plgriddata - Grid data from irregularly sampled data

x (PLFLT_VECTOR(3plplot), input)
              The input x vector.

       y (PLFLT_VECTOR(3plplot), input)
              The input y vector.

       z (PLFLT_VECTOR(3plplot), input)
              The input z vector. Each triple x[i], y[i], z[i] represents one data sample coordinate.

       npts (PLINT(3plplot), input)
              The number of data samples in the x, y and z vectors.

       xg (PLFLT_VECTOR(3plplot), input)
              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(3plplot), input)
              The number of points in the xg vector.

       yg (PLFLT_VECTOR(3plplot), input)
              A vector that specifies the grid spacing in the y direction. Similar to the xg parameter.

       nptsy (PLINT(3plplot), input)
              The number of points in the yg vector.

       zg (PLFLT_NC_MATRIX(3plplot), output)
              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 (PLINT(3plplot), input)
              The  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 (PLFLT(3plplot), 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.

       Many developers (who are credited at http://plplot.org/credits.php) have contributed to PLplot  over  its
       long history.

       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.

       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.

plgriddata - Grid data from irregularly sampled data

       PLplot documentation at http://plplot.org/documentation.php.

                                                   July, 2025                                PLGRIDDATA(3plplot)

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