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HH9uI)HHHrQ1 T TT TT TT TT TT TT THI9uILIHHxHUHAWAVSH(HuH#HEDvDuHG]܉]y pJHHt$H(HtD9uEt2LYIHt71H}111%JHH([A^A_]HxI"IHuMt3LLILH2IHHuHLLHHHHUHAWAVSH(HuHN#HED~D}HG]ԉ]؅y IHHtmLw(EMtIA)H}Au@HugIt/}EtEA8tAIL7HHt81>I1+E1#ILGHuHHH([A^A_]UHAVSH HuHm#HEDvDuHG]]y HHHt'H(HtD9uEt)xGHt*11H}111DH HNGHu)GHHH [A^]UHAVSH HuH#HEDvDuHG]]y GHHt+H(Ht"D9u!Et-HFHt(1.H}111GHFHuH6HHH [A^]f.DUHAVSH HuH$HEDvDuHG]]y "GHHt(H(HtD9uEt*H1FHt(1.H}111FHEHuHyHHH [A^]fUHAWAVSH(HuHP$HED~D}HG]ԉ]؅y pFHHtmLw(EMtIA)H}Au@HuWFt/}EtEA8tAIL'EHt81>E1+E1#ILDHuHHH([A^A_]UHAVSH HuH]$HEDvDuHG]]y EHHt'H(HtD9uEt)hDHt*11H}1114E H>DHuDHHH [A^]UHAVSH HuH$HEDvDuHG]]y DHHt+H(Ht"D9u!Et-HCHt(1.H}111DHCHuH&HHH [A^]f.DUHAVSH HuH$HEDvDuHG]]y DHHt(H(HtD9uEt*H1BHt(1.H}111CHBHuHiHHH [A^]ÐUHH=ՑH5nH,H 0B]uBHH=BH]ÐUH]DfDUHSPHH=pH5 HH ^BuBHAH=:1BH5H'HAt H tH[]H=H[]aBfUHAVSH0HuHMHEЋFEHEHEH}ȃu\Hu|Bt^H]H=HDAtH=UHDt HBLcHucH}111?RE1>HuCL?Ht6HH>t)H>HH1Q@H߾>1HH [A^]UHAWAVSH(HuH3#HED~D}HG]ԉ]؅y ?HHt`Lw(MtWA)Au;H5H}HU>}t4LHA@=HuHf HH}>1H([A^A_]fUHAVSH HuHO#HEDvDuHG]]y B>HHt H(HtD9u?H/=Ht1H}111= H'>HHH [A^]f.fUHAWAVSHHHuH#HED~D}HG]̉]Ѕy =HHtwLw(MtnA)AuRHuH}=tTHuH}=tCHUHMH}L>[<Hu%H=&Hu<H}=1HH[A^A_]fUHAWAVSH8HuHB#HED~D}HG]̉]Ѕy <HHtnLw(MteA)AuIHuH}<tKHuH}<t:HuHUL=H;HuH<H}N<1H8[A^A_]f.UHAVSH HuH"HEDvDuHG]]y <HHt H(HtD9um=H:Ht1H}111; H:HHH [A^]f.fUHAWAVSH(HuH"HED~D}HG]ԉ]؅y `;HHtXLw(MtOA)Au3HuH}];t5HuL<4:HuH HH}:1H([A^A_]ÐUHAWAVSH(HuHx"HED~D}HG]ԉ]؅y :HHt`Lw(MtWA)Au;H5#H}HU|:}t4LH;|9HuH HH}<:1H([A^A_]fUHAWAVSH(HuH]"HED~D}HG]ԉ]؅y 9HHtXLw(MtOA)Au3HuH}9t5HuL ;8HuH^HH}91H([A^A_]ÐUHAWAVSH(HuH "HED~D}HG]ԉ]؅y @9HHt`Lw(MtWA)Au;H5z"H}HU 9}t4LHW: 8HuHHH}81H([A^A_]ÐUHH=UH5H"H 07݊u/8H&H=7H]ÐUH]j9fDUHSPHH=H5H!H ~7xu7HH=Q7H5z!HH7t H tH[]H=H[]7fUHAVSH0HuH$BHEЋFEHEHEH}ȃHu7H]H= H9AtDH=H9t1H=IH9tH=IH9t H7Lc.6HuL-661H0[A^]UHAWAVSH(HuHLBHED~D}HG]ԉ]؅y6HHLw(HEMA)Au{HuH}6}L}tH=L8AtuH=˸L8tbH=HL8tOH=HL8t"1+E1#ILx!HuHHH([A^A_]ÐUHH=5{H5|HH 0!{u%!H|H=z Hz]ÐUH]"fDUHSPHH=zH5i|HtH X{u H{H=zq H5@HzH% t H tztH[]H=dzH[] fUHAVSH0HuHD+HEЋFEHEHEH}ȃHu H]H=H#AtDH=fKH#t1H=3H"tH=3H"t H. LcNHuLM 1H0[A^]UHAWAVSH(HuHl+HED~D}HG]ԉ]؅yHHLw(HEMA)Au{HuH}}L}tH=L"AtuH=MJL!tbH=2L!tOH=1L!tHHH([A^A_]f.DUHAVSH0HuHHEDvDuHG]܉]y HHt*H(Ht!D9u Et,EHt-16H}111q%HExHuEoHHH0[A^]UHAWAVSH(HuHHED~D}HG]ԉ]؅yHHLw(MA)AugHuH}ti}EtkA]fWfUf.u{AILHu6H}X1H([A^A_]ILVHuHHUHAVSH0HuHHEDvDuHG]܉]y HHt%H(HtD9uWEt$EHt-16H}111%HEHuEHHH0[A^]f.UHAVSH0HuHHEDvDuHG]܉]y 2HHt*H(Ht!D9u Et,EHt-16H}111%HEHuEHHH0[A^]UHAWAVSH(HuH?HED~D}HG]ԉ]؅y pHHtmLw(EMtIA)H}Au@HuWt/}EtEA8tAIL'Ht81>1+E1#ILHuHHH([A^A_]UHAVSH HuH2HEDvDuHG]]y HHt'H(HtD9uEt)hHt*11H}1114 H>HuHHH [A^]UHAVSH HuH^HEDvDuHG]]y HHt+H(Ht"D9u!Et-HHt(1.H}111HHuH&HHH [A^]f.DUHAVSH HuHoHEDvDuHG]]y HHt(H(HtD9uEt*H1Ht(1.H}111HHuHiHHH [A^]fUHAWAVSH(HuHHED~D}HG]ԉ]؅y`HHLw(HEMA)AHuH}1}H]MHL HtMtLHoMtLHHHOIIL/II 4J ;H9s J 8H9LHHyHHHƉH`sd1H}F1+HE1ILH>H\1H([A^A_]Idž%HH)13L30L0D3 L30D0 L00D3@L3PD0@L0PD3`L3pD0`L0pHHuHt"HHD3 3D0 0H HuI9AHHILHt'1f.  HH9uI)HHHrQ1 T TT TT TT TT TT TT THI9uIL HHxHUHAWAVSH(HuHPHEDvDuHG]܉]y p HHt$H(HtD9uEt2LY Ht71H}111% HH([A^A_]HI" HuMt3LILH2 HHu LL HHHUHAWAVSH(HuHHED~D}HG]ԉ]؅y HHtmLw(EMtIA)H}Au@Hug t/}EtEA8tAIL7 Ht81> 1+E1#IL HuHHH([A^A_]UHAVSH HuHHEDvDuHG]]y HHt'H(HtD9uEt)x Ht*11H}111D  HN Hu) HHH [A^]UHAVSH HuHHEDvDuHG]]y HHt+H(Ht"D9u!Et-H Ht(1.H}111 H HuH6HHH [A^]f.DUHAVSH HuHGHEDvDuHG]]y " HHt(H(HtD9uEt*H1 Ht(1.H}111 HHuHyHHH [A^]fUHAWAVSH(HuHHED~D}HG]ԉ]؅y p HHtmLw(EMtIA)H}Au@HuW t/}EtEA8tAIL'Ht81>1+E1#ILHuHHH([A^A_]UHAVSH HuHHEDvDuHG]]y HHt'H(HtD9uEt)hHt*11H}1114 H >HuHHH [A^]UHAVSH HuH/HEDvDuHG]]y HHt+H(Ht"D9u!Et-HHt(1.H}111H(HuH&HHH [A^]f.DUHAVSH HuH;HEDvDuHG]]y HHt(H(HtD9uEt*H1Ht(1.H}111H0HuHiHHH [A^]fUHAWAVSH(HuHXHED~D}HG]ԉ]؅y `HHtmLw(EMtIA)H}Au@HuGt/}EtEA8tAILHt81>1+E1#IL8HuHyHH([A^A_]UHAVSH HuHHEDvDuHG]]y rHHt'H(HtD9uEt)XHt*11H}111$ H@.Hu HHH [A^]UHAVSH HuHHEDvDuHG]]y HHt+H(Ht"D9u!Et-H8Ht(1.H}111pHH|HuHHHH [A^]f.DUHAVSH HuHHEDvDuHG]]y HHt(H(HtD9uEt*H18Ht(1.H}111HPHuHYHHH [A^]fUHAWAVSH(HuH,2HED~D}HG]ԉ]؅y PHHtmLw(EMtIA)H}Au@Hu7t/}EtEA8tAILHt81>1+E1#ILXHuHiHH([A^A_]UHAVSH HuH0HEDvDuHG]]y bHHt'H(HtD9uEt)HHt*11H}111 H`HuHHH [A^]UHAVSH HuH1HEDvDuHG]]y HHt+H(Ht"D9u!Et-HXHt(1.H}111`HhlHuHHHH [A^]f.DUHAVSH HuH0HEDvDuHG]]y HHt(H(HtD9uEt*H1XHt(1.H}111HpHuHIHHH [A^]fUHAWAVSH(HuHHED~D}HG]ԉ]؅y @HHtmLw(EMtIA)H}Au@Hu't/}EtEA8tAILHt81>1+E1#ILxHuHYHH([A^A_]UHAVSH HuHHEDvDuHG]]y RHHt'H(HtD9uEt)8Ht*11H}111 HHuHHH [A^]UHAVSH HuHHEDvDuHG]]y HHt+H(Ht"D9u!Et-HxHt(1.H}111PH\HuHHHH [A^]f.DUHAVSH HuH HEDvDuHG]]y HHt(H(HtD9uEt*H1xHt(1.H}111HHuH9HHH [A^]fUHAWAVSH(HuH#HED~D}HG]ԉ]؅y 0HHtmLw(EMtIA)H}Au@Hut/}EtEA8tAILHt81>1+E1#ILHuHIHH([A^A_]UHAVSH HuHHEDvDuHG]]y BHHt'H(HtD9uEt)(Ht*11H}111 HHuHHH [A^]UHAVSH HuHHEDvDuHG]]y HHt+H(Ht"D9u!Et-HtHt(1.H}111@HLHuHHHH [A^]f.DUHAVSH HuHrHEDvDuHG]]y HHt(H(HtD9uEt*H1Ht(1.H}111HHuH)HHH [A^]fUHAWAVSH(HuHIHED~D}HG]ԉ]؅y HHLw(HEMA)AHuH}}H]MHL HtMtLH/MtLHHIILII 4J ;H9s J 8H9LHHyHHHƉH`sd1H}1+HE1ILHH\1H([A^A_]Idž%HH)13L30L0D3 L30D0 L00D3@L3PD0@L0PD3`L3pD0`L0pHHuHt"HHD3 3D0 0H HuI9AHHILHt'1f.  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HH9uI)HHHrQ1 T TT TT TT TT TT TT THI9uILHHXHUHAWAVSH(HuH HEDvDuHG]܉]y PHHt$H(HtD9uEt2L9Ht71H}111HH([A^A_]HIHuMt3LnILHHHuLLHHdHUHAWAVSH(HuHw HED~D}HG]ԉ]؅y `HHtgLw(Mt^A)AuBHuH}WtD}utHA9tAILHu6H}1H([A^A_]ILHuH}HUHAVSH HuHX HEDvDuHG]]y HHt#H(HtD9uEt%lHt*11H}1118 HBHuHcAHHH [A^]ÐUHAVSH=RH5;TH\ H 0*SrHoSL5pS1Ht"HH5) LHH uHBHt"HH5LH|H uH}Ht"HH5LHKH uHH=QhHQ[A^]UH]lfDUHSPHH5cHQHt H QtH[]H=qQH[]nfDUHAVSH0HuHHEЋFEHEHEH}ȃHuH]H=HAtDH= Ht1H=HtH=Ht HLcHuL1H0[A^]UHAWAVSH(HuH<HED~D}HG]ԉ]؅yHHLw(HEMA)Au{HuH}u}L}tH=LAtuH= LtbH=LtOH=LtLc^HuL]$1H0[A^]UHAWAVSH(HuH|HED~D}HG]ԉ]؅yHHLw(HEMA)Au{HuH}}L}tH=LAtuH=LtbH=LtOH= Lt1+E1#ILhHuHiHH([A^A_]UHAVSH HuH,HEDvDuHG]]y bHHt'H(HtD9uEt)HHt*11H}111 HpHuHHH [A^]UHAVSH HuH HEDvDuHG]]y HHt+H(Ht"D9u!Et-HhHt(1.H}111`HxlHuHHHH [A^]f.DUHAVSH HuHHEDvDuHG]]y HHt(H(HtD9uEt*H1hHt(1.H}111HHuHIHHH [A^]fUHAWAVSH(HuHHED~D}HG]ԉ]؅y@HHLw(HEMA)AHuH}}H]MHL HtMtLHOMtL(HH/IILII 4J ;H9s J 8H9LHHyHHHƉH`sd1H}&1+HE1ILHH\1H([A^A_]Idž%HH)13L30L0D3 L30D0 L00D3@L3PD0@L0PD3`L3pD0`L0pHHuHt"HHD3 3D0 0H HuI9AHHILHt'1f.  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Superclass: vtkPassInputTypeAlgorithm This filter takes an input selection, vtkDataSetAttribute information, and data object and adds a bit array to the output vtkDataSetAttributes indicating whether each index was selected or not. IsTypeOfV.IsTypeOf(string) -> 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. IsAV.IsA(string) -> 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. SafeDownCastV.SafeDownCast(vtkObjectBase) -> vtkAddMembershipArray C++: static vtkAddMembershipArray *SafeDownCast(vtkObjectBase *o) NewInstanceV.NewInstance() -> vtkAddMembershipArray C++: vtkAddMembershipArray *NewInstance() GetFieldTypeV.GetFieldType() -> int C++: virtual int GetFieldType() The field type to add the membership array to. SetFieldTypeV.SetFieldType(int) C++: virtual void SetFieldType(int _arg) The field type to add the membership array to. GetFieldTypeMinValueV.GetFieldTypeMinValue() -> int C++: virtual int GetFieldTypeMinValue() The field type to add the membership array to. GetFieldTypeMaxValueV.GetFieldTypeMaxValue() -> int C++: virtual int GetFieldTypeMaxValue() The field type to add the membership array to. SetOutputArrayNameV.SetOutputArrayName(string) C++: virtual void SetOutputArrayName(const char *_arg) The name of the array added to the output vtkDataSetAttributes indicating membership. Defaults to "membership". GetOutputArrayNameV.GetOutputArrayName() -> string C++: virtual char *GetOutputArrayName() The name of the array added to the output vtkDataSetAttributes indicating membership. Defaults to "membership". SetInputArrayNameV.SetInputArrayName(string) C++: virtual void SetInputArrayName(const char *_arg) GetInputArrayNameV.GetInputArrayName() -> string C++: virtual char *GetInputArrayName() SetInputValuesV.SetInputValues(vtkAbstractArray) C++: void SetInputValues(vtkAbstractArray *) GetInputValuesV.GetInputValues() -> vtkAbstractArray C++: virtual vtkAbstractArray *GetInputValues() vtkPassInputTypeAlgorithmvtkAlgorithmvtkObjectvtkObjectBasevtkAbstractArrayvtkAdjacencyMatrixToEdgeTablevtkInfovisCorePython.vtkAdjacencyMatrixToEdgeTablevtkAdjacencyMatrixToEdgeTable - Treats a dense 2-way array of doubles as an adacency matrix and converts it into a vtkTable suitable for use as an edge table with vtkTableToGraph. Superclass: vtkTableAlgorithm @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkAdjacencyMatrixToEdgeTable C++: static vtkAdjacencyMatrixToEdgeTable *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkAdjacencyMatrixToEdgeTable C++: vtkAdjacencyMatrixToEdgeTable *NewInstance() GetSourceDimensionV.GetSourceDimension() -> int C++: virtual vtkIdType GetSourceDimension() Specifies whether rows or columns become the "source" in the output edge table. 0 = rows, 1 = columns. Default: 0 SetSourceDimensionV.SetSourceDimension(int) C++: virtual void SetSourceDimension(vtkIdType _arg) Specifies whether rows or columns become the "source" in the output edge table. 0 = rows, 1 = columns. Default: 0 GetValueArrayNameV.GetValueArrayName() -> string C++: virtual char *GetValueArrayName() Controls the name of the output table column that contains edge weights. Default: "value" SetValueArrayNameV.SetValueArrayName(string) C++: virtual void SetValueArrayName(const char *_arg) Controls the name of the output table column that contains edge weights. Default: "value" GetMinimumCountV.GetMinimumCount() -> int C++: virtual vtkIdType GetMinimumCount() Specifies the minimum number of adjacent edges to include for each source vertex. Default: 0 SetMinimumCountV.SetMinimumCount(int) C++: virtual void SetMinimumCount(vtkIdType _arg) Specifies the minimum number of adjacent edges to include for each source vertex. Default: 0 GetMinimumThresholdV.GetMinimumThreshold() -> float C++: virtual double GetMinimumThreshold() Specifies a minimum threshold that an edge weight must exceed to be included in the output. Default: 0.5 SetMinimumThresholdV.SetMinimumThreshold(float) C++: virtual void SetMinimumThreshold(double _arg) Specifies a minimum threshold that an edge weight must exceed to be included in the output. Default: 0.5 vtkTableAlgorithmvtkArrayNormvtkInfovisCorePython.vtkArrayNormvtkArrayNorm - Computes L-norms along one dimension of an array. Superclass: vtkArrayDataAlgorithm Given an input matrix (vtkTypedArray), computes the L-norm for each vector along either dimension, storing the results in a dense output vector (1D vtkDenseArray). The caller may optionally request the inverse norm as output (useful for subsequent normalization), and may limit the computation to a "window" of vector elements, to avoid data copying. @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkArrayNorm C++: static vtkArrayNorm *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkArrayNorm C++: vtkArrayNorm *NewInstance() GetDimensionV.GetDimension() -> int C++: virtual int GetDimension() Controls the dimension along which norms will be computed. For input matrices, For input matrices, use "0" (rows) or "1" (columns). Default: 0 SetDimensionV.SetDimension(int) C++: virtual void SetDimension(int _arg) Controls the dimension along which norms will be computed. For input matrices, For input matrices, use "0" (rows) or "1" (columns). Default: 0 GetLV.GetL() -> int C++: virtual int GetL() Controls the L-value. Default: 2 SetLV.SetL(int) C++: void SetL(int value) Controls the L-value. Default: 2 SetInvertV.SetInvert(int) C++: virtual void SetInvert(int _arg) Controls whether to invert output values. Default: false GetInvertV.GetInvert() -> int C++: virtual int GetInvert() Controls whether to invert output values. Default: false SetWindowV.SetWindow(vtkArrayRange) C++: void SetWindow(const vtkArrayRange &window) Defines an optional "window" used to compute the norm on a subset of the elements in a vector. GetWindowV.GetWindow() -> vtkArrayRange C++: vtkArrayRange GetWindow() Defines an optional "window" used to compute the norm on a subset of the elements in a vector. vtkArrayDataAlgorithmvtkArrayRangevtkArrayToTablevtkInfovisCorePython.vtkArrayToTablevtkArrayToTable - Converts one- and two-dimensional vtkArrayData objects to vtkTable Superclass: vtkTableAlgorithm @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkArrayToTable C++: static vtkArrayToTable *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkArrayToTable C++: vtkArrayToTable *NewInstance() vtkCollapseGraphvtkInfovisCorePython.vtkCollapseGraphvtkCollapseGraph - "Collapses" vertices onto their neighbors. Superclass: vtkGraphAlgorithm vtkCollapseGraph "collapses" vertices onto their neighbors, while maintaining connectivity. Two inputs are required - a graph (directed or undirected), and a vertex selection that can be converted to indices. Conceptually, each of the vertices specified in the input selection expands, "swallowing" adacent vertices. Edges to-or-from the "swallowed" vertices become edges to-or-from the expanding vertices, maintaining the overall graph connectivity. In the case of directed graphs, expanding vertices only swallow vertices that are connected via out edges. This rule provides intuitive behavior when working with trees, so that "child" vertices collapse into their parents when the parents are part of the input selection. Input port 0: graph Input port 1: selection V.SafeDownCast(vtkObjectBase) -> vtkCollapseGraph C++: static vtkCollapseGraph *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkCollapseGraph C++: vtkCollapseGraph *NewInstance() SetGraphConnectionV.SetGraphConnection(vtkAlgorithmOutput) C++: void SetGraphConnection(vtkAlgorithmOutput *) Convenience function provided for setting the graph input. SetSelectionConnectionV.SetSelectionConnection(vtkAlgorithmOutput) C++: void SetSelectionConnection(vtkAlgorithmOutput *) Convenience function provided for setting the selection input. vtkGraphAlgorithmvtkAlgorithmOutputvtkCollapseVerticesByArrayvtkInfovisCorePython.vtkCollapseVerticesByArrayvtkCollapseVerticesByArray - Collapse the graph given a vertex array Superclass: vtkGraphAlgorithm vtkCollapseVerticesByArray is a class which collapses the graph using a vertex array as the key. So if the graph has vertices sharing common traits then this class combines all these vertices into one. This class does not perform aggregation on vertex data but allow to do so for edge data. Users can choose one or more edge