Tue Oct 19 17:24:59 2021 test_interp_test(): Python version: 3.6.9 Test test_interp(). r8mat_transpose_print_test Python version: 3.6.9 r8mat_transpose_print prints an R8MAT. Here is an R8MAT, transposed: Row: 0 1 2 3 Col 0 : 11 21 31 41 1 : 12 22 32 42 2 : 13 23 33 43 r8mat_transpose_print_test: Normal end of execution. r8mat_transpose_print_some_test Python version: 3.6.9 r8mat_transpose_print_some prints some of an R8MAT, transposed. R8MAT, rows 0:2, cols 3:5: Row: 0 1 2 Col 3 : 14 24 34 4 : 15 25 35 5 : 16 26 36 r8mat_transpose_print_some_test: Normal end of execution. p00_prob_num_test Python version: 3.6.9 p00_prob_num returns the number of test problems. test_interp includes 8 test problems. p00_prob_num_test: Normal end of execution. p00_story_test Python version: 3.6.9 p00_story prints the "story" for any problem. 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 Carl deBoor and John 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. p00_story_test: Normal end of execution. p00_dim_num_test Python version: 3.6.9 p00_dim_num returns the spatial dimension for any problem. Problem Dimension 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 p00_dim_num_test: Normal end of execution. p00_data_num_test Python version: 3.6.9 p00_data_num returns the number of data points for any problem. Problem Data Num 1 18 2 18 3 11 4 8 5 9 6 49 7 4 8 12 p00_data_num_test: Normal end of execution. p00_data_test() tests p00_data Python version: 3.6.9 p00_data returns the actual (MxN) data for any problem. 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 : -0.4 0.16 4 : -0.2 0.04 5 : 0 0 6 : 0.2 0.04 7 : 0.20001 0.05 8 : 0.4 0.16 9 : 0.6 0.36 10 : 0.8 0.64 11 : 1 1 p00_data_test: Normal end of execution. p00_plot_test Python version: 3.6.9 p00_plot plots any test problem. test_interp includes 8 test problems. # 1 "p01_plot.png" # 2 "p02_plot.png" # 3 "p03_plot.png" # 4 "p04_plot.png" # 5 "p05_plot.png" # 6 "p06_plot.png" # 7 "p07_plot.png" # 8 "p08_plot.png" p00_plot_test: Normal end of execution. test_interp_test(): Normal end of execution. Tue Oct 19 17:25:00 2021