# encoding: utf-8 # module vtkmodules.vtkCommonDataModel # from C:\Users\xukai\Downloads\发票2\venv\Lib\site-packages\vtkmodules\vtkCommonDataModel.cp311-win_amd64.pyd # by generator 1.147 # no doc # imports import vtkmodules.vtkCommonCore as __vtkmodules_vtkCommonCore import vtkmodules.vtkCommonMath as __vtkmodules_vtkCommonMath import vtkmodules.vtkCommonTransforms as __vtkmodules_vtkCommonTransforms class vtkOrderedTriangulator(__vtkmodules_vtkCommonCore.vtkObject): """ vtkOrderedTriangulator - helper class to generate triangulations Superclass: vtkObject This class is used to generate unique triangulations of points. The uniqueness of the triangulation is controlled by the id of the inserted points in combination with a Delaunay criterion. The class is designed to be as fast as possible (since the algorithm can be slow) and uses block memory allocations to support rapid triangulation generation. Also, the assumption behind the class is that a maximum of hundreds of points are to be triangulated. If you desire more robust triangulation methods use vtkPolygon::Triangulate(), vtkDelaunay2D, or vtkDelaunay3D. @par Background: This work is documented in the technical paper: W.J. Schroeder, B. Geveci, M. Malaterre. Compatible Triangulations of Spatial Decompositions. In Proceedings of Visualization 2004, IEEE Press October 2004. @par Background: Delaunay triangulations are unique assuming a random distribution of input points. The 3D Delaunay criterion is as follows: the circumsphere of each tetrahedron contains no other points of the triangulation except for the four points defining the tetrahedron. In application this property is hard to satisfy because objects like cubes are defined by eight points all sharing the same circumsphere (center and radius); hence the Delaunay triangulation is not unique. These so-called degenerate situations are typically resolved by arbitrary selecting a triangulation. This code does something different: it resolves degenerate triangulations by modifying the "InCircumsphere" method to use a slightly smaller radius. Hence, degenerate points are always considered "out" of the circumsphere. This, in combination with an ordering (based on id) of the input points, guarantees a unique triangulation. @par Background: There is another related characteristic of Delaunay triangulations. Given a N-dimensional Delaunay triangulation, points laying on a (N-1) dimensional plane also form a (N-1) Delaunay triangulation. This means for example, that if a 3D cell is defined by a set of (2D) planar faces, then the face triangulations are Delaunay. Combining this with the method to generate unique triangulations described previously, the triangulations on the face are guaranteed unique. This fact can be used to triangulate 3D objects in such a way to guarantee compatible face triangulations. This is a very useful fact for parallel processing, or performing operations like clipping that require compatible triangulations across 3D cell faces. (See vtkClipVolume for an example.) @par Background: A special feature of this class is that it can generate triangulation templates on the fly. If template triangulation is enabled, then the ordered triangulator will first triangulate the cell using the slower ordered Delaunay approach, and then store the result as a template. Later, if the same cell type and cell configuration is encountered, then the template is reused which greatly speeds the triangulation. @warning Duplicate vertices will be ignored, i.e., if two points have the same coordinates the second one is discarded. The implications are that the user of this class must prevent duplicate points. Because the precision of this algorithm is double, it's also a good idea to merge points that are within some epsilon of one another. @warning The triangulation is performed using the parametric coordinates of the inserted points. Therefore the bounds (see InitTriangulation()) should represent the range of the parametric coordinates of the inserted points. @sa vtkDelaunay2D vtkDelaunay3D vtkPolygon """ def AddTetras(self, classification, ugrid): # real signature unknown; restored from __doc__ """ AddTetras(self, classification:int, ugrid:vtkUnstructuredGrid) -> int C++: vtkIdType AddTetras(int classification, vtkUnstructuredGrid *ugrid) AddTetras(self, classification:int, connectivity:vtkCellArray) -> int C++: vtkIdType AddTetras(int classification, vtkCellArray *connectivity) AddTetras(self, classification:int, locator:vtkIncrementalPointLocator, outConnectivity:vtkCellArray, inPD:vtkPointData, outPD:vtkPointData, inCD:vtkCellData, cellId:int, outCD:vtkCellData) -> int C++: vtkIdType