data arrays for aggregation using AddAggregateEdgeArray function. V.SafeDownCast(vtkObjectBase) -> vtkCollapseVerticesByArray C++: static vtkCollapseVerticesByArray *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkCollapseVerticesByArray C++: vtkCollapseVerticesByArray *NewInstance() GetAllowSelfLoopsV.GetAllowSelfLoops() -> bool C++: virtual bool GetAllowSelfLoops() Boolean to allow self loops during collapse. SetAllowSelfLoopsV.SetAllowSelfLoops(bool) C++: virtual void SetAllowSelfLoops(bool _arg) Boolean to allow self loops during collapse. AllowSelfLoopsOnV.AllowSelfLoopsOn() C++: virtual void AllowSelfLoopsOn() Boolean to allow self loops during collapse. AllowSelfLoopsOffV.AllowSelfLoopsOff() C++: virtual void AllowSelfLoopsOff() Boolean to allow self loops during collapse. AddAggregateEdgeArrayV.AddAggregateEdgeArray(string) C++: void AddAggregateEdgeArray(const char *arrName) Add arrays on which aggregation of data is allowed. Default if replaced by the last value. ClearAggregateEdgeArrayV.ClearAggregateEdgeArray() C++: void ClearAggregateEdgeArray() Clear the list of arrays on which aggregation was set to allow. GetVertexArrayV.GetVertexArray() -> string C++: virtual char *GetVertexArray() Set the array using which perform the collapse. SetVertexArrayV.SetVertexArray(string) C++: virtual void SetVertexArray(const char *_arg) Set the array using which perform the collapse. GetCountEdgesCollapsedV.GetCountEdgesCollapsed() -> bool C++: virtual bool GetCountEdgesCollapsed() Set if count should be made of how many edges collapsed. SetCountEdgesCollapsedV.SetCountEdgesCollapsed(bool) C++: virtual void SetCountEdgesCollapsed(bool _arg) Set if count should be made of how many edges collapsed. CountEdgesCollapsedOnV.CountEdgesCollapsedOn() C++: virtual void CountEdgesCollapsedOn() Set if count should be made of how many edges collapsed. CountEdgesCollapsedOffV.CountEdgesCollapsedOff() C++: virtual void CountEdgesCollapsedOff() Set if count should be made of how many edges collapsed. GetEdgesCollapsedArrayV.GetEdgesCollapsedArray() -> string C++: virtual char *GetEdgesCollapsedArray() Name of the array where the count of how many edges collapsed will be stored.By default the name of array is "EdgesCollapsedCountArray". SetEdgesCollapsedArrayV.SetEdgesCollapsedArray(string) C++: virtual void SetEdgesCollapsedArray(const char *_arg) Name of the array where the count of how many edges collapsed will be stored.By default the name of array is "EdgesCollapsedCountArray". GetCountVerticesCollapsedV.GetCountVerticesCollapsed() -> bool C++: virtual bool GetCountVerticesCollapsed() Get/Set if count should be made of how many vertices collapsed. SetCountVerticesCollapsedV.SetCountVerticesCollapsed(bool) C++: virtual void SetCountVerticesCollapsed(bool _arg) Get/Set if count should be made of how many vertices collapsed. CountVerticesCollapsedOnV.CountVerticesCollapsedOn() C++: virtual void CountVerticesCollapsedOn() Get/Set if count should be made of how many vertices collapsed. CountVerticesCollapsedOffV.CountVerticesCollapsedOff() C++: virtual void CountVerticesCollapsedOff() Get/Set if count should be made of how many vertices collapsed. GetVerticesCollapsedArrayV.GetVerticesCollapsedArray() -> string C++: virtual char *GetVerticesCollapsedArray() Name of the array where the count of how many vertices collapsed will be stored. By default name of the array is "VerticesCollapsedCountArray". SetVerticesCollapsedArrayV.SetVerticesCollapsedArray(string) C++: virtual void SetVerticesCollapsedArray(const char *_arg) Name of the array where the count of how many vertices collapsed will be stored. By default name of the array is "VerticesCollapsedCountArray". vtkContinuousScatterplotvtkInfovisCorePython.vtkContinuousScatterplotvtkContinuousScatterplot - Given a 3D domain space represented by an unstructured grid composed of tetrahedral cells with bivariate fields, this filter tessellates each cell in the domain to polyhedral fragments by intersecting the projection of Superclass: vtkImageAlgorithm the cell into 2-D range space against two sets of cutting planes, one set is defined along the first field, the second set is defined along the second field. The volume of these subdivided polyhedral fragments can be computed and aggregated over cells to depict the density distribution of the data projection in the bivariate range space. @section Introduction Given a bivariate field (f1,f2) defined on an unstructured grid which is composed of tetrahedral cells, we can initially subdivide each cell based on its projection in the range into a number of fragments along the first field f1, we refer to these polyhedral fragments as Frag(f1) = {frag(f1)_1, frag(f1)_2, ... , frag(f1)_n}, where frag(f1)_n refers to the nth fragment along the first field subdivision. Each fragment has a range value and the value difference between the neighbouring fragments is represented as fragment width fw_f1, which is uniformly distributed across the range. Based on the structure of Frag(f1), for each of its cell "frag(f1)_n", we can further subdivide this cell based on the second field f2 using fragment width fw_f2. The tessellation along the second field results in an even finer fragment collection which we refer to as Frag(f1,f2) = {frag(f1,f2)_1, frag(f1,f2)_2, ... , frag(f1,f2)_m}. We can observe that Frag(f1,f2) is a finer tessellation of the domain than Frag(f1) and will be used to compute the density distribution in the bivariate range space. The algorithm for fragment computation is similar to the first stage of the work in [0]. Each fragment "s" in Frag(f1,f2) has range values (f1(s), f2(s)) in the bivariate fields. These values can be further mapped to a 2-D bin with a resolution rexX * resY. The mapped bin index (binIndexX, binIndexY) of the fragment can be computed by linear interpolation on its range values : binIndexX = (int) resX * (f1(s) - f1_min) / (f1_max - f1_min) binIndexY = (int) resY * (f2(s) - f2_min) / (f2_max - f2_min), where (f1_min, f1_max) is the range in first field. Once we know which bin a fragment coincides, the density value in each bin equals to the total geometric volume of the fragments in this bin. This volume distribution over the bins will be exported as a point data array in the output data structure. If we map this 2-D bin to a 2-D image with each bin corresponding to a pixel and bin density to pixel transparency, then the image can be displayed as a continuous scatterplot. * @section Algorithm * The algorithm of this filter can be described as: * Require: R.1 The domain space is an unstructured grid data set composed of * tetrahedral cells; * R.2 The range space contains two scalar fields, say f1 and f2. * * The most important step is to compute the fragments. The implementation processes * the input grid one cell at a time, explicitly computing the intersection of the cell * with the cutting planes defined by the fragment boundaries in each scalar field. * In order to subdivide the cell, we need to define a list of cutting planes in each * field. The interval between neighbouring cutting planes is related to the output 2-D * bin resolution (resX, resY) and can be computed as : * fw_f1 = (f1_max - f1_min) / resX * fw_f2 = (f2_max - f2_min) / resY, * where (f1_max,f1_min) is the scalar range of first field. * * 1. For each tetrahedron T in the input grid: * * 1.1 Subdivide the cell T based on the first field f1, we will obtain a list * of fragments: Frag(f1) = {frag(f1)_1, frag(f1)_2, ... , frag(f1)_n}. The * steps for subdivision can be described as: * * 1.1.1 For each cutting plane s with respect to the first field f1, * its field value f1(s) = f1_min + n * fw_f1, where n refers to the n-th * cutting plane: * * 1.1.2. Traverse each edge e starting from point a to b in the cell, we * will maintain three data classes, namely fragmentFace, * residualFace and cutSet: * A. fragmentFace contains vertices in the current fragment. * B. cutSet contains vertices whose range values equal to f1(s). * This set contains the current cutting plane. * C. residualFace contains the rest of the vertices in the cell. * In order to classify edge vertices into these classes, the * following case table is used for each vertex "a" : * case 0 : f1(a)------ f1(s) ------f1(b) * condition: f1(a) < f1(s) , f1(b) > f1(s) * class: p(s,e), a -> fragmentFace * p(s,e) -> cutSet * p(s,e) -> residualFace * * case 1 : f1(b)------ f1(s) ------f1(a) * condition: f1(a) > f1(s) , f1(b) < f1(s) * class: p(s,e) -> fragmentFace * p(s,e) -> cutSet * a -> residualFace * * case 2 : f1(s),f1(a)-------------------f1(b) * condition: f1(s) == f1(a), f1(s) <= f1(b) * class: a -> fragmentFace * a -> residualFace * a -> cutSet * * case 3 : f1(a)-------------------f1(b), f1(s) * condition: f1(s) > f1(a), f1(s) == f1(b) * class: a -> fragmentFace * * case 4 : f1(s),f1(b)-------------------f1(a) * condition: f1(s) < f1(a), f1(s) == f1(b) * class: a -> residualFace * Remark: 1. we use "->" to indicate "belongs to" relation. * 2. p(s,e) refers to the interpolated point of range value * f1(s) on the edge e. * * 1.1.3. After we have traversed every edge in a cell for the cutting plane * s, three classes for storing fragment, cutting plane and residual * faces are updated. The faces of the current fragment frag(f1) * are the union of all elements in fragmentFace and cutSet. * * 1.2 Take the output of step 1.1, traverse each fragment in Frag(f1), define a list * of cutting planes with respect to field f2, further subdivide the fragments in * Frag(f1) following steps from 1.1.2 to 1.1.3. The output of this step will be * the fragment collection Frag(f1,f2). Each fragment in Frag(f1,f2) can be further * mapped to a 2-D bin based on its range values. The density value in each bin * equals to the total geometric volume of the fragments in this bin. This volume * distribution over the bins will be exported as a point data array in the output * data structure. * * @section VTK Filter Design * The input and output ports of the filter: * Input port : the input data set should be a vtkUnstructuredGrid, with each of its * cell defined as a tetrahedron. At least two scalar fields are * associated with the data. The user needs to specify the name of the * two scalar arrays beforehand. * Output port: the output data set is a 2D image stored as a vtkImageData. * The resolution of the output image can be set by the user. * The volume distribution of fragments in each pixel or bin * is stored in an point data array named "volume" in the output * vtkImageData. * * @section How To Use This Filter * Suppose we have a tetrahedral mesh stored in a vtkUnstructuredGrid, we call this * data set "inputData". This data set has two scalar arrays whose names are "f1" * and "f2" respectively. We would like the resolution of output image set to (resX,resY). * Given these input, this filter can be called as follows in c++ sample code: * * vtkSmartPointercsp = * vtkSmartPointer::New(); * csp->SetInputData(inputData); * csp->SetField1("f1",resX); * csp->SetField2("f2",resY); * csp->Update(); * * Then the output, "csp->GetOutput()", will be a vtkImageData containing a scalar * array whose name is "volume". This array contains the volume distribution of the * fragments. * * [0] H.Carr and D.Duke, Joint contour nets: Topological analysis of multivariate data. * IEEE Transactions on Visualization and Computer Graphics, volume 20, * issue 08, pages 1100-1113, 2014 V.SafeDownCast(vtkObjectBase) -> vtkContinuousScatterplot C++: static vtkContinuousScatterplot *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkContinuousScatterplot C++: vtkContinuousScatterplot *NewInstance() GetEpsilonV.GetEpsilon() -> float C++: virtual double GetEpsilon() Get the tolerance used when comparing floating point numbers for equality. SetEpsilonV.SetEpsilon(float) C++: virtual void SetEpsilon(double _arg) Set the tolerance used when comparing floating point numbers for equality. SetField1V.SetField1(string, int) C++: void SetField1(char *fieldName, vtkIdType ResX) Specify the name of the first field to be used in subdividing the dataset. Specify the resolution along x axis of the output image. SetField2V.SetField2(string, int) C++: void SetField2(char *fieldName, vtkIdType ResY) Specify the name of the second field to be used in subdividing the dataset. Specify the resolution along y axis of the output image. vtkImageAlgorithmvtkDataObjectToTablevtkInfovisCorePython.vtkDataObjectToTablevtkDataObjectToTable - extract field data as a table Superclass: vtkTableAlgorithm This filter is used to extract either the field, cell or point data of any data object as a table. V.SafeDownCast(vtkObjectBase) -> vtkDataObjectToTable C++: static vtkDataObjectToTable *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkDataObjectToTable C++: vtkDataObjectToTable *NewInstance() V.GetFieldType() -> int C++: virtual int GetFieldType() The field type to copy into the output table. Should be one of FIELD_DATA, POINT_DATA, CELL_DATA, VERTEX_DATA, EDGE_DATA. V.SetFieldType(int) C++: virtual void SetFieldType(int _arg) The field type to copy into the output table. Should be one of FIELD_DATA, POINT_DATA, CELL_DATA, VERTEX_DATA, EDGE_DATA. V.GetFieldTypeMinValue() -> int C++: virtual int GetFieldTypeMinValue() The field type to copy into the output table. Should be one of FIELD_DATA, POINT_DATA, CELL_DATA, VERTEX_DATA, EDGE_DATA. V.GetFieldTypeMaxValue() -> int C++: virtual int GetFieldTypeMaxValue() The field type to copy into the output table. Should be one of FIELD_DATA, POINT_DATA, CELL_DATA, VERTEX_DATA, EDGE_DATA. vtkDotProductSimilarityvtkInfovisCorePython.vtkDotProductSimilarityvtkDotProductSimilarity - compute dot-product similarity metrics. Superclass: vtkTableAlgorithm Treats matrices as collections of vectors and computes dot-product similarity metrics between vectors. The results are returned as an edge-table that lists the index of each vector and their computed similarity. The output edge-table is typically used with vtkTableToGraph to create a similarity graph. This filter can be used with one or two input matrices. If you provide a single matrix as input, every vector in the matrix is compared with every other vector. If you provide two matrices, every vector in the first matrix is compared with every vector in the second matrix. Note that this filter *only* computes the dot-product between each pair of vectors; if you want to compute the cosine of the angles between vectors, you will need to normalize the inputs yourself. Inputs: Input port 0: (required) A vtkDenseArraywith two dimensions (a matrix). Input port 1: (optional) A vtkDenseArraywith two dimensions (a matrix). Outputs: Output port 0: A vtkTable containing "source", "target", and "similarity" columns. @warning Note that the complexity of this filter is quadratic! It also requires dense arrays as input, in the future it should be generalized to accept sparse arrays. @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkDotProductSimilarity C++: static vtkDotProductSimilarity *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkDotProductSimilarity C++: vtkDotProductSimilarity *NewInstance() GetVectorDimensionV.GetVectorDimension() -> int C++: virtual vtkIdType GetVectorDimension() Controls whether to compute similarities for row-vectors or column-vectors. 