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The method works by resampling the scalars associated with points defined on an arbitrary dataset onto a volume (i.e., structured points) dataset. The influence functions are described as "inverse distance weighted". Once the interpolation is performed across the volume, the usual volume visualization techniques (e.g., iso-contouring or volume rendering) can be used. Note that this implementation also provides the ability to specify the power parameter p. Given the generalized Inverse Distance Weighting (IDW) function with distance between points measured as d(x,xi), p is defined as: u(x) = Sum(wi(x) * ui) / Sum(wi(x)) if d(x,xi) != 0 u(x) = ui if d(x,xi) == 0 where wi(x) = 1 / (d(x,xi)^p Typically p=2, so the weights wi(x) are the inverse of the distance squared. However, power parameters > 2 can be used which assign higher weights for data closer to the interpolated point; or <2 which assigns greater weight to points further away. (Note that if p!=2, performance may be significantly impacted as the algorihm is tuned for p=2.) @warning Strictly speaking, this is a modified Shepard's methodsince only points within the MaxiumDistance are used for interpolation. By setting the maximum distance to include the entire bounding box and therefore all points, the class executes much slower but incorporates all points into the interpolation process (i.e., a pure Shepard method). @warning The input to this filter is any dataset type. This filter can be used to resample the points of any type of dataset onto the output volume; i.e., the input data need not be unstructured with explicit point representations. @warning The bounds of the data (i.e., the sample space) is automatically computed if not set by the user. @warning If you use a maximum distance less than 1.0 (i.e., using a modified Shephard's method), some output points may never receive a contribution. The final value of these points can be specified with the "NullValue" instance variable. @warning This class has been threaded with vtkSMPTools. Using TBB or other non-sequential type (set in the CMake variable VTK_SMP_IMPLEMENTATION_TYPE) may improve performance significantly. @sa vtkGaussianSplatter vtkCheckerboardSplatter vtkImagingHybridPython.vtkShepardMethodV.IsTypeOf(string) -> int C++: static vtkTypeBool IsTypeOf(const char *type) Standard type and print methods. V.IsA(string) -> int C++: vtkTypeBool IsA(const char *type) override; Standard type and print methods. V.SafeDownCast(vtkObjectBase) -> vtkShepardMethod C++: static vtkShepardMethod *SafeDownCast(vtkObjectBase *o) Standard type and print methods. V.NewInstance() -> vtkShepardMethod C++: vtkShepardMethod *NewInstance() Standard type and print methods. V.SetSampleDimensions(int, int, int) C++: void SetSampleDimensions(int i, int j, int k) V.SetSampleDimensions([int, int, int]) C++: void SetSampleDimensions(int dim[3]) Set the i-j-k dimensions on which to interpolate the input points. V.GetSampleDimensions() -> (int, int, int) C++: int *GetSampleDimensions() Retrieve the i-j-k dimensions on which to interpolate the input points. V.SetMaximumDistance(float) C++: virtual void SetMaximumDistance(double _arg) Specify the maximum influence distance of each input point. This distance is a fraction of the length of the diagonal of the sample space. Thus, values of 1.0 will cause each input point to influence all points in the volume dataset. Values less than 1.0 can improve performance significantly. By default the maximum distance is 0.25. V.GetMaximumDistanceMinValue() -> float C++: virtual double GetMaximumDistanceMinValue() Specify the maximum influence distance of each input point. This distance is a fraction of the length of the diagonal of the sample space. Thus, values of 1.0 will cause each input point to influence all points in the volume dataset. Values less than 1.0 can improve performance significantly. By default the maximum distance is 0.25. V.GetMaximumDistanceMaxValue() -> float C++: virtual double GetMaximumDistanceMaxValue() Specify the maximum influence distance of each input point. This distance is a fraction of the length of the diagonal of the sample space. Thus, values of 1.0 will cause each input point to influence all points in the volume dataset. Values less than 1.0 can improve performance significantly. By default the maximum distance is 0.25. V.GetMaximumDistance() -> float C++: virtual double GetMaximumDistance() Specify the maximum influence distance of each input point. This distance is a fraction of the length of the diagonal of the sample space. Thus, values of 1.0 will cause each input point to influence all points in the volume dataset. Values less than 1.0 can improve performance significantly. By default the maximum distance is 0.25. V.SetNullValue(float) C++: virtual void SetNullValue(double _arg) Set the value for output points not receiving a contribution from any input point(s). Output points may not receive a contribution when the MaximumDistance < 1. V.GetNullValue() -> float C++: virtual double GetNullValue() Set the value for output points not receiving a contribution from any input point(s). Output points may not receive a contribution when the MaximumDistance < 1. V.SetModelBounds(float, float, float, float, float, float) C++: void SetModelBounds(double, double, double, double, double, double) V.SetModelBounds((float, float, float, float, float, float)) C++: void SetModelBounds(double a[6]) V.GetModelBounds() -> (float, float, float, float, float, float) C++: double *GetModelBounds() Specify the position in space to perform the sampling. The ModelBounds and SampleDimensions together define the output volume. (Note: if the ModelBounds are set to an invalid state [zero or negative volume] then the bounds are computed automatically.) V.SetPowerParameter(float) C++: virtual void SetPowerParameter(double _arg) Set / Get the power parameter p. By default p=2. Values (which must be a positive, real value) != 2 may affect performance significantly. V.GetPowerParameterMinValue() -> float C++: virtual double GetPowerParameterMinValue() Set / Get the power parameter p. By default p=2. Values (which must be a positive, real value) != 2 may affect performance significantly. V.GetPowerParameterMaxValue() -> float C++: virtual double GetPowerParameterMaxValue() Set / Get the power parameter p. By default p=2. Values (which must be a positive, real value) != 2 may affect performance significantly. V.GetPowerParameter() -> float C++: virtual double GetPowerParameter() Set / Get the power parameter p. By default p=2. Values (which must be a positive, real value) != 2 may affect performance significantly. V.ComputeModelBounds([float, float, float], [float, float, float]) -> float C++: double ComputeModelBounds(double origin[3], double ar[3]) Compute ModelBounds from the input geometry. 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