o hC@sdZddlZddlmZgdZddZeddId d ZdJd d ZdKddZ dLddZ ed         dMddZ e Z ed dNddZ ed dNddZ dOdd Zd!d"Zd#d$ZdPd%d&ZdQd'd(ZdQd)d*ZdJd+d,ZdRd/d0ZdSd2d3Zed4  5 6 7 8dTdd9d:d;Zed4 dUdd=d.d.dddd.d.ddd? d@dAZdVdBdCZdVdDdEZdd ddFdGdHZdS)Wa ****** Layout ****** Node positioning algorithms for graph drawing. For `random_layout()` the possible resulting shape is a square of side [0, scale] (default: [0, 1]) Changing `center` shifts the layout by that amount. For the other layout routines, the extent is [center - scale, center + scale] (default: [-1, 1]). Warning: Most layout routines have only been tested in 2-dimensions. N)np_random_state)bipartite_layoutcircular_layoutforceatlas2_layoutkamada_kawai_layout random_layoutrescale_layoutrescale_layout_dict shell_layout spring_layoutspectral_layout planar_layoutfruchterman_reingold_layout spiral_layoutmultipartite_layout bfs_layout arf_layoutcCshddl}t|tjst}|||}|dur||}n||}t||kr0d}t|||fS)Nrz;length of center coordinates must match dimension of layout) numpy isinstancenxGraphadd_nodes_fromzerosasarraylen ValueError)Gcenterdimnp empty_graphmsgr"k/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/networkx/drawing/layout.py_process_params*s     r$cCsJddl}t|||\}}|t|||}||j}tt||}|S)aNPosition 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) rN)rr$randrastypefloat32dictzip)rrrseedrposr"r"r#r?s ' rcCsddl}|dkr tdt|||\}}td|d}t|dkr%i}|St|dkr5tj||i}|S|ddt|dddd|j }| |j }| | ||||t||fg}t||d|}tt||}|S)abPosition nodes on a circle. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. If dim>2, the remaining dimensions are set to zero in the returned positions. If dim<2, a ValueError is raised. Returns ------- pos : dict A dictionary of positions keyed by node Raises ------ ValueError If dim < 2 Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.circular_layout(G) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. rNr&zcannot handle dimensions < 2r.scale)rrr$maxrrutilsarbitrary_elementlinspacepir(r) column_stackcossinrrr*r+)rr1rrrpaddimsr-thetar"r"r#rps&*   ( "rcCsddl}|dkr tdt|||\}}t|dkriSt|dkr*tj||iS|dur3t|g}|t|}t|ddkrDd}n|}|durQ|jt|}|} i} |D]5} |j dd|jt| d|j d| } || | | | | g|} | t| | ||7}| |7} qW| S) aPosition 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. rNr&can only handle 2 dimensionsr.F)endpointdtype)rrr$rrr3r4listr6r5r)r7r8r9updater+)rnlistrotater1rrr radius_bumpradius first_thetanposnodesr;r-r"r"r#r s80    " r verticalUUUUUU?cs8ddl}|dvrd}t|t||dd\}}t|dkriSd}||} | d|df} t|fdd |D} t| }|dt} || t| } |d|t}|d|t| }| | |g| }| | |g| }| ||g}t ||d |}|d kr|ddddd f}tt ||}|S) aPosition nodes in two straight lines. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. nodes : list or container Nodes in one node set of the bipartite graph. This set will be placed on left or top. align : string (default='vertical') The alignment of nodes. Vertical or horizontal. