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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/community/tests/test_quality.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/community/tests/test_quality.py
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+"""Unit tests for the :mod:`networkx.algorithms.community.quality`
+module.
+
+"""
+
+import pytest
+
+import networkx as nx
+from networkx import barbell_graph
+from networkx.algorithms.community import modularity, partition_quality
+from networkx.algorithms.community.quality import inter_community_edges
+
+
+class TestPerformance:
+    """Unit tests for the :func:`performance` function."""
+
+    def test_bad_partition(self):
+        """Tests that a poor partition has a low performance measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 4}, {2, 3, 5}]
+        assert 8 / 15 == pytest.approx(partition_quality(G, partition)[1], abs=1e-7)
+
+    def test_good_partition(self):
+        """Tests that a good partition has a high performance measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 2}, {3, 4, 5}]
+        assert 14 / 15 == pytest.approx(partition_quality(G, partition)[1], abs=1e-7)
+
+
+class TestCoverage:
+    """Unit tests for the :func:`coverage` function."""
+
+    def test_bad_partition(self):
+        """Tests that a poor partition has a low coverage measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 4}, {2, 3, 5}]
+        assert 3 / 7 == pytest.approx(partition_quality(G, partition)[0], abs=1e-7)
+
+    def test_good_partition(self):
+        """Tests that a good partition has a high coverage measure."""
+        G = barbell_graph(3, 0)
+        partition = [{0, 1, 2}, {3, 4, 5}]
+        assert 6 / 7 == pytest.approx(partition_quality(G, partition)[0], abs=1e-7)
+
+
+def test_modularity():
+    G = nx.barbell_graph(3, 0)
+    C = [{0, 1, 4}, {2, 3, 5}]
+    assert (-16 / (14**2)) == pytest.approx(modularity(G, C), abs=1e-7)
+    C = [{0, 1, 2}, {3, 4, 5}]
+    assert (35 * 2) / (14**2) == pytest.approx(modularity(G, C), abs=1e-7)
+
+    n = 1000
+    G = nx.erdos_renyi_graph(n, 0.09, seed=42, directed=True)
+    C = [set(range(n // 2)), set(range(n // 2, n))]
+    assert 0.00017154251389292754 == pytest.approx(modularity(G, C), abs=1e-7)
+
+    G = nx.margulis_gabber_galil_graph(10)
+    mid_value = G.number_of_nodes() // 2
+    nodes = list(G.nodes)
+    C = [set(nodes[:mid_value]), set(nodes[mid_value:])]
+    assert 0.13 == pytest.approx(modularity(G, C), abs=1e-7)
+
+    G = nx.DiGraph()
+    G.add_edges_from([(2, 1), (2, 3), (3, 4)])
+    C = [{1, 2}, {3, 4}]
+    assert 2 / 9 == pytest.approx(modularity(G, C), abs=1e-7)
+
+
+def test_modularity_resolution():
+    G = nx.barbell_graph(3, 0)
+    C = [{0, 1, 4}, {2, 3, 5}]
+    assert modularity(G, C) == pytest.approx(3 / 7 - 100 / 14**2)
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(3 / 7 - gamma * 100 / 14**2)
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(3 / 7 - gamma * 100 / 14**2)
+
+    C = [{0, 1, 2}, {3, 4, 5}]
+    assert modularity(G, C) == pytest.approx(6 / 7 - 98 / 14**2)
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(6 / 7 - gamma * 98 / 14**2)
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx(6 / 7 - gamma * 98 / 14**2)
+
+    G = nx.barbell_graph(5, 3)
+    C = [frozenset(range(5)), frozenset(range(8, 13)), frozenset(range(5, 8))]
+    gamma = 1
+    result = modularity(G, C, resolution=gamma)
+    # This C is maximal for gamma=1:  modularity = 0.518229
+    assert result == pytest.approx((22 / 24) - gamma * (918 / (48**2)))
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((22 / 24) - gamma * (918 / (48**2)))
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((22 / 24) - gamma * (918 / (48**2)))
+
+    C = [{0, 1, 2, 3}, {9, 10, 11, 12}, {5, 6, 7}, {4}, {8}]
+    gamma = 1
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((14 / 24) - gamma * (598 / (48**2)))
+    gamma = 2.5
+    result = modularity(G, C, resolution=gamma)
+    # This C is maximal for gamma=2.5:  modularity = -0.06553819
+    assert result == pytest.approx((14 / 24) - gamma * (598 / (48**2)))
+    gamma = 0.2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((14 / 24) - gamma * (598 / (48**2)))
+
+    C = [frozenset(range(8)), frozenset(range(8, 13))]
+    gamma = 1
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((23 / 24) - gamma * (1170 / (48**2)))
+    gamma = 2
+    result = modularity(G, C, resolution=gamma)
+    assert result == pytest.approx((23 / 24) - gamma * (1170 / (48**2)))
+    gamma = 0.3
+    result = modularity(G, C, resolution=gamma)
+    # This C is maximal for gamma=0.3:  modularity = 0.805990
+    assert result == pytest.approx((23 / 24) - gamma * (1170 / (48**2)))
+
+
+def test_inter_community_edges_with_digraphs():
+    G = nx.complete_graph(2, create_using=nx.DiGraph())
+    partition = [{0}, {1}]
+    assert inter_community_edges(G, partition) == 2
+
+    G = nx.complete_graph(10, create_using=nx.DiGraph())
+    partition = [{0}, {1, 2}, {3, 4, 5}, {6, 7, 8, 9}]
+    assert inter_community_edges(G, partition) == 70
+
+    G = nx.cycle_graph(4, create_using=nx.DiGraph())
+    partition = [{0, 1}, {2, 3}]
+    assert inter_community_edges(G, partition) == 2