28-Mar-2019 15:56:39 test_interp_test MATLAB version Test test_interp. This test also requires the R8LIB library. TEST_INTERP_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 a disaster. TEST_INTERP_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: 1 2 Col 1: 0.000000 4.000000 2: 1.000000 5.000000 3: 2.000000 6.000000 4: 4.000000 6.000000 5: 5.000000 5.000000 6: 6.000000 3.000000 7: 7.000000 1.000000 8: 8.000000 1.000000 9: 9.000000 1.000000 10: 10.000000 3.000000 11: 11.000000 4.000000 12: 12.000000 4.000000 13: 13.000000 3.000000 14: 14.000000 3.000000 15: 15.000000 4.000000 16: 16.000000 4.000000 17: 17.000000 3.000000 18: 18.000000 0.000000 Problem 2 DATA_NUM = 18 DIM_NUM = 2 Data array: Row: 1 2 Col 1: 0.000000 0.000000 2: 1.340000 5.000000 3: 5.000000 8.660000 4: 10.000000 10.000000 5: 10.600000 10.400000 6: 10.700000 12.000000 7: 10.705000 28.600000 8: 10.800000 30.200000 9: 11.400000 30.600000 10: 19.600000 30.600000 11: 20.200000 30.200000 12: 20.295000 28.600000 13: 20.300000 12.000000 14: 20.400000 10.400000 15: 21.000000 10.000000 16: 26.000000 8.660000 17: 29.660000 5.000000 18: 31.000000 0.000000 Problem 3 DATA_NUM = 11 DIM_NUM = 2 Data array: Row: 1 2 Col 1: 0.000000 0.000000 2: 2.000000 10.000000 3: 3.000000 10.000000 4: 5.000000 10.000000 5: 6.000000 10.000000 6: 8.000000 10.000000 7: 9.000000 10.500000 8: 11.000000 15.000000 9: 12.000000 50.000000 10: 14.000000 60.000000 11: 15.000000 85.000000 Problem 4 DATA_NUM = 8 DIM_NUM = 2 Data array: Row: 1 2 Col 1: 0.000000 0.000000 2: 0.050000 0.700000 3: 0.100000 1.000000 4: 0.200000 1.000000 5: 0.800000 0.300000 6: 0.850000 0.050000 7: 0.900000 0.100000 8: 1.000000 1.000000 Problem 5 DATA_NUM = 9 DIM_NUM = 2 Data array: Row: 1 2 Col 1: 0.000000 0.000000 2: 0.100000 0.900000 3: 0.200000 0.950000 4: 0.300000 0.900000 5: 0.400000 0.100000 6: 0.500000 0.050000 7: 0.600000 0.050000 8: 0.800000 0.200000 9: 1.000000 1.000000 Problem 6 DATA_NUM = 49 DIM_NUM = 2 Data array: Row: 1 2 Col 1: 595.000000 0.644000 2: 605.000000 0.622000 3: 615.000000 0.638000 4: 625.000000 0.649000 5: 635.000000 0.652000 6: 645.000000 0.639000 7: 655.000000 0.646000 8: 665.000000 0.657000 9: 675.000000 0.652000 10: 685.000000 0.655000 11: 695.000000 0.644000 12: 705.000000 0.663000 13: 715.000000 0.663000 14: 725.000000 0.668000 15: 735.000000 0.676000 16: 745.000000 0.676000 17: 755.000000 0.686000 18: 765.000000 0.679000 19: 775.000000 0.678000 20: 785.000000 0.683000 21: 795.000000 0.694000 22: 805.000000 0.699000 23: 815.000000 0.710000 24: 825.000000 0.730000 25: 835.000000 0.763000 26: 845.000000 0.812000 27: 855.000000 0.907000 28: 865.000000 1.044000 29: 875.000000 1.336000 30: 885.000000 1.881000 31: 895.000000 2.169000 32: 905.000000 2.075000 33: 915.000000 1.598000 34: 925.000000 1.211000 35: 935.000000 0.916000 36: 945.000000 0.746000 37: 955.000000 0.672000 38: 965.000000 0.627000 39: 975.000000 0.615000 40: 985.000000 0.607000 41: 995.000000 0.606000 42: 1005.000000 0.609000 43: 1015.000000 0.603000 44: 1025.000000 0.601000 45: 1035.000000 0.603000 46: 1045.000000 0.601000 47: 1055.000000 0.611000 48: 1065.000000 0.601000 49: 1075.000000 0.608000 Problem 7 DATA_NUM = 4 DIM_NUM = 2 Data array: Row: 1 2 Col 1: 0.000000 1.000000 2: 1.000000 2.000000 3: 4.000000 2.000000 4: 5.000000 1.000000 Problem 8 DATA_NUM = 12 DIM_NUM = 2 Data array: Row: 1 2 Col 1: -1.000000 1.000000 2: -0.800000 0.640000 3: -0.600000 0.360000 4: -0.400000 0.160000 5: -0.200000 0.040000 6: 0.000000 0.000000 7: 0.200000 0.040000 8: 0.200010 0.050000 9: 0.400000 0.160000 10: 0.600000 0.360000 11: 0.800000 0.640000 12: 1.000000 1.000000 Created graphics file "p01_plot.png". Created graphics file "p02_plot.png". Created graphics file "p03_plot.png". Created graphics file "p04_plot.png". Created graphics file "p05_plot.png". Created graphics file "p06_plot.png". Created graphics file "p07_plot.png". Created graphics file "p08_plot.png". test_interp_test Normal end of execution. 28-Mar-2019 15:56:42