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. aspect_ratio : number (default=4/3): The ratio of the width to the height of the layout. Returns ------- pos : dict A dictionary of positions keyed by node. Examples -------- >>> G = nx.bipartite.gnmk_random_graph(3, 5, 10, seed=123) >>> top = nx.bipartite.sets(G)[0] >>> pos = nx.bipartite_layout(G, top) Notes ----- This algorithm currently only works in two dimensions and does not try to minimize edge crossings. rNrI horizontal,align must be either vertical or horizontal.r&rrr.csg|]}|vr|qSr"r").0vtopr"r# Jsz$bipartite_layout..r0rLr/) rrr$rr*fromkeysr@repeatr5r7 concatenaterr+)rrHalignr1r aspect_ratiorr!heightwidthoffsetbottomleft_xsright_xsleft_ysright_ystop_pos bottom_posr-r"rQr#rs2,   r 2-C6?weightc  sddl} t||| \}}|dur:|durtd|D] } | |vr$tdqddt|D| fdd|D}|durrtdd |D} | dkrOd } | t|| | |}t|D]\}}||vrp| ||||<q_nd}d } t|dkr~iSt|d krt j | |iSz1t|d krtt j ||d d }|dur|dur|j\}}| | |}t||||||| | }Wn/tyt j||d}|dur|dur|j\}}| | |}t||||||| | }Ynw|dur|durt||d|}tt||}|S)a 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) rNz'nodes are fixed without positions givencSi|]\}}||qSr"r"rOinoder"r"r# z!spring_layout..csg|] }|vr|qSr"r")rOrjnfixedr"r#rSsz!spring_layout..css|] }|D]}|VqqdSNr")rOpos_tupcoordr"r"r# sz spring_layout..r.frfr?rfr0)rr$r enumeraterr2valuesr'rrr3r4rHto_scipy_sparse_arrayshapesqrt_sparse_fruchterman_reingoldto_numpy_array_fruchterman_reingoldrr*r+)rkr-fixed iterations thresholdrfr1rrr,rrjdom_sizepos_arrrinAnnodes_r"rmr#r ]sd`       r c Csddl}z|j\} } Wnty} zd} t| | d} ~ ww|dur1|j|| ||jd}n||j}|durB| d| }t t |j dt |j dt |j dt |j dd} | |d}|j |jd|jd|jdf|jd}t|D]u}|dd|jddf||jddddf}|jj|dd}|j|d d|d |d ||||d |||}|jj|dd}||d kd|}|d || |}|durd||<||7}| |8} |j|| |kr|Sq|S)Nr9fruchterman_reingold() takes an adjacency matrix as inputr??r.皙?r/axis{Gz?)outz ijk,ij->ikr&zij,i->ijr=)rrzAttributeErrorr NetworkXErrorrr'r?r(r{r2Tminrrangenewaxislinalgnormclipeinsumwhere)rrr-rrrrr,rrrerrr!tdtdelta iterationdistance displacementlength delta_posr"r"r#r~sF  > ( 0r~c Cs:ddl}ddl} z|j\} } Wnty#} zd} t| | d} ~ wwz|}Wnty;| j|}Ynw|durM|j | | ||j d}n| |j }|durYg}|durd| d| }tt|jdt|jdt|jdt|jdd}||d}||| f}t|D]}|d9}t|jdD]G}||vrq|||j}| |djdd}||d kd |}||}|dd|f||||d|||jdd7<q| |djdd}||d kd|}|||j}||7}||8}|j|| |kr|Sq|S) Nrrrrr.rr&rr)rscipyrzrrrtolilsparse coo_arrayrr'r?r(r{r2rrrrsumr getrowviewtoarrayrr)rrr-rrrrr,rsprrrr!rrrrrirrAirrr"r"r#r|9s^    >   r|c s:ddl}t|||\}}t|}|dkriS|dur#ttj||d}d|||f} t|D]#\} } | |vr9q0|| } t|D]\} }|| vrJqA| || | | <qAq0dur||dkrct||dn|dkrnt ||dntt || ddt|| fd d |D}t | ||t|d |tt |S) aPosition 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) rNrvg.Ar%)rr&r.cg|]}|qSr"r")rOrr-r"r#rSz'kamada_kawai_layout..r0)rr$rr*rshortest_path_lengthonesrwrrr+r5array_kamada_kawai_solver)rdistr-rfr1rrrnNodesdist_mtxrownrrdistcolncrr"rr#rs6+ rcCsbddl}ddl}d}|d|||jdd||f}|jjt|d|dd}|j d|fS)NrMbP?r.zL-BFGS-BT)methodargsjacr/) rreyerzoptimizeminimize_kamada_kawai_costfnravelxreshape)rrrrrmeanwtcostargs optresultr"r"r#rs$rc Cs|jd}|||f}|dd|jddf||jddddf}|jj|dd}|d|d|||d} ||d} d| ||<d|| d } |d || | |d || | } |j|dd} | d||| d 7} | || 7} | | fS) Nrr/rz ijk,ij->ijkr.rr?r&z ij,ij,ijk->ikz ij,ij,ijk->jk) rzrrrrrr diag_indicesrr)pos_vecrinvdist meanweightrrrrnodesep directionr[costgradsumposr"r"r#rs 0    rc Cs*ddl}t|||\}}t|dkrAt|dkr|g}nt|dkr+||g}n|||||dg}tt||Sz"t|dkrJttj ||dd}| r]|| |}t ||}Wn t tfytj||d }| r|||j7}t||}Ynwt||d |}tt||}|S) aPosition nodes using the eigenvectors of the graph Laplacian. Using the unnormalized Laplacian, the layout shows possible clusters of nodes which are an approximation of the ratio cut. If dim is the number of dimensions then the positions are the entries of the dim eigenvectors corresponding to the ascending eigenvalues starting from the second one. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. weight : string or None optional (default='weight') The edge attribute that holds the numerical value used for the edge weight. If None, then all edge weights are 1. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int Dimension of layout. Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.spectral_layout(G) Notes ----- Directed graphs will be considered as undirected graphs when positioning the nodes. For larger graphs (>500 nodes) this will use the SciPy sparse eigenvalue solver (ARPACK). rNr&r.@rsdrurvr0)rr$rrrr*r+rrry is_directed transpose_sparse_spectral ImportErrorr}r _spectralr)rrfr1rrrr-rr"r"r#r s2-      r c Csddl}z|j\}}Wnty}zd}t||d}~ww|j||jd|j|dd}||}|j |\} } | | d|d} | | dd| fS)Nrz-spectral() takes an adjacency matrix as inputrr.r) rrzrrridentityr?rreigargsortreal) rrrrrrr!DL eigenvalues eigenvectorsindexr"r"r#rMs rc Csddl}ddl}z|j\}}Wnty#}zd}t||d}~ww|j|j|j ddd||}||} |d} t d| dt | |} |jj j| | d| d\} } || d| }|| dd|fS)Nrz4sparse_spectral() takes an adjacency matrix as inputr.rr&SM)whichncv)rrrzrrrr csr_arrayspdiagsrr2intr{reigshrr)rrrrrrrr!rrrrrrrr"r"r#rbs  "rcsddl}|dkr tdt|||\}}t|dkriSt|tjr%|}nt|\}}|s3tdt |t |}| fdd|D |j t|d|tt|S) aPosition 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) rNr&r<zG is not planar.crr"r")rOrrr"r#rSrz!planar_layout..r0)rrr$rrrPlanarEmbeddingcheck_planarityNetworkXExceptioncombinatorial_embedding_to_posr@vstackr(float64rr*r+)rr1rrr embedding is_planar node_listr"rr#r }s"!     r ffffff?Fc Cs(ddl}|dkr tdt|||\}}t|dkriSt|dkr*tj||iSg}|rad}d} |} | || | 7} tt|D]} | | } | || 7} || | | | | | gqBn|j t|t d} || }| | || || |g}t|||d|}tt||}|S) aPosition nodes in a spiral layout. Parameters ---------- G : NetworkX graph or list of nodes A position will be assigned to every node in G. scale : number (default: 1) Scale factor for positions. center : array-like or None Coordinate pair around which to center the layout. dim : int, default=2 Dimension of layout, currently only dim=2 is supported. Other dimension values result in a ValueError. resolution : float, default=0.35 The compactness of the spiral layout returned. Lower values result in more compressed spiral layouts. equidistant : bool, default=False If True, nodes will be positioned equidistant from each other by decreasing angle further from center. If False, nodes will be positioned at equal angles from each other by increasing separation further from center. Returns ------- pos : dict A dictionary of positions keyed by node Raises ------ ValueError If dim != 2 Examples -------- >>> G = nx.path_graph(4) >>> pos = nx.spiral_layout(G) >>> nx.draw(G, pos=pos) Notes ----- This algorithm currently only works in two dimensions. rNr&r<r.rrr0)rrr$rrr3r4rappendr8r9arangefloatrrrr*r+)rr1rr resolution equidistantrr-chordstepr;rrrangler"r"r#rs2,   $$rsubsetcCsddl}|dvrd}t|t||dd\}}t|dkriSzt|tdd|Dkr4td Wn%tyZt ||}t|t|krRtd |tj |}Ynwz t t |}Wn typ|}Ynwd} g} t|} t|D]>\} } t| }|| |}|jd|td }| d d|d df}|||g|}| dur|} n|| |g} | | qt| |d |} |dkr| dddddf} t t| | } | S)aSPosition 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. rNrKrMr&rNcs|]}t|VqdSrorrOrHr"r"r#rr?z&multipartite_layout..z4all nodes must be in one subset of `subset_key` dictz'all nodes need a subset_key attribute: rr.r0rLr/)rrr$rrrxrrrget_node_attributesr3groupsr*sorteditems TypeErrorrwrUrrr7rVextendrr+)r subset_keyrWr1rrr!node_to_subsetlayersr-rHrZrilayerrYxsysr[ layer_posr"r"r#rsX.       rr,皙?