
plgriddata: Grid data from irregularly sampled data 

DESCRIPTION:

	Real world data is frequently irregularly sampled, but all PLplot 3D
	plots require data placed in a uniform grid. This function takes
	irregularly sampled data from three input arrays x[npts], y[npts], and
	z[npts], reads the desired grid location from input arrays xg[nptsx]
	and yg[nptsy], and returns the gridded data into output array
	zg[nptsx][nptsy].  The algorithm used to grid the data is specified
	with the argument type which can have one parameter specified in
	argument data. 

SYNOPSIS:

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

ARGUMENTS:

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

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

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

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

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

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

    yg (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. 

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

    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 on the algorithm read the source file plgridd.c. 

    data (PLFLT, input) :     Some gridding algorithms require extra data,
        which can be specified through this argument. Currently, for
        algoritm: 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 then data will be accepted. If
            0, all weigths will be accepted. 
