flake8-bugbear==22.10.27
 - repo: https://.com/asottile/yesqa
 rev: v1.4.0
 hooks:

@@ -241,7 +241,7 @@ def square_clustering(G, node_ids=None):
 A, degrees = G.get_properties("A degrees+") # TODO" how to handle self-edges?
 # P2 from https://arxiv.org/pdf/2007.11111.pdf; we'll also use it as scratch
 if node_ids is not None:
-v = Vector.from_values(node_ids, True, size=degrees.size)
+v = Vector.from_coo(node_ids, True, size=degrees.size)
 Asubset = binary.second(v & A).new(name="A_subset")
 else:
 Asubset = A
@@ -298,10 +298,10 @@ def generalized_degree(G, *, mask=None):
 else:
 Tri(A.S) << 0
 Tri(Tri.S, binary.second) << plus_pair(Tri @ A.T)
-rows, cols, vals = Tri.to_values()
+rows, cols, vals = Tri.to_coo()
 # The column index indicates the number of triangles an edge participates in.
 # The largest this can be is `A.ncols - 1`. Values is count of edges.
-return Matrix.from_values(
+return Matrix.from_coo(
 rows,
 vals,
 np.ones(vals.size, dtype=int),

@@ -28,7 +28,7 @@ def k_truss(G: Graph, k) -> Graph:
 S = C
 
 # Remove isolate nodes
-indices, _ = C.reduce_rowwise(monoid.any).to_values()
+indices, _ = C.reduce_rowwise(monoid.any).to_coo()
 Ktruss = C[indices, indices].new()
 
 # Convert back to networkx graph with correct node ids

@@ -10,7 +10,7 @@ def descendants(G, source):
 raise KeyError(f"The node {source} is not in the graph")
 index = G._key_to_id[source]
 A = G._A
-q = Vector.from_values(index, True, size=A.nrows, name="q")
+q = Vector.from_coo(index, True, size=A.nrows, name="q")
 rv = q.dup(name="descendants")
 for _ in range(A.nrows):
 q(~rv.S, replace) << lor_pair(q @ A)
@@ -26,7 +26,7 @@ def ancestors(G, source):
 raise KeyError(f"The node {source} is not in the graph")
 index = G._key_to_id[source]
 A = G._A
-q = Vector.from_values(index, True, size=A.nrows, name="q")
+q = Vector.from_coo(index, True, size=A.nrows, name="q")
 rv = q.dup(name="descendants")
 for _ in range(A.nrows):
 q(~rv.S, replace) << lor_pair(A @ q)

@@ -11,9 +11,9 @@ def has_path(G, source, target):
 if src == dst:
 return True
 A = G._A
-q_src = Vector.from_values(src, True, size=A.nrows, name="q_src")
+q_src = Vector.from_coo(src, True, size=A.nrows, name="q_src")
 seen_src = q_src.dup(name="seen_src")
-q_dst = Vector.from_values(dst, True, size=A.nrows, name="q_dst")
+q_dst = Vector.from_coo(dst, True, size=A.nrows, name="q_dst")
 seen_dst = q_dst.dup(name="seen_dst")
 for _ in range(A.nrows // 2):
 q_src(~seen_src.S, replace) << lor_pair(q_src @ A)

@@ -16,7 +16,7 @@ def is_simple_path(G, nodes):
 if len(indices) != len(nodes) or len(indices) > len(set(indices)):
 return False
 # Check all steps in path at once
-P = Matrix.from_values(indices[:-1], indices[1:], True, nrows=A.nrows, ncols=A.ncols)
+P = Matrix.from_coo(indices[:-1], indices[1:], True, nrows=A.nrows, ncols=A.ncols)
 P << binary.second(A & P)
 return P.nvals == len(indices) - 1
 # Alternative

@@ -15,7 +15,7 @@ def is_tournament(G):
 
 def score_sequence(G):
 degrees = G.get_property("row_degrees+")
-_, values = degrees.to_values(indices=False, sort=False)
+_, values = degrees.to_coo(indices=False, sort=False)
 values.sort()
 if degrees.nvals != degrees.size:
 values = np.pad(values, (degrees.size - degrees.nvals, 0))

