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, 2016                      PLGRIDDATA(3plplot)