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.