j3{ dZddlZddlZddlmZmZmZddlm Z m Z ddl m Z ddl mZmZmZmZmZmZmZmZmZmZmZ ddlmZmZdd lmZ ddl!Z"d+d Z#d eefd Z$d Z%dZ&dZ'dZ( d,dedeefdZ) d-de*de*deeded ef dZ+ d.dedefdZ,d/dZ-ded eefdZ.deded eeeffdZ/d0d Z0d,d!Z1d1d"Z2 d2d#eed$eefd%Z3 d/ded&eed ee*e*ffd'Z4d(Z5d/d)Z6d/d*Z7y#e$r@Zej@eZej@eZej@eZYdZ[dZ[wwxYw#e$rZej@eZ"YdZ[dZ[wwxYw)3z graph.py ------------- Deal with graph operations. Primarily deal with graphs in (n, 2) edge list form, and abstract the backend graph library being used. Currently uses networkx or scipy.sparse.csgraph backend. N) exceptionsgroupingutil)logtol)faces_to_edges) ArrayLikeGraphEngineTypeIntegerListNDArrayNumberOptionalSequenceTupleUnionint64) coo_matrixcsgraph)cKDTreec|#t|d\}}|jdn|j}|j}t j |d}t |dk(rtjd ||}|dddf|dddfk7}||}|jd|r,||dddf|}t |t |k(sJ||fS|S) a Returns an (n, 2) list of face indices. Each pair of faces in the list shares an edge, making them adjacent. Parameters ----------- faces : (n, 3) int, or None Vertex indices representing triangles mesh : Trimesh object If passed will used cached edges instead of generating from faces return_edges : bool Return the edges shared by adjacent faces Returns ---------- adjacency : (m, 2) int Indexes of faces that are adjacent edges: (m, 2) int Only returned if return_edges is True Indexes of vertices which make up the edges shared by the adjacent faces Examples ---------- This is useful for lots of things such as finding face- connected components: ```python >>> graph = nx.Graph() >>> graph.add_edges_from(mesh.face_adjacency) >>> groups = nx.connected_components(graph_connected) ``` NT) return_indexraxis require_countrz3No adjacent faces detected! Did you merge vertices?) r sort edges_sorted edges_facer group_rowslenrdebug) facesmesh return_edgesedgesr! edge_groups adjacency nondegenerateadjacency_edgess :/DATA/.local/lib/python3.12/site-packages/trimesh/graph.pyface_adjacencyr.1sH |+5tDz  !!__ %%e1=K ;1 GH ;'IadOyA6M-(INNN AqD 1- @A?#s9~555/)) returnc2|j}|j|z}|jd|j|j }t j |jdddf|jdddffdt j}|S)a Find faces that share a vertex i.e. 'neighbors' faces. Relies on the fact that an adjacency matrix at a power p contains the number of paths of length p connecting two nodes. Here we take the bipartite graph from mesh.faces_sparse to the power 2. The non-zeros are the faces connected by one vertex. Returns ---------- neighborhood : (n, 2) int Pairs of faces which share a vertex rN)rdtype) faces_sparseTsetdiageliminate_zerostocoonp concatenaterowcolr)r&VTTT neighborhoods r-face_neighborhoodr@s|   B BJJqM B>> 4"&&D/*288L r/c tj|jtjdz }|j}t |jj D]\}}|j|}tjtj||dddfjdk(||dddfjdk(}|jddk(}d||ddf<|||||f<|S)a Return the vertex index of the two vertices not in the shared edge between two adjacent faces Parameters ---------- mesh : Trimesh object Input mesh Returns ----------- vid_unshared : (len(mesh.face_adjacency), 2) int Indexes of mesh.vertices for degenerate faces without exactly one unshared vertex per face it will be -1 r3rNrr2rrF) r9 zeros_liker.rface_adjacency_edges enumerater5r% logical_not logical_orreshapesum)r& vid_unsharedr(ifidr%unsharedrow_oks r-face_adjacency_unsharedrPs(==!4!4BHHEIL  % %ED//1123 3>> MMq!t,,W55q!t,,W55  1%*$&!"'