plgriddata - Grid data from irregularly sampled data
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.
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.
- 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.
PLplot documentation at http://plplot.org/documentation.php.