ó |£*^c @s dZddlZddlZddddddgZd d dd „Zd d dd „Zdd d dd „Zd dddddd dd„Z e Z d ddddd„Z d ddddd„Z d dd dd„Z d d„Zd d„Zd d„Zd„ZdS(s ****** Layout ****** Node positioning algorithms for graph drawing. The default scales and centering for these layouts are typically squares with side [0, 1] or [0, scale]. The two circular layout routines (circular_layout and shell_layout) have size [-1, 1] or [-scale, scale]. iÿÿÿÿNtcircular_layoutt random_layoutt shell_layoutt spring_layouttspectral_layouttfruchterman_reingold_layoutigð?cCsqddl}t|ƒ|f}|jj|ƒ|}|dk r^||j|ƒd|7}ntt||ƒƒS(sAPosition nodes uniformly at random. For every node, a position is generated by choosing each of dim coordinates uniformly at random on the default interval [0.0, 1.0), or on an interval of length `scale` centered at `center`. 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. dim : int Dimension of layout. scale : float (default 1) Scale factor for positions center : array-like (default scale*0.5 in each dim) Coordinate around which to center the layout. Returns ------- pos : dict A dictionary of positions keyed by node Examples -------- >>> G = nx.lollipop_graph(4, 3) >>> pos = nx.random_layout(G) iÿÿÿÿNgà?(tnumpytlentrandomtNonetasarraytdicttzip(tGtdimtscaletcentertnptshapetpos((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyR!s !  cCs®ddl}t|ƒdkr"iSd|j}|jd||t|ƒƒ}|j|j|ƒ|j|ƒgƒ|}|dk r›||j|ƒ7}nt t ||ƒƒS(s™Position nodes on a circle. Parameters ---------- G : NetworkX graph or list of nodes dim : int Dimension of layout, currently only dim=2 is supported scale : float (default 1) Scale factor for positions, i.e. radius of circle. center : array-like (default origin) Coordinate around which to center the layout. Returns ------- dict : A dictionary of positions keyed by node 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. iÿÿÿÿNig@( RRtpitaranget column_stacktcostsinR R R R (R RRRRttwopitthetaR((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyRKs  + cCsvddl}t|ƒdkr"iS|dkr@t|ƒg}nt|ƒ}t|dƒdkrud}|d8}nd}|r‹||n|}||9}i} d|j} xx|D]p} |jd| | t| ƒƒ} |j|j| ƒ|j| ƒgƒ|} | j t | | ƒƒ||7}qµW|dk rr|j |ƒ}x+| j ƒD]\}}||| |>> 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. iÿÿÿÿNiiggð?g@( RRR tlistRRRRRtupdateR R titems(R tnlistRRRRt numb_shellstradiustgaptnposRtnodesRRtntp((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyRxs0$       + i2tweightc Cs~ddl} t|ƒdkr"iS|dk r™tt|tt|ƒƒƒƒ} | jg|D]} | | ^q\ƒ}|dkr™d} t| ƒ‚q™n|dk rT| jt |j ƒƒƒ} | j dƒ}| j dƒ|}t|ƒ|f}| j j |ƒ||}xIt|ƒD]2\}}||kr| j||ƒ||>> G=nx.path_graph(4) >>> pos=nx.spring_layout(G) # this function has two names: # spring_layout and fruchterman_reingold_layout >>> pos=nx.fruchterman_reingold_layout(G) iÿÿÿÿNis4Keyword pos must be specified if any nodes are fixediôR&tdtypetfgà?