/ VQY!3$ r/c |jtjdkD}tjdtj|j|z}|j |j }tj|djd}|j}tjtj|j |djd}tj|tj||jd|z}tj|}tjt!|j"tj$z} |||z | |<| |fS)a Compute an approximate radius between adjacent faces. Parameters -------------- mesh : trimesh.Trimesh Returns ------------- radii : (len(self.face_adjacency),) float Approximate radius between faces Parallel faces will have a value of np.inf span : (len(self.face_adjacency),) float Perpendicular projection distance of two unshared vertices onto the shared edge g{Gz?g@rr)r2rC)face_adjacency_anglesr9radiansabssinverticesrPdiffrIrErunitizesubtract diagonal_dotrow_normonesr#r.inf) r&nonzero denominator point_pairsvectorsr( edges_vecperpspanradiis r-face_adjacency_radiusrgs1,((2::d+;;G&&rvvd&@&@&IJJKK-- < <=Kggk*227;G  % %E RWWT]]5%9BJJ7STI ;;$##GY7??H9T D == D GGC++, - 6E'][0E'N $;r/cdtj}|j|j|S)a/ Returns a networkx graph representing the vertices and their connections in the mesh. Parameters ---------- mesh : Trimesh object Returns --------- graph : networkx.Graph Graph representing vertices and edges between them where vertices are nodes and edges are edges Examples ---------- This is useful for getting nearby vertices for a given vertex, potentially for some simple smoothing techniques. >>> graph = mesh.vertex_adjacency_graph >>> graph.neighbors(0) > [1, 3, 4] )nxGraphadd_edges_from edges_unique)r&gs r-vertex_adjacency_graphrns(.  AT&&' Hr/ctjt|d}tjt|d}tj||tj }|S)a Given two sets of faces, find the edges which are in both sets. Parameters --------- faces_a : (n, 3) int Array of faces faces_b : (m, 3) int Array of faces Returns --------- shared : (p, 2) int Edges shared between faces rr operation)r9rr r boolean_rows intersect1d)faces_afaces_be_ae_bshareds r- shared_edgesrysK ''.) 2C ''.) 2C  " "3r~~ FF Mr/enginefacet_thresholdc|tj}|j}|j}t j t |t}t j|tjkD}||||z dz|kD||<t|j|t jt |jd|}|S)an Find the list of parallel adjacent faces. Parameters ----------- mesh : trimesh.Trimesh engine Which graph engine to use facet_threshold : float Threshold for two facets to be considered coplanar Returns --------- facets : sequence of (n,) int Groups of face indexes of parallel adjacent faces. rBr)nodesmin_lenrz)rr{rgface_adjacency_spanr9r]r#boolrUzeroconnected_componentsr.aranger%)r&rzr{rfreparallelr_ componentss r-facetsr)s(--  & &E  # #Dwws5z.HffTlSXX%Gw$w-7A=OHW& H%iiDJJ( J r/only_watertightrepairr*c | |j}|rd}nd}t|tjt |j ||}|j |f||d|S)a3 Split a mesh into multiple meshes from face connectivity. If only_watertight is true it will only return watertight meshes and will attempt to repair single triangle or quad holes. Parameters ---------- mesh : trimesh.Trimesh The source multibody mesh to split only_watertight Only return watertight components and discard any connected component that isn't fully watertight. repair If set try to fill small holes in a mesh, before the discard step in `only_watertight. adjacency : (n, 2) int If passed will be used instead of `mesh.face_adjacency` engine Which graph engine to use for the connected components. Returns ---------- meshes : (m,) trimesh.Trimesh Results of splitting based on