| 
	    plgriddata
	  ( | x, | 
| y, | |
| z, | |
| npts, | |
| xg, | |
| nptsx, | |
| yg, | |
| nptsy, | |
| zg, | |
| type, | |
| data ); | 
      Real world data is frequently irregularly sampled, but PLplot 3D
      plots require data organized as a grid, i.e., with x sample point values independent
      of y coordinate and vice versa.  This function takes
      irregularly sampled data from the
      x[npts], y[npts], and
      z[npts] vectors; reads the desired grid location from
      the input vectors xg[nptsx] and
      yg[nptsy]; and returns the interpolated result on that grid
      using the output matrix zg[nptsx][nptsy].  The algorithm used
      to interpolate the data to the grid is specified with the argument
      type which can have one parameter specified in
      argument data.
    
x
	  (PLFLT_VECTOR
	    The input x vector.
	  
y
	  (PLFLT_VECTOR
	    The input y vector.
	  
z
	  (PLFLT_VECTOR
	    The input z vector. Each triple
	    x[i], y[i],
	    z[i] represents one data sample coordinate.
	  
npts
	  (PLINT
	    The number of data samples in the x,
	    y and z vectors.
	  
xg
	  (PLFLT_VECTOR
	    A vector 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 vector.
	  
nptsx
	  (PLINT
	    The number of points in the xg vector.
	  
yg
	  (PLFLT_VECTOR
	    A vector that specifies the grid spacing in the y
	    direction. Similar to the xg parameter.
	  
nptsy
	  (PLINT
	    The number of points in the yg vector.
	  
zg
	  (PLFLT_NC_MATRIX
	    The matrix of interpolated results where data lies in the grid
	    specified by xg and yg.
	    Therefore the zg matrix must be dimensioned
	    nptsx by nptsy.
	  
type
	  (PLINTThe type of grid interpolation 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
	  (PLFLTSome 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.
	      
Redacted form:
	    General: plgriddata(x, y, z, xg, yg, zg, type, data)
	  
	    Python: zg=plgriddata(x, y, z, xg, yg, type, data)
This function is used in example 21.