0 = rows, 1 = columns. SetVectorDimensionV.SetVectorDimension(int) C++: virtual void SetVectorDimension(vtkIdType _arg) Controls whether to compute similarities for row-vectors or column-vectors. 0 = rows, 1 = columns. GetUpperDiagonalV.GetUpperDiagonal() -> int C++: virtual int GetUpperDiagonal() When computing similarities for a single input matrix, controls whether the results will include the upper diagonal of the similarity matrix. Default: true. SetUpperDiagonalV.SetUpperDiagonal(int) C++: virtual void SetUpperDiagonal(int _arg) When computing similarities for a single input matrix, controls whether the results will include the upper diagonal of the similarity matrix. Default: true. GetDiagonalV.GetDiagonal() -> int C++: virtual int GetDiagonal() When computing similarities for a single input matrix, controls whether the results will include the diagonal of the similarity matrix. Default: false. SetDiagonalV.SetDiagonal(int) C++: virtual void SetDiagonal(int _arg) When computing similarities for a single input matrix, controls whether the results will include the diagonal of the similarity matrix. Default: false. GetLowerDiagonalV.GetLowerDiagonal() -> int C++: virtual int GetLowerDiagonal() When computing similarities for a single input matrix, controls whether the results will include the lower diagonal of the similarity matrix. Default: false. SetLowerDiagonalV.SetLowerDiagonal(int) C++: virtual void SetLowerDiagonal(int _arg) When computing similarities for a single input matrix, controls whether the results will include the lower diagonal of the similarity matrix. Default: false. GetFirstSecondV.GetFirstSecond() -> int C++: virtual int GetFirstSecond() When computing similarities for two input matrices, controls whether the results will include comparisons from the first matrix to the second matrix. SetFirstSecondV.SetFirstSecond(int) C++: virtual void SetFirstSecond(int _arg) When computing similarities for two input matrices, controls whether the results will include comparisons from the first matrix to the second matrix. GetSecondFirstV.GetSecondFirst() -> int C++: virtual int GetSecondFirst() When computing similarities for two input matrices, controls whether the results will include comparisons from the second matrix to the first matrix. SetSecondFirstV.SetSecondFirst(int) C++: virtual void SetSecondFirst(int _arg) When computing similarities for two input matrices, controls whether the results will include comparisons from the second matrix to the first matrix. V.GetMinimumThreshold() -> float C++: virtual double GetMinimumThreshold() Specifies a minimum threshold that a similarity must exceed to be included in the output. V.SetMinimumThreshold(float) C++: virtual void SetMinimumThreshold(double _arg) Specifies a minimum threshold that a similarity must exceed to be included in the output. V.GetMinimumCount() -> int C++: virtual vtkIdType GetMinimumCount() Specifies a minimum number of edges to include for each vector. V.SetMinimumCount(int) C++: virtual void SetMinimumCount(vtkIdType _arg) Specifies a minimum number of edges to include for each vector. GetMaximumCountV.GetMaximumCount() -> int C++: virtual vtkIdType GetMaximumCount() Specifies a maximum number of edges to include for each vector. SetMaximumCountV.SetMaximumCount(int) C++: virtual void SetMaximumCount(vtkIdType _arg) Specifies a maximum number of edges to include for each vector. vtkExtractSelectedTreevtkInfovisCorePython.vtkExtractSelectedTreevtkExtractSelectedTree - return a subtree from a vtkTree Superclass: vtkTreeAlgorithm input 0 --- a vtkTree input 1 --- a vtkSelection, containing selected vertices. It may have FILED_type set to POINTS ( a vertex selection) or CELLS (an edge selection). A vertex selection preserves the edges that connect selected vertices. An edge selection perserves the vertices that are adjacent to at least one selected edges. V.SafeDownCast(vtkObjectBase) -> vtkExtractSelectedTree C++: static vtkExtractSelectedTree *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkExtractSelectedTree C++: vtkExtractSelectedTree *NewInstance() V.SetSelectionConnection(vtkAlgorithmOutput) C++: void SetSelectionConnection(vtkAlgorithmOutput *in) A convenience method for setting the second input (i.e. the selection). FillInputPortInformationV.FillInputPortInformation(int, vtkInformation) -> int C++: int FillInputPortInformation(int port, vtkInformation *info) override; Fill the input port information objects for this algorithm. This is invoked by the first call to GetInputPortInformation for each port so subclasses can specify what they can handle. vtkTreeAlgorithmvtkInformationvtkEdgeCentersvtkInfovisCorePython.vtkEdgeCentersvtkEdgeCenters - generate points at center of edges Superclass: vtkPolyDataAlgorithm vtkEdgeCenters is a filter that takes as input any graph and generates on output points at the center of the cells in the dataset. These points can be used for placing glyphs (vtkGlyph3D) or labeling (vtkLabeledDataMapper). (The center is the parametric center of the cell, not necessarily the geometric or bounding box center.) The edge attributes will be associated with the points on output. @warning You can choose to generate just points or points and vertex cells. Vertex cells are drawn during rendering; points are not. Use the ivar VertexCells to generate cells. @sa vtkGlyph3D vtkLabeledDataMapper V.SafeDownCast(vtkObjectBase) -> vtkEdgeCenters C++: static vtkEdgeCenters *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkEdgeCenters C++: vtkEdgeCenters *NewInstance() SetVertexCellsV.SetVertexCells(int) C++: virtual void SetVertexCells(int _arg) Enable/disable the generation of vertex cells. GetVertexCellsV.GetVertexCells() -> int C++: virtual int GetVertexCells() Enable/disable the generation of vertex cells. VertexCellsOnV.VertexCellsOn() C++: virtual void VertexCellsOn() Enable/disable the generation of vertex cells. VertexCellsOffV.VertexCellsOff() C++: virtual void VertexCellsOff() Enable/disable the generation of vertex cells. vtkPolyDataAlgorithmvtkExpandSelectedGraphvtkInfovisCorePython.vtkExpandSelectedGraphvtkExpandSelectedGraph - expands a selection set of a vtkGraph Superclass: vtkSelectionAlgorithm The first input is a vtkSelection containing the selected vertices. The second input is a vtkGraph. This filter 'grows' the selection set in one of the following ways 1) SetBFSDistance controls how many 'hops' the selection is grown from each seed point in the selection set (defaults to 1) 2) IncludeShortestPaths controls whether this filter tries to 'connect' the vertices in the selection set by computing the shortest path between the vertices (if such a path exists) Note: IncludeShortestPaths is currently non-functional V.SafeDownCast(vtkObjectBase) -> vtkExpandSelectedGraph C++: static vtkExpandSelectedGraph *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkExpandSelectedGraph C++: vtkExpandSelectedGraph *NewInstance() V.SetGraphConnection(vtkAlgorithmOutput) C++: void SetGraphConnection(vtkAlgorithmOutput *in) A convenience method for setting the second input (i.e. the graph). V.FillInputPortInformation(int, vtkInformation) -> int C++: int FillInputPortInformation(int port, vtkInformation *info) override; Specify the first vtkSelection input and the second vtkGraph input. SetBFSDistanceV.SetBFSDistance(int) C++: virtual void SetBFSDistance(int _arg) Set/Get BFSDistance which controls how many 'hops' the selection is grown from each seed point in the selection set (defaults to 1) GetBFSDistanceV.GetBFSDistance() -> int C++: virtual int GetBFSDistance() Set/Get BFSDistance which controls how many 'hops' the selection is grown from each seed point in the selection set (defaults to 1) SetIncludeShortestPathsV.SetIncludeShortestPaths(bool) C++: virtual void SetIncludeShortestPaths(bool _arg) Set/Get IncludeShortestPaths controls whether this filter tries to 'connect' the vertices in the selection set by computing the shortest path between the vertices (if such a path exists) Note: IncludeShortestPaths is currently non-functional GetIncludeShortestPathsV.GetIncludeShortestPaths() -> bool C++: virtual bool GetIncludeShortestPaths() Set/Get IncludeShortestPaths controls whether this filter tries to 'connect' the vertices in the selection set by computing the shortest path between the vertices (if such a path exists) Note: IncludeShortestPaths is currently non-functional IncludeShortestPathsOnV.IncludeShortestPathsOn() C++: virtual void IncludeShortestPathsOn() Set/Get IncludeShortestPaths controls whether this filter tries to 'connect' the vertices in the selection set by computing the shortest path between the vertices (if such a path exists) Note: IncludeShortestPaths is currently non-functional IncludeShortestPathsOffV.IncludeShortestPathsOff() C++: virtual void IncludeShortestPathsOff() Set/Get IncludeShortestPaths controls whether this filter tries to 'connect' the vertices in the selection set by computing the shortest path between the vertices (if such a path exists) Note: IncludeShortestPaths is currently non-functional SetDomainV.SetDomain(string) C++: virtual void SetDomain(const char *_arg) Set/Get the vertex domain to use in the expansion. GetDomainV.GetDomain() -> string C++: virtual char *GetDomain() Set/Get the vertex domain to use in the expansion. SetUseDomainV.SetUseDomain(bool) C++: virtual void SetUseDomain(bool _arg) Whether or not to use the domain when deciding to add a vertex to the expansion. Defaults to false. GetUseDomainV.GetUseDomain() -> bool C++: virtual bool GetUseDomain() Whether or not to use the domain when deciding to add a vertex to the expansion. Defaults to false. UseDomainOnV.UseDomainOn() C++: virtual void UseDomainOn() Whether or not to use the domain when deciding to add a vertex to the expansion. Defaults to false. UseDomainOffV.UseDomainOff() C++: virtual void UseDomainOff() Whether or not to use the domain when deciding to add a vertex to the expansion. Defaults to false. vtkSelectionAlgorithmvtkExtractSelectedGraphvtkInfovisCorePython.vtkExtractSelectedGraphvtkExtractSelectedGraph - return a subgraph of a vtkGraph Superclass: vtkGraphAlgorithm The first input is a vtkGraph to take a subgraph from. The second input (optional) is a vtkSelection containing selected indices. The third input (optional) is a vtkAnnotationsLayers whose annotations contain selected specifying selected indices. The vtkSelection may have FIELD_TYPE set to POINTS (a vertex selection) or CELLS (an edge selection). A vertex selection preserves all edges that connect selected vertices. An edge selection preserves all vertices that are adjacent to at least one selected edge. Alternately, you may indicate that an edge selection should maintain the full set of vertices, by turning RemoveIsolatedVertices off. V.SafeDownCast(vtkObjectBase) -> vtkExtractSelectedGraph C++: static vtkExtractSelectedGraph *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkExtractSelectedGraph C++: vtkExtractSelectedGraph *NewInstance() SetAnnotationLayersConnectionV.SetAnnotationLayersConnection(vtkAlgorithmOutput) C++: void SetAnnotationLayersConnection(vtkAlgorithmOutput *in) A convenience method for setting the third input (i.e. the annotation layers). SetRemoveIsolatedVerticesV.SetRemoveIsolatedVertices(bool) C++: virtual void SetRemoveIsolatedVertices(bool _arg) If set, removes vertices with no adjacent edges in an edge selection. A vertex selection ignores this flag and always returns the full set of selected vertices. Default is on. GetRemoveIsolatedVerticesV.GetRemoveIsolatedVertices() -> bool C++: virtual bool GetRemoveIsolatedVertices() If set, removes vertices with no adjacent edges in an edge selection. A vertex selection ignores this flag and always returns the full set of selected vertices. Default is on. RemoveIsolatedVerticesOnV.RemoveIsolatedVerticesOn() C++: virtual void RemoveIsolatedVerticesOn() If set, removes vertices with no adjacent edges in an edge selection. A vertex selection ignores this flag and always returns the full set of selected vertices. Default is on. RemoveIsolatedVerticesOffV.RemoveIsolatedVerticesOff() C++: virtual void RemoveIsolatedVerticesOff() If set, removes vertices with no adjacent edges in an edge selection. A vertex selection ignores this flag and always returns the full set of selected vertices. Default is on. V.FillInputPortInformation(int, vtkInformation) -> int C++: int FillInputPortInformation(int port, vtkInformation *info) override; Specify the first vtkGraph input and the second vtkSelection input. vtkGenerateIndexArrayvtkInfovisCorePython.vtkGenerateIndexArrayvtkGenerateIndexArray - Generates a new vtkIdTypeArray containing zero-base indices. Superclass: vtkDataObjectAlgorithm vtkGenerateIndexArray operates in one of two distinct "modes". By default, it simply generates an index array containing monotonically-increasing integers in the range [0, N), where N is appropriately sized for the field type that will store the results. This mode is useful for generating a unique ID field for datasets that have none. The second "mode" uses an existing array from the input data object as a "reference". Distinct values from the reference array are sorted in ascending order, and an integer index in the range [0, N) is assigned to each. The resulting map is used to populate the output index array, mapping