ư>rr,csddl}ddl} |dkrd} t| tj||d} |dur | }n|D]} | |vr2| | || <q$t|} | dkr=|S| | | f| | }ddt |D| D]\}}||krqfdd ||fD\}}||||f<qV| t |}|| | }|d}d}||kr|dd| jf|| j}| jj|d d d | jf}||d |d | jf||||}Wdn1swY| j|dd }|||7}| jj|d d }||krn|d7}||kstt||S)aArf 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) rNr.z'The parameter a should be larger than 1rcSrgr"r"rhr"r"r#rkrlzarf_layout..c3s|]}|VqdSror")rOri node_orderr"r#rrrzarf_layout..r/r.ignore)warningsrrrrrHcopyrrrrwedgesrr@rxr{rrrcatch_warnings simplefilternansumrr*r+)rr-scalingaetolrmax_iterr,rrr!pos_tmprjNKryidxjdxprhoerrorn_iterdiffrchanger"rr#rgsR@      rdrr) r"jitter_tolerance scaling_ratiogravitydistributed_actionstrong_gravity node_mass node_sizerf dissuade_hubslinlogr,rc ,CsFddl}t|dkr iS|dur"tj||| d}|t|}n;|t|}|jdd}|jdd}|j }|| t||||}t |D]\}}||vr\|| ||<qL| t|}| t|}d}| durti} nd}|dur|i}t |D]\}}||||d||<| |d||<qt|}| ||f}| ||f}| ||f}tj|| d}d d }d}d} d}!d}"t|D]P}#|dddf|d}$|jj|$d d}%| r|d|% |%}||d|d ||}|d |$|}n|d |$| }|r||dddf}|dddf|d}&|r6|%|dddf |d7}%|%d}'||&d||'d|&|'|}(|d |$|(}| |dddf||jj|d ddddf}|r||jj|d ddddf9}|||})|!||jj||)d d7}!|"d||jj||)d d7}"|||!|"|| |\}} |r||jj|)d d}*d|d|||*}(|jj|)d d}+||(|+d||+j|+}(n||jj|)d d}*|d|||*}(||)|(dddf7}t|)|(dddfdkrnqtt||S)a 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 rN)rr,rFTr.rvcSsddl}d||}||}d} d} t| |||d} |t|| } ||dkr9|| kr4|d9}t| |} |dkrA|j} n| |||} || |krX|| krW|d9}n|d kr`|d 9}d}|t| |||}||fS) aComputes 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. rNg?rcr&rrgffffff?rg?)rr{rr2inf)rswingtractionspeedspeed_efficiencyr0r opt_jitter min_jitter max_jittermin_speed_efficiencyotherjitter target_speedmax_riser"r"r#estimate_factorVs."    z+forceatlas2_layout..estimate_factorr/z ij, ij -> ijz ijk, ij -> ikr&rrg$@g|=)rrrrrr@rxr2rsizer'rwrrgetdegreer}rrrlog fill_diagonalrrr{minimumrrzabsr*r+),rr-r"r0r1r2r3r4r5r6rfr7r8r,rrrpos_initmax_posmin_posr'rjmassrG adjust_sizesr gravities attraction repulsionrrFr<r=r:r;rr-rtmpd2factorrAswingingdfr"r"r#rsK   @       $ ""rcCs>ddl}||jdd8}||}|dkr|||9}|S)aReturns 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 rNr)rmeanrMr2)r-r1rlimr"r"r#rs  rcCs<ddl}|siS|t|}t||d}tt||S)aReturn 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 rNr0)rrr@rxrr*r+)r-r1rpos_vr"r"r#r s  r )rWr1rcCs^t||d\}}ttt||}t|tdd|Dkr&tdt |||||dS)auPosition 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. r&csrrorrr"r"r#rrUrzbfs_layout..zwbfs_layout didn't include all nodes. Perhaps use input graph: G.subgraph(nx.node_connected_component(G, start)))r rWr1r) r$r*rwr bfs_layersrrrxrr)rstartrWr1rr r"r"r#r2s r)Nr&N)r.Nr&)NNr.Nr&)rIr.NrJ) NNNrdrerfr.Nr&N)NNNrdrer&N)NNrfr.Nr&)rfr.Nr&)r&)r.Nr&rF)rrIr.N)Nr.rrrrro)r.)__doc__networkxrnetworkx.utilsr__all__r$rrr rr rr~r|rrrr rrr rrrrrr rr"r"r"r#s   0 D\ N  = I M  O   9 Ob y  (&