30-Sep-2022 15:10:42

test_interp_test()
  MATLAB/Octave version 4.2.2
  Test test_interp().
  This test also requires r8lib().

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           4
    2:           1           5
    3:           2           6
    4:           4           6
    5:           5           5
    6:           6           3
    7:           7           1
    8:           8           1
    9:           9           1
   10:          10           3
   11:          11           4
   12:          12           4
   13:          13           3
   14:          14           3
   15:          15           4
   16:          16           4
   17:          17           3
   18:          18           0

  Problem 2
  DATA_NUM = 18
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:           0           0
    2:        1.34           5
    3:           5        8.66
    4:          10          10
    5:        10.6        10.4
    6:        10.7          12
    7:      10.705        28.6
    8:        10.8        30.2
    9:        11.4        30.6
   10:        19.6        30.6
   11:        20.2        30.2
   12:      20.295        28.6
   13:        20.3          12
   14:        20.4        10.4
   15:          21          10
   16:          26        8.66
   17:       29.66           5
   18:          31           0

  Problem 3
  DATA_NUM = 11
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:           0           0
    2:           2          10
    3:           3          10
    4:           5          10
    5:           6          10
    6:           8          10
    7:           9        10.5
    8:          11          15
    9:          12          50
   10:          14          60
   11:          15          85

  Problem 4
  DATA_NUM = 8
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:           0           0
    2:        0.05         0.7
    3:         0.1           1
    4:         0.2           1
    5:         0.8         0.3
    6:        0.85        0.05
    7:         0.9         0.1
    8:           1           1

  Problem 5
  DATA_NUM = 9
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:           0           0
    2:         0.1         0.9
    3:         0.2        0.95
    4:         0.3         0.9
    5:         0.4         0.1
    6:         0.5        0.05
    7:         0.6        0.05
    8:         0.8         0.2
    9:           1           1

  Problem 6
  DATA_NUM = 49
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:         595       0.644
    2:         605       0.622
    3:         615       0.638
    4:         625       0.649
    5:         635       0.652
    6:         645       0.639
    7:         655       0.646
    8:         665       0.657
    9:         675       0.652
   10:         685       0.655
   11:         695       0.644
   12:         705       0.663
   13:         715       0.663
   14:         725       0.668
   15:         735       0.676
   16:         745       0.676
   17:         755       0.686
   18:         765       0.679
   19:         775       0.678
   20:         785       0.683
   21:         795       0.694
   22:         805       0.699
   23:         815        0.71
   24:         825        0.73
   25:         835       0.763
   26:         845       0.812
   27:         855       0.907
   28:         865       1.044
   29:         875       1.336
   30:         885       1.881
   31:         895       2.169
   32:         905       2.075
   33:         915       1.598
   34:         925       1.211
   35:         935       0.916
   36:         945       0.746
   37:         955       0.672
   38:         965       0.627
   39:         975       0.615
   40:         985       0.607
   41:         995       0.606
   42:        1005       0.609
   43:        1015       0.603
   44:        1025       0.601
   45:        1035       0.603
   46:        1045       0.601
   47:        1055       0.611
   48:        1065       0.601
   49:        1075       0.608

  Problem 7
  DATA_NUM = 4
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:           0           1
    2:           1           2
    3:           4           2
    4:           5           1

  Problem 8
  DATA_NUM = 12
  DIM_NUM  = 2

  Data array:

  Row:       1             2       
  Col
    1:          -1           1
    2:        -0.8        0.64
    3:        -0.6        0.36
    4:        -0.4        0.16
    5:        -0.2        0.04
    6:           0           0
    7:         0.2        0.04
    8:     0.20001        0.05
    9:         0.4        0.16
   10:         0.6        0.36
   11:         0.8        0.64
   12:           1           1

  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.

30-Sep-2022 15:10:44