Ńň n,2Jc @s¨ddkZyeWn#ej oddklZnXddkZddklZddk l Z l Z ddgZ de fd„ƒYZdefd„ƒYZdS( i˙˙˙˙N(tSet(tdelaunay(tLinearInterpolatortNNInterpolatort TriangulationtDuplicatePointWarningcBseZdZRS(sBDuplicate points were passed in to the triangulation routine. (t__name__t __module__t__doc__(((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyRscBs€eZdZd„Zd„Zd„Zeid„Zeid„Z d d„Z d eid„Z d eid„Z d „ZRS( swA Delaunay triangulation of points in a plane. Triangulation(x, y) x, y -- the coordinates of the points as 1-D arrays of floats Let us make the following definitions: npoints = number of points input nedges = number of edges in the triangulation ntriangles = number of triangles in the triangulation point_id = an integer identifying a particular point (specifically, an index into x and y), range(0, npoints) edge_id = an integer identifying a particular edge, range(0, nedges) triangle_id = an integer identifying a particular triangle range(0, ntriangles) Attributes: (all should be treated as read-only to maintain consistency) x, y -- the coordinates of the points as 1-D arrays of floats. circumcenters -- (ntriangles, 2) array of floats giving the (x,y) coordinates of the circumcenters of each triangle (indexed by a triangle_id). edge_db -- (nedges, 2) array of point_id's giving the points forming each edge in no particular order; indexed by an edge_id. triangle_nodes -- (ntriangles, 3) array of point_id's giving the points forming each triangle in counter-clockwise order; indexed by a triangle_id. triangle_neighbors -- (ntriangles, 3) array of triangle_id's giving the neighboring triangle; indexed by a triangle_id. The value can also be -1 meaning that that edge is on the convex hull of the points and there is no neighbor on that edge. The values are ordered such that triangle_neighbors[tri, i] corresponds with the edge *opposite* triangle_nodes[tri, i]. As such, these neighbors are also in counter-clockwise order. hull -- list of point_id's giving the nodes which form the convex hull of the point set. This list is sorted in counter-clockwise order. cCs5ti|dtiƒ|_ti|dtiƒ|_|ii|iijpt|iiƒdjotdƒ‚n|ii|_|i ƒ}|i|iijoCt i dt ƒ||_ |i|i |_|i|i |_n d|_ t|i|iƒ\|_|_|_|_|iƒ|_dS(Ntdtypeis#x,y must be equal-length 1-D arrayssBInput data contains duplicate x,y points; some values are ignored.(tnptasarraytfloat64txtytshapetlent ValueErrort old_shapet_collapse_duplicate_pointstwarningstwarnRtj_uniquetNoneRt circumcenterstedge_dbttriangle_nodesttriangle_neighborst_compute_convex_hullthull(tselfR RR((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyt__init__?s 2   -cCsktid|i|ifƒ}titti|i|ƒdjti|i|ƒdjBgƒ}||S(s›Generate index array that picks out unique x,y points. This appears to be required by the underlying delaunay triangulation code. tkeysi(R tlexsortR RthstacktTruetdiff(Rtj_sortedt mask_unique((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyRZs <cCsœ|idj}h}|itt|i|dd…dfdd…df|i|dd…dfdd…dfƒƒƒ|itt|i|dd…dfdd…df|i|dd…dfdd…dfƒƒƒ|itt|i|dd…dfdd…df|i|dd…dfdd…dfƒƒƒt|iƒƒ}x&|o|i|i|dƒƒqhW|iƒ|S(sÁExtract the convex hull from the triangulation information. The output will be a list of point_id's in counter-clockwise order forming the convex hull of the data set. i˙˙˙˙Niii( RtupdatetdicttzipRtlisttpopitemtappendtpop(RtbordertedgesR((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyRhs646464 cCslti|dtiƒ}|i|ijotdƒ‚n|idj o||i}nt|||ƒS(sâGet an object which can interpolate within the convex hull by assigning a plane to each triangle. z -- an array of floats giving the known function values at each point in the triangulation. R s#z must be the same shape as x and yN( R R R RRRRRR(Rtzt default_value((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pytlinear_interpolator‚s cCslti|dtiƒ}|i|ijotdƒ‚n|idj o||i}nt|||ƒS(sÜGet an object which can interpolate within the convex hull by the natural neighbors method. z -- an array of floats giving the known function values at each point in the triangulation. R s#z must be the same shape as x and yN( R R R RRRRRR(RR0R1((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pytnn_interpolator‘s cCs^|djo2|id|id|id|idf}nti|tiƒ\}}}}t|tii|iƒƒ}t|tii|iƒƒ}t |ti i|iƒƒ}t |ti i|iƒƒ}t ||d||dƒ}||d}||d} ti |d|||d|g| | d|| d|ggƒ\} } ti |i| fƒ} ti |i| fƒ} |i | | ƒ}tit|iƒdftiƒ}|i|dd…df<|i|dd…dfR1RJRP((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pytnn_extrapolatorźscCs(|i||ƒ\}}|i||ƒS(N(RQR2(RR0R>R1RJRP((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pytlinear_extrapolatorŔscCshh}x[|iD]P\}}|i|tƒƒ}|i|ƒ|i|tƒƒ}|i|ƒqW|S(sľReturn a graph of node_id's pointing to node_id's. The arcs of the graph correspond to the edges in the triangulation. {node_id: set([node_id, ...]), ...} (Rt setdefaulttsettadd(RtgtitjRO((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyt node_graphÄs  N(RRRRRRR tnanR2R3RRQRRRSRZ(((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyRs*    (RRUt NameErrortsetsRtnumpyR tmatplotlib._delaunayRt interpolateRRt__all__tRuntimeWarningRtobjectR(((sOC:\graphics\Tools\Python26\Lib\site-packages\matplotlib\delaunay\triangulate.pyts