parameters. r)r(r}r~rz)rr)r.rr9rr#r%submesh)r&rrr*rzkwargsr~rs r-splitr\stH'' %ryyTZZ97SYJ 4<< $3F FL r/r~c fd} fd}tjtjtjt dk(rgSt dk(r+dkr$tj dj SgStjds tddg}t dkDr|jjt dkDr|jjtj|dz tj t}d |<|jd }|tj d |fd |ff} || vr | |S| j#D] } | cSt'd #t$$rY$wxYw)a Find groups of connected nodes from an edge list. Parameters ----------- edges : (n, 2) int Edges between nodes nodes : (m, ) int or None List of nodes that exist min_len : int Minimum length of a component group to return engine : str or None Which graph engine to use (None for automatic): (None, 'networkx', 'scipy') Returns ----------- components : (n,) sequence of (*,) int Nodes which are connected ctj}dkr|jtj|Dcgc] }t |c}Scc}w)z: Find connected components using networkx r)ri from_edgelistadd_nodes_fromrlist)graphrLr(r~r}s r-components_networkxz1connected_components..components_networkxsU  ' a<   '!#!8!8!?@!?AQ!?@@@sAct}tjt}d|<tjtj |}t j||}|Dcgc]}|| c}Scc}w)zF Find connected components using scipy.sparse.csgraph ) node_countrBT)r~)connected_component_labelsr9zerosrrrrgroup) labels containedindexrcr(r~rr}s r-components_csgraphz0connected_components..components_csgraphsw ,EjIHHZt4  % *BHH5i@^^F9$5wG ",-*Qa*---s5 BrBrrrCr2redges must be (n, 2)!Trscipynetworkxzno graph engines available!)r9 asanyarrayruniquer#rItolistris_shape ValueErrorappendmaxrrall collections OrderedDictvalues BaseException ImportError) r(r~r}rzrrcountsmaskedges_okenginesfunctionrs ``` @r-rrs2 A$ MM%rxx 0E } %  5zQ Uq a<::eW-446 6I == (011SF 5zA~ eiik" 5zA~ eiik"!#J 88Jd +DDKE{A&H (OE%% % &5H(IJG wv  NN$ : % 3 44   s&F;; GGcvt||}tj|d\}}|t||k(sJ|S)a? Label graph nodes from an edge list, using scipy.sparse.csgraph Parameters ----------- edges : (n, 2) int Edges of a graph node_count : int, or None The largest node in the graph. Returns ---------- labels : (node_count,) int Component labels for each node F)directed) edges_to_coorrr#)r(rmatrix _body_countrs r-rrsD % ,F!66vNK6{j((( Mr/ traversalc tj|dd|ddf}|jtj|dddk}|j r|g}nOt j |dd}|Dcgc],}tj|dddf|||dddg.}}tj|Dcgc]G}t|d kDxr5|d|dk7xr(|jt|d|dgddkIc}}|jr@tj|dD]%} tj|| || ddg|| <'tjr`|D][}tjtj|dd|ddfd} |j| ddkj r[J|Scc}wcc}w) a Given a traversal as a list of nodes split the traversal if a sequential index pair is not in the given edges. Useful since the implementation of DFS we're using will happily return disconnected values in a flat traversal. Parameters -------------- traversal : (m,) int Traversal through edges edges_tree : cKDTree A way to reconstruct original edge indices from sorted (n, 2) edge values. This is a slight misuse of a kdtree since one could just hash the tuples of the integer edges, but that isn't possible with numpy arrays easily and this allows a vectorized reconstruction. Returns --------------- split : sequence of (p,) int Traversals split into only connected paths. Nr2rrrg|=T)r~ only_nonzeror)r9 column_stackqueryrrrblocksr:arrayr#sortedanyr_rstrict) r edges_tree trav_edgeexistsrrbs needs_closerL check_edges r-_split_traversalrs23B12 ?