each value in the reference array to its corresponding index and storing the result in the output array. This mode is especially useful when generating tensors, since it allows us to "map" from an array with arbitrary contents to an index that can be used as tensor coordinates. V.SafeDownCast(vtkObjectBase) -> vtkGenerateIndexArray C++: static vtkGenerateIndexArray *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkGenerateIndexArray C++: vtkGenerateIndexArray *NewInstance() SetArrayNameV.SetArrayName(string) C++: virtual void SetArrayName(const char *_arg) Control the output index array name. Default: "index". GetArrayNameV.GetArrayName() -> string C++: virtual char *GetArrayName() Control the output index array name. Default: "index". V.SetFieldType(int) C++: virtual void SetFieldType(int _arg) Control the location where the index array will be stored. V.GetFieldType() -> int C++: virtual int GetFieldType() Control the location where the index array will be stored. SetReferenceArrayNameV.SetReferenceArrayName(string) C++: virtual void SetReferenceArrayName(const char *_arg) Specifies an optional reference array for index-generation. GetReferenceArrayNameV.GetReferenceArrayName() -> string C++: virtual char *GetReferenceArrayName() Specifies an optional reference array for index-generation. SetPedigreeIDV.SetPedigreeID(int) C++: virtual void SetPedigreeID(int _arg) Specifies whether the index array should be marked as pedigree ids. Default: false. GetPedigreeIDV.GetPedigreeID() -> int C++: virtual int GetPedigreeID() Specifies whether the index array should be marked as pedigree ids. Default: false. vtkDataObjectAlgorithmvtkGraphHierarchicalBundleEdgesvtkInfovisCorePython.vtkGraphHierarchicalBundleEdgesvtkGraphHierarchicalBundleEdges - layout graph arcs in bundles Superclass: vtkGraphAlgorithm This algorithm creates a vtkPolyData from a vtkGraph. As opposed to vtkGraphToPolyData, which converts each arc into a straight line, each arc is converted to a polyline, following a tree structure. The filter requires both a vtkGraph and vtkTree as input. The tree vertices must be a superset of the graph vertices. A common example is when the graph vertices correspond to the leaves of the tree, but the internal vertices of the tree represent groupings of graph vertices. The algorithm matches the vertices using the array "PedigreeId". The user may alternately set the DirectMapping flag to indicate that the two structures must have directly corresponding offsets (i.e. node i in the graph must correspond to node i in the tree). The vtkGraph defines the topology of the output vtkPolyData (i.e. the connections between nodes) while the vtkTree defines the geometry (i.e. the location of nodes and arc routes). Thus, the tree must have been assigned vertex locations, but the graph does not need locations, in fact they will be ignored. The edges approximately follow the path from the source to target nodes in the tree. A bundling parameter controls how closely the edges are bundled together along the tree structure. You may follow this algorithm with vtkSplineFilter in order to make nicely curved edges. @par Thanks: This algorithm was developed in the paper Danny Holten. Hierarchical Edge Bundles: Visualization of Adjacency Relations Relations in Hierarchical Data. IEEE Transactions on Visualization and Computer Graphics, Vol. 12, No. 5, 2006. pp. 741-748. V.SafeDownCast(vtkObjectBase) -> vtkGraphHierarchicalBundleEdges C++: static vtkGraphHierarchicalBundleEdges *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkGraphHierarchicalBundleEdges C++: vtkGraphHierarchicalBundleEdges *NewInstance() SetBundlingStrengthV.SetBundlingStrength(float) C++: virtual void SetBundlingStrength(double _arg) The level of arc bundling in the graph. A strength of 0 creates straight lines, while a strength of 1 forces arcs to pass directly through hierarchy node points. The default value is 0.8. GetBundlingStrengthMinValueV.GetBundlingStrengthMinValue() -> float C++: virtual double GetBundlingStrengthMinValue() The level of arc bundling in the graph. A strength of 0 creates straight lines, while a strength of 1 forces arcs to pass directly through hierarchy node points. The default value is 0.8. GetBundlingStrengthMaxValueV.GetBundlingStrengthMaxValue() -> float C++: virtual double GetBundlingStrengthMaxValue() The level of arc bundling in the graph. A strength of 0 creates straight lines, while a strength of 1 forces arcs to pass directly through hierarchy node points. The default value is 0.8. GetBundlingStrengthV.GetBundlingStrength() -> float C++: virtual double GetBundlingStrength() The level of arc bundling in the graph. A strength of 0 creates straight lines, while a strength of 1 forces arcs to pass directly through hierarchy node points. The default value is 0.8. SetDirectMappingV.SetDirectMapping(bool) C++: virtual void SetDirectMapping(bool _arg) If on, uses direct mapping from tree to graph vertices. If off, both the graph and tree must contain PedigreeId arrays which are used to match graph and tree vertices. Default is off. GetDirectMappingV.GetDirectMapping() -> bool C++: virtual bool GetDirectMapping() If on, uses direct mapping from tree to graph vertices. If off, both the graph and tree must contain PedigreeId arrays which are used to match graph and tree vertices. Default is off. DirectMappingOnV.DirectMappingOn() C++: virtual void DirectMappingOn() If on, uses direct mapping from tree to graph vertices. If off, both the graph and tree must contain PedigreeId arrays which are used to match graph and tree vertices. Default is off. DirectMappingOffV.DirectMappingOff() C++: virtual void DirectMappingOff() If on, uses direct mapping from tree to graph vertices. If off, both the graph and tree must contain PedigreeId arrays which are used to match graph and tree vertices. Default is off. V.FillInputPortInformation(int, vtkInformation) -> int C++: int FillInputPortInformation(int port, vtkInformation *info) override; Set the input type of the algorithm to vtkGraph. vtkGroupLeafVerticesvtkInfovisCorePython.vtkGroupLeafVerticesvtkGroupLeafVertices - Filter that expands a tree, categorizing leaf vertices Superclass: vtkTreeAlgorithm Use SetInputArrayToProcess(0, ...) to set the array to group on. Currently this array must be a vtkStringArray. V.SafeDownCast(vtkObjectBase) -> vtkGroupLeafVertices C++: static vtkGroupLeafVertices *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkGroupLeafVertices C++: vtkGroupLeafVertices *NewInstance() SetGroupDomainV.SetGroupDomain(string) C++: virtual void SetGroupDomain(const char *_arg) The name of the domain that non-leaf vertices will be assigned to. If the input graph already contains vertices in this domain: - If the ids for this domain are numeric, starts assignment with max id - If the ids for this domain are strings, starts assignment with "group X" where "X" is the max id. Default is "group_vertex". GetGroupDomainV.GetGroupDomain() -> string C++: virtual char *GetGroupDomain() The name of the domain that non-leaf vertices will be assigned to. If the input graph already contains vertices in this domain: - If the ids for this domain are numeric, starts assignment with max id - If the ids for this domain are strings, starts assignment with "group X" where "X" is the max id. Default is "group_vertex". vtkMergeColumnsvtkInfovisCorePython.vtkMergeColumnsvtkMergeColumns - merge two columns into a single column Superclass: vtkTableAlgorithm vtkMergeColumns replaces two columns in a table with a single column containing data in both columns. The columns are set using SetInputArrayToProcess(0, 0, 0, vtkDataObject::FIELD_ASSOCIATION_ROWS, "col1") and SetInputArrayToProcess(1, 0, 0, vtkDataObject::FIELD_ASSOCIATION_ROWS, "col2") where "col1" and "col2" are the names of the columns to merge. The user may also specify the name of the merged column. The arrays must be of the same type. If the arrays are numeric, the values are summed in the merged column. If the arrays are strings, the values are concatenated. The strings are separated by a space if they are both nonempty. V.SafeDownCast(vtkObjectBase) -> vtkMergeColumns C++: static vtkMergeColumns *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkMergeColumns C++: vtkMergeColumns *NewInstance() SetMergedColumnNameV.SetMergedColumnName(string) C++: virtual void SetMergedColumnName(const char *_arg) The name to give the merged column created by this filter. GetMergedColumnNameV.GetMergedColumnName() -> string C++: virtual char *GetMergedColumnName() The name to give the merged column created by this filter. vtkMergeGraphsvtkInfovisCorePython.vtkMergeGraphsvtkMergeGraphs - combines two graphs Superclass: vtkGraphAlgorithm vtkMergeGraphs combines information from two graphs into one. Both graphs must have pedigree ids assigned to the vertices. The output will contain the vertices/edges in the first graph, in addition to: - vertices in the second graph whose pedigree id does not match a vertex in the first input - edges in the second graph The output will contain the same attribute structure as the input; fields associated only with the second input graph will not be passed to the output. When possible, the vertex/edge data for new vertices and edges will be populated with matching attributes on the second graph. To be considered a matching attribute, the array must have the same name, type, and number of components. @warning This filter is not "domain-aware". Pedigree ids are assumed to be globally unique, regardless of their domain. V.SafeDownCast(vtkObjectBase) -> vtkMergeGraphs C++: static vtkMergeGraphs *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkMergeGraphs C++: vtkMergeGraphs *NewInstance() ExtendGraphV.ExtendGraph(vtkMutableGraphHelper, vtkGraph) -> int C++: int ExtendGraph(vtkMutableGraphHelper *g1, vtkGraph *g2) This is the core functionality of the algorithm. Adds edges and vertices from g2 into g1. SetUseEdgeWindowV.SetUseEdgeWindow(bool) C++: virtual void SetUseEdgeWindow(bool _arg) Whether to use an edge window array. The default is to not use a window array. GetUseEdgeWindowV.GetUseEdgeWindow() -> bool C++: virtual bool GetUseEdgeWindow() Whether to use an edge window array. The default is to not use a window array. UseEdgeWindowOnV.UseEdgeWindowOn() C++: virtual void UseEdgeWindowOn() Whether to use an edge window array. The default is to not use a window array. UseEdgeWindowOffV.UseEdgeWindowOff() C++: virtual void UseEdgeWindowOff() Whether to use an edge window array. The default is to not use a window array. SetEdgeWindowArrayNameV.SetEdgeWindowArrayName(string) C++: virtual void SetEdgeWindowArrayName(const char *_arg) The edge window array. The default array name is "time". GetEdgeWindowArrayNameV.GetEdgeWindowArrayName() -> string C++: virtual char *GetEdgeWindowArrayName() The edge window array. The default array name is "time". SetEdgeWindowV.SetEdgeWindow(float) C++: virtual void SetEdgeWindow(double _arg) The time window amount. Edges with values lower than the maximum value minus this window will be removed from the graph. The default edge window is 10000. GetEdgeWindowV.GetEdgeWindow() -> float C++: virtual double GetEdgeWindow() The time window amount. Edges with values lower than the maximum value minus this window will be removed from the graph. The default edge window is 10000. vtkMutableGraphHelpervtkGraphvtkMergeTablesvtkInfovisCorePython.vtkMergeTablesvtkMergeTables - combine two tables Superclass: vtkTableAlgorithm Combines the columns of two tables into one larger table. The number of rows in the resulting table is the sum of the number of rows in each of the input tables. The number of columns in the output is generally the sum of the number of columns in each input table, except in the case where column names are duplicated in both tables. In this case, if MergeColumnsByName is on (the default), the two columns will be merged into a single column of the same name. If MergeColumnsByName is off, both columns will exist in the output. You may set the FirstTablePrefix and SecondTablePrefix to define how the columns named are modified. One of these prefixes may be the empty string, but they must be different. V.SafeDownCast(vtkObjectBase) -> vtkMergeTables C++: static vtkMergeTables *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkMergeTables C++: vtkMergeTables *NewInstance() SetFirstTablePrefixV.SetFirstTablePrefix(string) C++: virtual void SetFirstTablePrefix(const char *_arg) The prefix to give to same-named fields from the first table. Default is "Table1.". GetFirstTablePrefixV.GetFirstTablePrefix() -> string C++: virtual char *GetFirstTablePrefix() The prefix to give to same-named fields from the first table. Default is "Table1.". SetSecondTablePrefixV.SetSecondTablePrefix(string) C++: virtual void SetSecondTablePrefix(const char *_arg) The prefix to give to same-named fields from the second table. Default is "Table2.". GetSecondTablePrefixV.GetSecondTablePrefix() -> string C++: virtual char *GetSecondTablePrefix() The prefix to give to same-named fields from the second table. Default is "Table2.". SetMergeColumnsByNameV.SetMergeColumnsByName(bool) C++: virtual void SetMergeColumnsByName(bool _arg) If on, merges columns with the same name. If off, keeps both columns, but calls one FirstTablePrefix + name, and the other SecondTablePrefix + name. Default is on. GetMergeColumnsByNameV.GetMergeColumnsByName() -> bool C++: virtual bool GetMergeColumnsByName() If on, merges columns with the same name. If off, keeps both columns, but calls one FirstTablePrefix + name, and the other SecondTablePrefix + name. Default is on. MergeColumnsByNameOnV.MergeColumnsByNameOn() C++: virtual void MergeColumnsByNameOn() If on, merges columns with the same name. If off, keeps both columns, but calls one FirstTablePrefix + name, and the other SecondTablePrefix + name. Default is on. MergeColumnsByNameOffV.MergeColumnsByNameOff() C++: virtual void MergeColumnsByNameOff() If on, merges columns with the same name. If off, keeps both columns, but calls one FirstTablePrefix + name, and the other SecondTablePrefix + name. Default is on. SetPrefixAllButMergedV.SetPrefixAllButMerged(bool) C++: virtual void SetPrefixAllButMerged(bool _arg) If on, all columns will have prefixes except merged columns. If off, only unmerged columns