two version of R2R are hereHEADmaster

1 files changed, 302 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/centrality/tests/test_trophic.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/centrality/tests/test_trophic.py
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+"""Test trophic levels, trophic differences and trophic coherence"""
+
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+
+
+def test_trophic_levels():
+    """Trivial example"""
+    G = nx.DiGraph()
+    G.add_edge("a", "b")
+    G.add_edge("b", "c")
+
+    d = nx.trophic_levels(G)
+    assert d == {"a": 1, "b": 2, "c": 3}
+
+
+def test_trophic_levels_levine():
+    """Example from Figure 5 in Stephen Levine (1980) J. theor. Biol. 83,
+    195-207
+    """
+    S = nx.DiGraph()
+    S.add_edge(1, 2, weight=1.0)
+    S.add_edge(1, 3, weight=0.2)
+    S.add_edge(1, 4, weight=0.8)
+    S.add_edge(2, 3, weight=0.2)
+    S.add_edge(2, 5, weight=0.3)
+    S.add_edge(4, 3, weight=0.6)
+    S.add_edge(4, 5, weight=0.7)
+    S.add_edge(5, 4, weight=0.2)
+
+    # save copy for later, test intermediate implementation details first
+    S2 = S.copy()
+
+    # drop nodes of in-degree zero
+    z = [nid for nid, d in S.in_degree if d == 0]
+    for nid in z:
+        S.remove_node(nid)
+
+    # find adjacency matrix
+    q = nx.linalg.graphmatrix.adjacency_matrix(S).T
+
+    # fmt: off
+    expected_q = np.array([
+        [0, 0, 0., 0],
+        [0.2, 0, 0.6, 0],
+        [0, 0, 0, 0.2],
+        [0.3, 0, 0.7, 0]
+    ])
+    # fmt: on
+    assert np.array_equal(q.todense(), expected_q)
+
+    # must be square, size of number of nodes
+    assert len(q.shape) == 2
+    assert q.shape[0] == q.shape[1]
+    assert q.shape[0] == len(S)
+
+    nn = q.shape[0]
+
+    i = np.eye(nn)
+    n = np.linalg.inv(i - q)
+    y = np.asarray(n) @ np.ones(nn)
+
+    expected_y = np.array([1, 2.07906977, 1.46511628, 2.3255814])
+    assert np.allclose(y, expected_y)
+
+    expected_d = {1: 1, 2: 2, 3: 3.07906977, 4: 2.46511628, 5: 3.3255814}
+
+    d = nx.trophic_levels(S2)
+
+    for nid, level in d.items():
+        expected_level = expected_d[nid]
+        assert expected_level == pytest.approx(level, abs=1e-7)
+
+
+def test_trophic_levels_simple():
+    matrix_a = np.array([[0, 0], [1, 0]])
+    G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph)
+    d = nx.trophic_levels(G)
+    assert d[0] == pytest.approx(2, abs=1e-7)
+    assert d[1] == pytest.approx(1, abs=1e-7)
+
+
+def test_trophic_levels_more_complex():
+    # fmt: off
+    matrix = np.array([
+        [0, 1, 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1],
+        [0, 0, 0, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
+    d = nx.trophic_levels(G)
+    expected_result = [1, 2, 3, 4]
+    for ind in range(4):
+        assert d[ind] == pytest.approx(expected_result[ind], abs=1e-7)
+
+    # fmt: off
+    matrix = np.array([
+        [0, 1, 1, 0],
+        [0, 0, 1, 1],
+        [0, 0, 0, 1],
+        [0, 0, 0, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
+    d = nx.trophic_levels(G)
+
+    expected_result = [1, 2, 2.5, 3.25]
+    print("Calculated result: ", d)
+    print("Expected Result: ", expected_result)
+
+    for ind in range(4):
+        assert d[ind] == pytest.approx(expected_result[ind], abs=1e-7)
+
+
+def test_trophic_levels_even_more_complex():
+    # fmt: off
+    # Another, bigger matrix
+    matrix = np.array([
+        [0, 0, 0, 0, 0],
+        [0, 1, 0, 1, 0],
+        [1, 0, 0, 0, 0],
+        [0, 1, 0, 0, 0],
+        [0, 0, 0, 1, 0]
+    ])
+    # Generated this linear system using pen and paper:
+    K = np.array([
+        [1, 0, -1, 0, 0],
+        [0, 0.5, 0, -0.5, 0],
+        [0, 0, 1, 0, 0],
+        [0, -0.5, 0, 1, -0.5],
+        [0, 0, 0, 0, 1],
+    ])
+    # fmt: on
+    result_1 = np.ravel(np.linalg.inv(K) @ np.ones(5))
+    G = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
+    result_2 = nx.trophic_levels(G)
+
+    for ind in range(5):
+        assert result_1[ind] == pytest.approx(result_2[ind], abs=1e-7)
+
+
+def test_trophic_levels_singular_matrix():
+    """Should raise an error with graphs with only non-basal nodes"""
+    matrix = np.identity(4)
+    G = nx.from_numpy_array(matrix, create_using=nx.DiGraph)
+    with pytest.raises(nx.NetworkXError) as e:
+        nx.trophic_levels(G)
+    msg = (
+        "Trophic levels are only defined for graphs where every node "
+        + "has a path from a basal node (basal nodes are nodes with no "
+        + "incoming edges)."
+    )
+    assert msg in str(e.value)
+
+
+def test_trophic_levels_singular_with_basal():
+    """Should fail to compute if there are any parts of the graph which are not
+    reachable from any basal node (with in-degree zero).
+    """
+    G = nx.DiGraph()
+    # a has in-degree zero
+    G.add_edge("a", "b")
+
+    # b is one level above a, c and d
+    G.add_edge("c", "b")
+    G.add_edge("d", "b")
+
+    # c and d form a loop, neither are reachable from a
+    G.add_edge("c", "d")
+    G.add_edge("d", "c")
+
+    with pytest.raises(nx.NetworkXError) as e:
+        nx.trophic_levels(G)
+    msg = (
+        "Trophic levels are only defined for graphs where every node "
+        + "has a path from a basal node (basal nodes are nodes with no "
+        + "incoming edges)."
+    )
+    assert msg in str(e.value)
+
+    # if self-loops are allowed, smaller example:
+    G = nx.DiGraph()
+    G.add_edge("a", "b")  # a has in-degree zero
+    G.add_edge("c", "b")  # b is one level above a and c
+    G.add_edge("c", "c")  # c has a self-loop
+    with pytest.raises(nx.NetworkXError) as e:
+        nx.trophic_levels(G)
+    msg = (
+        "Trophic levels are only defined for graphs where every node "
+        + "has a path from a basal node (basal nodes are nodes with no "
+        + "incoming edges)."
+    )
+    assert msg in str(e.value)
+
+
+def test_trophic_differences():
+    matrix_a = np.array([[0, 1], [0, 0]])
+    G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph)
+    diffs = nx.trophic_differences(G)
+    assert diffs[(0, 1)] == pytest.approx(1, abs=1e-7)
+
+    # fmt: off
+    matrix_b = np.array([
+        [0, 1, 1, 0],
+        [0, 0, 1, 1],
+        [0, 0, 0, 1],
+        [0, 0, 0, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix_b, create_using=nx.DiGraph)
+    diffs = nx.trophic_differences(G)
+
+    assert diffs[(0, 1)] == pytest.approx(1, abs=1e-7)
+    assert diffs[(0, 2)] == pytest.approx(1.5, abs=1e-7)
+    assert diffs[(1, 2)] == pytest.approx(0.5, abs=1e-7)
+    assert diffs[(1, 3)] == pytest.approx(1.25, abs=1e-7)
+    assert diffs[(2, 3)] == pytest.approx(0.75, abs=1e-7)
+
+
+def test_trophic_incoherence_parameter_no_cannibalism():
+    matrix_a = np.array([[0, 1], [0, 0]])
+    G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=False)
+    assert q == pytest.approx(0, abs=1e-7)
+
+    # fmt: off
+    matrix_b = np.array([
+        [0, 1, 1, 0],
+        [0, 0, 1, 1],
+        [0, 0, 0, 1],
+        [0, 0, 0, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix_b, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=False)
+    assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7)
+
+    # fmt: off
+    matrix_c = np.array([
+        [0, 1, 1, 0],
+        [0, 1, 1, 1],
+        [0, 0, 0, 1],
+        [0, 0, 0, 1]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix_c, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=False)
+    # Ignore the -link
+    assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7)
+
+    # no self-loops case
+    # fmt: off
+    matrix_d = np.array([
+        [0, 1, 1, 0],
+        [0, 0, 1, 1],
+        [0, 0, 0, 1],
+        [0, 0, 0, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix_d, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=False)
+    # Ignore the -link
+    assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7)
+
+
+def test_trophic_incoherence_parameter_cannibalism():
+    matrix_a = np.array([[0, 1], [0, 0]])
+    G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=True)
+    assert q == pytest.approx(0, abs=1e-7)
+
+    # fmt: off
+    matrix_b = np.array([
+        [0, 0, 0, 0, 0],
+        [0, 1, 0, 1, 0],
+        [1, 0, 0, 0, 0],
+        [0, 1, 0, 0, 0],
+        [0, 0, 0, 1, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix_b, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=True)
+    assert q == pytest.approx(2, abs=1e-7)
+
+    # fmt: off
+    matrix_c = np.array([
+        [0, 1, 1, 0],
+        [0, 0, 1, 1],
+        [0, 0, 0, 1],
+        [0, 0, 0, 0]
+    ])
+    # fmt: on
+    G = nx.from_numpy_array(matrix_c, create_using=nx.DiGraph)
+    q = nx.trophic_incoherence_parameter(G, cannibalism=True)
+    # Ignore the -link
+    assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7)