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