""" ****** Layout ****** Node positioning algorithms for graph drawing. For `random_layout()` the possible resulting shape is a square of side [0, scale] (default: [0, 1]) Changing `center` shifts the layout by that amount. For the other layout routines, the extent is [center - scale, center + scale] (default: [-1, 1]). Warning: Most layout routines have only been tested in 2-dimensions. """ import networkx as nx from networkx.utils import np_random_state __all__ = [ "bipartite_layout", "circular_layout", "forceatlas2_layout", "kamada_kawai_layout", "random_layout", "rescale_layout", "rescale_layout_dict", "shell_layout", "spring_layout", "spectral_layout", "planar_layout", "fruchterman_reingold_layout", "spiral_layout", "multipartite_layout", "bfs_layout", "arf_layout", ] def _process_params(G, center, dim): # Some boilerplate code. import numpy as np if not isinstance(G, nx.Graph): empty_graph = nx.Graph() empty_graph.add_nodes_from(G) G = empty_graph if center is None: center = np.zeros(dim) else: center = np.asarray(center) if len(center) != dim: msg = "length of center coordinates must match dimension of layout" raise ValueError(msg) return G, center @np_random_state(3) def random_layout(G, center=None, dim=2, seed=None): """Position nodes uniformly at random in the unit square. For every node, a position is generated by choosing each of dim coordinates uniformly at random on the interval [0.0, 1.0). NumPy (http://scipy.org) is required for this function. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. seed : int, RandomState instance or None optional (default=None) Set the random state for deterministic node layouts. If int, `seed` is the seed used by the random number generator, if numpy.random.RandomState instance, `seed` is the random number generator, if None, the random number generator is the RandomState instance used by numpy.random. Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.lollipop_graph(4, 3) >>> pos = nx.random_layout(G) """ import numpy as np G, center = _process_params(G, center, dim) pos = seed.rand(len(G), dim) + center pos = pos.astype(np.float32) pos = dict(zip(G, pos)) return pos def circular_layout(G, scale=1, center=None, dim=2): # dim=2 only """Position nodes on a circle. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. If dim>2, the remaining dimensions are set to zero in the returned positions. If dim<2, a ValueError is raised. Returns ------- pos : dict A dictionary of positions keyed by node Raises ------ ValueError If dim < 2 Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.circular_layout(G) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. """ import numpy as np if dim < 2: raise ValueError("cannot handle dimensions < 2") G, center = _process_params(G, center, dim) paddims = max(0, (dim - 2)) if len(G) == 0: pos = {} elif len(G) == 1: pos = {nx.utils.arbitrary_element(G): center} else: # Discard the extra angle since it matches 0 radians. theta = np.linspace(0, 1, len(G) + 1)[:-1] * 2 * np.pi theta = theta.astype(np.float32) pos = np.column_stack( [np.cos(theta), np.sin(theta), np.zeros((len(G), paddims))] ) pos = rescale_layout(pos, scale=scale) + center pos = dict(zip(G, pos)) return pos def shell_layout(G, nlist=None, rotate=None, scale=1, center=None, dim=2): """Position nodes in concentric circles. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. nlist : list of lists List of node lists for each shell. rotate : angle in radians (default=pi/len(nlist)) Angle by which to rotate the starting position of each shell relative to the starting position of the previous shell. To recreate behavior before v2.5 use rotate=0. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout, currently only dim=2 is supported. Other dimension values result in a ValueError. Returns ------- pos : dict A dictionary of positions keyed by node Raises ------ ValueError If dim != 2 Examples -------- >>> G = nx.path_graph(4) >>> shells = [[0], [1, 2, 3]] >>> pos = nx.shell_layout(G, shells) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. """ import numpy as np if dim != 2: raise ValueError("can only handle 2 dimensions") G, center = _process_params(G, center, dim) if len(G) == 0: return {} if len(G) == 1: return {nx.utils.arbitrary_element(G): center} if nlist is None: # draw the whole graph in one shell nlist = [list(G)] radius_bump = scale / len(nlist) if len(nlist[0]) == 1: # single node at center radius = 0.0 else: # else start at r=1 radius = radius_bump if rotate is None: rotate = np.pi / len(nlist) first_theta = rotate npos = {} for nodes in nlist: # Discard the last angle (endpoint=False) since 2*pi matches 0 radians theta = ( np.linspace(0, 2 * np.pi, len(nodes), endpoint=False, dtype=np.float32) + first_theta ) pos = radius * np.column_stack([np.cos(theta), np.sin(theta)]) + center npos.update(zip(nodes, pos)) radius += radius_bump first_theta += rotate return npos def bipartite_layout( G, nodes, align="vertical", scale=1, center=None, aspect_ratio=4 / 3 ): """Position nodes in two straight lines. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. nodes : list or container Nodes in one node set of the bipartite graph. This set will be placed on left or top. align : string (default='vertical') The alignment of nodes. Vertical or horizontal. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. aspect_ratio : number (default=4/3): The ratio of the width to the height of the layout. Returns ------- pos : dict A dictionary of positions keyed by node. Examples -------- >>> G = nx.bipartite.gnmk_random_graph(3, 5, 10, seed=123) >>> top = nx.bipartite.sets(G)[0] >>> pos = nx.bipartite_layout(G, top) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. """ import numpy as np if align not in ("vertical", "horizontal"): msg = "align must be either vertical or horizontal." raise ValueError(msg) G, center = _process_params(G, center=center, dim=2) if len(G) == 0: return {} height = 1 width = aspect_ratio * height offset = (width / 2, height / 2) top = dict.fromkeys(nodes) bottom = [v for v in G if v not in top] nodes = list(top) + bottom left_xs = np.repeat(0, len(top)) right_xs = np.repeat(width, len(bottom)) left_ys = np.linspace(0, height, len(top)) right_ys = np.linspace(0, height, len(bottom)) top_pos = np.column_stack([left_xs, left_ys]) - offset bottom_pos = np.column_stack([right_xs, right_ys]) - offset pos = np.concatenate([top_pos, bottom_pos]) pos = rescale_layout(pos, scale=scale) + center if align == "horizontal": pos = pos[:, ::-1] # swap x and y coords pos = dict(zip(nodes, pos)) return pos @np_random_state(10) def spring_layout( G, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, weight="weight", scale=1, center=None, dim=2, seed=None, ): """Position nodes using Fruchterman-Reingold force-directed algorithm. The algorithm simulates a force-directed representation of the network treating edges as springs holding nodes close, while treating nodes as repelling objects, sometimes called an anti-gravity force. Simulation continues until the positions are close to an equilibrium. There are some hard-coded values: minimal distance between nodes (0.01) and "temperature" of 0.1 to ensure nodes don't fly away. During the simulation, `k` helps determine the distance between nodes, though `scale` and `center` determine the size and place after rescaling occurs at the end of the simulation. Fixing some nodes doesn't allow them to move in the simulation. It also turns off the rescaling feature at the simulation's end. In addition, setting `scale` to `None` turns off rescaling. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. k : float (default=None) Optimal distance between nodes. If None the distance is set to 1/sqrt(n) where n is the number of nodes. Increase this value to move nodes farther apart. pos : dict or None optional (default=None) Initial positions for nodes as a dictionary with node as keys and values as a coordinate list or tuple. If None, then use random initial positions. fixed : list or None optional (default=None) Nodes to keep fixed at initial position. Nodes not in ``G.nodes`` are ignored. ValueError raised if `fixed` specified and `pos` not. iterations : int optional (default=50) Maximum number of iterations taken threshold: float optional (default = 1e-4) Threshold for relative error in node position changes. The iteration stops if the error is below this threshold. weight : string or None optional (default='weight') The edge attribute that holds the numerical value used for the edge weight. Larger means a stronger attractive force. If None, then all edge weights are 1. scale : number or None (default: 1) Scale factor for positions. Not used unless `fixed is None`. If scale is None, no rescaling is performed. center : array-like or None Coordinate pair around which to center the layout. Not used unless `fixed is None`. dim : int Dimension of layout. seed : int, RandomState instance or None optional (default=None) Used only for the initial positions in the algorithm. Set the random state for deterministic node layouts. If int, `seed` is the seed used by the random number generator, if numpy.random.RandomState instance, `seed` is the random number generator, if None, the random number generator is the RandomState instance used by numpy.random. Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.spring_layout(G) # The same using longer but equivalent function name >>> pos = nx.fruchterman_reingold_layout(G) """ import numpy as np G, center = _process_params(G, center, dim) if fixed is not None: if pos is None: raise ValueError("nodes are fixed without positions given") for node in fixed: if node not in pos: raise ValueError("nodes are fixed without positions given") nfixed = {node: i for i, node in enumerate(G)} fixed = np.asarray([nfixed[node] for node in fixed if node in nfixed]) if pos is not None: # Determine size of existing domain to adjust initial positions dom_size = max(coord for pos_tup in pos.values() for coord in pos_tup) if dom_size == 0: dom_size = 1 pos_arr = seed.rand(len(G), dim) * dom_size + center for i, n in enumerate(G): if n in pos: pos_arr[i] = np.asarray(pos[n]) else: pos_arr = None dom_size = 1 if len(G) == 0: return {} if len(G) == 1: return {nx.utils.arbitrary_element(G.nodes()): center} try: # Sparse matrix if len(G) < 500: # sparse solver for large graphs raise ValueError A = nx.to_scipy_sparse_array(G, weight=weight, dtype="f") if k is None and fixed is not None: # We must adjust k by domain size for layouts not near 1x1 nnodes, _ = A.shape k = dom_size / np.sqrt(nnodes) pos = _sparse_fruchterman_reingold( A, k, pos_arr, fixed, iterations, threshold, dim, seed ) except ValueError: A = nx.to_numpy_array(G, weight=weight) if k is None and fixed is not None: # We must adjust k by domain size for layouts not near 1x1 nnodes, _ = A.shape k = dom_size / np.sqrt(nnodes) pos = _fruchterman_reingold( A, k, pos_arr, fixed, iterations, threshold, dim, seed ) if fixed is None and scale is not None: pos = rescale_layout(pos, scale=scale) + center pos = dict(zip(G, pos)) return pos fruchterman_reingold_layout = spring_layout @np_random_state(7) def _fruchterman_reingold( A, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, dim=2, seed=None ): # Position nodes in adjacency matrix A using Fruchterman-Reingold # Entry point for NetworkX graph is fruchterman_reingold_layout() import numpy as np try: nnodes, _ = A.shape except AttributeError as err: msg = "fruchterman_reingold() takes an adjacency matrix as input" raise nx.NetworkXError(msg) from err if pos is None: # random initial positions pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype) else: # make sure positions are of same type as matrix pos = pos.astype(A.dtype) # optimal distance between nodes if k is None: k = np.sqrt(1.0 / nnodes) # the initial "temperature" is about .1 of domain area (=1x1) # this is the largest step allowed in the dynamics. # We need to calculate this in case our fixed positions force our domain # to be much bigger than 1x1 t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1 # simple cooling scheme. # linearly step down by dt on each iteration so last iteration is size dt. dt = t / (iterations + 1) delta = np.zeros((pos.shape[0], pos.shape[0], pos.shape[1]), dtype=A.dtype) # the inscrutable (but fast) version # this is still O(V^2) # could use multilevel methods to speed this up significantly for iteration in range(iterations): # matrix of difference between points delta = pos[:, np.newaxis, :] - pos[np.newaxis, :, :] # distance between points distance = np.linalg.norm(delta, axis=-1) # enforce minimum distance of 0.01 np.clip(distance, 0.01, None, out=distance) # displacement "force" displacement = np.einsum( "ijk,ij->ik", delta, (k * k / distance**2 - A * distance / k) ) # update positions length = np.linalg.norm(displacement, axis=-1) length = np.where(length < 0.01, 0.1, length) delta_pos = np.einsum("ij,i->ij", displacement, t / length) if fixed is not None: # don't change positions of fixed nodes delta_pos[fixed] = 0.0 pos += delta_pos # cool temperature t -= dt if (np.linalg.norm(delta_pos) / nnodes) < threshold: break return pos @np_random_state(7) def _sparse_fruchterman_reingold( A, k=None, pos=None, fixed=None, iterations=50, threshold=1e-4, dim=2, seed=None ): # Position nodes in adjacency matrix A using Fruchterman-Reingold # Entry point for NetworkX graph is fruchterman_reingold_layout() # Sparse version import numpy as np import scipy as sp try: nnodes, _ = A.shape except AttributeError as err: msg = "fruchterman_reingold() takes an adjacency matrix as input" raise nx.NetworkXError(msg) from err # make sure we have a LIst of Lists representation try: A = A.tolil() except AttributeError: A = (sp.sparse.coo_array(A)).tolil() if pos is None: # random initial positions pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype) else: # make sure positions are of same type as matrix pos = pos.astype(A.dtype) # no fixed nodes if fixed is None: fixed = [] # optimal distance between nodes if k is None: k = np.sqrt(1.0 / nnodes) # the initial "temperature" is about .1 of domain area (=1x1) # this is the largest step allowed in the dynamics. t = max(max(pos.T[0]) - min(pos.T[0]), max(pos.T[1]) - min(pos.T[1])) * 0.1 # simple cooling scheme. # linearly step down by dt on each iteration so last iteration is size dt. dt = t / (iterations + 1) displacement = np.zeros((dim, nnodes)) for iteration in range(iterations): displacement *= 0 # loop over rows for i in range(A.shape[0]): if i in fixed: continue # difference between this row's node position and all others delta = (pos[i] - pos).T # distance between points distance = np.sqrt((delta**2).sum(axis=0)) # enforce minimum distance of 0.01 distance = np.where(distance < 0.01, 0.01, distance) # the adjacency matrix row Ai = A.getrowview(i).toarray() # TODO: revisit w/ sparse 1D container # displacement "force" displacement[:, i] += ( delta * (k * k / distance**2 - Ai * distance / k) ).sum(axis=1) # update positions length = np.sqrt((displacement**2).sum(axis=0)) length = np.where(length < 0.01, 0.1, length) delta_pos = (displacement * t / length).T pos += delta_pos # cool temperature t -= dt if (np.linalg.norm(delta_pos) / nnodes) < threshold: break return pos def kamada_kawai_layout( G, dist=None, pos=None, weight="weight", scale=1, center=None, dim=2 ): """Position nodes using Kamada-Kawai path-length cost-function. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. dist : dict (default=None) A two-level dictionary of optimal distances between nodes, indexed by source and destination node. If None, the distance is computed using shortest_path_length(). pos : dict or None optional (default=None) Initial positions for nodes as a dictionary with node as keys and values as a coordinate list or tuple. If None, then use circular_layout() for dim >= 2 and a linear layout for dim == 1. weight : string or None optional (default='weight') The edge attribute that holds the numerical value used for the edge weight. If None, then all edge weights are 1. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.kamada_kawai_layout(G) """ import numpy as np G, center = _process_params(G, center, dim) nNodes = len(G) if nNodes == 0: return {} if dist is None: dist = dict(nx.shortest_path_length(G, weight=weight)) dist_mtx = 1e6 * np.ones((nNodes, nNodes)) for row, nr in enumerate(G): if nr not in dist: continue rdist = dist[nr] for col, nc in enumerate(G): if nc not in rdist: continue dist_mtx[row][col] = rdist[nc] if pos is None: if dim >= 3: pos = random_layout(G, dim=dim) elif dim == 2: pos = circular_layout(G, dim=dim) else: pos = dict(zip(G, np.linspace(0, 1, len(G)))) pos_arr = np.array([pos[n] for n in G]) pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim) pos = rescale_layout(pos, scale=scale) + center return dict(zip(G, pos)) def _kamada_kawai_solve(dist_mtx, pos_arr, dim): # Anneal node locations based on the Kamada-Kawai cost-function, # using the supplied matrix of preferred inter-node distances, # and starting locations. import numpy as np import scipy as sp meanwt = 1e-3 costargs = (np, 1 / (dist_mtx + np.eye(dist_mtx.shape[0]) * 1e-3), meanwt, dim) optresult = sp.optimize.minimize( _kamada_kawai_costfn, pos_arr.ravel(), method="L-BFGS-B", args=costargs, jac=True, ) return optresult.x.reshape((-1, dim)) def _kamada_kawai_costfn(pos_vec, np, invdist, meanweight, dim): # Cost-function and gradient for Kamada-Kawai layout algorithm nNodes = invdist.shape[0] pos_arr = pos_vec.reshape((nNodes, dim)) delta = pos_arr[:, np.newaxis, :] - pos_arr[np.newaxis, :, :] nodesep = np.linalg.norm(delta, axis=-1) direction = np.einsum("ijk,ij->ijk", delta, 1 / (nodesep + np.eye(nNodes) * 1e-3)) offset = nodesep * invdist - 1.0 offset[np.diag_indices(nNodes)] = 0 cost = 0.5 * np.sum(offset**2) grad = np.einsum("ij,ij,ijk->ik", invdist, offset, direction) - np.einsum( "ij,ij,ijk->jk", invdist, offset, direction ) # Additional parabolic term to encourage mean position to be near origin: sumpos = np.sum(pos_arr, axis=0) cost += 0.5 * meanweight * np.sum(sumpos**2) grad += meanweight * sumpos return (cost, grad.ravel()) def spectral_layout(G, weight="weight", scale=1, center=None, dim=2): """Position nodes using the eigenvectors of the graph Laplacian. Using the unnormalized Laplacian, the layout shows possible clusters of nodes which are an approximation of the ratio cut. If dim is the number of dimensions then the positions are the entries of the dim eigenvectors corresponding to the ascending eigenvalues starting from the second one. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. weight : string or None optional (default='weight') The edge attribute that holds the numerical value used for the edge weight. If None, then all edge weights are 1. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.spectral_layout(G) Notes ----- Directed graphs will be considered as undirected graphs when positioning the nodes. For larger graphs (>500 nodes) this will use the SciPy sparse eigenvalue solver (ARPACK). """ # handle some special cases that break the eigensolvers import numpy as np G, center = _process_params(G, center, dim) if len(G) <= 2: if len(G) == 0: pos = np.array([]) elif len(G) == 1: pos = np.array([center]) else: pos = np.array([np.zeros(dim), np.array(center) * 2.0]) return dict(zip(G, pos)) try: # Sparse matrix if len(G) < 500: # dense solver is faster for small graphs raise ValueError A = nx.to_scipy_sparse_array(G, weight=weight, dtype="d") # Symmetrize directed graphs if G.is_directed(): A = A + np.transpose(A) pos = _sparse_spectral(A, dim) except (ImportError, ValueError): # Dense matrix A = nx.to_numpy_array(G, weight=weight) # Symmetrize directed graphs if G.is_directed(): A += A.T pos = _spectral(A, dim) pos = rescale_layout(pos, scale=scale) + center pos = dict(zip(G, pos)) return pos def _spectral(A, dim=2): # Input adjacency matrix A # Uses dense eigenvalue solver from numpy import numpy as np try: nnodes, _ = A.shape except AttributeError as err: msg = "spectral() takes an adjacency matrix as input" raise nx.NetworkXError(msg) from err # form Laplacian matrix where D is diagonal of degrees D = np.identity(nnodes, dtype=A.dtype) * np.sum(A, axis=1) L = D - A eigenvalues, eigenvectors = np.linalg.eig(L) # sort and keep smallest nonzero index = np.argsort(eigenvalues)[1 : dim + 1] # 0 index is zero eigenvalue return np.real(eigenvectors[:, index]) def _sparse_spectral(A, dim=2): # Input adjacency matrix A # Uses sparse eigenvalue solver from scipy # Could use multilevel methods here, see Koren "On spectral graph drawing" import numpy as np import scipy as sp try: nnodes, _ = A.shape except AttributeError as err: msg = "sparse_spectral() takes an adjacency matrix as input" raise nx.NetworkXError(msg) from err # form Laplacian matrix # TODO: Rm csr_array wrapper in favor of spdiags array constructor when available D = sp.sparse.csr_array(sp.sparse.spdiags(A.sum(axis=1), 0, nnodes, nnodes)) L = D - A k = dim + 1 # number of Lanczos vectors for ARPACK solver.What is the right scaling? ncv = max(2 * k + 1, int(np.sqrt(nnodes))) # return smallest k eigenvalues and eigenvectors eigenvalues, eigenvectors = sp.sparse.linalg.eigsh(L, k, which="SM", ncv=ncv) index = np.argsort(eigenvalues)[1:k] # 0 index is zero eigenvalue return np.real(eigenvectors[:, index]) def planar_layout(G, scale=1, center=None, dim=2): """Position nodes without edge intersections. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. If G is of type nx.PlanarEmbedding, the positions are selected accordingly. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. Returns ------- pos : dict A dictionary of positions keyed by node Raises ------ NetworkXException If G is not planar Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.planar_layout(G) """ import numpy as np if dim != 2: raise ValueError("can only handle 2 dimensions") G, center = _process_params(G, center, dim) if len(G) == 0: return {} if isinstance(G, nx.PlanarEmbedding): embedding = G else: is_planar, embedding = nx.check_planarity(G) if not is_planar: raise nx.NetworkXException("G is not planar.") pos = nx.combinatorial_embedding_to_pos(embedding) node_list = list(embedding) pos = np.vstack([pos[x] for x in node_list]) pos = pos.astype(np.float64) pos = rescale_layout(pos, scale=scale) + center return dict(zip(node_list, pos)) def spiral_layout(G, scale=1, center=None, dim=2, resolution=0.35, equidistant=False): """Position nodes in a spiral layout. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int, default=2 Dimension of layout, currently only dim=2 is supported. Other dimension values result in a ValueError. resolution : float, default=0.35 The compactness of the spiral layout returned. Lower values result in more compressed spiral layouts. equidistant : bool, default=False If True, nodes will be positioned equidistant from each other by decreasing angle further from center. If False, nodes will be positioned at equal angles from each other by increasing separation further from center. Returns ------- pos : dict A dictionary of positions keyed by node Raises ------ ValueError If dim != 2 Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.spiral_layout(G) >>> nx.draw(G, pos=pos) Notes ----- This algorithm currently only works in two dimensions. """ import numpy as np if dim != 2: raise ValueError("can only handle 2 dimensions") G, center = _process_params(G, center, dim) if len(G) == 0: return {} if len(G) == 1: return {nx.utils.arbitrary_element(G): center} pos = [] if equidistant: chord = 1 step = 0.5 theta = resolution theta += chord / (step * theta) for _ in range(len(G)): r = step * theta theta += chord / r pos.append([np.cos(theta) * r, np.sin(theta) * r]) else: dist = np.arange(len(G), dtype=float) angle = resolution * dist pos = np.transpose(dist * np.array([np.cos(angle), np.sin(angle)])) pos = rescale_layout(np.array(pos), scale=scale) + center pos = dict(zip(G, pos)) return pos def multipartite_layout(G, subset_key="subset", align="vertical", scale=1, center=None): """Position nodes in layers of straight lines. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. subset_key : string or dict (default='subset') If a string, the key of node data in G that holds the node subset. If a dict, keyed by layer number to the nodes in that layer/subset. align : string (default='vertical') The alignment of nodes. Vertical or horizontal. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. Returns ------- pos : dict A dictionary of positions keyed by node. Examples -------- >>> G = nx.complete_multipartite_graph(28, 16, 10) >>> pos = nx.multipartite_layout(G) or use a dict to provide the layers of the layout >>> G = nx.Graph([(0, 1), (1, 2), (1, 3), (3, 4)]) >>> layers = {"a": [0], "b": [1], "c": [2, 3], "d": [4]} >>> pos = nx.multipartite_layout(G, subset_key=layers) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. Network does not need to be a complete multipartite graph. As long as nodes have subset_key data, they will be placed in the corresponding layers. """ import numpy as np if align not in ("vertical", "horizontal"): msg = "align must be either vertical or horizontal." raise ValueError(msg) G, center = _process_params(G, center=center, dim=2) if len(G) == 0: return {} try: # check if subset_key is dict-like if len(G) != sum(len(nodes) for nodes in subset_key.values()): raise nx.NetworkXError( "all nodes must be in one subset of `subset_key` dict" ) except AttributeError: # subset_key is not a dict, hence a string node_to_subset = nx.get_node_attributes(G, subset_key) if len(node_to_subset) != len(G): raise nx.NetworkXError( f"all nodes need a subset_key attribute: {subset_key}" ) subset_key = nx.utils.groups(node_to_subset) # Sort by layer, if possible try: layers = dict(sorted(subset_key.items())) except TypeError: layers = subset_key pos = None nodes = [] width = len(layers) for i, layer in enumerate(layers.values()): height = len(layer) xs = np.repeat(i, height) ys = np.arange(0, height, dtype=float) offset = ((width - 1) / 2, (height - 1) / 2) layer_pos = np.column_stack([xs, ys]) - offset if pos is None: pos = layer_pos else: pos = np.concatenate([pos, layer_pos]) nodes.extend(layer) pos = rescale_layout(pos, scale=scale) + center if align == "horizontal": pos = pos[:, ::-1] # swap x and y coords pos = dict(zip(nodes, pos)) return pos @np_random_state("seed") def arf_layout( G, pos=None, scaling=1, a=1.1, etol=1e-6, dt=1e-3, max_iter=1000, *, seed=None, ): """Arf layout for networkx The attractive and repulsive forces (arf) layout [1] improves the spring layout in three ways. First, it prevents congestion of highly connected nodes due to strong forcing between nodes. Second, it utilizes the layout space more effectively by preventing large gaps that spring layout tends to create. Lastly, the arf layout represents symmetries in the layout better than the default spring layout. Parameters ---------- G : nx.Graph or nx.DiGraph Networkx graph. pos : dict Initial position of the nodes. If set to None a random layout will be used. scaling : float Scales the radius of the circular layout space. a : float Strength of springs between connected nodes. Should be larger than 1. The greater a, the clearer the separation ofunconnected sub clusters. etol : float Gradient sum of spring forces must be larger than `etol` before successful termination. dt : float Time step for force differential equation simulations. max_iter : int Max iterations before termination of the algorithm. seed : int, RandomState instance or None optional (default=None) Set the random state for deterministic node layouts. If int, `seed` is the seed used by the random number generator, if numpy.random.RandomState instance, `seed` is the random number generator, if None, the random number generator is the RandomState instance used by numpy.random. References .. [1] "Self-Organization Applied to Dynamic Network Layout", M. Geipel, International Journal of Modern Physics C, 2007, Vol 18, No 10, pp. 1537-1549. https://doi.org/10.1142/S0129183107011558 https://arxiv.org/abs/0704.1748 Returns ------- pos : dict A dictionary of positions keyed by node. Examples -------- >>> G = nx.grid_graph((5, 5)) >>> pos = nx.arf_layout(G) """ import warnings import numpy as np if a <= 1: msg = "The parameter a should be larger than 1" raise ValueError(msg) pos_tmp = nx.random_layout(G, seed=seed) if pos is None: pos = pos_tmp else: for node in G.nodes(): if node not in pos: pos[node] = pos_tmp[node].copy() # Initialize spring constant matrix N = len(G) # No nodes no computation if N == 0: return pos # init force of springs K = np.ones((N, N)) - np.eye(N) node_order = {node: i for i, node in enumerate(G)} for x, y in G.edges(): if x != y: idx, jdx = (node_order[i] for i in (x, y)) K[idx, jdx] = a # vectorize values p = np.asarray(list(pos.values())) # equation 10 in [1] rho = scaling * np.sqrt(N) # looping variables error = etol + 1 n_iter = 0 while error > etol: diff = p[:, np.newaxis] - p[np.newaxis] A = np.linalg.norm(diff, axis=-1)[..., np.newaxis] # attraction_force - repulsions force # suppress nans due to division; caused by diagonal set to zero. # Does not affect the computation due to nansum with warnings.catch_warnings(): warnings.simplefilter("ignore") change = K[..., np.newaxis] * diff - rho / A * diff change = np.nansum(change, axis=0) p += change * dt error = np.linalg.norm(change, axis=-1).sum() if n_iter > max_iter: break n_iter += 1 return dict(zip(G.nodes(), p)) @np_random_state("seed") def forceatlas2_layout( G, pos=None, *, max_iter=100, jitter_tolerance=1.0, scaling_ratio=2.0, gravity=1.0, distributed_action=False, strong_gravity=False, node_mass=None, node_size=None, weight=None, dissuade_hubs=False, linlog=False, seed=None, dim=2, ): """Position