AddTetras(int classification, vtkIncrementalPointLocator *locator, vtkCellArray *outConnectivity, vtkPointData *inPD, vtkPointData *outPD, vtkCellData *inCD, vtkIdType cellId, vtkCellData *outCD) AddTetras(self, classification:int, ptIds:vtkIdList, pts:vtkPoints) -> int C++: vtkIdType AddTetras(int classification, vtkIdList *ptIds, vtkPoints *pts) Add the tetras to the unstructured grid provided. The unstructured grid is assumed to have been initialized (with Allocate()) and points set (with SetPoints()). The tetrahdera added are of the type specified (0=inside,1=outside,2=all). Inside tetrahedron are those whose points are classified "inside" or on the "boundary." Outside tetrahedron have at least one point classified "outside." The method returns the number of tetrahedrahedron of the type requested. """ return 0 def AddTriangles(self, connectivity): # real signature unknown; restored from __doc__ """ AddTriangles(self, connectivity:vtkCellArray) -> int C++: vtkIdType AddTriangles(vtkCellArray *connectivity) AddTriangles(self, id:int, connectivity:vtkCellArray) -> int C++: vtkIdType AddTriangles(vtkIdType id, vtkCellArray *connectivity) Add the triangle faces classified (2=boundary) to the connectivity list provided. The method returns the number of triangles. """ return 0 def GetNextTetra(self, classification, tet, cellScalars, tetScalars): # real signature unknown; restored from __doc__ """ GetNextTetra(self, classification:int, tet:vtkTetra, cellScalars:vtkDataArray, tetScalars:vtkDoubleArray) -> int C++: int GetNextTetra(int classification, vtkTetra *tet, vtkDataArray *cellScalars, vtkDoubleArray *tetScalars) Methods to get one tetra at a time. Start with InitTetraTraversal() and then invoke GetNextTetra() until the method returns 0. cellScalars are point-centered scalars on the original cell. tetScalars are point-centered scalars on the tetra: the values will be copied from cellScalars. \pre tet_exists: tet!=0 \pre cellScalars_exists: cellScalars!=0 \pre tetScalars_exists: tetScalars!=0 \pre tetScalars_valid_size: tetScalars->GetNumberOfTuples()==4 """ return 0 def GetNumberOfGenerationsFromBase(self, type): # real signature unknown; restored from __doc__ """ GetNumberOfGenerationsFromBase(self, type:str) -> int C++: vtkIdType GetNumberOfGenerationsFromBase(const char *type) override; Given the name of a base class of this class type, return the distance of inheritance between this class type and the named class (how many generations of inheritance are there between this class and the named class). If the named class is not in this class's inheritance tree, return a negative value. Valid responses will always be nonnegative. This method works in combination with vtkTypeMacro found in vtkSetGet.h. """ return 0 def GetNumberOfGenerationsFromBaseType(self, type): # real signature unknown; restored from __doc__ """ GetNumberOfGenerationsFromBaseType(type:str) -> int C++: static vtkIdType GetNumberOfGenerationsFromBaseType( const char *type) Given a the name of a base class of this class type, return the distance of inheritance between this class type and the named class (how many generations of inheritance are there between this class and the named class). If the named class is not in this class's inheritance tree, return a negative value. Valid responses will always be nonnegative. This method works in combination with vtkTypeMacro found in vtkSetGet.h. """ return 0 def GetNumberOfPoints(self): # real signature unknown; restored from __doc__ """ GetNumberOfPoints(self) -> int C++: virtual int GetNumberOfPoints() Return the number of inserted points. """ return 0 def GetPointId(self, internalId): # real signature unknown; restored from __doc__ """ GetPointId(self, internalId:int) -> int C++: vtkIdType GetPointId(vtkIdType internalId) Return the Id of point `internalId'. This id is the one passed in argument of InsertPoint. It assumes that the point has already been inserted. The method should be invoked prior to the Triangulate method. \pre valid_range: internalId>=0 && internalIdGetNumberOfPoints() """ return 0 def GetPointLocation(self, internalId): # real signature unknown; restored from __doc__ """ GetPointLocation(self, internalId:int) -> Pointer C++: double *GetPointLocation(vtkIdType internalId) Return the global coordinates of point `internalId'. It assumes that the point has already been inserted. The method should be invoked prior to the Triangulate method. \pre valid_range: internalId>=0 && internalIdGetNumberOfPoints() """ pass def GetPointPosition(self, internalId): # real signature unknown; restored from __doc__ """ GetPointPosition(self, internalId:int) -> Pointer C++: double *GetPointPosition(vtkIdType internalId) Return the parametric coordinates of point `internalId'. It assumes that the point has already been inserted. The method should be invoked prior to the Triangulate method. \pre valid_range: internalId>=0 && internalIdGetNumberOfPoints() """ pass def GetPreSorted(self): # real signature unknown; restored from __doc__ """ GetPreSorted(self) -> int C++: virtual vtkTypeBool GetPreSorted() """ return 0 def GetTetras(self, classification, ugrid): # real signature unknown; restored from __doc__ """ GetTetras(self, classification:int, ugrid:vtkUnstructuredGrid) -> int C++: vtkIdType GetTetras(int classification, vtkUnstructuredGrid *ugrid) Initialize and add the tetras and points from the triangulation to the unstructured grid provided. New points are created and the mesh is allocated. (This method differs from AddTetras() in that it inserts points and cells; AddTetras only adds the tetra cells.) The tetrahdera added are of the type specified (0=inside,1=outside,2=all). Inside tetrahedron are those whose points are classified "inside" or on the "boundary." Outside tetrahedron have at least one point classified "outside." The method returns the number of tetrahedrahedron of the type requested. """ return 0 def GetUseTemplates(self): # real signature unknown; restored from __doc__ """ GetUseTemplates(self) -> int C++: virtual vtkTypeBool GetUseTemplates() """ return 0 def GetUseTwoSortIds(self): # real signature unknown; restored from __doc__ """ GetUseTwoSortIds(self) -> int C++: virtual vtkTypeBool GetUseTwoSortIds() """ return 0 def InitTetraTraversal(self): # real signature unknown; restored from __doc__ """ InitTetraTraversal(self) -> None C++: void InitTetraTraversal() Methods to get one tetra at a time. Start with InitTetraTraversal() and then invoke GetNextTetra() until the method returns 0. """ pass def InitTriangulation(self, xmin, xmax, ymin, ymax, zmin, zmax, numPts): # real signature unknown; restored from __doc__ """ InitTriangulation(self, xmin:float, xmax:float, ymin:float, ymax:float, zmin:float, zmax:float, numPts:int) -> None C++: void InitTriangulation(double xmin, double xmax, double ymin, double ymax, double zmin, double zmax, int numPts) InitTriangulation(self, bounds:[float, float, float, float, float, float], numPts:int) -> None C++: void InitTriangulation(double bounds[6], int numPts) Initialize the triangulation process. Provide a bounding box and the maximum number of points to be inserted. Note that since the triangulation is performed using parametric coordinates (see InsertPoint()) the bounds should be represent the range of the parametric coordinates inserted. \post no_point_inserted: GetNumberOfPoints()==0 """ pass def InsertPoint(self, id, x, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ InsertPoint(self, id:int, x:[float, float, float], p:[float, float, float], type:int) -> int C++: vtkIdType InsertPoint(vtkIdType id, double x[3], double p[3], int type) InsertPoint(self, id:int, sortid:int, x:[float, float, float], p:[float, float, float], type:int) -> int C++: vtkIdType InsertPoint(vtkIdType id, vtkIdType sortid, double x[3], double p[3], int type) InsertPoint(self, id:int, sortid:int, sortid2:int, x:[float, float, float], p:[float, float, float], type:int) -> int C++: vtkIdType InsertPoint(vtkIdType id, vtkIdType sortid, vtkIdType sortid2, double x[3], double p[3], int type) For each point to be inserted, provide an id, a position x, parametric coordinate p, and whether the point is inside (type=0), outside (type=1), or on the boundary (type=2). You must call InitTriangulation() prior to invoking this method. Make sure that the number of points inserted does not exceed the numPts specified in InitTriangulation(). Also note that the "id" can be any integer and can be greater than numPts. It is used to create tetras (in AddTetras()) with the appropriate connectivity ids. The method returns an internal id that can be used prior to the Triangulate() method to update the type of the point with UpdatePointType(). (Note: the algorithm triangulated with the parametric coordinate p[3] and creates tetras with the global coordinate x[3]. The parametric coordinates and global coordinates may be the same.) """ pass def IsA(self, type): # real signature unknown; restored from __doc__ """ IsA(self, type:str) -> int C++: vtkTypeBool IsA(const char *type) override; Return 1 if this class is the same type of (or a subclass of) the named class. Returns 0 otherwise. This method works in combination with vtkTypeMacro found in vtkSetGet.h. """ return 0 def IsTypeOf(self, type): # real signature unknown; restored from __doc__ """ IsTypeOf(type:str) -> int C++: static vtkTypeBool IsTypeOf(const char *type) Return 1 if this class type is the same type of (or a subclass of) the named class. Returns 0 otherwise. This method works in combination with vtkTypeMacro found in vtkSetGet.h. """ return 0 def NewInstance(self): # real signature unknown; restored from __doc__ """ NewInstance(self) -> vtkOrderedTriangulator C++: vtkOrderedTriangulator *NewInstance() """ return vtkOrderedTriangulator def PreSortedOff(self): # real signature unknown; restored from __doc__ """ PreSortedOff(self) -> None C++: virtual void PreSortedOff() """ pass def PreSortedOn(self): # real signature unknown; restored from __doc__ """ PreSortedOn(self) -> None C++: virtual void PreSortedOn() """ pass def SafeDownCast(self, o): # real signature unknown; restored from __doc__ """ SafeDownCast(o:vtkObjectBase) -> vtkOrderedTriangulator C++: static vtkOrderedTriangulator *SafeDownCast(vtkObjectBase *o) """ return vtkOrderedTriangulator def SetPreSorted(self, _arg): # real signature unknown; restored from __doc__ """ SetPreSorted(self, _arg:int) -> None C++: virtual void SetPreSorted(vtkTypeBool _arg) Boolean indicates whether the points have been pre-sorted. If pre-sorted is enabled, the points are not sorted on point id. By default, presorted is off. (The point id is defined in InsertPoint().) """ pass def SetUseTemplates(self, _arg): # real signature unknown; restored from __doc__ """ SetUseTemplates(self, _arg:int) -> None C++: virtual void SetUseTemplates(vtkTypeBool _arg) If this flag is set, then the ordered triangulator will create and use templates for the triangulation. To use templates, the TemplateTriangulate() method should be called when appropriate. (Note: the TemplateTriangulate() method works for complete (interior) cells without extra points due to intersection, etc.) """ pass def SetUseTwoSortIds(self, _arg): # real signature unknown; restored from __doc__ """ SetUseTwoSortIds(self, _arg:int) -> None C++: virtual void SetUseTwoSortIds(vtkTypeBool _arg) Tells the triangulator that a second sort id is provided for each point and should also be considered when sorting. """ pass def TemplateTriangulate(self, cellType, numPts, numEdges): # real signature unknown; restored from __doc__ """ TemplateTriangulate(self, cellType:int, numPts:int, numEdges:int) -> None C++: void TemplateTriangulate(int cellType, int numPts, int numEdges) """ pass def Triangulate(self): # real signature unknown; restored from __doc__ """ Triangulate(self) -> None C++: void Triangulate() Perform the triangulation. (Complete all calls to InsertPoint() prior to invoking this method.) A special version is available when templates should be used. """ pass def UpdatePointType(self, internalId, type): # real signature unknown; restored from __doc__ """ UpdatePointType(self, internalId:int, type:int) -> None C++: void UpdatePointType(vtkIdType internalId, int type) Update the point type. This is useful when the merging of nearly coincident points is performed. The id is the internal id returned from InsertPoint(). The method should be invoked prior to the Triangulate method. The type is specified as inside (type=0), outside (type=1), or on the boundary (type=2). \pre valid_range: internalId>=0 && internalIdGetNumberOfPoints() """ pass def UseTemplatesOff(self): # real signature unknown; restored from __doc__ """ UseTemplatesOff(self) -> None C++: virtual void UseTemplatesOff() """ pass def UseTemplatesOn(self): # real signature unknown; restored from __doc__ """ UseTemplatesOn(self) -> None C++: virtual void UseTemplatesOn() """ pass def UseTwoSortIdsOff(self): # real signature unknown; restored from __doc__ """ UseTwoSortIdsOff(self) -> None C++: virtual void UseTwoSortIdsOff() """ pass def UseTwoSortIdsOn(self): # real signature unknown; restored from __doc__ """ UseTwoSortIdsOn(self) -> None C++: virtual void UseTwoSortIdsOn() """ pass def __delattr__(self, *args, **kwargs): # real signature unknown """ Implement delattr(self, name). """ pass def __getattribute__(self, *args, **kwargs): # real signature unknown """ Return getattr(self, name). """ pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __repr__(self, *args, **kwargs): # real signature unknown """ Return repr(self). """ pass def __setattr__(self, *args, **kwargs): # real signature unknown """ Implement setattr(self, name, value). """ pass def __str__(self, *args, **kwargs): # real signature unknown """ Return str(self). """ pass __this__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Pointer to the C++ object.""" __dict__ = None # (!) real value is 