446 lines
17 KiB
Python
446 lines
17 KiB
Python
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"""Unit tests for layout functions."""
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import pytest
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import networkx as nx
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np = pytest.importorskip("numpy")
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pytest.importorskip("scipy")
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class TestLayout:
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@classmethod
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def setup_class(cls):
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cls.Gi = nx.grid_2d_graph(5, 5)
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cls.Gs = nx.Graph()
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nx.add_path(cls.Gs, "abcdef")
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cls.bigG = nx.grid_2d_graph(25, 25) # > 500 nodes for sparse
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def test_spring_fixed_without_pos(self):
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G = nx.path_graph(4)
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pytest.raises(ValueError, nx.spring_layout, G, fixed=[0])
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pos = {0: (1, 1), 2: (0, 0)}
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pytest.raises(ValueError, nx.spring_layout, G, fixed=[0, 1], pos=pos)
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nx.spring_layout(G, fixed=[0, 2], pos=pos) # No ValueError
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def test_spring_init_pos(self):
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# Tests GH #2448
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import math
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G = nx.Graph()
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G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3)])
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init_pos = {0: (0.0, 0.0)}
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fixed_pos = [0]
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pos = nx.fruchterman_reingold_layout(G, pos=init_pos, fixed=fixed_pos)
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has_nan = any(math.isnan(c) for coords in pos.values() for c in coords)
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assert not has_nan, "values should not be nan"
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def test_smoke_empty_graph(self):
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G = []
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nx.random_layout(G)
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nx.circular_layout(G)
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nx.planar_layout(G)
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nx.spring_layout(G)
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nx.fruchterman_reingold_layout(G)
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nx.spectral_layout(G)
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nx.shell_layout(G)
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nx.bipartite_layout(G, G)
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nx.spiral_layout(G)
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nx.multipartite_layout(G)
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nx.kamada_kawai_layout(G)
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def test_smoke_int(self):
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G = self.Gi
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nx.random_layout(G)
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nx.circular_layout(G)
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nx.planar_layout(G)
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nx.spring_layout(G)
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nx.fruchterman_reingold_layout(G)
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nx.fruchterman_reingold_layout(self.bigG)
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nx.spectral_layout(G)
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nx.spectral_layout(G.to_directed())
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nx.spectral_layout(self.bigG)
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nx.spectral_layout(self.bigG.to_directed())
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nx.shell_layout(G)
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nx.spiral_layout(G)
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nx.kamada_kawai_layout(G)
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nx.kamada_kawai_layout(G, dim=1)
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nx.kamada_kawai_layout(G, dim=3)
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def test_smoke_string(self):
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G = self.Gs
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nx.random_layout(G)
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nx.circular_layout(G)
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nx.planar_layout(G)
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nx.spring_layout(G)
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nx.fruchterman_reingold_layout(G)
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nx.spectral_layout(G)
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nx.shell_layout(G)