(RRR R R trangeR t ValueErrortarrayRtvaluestmintmaxRt enumeratetsqrttnxtto_scipy_sparse_matrixt_sparse_fruchterman_reingoldtto_numpy_matrixt_fruchterman_reingoldt_rescale_layout(R RtkRtfixedt iterationsR&RRRtnfixedtvtmsgt pos_coordst min_coordst domain_sizeRtpos_arrtiR$tA((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyRÁsD:  !&   !"   !cCsòddl}y|j\}}Wn tk rAtjdƒ‚nX|j|ƒ}|dkrŠ|j|jj||fƒd|jƒ}n|j |jƒ}|dkr¾|j d|ƒ}nt t |j dƒt |j dƒt |j dƒt |j dƒƒd} | t|dƒ} |j|jd|jd|jdfd|jƒ} x”t|ƒD]†} xat|jdƒD]L} |dd…| df|dd…| f| dd…dd…| f_sparse_fruchterman_reingold() scipy numpy: http://scipy.org/ R'gð?iigš™™™™™¹?iRCg{®Gáz„?(RRRDR1REt scipy.sparseRWRXt ImportErrorttolilR R RR'RFR0R.RGR-RHRIR)RJRKt getrowviewttoarrayR6(RBRR7RR8R9RRMRNRWRXRORPRTRRRARQRStAiRU((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyR3gsT     -   O  2  cCsddl}t|ƒdkrìt|ƒdkr4iSt|ƒdkr„|dk rd|j|ƒ}qÙ|jd|fƒ|d}nU|j|j|ƒ|j|ƒ|gƒ}|dk rÙ||j|ƒ|d7}ntt||ƒƒSykt|ƒdkr t ‚nt j |d|d d ƒ}|j ƒrG||j |ƒ}nt||ƒ}Wn]tt fk r¶t j|d|ƒ}|j ƒr¤||j |ƒ}nt||ƒ}nXt||ƒ}|dk rð||j|ƒd|7}ntt||ƒƒS( sPosition nodes using the eigenvectors of the graph Laplacian. Parameters ---------- G : NetworkX graph or list of nodes dim : int Dimension of layout 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 : float optional (default 1) Scale factor for positions, i.e. nodes placed in a box with side [0, scale] or centered on `center` if provided. center : array-like (default scale/2 in each dim) Coordinate around which to center the layout. Returns ------- 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). iÿÿÿÿNiiigà?iôR&R'td(RRR R tonesR+RIR R R*R1R2t is_directedRLt_sparse_spectralRZR4t _spectralR6(R RR&RRRRRB((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyR¯s8(   +     c Csÿyddl}Wntk r/tdƒ‚nXy|j\}}Wn tk retjdƒ‚nX|j|ƒ}|j|d|jƒ}||j |ddƒ}||}|j j |ƒ\}} |j |ƒd|d!} |j | dd…| fƒS(Niÿÿÿÿs4spectral_layout() requires numpy: http://scipy.org/ s-spectral() takes an adjacency matrix as inputR'RCi(RRZRRDR1RER tidentityR'RJtlinalgteigtargsorttreal( RBRRRMRNtItDtLt eigenvaluest eigenvectorstindex((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyRcþs     cCsoy ddl}ddlm}Wntk r?tdƒ‚nXyddlm}Wn!tk rwddlm}nXy|j\}}Wn t k r­t j dƒ‚nX|j |j ddƒjƒ}||d ||ƒ}||} |d} td | dt|j|ƒƒƒ} || | d d d | ƒ\} } |j| ƒd| !}|j| dd…|fƒS(Niÿÿÿÿ(RWs=_sparse_spectral() requires scipy & numpy: http://scipy.org/ (teigsh(teigen_symmetrics4sparse_spectral() takes an adjacency matrix as inputRCiiitwhichtSMtncv(RRYRWRZtscipy.sparse.linalg.eigenRotscipy.sparse.linalgRpRRDR1RER RJRGR.tintR0RgRh(RBRRRWRoRMRNtdataRjRkR7RsRlRmRn((sk/home/gitlab-runner/builds/8480fa44/0/bergerc/fluidmanager-web/art-framework/bin/networkx/drawing/layout.pyRbs,       &!cCsÌd}xrt|jdƒD]]}|dd…|fc|dd…|fjƒ8t|jdƒD]&}|dd…|fc||9 s6   *-I b<  GO  "