with the same name will have prefixes. Default is off. GetPrefixAllButMergedV.GetPrefixAllButMerged() -> bool C++: virtual bool GetPrefixAllButMerged() If on, all columns will have prefixes except merged columns. If off, only unmerged columns with the same name will have prefixes. Default is off. PrefixAllButMergedOnV.PrefixAllButMergedOn() C++: virtual void PrefixAllButMergedOn() If on, all columns will have prefixes except merged columns. If off, only unmerged columns with the same name will have prefixes. Default is off. PrefixAllButMergedOffV.PrefixAllButMergedOff() C++: virtual void PrefixAllButMergedOff() If on, all columns will have prefixes except merged columns. If off, only unmerged columns with the same name will have prefixes. Default is off. vtkInfovisCorePython.vtkMutableGraphHelpervtkMutableGraphHelper - Helper class for building a directed or directed graph Superclass: vtkObject vtkMutableGraphHelper has helper methods AddVertex and AddEdge which add vertices/edges to the underlying mutable graph. This is helpful in filters which need to (re)construct graphs which may be either directed or undirected. @sa vtkGraph vtkMutableDirectedGraph vtkMutableUndirectedGraph V.SafeDownCast(vtkObjectBase) -> vtkMutableGraphHelper C++: static vtkMutableGraphHelper *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkMutableGraphHelper C++: vtkMutableGraphHelper *NewInstance() SetGraphV.SetGraph(vtkGraph) C++: void SetGraph(vtkGraph *g) Set the underlying graph that you want to modify with this helper. The graph must be an instance of vtkMutableDirectedGraph or vtkMutableUndirectedGraph. GetGraphV.GetGraph() -> vtkGraph C++: vtkGraph *GetGraph() Set the underlying graph that you want to modify with this helper. The graph must be an instance of vtkMutableDirectedGraph or vtkMutableUndirectedGraph. AddEdgeV.AddEdge(int, int) -> vtkEdgeType C++: vtkEdgeType AddEdge(vtkIdType u, vtkIdType v) Add an edge to the underlying mutable graph. AddGraphEdgeV.AddGraphEdge(int, int) -> vtkGraphEdge C++: vtkGraphEdge *AddGraphEdge(vtkIdType u, vtkIdType v) AddVertexV.AddVertex() -> int C++: vtkIdType AddVertex() Add a vertex to the underlying mutable graph. RemoveVertexV.RemoveVertex(int) C++: void RemoveVertex(vtkIdType v) Remove a vertex from the underlying mutable graph. RemoveVerticesV.RemoveVertices(vtkIdTypeArray) C++: void RemoveVertices(vtkIdTypeArray *verts) Remove a collection of vertices from the underlying mutable graph. RemoveEdgeV.RemoveEdge(int) C++: void RemoveEdge(vtkIdType e) Remove an edge from the underlying mutable graph. RemoveEdgesV.RemoveEdges(vtkIdTypeArray) C++: void RemoveEdges(vtkIdTypeArray *edges) Remove a collection of edges from the underlying mutable graph. vtkEdgeTypevtkIdTypeArrayvtkNetworkHierarchyvtkInfovisCorePython.vtkNetworkHierarchyvtkNetworkHierarchy - Filter that takes a graph and makes a tree out of the network ip addresses in that graph. Superclass: vtkTreeAlgorithm Use SetInputArrayToProcess(0, ...) to set the array to that has the network ip addresses. Currently this array must be a vtkStringArray. V.SafeDownCast(vtkObjectBase) -> vtkNetworkHierarchy C++: static vtkNetworkHierarchy *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkNetworkHierarchy C++: vtkNetworkHierarchy *NewInstance() GetIPArrayNameV.GetIPArrayName() -> string C++: virtual char *GetIPArrayName() Used to store the ip array name SetIPArrayNameV.SetIPArrayName(string) C++: virtual void SetIPArrayName(const char *_arg) Used to store the ip array name vtkPipelineGraphSourcevtkInfovisCorePython.vtkPipelineGraphSourcevtkPipelineGraphSource - a graph constructed from a VTK pipeline Superclass: vtkDirectedGraphAlgorithm V.SafeDownCast(vtkObjectBase) -> vtkPipelineGraphSource C++: static vtkPipelineGraphSource *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkPipelineGraphSource C++: vtkPipelineGraphSource *NewInstance() AddSinkV.AddSink(vtkObject) C++: void AddSink(vtkObject *object) RemoveSinkV.RemoveSink(vtkObject) C++: void RemoveSink(vtkObject *object) vtkDirectedGraphAlgorithmvtkPruneTreeFiltervtkInfovisCorePython.vtkPruneTreeFiltervtkPruneTreeFilter - prune a subtree out of a vtkTree Superclass: vtkTreeAlgorithm Removes a subtree rooted at a particular vertex in a vtkTree. V.SafeDownCast(vtkObjectBase) -> vtkPruneTreeFilter C++: static vtkPruneTreeFilter *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkPruneTreeFilter C++: vtkPruneTreeFilter *NewInstance() GetParentVertexV.GetParentVertex() -> int C++: virtual vtkIdType GetParentVertex() Set the parent vertex of the subtree to remove. SetParentVertexV.SetParentVertex(int) C++: virtual void SetParentVertex(vtkIdType _arg) Set the parent vertex of the subtree to remove. GetShouldPruneParentVertexV.GetShouldPruneParentVertex() -> bool C++: virtual bool GetShouldPruneParentVertex() Should we remove the parent vertex, or just its descendants? Default behavior is to remove the parent vertex. SetShouldPruneParentVertexV.SetShouldPruneParentVertex(bool) C++: virtual void SetShouldPruneParentVertex(bool _arg) Should we remove the parent vertex, or just its descendants? Default behavior is to remove the parent vertex. vtkRandomGraphSourcevtkInfovisCorePython.vtkRandomGraphSourcevtkRandomGraphSource - a graph with random edges Superclass: vtkGraphAlgorithm Generates a graph with a specified number of vertices, with the density of edges specified by either an exact number of edges or the probability of an edge. You may additionally specify whether to begin with a random tree (which enforces graph connectivity). V.SafeDownCast(vtkObjectBase) -> vtkRandomGraphSource C++: static vtkRandomGraphSource *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkRandomGraphSource C++: vtkRandomGraphSource *NewInstance() GetNumberOfVerticesV.GetNumberOfVertices() -> int C++: virtual int GetNumberOfVertices() The number of vertices in the graph. SetNumberOfVerticesV.SetNumberOfVertices(int) C++: virtual void SetNumberOfVertices(int _arg) The number of vertices in the graph. GetNumberOfVerticesMinValueV.GetNumberOfVerticesMinValue() -> int C++: virtual int GetNumberOfVerticesMinValue() The number of vertices in the graph. GetNumberOfVerticesMaxValueV.GetNumberOfVerticesMaxValue() -> int C++: virtual int GetNumberOfVerticesMaxValue() The number of vertices in the graph. GetNumberOfEdgesV.GetNumberOfEdges() -> int C++: virtual int GetNumberOfEdges() If UseEdgeProbability is off, creates a graph with the specified number of edges. Duplicate (parallel) edges are allowed. SetNumberOfEdgesV.SetNumberOfEdges(int) C++: virtual void SetNumberOfEdges(int _arg) If UseEdgeProbability is off, creates a graph with the specified number of edges. Duplicate (parallel) edges are allowed. GetNumberOfEdgesMinValueV.GetNumberOfEdgesMinValue() -> int C++: virtual int GetNumberOfEdgesMinValue() If UseEdgeProbability is off, creates a graph with the specified number of edges. Duplicate (parallel) edges are allowed. GetNumberOfEdgesMaxValueV.GetNumberOfEdgesMaxValue() -> int C++: virtual int GetNumberOfEdgesMaxValue() If UseEdgeProbability is off, creates a graph with the specified number of edges. Duplicate (parallel) edges are allowed. GetEdgeProbabilityV.GetEdgeProbability() -> float C++: virtual double GetEdgeProbability() If UseEdgeProbability is on, adds an edge with this probability between 0 and 1 for each pair of vertices in the graph. SetEdgeProbabilityV.SetEdgeProbability(float) C++: virtual void SetEdgeProbability(double _arg) If UseEdgeProbability is on, adds an edge with this probability between 0 and 1 for each pair of vertices in the graph. GetEdgeProbabilityMinValueV.GetEdgeProbabilityMinValue() -> float C++: virtual double GetEdgeProbabilityMinValue() If UseEdgeProbability is on, adds an edge with this probability between 0 and 1 for each pair of vertices in the graph. GetEdgeProbabilityMaxValueV.GetEdgeProbabilityMaxValue() -> float C++: virtual double GetEdgeProbabilityMaxValue() If UseEdgeProbability is on, adds an edge with this probability between 0 and 1 for each pair of vertices in the graph. SetIncludeEdgeWeightsV.SetIncludeEdgeWeights(bool) C++: virtual void SetIncludeEdgeWeights(bool _arg) When set, includes edge weights in an array named "edge_weights". Defaults to off. Weights are random between 0 and 1. GetIncludeEdgeWeightsV.GetIncludeEdgeWeights() -> bool C++: virtual bool GetIncludeEdgeWeights() When set, includes edge weights in an array named "edge_weights". Defaults to off. Weights are random between 0 and 1. IncludeEdgeWeightsOnV.IncludeEdgeWeightsOn() C++: virtual void IncludeEdgeWeightsOn() When set, includes edge weights in an array named "edge_weights". Defaults to off. Weights are random between 0 and 1. IncludeEdgeWeightsOffV.IncludeEdgeWeightsOff() C++: virtual void IncludeEdgeWeightsOff() When set, includes edge weights in an array named "edge_weights". Defaults to off. Weights are random between 0 and 1. SetEdgeWeightArrayNameV.SetEdgeWeightArrayName(string) C++: virtual void SetEdgeWeightArrayName(const char *_arg) The name of the edge weight array. Default "edge weight". GetEdgeWeightArrayNameV.GetEdgeWeightArrayName() -> string C++: virtual char *GetEdgeWeightArrayName() The name of the edge weight array. Default "edge weight". SetDirectedV.SetDirected(bool) C++: virtual void SetDirected(bool _arg) When set, creates a directed graph, as opposed to an undirected graph. GetDirectedV.GetDirected() -> bool C++: virtual bool GetDirected() When set, creates a directed graph, as opposed to an undirected graph. DirectedOnV.DirectedOn() C++: virtual void DirectedOn() When set, creates a directed graph, as opposed to an undirected graph. DirectedOffV.DirectedOff() C++: virtual void DirectedOff() When set, creates a directed graph, as opposed to an undirected graph. SetUseEdgeProbabilityV.SetUseEdgeProbability(bool) C++: virtual void SetUseEdgeProbability(bool _arg) When set, uses the EdgeProbability parameter to determine the density of edges. Otherwise, NumberOfEdges is used. GetUseEdgeProbabilityV.GetUseEdgeProbability() -> bool C++: virtual bool GetUseEdgeProbability() When set, uses the EdgeProbability parameter to determine the density of edges. Otherwise, NumberOfEdges is used. UseEdgeProbabilityOnV.UseEdgeProbabilityOn() C++: virtual void UseEdgeProbabilityOn() When set, uses the EdgeProbability parameter to determine the density of edges. Otherwise, NumberOfEdges is used. UseEdgeProbabilityOffV.UseEdgeProbabilityOff() C++: virtual void UseEdgeProbabilityOff() When set, uses the EdgeProbability parameter to determine the density of edges. Otherwise, NumberOfEdges is used. SetStartWithTreeV.SetStartWithTree(bool) C++: virtual void SetStartWithTree(bool _arg) When set, builds a random tree structure first, then adds additional random edges. GetStartWithTreeV.GetStartWithTree() -> bool C++: virtual bool GetStartWithTree() When set, builds a random tree structure first, then adds additional random edges. StartWithTreeOnV.StartWithTreeOn() C++: virtual void StartWithTreeOn() When set, builds a random tree structure first, then adds additional random edges. StartWithTreeOffV.StartWithTreeOff() C++: virtual void StartWithTreeOff() When set, builds a random tree structure first, then adds additional random edges. V.SetAllowSelfLoops(bool) C++: virtual void SetAllowSelfLoops(bool _arg) If this flag is set to true, edges where the source and target vertex are the same can be generated. The default is to forbid such loops. V.GetAllowSelfLoops() -> bool C++: virtual bool GetAllowSelfLoops() If this flag is set to true, edges where the source and target vertex are the same can be generated. The default is to forbid such loops. V.AllowSelfLoopsOn() C++: virtual void AllowSelfLoopsOn() If this flag is set to true, edges where the source and target vertex are the same can be generated. The default is to forbid such loops. V.AllowSelfLoopsOff() C++: virtual void AllowSelfLoopsOff() If this flag is set to true, edges where the source and target vertex are the same can be generated. The default is to forbid such loops. SetAllowParallelEdgesV.SetAllowParallelEdges(bool) C++: virtual void SetAllowParallelEdges(bool _arg) When set, multiple edges from a source to a target vertex are allowed. The default is to forbid such loops. GetAllowParallelEdgesV.GetAllowParallelEdges() -> bool C++: virtual bool GetAllowParallelEdges() When set, multiple edges from a source to a target vertex are allowed. The default is to forbid such loops. AllowParallelEdgesOnV.AllowParallelEdgesOn() C++: virtual void AllowParallelEdgesOn() When set, multiple edges from a source to a target vertex are allowed. The default is to forbid such loops. AllowParallelEdgesOffV.AllowParallelEdgesOff() C++: virtual void AllowParallelEdgesOff() When set, multiple edges from a source to a target vertex are allowed. The default is to forbid such loops. SetGeneratePedigreeIdsV.SetGeneratePedigreeIds(bool) C++: virtual void SetGeneratePedigreeIds(bool _arg) Add pedigree ids to vertex and edge data. GetGeneratePedigreeIdsV.GetGeneratePedigreeIds() -> bool C++: virtual bool GetGeneratePedigreeIds() Add pedigree ids to vertex and edge data. GeneratePedigreeIdsOnV.GeneratePedigreeIdsOn() C++: virtual void GeneratePedigreeIdsOn() Add pedigree ids to vertex and edge data. GeneratePedigreeIdsOffV.GeneratePedigreeIdsOff() C++: virtual void GeneratePedigreeIdsOff() Add pedigree ids to vertex and edge data. SetVertexPedigreeIdArrayNameV.SetVertexPedigreeIdArrayName(string) C++: virtual void SetVertexPedigreeIdArrayName(const char *_arg) The name of the vertex pedigree id array. Default "vertex id". GetVertexPedigreeIdArrayNameV.GetVertexPedigreeIdArrayName() -> string C++: virtual char *GetVertexPedigreeIdArrayName() The name of the vertex pedigree id array. Default "vertex id". SetEdgePedigreeIdArrayNameV.SetEdgePedigreeIdArrayName(string) C++: virtual void SetEdgePedigreeIdArrayName(const char *_arg) The name of the edge pedigree id array. Default "edge id". GetEdgePedigreeIdArrayNameV.GetEdgePedigreeIdArrayName() -> string C++: virtual char *GetEdgePedigreeIdArrayName() The name of the edge pedigree id array. Default "edge id". SetSeedV.SetSeed(int) C++: virtual void SetSeed(int _arg) Control the seed used for pseudo-random-number generation. This ensures that vtkRandomGraphSource can produce repeatable results. GetSeedV.GetSeed() -> int