GNU Scientific Library – Reference Manual: 1D Interpolation Example programs

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### 28.7 Examples of 1D Interpolation

The following program demonstrates the use of the interpolation and spline functions. It computes a cubic spline interpolation of the 10-point dataset (x_i, y_i) where x_i = i + \sin(i)/2 and y_i = i + \cos(i^2) for i = 0 \dots 9.

    #include <stdlib.h>
    #include <stdio.h>
    #include <math.h>
    #include <gsl/gsl_errno.h>
    #include <gsl/gsl_spline.h>

    int
    main (void)
    {
      int i;
      double xi, yi, x[10], y[10];

      printf ("#m=0,S=2\n");

      for (i = 0; i < 10; i++)
        {
          x[i] = i + 0.5 * sin (i);
          y[i] = i + cos (i * i);
          printf ("%g %g\n", x[i], y[i]);
        }

      printf ("#m=1,S=0\n");

      {
        gsl_interp_accel *acc
          = gsl_interp_accel_alloc ();
        gsl_spline *spline
          = gsl_spline_alloc (gsl_interp_cspline, 10);

        gsl_spline_init (spline, x, y, 10);

        for (xi = x[0]; xi < x[9]; xi += 0.01)
          {
            yi = gsl_spline_eval (spline, xi, acc);
            printf ("%g %g\n", xi, yi);
          }
        gsl_spline_free (spline);
        gsl_interp_accel_free (acc);
      }
      return 0;
    }

The output is designed to be used with the GNU plotutils `graph` program,

    $ ./a.out > interp.dat
    $ graph -T ps < interp.dat > interp.ps

The result shows a smooth interpolation of the original points. The interpolation method can be changed simply by varying the first argument of `gsl_spline_alloc`.

The next program demonstrates a periodic cubic spline with 4 data points. Note that the first and last points must be supplied with the same y-value for a periodic spline.

    #include <stdlib.h>
    #include <stdio.h>
    #include <math.h>
    #include <gsl/gsl_errno.h>
    #include <gsl/gsl_spline.h>

    int
    main (void)
    {
      int N = 4;
      double x[4] = {0.00, 0.10,  0.27,  0.30};
      double y[4] = {0.15, 0.70, -0.10,  0.15};
                 /* Note: y[0] == y[3] for periodic data */

      gsl_interp_accel *acc = gsl_interp_accel_alloc ();
      const gsl_interp_type *t = gsl_interp_cspline_periodic;
      gsl_spline *spline = gsl_spline_alloc (t, N);

      int i; double xi, yi;

      printf ("#m=0,S=5\n");
      for (i = 0; i < N; i++)
        {
          printf ("%g %g\n", x[i], y[i]);
        }

      printf ("#m=1,S=0\n");
      gsl_spline_init (spline, x, y, N);

      for (i = 0; i <= 100; i++)
        {
          xi = (1 - i / 100.0) * x[0] + (i / 100.0) * x[N-1];
          yi = gsl_spline_eval (spline, xi, acc);
          printf ("%g %g\n", xi, yi);
        }

      gsl_spline_free (spline);
      gsl_interp_accel_free (acc);
      return 0;
    }

The output can be plotted with GNU `graph`.

    $ ./a.out > interp.dat
    $ graph -T ps < interp.dat > interp.ps

The result shows a periodic interpolation of the original points. The slope of the fitted curve is the same at the beginning and end of the data, and the second derivative is also.

The next program illustrates the difference between the cubic spline, Akima, and Steffen interpolation types on a difficult dataset.

    #include <stdio.h>
    #include <stdlib.h>
    #include <math.h>

    #include <gsl/gsl_math.h>
    #include <gsl/gsl_spline.h>

    int
    main(void)
    {
      size_t i;
      const size_t N = 9;

      /* this dataset is taken from
       * J. M. Hyman, Accurate Monotonicity preserving cubic interpolation,
       * SIAM J. Sci. Stat. Comput. 4, 4, 1983. */
      const double x[] = { 7.99, 8.09, 8.19, 8.7, 9.2,
                           10.0, 12.0, 15.0, 20.0 };
      const double y[] = { 0.0, 2.76429e-5, 4.37498e-2,
                           0.169183, 0.469428, 0.943740,
                           0.998636, 0.999919, 0.999994 };

      gsl_interp_accel *acc = gsl_interp_accel_alloc();
      gsl_spline *spline_cubic = gsl_spline_alloc(gsl_interp_cspline, N);
      gsl_spline *spline_akima = gsl_spline_alloc(gsl_interp_akima, N);
      gsl_spline *spline_steffen = gsl_spline_alloc(gsl_interp_steffen, N);

      gsl_spline_init(spline_cubic, x, y, N);
      gsl_spline_init(spline_akima, x, y, N);
      gsl_spline_init(spline_steffen, x, y, N);

      for (i = 0; i < N; ++i)
        printf("%g %g\n", x[i], y[i]);

      printf("\n\n");

      for (i = 0; i <= 100; ++i)
        {
          double xi = (1 - i / 100.0) * x[0] + (i / 100.0) * x[N-1];
          double yi_cubic = gsl_spline_eval(spline_cubic, xi, acc);
          double yi_akima = gsl_spline_eval(spline_akima, xi, acc);
          double yi_steffen = gsl_spline_eval(spline_steffen, xi, acc);

          printf("%g %g %g %g\n", xi, yi_cubic, yi_akima, yi_steffen);
        }

      gsl_spline_free(spline_cubic);
      gsl_spline_free(spline_akima);
      gsl_spline_free(spline_steffen);
      gsl_interp_accel_free(acc);

      return 0;
    }

The cubic method exhibits a local maxima between the 6th and 7th data points and continues oscillating for the rest of the data. Akima also shows a local maxima but recovers and follows the data well after the 7th grid point. Steffen preserves monotonicity in all intervals and does not exhibit oscillations, at the expense of having a discontinuous second derivative.

Next: 1D Interpolation References and Further Reading, Previous: 1D Higher-level Interface, Up: Interpolation [Index]