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| """Unit tests for layout functions.""" | |
| import pytest | |
| import networkx as nx | |
| np = pytest.importorskip("numpy") | |
| pytest.importorskip("scipy") | |
| class TestLayout: | |
| def setup_class(cls): | |
| cls.Gi = nx.grid_2d_graph(5, 5) | |
| cls.Gs = nx.Graph() | |
| nx.add_path(cls.Gs, "abcdef") | |
| cls.bigG = nx.grid_2d_graph(25, 25) # > 500 nodes for sparse | |
| def test_spring_fixed_without_pos(self): | |
| G = nx.path_graph(4) | |
| pytest.raises(ValueError, nx.spring_layout, G, fixed=[0]) | |
| pos = {0: (1, 1), 2: (0, 0)} | |
| pytest.raises(ValueError, nx.spring_layout, G, fixed=[0, 1], pos=pos) | |
| nx.spring_layout(G, fixed=[0, 2], pos=pos) # No ValueError | |
| def test_spring_init_pos(self): | |
| # Tests GH #2448 | |
| import math | |
| G = nx.Graph() | |
| G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3)]) | |
| init_pos = {0: (0.0, 0.0)} | |
| fixed_pos = [0] | |
| pos = nx.fruchterman_reingold_layout(G, pos=init_pos, fixed=fixed_pos) | |
| has_nan = any(math.isnan(c) for coords in pos.values() for c in coords) | |
| assert not has_nan, "values should not be nan" | |
| def test_smoke_empty_graph(self): | |
| G = [] | |
| nx.random_layout(G) | |
| nx.circular_layout(G) | |
| nx.planar_layout(G) | |
| nx.spring_layout(G) | |
| nx.fruchterman_reingold_layout(G) | |
| nx.spectral_layout(G) | |
| nx.shell_layout(G) | |
| nx.bipartite_layout(G, G) | |
| nx.spiral_layout(G) | |
| nx.multipartite_layout(G) | |
| nx.kamada_kawai_layout(G) | |
| def test_smoke_int(self): | |
| G = self.Gi | |
| nx.random_layout(G) | |
| nx.circular_layout(G) | |
| nx.planar_layout(G) | |
| nx.spring_layout(G) | |
| nx.fruchterman_reingold_layout(G) | |
| nx.fruchterman_reingold_layout(self.bigG) | |
| nx.spectral_layout(G) | |
| nx.spectral_layout(G.to_directed()) | |
| nx.spectral_layout(self.bigG) | |
| nx.spectral_layout(self.bigG.to_directed()) | |
| nx.shell_layout(G) | |
| nx.spiral_layout(G) | |
| nx.kamada_kawai_layout(G) | |
| nx.kamada_kawai_layout(G, dim=1) | |
| nx.kamada_kawai_layout(G, dim=3) | |
| nx.arf_layout(G) | |
| def test_smoke_string(self): | |
| G = self.Gs | |
| nx.random_layout(G) | |
| nx.circular_layout(G) | |
| nx.planar_layout(G) | |
| nx.spring_layout(G) | |
| nx.fruchterman_reingold_layout(G) | |
| nx.spectral_layout(G) | |
| nx.shell_layout(G) | |
| nx.spiral_layout(G) | |
| nx.kamada_kawai_layout(G) | |
| nx.kamada_kawai_layout(G, dim=1) | |
| nx.kamada_kawai_layout(G, dim=3) | |
| nx.arf_layout(G) | |
| def check_scale_and_center(self, pos, scale, center): | |
| center = np.array(center) | |
| low = center - scale | |
| hi = center + scale | |
| vpos = np.array(list(pos.values())) | |
| length = vpos.max(0) - vpos.min(0) | |
| assert (length <= 2 * scale).all() | |
| assert (vpos >= low).all() | |
| assert (vpos <= hi).all() | |
| def test_scale_and_center_arg(self): | |
| sc = self.check_scale_and_center | |
| c = (4, 5) | |
| G = nx.complete_graph(9) | |
| G.add_node(9) | |
| sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5)) | |
| # rest can have 2*scale length: [-scale, scale] | |
| sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c) | |
| sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c) | |
| sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c) | |
| sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c) | |
| sc(nx.spiral_layout(G, scale=2, center=c), scale=2, center=c) | |
| sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c) | |
| c = (2, 3, 5) | |
| sc(nx.kamada_kawai_layout(G, dim=3, scale=2, center=c), scale=2, center=c) | |
| def test_planar_layout_non_planar_input(self): | |
| G = nx.complete_graph(9) | |
| pytest.raises(nx.NetworkXException, nx.planar_layout, G) | |
| def test_smoke_planar_layout_embedding_input(self): | |
| embedding = nx.PlanarEmbedding() | |
| embedding.set_data({0: [1, 2], 1: [0, 2], 2: [0, 1]}) | |