nodes using the ForceAtlas2 force-directed layout algorithm. This function applies the ForceAtlas2 layout algorithm [1]_ to a NetworkX graph, positioning the nodes in a way that visually represents the structure of the graph. The algorithm uses physical simulation to minimize the energy of the system, resulting in a more readable layout. Parameters ---------- G : nx.Graph A NetworkX graph to be laid out. pos : dict or None, optional Initial positions of the nodes. If None, random initial positions are used. max_iter : int (default: 100) Number of iterations for the layout optimization. jitter_tolerance : float (default: 1.0) Controls the tolerance for adjusting the speed of layout generation. scaling_ratio : float (default: 2.0) Determines the scaling of attraction and repulsion forces. distributed_attraction : bool (default: False) Distributes the attraction force evenly among nodes. strong_gravity : bool (default: False) Applies a strong gravitational pull towards the center. node_mass : dict or None, optional Maps nodes to their masses, influencing the attraction to other nodes. node_size : dict or None, optional Maps nodes to their sizes, preventing crowding by creating a halo effect. dissuade_hubs : bool (default: False) Prevents the clustering of hub nodes. linlog : bool (default: False) Uses logarithmic attraction instead of linear. seed : int, RandomState instance or None optional (default=None) Used only for the initial positions in the algorithm. Set the random state for deterministic node layouts. If int, `seed` is the seed used by the random number generator, if numpy.random.RandomState instance, `seed` is the random number generator, if None, the random number generator is the RandomState instance used by numpy.random. dim : int (default: 2) Sets the dimensions for the layout. Ignored if `pos` is provided. Examples -------- >>> import networkx as nx >>> G = nx.florentine_families_graph() >>> pos = nx.forceatlas2_layout(G) >>> nx.draw(G, pos=pos) References ---------- .. [1] Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one, 9(6), e98679. https://doi.org/10.1371/journal.pone.0098679 """ import numpy as np if len(G) == 0: return {} # parse optional pos positions if pos is None: pos = nx.random_layout(G, dim=dim, seed=seed) pos_arr = np.array(list(pos.values())) else: # set default node interval within the initial pos values pos_init = np.array(list(pos.values())) max_pos = pos_init.max(axis=0) min_pos = pos_init.min(axis=0) dim = max_pos.size pos_arr = min_pos + seed.rand(len(G), dim) * (max_pos - min_pos) for idx, node in enumerate(G): if node in pos: pos_arr[idx] = pos[node].copy() mass = np.zeros(len(G)) size = np.zeros(len(G)) # Only adjust for size when the users specifies size other than default (1) adjust_sizes = False if node_size is None: node_size = {} else: adjust_sizes = True if node_mass is None: node_mass = {} for idx, node in enumerate(G): mass[idx] = node_mass.get(node, G.degree(node) + 1) size[idx] = node_size.get(node, 1) n = len(G) gravities = np.zeros((n, dim)) attraction = np.zeros((n, dim)) repulsion = np.zeros((n, dim)) A = nx.to_numpy_array(G, weight=weight) def estimate_factor(n, swing, traction, speed, speed_efficiency, jitter_tolerance): """Computes the scaling factor for the force in the ForceAtlas2 layout algorithm. This helper function adjusts the speed and efficiency of the layout generation based on the current state of the system, such as the number of nodes, current swing, and traction forces. Parameters ---------- n : int Number of nodes in the graph. swing : float The current swing, representing the oscillation of the nodes. traction : float The current traction force, representing the attraction between nodes. speed : float The current speed of the layout generation. speed_efficiency : float The efficiency of the current speed, influencing how fast the layout converges. jitter_tolerance : float The tolerance for jitter, affecting how much speed adjustment is allowed. Returns ------- tuple A tuple containing the updated speed and speed efficiency. Notes ----- This function is a part of the ForceAtlas2 layout algorithm and is used to dynamically adjust the layout parameters to achieve an optimal and stable visualization. """ import numpy as np # estimate jitter opt_jitter = 0.05 * np.sqrt(n) min_jitter = np.sqrt(opt_jitter) max_jitter = 10 min_speed_efficiency = 0.05 other = min(max_jitter, opt_jitter * traction / n**2) jitter = jitter_tolerance * max(min_jitter, other) if swing / traction > 2.0: if speed_efficiency > min_speed_efficiency: speed_efficiency *= 0.5 jitter = max(jitter, jitter_tolerance) if swing == 0: target_speed = np.inf else: target_speed = jitter * speed_efficiency * traction / swing if swing > jitter * traction: if speed_efficiency > min_speed_efficiency: speed_efficiency *= 