@I  bggia8 9! ?E!H, zzA!CR&!AB%!AJJ$$Z03e;@@B BB L1  s :1GA G traversalsr(c $tj|d}t|}t|dk(r|j Sg}|D]}|j t || tj|Dcgc]}tj|dd|ddf!c}}t|dkDrI|jd|j tj||tj|S|j }|Scc}w)a Convert a traversal of a list of edges into a sequence of traversals where every pair of consecutive node indexes is an edge in a passed edge list Parameters ------------- traversals : sequence of (m,) int Node indexes of traversals of a graph edges : (n, 2) int Pairs of connected node indexes Returns -------------- splits : sequence of (p,) int Node indexes of connected traversals rrr)rrNr2rp) r9rrr#copyextendrr vstack_emptyrrrr setdiff1d)rr(rsplitsr}rLincludeds r-fill_traversalsrSs& GGE "EJ :!zz| F &:NO   F!SFq"//1Sb61QR5/"BF!STH 8}q 1   h++E8r||TU M M"Ts9$D ctj|tj}t|dk(rgSt j |ds t dt|jj}|dk(rtj}n!|dk(rtj}n t d|jd t|}tj|j!}tj"|dk(d}tj"|dkDd}g}tj$t|dztj&}tj(||fD]G} || r ||| d d j+tj} |j-| d || <I|S) a Given an edge list generate a sequence of ordered depth first search traversals using scipy.csgraph routines. Parameters ------------ edges : (n, 2) int Undirected edges of a graph mode : str Traversal type, 'bfs' or 'dfs' Returns ----------- traversals : (m,) sequence of (p,) int Ordered DFS or BFS traversals of the graph. rBrrzedges are not (n, 2)!bfsdfsz(traversal mode must be either dfs or bfsrrF)i_startreturn_predecessorsrT)r9rrr#rrrstrlowerstriprbreadth_first_orderdepth_first_orderrrbincountravelr_rbool_r:astyper) r(modefuncrr nodes_leafr}rvisitedstartordereds r-rrs}" HHU"(( +E 5zQ ]]5' *011 t9??  " " $D u}** ((CDD JJAJ  E{{5;;=)HHM*1-J JJx!| $Q 'EJhhs8}q(9G U 34 5>  5ee &   '"5 r/ctj|tj}t|dk(s!t j |ds t d||jdz}t|}|$tjt|t}t||jf|j||fS)a Given an edge list, return a boolean scipy.sparse.coo_matrix representing the edges in matrix form. Parameters ------------ edges : (n, 2) int Edges of a graph count : int The total number of nodes in the graph if None: count = edges.max() + 1 data : (n,) any Assign data to each edge, if None will be bool True for each specified edge Returns ------------ matrix: (count, count) scipy.sparse.coo_matrix Sparse COO rBrrrr)r3shape)r9rrr#rrrrintr]rrr5r3)r(countdatas r-rrs* MM%rxx 0E J!Ot}}UG<011 } a JE |wws5z. tUWWoTZZu~ NNr/ctjt}|r)|Dcgc]}||dj|dc}nB|Dcgc]6}||dj|d||dj|df8c}||j dz}t |Dcgc]}t ||}}|Scc}wcc}wcc}w)a Find the neighbors for each node in an edgelist graph. TODO : re-write this with sparse matrix operations Parameters ------------ edges : (n, 2) int Connected nodes directed : bool If True, only connect edges in one direction Returns --------- neighbors : sequence Vertex index corresponds to set of other vertex indices rr)r defaultdictsetaddrranger)r( max_indexr neighborsedgerLrs r-rrs$'',I5:;UT47   Q (U; tAw  # #DG ,iQ.@.D.DT!W.M N IIK!O ).y)9 :)9AT)A, )9E : L <  ;s!C ;C)C angle facet_minareacd|tjd}t|jdk(r|j S|j |k}|j|}g}d}||j }|j|z } |jD cgc]} || j|kDs| }} t|dkDrstjt|jt} d| tj|<|| |jd}tj|}t'|d | } t|dkDr| j)|t| dk(r|j Stjtj| } t| t|jk7r\tj*tj,t|j| } | j)| j/d |j1| dd }| |j2d<t|jt|jk7rt#j$d|Scc} w#t $rt#j$dd Y}wxYw)az Return a non-watertight version of the mesh which will render nicely with smooth shading by disconnecting faces at sharp angles to each other. Parameters ----------- mesh : trimesh.Trimesh Source geometry angle : float or None Angle in radians face pairs with angles smaller than this will appear smoothed