| nx.planar_layout(embedding) | |
| def test_default_scale_and_center(self): | |
| sc = self.check_scale_and_center | |
| c = (0, 0) | |
| G = nx.complete_graph(9) | |
| G.add_node(9) | |
| sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5)) | |
| sc(nx.spring_layout(G), scale=1, center=c) | |
| sc(nx.spectral_layout(G), scale=1, center=c) | |
| sc(nx.circular_layout(G), scale=1, center=c) | |
| sc(nx.shell_layout(G), scale=1, center=c) | |
| sc(nx.spiral_layout(G), scale=1, center=c) | |
| sc(nx.kamada_kawai_layout(G), scale=1, center=c) | |
| c = (0, 0, 0) | |
| sc(nx.kamada_kawai_layout(G, dim=3), scale=1, center=c) | |
| def test_circular_planar_and_shell_dim_error(self): | |
| G = nx.path_graph(4) | |
| pytest.raises(ValueError, nx.circular_layout, G, dim=1) | |
| pytest.raises(ValueError, nx.shell_layout, G, dim=1) | |
| pytest.raises(ValueError, nx.shell_layout, G, dim=3) | |
| pytest.raises(ValueError, nx.planar_layout, G, dim=1) | |
| pytest.raises(ValueError, nx.planar_layout, G, dim=3) | |
| def test_adjacency_interface_numpy(self): | |
| A = nx.to_numpy_array(self.Gs) | |
| pos = nx.drawing.layout._fruchterman_reingold(A) | |
| assert pos.shape == (6, 2) | |
| pos = nx.drawing.layout._fruchterman_reingold(A, dim=3) | |
| assert pos.shape == (6, 3) | |
| pos = nx.drawing.layout._sparse_fruchterman_reingold(A) | |
| assert pos.shape == (6, 2) | |
| def test_adjacency_interface_scipy(self): | |
| A = nx.to_scipy_sparse_array(self.Gs, dtype="d") | |
| pos = nx.drawing.layout._sparse_fruchterman_reingold(A) | |
| assert pos.shape == (6, 2) | |
| pos = nx.drawing.layout._sparse_spectral(A) | |
| assert pos.shape == (6, 2) | |
| pos = nx.drawing.layout._sparse_fruchterman_reingold(A, dim=3) | |
| assert pos.shape == (6, 3) | |
| def test_single_nodes(self): | |
| G = nx.path_graph(1) | |
| vpos = nx.shell_layout(G) | |
| assert not vpos[0].any() | |
| G = nx.path_graph(4) | |
| vpos = nx.shell_layout(G, [[0], [1, 2], [3]]) | |
| assert not vpos[0].any() | |
| assert vpos[3].any() # ensure node 3 not at origin (#3188) | |
| assert np.linalg.norm(vpos[3]) <= 1 # ensure node 3 fits (#3753) | |
| vpos = nx.shell_layout(G, [[0], [1, 2], [3]], rotate=0) | |
| assert np.linalg.norm(vpos[3]) <= 1 # ensure node 3 fits (#3753) | |
| def test_smoke_initial_pos_fruchterman_reingold(self): | |
| pos = nx.circular_layout(self.Gi) | |
| npos = nx.fruchterman_reingold_layout(self.Gi, pos=pos) | |
| def test_smoke_initial_pos_arf(self): | |
| pos = nx.circular_layout(self.Gi) | |
| npos = nx.arf_layout(self.Gi, pos=pos) | |
| def test_fixed_node_fruchterman_reingold(self): | |
| # Dense version (numpy based) | |
| pos = nx.circular_layout(self.Gi) | |
| npos = nx.spring_layout(self.Gi, pos=pos, fixed=[(0, 0)]) | |
| assert tuple(pos[(0, 0)]) == tuple(npos[(0, 0)]) | |
| # Sparse version (scipy based) | |
| pos = nx.circular_layout(self.bigG) | |
| npos = nx.spring_layout(self.bigG, pos=pos, fixed=[(0, 0)]) | |
| for axis in range(2): | |
| assert pos[(0, 0)][axis] == pytest.approx(npos[(0, 0)][axis], abs=1e-7) | |
| def test_center_parameter(self): | |
| G = nx.path_graph(1) | |
| nx.random_layout(G, center=(1, 1)) | |
| vpos = nx.circular_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| vpos = nx.planar_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| vpos = nx.spring_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| vpos = nx.fruchterman_reingold_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| vpos = nx.spectral_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| vpos = nx.shell_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| vpos = nx.spiral_layout(G, center=(1, 1)) | |
| assert tuple(vpos[0]) == (1, 1) | |
| def test_center_wrong_dimensions(self): | |
| G = nx.path_graph(1) | |
| assert id(nx.spring_layout) == id(nx.fruchterman_reingold_layout) | |
