Prepare for 2.2.0 roll (#40)

* Prepare for 2.2.0 roll to pypi

* Formatting fixes with Black

* Fixes with ruff

CHANGELOG.md

@@ -1,5 +1,12 @@
 # CHANGELOG
 
+## [v2.2.0 (January 11 2022)](https://pypi.org/project/grandiso/2.2.0/)
+
+-   Features
+    -   (#39) Adds streaming match generators (thanks @davidmezzetti!)
+-   Fixes
+    -   (#37) Improve performance of `find_motifs` by remembering to actually update `_greatest_backbone_count` (thanks @nobutoba!)
+
 ## [v2.1.1 (December 21 2021)](https://pypi.org/project/grandiso/2.1.0/)
 
 -   Fixes

grandiso/__init__.py

@@ -17,13 +17,13 @@
       attribute search.
 """
 
-from typing import Dict, Generator, Hashable, List, Optional, Union, Tuple
+from typing import Dict, Generator, Hashable, List, Union, Tuple
 import itertools
 from functools import lru_cache
 
 import networkx as nx
 
-__version__ = "2.1.1"
+__version__ = "2.2.0"
 
 
 @lru_cache()
@@ -161,8 +161,9 @@ def get_next_backbone_candidates(
         # Let's return ALL possible node choices for this next_node. To do this
         # without being an insane person, let's filter on max degree in host:
         for n in host.nodes:
-            if is_node_attr_match(next_node, n, motif, host) \
-            and is_node_structural_match(next_node, n, motif, host):
+            if is_node_attr_match(
+                next_node, n, motif, host
+            ) and is_node_structural_match(next_node, n, motif, host):
                 yield {next_node: n}
         return
 
@@ -248,7 +249,7 @@ def get_next_backbone_candidates(
         # This is neato :) It means that there are multiple edges in the host
         # graph that we can use to downselect the number of candidate nodes.
         candidate_nodes_set = set()
-        for (source, _, target) in required_edges:
+        for source, _, target in required_edges:
             if directed:
                 if source is not None:
                     # this is a "from" edge:
@@ -281,9 +282,11 @@ def get_next_backbone_candidates(
 
     def tentative_results():
         for c in candidate_nodes:
-            if c not in backbone.values() \
-            and is_node_attr_match(next_node, c, motif, host) \
-            and is_node_structural_match(next_node, c, motif, host):
+            if (
+                c not in backbone.values()
+                and is_node_attr_match(next_node, c, motif, host)
+                and is_node_structural_match(next_node, c, motif, host)
+            ):
                 yield {**backbone, next_node: c}
 
     # One last filtering step here. This is to catch the cases where you have
@@ -323,7 +326,7 @@ def monomorphism_candidates():
     # to confirm that no spurious edges exist in the induced subgraph:
     def isomorphism_candidates():
         for result in monomorphism_candidates():
-            for (motif_u, motif_v) in itertools.product(result.keys(), result.keys()):
+            for motif_u, motif_v in itertools.product(result.keys(), result.keys()):
                 # if the motif has this edge, then it doesn't rule any of the
                 # above results out as an isomorphism.
                 # if the motif does NOT have the edge, then NO RESULT may have
@@ -338,6 +341,7 @@ def isomorphism_candidates():
 
     yield from isomorphism_candidates()
 
+
 def uniform_node_interestingness(motif: nx.Graph) -> dict:
     """
     Sort the nodes in a motif by their interestingness.
@@ -422,6 +426,7 @@ def walk(path):
     for path in paths:
         yield from walk(path)
 
+
 def find_motifs(
     motif: nx.Graph,
     host: nx.Graph,

grandiso/queues.py

@@ -5,7 +5,7 @@
 
 try:
     SimpleQueue = queue.SimpleQueue
-except:
+except ImportError:
     SimpleQueue = queue.Queue
 
 

grandiso/test_grandiso.py

@@ -1,5 +1,3 @@
-import time
-import copy
 import random
 import pytest
 
@@ -318,7 +316,7 @@ def _random_host(directed=False, n=20, p=0.1):
 def _random_directed_motif():
     motif = _random_motif()
     dmotif = nx.DiGraph()
-    for (u, v) in motif.edges():
+    for u, v in motif.edges():
         dmotif.add_edge(*random.choice([(u, v), (v, u)]))
     return dmotif
 

grandiso/test_queues.py

@@ -11,19 +11,21 @@
 
 
 @pytest.mark.parametrize(
-    "queue,queue_args", all_queues,
+    "queue,queue_args",
+    all_queues,
 )
 def test_empty(queue, queue_args):
     q = queue(*queue_args)
     assert q.empty()
     q.put(1)
-    assert q.empty() == False
+    assert q.empty() is False
     q.get()
     assert q.empty()
 
 
 @pytest.mark.parametrize(
-    "queue,queue_args", all_queues,
+    "queue,queue_args",
+    all_queues,
 )
 def test_can_put_and_pop(queue, queue_args):
     q = queue(*queue_args)

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="grandiso",
-    version="2.1.1",
+    version="2.2.0",
     author="Jordan Matelsky",
     author_email="opensource@matelsky.com",
     description="Performant subgraph isomorphism",
@@ -18,4 +18,5 @@
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
+    install_requires=["networkx>=2.5.1"],
 )