facet_minarea : float or None Minimum area fraction to consider IE for `facets_minarea=25` only facets larger than `mesh.area / 25` will be considered. Returns --------- smooth : trimesh.Trimesh Geometry with disconnected face patches NrrBFrrzfailed to calculate facetsT)exc_infor)r~r}rC)rroriginal_componentszface count in smooth wrong!)r9rTr#r.rrS area_facesarearrJr]r%rhstackrrrrwarningrrrrrIrmetadata)r&rrangle_okr*rr}areasmin_areafrrrbrokesmooths r- smooth_shaders=2 } 2 4  1$yy{))E1H##H-IF E 99}, E"&JAa 0IaFJ6{Qwws4::d;*/RYYv&'%d9o&9&9q&9&AB  ),&i%HJ 6{Q&! :!yy{YYryy, -F 6{c$**o% RYYs4::7@%--01\\*eD\ IF-7FOO)* 6<<C O+ 12 MOK E KK4t D D Es+J J*J.BJ J J/.J/r cP|tj|d}tj|d}t t |dzt |k(}||j dddddfj}t tj|j}||fS)ah Parameters ----------- edges : (n, 2) int List of vertex indices edges_sorted : (n, 2) int Pass vertex indices sorted on axis 1 as a speedup Returns --------- watertight : boolean Whether every edge is shared by an even number of faces winding : boolean Whether every shared edge is reversed Nrrrr)r2rrR) r9rrr"rr#rIr5equalr)r(r groups watertightopposingwindings r- is_watertightrks(wwu1-  Q ?Fs6{Q3u:56JV}$$W-a1f577H288X&**,-G w r/cddl}ddl}|j5}tjj j ||j|jd|jdg}ddd|S#1swYSxYw)z Turn a networkx graph into an SVG string using graphviz `dot`. Parameters ---------- graph: networkx graph Returns --------- svg: string, pictoral layout in SVG format rNdotz-Tsvg) subprocesstempfileNamedTemporaryFileridrawing nx_agraph write_dotname check_output)rrrdot_filesvgs r- graph_to_svgrsj  $ $ &( &&uhmm<%%uhmmW&EF ' J ' Js AA66Bc|6t|jt|jzdz}|dfg}g}g}t|D]}|dd}||}t|dk(r3|j |t|dk(r|S|j }Qd} |j D]L} || j D]4} | r|j | | fd} |j |dd| | fgz6N|S)a* For a networkx MultiDiGraph, find all paths from a source node to leaf nodes. This function returns edge instance numbers in addition to nodes, unlike networkx.all_simple_paths. Parameters --------------- G : networkx.MultiDiGraph Graph to evaluate source : hashable Node to start traversal at cutoff : int Number of nodes to visit If None will visit all nodes Returns ---------- traversals : (n,) list of [(node, edge instance index), ] paths Traversals of the multigraph Nrrr2TF)r#r(r}rrpopkeys) Gsourcecutoffcurrentqueuer_ current_nodechildrnodeinstances r-multigraph_pathsr)s*~aggi.3qwwy>1Q6{mG EJ 6]r{1~ , u:?   g &5zQ* %iikGE  %d 0 0 2H h'78 %  WSb\dH5E4F%FG!3%+D r/cg}tj|D]A\}}||d|d|d}||j|.|j||C|S)a\ Given a MultiDiGraph traversal, collect attributes along it. Parameters ------------- G: networkx.MultiDiGraph traversal: (n) list of (node, instance) tuples attrib: dict key, name to collect. If None, will return all Returns ------------- collected: (len(traversal) - 1) list of attributes rr)rpairwiser)rrattrib collecteduvattribss r-multigraph_collectr1skI i(1AaD'!A$-!% >   W %   WV_ - ) r/)NNF)NN)TTNN)rNN)N)r)NF)Ng$@)8__doc__rnumpyr9rrr constantsrrgeometryr typedr r r r rrrrrrr scipy.sparserr scipy.spatialrrEExceptionWrapperrrir.r@rPrgrnryrrrrrrrrrrrrrr)r1r/r-r=s-(($    00%(K\wu~2+\.b 8.OS0!0;CF;K0j!%)" 33 3 " 3  3 3nHLf5f55Df5R2<<W <~,,,uXwEV?W,^DN$ONFMQV&!V9A&9IVt;?" "$,Y$7" 4:"J.AHQ0)j))!,G)j))!,G,,,Q/J 0( % $ $Q 'B(s/C1 D91D666D11D69E>EE