C++: virtual int GetSeed() Control the seed used for pseudo-random-number generation. This ensures that vtkRandomGraphSource can produce repeatable results. vtkReduceTableMEANMEDIANMODEvtkInfovisCorePython.vtkReduceTablevtkReduceTable - combine some of the rows of a table Superclass: vtkTableAlgorithm Collapses the rows of the input table so that one particular column (the IndexColumn) does not contain any duplicate values. Thus the output table will have the same columns as the input table, but potentially fewer rows. One example use of this class would be to generate a summary table from a table of observations. When two or more rows of the input table share a value in the IndexColumn, the values from these rows will be combined on a column-by-column basis. By default, such numerical values will be reduced to their mean, and non-numerical values will be reduced to their mode. This default behavior can be changed by calling SetNumericalReductionMethod() or SetNonNumericalReductionMethod(). You can also specify the reduction method to use for a particular column by calling SetReductionMethodForColumn(). V.SafeDownCast(vtkObjectBase) -> vtkReduceTable C++: static vtkReduceTable *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkReduceTable C++: vtkReduceTable *NewInstance() GetIndexColumnV.GetIndexColumn() -> int C++: virtual vtkIdType GetIndexColumn() Get/Set the column that will be used to reduce the input table. Any rows sharing a value in this column will be collapsed into a single row in the output table. SetIndexColumnV.SetIndexColumn(int) C++: virtual void SetIndexColumn(vtkIdType _arg) Get/Set the column that will be used to reduce the input table. Any rows sharing a value in this column will be collapsed into a single row in the output table. GetNumericalReductionMethodV.GetNumericalReductionMethod() -> int C++: virtual int GetNumericalReductionMethod() Get/Set the method that should be used to combine numerical values. SetNumericalReductionMethodV.SetNumericalReductionMethod(int) C++: virtual void SetNumericalReductionMethod(int _arg) Get/Set the method that should be used to combine numerical values. GetNonNumericalReductionMethodV.GetNonNumericalReductionMethod() -> int C++: virtual int GetNonNumericalReductionMethod() Get/Set the method that should be used to combine non-numerical values. SetNonNumericalReductionMethodV.SetNonNumericalReductionMethod(int) C++: virtual void SetNonNumericalReductionMethod(int _arg) Get/Set the method that should be used to combine non-numerical values. GetReductionMethodForColumnV.GetReductionMethodForColumn(int) -> int C++: int GetReductionMethodForColumn(vtkIdType col) Get the method that should be used to combine the values within the specified column. Returns -1 if no method has been set for this particular column. SetReductionMethodForColumnV.SetReductionMethodForColumn(int, int) C++: void SetReductionMethodForColumn(vtkIdType col, int method) Set the method that should be used to combine the values within the specified column. vtkRemoveIsolatedVerticesvtkInfovisCorePython.vtkRemoveIsolatedVerticesvtkRemoveIsolatedVertices - remove vertices of a vtkGraph with degree zero. Superclass: vtkGraphAlgorithm V.SafeDownCast(vtkObjectBase) -> vtkRemoveIsolatedVertices C++: static vtkRemoveIsolatedVertices *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkRemoveIsolatedVertices C++: vtkRemoveIsolatedVertices *NewInstance() vtkSparseArrayToTablevtkInfovisCorePython.vtkSparseArrayToTablevtkSparseArrayToTable - Converts a sparse array to a vtkTable. Superclass: vtkTableAlgorithm Converts any sparse array to a vtkTable containing one row for each value stored in the array. The table will contain one column of coordinates for each dimension in the source array, plus one column of array values. A common use-case for vtkSparseArrayToTable would be converting a sparse array into a table suitable for use as an input to vtkTableToGraph. The coordinate columns in the output table will be named using the dimension labels from the source array, The value column name can be explicitly set using SetValueColumn(). @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkSparseArrayToTable C++: static vtkSparseArrayToTable *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkSparseArrayToTable C++: vtkSparseArrayToTable *NewInstance() GetValueColumnV.GetValueColumn() -> string C++: virtual char *GetValueColumn() Specify the name of the output table column that contains array values. Default: "value" SetValueColumnV.SetValueColumn(string) C++: virtual void SetValueColumn(const char *_arg) Specify the name of the output table column that contains array values. Default: "value" vtkStreamGraphvtkInfovisCorePython.vtkStreamGraphvtkStreamGraph - combines two graphs Superclass: vtkGraphAlgorithm vtkStreamGraph iteratively collects information from the input graph and combines it in the output graph. It internally maintains a graph instance that is incrementally updated every time the filter is called. Each update, vtkMergeGraphs is used to combine this filter's input with the internal graph. If you can use an edge window array to filter out old edges based on a moving threshold. V.SafeDownCast(vtkObjectBase) -> vtkStreamGraph C++: static vtkStreamGraph *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkStreamGraph C++: vtkStreamGraph *NewInstance() vtkStringToCategoryvtkInfovisCorePython.vtkStringToCategoryvtkStringToCategory - Creates a category array from a string array Superclass: vtkDataObjectAlgorithm vtkStringToCategory creates an integer array named "category" based on the values in a string array. You may use this filter to create an array that you may use to color points/cells by the values in a string array. Currently there is not support to color by a string array directly. The category values will range from zero to N-1, where N is the number of distinct strings in the string array. Set the string array to process with SetInputArrayToProcess(0,0,0,...). The array may be in the point, cell, or field data of the data object. The list of unique strings, in the order they are mapped, can also be retrieved from output port 1. They are in a vtkTable, stored in the "Strings" column as a vtkStringArray. V.SafeDownCast(vtkObjectBase) -> vtkStringToCategory C++: static vtkStringToCategory *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkStringToCategory C++: vtkStringToCategory *NewInstance() SetCategoryArrayNameV.SetCategoryArrayName(string) C++: virtual void SetCategoryArrayName(const char *_arg) The name to give to the output vtkIntArray of category values. GetCategoryArrayNameV.GetCategoryArrayName() -> string C++: virtual char *GetCategoryArrayName() The name to give to the output vtkIntArray of category values. vtkStringToNumericvtkInfovisCorePython.vtkStringToNumericvtkStringToNumeric - Converts string arrays to numeric arrays Superclass: vtkDataObjectAlgorithm vtkStringToNumeric is a filter for converting a string array into a numeric arrays. V.SafeDownCast(vtkObjectBase) -> vtkStringToNumeric C++: static vtkStringToNumeric *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkStringToNumeric C++: vtkStringToNumeric *NewInstance() SetForceDoubleV.SetForceDouble(bool) C++: virtual void SetForceDouble(bool _arg) Convert all numeric columns to vtkDoubleArray, even if they contain only integer values. Default is off. GetForceDoubleV.GetForceDouble() -> bool C++: virtual bool GetForceDouble() Convert all numeric columns to vtkDoubleArray, even if they contain only integer values. Default is off. ForceDoubleOnV.ForceDoubleOn() C++: virtual void ForceDoubleOn() Convert all numeric columns to vtkDoubleArray, even if they contain only integer values. Default is off. ForceDoubleOffV.ForceDoubleOff() C++: virtual void ForceDoubleOff() Convert all numeric columns to vtkDoubleArray, even if they contain only integer values. Default is off. SetDefaultIntegerValueV.SetDefaultIntegerValue(int) C++: virtual void SetDefaultIntegerValue(int _arg) Set the default integer value assigned to arrays. Default is 0. GetDefaultIntegerValueV.GetDefaultIntegerValue() -> int C++: virtual int GetDefaultIntegerValue() Set the default integer value assigned to arrays. Default is 0. SetDefaultDoubleValueV.SetDefaultDoubleValue(float) C++: virtual void SetDefaultDoubleValue(double _arg) Set the default double value assigned to arrays. Default is 0.0 GetDefaultDoubleValueV.GetDefaultDoubleValue() -> float C++: virtual double GetDefaultDoubleValue() Set the default double value assigned to arrays. Default is 0.0 SetTrimWhitespacePriorToNumericConversionV.SetTrimWhitespacePriorToNumericConversion(bool) C++: virtual void SetTrimWhitespacePriorToNumericConversion( bool _arg) Whether to trim whitespace from strings prior to conversion to a numeric. Default is false to preserve backward compatibility. * vtkVariant handles whitespace inconsistently, so trim it before we try to * convert it. For example: * vtkVariant(" 2.0").ToDouble() == 2.0 <-- leading whitespace is not a problem * vtkVariant(" 2.0 ").ToDouble() == NaN <-- trailing whitespace is a problem * vtkVariant(" infinity ").ToDouble() == NaN <-- any whitespace is a problem * In these cases, trimming the whitespace gives us the result we expect: * 2.0 and INF respectively. GetTrimWhitespacePriorToNumericConversionV.GetTrimWhitespacePriorToNumericConversion() -> bool C++: virtual bool GetTrimWhitespacePriorToNumericConversion() Whether to trim whitespace from strings prior to conversion to a numeric. Default is false to preserve backward compatibility. * vtkVariant handles whitespace inconsistently, so trim it before we try to * convert it. For example: * vtkVariant(" 2.0").ToDouble() == 2.0 <-- leading whitespace is not a problem * vtkVariant(" 2.0 ").ToDouble() == NaN <-- trailing whitespace is a problem * vtkVariant(" infinity ").ToDouble() == NaN <-- any whitespace is a problem * In these cases, trimming the whitespace gives us the result we expect: * 2.0 and INF respectively. TrimWhitespacePriorToNumericConversionOnV.TrimWhitespacePriorToNumericConversionOn() C++: virtual void TrimWhitespacePriorToNumericConversionOn() Whether to trim whitespace from strings prior to conversion to a numeric. Default is false to preserve backward compatibility. * vtkVariant handles whitespace inconsistently, so trim it before we try to * convert it. For example: * vtkVariant(" 2.0").ToDouble() == 2.0 <-- leading whitespace is not a problem * vtkVariant(" 2.0 ").ToDouble() == NaN <-- trailing whitespace is a problem * vtkVariant(" infinity ").ToDouble() == NaN <-- any whitespace is a problem * In these cases, trimming the whitespace gives us the result we expect: * 2.0 and INF respectively. TrimWhitespacePriorToNumericConversionOffV.TrimWhitespacePriorToNumericConversionOff() C++: virtual void TrimWhitespacePriorToNumericConversionOff() Whether to trim whitespace from strings prior to conversion to a numeric. Default is false to preserve backward compatibility. * vtkVariant handles whitespace inconsistently, so trim it before we try to * convert it. For example: * vtkVariant(" 2.0").ToDouble() == 2.0 <-- leading whitespace is not a problem * vtkVariant(" 2.0 ").ToDouble() == NaN <-- trailing whitespace is a problem * vtkVariant(" infinity ").ToDouble() == NaN <-- any whitespace is a problem * In these cases, trimming the whitespace gives us the result we expect: * 2.0 and INF respectively. SetConvertFieldDataV.SetConvertFieldData(bool) C++: virtual void SetConvertFieldData(bool _arg) Whether to detect and convert field data arrays. Default is on. GetConvertFieldDataV.GetConvertFieldData() -> bool C++: virtual bool GetConvertFieldData() Whether to detect and convert field data arrays. Default is on. ConvertFieldDataOnV.ConvertFieldDataOn() C++: virtual void ConvertFieldDataOn() Whether to detect and convert field data arrays. Default is on. ConvertFieldDataOffV.ConvertFieldDataOff() C++: virtual void ConvertFieldDataOff() Whether to detect and convert field data arrays. Default is on. SetConvertPointDataV.SetConvertPointData(bool) C++: virtual void SetConvertPointData(bool _arg) Whether to detect and convert cell data arrays. Default is on. GetConvertPointDataV.GetConvertPointData() -> bool C++: virtual bool GetConvertPointData() Whether to detect and convert cell data arrays. Default is on. ConvertPointDataOnV.ConvertPointDataOn() C++: virtual void ConvertPointDataOn() Whether to detect and convert cell data arrays. Default is on. ConvertPointDataOffV.ConvertPointDataOff() C++: virtual void ConvertPointDataOff() Whether to detect and convert cell data arrays. Default is on. SetConvertCellDataV.SetConvertCellData(bool) C++: virtual void SetConvertCellData(bool _arg) Whether to detect and convert point data arrays. Default is on. GetConvertCellDataV.GetConvertCellData() -> bool C++: virtual bool GetConvertCellData() Whether to detect and convert point data arrays. Default is on. ConvertCellDataOnV.ConvertCellDataOn() C++: virtual void ConvertCellDataOn() Whether to detect and convert point data arrays. Default is on. ConvertCellDataOffV.ConvertCellDataOff() C++: virtual void ConvertCellDataOff() Whether to detect and convert point data arrays. Default is on. SetConvertVertexDataV.SetConvertVertexData(bool) C++: virtual void SetConvertVertexData(bool b) Whether to detect and convert vertex data arrays. Default is on. GetConvertVertexDataV.GetConvertVertexData() -> bool C++: virtual bool GetConvertVertexData() ConvertVertexDataOnV.ConvertVertexDataOn() C++: virtual void ConvertVertexDataOn() ConvertVertexDataOffV.ConvertVertexDataOff() C++: virtual void ConvertVertexDataOff() SetConvertEdgeDataV.SetConvertEdgeData(bool) C++: virtual void SetConvertEdgeData(bool b) Whether to detect and convert edge data arrays. Default is on. GetConvertEdgeDataV.GetConvertEdgeData() -> bool C++: virtual bool GetConvertEdgeData() ConvertEdgeDataOnV.ConvertEdgeDataOn() C++: virtual void ConvertEdgeDataOn() ConvertEdgeDataOffV.ConvertEdgeDataOff() C++: virtual void ConvertEdgeDataOff() SetConvertRowDataV.SetConvertRowData(bool) C++: virtual void SetConvertRowData(bool b) Whether to detect and convert row data arrays. Default is on. GetConvertRowDataV.GetConvertRowData() -> bool C++: virtual bool GetConvertRowData() ConvertRowDataOnV.ConvertRowDataOn() C++: virtual void