0.7 elif speed < 1000: speed_efficiency *= 1.3 max_rise = 0.5 speed = speed + min(target_speed - speed, max_rise * speed) return speed, speed_efficiency speed = 1 speed_efficiency = 1 swing = 1 traction = 1 for _ in range(max_iter): # compute pairwise difference diff = pos_arr[:, None] - pos_arr[None] # compute pairwise distance distance = np.linalg.norm(diff, axis=-1) # linear attraction if linlog: attraction = -np.log(1 + distance) / distance np.fill_diagonal(attraction, 0) attraction = np.einsum("ij, ij -> ij", attraction, A) attraction = np.einsum("ijk, ij -> ik", diff, attraction) else: attraction = -np.einsum("ijk, ij -> ik", diff, A) if distributed_action: attraction /= mass[:, None] # repulsion tmp = mass[:, None] @ mass[None] if adjust_sizes: distance += -size[:, None] - size[None] d2 = distance**2 # remove self-interaction np.fill_diagonal(tmp, 0) np.fill_diagonal(d2, 1) factor = (tmp / d2) * scaling_ratio repulsion = np.einsum("ijk, ij -> ik", diff, factor) # gravity gravities = ( -gravity * mass[:, None] * pos_arr / np.linalg.norm(pos_arr, axis=-1)[:, None] ) if strong_gravity: gravities *= np.linalg.norm(pos_arr, axis=-1)[:, None] # total forces update = attraction + repulsion + gravities # compute total swing and traction swing += (mass * np.linalg.norm(pos_arr - update, axis=-1)).sum() traction += (0.5 * mass * np.linalg.norm(pos_arr + update, axis=-1)).sum() speed, speed_efficiency = estimate_factor( n, swing, traction, speed, speed_efficiency, jitter_tolerance, ) # update pos if adjust_sizes: swinging = mass * np.linalg.norm(update, axis=-1) factor = 0.1 * speed / (1 + np.sqrt(speed * swinging)) df = np.linalg.norm(update, axis=-1) factor = np.minimum(factor * df, 10.0 * np.ones(df.shape)) / df else: swinging = mass * np.linalg.norm(update, axis=-1) factor = speed / (1 + np.sqrt(speed * swinging)) pos_arr += update * factor[:, None] if abs((update * factor[:, None]).sum()) < 1e-10: break return dict(zip(G, pos_arr)) def rescale_layout(pos, scale=1): """Returns scaled position array to (-scale, scale) in all axes. The function acts on NumPy arrays which hold position information. Each position is one row of the array. The dimension of the space equals the number of columns. Each coordinate in one column. To rescale, the mean (center) is subtracted from each axis separately. Then all values are scaled so that the largest magnitude value from all axes equals `scale` (thus, the aspect ratio is preserved). The resulting NumPy Array is returned (order of rows unchanged). Parameters ---------- pos : numpy array positions to be scaled. Each row is a position. scale : number (default: 1) The size of the resulting extent in all directions. Returns ------- pos : numpy array scaled positions. Each row is a position. See Also -------- rescale_layout_dict """ import numpy as np # Find max length over all dimensions pos -= pos.mean(axis=0) lim = np.abs(pos).max() # max coordinate for all axes # rescale to (-scale, scale) in all directions, preserves aspect if lim > 0: pos *= scale / lim return pos def rescale_layout_dict(pos, scale=1): """Return a dictionary of scaled positions keyed by node Parameters ---------- pos : A dictionary of positions keyed by node scale : number (default: 1) The size of the resulting extent in all directions. Returns ------- pos : A dictionary of positions keyed by node Examples -------- >>> import numpy as np >>> pos = {0: np.array((0, 0)), 1: np.array((1, 1)), 2: np.array((0.5, 0.5))} >>> nx.rescale_layout_dict(pos) {0: array([-1., -1.]), 1: array([1., 1.]), 2: array([0., 0.])} >>> pos = {0: np.array((0, 0)), 1: np.array((-1, 1)), 2: np.array((-0.5, 0.5))} >>> nx.rescale_layout_dict(pos, scale=2) {0: array([ 2., -2.]), 1: array([-2., 2.]), 2: array([0., 0.])} See Also -------- rescale_layout """ import numpy as np if not pos: # empty_graph return {} pos_v = np.array(list(pos.values())) pos_v = rescale_layout(pos_v, scale=scale) return dict(zip(pos, pos_v)) def bfs_layout(G, start, *, align="vertical", scale=1, center=None): """Position nodes according to breadth-first search algorithm. Parameters ---------- G : NetworkX graph A position will be assigned to every node in G. start : node in `G` Starting node for bfs center : array-like or None Coordinate pair around which to center the layout. Returns ------- pos : dict A dictionary of positions keyed by node. Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.bfs_layout(G, 0) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. """ G, center = _process_params(G, center, 2) # Compute layers with BFS layers = dict(enumerate(nx.bfs_layers(G, start))) if len(G) != sum(len(nodes) for nodes in layers.values()): raise nx.NetworkXError( "bfs_layout didn't include all nodes. Perhaps use input graph:\n" " G.subgraph(nx.node_connected_component(G, start))" ) # Compute node positions with multipartite_layout return multipartite_layout( G, subset_key=layers, align=align, scale=scale, center=center )