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nx.spiral_layout(G)
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nx.kamada_kawai_layout(G)
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nx.kamada_kawai_layout(G, dim=1)
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nx.kamada_kawai_layout(G, dim=3)
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def check_scale_and_center(self, pos, scale, center):
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center = np.array(center)
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low = center - scale
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hi = center + scale
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vpos = np.array(list(pos.values()))
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length = vpos.max(0) - vpos.min(0)
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assert (length <= 2 * scale).all()
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assert (vpos >= low).all()
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assert (vpos <= hi).all()
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def test_scale_and_center_arg(self):
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sc = self.check_scale_and_center
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c = (4, 5)
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G = nx.complete_graph(9)
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G.add_node(9)
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sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5))
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# rest can have 2*scale length: [-scale, scale]
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sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c)
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sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c)
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sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c)
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sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c)
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sc(nx.spiral_layout(G, scale=2, center=c), scale=2, center=c)
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sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c)
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c = (2, 3, 5)
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sc(nx.kamada_kawai_layout(G, dim=3, scale=2, center=c), scale=2, center=c)
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def test_planar_layout_non_planar_input(self):
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G = nx.complete_graph(9)
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pytest.raises(nx.NetworkXException, nx.planar_layout, G)
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def test_smoke_planar_layout_embedding_input(self):
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embedding = nx.PlanarEmbedding()
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embedding.set_data({0: [1, 2], 1: [0, 2], 2: [0, 1]})
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nx.planar_layout(embedding)
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def test_default_scale_and_center(self):
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sc = self.check_scale_and_center
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c = (0, 0)
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G = nx.complete_graph(9)
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G.add_node(9)
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sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
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sc(nx.spring_layout(G), scale=1, center=c)
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sc(nx.spectral_layout(G), scale=1, center=c)
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sc(nx.circular_layout(G), scale=1, center=c)
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sc(nx.shell_layout(G), scale=1, center=c)
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sc(nx.spiral_layout(G), scale=1, center=c)
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sc(nx.kamada_kawai_layout(G), scale=1, center=c)
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c = (0, 0, 0)
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sc(nx.kamada_kawai_layout(G, dim=3), scale=1, center=c)
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def test_circular_planar_and_shell_dim_error(self):
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G = nx.path_graph(4)
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pytest.raises(ValueError, nx.circular_layout, G, dim=1)
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pytest.raises(ValueError, nx.shell_layout, G, dim=1)
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pytest.raises(ValueError, nx.shell_layout, G, dim=3)
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pytest.raises(ValueError, nx.planar_layout, G, dim=1)
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pytest.raises(ValueError, nx.planar_layout, G, dim=3)
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def test_adjacency_interface_numpy(self):
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A = nx.to_numpy_array(self.Gs)
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pos = nx.drawing.layout._fruchterman_reingold(A)
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assert pos.shape == (6, 2)