ConvertRowDataOn() ConvertRowDataOffV.ConvertRowDataOff() C++: virtual void ConvertRowDataOff() vtkTableToArrayvtkInfovisCorePython.vtkTableToArrayvtkTableToArray - converts a vtkTable to a matrix. Superclass: vtkArrayDataAlgorithm Converts a vtkTable into a dense matrix. Use AddColumn() to designate one-to-many table columns that will become columns in the output matrix.a Using AddColumn() it is possible to duplicate / reorder columns in arbitrary ways. @warning Only produces vtkDenseArray, regardless of the input table column types. @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkTableToArray C++: static vtkTableToArray *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTableToArray C++: vtkTableToArray *NewInstance() ClearColumnsV.ClearColumns() C++: void ClearColumns() Reset the list of input table columns that will be mapped to columns in the output matrix. AddColumnV.AddColumn(string) C++: void AddColumn(const char *name) V.AddColumn(int) C++: void AddColumn(vtkIdType index) Add a column by name to the list of input table columns that will be mapped to columns in the output matrix. AddAllColumnsV.AddAllColumns() C++: void AddAllColumns() Add every input table column to the output matrix. @z@kvtkTableToGraphvtkInfovisCorePython.vtkTableToGraphvtkTableToGraph - convert a vtkTable into a vtkGraph Superclass: vtkGraphAlgorithm vtkTableToGraph converts a table to a graph using an auxiliary link graph. The link graph specifies how each row in the table should be converted to an edge, or a collection of edges. It also specifies which columns of the table should be considered part of the same domain, and which columns should be hidden. A second, optional, table may be provided as the vertex table. This vertex table must have one or more domain columns whose values match values in the edge table. The linked column name is specified in the domain array in the link graph. The output graph will only contain vertices corresponding to a row in the vertex table. For heterogeneous graphs, you may want to use vtkMergeTables to create a single vertex table. The link graph contains the following arrays: (1) The "column" array has the names of the columns to connect in each table row. This array is required. (2) The optional "domain" array provides user-defined domain names for each column. Matching domains in multiple columns will merge vertices with the same value from those columns. By default, all columns are in the same domain. If a vertex table is supplied, the domain indicates the column in the vertex table that the edge table column associates with. If the user provides a vertex table but no domain names, the output will be an empty graph. Hidden columns do not need valid domain names. (3) The optional "hidden" array is a bit array specifying whether the column should be hidden. The resulting graph will contain edges representing connections "through" the hidden column, but the vertices for that column will not be present. By default, no columns are hidden. Hiding a column in a particular domain hides all columns in that domain. The output graph will contain three additional arrays in the vertex data. The "domain" column is a string array containing the domain of each vertex. The "label" column is a string version of the distinct value that, along with the domain, defines that vertex. The "ids" column also contains the distinguishing value, but as a vtkVariant holding the raw value instead of being converted to a string. The "ids" column is set as the vertex pedigree ID attribute. V.SafeDownCast(vtkObjectBase) -> vtkTableToGraph C++: static vtkTableToGraph *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTableToGraph C++: vtkTableToGraph *NewInstance() AddLinkVertexV.AddLinkVertex(string, string, int) C++: void AddLinkVertex(const char *column, const char *domain=nullptr, int hidden=0) Add a vertex to the link graph. Specify the column name, the domain name for the column, and whether the column is hidden. ClearLinkVerticesV.ClearLinkVertices() C++: void ClearLinkVertices() Clear the link graph vertices. This also clears all edges. AddLinkEdgeV.AddLinkEdge(string, string) C++: void AddLinkEdge(const char *column1, const char *column2) Add an edge to the link graph. Specify the names of the columns to link. ClearLinkEdgesV.ClearLinkEdges() C++: void ClearLinkEdges() Clear the link graph edges. The graph vertices will remain. GetLinkGraphV.GetLinkGraph() -> vtkMutableDirectedGraph C++: virtual vtkMutableDirectedGraph *GetLinkGraph() The graph describing how to link the columns in the table. SetLinkGraphV.SetLinkGraph(vtkMutableDirectedGraph) C++: void SetLinkGraph(vtkMutableDirectedGraph *g) The graph describing how to link the columns in the table. LinkColumnPathV.LinkColumnPath(vtkStringArray, vtkStringArray, vtkBitArray) C++: void LinkColumnPath(vtkStringArray *column, vtkStringArray *domain=nullptr, vtkBitArray *hidden=nullptr) Links the columns in a specific order. This creates a simple path as the link graph. V.SetDirected(bool) C++: virtual void SetDirected(bool _arg) Specify the directedness of the output graph. V.GetDirected() -> bool C++: virtual bool GetDirected() Specify the directedness of the output graph. V.DirectedOn() C++: virtual void DirectedOn() Specify the directedness of the output graph. V.DirectedOff() C++: virtual void DirectedOff() Specify the directedness of the output graph. GetMTimeV.GetMTime() -> int C++: vtkMTimeType GetMTime() override; Get the current modified time. SetVertexTableConnectionV.SetVertexTableConnection(vtkAlgorithmOutput) C++: void SetVertexTableConnection(vtkAlgorithmOutput *in) A convenience method for setting the vertex table input. This is mainly for the benefit of the VTK client/server layer, vanilla VTK code should use e.g: * table_to_graph->SetInputConnection(1, vertex_table->output()); vtkMutableDirectedGraphvtkStringArrayvtkBitArrayvtkTableToSparseArrayvtkInfovisCorePython.vtkTableToSparseArrayvtkTableToSparseArray - converts a vtkTable into a sparse array. Superclass: vtkArrayDataAlgorithm Converts a vtkTable into a sparse array. Use AddCoordinateColumn() to designate one-to-many table columns that contain coordinates for each array value, and SetValueColumn() to designate the table column that contains array values. Thus, the number of dimensions in the output array will equal the number of calls to AddCoordinateColumn(). The coordinate columns will also be used to populate dimension labels in the output array. By default, the extent of the output array will be set to the range [0, largest coordinate + 1) along each dimension. In some situations you may prefer to set the extents explicitly, using the SetOutputExtents() method. This is useful when the output array should be larger than its largest coordinates, or when working with partitioned data. @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkTableToSparseArray C++: static vtkTableToSparseArray *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTableToSparseArray C++: vtkTableToSparseArray *NewInstance() ClearCoordinateColumnsV.ClearCoordinateColumns() C++: void ClearCoordinateColumns() Specify the set of input table columns that will be mapped to coordinates in the output sparse array. AddCoordinateColumnV.AddCoordinateColumn(string) C++: void AddCoordinateColumn(const char *name) Specify the set of input table columns that will be mapped to coordinates in the output sparse array. V.SetValueColumn(string) C++: void SetValueColumn(const char *name) Specify the input table column that will be mapped to values in the output array. V.GetValueColumn() -> string C++: const char *GetValueColumn() Specify the input table column that will be mapped to values in the output array. ClearOutputExtentsV.ClearOutputExtents() C++: void ClearOutputExtents() Explicitly specify the extents of the output array. SetOutputExtentsV.SetOutputExtents(vtkArrayExtents) C++: void SetOutputExtents(const vtkArrayExtents &extents) Explicitly specify the extents of the output array. vtkArrayExtentsvtkTableToTreeFiltervtkInfovisCorePython.vtkTableToTreeFiltervtkTableToTreeFilter - Filter that converts a vtkTable to a vtkTree Superclass: vtkTreeAlgorithm vtkTableToTreeFilter is a filter for converting a vtkTable data structure into a vtkTree datastructure. Currently, this will convert the table into a star, with each row of the table as a child of a new root node. The columns of the table are passed as node fields of the tree. V.SafeDownCast(vtkObjectBase) -> vtkTableToTreeFilter C++: static vtkTableToTreeFilter *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTableToTreeFilter C++: vtkTableToTreeFilter *NewInstance() vtkThresholdGraphvtkInfovisCorePython.vtkThresholdGraphvtkThresholdGraph - Returns a subgraph of a vtkGraph. Superclass: vtkGraphAlgorithm Requires input array, lower and upper threshold. This filter than extracts the subgraph based on these three parameters. V.SafeDownCast(vtkObjectBase) -> vtkThresholdGraph C++: static vtkThresholdGraph *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkThresholdGraph C++: vtkThresholdGraph *NewInstance() GetLowerThresholdV.GetLowerThreshold() -> float C++: virtual double GetLowerThreshold() Get/Set lower threshold. This would be the value against which edge or vertex data array value will be compared. SetLowerThresholdV.SetLowerThreshold(float) C++: virtual void SetLowerThreshold(double _arg) Get/Set lower threshold. This would be the value against which edge or vertex data array value will be compared. GetUpperThresholdV.GetUpperThreshold() -> float C++: virtual double GetUpperThreshold() Get/Set upper threshold. This would be the value against which edge or vertex data array value will be compared. SetUpperThresholdV.SetUpperThreshold(float) C++: virtual void SetUpperThreshold(double _arg) Get/Set upper threshold. This would be the value against which edge or vertex data array value will be compared. vtkThresholdTableACCEPT_LESS_THANACCEPT_GREATER_THANACCEPT_BETWEENACCEPT_OUTSIDEvtkInfovisCorePython.vtkThresholdTablevtkThresholdTable - Thresholds table rows. Superclass: vtkTableAlgorithm vtkThresholdTable uses minimum and/or maximum values to threshold table rows based on the values in a particular column. The column to threshold is specified using SetInputArrayToProcess(0, ...). V.SafeDownCast(vtkObjectBase) -> vtkThresholdTable C++: static vtkThresholdTable *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkThresholdTable C++: vtkThresholdTable *NewInstance() SetModeV.SetMode(int) C++: virtual void SetMode(int _arg) The mode of the threshold filter. Options are: ACCEPT_LESS_THAN (0) accepts rows with values < MaxValue; ACCEPT_GREATER_THAN (1) accepts rows with values > MinValue; ACCEPT_BETWEEN (2) accepts rows with values > MinValue and < MaxValue; ACCEPT_OUTSIDE (3) accepts rows with values < MinValue or > MaxValue. GetModeMinValueV.GetModeMinValue() -> int C++: virtual int GetModeMinValue() The mode of the threshold filter. Options are: ACCEPT_LESS_THAN (0) accepts rows with values < MaxValue; ACCEPT_GREATER_THAN (1) accepts rows with values > MinValue; ACCEPT_BETWEEN (2) accepts rows with values > MinValue and < MaxValue; ACCEPT_OUTSIDE (3) accepts rows with values < MinValue or > MaxValue. GetModeMaxValueV.GetModeMaxValue() -> int C++: virtual int GetModeMaxValue() The mode of the threshold filter. Options are: ACCEPT_LESS_THAN (0) accepts rows with values < MaxValue; ACCEPT_GREATER_THAN (1) accepts rows with values > MinValue; ACCEPT_BETWEEN (2) accepts rows with values > MinValue and < MaxValue; ACCEPT_OUTSIDE (3) accepts rows with values < MinValue or > MaxValue. GetModeV.GetMode() -> int C++: virtual int GetMode() The mode of the threshold filter. Options are: ACCEPT_LESS_THAN (0) accepts rows with values < MaxValue; ACCEPT_GREATER_THAN (1) accepts rows with values > MinValue; ACCEPT_BETWEEN (2) accepts rows with values > MinValue and < MaxValue; ACCEPT_OUTSIDE (3) accepts rows with values < MinValue or > MaxValue. SetMinValueV.SetMinValue(vtkVariant) C++: virtual void SetMinValue(vtkVariant v) V.SetMinValue(float) C++: void SetMinValue(double v) The minimum value for the threshold. This may be any data type stored in a vtkVariant. GetMinValueV.GetMinValue() -> vtkVariant C++: virtual vtkVariant GetMinValue() The minimum value for the threshold. This may be any data type stored in a vtkVariant. SetMaxValueV.SetMaxValue(vtkVariant) C++: virtual void SetMaxValue(vtkVariant v) V.SetMaxValue(float) C++: void SetMaxValue(double v) The maximum value for the threshold. This may be any data type stored in a vtkVariant. GetMaxValueV.GetMaxValue() -> vtkVariant C++: virtual vtkVariant GetMaxValue() The maximum value for the threshold. This may be any data type stored in a vtkVariant. ThresholdBetweenV.ThresholdBetween(vtkVariant, vtkVariant) C++: void ThresholdBetween(vtkVariant lower, vtkVariant upper) V.ThresholdBetween(float, float) C++: void ThresholdBetween(double lower, double upper) Criterion is rows whose scalars are between lower and upper thresholds (inclusive of the end values). @W vtkVariant@dvtkVariant@WW vtkVariant vtkVariant@ddvtkTransferAttributesvtkInfovisCorePython.vtkTransferAttributesvtkTransferAttributes - transfer data from a graph representation to a tree representation using direct mapping or pedigree ids. Superclass: vtkPassInputTypeAlgorithm The filter requires both a vtkGraph and vtkTree as input. The tree vertices must be a superset of the graph vertices. A common example is when the graph vertices correspond to the leaves of the tree, but the internal vertices of the tree represent groupings of graph vertices. The algorithm matches the vertices using the array "PedigreeId". The user may alternately set the DirectMapping flag to indicate that the two structures must have directly corresponding offsets (i.e. node i in the graph must correspond to node i in the tree). V.SafeDownCast(vtkObjectBase) -> vtkTransferAttributes C++: static vtkTransferAttributes *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTransferAttributes C++: vtkTransferAttributes *NewInstance() GetSourceArrayNameV.GetSourceArrayName() -> string C++: virtual char *GetSourceArrayName() The field name to use for storing the source array. SetSourceArrayNameV.SetSourceArrayName(string) C++: virtual void SetSourceArrayName(const char *_arg) The field name to use for storing the source