NAME

       plgriddata - Grid data from irregularly sampled data

SYNOPSIS

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

       Redacted  form:   General:  plgriddata(x, y, z, xg, yg, zg, type, data) Perl/PDL: Not available?  Python:
       zg=plgriddata(x, y, z, xg, yg, type, data)

       This function is used in example 21.

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.

AUTHORS

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

SEE ALSO

       PLplot documentation at http://plplot.sourceforge.net/documentation.php.

                                                 February, 2014                              PLGRIDDATA(3plplot)