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pos = nx.drawing.layout._fruchterman_reingold(A, dim=3)
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assert pos.shape == (6, 3)
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pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
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assert pos.shape == (6, 2)
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def test_adjacency_interface_scipy(self):
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A = nx.to_scipy_sparse_array(self.Gs, dtype="d")
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pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
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assert pos.shape == (6, 2)
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pos = nx.drawing.layout._sparse_spectral(A)
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assert pos.shape == (6, 2)
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pos = nx.drawing.layout._sparse_fruchterman_reingold(A, dim=3)
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assert pos.shape == (6, 3)
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def test_single_nodes(self):
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G = nx.path_graph(1)
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vpos = nx.shell_layout(G)
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assert not vpos[0].any()
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G = nx.path_graph(4)
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vpos = nx.shell_layout(G, [[0], [1, 2], [3]])
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assert not vpos[0].any()
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assert vpos[3].any() # ensure node 3 not at origin (#3188)
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assert np.linalg.norm(vpos[3]) <= 1 # ensure node 3 fits (#3753)
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vpos = nx.shell_layout(G, [[0], [1, 2], [3]], rotate=0)
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assert np.linalg.norm(vpos[3]) <= 1 # ensure node 3 fits (#3753)
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def test_smoke_initial_pos_fruchterman_reingold(self):
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pos = nx.circular_layout(self.Gi)
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npos = nx.fruchterman_reingold_layout(self.Gi, pos=pos)
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def test_fixed_node_fruchterman_reingold(self):
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# Dense version (numpy based)
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pos = nx.circular_layout(self.Gi)
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npos = nx.spring_layout(self.Gi, pos=pos, fixed=[(0, 0)])
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assert tuple(pos[(0, 0)]) == tuple(npos[(0, 0)])
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# Sparse version (scipy based)
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pos = nx.circular_layout(self.bigG)
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npos = nx.spring_layout(self.bigG, pos=pos, fixed=[(0, 0)])
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for axis in range(2):
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assert pos[(0, 0)][axis] == pytest.approx(npos[(0, 0)][axis], abs=1e-7)
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def test_center_parameter(self):
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G = nx.path_graph(1)
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nx.random_layout(G, center=(1, 1))
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vpos = nx.circular_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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vpos = nx.planar_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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vpos = nx.spring_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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vpos = nx.spectral_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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vpos = nx.shell_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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vpos = nx.spiral_layout(G, center=(1, 1))
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assert tuple(vpos[0]) == (1, 1)
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def test_center_wrong_dimensions(self):
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G = nx.path_graph(1)
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assert id(nx.spring_layout) == id(nx.fruchterman_reingold_layout)
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pytest.raises(ValueError, nx.random_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.circular_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.planar_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.spring_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.spring_layout, G, dim=3, center=(1, 1))
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pytest.raises(ValueError, nx.spectral_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.spectral_layout, G, dim=3, center=(1, 1))
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pytest.raises(ValueError, nx.shell_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.spiral_layout, G, center=(1, 1, 1))
