A (3, n) array containing the random x, y, z points to be gridded.grid_x
The x dimension of the grid. The x values are scaled to fit this dimension.grid_y
The y dimension of the grid. The y values are scaled to fit this dimension.
Returns agridded, 2D array containing z values, given random x-, y-, z-coordinates (this function works best with dense data points).
A (3, n) array containing the random x, y, z points to be gridded.grid_x
The x dimension of the grid. The x values are scaled to fit this dimension.grid_y
The y dimension of the grid. The y values are scaled to fit this dimension.
A gridded, 2D array containing z values.
The number of iterations on the smooth function. The execution time increases linearly with the number of iterations. The default is 3, but any non-negative integer (including zero) may be specified.Nghbr
The size of the neighborhood to smooth. If not supplied, the neighborhood size is calculated from the distribution of the points. The amount of memory required increases by the square of the neighborhood size.No_Avg
Normally, if multiple data points fall in the same cell in the gridded array, then the value of that cell is the average value of all the data points that fall in that cell.If the No_Avg keyword is present and nonzero, however, the value of the cell in the gridded array is the total of all the points that fall in that cell.
XMin
The x-coordinate of the left edge of the grid. If omitted, maps the minimum x value found in the points(0, *) array to the left edge of the grid.XMax
The x-coordinate of the right edge of the grid. If omitted, maps the maximum x value found in the points(0, *) array to the right edge of the grid.YMin
The y-coordinate of the bottom edge of the grid. If omitted, maps the minimum y value found in the points(1, *) array to the bottom edge of the grid.YMax
The y-coordinate of the top edge of the grid. If omitted, maps the maximum y value found in the points(1, *) array to the top edge of the grid.
FAST_GRID3 is similar to GRID_3D. FAST_GRID3, however, works best with dense data points (more than 1000 points to be gridded) and is considerably faster, but slightly less accurate, than GRID_3D. (GRID_3D works best with sparse data points and is stable when extrapolating into large void areas.)
TIP: For best results, use a small neighborhood (such as 3) and a large number of iterations (more than 16).
PRO f_gridemo3
points = RANDOMU(s, 3, 1000) points(0, *) = points(0, *) * 10.0 points(1, *) = points(1, *) * 10.0 points(*, 0) = [1.7, 1.6, 2.9] points(*, 1) = [1.4, 1.2, 3.7] points(*, 2) = [9.8, 9.2, 5.5] points(*, 3) = [9.8, 8.4, 0.1] points(*, 4) = [4.8, 9.9, 6.3] points(*, 5) = [0.2, 9.0, 9.0] points(*, 6) = [3.1, 7.2, 15.2] points(*, 7) = [5.6, 6.0, -5.9] points(*, 8) = [0.3, 0.5, 3.3] points(*, 9) = [9.7, 0.7, -1.6]
zval = FAST_GRID3(points, 48, 32)
WINDOW, 0, Colors=128 SURFR SURFACE, zval, Bottom=90, Ax=30.0, Az=30.0, /T3d
END
UNIX and OpenVMS Users: For information on the optional software package for advanced gridding, PV-WAVE:GTGRID, contact your Visual Numerics account representative.