| pytest.raises(ValueError, nx.random_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.circular_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.planar_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.spring_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.spring_layout, G, dim=3, center=(1, 1)) | |
| pytest.raises(ValueError, nx.spectral_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.spectral_layout, G, dim=3, center=(1, 1)) | |
| pytest.raises(ValueError, nx.shell_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.spiral_layout, G, center=(1, 1, 1)) | |
| pytest.raises(ValueError, nx.kamada_kawai_layout, G, center=(1, 1, 1)) | |
| def test_empty_graph(self): | |
| G = nx.empty_graph() | |
| vpos = nx.random_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.circular_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.planar_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.bipartite_layout(G, G) | |
| assert vpos == {} | |
| vpos = nx.spring_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.fruchterman_reingold_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.spectral_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.shell_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.spiral_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.multipartite_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.kamada_kawai_layout(G, center=(1, 1)) | |
| assert vpos == {} | |
| vpos = nx.arf_layout(G) | |
| assert vpos == {} | |
| def test_bipartite_layout(self): | |
| G = nx.complete_bipartite_graph(3, 5) | |
| top, bottom = nx.bipartite.sets(G) | |
| vpos = nx.bipartite_layout(G, top) | |
| assert len(vpos) == len(G) | |
| top_x = vpos[list(top)[0]][0] | |
| bottom_x = vpos[list(bottom)[0]][0] | |
| for node in top: | |
| assert vpos[node][0] == top_x | |
| for node in bottom: | |
| assert vpos[node][0] == bottom_x | |
| vpos = nx.bipartite_layout( | |
| G, top, align="horizontal", center=(2, 2), scale=2, aspect_ratio=1 | |
| ) | |
| assert len(vpos) == len(G) | |
| top_y = vpos[list(top)[0]][1] | |
| bottom_y = vpos[list(bottom)[0]][1] | |
| for node in top: | |
| assert vpos[node][1] == top_y | |
| for node in bottom: | |
| assert vpos[node][1] == bottom_y | |
| pytest.raises(ValueError, nx.bipartite_layout, G, top, align="foo") | |
| def test_multipartite_layout(self): | |
| sizes = (0, 5, 7, 2, 8) | |
| G = nx.complete_multipartite_graph(*sizes) | |
| vpos = nx.multipartite_layout(G) | |
| assert len(vpos) == len(G) | |
| start = 0 | |
| for n in sizes: | |
| end = start + n | |
| assert all(vpos[start][0] == vpos[i][0] for i in range(start + 1, end)) | |
| start += n | |
| vpos = nx.multipartite_layout(G, align="horizontal", scale=2, center=(2, 2)) | |
| assert len(vpos) == len(G) | |
| start = 0 | |
| for n in sizes: | |
| end = start + n | |
| assert all(vpos[start][1] == vpos[i][1] for i in range(start + 1, end)) | |
| start += n | |
| pytest.raises(ValueError, nx.multipartite_layout, G, align="foo") | |
| def test_kamada_kawai_costfn_1d(self): | |
| costfn = nx.drawing.layout._kamada_kawai_costfn | |
| pos = np.array([4.0, 7.0]) | |
| invdist = 1 / np.array([[0.1, 2.0], [2.0, 0.3]]) | |
| cost, grad = costfn(pos, np, invdist, meanweight=0, dim=1) | |
| assert cost == pytest.approx(((3 / 2.0 - 1) ** 2), abs=1e-7) | |
| assert grad[0] == pytest.approx((-0.5), abs=1e-7) | |
| 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_arf_layout_partial_input_test(self): | |
| """ | |
| Checks whether partial pos input still returns a proper position. | |
| """ | |
| G = self.Gs | |
| node = nx.utils.arbitrary_element(G) | |
| pos = nx.circular_layout(G) | |
| del pos[node] | |
| pos = nx.arf_layout(G, pos=pos) | |
| assert len(pos) == len(G) | |
| def test_arf_layout_negative_a_check(self): | |
| """ | |
| Checks input parameters correctly raises errors. For example, `a` should be larger than 1 | |
| """ | |
| G = self.Gs | |
| pytest.raises(ValueError, nx.arf_layout, G=G, a=-1) | |
| 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() | |