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pytest.raises(ValueError, nx.kamada_kawai_layout, G, center=(1, 1, 1))
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def test_empty_graph(self):
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G = nx.empty_graph()
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vpos = nx.random_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.circular_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.planar_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.bipartite_layout(G, G)
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assert vpos == {}
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vpos = nx.spring_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.spectral_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.shell_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.spiral_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.multipartite_layout(G, center=(1, 1))
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assert vpos == {}
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vpos = nx.kamada_kawai_layout(G, center=(1, 1))
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assert vpos == {}
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def test_bipartite_layout(self):
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G = nx.complete_bipartite_graph(3, 5)
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top, bottom = nx.bipartite.sets(G)
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vpos = nx.bipartite_layout(G, top)
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assert len(vpos) == len(G)
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top_x = vpos[list(top)[0]][0]
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bottom_x = vpos[list(bottom)[0]][0]
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for node in top:
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assert vpos[node][0] == top_x
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for node in bottom:
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assert vpos[node][0] == bottom_x
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vpos = nx.bipartite_layout(
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G, top, align="horizontal", center=(2, 2), scale=2, aspect_ratio=1
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)
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assert len(vpos) == len(G)
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top_y = vpos[list(top)[0]][1]
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bottom_y = vpos[list(bottom)[0]][1]
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for node in top:
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assert vpos[node][1] == top_y
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for node in bottom:
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assert vpos[node][1] == bottom_y
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pytest.raises(ValueError, nx.bipartite_layout, G, top, align="foo")
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def test_multipartite_layout(self):
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sizes = (0, 5, 7, 2, 8)
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G = nx.complete_multipartite_graph(*sizes)
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vpos = nx.multipartite_layout(G)
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assert len(vpos) == len(G)
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start = 0
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for n in sizes:
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end = start + n
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assert all(vpos[start][0] == vpos[i][0] for i in range(start + 1, end))
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start += n
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vpos = nx.multipartite_layout(G, align="horizontal", scale=2, center=(2, 2))
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assert len(vpos) == len(G)
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start = 0
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for n in sizes:
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end = start + n
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assert all(vpos[start][1] == vpos[i][1] for i in range(start + 1, end))
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start += n
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pytest.raises(ValueError, nx.multipartite_layout, G, align="foo")
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def test_kamada_kawai_costfn_1d(self):
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costfn = nx.drawing.layout._kamada_kawai_costfn
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pos = np.array([4.0, 7.0])
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invdist = 1 / np.array([[0.1, 2.0], [2.0, 0.3]])
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cost, grad = costfn(pos, np, invdist, meanweight=0, dim=1)
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assert cost == pytest.approx(((3 / 2.0 - 1) ** 2), abs=1e-7)
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assert grad[0] == pytest.approx((-0.5), abs=1e-7)
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||
|
|
assert grad[1] == pytest.approx(0.5, abs=1e-7)
|
||
|
|
|
||
|
|
def check_kamada_kawai_costfn(self, pos, invdist, meanwt, dim):
|
||
|
|
costfn = nx.drawing.layout._kamada_kawai_costfn