array. GetTargetArrayNameV.GetTargetArrayName() -> string C++: virtual char *GetTargetArrayName() The field name to use for storing the source array. SetTargetArrayNameV.SetTargetArrayName(string) C++: virtual void SetTargetArrayName(const char *_arg) The field name to use for storing the source array. GetSourceFieldTypeV.GetSourceFieldType() -> int C++: virtual int GetSourceFieldType() The source field type for accessing the source array. Valid values are those from enum vtkDataObject::FieldAssociations. SetSourceFieldTypeV.SetSourceFieldType(int) C++: virtual void SetSourceFieldType(int _arg) The source field type for accessing the source array. Valid values are those from enum vtkDataObject::FieldAssociations. GetTargetFieldTypeV.GetTargetFieldType() -> int C++: virtual int GetTargetFieldType() The target field type for accessing the target array. Valid values are those from enum vtkDataObject::FieldAssociations. SetTargetFieldTypeV.SetTargetFieldType(int) C++: virtual void SetTargetFieldType(int _arg) The target field type for accessing the target array. Valid values are those from enum vtkDataObject::FieldAssociations. GetDefaultValueV.GetDefaultValue() -> vtkVariant C++: vtkVariant GetDefaultValue() Method to get/set the default value. SetDefaultValueV.SetDefaultValue(vtkVariant) C++: void SetDefaultValue(vtkVariant value) Method to get/set the default value. vtkTransposeMatrixvtkInfovisCorePython.vtkTransposeMatrixvtkTransposeMatrix - Computes the transpose of an input matrix. Superclass: vtkArrayDataAlgorithm @par Thanks: Developed by Timothy M. Shead (tshead@sandia.gov) at Sandia National Laboratories. V.SafeDownCast(vtkObjectBase) -> vtkTransposeMatrix C++: static vtkTransposeMatrix *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTransposeMatrix C++: vtkTransposeMatrix *NewInstance() vtkTreeFieldAggregatorvtkInfovisCorePython.vtkTreeFieldAggregatorvtkTreeFieldAggregator - aggregate field values from the leaves up the tree Superclass: vtkTreeAlgorithm vtkTreeFieldAggregator may be used to assign sizes to all the vertices in the tree, based on the sizes of the leaves. The size of a vertex will equal the sum of the sizes of the child vertices. If you have a data array with values for all leaves, you may specify that array, and the values will be filled in for interior tree vertices. If you do not yet have an array, you may tell the filter to create a new array, assuming that the size of each leaf vertex is 1. You may optionally set a flag to first take the log of all leaf values before aggregating. V.SafeDownCast(vtkObjectBase) -> vtkTreeFieldAggregator C++: static vtkTreeFieldAggregator *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTreeFieldAggregator C++: vtkTreeFieldAggregator *NewInstance() GetFieldV.GetField() -> string C++: virtual char *GetField() The field to aggregate. If this is a string array, the entries are converted to double. TODO: Remove this field and use the ArrayToProcess in vtkAlgorithm. SetFieldV.SetField(string) C++: virtual void SetField(const char *_arg) The field to aggregate. If this is a string array, the entries are converted to double. TODO: Remove this field and use the ArrayToProcess in vtkAlgorithm. V.GetMinValue() -> float C++: virtual double GetMinValue() If the value of the vertex is less than MinValue then consider it's value to be minVal. V.SetMinValue(float) C++: virtual void SetMinValue(double _arg) If the value of the vertex is less than MinValue then consider it's value to be minVal. SetLeafVertexUnitSizeV.SetLeafVertexUnitSize(bool) C++: virtual void SetLeafVertexUnitSize(bool _arg) If set, the algorithm will assume a size of 1 for each leaf vertex. GetLeafVertexUnitSizeV.GetLeafVertexUnitSize() -> bool C++: virtual bool GetLeafVertexUnitSize() If set, the algorithm will assume a size of 1 for each leaf vertex. LeafVertexUnitSizeOnV.LeafVertexUnitSizeOn() C++: virtual void LeafVertexUnitSizeOn() If set, the algorithm will assume a size of 1 for each leaf vertex. LeafVertexUnitSizeOffV.LeafVertexUnitSizeOff() C++: virtual void LeafVertexUnitSizeOff() If set, the algorithm will assume a size of 1 for each leaf vertex. SetLogScaleV.SetLogScale(bool) C++: virtual void SetLogScale(bool _arg) If set, the leaf values in the tree will be logarithmically scaled (base 10). GetLogScaleV.GetLogScale() -> bool C++: virtual bool GetLogScale() If set, the leaf values in the tree will be logarithmically scaled (base 10). LogScaleOnV.LogScaleOn() C++: virtual void LogScaleOn() If set, the leaf values in the tree will be logarithmically scaled (base 10). LogScaleOffV.LogScaleOff() C++: virtual void LogScaleOff() If set, the leaf values in the tree will be logarithmically scaled (base 10). vtkTreeDifferenceFiltervtkInfovisCorePython.vtkTreeDifferenceFiltervtkTreeDifferenceFilter - compare two trees Superclass: vtkGraphAlgorithm vtkTreeDifferenceFilter compares two trees by analyzing a vtkDoubleArray. Each tree must have a copy of this array. A user of this filter should call SetComparisonArrayName to specify the array that should be used as the basis of coparison. This array can either be part of the trees' EdgeData or VertexData. V.SafeDownCast(vtkObjectBase) -> vtkTreeDifferenceFilter C++: static vtkTreeDifferenceFilter *SafeDownCast( vtkObjectBase *o) V.NewInstance() -> vtkTreeDifferenceFilter C++: vtkTreeDifferenceFilter *NewInstance() SetIdArrayNameV.SetIdArrayName(string) C++: virtual void SetIdArrayName(const char *_arg) Set/Get the name of the identifier array in the trees' VertexData. This array is used to find corresponding vertices in the two trees. If this array name is not set, then we assume that the vertices in the two trees to compare have corresponding vtkIdTypes. Otherwise, the named array must be a vtkStringArray. The identifier array does not necessarily have to specify a name for each vertex in the tree. If some vertices are unnamed, then this filter will assign correspondence between ancestors of named vertices. GetIdArrayNameV.GetIdArrayName() -> string C++: virtual char *GetIdArrayName() Set/Get the name of the identifier array in the trees' VertexData. This array is used to find corresponding vertices in the two trees. If this array name is not set, then we assume that the vertices in the two trees to compare have corresponding vtkIdTypes. Otherwise, the named array must be a vtkStringArray. The identifier array does not necessarily have to specify a name for each vertex in the tree. If some vertices are unnamed, then this filter will assign correspondence between ancestors of named vertices. SetComparisonArrayNameV.SetComparisonArrayName(string) C++: virtual void SetComparisonArrayName(const char *_arg) Set/Get the name of the array that we're comparing between the two trees. The named array must be a vtkDoubleArray. GetComparisonArrayNameV.GetComparisonArrayName() -> string C++: virtual char *GetComparisonArrayName() Set/Get the name of the array that we're comparing between the two trees. The named array must be a vtkDoubleArray. V.SetOutputArrayName(string) C++: virtual void SetOutputArrayName(const char *_arg) Set/Get the name of a new vtkDoubleArray that will contain the results of the comparison between the two trees. This new array will be added to the input tree's VertexData or EdgeData, based on the value of ComparisonArrayIsVertexData. If this method is not called, the new vtkDoubleArray will be named "difference" by default. V.GetOutputArrayName() -> string C++: virtual char *GetOutputArrayName() Set/Get the name of a new vtkDoubleArray that will contain the results of the comparison between the two trees. This new array will be added to the input tree's VertexData or EdgeData, based on the value of ComparisonArrayIsVertexData. If this method is not called, the new vtkDoubleArray will be named "difference" by default. SetComparisonArrayIsVertexDataV.SetComparisonArrayIsVertexData(bool) C++: virtual void SetComparisonArrayIsVertexData(bool _arg) Specify whether the comparison array is within the trees' vertex data or not. By default, we assume that the array to compare is within the trees' EdgeData(). GetComparisonArrayIsVertexDataV.GetComparisonArrayIsVertexData() -> bool C++: virtual bool GetComparisonArrayIsVertexData() Specify whether the comparison array is within the trees' vertex data or not. By default, we assume that the array to compare is within the trees' EdgeData(). vtkTreeLevelsFiltervtkInfovisCorePython.vtkTreeLevelsFiltervtkTreeLevelsFilter - adds level and leaf fields to a vtkTree Superclass: vtkTreeAlgorithm The filter currently add two arrays to the incoming vtkTree datastructure. 1) "levels" this is the distance from the root of the vertex. Root = 0 and you add 1 for each level down from the root 2) "leaf" this array simply indicates whether the vertex is a leaf or not @par Thanks: Thanks to Brian Wylie from Sandia National Laboratories for creating this class. V.SafeDownCast(vtkObjectBase) -> vtkTreeLevelsFilter C++: static vtkTreeLevelsFilter *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkTreeLevelsFilter C++: vtkTreeLevelsFilter *NewInstance() vtkVertexDegreevtkInfovisCorePython.vtkVertexDegreevtkVertexDegree - Adds an attribute array with the degree of each vertex Superclass: vtkGraphAlgorithm Adds an attribute array with the degree of each vertex. By default the name of the array will be "VertexDegree", but that can be changed by calling SetOutputArrayName("foo"); V.SafeDownCast(vtkObjectBase) -> vtkVertexDegree C++: static vtkVertexDegree *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkVertexDegree C++: vtkVertexDegree *NewInstance() V.SetOutputArrayName(string) C++: virtual void SetOutputArrayName(const char *_arg) Set the output array name. If no output array name is set then the name 'VertexDegree' is used. vtkRemoveHiddenDatavtkInfovisCorePython.vtkRemoveHiddenDatavtkRemoveHiddenData - Removes the rows/edges/vertices of input data flagged by ann. Superclass: vtkPassInputTypeAlgorithm Output only those rows/vertices/edges of the input vtkDataObject that are visible, as defined by the vtkAnnotation::HIDE() flag of the input vtkAnnotationLayers. Inputs: Port 0 - vtkDataObject Port 1 - vtkAnnotationLayers (optional) V.SafeDownCast(vtkObjectBase) -> vtkRemoveHiddenData C++: static vtkRemoveHiddenData *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkRemoveHiddenData C++: vtkRemoveHiddenData *NewInstance() vtkKCoreDecompositionvtkInfovisCorePython.vtkKCoreDecompositionvtkKCoreDecomposition - Compute the k-core decomposition of the input graph. Superclass: vtkGraphAlgorithm The k-core decomposition is a graph partitioning strategy that is useful for analyzing the structure of large networks. A k-core of a graph G is a maximal connected subgraph of G in which all vertices have degree at least k. The k-core membership for each vertex of the input graph is found on the vertex data of the output graph as an array named 'KCoreDecompositionNumbers' by default. The algorithm used to find the k-cores has O(number of graph edges) running time, and is described in the following reference paper. An O(m) Algorithm for Cores Decomposition of Networks V. Batagelj, M. Zaversnik, 2001 @par Thanks: Thanks to Thomas Otahal from Sandia National Laboratories for providing this implementation. V.SafeDownCast(vtkObjectBase) -> vtkKCoreDecomposition C++: static vtkKCoreDecomposition *SafeDownCast(vtkObjectBase *o) V.NewInstance() -> vtkKCoreDecomposition C++: vtkKCoreDecomposition *NewInstance() V.SetOutputArrayName(string) C++: virtual void SetOutputArrayName(const char *_arg) Set the output array name. If no output array name is set then the name 'KCoreDecompositionNumbers' is used. SetUseInDegreeNeighborsV.SetUseInDegreeNeighbors(bool) C++: virtual void SetUseInDegreeNeighbors(bool _arg) Directed graphs only. Use only the in edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. GetUseInDegreeNeighborsV.GetUseInDegreeNeighbors() -> bool C++: virtual bool GetUseInDegreeNeighbors() Directed graphs only. Use only the in edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. UseInDegreeNeighborsOnV.UseInDegreeNeighborsOn() C++: virtual void UseInDegreeNeighborsOn() Directed graphs only. Use only the in edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. UseInDegreeNeighborsOffV.UseInDegreeNeighborsOff() C++: virtual void UseInDegreeNeighborsOff() Directed graphs only. Use only the in edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. SetUseOutDegreeNeighborsV.SetUseOutDegreeNeighbors(bool) C++: virtual void SetUseOutDegreeNeighbors(bool _arg) Directed graphs only. Use only the out edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. GetUseOutDegreeNeighborsV.GetUseOutDegreeNeighbors() -> bool C++: virtual bool GetUseOutDegreeNeighbors() Directed graphs only. Use only the out edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. UseOutDegreeNeighborsOnV.UseOutDegreeNeighborsOn() C++: virtual void UseOutDegreeNeighborsOn() Directed graphs only. Use only the out edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. UseOutDegreeNeighborsOffV.UseOutDegreeNeighborsOff() C++: virtual void UseOutDegreeNeighborsOff() Directed graphs only. Use only the out edges to compute the vertex degree of a vertex. The default is to use both in and out edges to compute vertex degree. SetCheckInputGraphV.SetCheckInputGraph(bool) C++: virtual void SetCheckInputGraph(bool _arg) Check the input graph for self loops and parallel edges. The k-core is not defined for graphs that contain either of these. Default is on. GetCheckInputGraphV.GetCheckInputGraph() -> bool C++: virtual bool GetCheckInputGraph() Check the input graph for self loops and parallel edges. The k-core is not defined for graphs that contain either of these. Default is on. CheckInputGraphOnV.CheckInputGraphOn() C++: virtual void CheckInputGraphOn() Check the input graph for self loops and parallel edges. The k-core is not defined for graphs that contain either of these. Default is on. CheckInputGraphOffV.CheckInputGraphOff() C++: virtual void CheckInputGraphOff() Check the input graph for self loops and parallel edges. The k-core is not defined for graphs that contain either of these. Default is on. real_initvtkInfovisCorePythoncan't get dictionary for module vtkInfovisCorePythonvtkInfovisCorePython?..K h UK h U(T_(Q8=__d. 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