1PLGRIDDATA(3plplot) PLplot API PLGRIDDATA(3plplot)
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6 plgriddata - Grid data from irregularly sampled data
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9 plgriddata(x, y, z, npts, xg, nptsx, yg, nptsy, zg, type, data)
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12 Real world data is frequently irregularly sampled, but PLplot 3D plots
13 require data organized as a grid, i.e., with x sample point values in‐
14 dependent of y coordinate and vice versa. This function takes irregu‐
15 larly sampled data from the x[npts], y[npts], and z[npts] vectors;
16 reads the desired grid location from the input vectors xg[nptsx] and
17 yg[nptsy]; and returns the interpolated result on that grid using the
18 output matrix zg[nptsx][nptsy]. The algorithm used to interpolate the
19 data to the grid is specified with the argument type which can have one
20 parameter specified in argument data.
21 Redacted form: General: plgriddata(x, y, z, xg, yg, zg, type, data)
23 Python: zg=plgriddata(x, y, z, xg, yg, type, data)
24 This function is used in example 21.
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29 x (PLFLT_VECTOR(3plplot), input)
30 The input x vector.
31 y (PLFLT_VECTOR(3plplot), input)
33 The input y vector.
34 z (PLFLT_VECTOR(3plplot), input)
36 The input z vector. Each triple x[i], y[i], z[i] represents one
37 data sample coordinate.
38 npts (PLINT(3plplot), input)
40 The number of data samples in the x, y and z vectors.
41 xg (PLFLT_VECTOR(3plplot), input)
43 A vector that specifies the grid spacing in the x direction.
44 Usually xg has nptsx equally spaced values from the minimum to
45 the maximum values of the x input vector.
46 nptsx (PLINT(3plplot), input)
48 The number of points in the xg vector.
49 yg (PLFLT_VECTOR(3plplot), input)
51 A vector that specifies the grid spacing in the y direction.
52 Similar to the xg parameter.
53 nptsy (PLINT(3plplot), input)
55 The number of points in the yg vector.
56 zg (PLFLT_NC_MATRIX(3plplot), output)
58 The matrix of interpolated results where data lies in the grid
59 specified by xg and yg. Therefore the zg matrix must be dimen‐
60 sioned nptsx by nptsy.
61 type (PLINT(3plplot), input)
63 The type of grid interpolation algorithm to use, which can be:
64 GRID_CSA: Bivariate Cubic Spline approximation GRID_DTLI: Delau‐
65 nay Triangulation Linear Interpolation GRID_NNI: Natural Neigh‐
66 bors Interpolation GRID_NNIDW: Nearest Neighbors Inverse Dis‐
67 tance Weighted GRID_NNLI: Nearest Neighbors Linear Interpolation
68 GRID_NNAIDW: Nearest Neighbors Around Inverse Distance Weighted
69 For details of the algorithms read the source file plgridd.c.
70 data (PLFLT(3plplot), input)
72 Some gridding algorithms require extra data, which can be speci‐
73 fied through this argument. Currently, for algorithm:
74 GRID_NNIDW, data specifies the number of neighbors to use, the
75 lower the value, the noisier (more local) the approximation is.
76 GRID_NNLI, data specifies what a thin triangle is, in the range
77 [1. .. 2.]. High values enable the usage of very thin triangles
78 for interpolation, possibly resulting in error in the approxima‐
79 tion. GRID_NNI, only weights greater than data will be ac‐
80 cepted. If 0, all weights will be accepted.
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85 Many developers (who are credited at http://plplot.org/credits.php)
86 have contributed to PLplot over its long history.
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89 PLplot documentation at http://plplot.org/documentation.php.
90 January, 2021 PLGRIDDATA(3plplot)