@@ -52,7 +52,7 @@ def __getitem__(self, key):
 def __iter__(self):
 # Slow if we iterate over one; fast if we iterate over all
 return map(
-self.id_to_key.__getitem__, self.vector.to_values(values=False, sort=False)[0].tolist()
+self.id_to_key.__getitem__, self.vector.to_coo(values=False, sort=False)[0].tolist()
 )
 
 def __len__(self):
@@ -123,7 +123,7 @@ def __getitem__(self, key):
 
 def __iter__(self):
 # Slow if we iterate over one; fast if we iterate over all
-return iter(self.vector.to_values(values=False, sort=False)[0].tolist())
+return iter(self.vector.to_coo(values=False, sort=False)[0].tolist())
 
 def __len__(self):
 return self.vector.nvals
@@ -232,7 +232,7 @@ def __iter__(self):
 # Slow if we iterate over one; fast if we iterate over all
 return map(
 self.id_to_key.__getitem__,
-self._get_rows().to_values(values=False, sort=False)[0].tolist(),
+self._get_rows().to_coo(values=False, sort=False)[0].tolist(),
 )
 
 def __len__(self):

@@ -46,7 +46,7 @@ def __contains__(self, x):
 def __iter__(self):
 # Slow if we iterate over one; fast if we iterate over all
 return map(
-self.id_to_key.__getitem__, self.vector.to_values(values=False, sort=False)[0].tolist()
+self.id_to_key.__getitem__, self.vector.to_coo(values=False, sort=False)[0].tolist()
 )
 
 def __len__(self):

@@ -1,5 +1,3 @@
-import pytest
-
 from . import nxapi
 
 
@@ -93,6 +91,10 @@ def convert_to_nx(obj, *, name=None):
 
 @staticmethod
 def on_start_tests(items):
+try:
+import pytest
+except ImportError: # pragma: no cover (import)
+return
 skip = [
 ("test_attributes", {"TestBoruvka", "test_mst.py"}),
 ("test_weight_attribute", {"TestBoruvka", "test_mst.py"}),

@@ -21,7 +21,7 @@ def edge_boundary(G, nbunch1, nbunch2=None, data=False, keys=False, default=None
 v1 = G.set_to_vector(nbunch1, ignore_extra=True)
 v2 = G.set_to_vector(nbunch2, ignore_extra=True)
 result = algorithms.edge_boundary(G, v1, v2, is_weighted=is_multigraph or data)
-rows, cols, vals = result.to_values(values=is_multigraph or data)
+rows, cols, vals = result.to_coo(values=is_multigraph or data)
 id_to_key = G.id_to_key
 if data:
 it = zip(

@@ -95,7 +95,7 @@ def _split(L, k):
 # TODO: should this move into algorithms?
 def _square_clustering_split(G, node_ids=None, *, nsplits):
 if node_ids is None:
-node_ids = G._A.reduce_rowwise(monoid.any).to_values()[0]
+node_ids = G._A.reduce_rowwise(monoid.any).to_coo()[0]
 result = None
 for chunk_ids in _split(node_ids, nsplits):
 res = algorithms.square_clustering(G, chunk_ids)

@@ -28,26 +28,17 @@
 
 def isdised(func):
 """Can this NetworkX function dis to other backends?"""
-# Haha, there should be a better way to know this
-registered_algorithms = backends._registered_algorithms
-try:
-return (
-func.__globals__.get("_registered_algorithms") is registered_algorithms
-and func.__module__.startswith("networkx")
-and func.__module__ != "networkx.classes.backends"
-and set(func.__code__.co_freevars) == {"func", "name"}
-)
-except Exception:
-return False
+return (
+callable(func) and hasattr(func, "disname") and func.__module__.startswith("networkx")
+)
 
 
 def disname(func):
 """The dised name of the disable NetworkX function"""
 # Haha, there should be a better way to get this
 if not isdised(func):
 raise ValueError(f"Function is not dised in NetworkX: {func.__name__}")
-index = func.__code__.co_freevars.index("name")
-return func.__closure__[index].cell_contents
+return func.disname
 
 
 def fullname(func):

@@ -1 +1 @@
-python-graphblas >=2022.10.1
+python-graphblas >=2022.11.0

@@ -178,14 +178,14 @@ def main(filename, backend, time, n, verify, alpha, tol, _get_result=False):
 
 start = timeit.default_timer()
 df = pd.read_csv(filename, delimiter="\t", names=["row", "col"])
-G = Matrix.from_values(df["row"].values, df["col"].values, 1)
+G = Matrix.from_coo(df["row"].values, df["col"].values, 1)
 stop = timeit.default_timer()
 num_nodes = G.nrows
 num_edges = G.nvals
 if _get_result:
 result = pagerank(G, alpha=alpha, tol=tol)
 result(~result.S) << 0 # Densify just in case
-return result.to_values()[1]
+return result.to_coo()[1]
 
 elif backend == "scipy":
 import pandas as pd