'mappingproxy({\'__vtkname__\': \'vtkOrderedTriangulator\', \'IsTypeOf\': , \'IsA\': , \'SafeDownCast\': , \'NewInstance\': , \'GetNumberOfGenerationsFromBaseType\': , \'GetNumberOfGenerationsFromBase\': , \'InitTriangulation\': , \'InsertPoint\': , \'Triangulate\': , \'TemplateTriangulate\': , \'UpdatePointType\': , \'GetPointPosition\': , \'GetPointLocation\': , \'GetPointId\': , \'GetNumberOfPoints\': , \'SetUseTemplates\': , \'GetUseTemplates\': , \'UseTemplatesOn\': , \'UseTemplatesOff\': , \'SetPreSorted\': , \'GetPreSorted\': , \'PreSortedOn\': , \'PreSortedOff\': , \'SetUseTwoSortIds\': , \'GetUseTwoSortIds\': , \'UseTwoSortIdsOn\': , \'UseTwoSortIdsOff\': , \'GetTetras\': , \'AddTetras\': , \'AddTriangles\': , \'InitTetraTraversal\': , \'GetNextTetra\': , \'__new__\': , \'__repr__\': , \'__str__\': , \'__getattribute__\': , \'__setattr__\': , \'__delattr__\': , \'__dict__\': , \'__this__\': , \'__doc__\': \'vtkOrderedTriangulator - helper class to generate triangulations\\n\\nSuperclass: vtkObject\\n\\nThis class is used to generate unique triangulations of points. The\\nuniqueness of the triangulation is controlled by the id of the\\ninserted points in combination with a Delaunay criterion. The class\\nis designed to be as fast as possible (since the algorithm can be\\nslow) and uses block memory allocations to support rapid\\ntriangulation generation. Also, the assumption behind the class is\\nthat a maximum of hundreds of points are to be triangulated. If you\\ndesire more robust triangulation methods use\\nvtkPolygon::Triangulate(), vtkDelaunay2D, or vtkDelaunay3D.\\n\\n@par Background: This work is documented in the technical paper: W.J.\\nSchroeder, B. Geveci, M. Malaterre. Compatible Triangulations of\\nSpatial Decompositions. In Proceedings of Visualization 2004, IEEE\\nPress October 2004.\\n\\n@par Background: Delaunay triangulations are unique assuming a random\\ndistribution of input points. The 3D Delaunay criterion is as\\nfollows: the circumsphere of each tetrahedron contains no other\\npoints of the triangulation except for the four points defining the\\ntetrahedron. In application this property is hard to satisfy because\\nobjects like cubes are defined by eight points all sharing the same\\ncircumsphere (center and radius); hence the Delaunay triangulation is\\nnot unique. These so-called degenerate situations are typically\\nresolved by arbitrary selecting a triangulation. This code does\\nsomething different: it resolves degenerate triangulations by\\nmodifying the "InCircumsphere" method to use a slightly smaller\\nradius. Hence, degenerate points are always considered "out" of the\\ncircumsphere. This, in combination with an ordering (based on id) of\\nthe input points, guarantees a unique triangulation.\\n\\n@par Background: There is another related characteristic of Delaunay\\ntriangulations. Given a N-dimensional Delaunay triangulation, points\\nlaying on a (N-1) dimensional plane also form a (N-1) Delaunay\\ntriangulation. This means for example, that if a 3D cell is defined\\nby a set of (2D) planar faces, then the face triangulations are\\nDelaunay. Combining this with the method to generate unique\\ntriangulations described previously, the triangulations on the face\\nare guaranteed unique. This fact can be used to triangulate 3D\\nobjects in such a way to guarantee compatible face triangulations.\\nThis is a very useful fact for parallel processing, or performing\\noperations like clipping that require compatible triangulations\\nacross 3D cell faces. (See vtkClipVolume for an example.)\\n\\n@par Background: A special feature of this class is that it can\\ngenerate triangulation templates on the fly. If template\\ntriangulation is enabled, then the ordered triangulator will first\\ntriangulate the cell using the slower ordered Delaunay approach, and\\nthen store the result as a template. Later, if the same cell type and\\ncell configuration is encountered, then the template is reused which\\ngreatly speeds the triangulation.\\n\\n@warning\\nDuplicate vertices will be ignored, i.e., if two points have the same\\ncoordinates the second one is discarded. The implications are that\\nthe user of this class must prevent duplicate points. Because the\\nprecision of this algorithm is double, it\\\'s also a good idea to merge\\npoints that are within some epsilon of one another.\\n\\n@warning\\nThe triangulation is performed using the parametric coordinates of\\nthe inserted points. Therefore the bounds (see InitTriangulation())\\nshould represent the range of the parametric coordinates of the\\ninserted points.\\n\\n@sa\\nvtkDelaunay2D vtkDelaunay3D vtkPolygon\\n\\n\'})' __vtkname__ = 'vtkOrderedTriangulator'