26 February 2022 01:38:39 PM
TEST_INTERP_TEST
C version
Test the TEST_INTERP library.
This test also requires the R8LIB library.
TEST01
P00_STORY prints the problem "story".
Problem 1
This example is due to Hans-Joerg Wenz.
It is an example of good data, which is dense enough in areas
where the expected curvature of the interpolant is large.
Good results can be expected with almost any reasonable
interpolation method.
Problem 2
This example is due to ETY Lee of Boeing.
Data near the corners is more dense than in regions of small curvature.
A local interpolation method will produce a more plausible
interpolant than a nonlocal interpolation method, such as
cubic splines.
Problem 3
This example is due to Fred Fritsch and Ralph Carlson.
This data can cause problems for interpolation methods.
There are sudden changes in direction, and at the same time,
sparsely-placed data. This can cause an interpolant to overshoot
the data in a way that seems implausible.
Problem 4
This example is due to Larry Irvine, Samuel Marin and Philip Smith.
This data can cause problems for interpolation methods.
There are sudden changes in direction, and at the same time,
sparsely-placed data. This can cause an interpolant to overshoot
the data in a way that seems implausible.
Problem 5
This example is due to Larry Irvine, Samuel Marin and Philip Smith.
This data can cause problems for interpolation methods.
There are sudden changes in direction, and at the same time,
sparsely-placed data. This can cause an interpolant to overshoot
the data in a way that seems implausible.
Problem 6
The data is due to deBoor and Rice.
The data represents a temperature dependent property of titanium.
The data has been used extensively as an example in spline
approximation with variably-spaced knots.
DeBoor considers two sets of knots:
(595,675,755,835,915,995,1075)
and
(595,725,850,910,975,1040,1075).
Problem 7
This data is a simple symmetric set of 4 points,
for which it is interesting to develop the Shepard
interpolants for varying values of the exponent p.
Problem 8
This is equally spaced data for y = x^2,
except for one extra point whose x value is
close to another, but whose y value is not so close.
A small disagreement in nearby data can be disaster.
TEST02
P00_DATA_NUM returns N, the number of data points.
P00_DIM_NUM returns M, the dimension of data.
P00_DATA returns the actual (MxN) data.
Problem 1
DATA_NUM 18
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 0 4
1: 1 5
2: 2 6
3: 4 6
4: 5 5
5: 6 3
6: 7 1
7: 8 1
8: 9 1
9: 10 3
10: 11 4
11: 12 4
12: 13 3
13: 14 3
14: 15 4
15: 16 4
16: 17 3
17: 18 0
Problem 2
DATA_NUM 18
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 0 0
1: 1.34 5
2: 5 8.66
3: 10 10
4: 10.6 10.4
5: 10.7 12
6: 10.705 28.6
7: 10.8 30.2
8: 11.4 30.6
9: 19.6 30.6
10: 20.2 30.2
11: 20.295 28.6
12: 20.3 12
13: 20.4 10.4
14: 21 10
15: 26 8.66
16: 29.66 5
17: 31 0
Problem 3
DATA_NUM 11
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 0 0
1: 2 10
2: 3 10
3: 5 10
4: 6 10
5: 8 10
6: 9 10.5
7: 11 15
8: 12 50
9: 14 60
10: 15 85
Problem 4
DATA_NUM 8
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 0 0
1: 0.05 0.7
2: 0.1 1
3: 0.2 1
4: 0.8 0.3
5: 0.85 0.05
6: 0.9 0.1
7: 1 1
Problem 5
DATA_NUM 9
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 0 0
1: 0.1 0.9
2: 0.2 0.95
3: 0.3 0.9
4: 0.4 0.1
5: 0.5 0.05
6: 0.6 0.05
7: 0.8 0.2
8: 1 1
Problem 6
DATA_NUM 49
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 595 0.644
1: 605 0.622
2: 615 0.638
3: 625 0.649
4: 635 0.652
5: 645 0.639
6: 655 0.646
7: 665 0.657
8: 675 0.652
9: 685 0.655
10: 695 0.644
11: 705 0.663
12: 715 0.663
13: 725 0.668
14: 735 0.676
15: 745 0.676
16: 755 0.686
17: 765 0.679
18: 775 0.678
19: 785 0.683
20: 795 0.694
21: 805 0.699
22: 815 0.71
23: 825 0.73
24: 835 0.763
25: 845 0.812
26: 855 0.907
27: 865 1.044
28: 875 1.336
29: 885 1.881
30: 895 2.169
31: 905 2.075
32: 915 1.598
33: 925 1.211
34: 935 0.916
35: 945 0.746
36: 955 0.672
37: 965 0.627
38: 975 0.615
39: 985 0.607
40: 995 0.606
41: 1005 0.609
42: 1015 0.603
43: 1025 0.601
44: 1035 0.603
45: 1045 0.601
46: 1055 0.611
47: 1065 0.601
48: 1075 0.608
Problem 7
DATA_NUM 4
DIM_NUM 2
Data array:
Row: 0 1
Col
0: 0 1
1: 1 2
2: 4 2
3: 5 1
Problem 8
DATA_NUM 12
DIM_NUM 2
Data array:
Row: 0 1
Col
0: -1 1
1: -0.8 0.64
2: -0.6 0.36
3: