
plgriddata: Grid data from irregularly sampled data 

DESCRIPTION:

    This function is used in example 21. 

SYNOPSIS:

plgriddata(x, y, z, npts, xg, nptsx, yg, nptsy, zg, type, data)

ARGUMENTS:

    x (const PLFLT *, input) :      The input x array. 

    y (const PLFLT *, input) :      The input y array. 

    z (const PLFLT *, input) :      The input z array. Each triple x[i],
    y[i], z[i] represents one data sample coordinate. 

    npts (PLINT, input) :    The number of data samples in the x, y and z
    arrays. 

    xg (const PLFLT *, input) :    The input array that specifies the grid
    spacing in the x direction. Usually xg has nptsx equally spaced
    values from the minimum to the maximum values of the x input
    array. 

    nptsx (PLINT, input) :    The number of points in the xg array. 

    yg (const PLFLT *, input) :    The input array that specifies the grid
    spacing in the y direction. Similar to the xg parameter. 

    nptsy (PLINT, input) :    The number of points in the yg array. 

    zg (PLFLT **, output) :    The output array, where data lies in the
    regular grid specified by xg and yg. the zg array must exist or be
    allocated by the user prior to the call, and must have dimension
    zg[nptsx][nptsy]. 

    type (PLINT, input) :    The type of gridding algorithm to use, which
    can be: GRID_CSA: Bivariate Cubic Spline approximation 
        GRID_DTLI: Delaunay Triangulation Linear Interpolation 
        GRID_NNI: Natural Neighbors Interpolation 
        GRID_NNIDW: Nearest Neighbors Inverse Distance Weighted 
        GRID_NNLI: Nearest Neighbors Linear Interpolation 
        GRID_NNAIDW:  Nearest Neighbors Around Inverse Distance
        Weighted 
    For details of the algorithms read the source file plgridd.c. 

    data (PLFLT, input) :    Some gridding algorithms require extra data,
    which can be specified through this argument. Currently, for
    algorithm: GRID_NNIDW, data specifies the number of neighbors to
    use, the lower the value, the noisier (more local) the
    approximation is. 
        GRID_NNLI, data specifies what a thin triangle is, in the
        range [1. .. 2.]. High values enable the usage of very thin
        triangles for interpolation, possibly resulting in error in
        the approximation. 
        GRID_NNI, only weights greater than data will be accepted. If
        0, all weights will be accepted. 
