ocmath_parametric_interpolate_eval

1 Description
2 Syntax
3 Parameters
4 Return
5 Examples
6 Remark
7 See Also
8 header to Include
9 Reference

Description

Get interpolated data using parametric interpolation. For every dimension of pData call the function ocmath_interpolate to interpolate.

Syntax

int ocmath_parametric_interpolate_eval( const double * pEvalT, double * pEvalData, UINT nEvalSize, const double * pT, const double * pData, UINT nDataSize, UINT nDimension = 2, int nMode = INTERP_TYPE_SPLINE, double dSmoothingFactor = 0, const double * pWeights = NULL, int iOption = OPTION_EXTRAPOLATE )

Parameters

pEvalT: [input]parameter points to be evaluated, if one element is outside of valid range, relative interpolated data will be NANUM
pEvalData: [output]interpolated data, with size nDimension by nEvalSize
nEvalSize: [input] point number to be evaluated, size of pEvalT
pT: [input]parameter data, size of nDataSize
pData: [input]source data, with size nDimension by nDataSize
nDataSize: [input] size of each dimension
nDimension: [input] dimension of source data, nDimension >= 2
nMode: interpolation method. Must be one of the three modes:; INTERP_TYPE_LINEAR(linear interpolation),; INTERP_TYPE_SPLINE(cubic spline interpolation with natural boundary condition),; INTERP_TYPE_BSPLINE(B-Spline curve fitting using method by Dierckx.P)
dSmoothingFactor: [input] This argument specifies the closeness to the original data. It is only useful when nMode = INTERP_TYPE_BSPLINE. dSmoothingFactor >= 0.; By means of this parameter, the user can control the tradeoff between closeness of fit and smoothness of fit of the approximation.; If dSmoothingFactor is too large, the spline will be too smooth and signal will be lost ; if it is too small the spline will pick up too much noise.; In the extreme cases the program will return an interpolating spline if dSmoothingFactor=0 and the weighted least-squares polynomial of degree 3 if s is very large.
pWeights: [input]pointer to weights, which is only used in method INTERP_TYPE_BSPLINE, by default(pWeights = NULL) all weights are 1. size is nDataSize
iOption: [input] specify how to extrapolate Y values in extrapolated range. Must be one of the three values:; OPTION_EXTRAPOLATE(extrapolate Y using the last two points),; OPTION_SET_MISSING(set all Y values in the extrapolated range to be missing values),; OPTION_REPEAT_LAST(use the Y value of the closest input X value for all values in the extrapolated range)

Return

Returns 0 on success, otherwise returns error code in enum Err_Spline.

Examples

EX1

void    ocmath_parametric_interpolate_eval_ex1()
{
    const int nDataSize = 5;
    vector vT(nDataSize);
    matrix mData = {{0, 1, -1, 0, 3},{0, 2, 3, 1, 0}};
    if(OE_NOERROR != ocmath_parametric_interpolate_range(vT, mData, nDataSize))
    {
        out_str("error in calculating range");
        return;
    }
    vector vEvalT;
    vEvalT.Data(vT[0], vT[nDataSize - 1], (vT[nDataSize - 1] - vT[0])/20);
    int nEvalPoints = vEvalT.GetSize();
    matrix mEvalData(2, nEvalPoints);
    if(0 != ocmath_parametric_interpolate_eval(vEvalT, mEvalData, nEvalPoints, vT, mData, nDataSize))
    {
        out_str("error in evaluation");
        return;
    }
    for(int ii = 0; ii < nEvalPoints; ii++)
    {
        printf("%f\t%f\t%f\n", vEvalT[ii], mEvalData[0][ii], mEvalData[1][ii]);
    }
}