|
||
|
|
|
||
|
|
cost, grad = costfn(pos.ravel(), np, invdist, meanweight=meanwt, dim=dim)
|
||
|
|
|
||
|
|
expected_cost = 0.5 * meanwt * np.sum(np.sum(pos, axis=0) ** 2)
|
||
|
|
for i in range(pos.shape[0]):
|
||
|
|
for j in range(i + 1, pos.shape[0]):
|
||
|
|
diff = np.linalg.norm(pos[i] - pos[j])
|
||
|
|
expected_cost += (diff * invdist[i][j] - 1.0) ** 2
|
||
|
|
|
||
|
|
assert cost == pytest.approx(expected_cost, abs=1e-7)
|
||
|
|
|
||
|
|
dx = 1e-4
|
||
|
|
for nd in range(pos.shape[0]):
|
||
|
|
for dm in range(pos.shape[1]):
|
||
|
|
idx = nd * pos.shape[1] + dm
|
||
|
|
ps = pos.flatten()
|
||
|
|
|
||
|
|
ps[idx] += dx
|
||
|
|
cplus = costfn(ps, np, invdist, meanweight=meanwt, dim=pos.shape[1])[0]
|
||
|
|
|
||
|
|
ps[idx] -= 2 * dx
|
||
|
|
cminus = costfn(ps, np, invdist, meanweight=meanwt, dim=pos.shape[1])[0]
|
||
|
|
|
||
|
|
assert grad[idx] == pytest.approx((cplus - cminus) / (2 * dx), abs=1e-5)
|
||
|
|
|
||
|
|
def test_kamada_kawai_costfn(self):
|
||
|
|
invdist = 1 / np.array([[0.1, 2.1, 1.7], [2.1, 0.2, 0.6], [1.7, 0.6, 0.3]])
|
||
|
|
meanwt = 0.3
|
||
|
|
|
||
|
|
# 2d
|
||
|
|
pos = np.array([[1.3, -3.2], [2.7, -0.3], [5.1, 2.5]])
|
||
|
|
|
||
|
|
self.check_kamada_kawai_costfn(pos, invdist, meanwt, 2)
|
||
|
|
|
||
|
|
# 3d
|
||
|
|
pos = np.array([[0.9, 8.6, -8.7], [-10, -0.5, -7.1], [9.1, -8.1, 1.6]])
|
||
|
|
|
||
|
|
self.check_kamada_kawai_costfn(pos, invdist, meanwt, 3)
|
||
|
|
|
||
|
|
def test_spiral_layout(self):
|
||
|
|
|
||
|
|
G = self.Gs
|
||
|
|
|
||
|
|
# a lower value of resolution should result in a more compact layout
|
||
|
|
# intuitively, the total distance from the start and end nodes
|
||
|
|
# via each node in between (transiting through each) will be less,
|
||
|
|
# assuming rescaling does not occur on the computed node positions
|
||
|
|
pos_standard = np.array(list(nx.spiral_layout(G, resolution=0.35).values()))
|
||
|
|
pos_tighter = np.array(list(nx.spiral_layout(G, resolution=0.34).values()))
|
||
|
|
distances = np.linalg.norm(pos_standard[:-1] - pos_standard[1:], axis=1)
|
||
|
|
distances_tighter = np.linalg.norm(pos_tighter[:-1] - pos_tighter[1:], axis=1)
|
||
|
|
assert sum(distances) > sum(distances_tighter)
|
||
|
|
|
||
|
|
# return near-equidistant points after the first value if set to true
|
||
|
|
pos_equidistant = np.array(list(nx.spiral_layout(G, equidistant=True).values()))
|
||
|
|
distances_equidistant = np.linalg.norm(
|
||
|
|
pos_equidistant[:-1] - pos_equidistant[1:], axis=1
|
||
|
|
)
|
||
|
|
assert np.allclose(
|
||
|
|
distances_equidistant[1:], distances_equidistant[-1], atol=0.01
|
||
|
|
)
|
||
|
|
|
||
|
|
def test_spiral_layout_equidistant(self):
|
||
|
|
G = nx.path_graph(10)
|
||
|
|
pos = nx.spiral_layout(G, equidistant=True)
|
||
|
|
# Extract individual node positions as an array
|
||
|
|
p = np.array(list(pos.values()))
|
||
|
|
# Elementwise-distance between node positions
|
||
|
|
dist = np.linalg.norm(p[1:] - p[:-1], axis=1)
|
||
|
|
assert np.allclose(np.diff(dist), 0, atol=1e-3)
|
||
|
|
|
||
|
|
def test_rescale_layout_dict(self):
|
||
|
|
G = nx.empty_graph()
|
||
|
|
vpos = nx.random_layout(G, center=(1, 1))
|
||
|
|
assert nx.rescale_layout_dict(vpos) == {}
|
||
|
|
|
||
|
|
G = nx.empty_graph(2)
|
||
|
|
vpos = {0: (0.0, 0.0), 1: (1.0, 1.0)}
|
||
|
|
s_vpos = nx.rescale_layout_dict(vpos)
|
||
|
|
assert np.linalg.norm([sum(x) for x in zip(*s_vpos.values())]) < 1e-6
|
||
|
|
|
||
|
|
G = nx.empty_graph(3)
|
||
|
|
vpos = {0: (0, 0), 1: (1, 1), 2: (0.5, 0.5)}
|
||
|
|
s_vpos = nx.rescale_layout_dict(vpos)
|
||
|
|
|
||
|
|
expectation = {
|
||
|
|
0: np.array((-1, -1)),
|
||
|
|
1: np.array((1, 1)),
|
||
|
|
2: np.array((0, 0)),
|
||
|
|
}
|
||
|
|
for k, v in expectation.items():
|
||
|
|
assert (s_vpos[k] == v).all()
|
||
|
|
s_vpos = nx.rescale_layout_dict(vpos, scale=2)
|
||
|
|
|
||
|
|
expectation = {
|
||
|
|
0: np.array((-2, -2)),
|
||
|
|
1: np.array((2, 2)),
|
||
|
|
2: np.array((0, 0)),
|
||
|
|
}
|
||
|
|
for k, v in expectation.items():
|
||
|
|
assert (s_vpos[k] == v).all()
|
||
|
|
|
||
|
|
|
||
|
|
def test_multipartite_layout_nonnumeric_partition_labels():
|
||
|
|
"""See gh-5123."""
|
||
|
|
G = nx.Graph()
|
||
|
|
G.add_node(0, subset="s0")
|
||
|
|
G.add_node(1, subset="s0")
|
||
|
|
G.add_node(2, subset="s1")
|
||
|
|
G.add_node(3, subset="s1")
|
||
|
|
G.add_edges_from([(0, 2), (0, 3), (1, 2)])
|
||
|
|
pos = nx.multipartite_layout(G)
|
||
|
|
assert len(pos) == len(G)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multipartite_layout_layer_order():
|
||
|
|
"""Return the layers in sorted order if the layers of the multipartite
|
||
|
|
graph are sortable. See gh-5691"""
|
||
|
|
G = nx.Graph()
|
||
|
|
for node, layer in zip(("a", "b", "c", "d", "e"), (2, 3, 1, 2, 4)):
|
||
|
|
G.add_node(node, subset=layer)
|
||
|
|
|
||
|
|
# Horizontal alignment, therefore y-coord determines layers
|
||
|
|
pos = nx.multipartite_layout(G, align="horizontal")
|
||
|
|
|
||
|
|
# Nodes "a" and "d" are in the same layer
|
||
|
|
assert pos["a"][-1] == pos["d"][-1]
|
||
|
|
# positions should be sorted according to layer
|
||
|
|
assert pos["c"][-1] < pos["a"][-1] < pos["b"][-1] < pos["e"][-1]
|
||
|
|
|
||
|
|
# Make sure that multipartite_layout still works when layers are not sortable
|
||
|
|
G.nodes["a"]["subset"] = "layer_0" # Can't sort mixed strs/ints
|
||
|
|
pos_nosort = nx.multipartite_layout(G) # smoke test: this should not raise
|
||
|
|
assert pos_nosort.keys() == pos.keys()
|