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view env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_trophic.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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"""Test trophic levels, trophic differences and trophic coherence """ import pytest np = pytest.importorskip("numpy") import networkx as nx from networkx.testing import almost_equal 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.dot(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 almost_equal(expected_level, level) 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 almost_equal(d[0], 2) assert almost_equal(d[1], 1) 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 almost_equal(d[ind], expected_result[ind]) # 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 almost_equal(d[ind], expected_result[ind]) 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.matmul(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 almost_equal(result_1[ind], result_2[ind]) 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 almost_equal(diffs[(0, 1)], 1) # 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 almost_equal(diffs[(0, 1)], 1) assert almost_equal(diffs[(0, 2)], 1.5) assert almost_equal(diffs[(1, 2)], 0.5) assert almost_equal(diffs[(1, 3)], 1.25) assert almost_equal(diffs[(2, 3)], 0.75) 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 almost_equal(q, 0) # 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 almost_equal(q, np.std([1, 1.5, 0.5, 0.75, 1.25])) # 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 almost_equal(q, np.std([1, 1.5, 0.5, 0.75, 1.25])) # 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 almost_equal(q, np.std([1, 1.5, 0.5, 0.75, 1.25])) 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 almost_equal(q, 0) # 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 almost_equal(q, 2) # 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 almost_equal(q, np.std([1, 1.5, 0.5, 0.75, 1.25]))